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Beyond Size: Multistage Coalescence and Crystallinity-dependent Nucleation Pathways

DOI: 10.31038/NAMS.2026912

Abstract

Within classical nucleation theory (CNT), an initially formed crystal nucleus is often treated analogously to a droplet, with its evolution primarily described by changes in cluster size. However, increasing evidence suggests that the internal phase state of a cluster, including density for liquids and crystallinity for solids, evolves together with size and plays a decisive role in determining nucleation pathways and rates. Here we relate in situ transmission electron microscopy (TEM) observations of intermediate monomers to recent theoretical simulations, showing a phase-aware description of nucleation. Time-resolved TEM reveals that (i) coalescence proceeds through discrete, multistage events assisted by e-beam energy input; (ii) intermediate monomers repeatedly switch roles between reactants and products as their phase stability evolves; and (iii) mass redistribution can occur, consistent with crystallinity-dependent chemical potential and phase-dependent susceptibility to vaporization and redeposition. Motivated by these observations, we propose a more general framework that extends classical nucleation descriptions by incorporating coupled size and phase variables together with external driving fields. This phase aware description reconciles nonclassical multistage pathways with a tractable energetic model and suggests experimentally accessible control parameters, such as dose rate and dwell time, for steering early stage nanocrystal evolution.

Keywords

Nucleation, Nonclassical crystallization, In Situ TEM, Electron beam

Introduction

Understanding nucleation and early growth is central to controlling nanomaterial synthesis and transformation. Although classical nucleation theory (CNT) remains a useful baseline, its size-only description implicitly assumes that a nascent nucleus already possesses the bulk properties of the product phase. Recent rare-event simulations of Lennard–Jones vapor condensation demonstrate that this assumption can fail even for simple fluids: critical droplets nucleate with a density substantially different from the macroscopic liquid, and the reactive pathway involves simultaneous growth and densification [1]. In solids, an analogous internal variable is structural order (crystallinity, defect density, polymorph), which can evolve concurrently with size and invert size-only expectations such as anti-Ostwald ripening [2].

ZnO is a particularly suitable model to explore this coupling because it exhibits crystalline, poorly crystalline and amorphous states on the nanoscale, and these states respond differently to electron irradiation. In earlier in situ TEM studies, we observed multistage transitions among ZnO intermediate species and anti-Ostwald-like mass diffusion, indicating that intermediate monomers are not rigid building blocks but continually change size and phase [2]. More recently, we showed that e-beam irradiation can facilitate crystal reconstruction and even promote vapor-mediated mass transfer between unconnected particles, with the direction set by crystallinity- dependent stability [3].

In this paper, as an extension of our previous studies [2,3], we revisit representative multistage coalescence events in the pulsed- laser-produced ZnO system and use them to formulate a concise, phase-aware nucleation framework that bridges liquid condensation and nanocrystal evolution. Our goals are to distill experimental signatures of coupled size and phase evolution under e-beam driving, and to provide a minimal model that can be used to interpret and control such pathways in nanoscale crystallization.

Results and Discussion

Revisiting Classical Nucleation Theory

The theoretical fundamentals of crystal synthesis and crystallization are mainly based on classical theories, ascending to Gibbs free energy theories [4]. In CNT, when a crystal is initially formed, it could be regarded as a droplet. The Gibbs free energy ΔG of a droplet (assuming in a spherical shape) is described as in the following:

where is the bulk energy, is the surface energy also referred to as the resistance force for nucleation, and r is the radius of the droplet. is often referred to as the driving force of nucleation. The difference in bulk free energy between product and reactant is related to saturation of solution, which is expressed as:

where C is the concentration of a solution, C₀ is the concentration of a solution when it is saturated, k is the Boltzmann constant, and T is temperature.

The Gibbs free energy change with the crystal radius is shown in Figure 1. A diagram of Gibbs free energy is provided vs. radius of nucleation, showing graphs of interface free energy, , the Gibbs free energy, ΔG, and bulk energy, . The maximum value of free energy is the energy barrier for nucleation, ΔG*, when Correspondingly, the value of r* at the energy barrier is the critical size of nucleation, after which the addition of new molecules to nuclei decreases the free energy, so nucleation is favorable.

Figure 1: Free Gibbs energy diagram of nucleation in classical nucleation theory.

CNT remains a foundational framework because it reduces an intrinsically collective, many body process to a simple thermodynamic competition between a bulk free energy gain that scales with cluster volume and an interfacial free energy penalty that scales with cluster area. This reduction yields a single critical cluster size and a corresponding free energy barrier, providing a convenient and widely adopted basis for comparing nucleation behavior across different systems and conditions. However, the most consequential assumption underlying CNT is not the capillary approximation itself, but rather the implicit identification of the nucleating cluster with the equilibrium bulk phase. While cluster size quantifies the amount of material involved in nucleation, it does not specify the internal thermodynamic or structural state of that material.

In many nucleation processes, internal equilibration within the cluster is not instantaneous. Liquid droplets may initially form with reduced density and undergo subsequent densification, while crystalline nuclei may first appear as amorphous or poorly ordered aggregates before transforming into an ordered phase. Similarly, nanoparticles can nucleate in metastable polymorphs or defect rich configurations and later evolve toward more stable structures. When internal ordering and cluster growth occur on comparable time scales, nucleation cannot be adequately described by a single size parameter, but must instead be treated as a coupled process involving at least two collective variables: a size coordinate, such as the cluster radius R, particle number n, or volume, and an internal phase or order parameter, such as density ρ in fluids or crystallinity and structural order α in solids, with additional variables including composition or strain becoming relevant in more complex systems.

In literatures [5], the authors regarded the intermediate monomers, i.e., the primary particles, having energy at its minimum as , where is its energy averaging over both surface and bulk energies. The intermediate monomers were proposed to contribute the free energy of the crystallization system by . Therefore, the critical energy of the system change is It implied that the primary particle was a precursor-like reactant, which has the effect equivalent to a change in the saturation of the solution, from S₀S_px S, where the supersaturation is defined as , where C is the concentration and C_sis the solubility limit. As a result, the intermediate monomers affects the free energy of the system simply by adding/subtracting an amount of free energy (depending on the primary particles metastable/stable).

In the model proposed by Mirabello and coworkers [6], the process of non-classical crystallization was described as a three-stage transition: (1) the formation particle (P); (2) the aggregation of Ps in which the Gibbs free energy changes due to the change of surface area; (3) the phase transition into the final crystalline phase. In the stage of the phase transition, a factor was assigned to describe the degree of the conversion. However, the definition of the conversion factor Φ is intuitional. Neither mass conservation nor additional nucleation was considered. Moreover, the model identified a critical cluster size, R_conv, by the condition: It implies that the transition direction is predetermined, from P to crystals (C) with increasing of Φ, and the process is size depended that in the early stage of R < R_convthe phase is dominated by P, and the conversion to C becomes favorable above R_conv. However, this predetermined phase change direction excludes the possibility of phase change from C to amorphous (A), or the possibilities of the growth of P consuming C, as we observed in this study.

Observation of Multistages of Crystal Evolution

ZnO crystals or intermediate monomers in this study were produced by the pulsed laser method, similar to the previous studies [7]. The precursor solution for producing ZnO crystals was prepared from zinc acetate dihydrate in alcoholic solution under basic conditions [8]. Specifically, 1.5mM Zn(CH₃COO)₂ · 2H₂O was dissolved in 25 mL DI water and mixed with 25mL ethanol (all chemicals from Sigma-Aldrich). Additionally, 1mL dilute NH₃ · H₂O solution (pH = 10) was added dropwise under vigorous stirring at room temperature. The substrate of Si (100) in a size of 1 cm × 1 cm was cleaned by DI water and immersed in the precursor solution. Copper grid holder for Transmission Electron Microscope (TEM) was placed on the silicon substrate, with film side face-up, and the grid edge was sealed by copper tape. The copper grid has 400 mesh with a thin film of pure silicon monoxide (15 – 30 nm) (SF400-Cu from Electron Microscopy Sciences, Hatfield, PA). The substrate was irradiated by pulsed laser for 1s. Ytterbium pulsed fiber laser with a wavelength of 1064 nm, pulse width of 100 ns was used to irradiate the substrate to trigger the hydrothermal reaction. Pulsed laser power density is 1.27 kW/cm² in a repetition rate of 100kHz. After irradiated by the pulsed laser, the grid was taken out and rinsed with DI water. After dried by air, it was cleaned by plasma (Ar) for 40s before TEM observation. FEI Tecnai G2 20 TEM with 200 kV LaB₆ filament was used. The CCD camera is a bottom mount Gatan US1000 2K x 2K, and videos were captured with a frame rate of ~30s^-1 [frames per second (fps)].

Figure 2 shows an in-situ observation of multistages of crystallization. Starting from 1 s, two adjacent ZnO particles (intermediate monomers) can be resolved as distinct entities in the time resolved TEM sequence, each maintaining its own boundary and contrast. Under continuous electron beam irradiation, the pair evolves progressively toward integration through coupled interfacial and structural relaxation processes. As irradiation proceeds, surface atom diffusion smooths initially rough or high energy surface segments and promotes the development of a contact neck at the junction, while gradual lattice rearrangement and rotation reduce the crystallographic mismatch across the interface. Concurrently, local mass transport and boundary relaxation decrease the interfacial free energy, leading to a steady loss of boundary sharpness and a more coherent morphology. By the end of the 60 s observation window, the two monomers have merged into a single larger particle that exhibits a more uniform, crystal like contrast, consistent with the electron beam facilitated structural evolution mechanisms previously reported for multiphase nano ZnO, including surface diffusion, grain rotation, and irradiation activated mass transfer that collectively drive the system toward a lower energy configuration [3].

Figure 2: Particle coalescence processes. The frame size of each picture: w:153 nm, h:133 nm.

The theoratical model could be summarized in Figure 3 that in one stage of the reaction during the multi-stage process, from monomer (M_i) at stage i, which composed of two monomers (M_i1and M_i2) to monomer (M_i+1) at stage i+1, representing the aggregated monomer (M_i1+M_i2). ΔE represents the energy barrier of particles’ moving, attaching, and aggregating under the assistance of the mass diffusion. The electron beam provided the energy for overcoming the energy barrier. Monomer M_ihas a Gibbs energy of G_i, and monomer M_i+1has a smaller energy of G_i+1. ΔG<0 indicates that M_i+1 is more stable than M_i. Monomer M_i+1will continue interacting with surrounding monomers, evolving in the following stages.

Figure 3: Energy diagram of the particle coalescence processes shown in Figure 2.

Mass Redistribution Among Clusters

Mass transfer among monomers was experimentally observed and shown as an important mechanism in the crystallization process. The continuous change of individual monomers (particles) indicated that successive mass transfer and phase change was induced by e-beam. Figure 4 was viewed from a TEM view window; each frame size is about 1.28× 0.81 μm. Two particles were tracked over 100s; the particle on the left is marked as p1, and the particle on the right is marked as p2. Two particles didn’t have significant change during the first 10s. At 12.90s, e-beam intensity increased artificially. As a result, particles shrank and p1 was hardly observed. At 22.83s, particles reappeared and continue to grow. At about 53.87s, p1 and p2 showed a crystalline structure rather than round shape. After 66.27s, particles continue to shrank. p1 disappeared at 73.93s and reappeared at 78.63s, and p2 disappeared at 78.63s. Although they disappeared eventually, a series of shrinkage and growth indicated the existence of local minimums in crystal growth energy diagrams.

Figure 4: A series of phase changes of two particles due to e-beam. Snapshots from videos of observations from TEM view window. Magnification is 19.5k. Each frame size is about 1.28 × 0.81 μm. E-beam intensity increase at 12.90 s.

The reaction is found dose sensitive, in which a high dose rate leads to a high reaction rate. Particles didn’t have significant change until increasing the e-beam intensity (dose rate) at 12.90 s. In-situ measurement of the intensity of e-beam interacting with the sample is difficult (not allowed at our equipment). However, it could be a good reference using the dose rate projected at the screen, which records the electron density transmitted through the sample to detectors sitting below the sample. In this study, a dose rate of 673 e/(nm²s) was used at a magnification of 19.5 k; 1560 e/(nm²s) at a magnification of 29 k; and 3330 e/(nm²s) at a magnification of 43 k. Convert the electron flux to dose rate, ψ, on samples, using equation [9]:

where S is total stopping power for water, 2.79 (MeV cm² g^-1), a is the radius of irradiation area, I is the beam current, unit of Gy is defined as the absorption of one joule per kilogram of matter. Dose- related effect was studied by the effect of e-beam induced damage effect [10,11]. Therefore, the dose rate is then 3.01 × 10⁷ Gy/s for 19.5 k, 6.98 × 10⁷ Gy/s for 29 k, and 1.49 × 10⁸ Gy/s for 43 k. Although it’s hard to have precise quantitative results between dose rate and crystallization kinetics, this result shows that electron beams could not only be used for imaging crystallization processes, but also as a promising tool to initiate and control the reactions in the observed area.

A General Framework of Crystallinity-dependent Nucleation Pathways

These observations align with our prior in situ TEM and simulation results on multiphase nano ZnO under electron irradiation, where the electron beam can induce constructive microstructural evolution rather than only damage [3]. Here, as an extension of our previous studies, we presented a generic model by extending the Gibbs energy profile of classical nucleation theories. The cases in ref. [5,6] discussed above could be included in this model. Moreover, two new features described by this model need to be stressed: firstly, considering that the crystal evolution results from the equilibrium among M, P, and C or A, intermediate monomers change their roles dynamically. Representative monomer types are schematically illustrated in Figure 5a. The relatively stable phase will consume less stable ones, e.g., the growth of stable P may consume adjacent C or A. Secondly, the phase transition and size change has no specific direction. The multi-stages reactions could result from either change of μ_Mi, γ_Mior R_Mi, as long as the system reduce the Gibbs free energy as a whole, for example, a transition from A to C by reducing R_Mi .

The crystal evolution includes multi-stages reaction, and intermediate monomers at different stages indicated the local minimums in energy diagrams (shown in Figure 5b). Figure 5c describes a schematic overview of crystal evolution pathways, starting from intermediate monomers. Intermediate monomers were a mixture of ion-molecule monomers, dense liquid, amorous particles, poorly crystalline particles and nanocrystals, generated by the pulsed laser method from indirect nucleation in our system. The intermediate monomers under e-beam continue to undergo a kinetically favored pathway, in which mass- transfer induced new monomer formation and existed monomer dissolving lower energy barriers and alter evolution pathways.

In each stage (shown in Figure 5c), monomers move and attach, with or without adjustment of crystalline orientation depending on the phase properties of monomers, as discussed above. Then monomers coalesce directly or through mass distribution to form a new crystalline phase. The new crystalline phase then transforms into a more stable phase by mass redistribution and phase adjustment. Finally, the large newly formed phase shows as a more stable monomer, usually a crystalline phase, with lower Gibbs free energy and serves as the reactant for the next stage. The final crystal is the product of several stages of monomer interactions. In addition, as the trajectories of monomer interactions may not remain in the final crystals, single crystals could also be formed from various monomers’ interactions.

Figure 5: (a) Various monomers types shown in blue dash block, adapted from [12]. (b) Proposed crystallization dynamics schematic diagram showing multi-stages transformation from initial monomers M₁ to final crystals. (c) A proposed example of a certain stage of crystal evolution. The mass and phase transfer from unstable phases to more stable phases. The product of a certain stage (M₂) continue react and contribute to the crystal evolution in next stage.

The proposed mechanism in literatrues are certain possible crystal evolution pathways. Here, we extend the diversity of the intermediate reactions, in which the role of the primary particles or intermediate monomers could be more complex. The reaction from molecules (M) to crystals (C) or amorphous (A) involving primary particle (P) is not only in the form of M + P C or A as implied in ref. [5], but could be considered as the equilibrium among M, P and C or A as:

If P is unstable (S_p>1), reaction (1) will be reversely favored, and reaction (2)(3) is forwardly promoted. Similar to the case described in Figure S4D-E of Ref.5, consuming P will lower the total free energy of the system. However, if P is stable (S_p<1), we should consider whether it will still be consumed and increase the energy barriers, as described in Figure 3C-D of Ref. [5]. If it favors reaction in a different way, such as by the direct growth of P, the role of the primary particle will not always be a reactant but could be a product, and its effect on the free energy diagram of the system would be more complicated. The generic model proposed here implies all those possible roles of intermediate monomers and all the possible evolution pathways.

Conclusions

In summary, we combined in-situ intermediate monomer observations with a phase-aware nucleation viewpoint to highlight a unifying principle: nucleation and early growth are governed by coupled evolution of size and an internal phase coordinate. In the ZnO system, e-beam irradiation facilitates discrete multistage coalescence events, enables dynamic role switching of intermediates, and drives crystallinity-dependent, sometimes anti-Ostwald, mass redistribution. By casting these behaviors on a minimal two-coordinate free-energy landscape and including an explicit driving term for e-beam energy input, we obtain a compact framework that connects nanocrystal evolution to recently established variable-density nucleation pathways in liquid condensation. This framework motivates phase-aware experimental metrics and provides actionable control knobs for e-beam-assisted nanomanufacturing.

Notes: The authors declare no competing financial interest.

References

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Assessing Perceptions of the Healthcare Community, Perceived Stress, Perceived Racism, Postpartum Depression, and Differences in Income among Black Women

Examining the Impact of Cultural Education on Fertility Behaviours Among Schoolgirls in South African Schools

2 Replies

DOI: 10.31038/AWHC.2025843

Abstract

The South African government has introduced various policies designed to prevent and manage learner pregnancy within schools. These initiatives form part of a broader national effort to address the social and health challenges that young people encounter across the country. Despite various interventions, teenage pregnancy remains a persistent social and public health challenge in South Africa with the rates of pregnancy and subsequent school dropouts among these young mothers persist, leading to severe economic consequences and perpetuating cycles of poverty. This paper examines how cultural education affects fertility behaviors among schoolgirls in South Africa, drawing from contemporary literature on cultural norms, teenage pregnancy, and school-based interventions. Evidence suggests that culturally grounded learning improves critical awareness, supports contraceptive uptake, and empowers girls to navigate pressures from families and communities. However, cultural barriers, religious norms, and moralistic attitudes often limit the effectiveness of these initiatives.

Keywords

Teenage pregnancy, Poverty, Cultural barriers, Prevention, Contraceptives

Introduction

Teenage pregnancy is a major concern in South African schools, with thousands of pregnancies recorded each year [1]. In support of this is [2] who argues that South Africa has one of the highest teenage pregnancy rates in the world, with nearly one in four girls becoming pregnant before the age of 20. According to Statistics South Africa, 90,037 girls aged 10 to 19 gave birth across the nine provinces between March 2021 and April 2022 (Stats SA [3]). This pattern highlights the urgent need for comprehensive sexual and reproductive health education and adolescent‑focused services to help curb rising teenage pregnancy rates, particularly during periods of crisis [4].

Many of pregnancies occur in contexts shaped by cultural expectations related to femininity, sexuality, and motherhood [5]. According to [6] cultural norms often transmitted through families, communities, and religious institutions play a significant role in shaping adolescents’ fertility decisions. Hence, [7], argues that 85% of pregnant women indicated that their families favoured their pregnancy. Research also shows that peer education and cultural dialogue are important interventions in addressing contraceptive behavior [8] and [2], but these efforts must meaningfully engage cultural and religious influences to be effective.

Therefore, cultural education, when integrated into school curricula, can challenge harmful norms, promote informed reproductive choices, and empower schoolgirls. Hence, is of paramount important to undergo this study as it aims to explore how such education affects fertility behavior among schoolgirls.

Literature Review

Cultural Norms as Predictors of Fertility Behavior

Socio‑cultural factors such as religion, ethnicity, and community beliefs significantly predict teenage pregnancy in South Africa. In the same vein, [9] argus that cultural beliefs and principles play a significant role in shaping the social landscape of the macrosystem. For instance, cultural predictors such as religious affiliation and ethnicity strongly influence adolescent fertility, especially in urban areas. In the same breath, [1] argues that the influence of ethnicity and religion play a crucial role more so in urban areas than in rural areas. All these are found in the macrosystem the adolescent leaves in. Above all, these norms shape beliefs about ideal family size, the acceptability of contraceptive use, and the meaning of womanhood [2]. In the same breath, [10] argues that women’s fertility preferences such as the number of children they desire and their willingness to use modern family planning methods are shaped by a complex interplay of social, economic, and cultural factors operating at multiple levels, often reinforcing or counterbalancing one another. Demographic characteristics including age and geographic context, levels of education [11‑13], religious orientation [14], as well as prevailing social norms and cultural beliefs [15], all play influential roles in determining women’s fertility aspirations and their adoption of family planning practices. Together, these factors contribute significantly to broader fertility patterns.

The Role of Education in Shaping Reproductive Behavior

Educational interventions, particularly peer education programs, have been shown to improve young people’s sexual health knowledge and reduce risky behavior. Hence, [2] argues that cultures, values, and traditions influence the prevailing negative perception of teenage mothers. However, their effectiveness is constrained if they do not address cultural and religious pressures. As one study found, cultural norms, church teachings, and community expectations limited adolescents’ ability to mentor peers on contraception, despite improved knowledge [1]. In the same vein, [16], argues that such perceptions have a key influence on decision‑ making processes and behaviour and may be particularly important in contexts of subsistence livelihoods and resource constraints, where households are vulnerable to both food insecurity and environmental degradation.

School Policy, Gender Norms, and Reproductive Outcomes

South African schools often operate within moralistic and conservative discourses around sexuality. These school‑based cultural norms can affect students’ reproductive choices. Research indicates that dominant school cultures that frame teenage pregnancy as moral failure reinforce shame, exclusion, and silence factors that limit informed fertility choices [17].

Cultural Education as an Intervention

Cultural education helps learners question harmful traditions, challenge myths about contraception, and understand the cultural roots of gender expectations. By discussing cultural norms openly, girls can develop stronger decision‑making skills. Literature further suggests that including cultural discussions in reproductive health interventions increases adolescents’ acceptance of contraception and reduces pregnancy risk [18]. On the other hand, [19] argues that cultural education serves as a mechanism to challenge and reshape these dominant social norms by engaging students in discussions about traditions, beliefs, and cultural misconceptions.

Theoretical Framework

This study is underpinned by Social Norms Theory (SNT). Social norms theory explains how individuals often misjudge the attitudes or behaviours of their peers and wider community, assuming them to differ from their own. This pattern of misunderstanding referred to as pluralistic ignorance [20] can arise in relation to both risky or problematic behaviours as well as healthy or protective ones. Such misperceptions may lead people to modify their own behaviour to align with what they mistakenly believe to be the norm [21]. As a result, individuals may adopt or justify harmful behaviours or, conversely, suppress positive, health‑promoting behaviours.

The Social Norms Theory asserts that individuals’ behaviours are shaped by perceived social expectations, cultural beliefs, and community norms. This theory deemed relevant to the current study as it allows the researcher to explain why schoolgirls may conform to social expectations encouraging early sexual activity or discouraging contraceptive use. Furthermore, it will guide the researcher to propose the interventions which will correct these misperceptions by revealing the actual, healthier norm that will have a beneficial effect on adolescent girl in, to reduce their participation in potentially problematic behaviour or be encouraged to engage in protective, healthy behaviours [22].

Methodology

Research Design

The methodology employed in this study involved the implementation of a systematic literature review research strategy synthesizing findings from peer‑reviewed studies, government reports, and policy documents exploring cultural influences on adolescent fertility. The researcher searched for peer‑reviewed studies on socio‑cultural predictors of teenage pregnancy, school‑based responses to teenage pregnancy, South African public health and educational reports and studies examining the interaction between culture and contraceptive use via google scholar.

This approach involved the meticulous identification and selection of relevant articles, as well as the exclusion of those that did not meet the predetermined inclusion criteria [2]. The detailed process of article retrieval, inclusion, and exclusion is elaborated upon in this section. According to [23], as cited in [24], conducting reviews transparently and reproducibly is what the systematic literature review as a research method aims to achieve. Above all, it enables the researcher to make selection of articles to incorporate or omit in the study [24]. Upon completing the reading, themes were identified across the literature reviewed, which includes, cultural norms and fertility, school‑based cultural influences, effects of cultural education, and barriers to culturally relevant sexual education.

Discussion

Cultural Education Improves Reproductive Knowledge

Evidence shows that when cultural beliefs are openly discussed in education settings, students gain a more nuanced understanding of fertility and contraception. Peer‑education programs, when culturally grounded, increase knowledge and enable adolescents to critically examine cultural messages about sexuality [1].

Challenging Myths and Misconceptions

Several scholars [25‑27] contend that rumours surrounding contraceptives such as beliefs that injectable methods damage the body negatively and oral contraceptives cause lower rates of acne, hirsutism, and weight gain, but higher rates of venous thromboembolism [28] influence both access to and utilisation of contraception. These misconceptions contribute to low contraceptive uptake, which in turn results in high levels of unmet need and increased rates of unintended pregnancy among adolescent girls and women. Similarly, [29] notes that many individuals are more preoccupied with the perceived negative side effects of contraceptives than with their effectiveness in preventing unwanted pregnancies. [27] further report that numerous adolescent girls and women cite side effects such as weight gain or loss, irregular bleeding, amenorrhea, and other bodily changes as reasons for avoiding contraceptive use. Participants also expressed fears that contraception could cause infertility or make the vagina “too wet,” leading to concerns about being viewed as undesirable by partners. Condoms, on the other hand, were often associated with discomfort, irritation from lubricants, and suspicions of infidelity. These misconceptions persist largely due to limited contraceptive education, particularly in low‑ and middle‑income countries (LMICs) [27]. Cultural beliefs such as the idea that contraceptives cause infertility also to exacerbate these concerns. [30] argue that dismissing these beliefs as mere rumours simply because they stem from social accounts of other women’s experiences rather than from clinical providers undermines a valuable and legitimate source of knowledge for many women.

Consequently, Jones (2023) argues that the decision to initiate contraception and select a method requires careful consideration; however, conflicting and confusing information often leaves individuals without the necessary knowledge to make informed choices. Culturally sensitive education has therefore been identified as essential for dispelling myths, reducing fear, and promoting informed contraceptive decision‑making. On the other hand, many programs recommend that providers address women’s concerns about the possible negative health effects of contraceptive methods by reassuring them that such fears are unfounded and that any side effects are likely to be minimal [31,32]. This type of counselling is commonly advocated in research that identifies associations between women’s beliefs and their contraceptive use [26,33‑35].

Empowering Girls Against Harmful Gender Norms

Many South African girls face cultural pressure to prove fertility, maintain submissiveness to partners, or conform to traditional femininity. Cultural education empowers girls to question these norms, advocate for themselves, and resist early sexual debut. According to [1] statistics of girls affiliating to traditional African religion were significantly positive with a 24% higher likelihood of pregnancy compared with Christian girls, while it was not significant and almost equal with a 1% lower chance of pregnancy among rural dwellers. This indicates that cultural education has its ways of decreasing teenage pregnancy. As observed in a practice of Basotho culture, where girls are not allowed to eat food such as eggs and intestine . This kind of practice is pass down to girls through gathering such as pitiki. According to [36], Basotho women gather during pitiki celebrations to support one another, share their experiences, and offer advice on addressing various challenges, particularly those involving their marriages or relationships with men. Consequently, ([37]: 88) agrees with [36] by pointing out that ‘we need new alternatives and sometimes this means revisiting the old wisdoms and tapping into the maternal legacies of knowledge in Africa’. Hence, pitiki is seen as a good space were Indigenous Basotho women’s knowledges related to sexual, reproductive health and well‑being, maternal and child health within and around the Indigenous are discussed [38]. In this space, girls are free to discuss any matters related to sex, as such this helps to reduce teenage pregnancy.

Addressing Cultural and Religious Barriers

Studies show that religion and cultural identity strongly influence teenage fertility. Cultural education enables learners to navigate these influences by offering critical reflection rather than dismissing cultural identity. Programs that ignore culture tend to be ineffective [2]. Previous research has highlighted religion understood as systems of faith and worship as a factor that influences teenage pregnancy [39,40]. In the South African context, the influence of religion on teenage pregnancy appears to stem largely from religious teachings that discourage the use of contraceptives. For example, a study conducted in the North‑West province found that 6.83% of participants believed that using contraception was against their religious beliefs [41]. Likewise, research carried out in the Vhembe district of Limpopo reported that, because most teenagers (94.1%) identified as Christian, religious beliefs played a significant role in teenage pregnancy, as contraception was generally not supported [42]. Concurring to this is [1] who argues that the most affected groups are Nguni group (51%), Christian faith (84.9%) and lived in urban areas (53%) in KwaZulu‑Natal (21%). Therefore, understanding these cultural and religious influences is essential for developing effective, culturally and religiously sensitive reproductive health strategies that respect community beliefs while promoting women’s autonomy and well‑being.

The Role of Schools

The South African government has introduced various policies designed to prevent and manage learner pregnancy within schools. Amongst those policies is the Policy on the Prevention and Management of Learner Pregnancy in that was finalised in 2021 by the Department of Basic Education. The policy expressly provides that a school may not discriminate against a learner based on her pregnancy status [43]. The School Governing Body (SGB) is given the mandate to implement and fulfil the need of the policies governing the school [44]. This suggest that SBG in their policy will be in position to suggest the programmes that raise awareness on teenage pregnancy. Therefore, these initiatives form part of a broader national effort to address the social and health challenges that young people encounter across the country.

To reduce the likelihood of young people engaging in unsafe sexual practices, it is essential to equip them with the knowledge and skills necessary for practicing safe sex [45‑47]. Schools serve as an ideal setting for delivering this education, as they provide access to most children and adolescents. I therefore, argue that recently schools often perpetuate moralistic messages about sexuality, which create stigma around pregnancy and contraceptive use. This is because teachers offering sex education often use more passive forms of learning and tend to disregard skill‑oriented learning activities [48‑50]. On the same breath, [51] argue that cultural and societal taboos surrounding sexual and reproductive health strongly shape both educational content and public health policies. In many countries, school‑based sex education is either highly restricted or entirely absent due to cultural sensitivities and political opposition. The United States, for example, has long experienced controversy over abstinence‑only versus comprehensive sex education, with the preference for abstinence‑only approaches often grounded in religious and cultural norms [52]. In contrast, nations that adopt more open and comprehensive approaches to sexual health such as the Netherlands report lower rates of teenage pregnancy and sexually transmitted infections [53]. These comparisons demonstrate how cultural contexts are both reflected in and reinforced by policy choices, which signal varying levels of societal comfort and openness toward sexual and reproductive health topics. Therefore, changing school culture through inclusive cultural education can create safer environments for discussing reproductive choices [2].

Conclusion

It has been observed that fertility choices such as preferred family size, timing of childbearing and contraceptive use are deeply shaped by cultural and religious norms as well as social expectations. In many communities, beliefs about womanhood, lineage continuation, masculinity, religious teachings, and social status influence how women make reproductive decisions. Cultural education significantly shapes fertility behavior among South African schoolgirls. By addressing cultural norms, challenging myths, and empowering girls, it contributes to informed reproductive decision‑making and helps reduce teenage pregnancy. However, cultural education must be delivered in ways that respect community traditions while promoting girls’ rights and autonomy. Schools, families, and communities must collaborate to develop culturally sensitive and empowering educational interventions. As observed, high fertility rates pose challenges not only for country, but also for food security and the general welfare of households [54].

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Using Mind Genomics Thinking and AI Simulation to Study Socio-political Issues: Possible Individual ‘Fall-Out’ from the US DOGE Efforts

After 45 Years of Pancreatic Islet Transplantation, Is It Time to Seriously Consider Molecular Alternatives to Cure Type 1 Diabetes?

4 Replies

DOI: 10.31038/EDMJ.20261022

Abstract

Type 1 diabetes (T1D) is caused by the autoimmune destruction of the insulin-producing beta (b)-cells of the pancreas resulting in hyperglycaemia and life-threatening complications. The promise of a β-cell replacement therapy has yet to be fulfilled due to the shortage of donor pancreata and the requirement for lifelong immunosuppression to prevent recurrent autoimmunity and allograft rejection, both of which pose significant clinical risks. Therefore, islet/ pancreas transplantation is unlikely to be a realistic cure for most patients. In this opinion summary, we build on our laboratory’s work and that of other researchers demonstrating the successful genetic engineering of surrogate b-cells from hepatocytes that are capable of synthesising and secreting insulin in response to physiological metabolic cues, akin to pancreatic b-cells. We propose that these advances highlight the feasibility of clinical translation and provide proof-of-principal that a patient’s own hepatocytes could ultimately be reprogrammed in vivo towards a b-cell phenotype. This approach bypasses the challenges of both donor tissue availability and immune-mediated rejection that complicate allogeneic islet and pancreas transplantation.

Introduction and Background

Type 1 diabetes (T1D) is caused by the autoimmune destruction of the insulin-producing pancreatic beta (β)-cells. It is the most common chronic disease of childhood in developed nations and its incidence continues to rise each year. Current treatment constitutes multiple daily insulin injections and blood glucose monitoring. Tight glucose control achieved through intensive insulin therapy can delay, but not eliminate, complications such as nephropathy, retinopathy, cardiovascular disease, and neurological impairment, which collectively contribute to significant morbidity and mortality. Since the 1970s pancreatic (β)-cell replacement has been considered a promising approach for the treatment of T1D, where pancreatic islets, purified from either an allogeneic or xenogeneic donor pancreas are administered to the patient, most commonly through portal vein infusion [1]. However, this procedure is limited by a shortage of donor pancreata and the requirement for lifelong immunosuppression, with its associated adverse side effects, together with a number of other unresolved issues, including limitations in encapsulation technology. Microencapsulation technology isolates islets in a thin layer of biomaterials, such as alginate, which allow the exchange of nutrients, insulin and other substances, improving immuno-rejection. However, studies have encountered several challenges, including pores blockage or obstruction, reduced cell viability and cell death and diminished insulin response to glucose levels [1].

Before the clinical onset of T1D, islet autoantibodies signal the initiation of silent and progressive destructive autoimmune processes, often appearing months or even years before hyperglycaemia develops. This pre-symptomatic phase presents a unique opportunity for early intervention. Indeed, population-based screening programs for T1D have been shown to reduce the incidence of diabetic ketoacidosis at diagnosis by enabling earlier detection [2]. To date the most successful studies aimed at delaying T1D onset has been the administration of anti-CD3 monoclonal antibodies, such as Teplizumab [3]. So far, immunotherapies have not had ultimate successes in altering T1D disease course. Their benefits are typically short term and long term favourable immune responses or regulation has remained difficult to sustain.

Thus, alternative therapeutic strategies are urgently needed. Gene therapy, whereby an “artificial β-cell”, capable of synthesising and secreting insulin in response to the physiological metabolic signals, is generated by genetically engineering the patient’s own cells. This approach would circumvent the issues of tissue rejection inherent to both allogeneic transplantation of islets and pancreas. Our laboratory (and others) has shown that a number of cell types can be used for the genetic engineering of artificial β-cells [4-15], but hepatocytes are particularly useful as they as they derive from the same endodermal precursors as pancreatic cells and possess similar characteristics to pancreatic β-cells, including an ability to process and secrete proteins, and a glucose sensing system (glucose transporter 2 [GLUT2] and glucokinase [Gck] [ 6-8,12,14,15].

Recent Work from Our Laboratory

Our team and others have established that specific combinations of β-cell transcription factors exert a synergistic effect in stimulating β-cell transdifferentiation, storage of insulin in granules, regulated insulin secretion to glucose and other β-cell secretogogues, and, most importantly, permanent reversal of diabetes [5,6,13]. Additionally, and very importantly, this transdifferentiation process does not result in recurrent autoimmune reactions against the surrogate β-cells [14]. Together, these findings highlight that only approaches combining immune regulation with enhanced β-cell survival have the potential to delay, prevent and ultimately cure T1D.

A recent study investigated a novel gene therapy approach that prevented disease development by replacing pancreatic β-cell function with insulin-producing cells generated through hepatic transdifferentiation [14]. In this model, a clinically applicable third- generation lentiviral vector (based on the pRRLSIN.cPPT.PGK-GFP. wpre vector) was used to deliver a cocktail of β-cell transcription factors: pancreatic and duodenal homeobox 1 (Pdx1), neuronal differentiation 1 (ND1), and MAF BZIP Transcription Factor A (MafA) to the portal vein of 5-6-week-old non-obese diabetic (NOD) mice. At the experimental endpoint of 30-weeks, all (100%) of the treated NOD mice remained normoglycemic, exhibited normal intraperitoneal glucose tolerance responses and demonstrated an ability to regulate blood glucose as effectively as the non-diabetic controls. A range of pancreatic markers, including somatostatin, Glut 2 and, most importantly mouse insulin (Ins1 and Ins2), were detected in the liver, and liver function tests remained normal. Collectively, these findings showed that expression of these β-cell transcription factors induced partial pancreatic transdifferentiation and prevented the onset of hyperglycemia and impaired glucose tolerance, as the transduced hepatocytes effectively assumed β-cell function. Immunohistochemistry confirmed that endogenous β-cells had been destroyed by the autoimmune process. As lentiviral vectors permanently transduce cells, this approach therefore holds substantial promise as a potential clinical therapy, particularly if applied in individuals at early stages of Type 1 diabetes.

Author Contributions

Both authors contributed to the writing and review of the manuscript and approved the final version.

Acknowledgements

The authors have nothing to report.

Funding

This study was supported by the National Health and Medical Research Council (NHMRC) of Australia, Ideas grant, number 1187040.

Disclosure Provenance and Peer Review

Nothing to report.

References

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Algal Blooms, Dinoflagellates and Petroleum Resources

2 Replies

DOI: 10.31038/GEMS.2026813

Abstract

A theory of petroleum formation from algal blooms is revised in the light of recently obtained data on biochemical composition of dinoflagellate lipids, microalgal blooms, “marine snow” and its composition, taxonomy of major phytoplankton groups and their relative abundances. The article is illustrated with electron micrographs of dinoflagellates, a scheme of petroleum formation and a map. It is concluded that seasonal dinoflagellate blooms or those of a longer period may be the principal agent in the petroleum formation.

Keywords

Algal blooms, Dinoflagellata, Marine phytoplankton, Microalgae, Oil, Petroleum formation, Petroleum resources

Introduction

Petroleum (crude oil) has played an important role for humans worldwide. During the 20th century until the present it has been an important factor in geopolitics and conflicts between countries, determining the welfare of individual humans through that of entire nations. During wars that depended on energy sources for military vehicles, it has been considered the blood of war and the blood of the economy. Today oil is the primary source of energy and the most important commodity traded among many countries. More than half of the energy that powers our civilization comes from this non- renewable energy source. Therefore, it is a strategic resource whose scarcity would cause the decline of the global economy [1,2]. Oil is not distributed evenly around the world, so many countries are heavily dependent on nations with oil resources. At present, the world’s largest proven petroleum reserves are in Venezuela, Saudi Arabia, Iran, Canada, Iraq, United Arab Emirates, Kuwait, Russia, United States of America, and Libya.

Among the hypotheses that could explain the origin of petroleum during the geological history of the Earth, there is one based on the suggestion that marine phytoplankton (in particular, algal blooms caused by dinoflagellates) are the principal source of petroleum resources [3]. Herein are some suggestions that we consider relevant:

“Judging from… rough correspondences between distribution of areas of oil fields and supposedly great expanse of the so- called Old Tethys Sea, or became more and more preferable to conclude that petroleum was derived primarily from phytoplankton of this sea.”
“Frequent occurrences of intense red water blooms in coastal shallow water may be responsible for petroleum formation throughout long continued geological periods.”
“Planktonic diatoms are non-motile, but usually possess spiny surface extensions which give them resistance to passage through water. Largely due to this, over-crowding of diatoms is theoretically limited. In contrast, dinoflagellates are motile but frequently do not possess floatation structures.”
“Poisonous effects caused by dinoflagellate also So that it is not phytoplankton only, but also all of higher marine organisms killed by intense red water occurrence that are most highly responsible for the formation of petroleum.”

The present study reviews some aspects of this hypothesis emphasizing the advances in our knowledge of algal blooms and major microalgal taxonomic groups, in particular, of recent marine dinoflagellates.

Material and Methods

To discuss Abe’s suggestions [3] previously presented, some old and modern literature was selected and reviewed. Some dinoflagellate species were selected to illustrate dinoflagellate morphological diversity. The cells were collected in the coastal water of the Peninsula of Yucatán, the southern Gulf of Mexico during the period of 2009-2012. Phytoplankton samples were fixed with a stock formaldehyde solution to a final concentration of 4% and stored in 100-ml plastic bottles. They were examined in a JEOL JSM-7600F scanning electron microscope (SEM) at a working distance of 8 to 15 mm and a voltage of 5.0 kV after a preliminary wash in distilled water, followed by dehydration in a series of ethanol solutions of increasing concentration (30, 50, 70 and 90% and twice in 100%), air drying on 0.5” aluminum mounts and sputter coating with gold-palladium using a Polaron SC7640 High Resolution Sputter Coater (Quorum Technologies, Newhaven, East Sussex, U.K.).

Results and Discussion

Elemental Composition and Generation of Petroleum Based on Organic Matter

Hydrocarbons (petroleum) originate from the thermogenic transformation of organic matter preserved in sedimentary basins. From a geological perspective, the occurrence of oil and gas reservoirs is intrinsically linked to the presence of source rocks within stratigraphic sequences [4]. Although approximately 98% of the Earth surface crust is composed of sediments or sedimentary rocks, the confluence of critical conditions for the formation of petroleum systems, such as generation, migration and entrapment, is a restrictive phenomenon, present in less than 1% of this surface [5]. At the geochemical level, while the quality of crude oil exhibits subtle variations depending on its geographic origin and thermal maturity, its elemental composition is remarkably constant. Petroleum is a complex mixture of organic compounds where the predominant elements are carbon and hydrogen, which represent approximately 95% of the total mass. The remaining percentage (between 1% and 7%) corresponds to heteroatoms, mainly sulfur, oxygen and nitrogen, whose presence and proportion define critical properties, such as viscosity and the degree of corrosivity of the fluid [6]. The generation of oil based on the amount of organic matter in marine and terrestrial systems has been accepted since the 1990s. Deng et al. (2023) [7] described the theoretical basis of oil and gas generation worldwide and confirmed that amount of hydrocarbons is closely correlated to the organic matter load present in sedimentary rocks. Furthermore, the organic matter used for oil production depends primarily on photosynthetic organisms (phytoplankton) in marine systems.

Biochemical Composition of Dinoflagellate Lipids

Due to their composition and origin, lipids produced by phytoplankton have been the subject of study for the last 50 years [8], with triacylglycerols, galactolipids and phospholipids being the main lipid components in these microorganisms [9,10]. These lipids have been of great importance in the field of biotechnology, mainly for the synthesis of a variety of saturated fatty acids, monounsaturated fatty acids, and polyunsaturated fatty acids (e.g., oleic acid (OA; 18:1(n-9), arachidonic acid (AA; 20:4(n-6), eicosapentaenoic acid (EPA; 20:5(n- 3), docosapentaenoic acid (DPA; 22:5(n-3), and docosahexaenoic acid (DHA; 22:6(n-3) that make up 5 to 60% of the cell dry weight [11,12]. In particular, triacylglycerol contributes up to 30%, free fatty acids up to 10%, and sterols approximately 5% of the phytoplankton lipid composition [10]. These lipids have multiple functions, from energy storage to the synthesis of membranes and bioactive substances that ensure their ecological success [13,14]. In particular, dinoflagellates can synthesize a mixture of highly unsaturated long-chain fatty acids (C28) and complex sterols, such as 4-methyl sterols and 4-desmethyl sterols, which serve as characteristic chemotaxonomic indicators [15,16]. These lipids are highly similar to crude oil, mainly due to the high carbon content (>85%) [4]. This is relevant for elucidating the origins of petroleum because of the quality of its organic matter; it is rich in complex lipids and specific biomarkers called dinosteranes (Table 1). These compounds are highly resistant and contribute to the formation of type I and II kerogen, both precursors of crude oil [17].

Table 1: Contribution of some groups of microorganisms to marine organic matter; Phyto – phytoplankton; Zoo – zooplankton.

Taxonomic group	Type	Main contribution to organic matter	Sedimentation rate (m day^-1)	Petroleum potential (kerogen)
Diatoms	Phyto	Lipids and silica	0.5-100*	Very high: Primary source of lipids (Type II)
Dinoflagellates	Phyto	Dinidporins (polymers) and sterols	0.5-10	High: Excellent preservation of biopolymers
Coccolithophorids	Phyto	Calcium carbonate	1-50*	Medium: Provide ballast for massive sinking
Cyanobacteria	Phyto	Labile organic matter (proteins)	< 0.5	Low: Mostly recycled at the surface
Copepods	Zoo	Compact fecal pellets	30-160	Indirect: Package and protect lipids
Krill (Euphausiaceae)	Zoo	Fecal pellets and active transport	150-350	Medium: Inject carbon at great depths
Salps	Zoo	Large and dense fecal pellets	400-1,200	High: Massive and ultra-fast carbon export
Appendicularians	Zoo	Marine snow (mucus houses)	50-200	Medium: Trap fine particles in aggregates
Foraminiferans	Zoo	Calcareous skeletons	100-500	Low-medium: Key to source rock density

Algal Blooms

An algal bloom caused by dinoflagellates is a highly productive event that drastically alters carbon fluxes in the ocean. The biomass of these microorganisms can reach massive concentrations, commonly known as “red tides” [20]. Unlike diatoms, dinoflagellates possess a particular chemical composition that influences the production of organic matter, which is ultimately preserved [21]. During bloom events, the concentration of organic carbon can exceed 500 to 1,000 µg of carbon per liter, which is 10 to 50 times higher than under normal ocean conditions [22]. Furthermore, net primary production rates of 2 to 5 g C m^-2 day^-1 have been recorded during the peak of the bloom; additionally, many dinoflagellates produce resistant cysts, whose walls are made of dinosporins (polymers similar to sporopollenin), which increases the rate of sedimentation and transport of carbon in the deep zones [17].

Much literature has been published about harmful algal blooms (HAB), caused by both toxic and non-toxic species, and various aspects of the so-called HAB science. Most bloom-forming marine species are dinoflagellates, and many of them have been reported toxic in any part of the world ocean, in marine environments, brackish and freshwater bodies [23]. At present, pelagic and benthic blooms are distinguished. Recently, (epi)benthic species have been paid much attention, and 242 dinoflagellate species from 63 genera have been described: most of them are sand-dwelling followed by epiphytic [24]. As for the duration of HAB events, they may last from several hours to more than a year. For example, the Karenia brevis (C.C. Davis) Gert Hansen & Moestrup (= Gymnodinium breve C.C. Davis) blooms on the West Florida shelf may last more than a year, and 8-12-month blooms are not uncommon there [25]. While in freshwater most toxic species are cyanobacteria (97 species from 10 orders), in the marine environment, especially in the coastal zone, dinoflagellates have the highest species richness (118 species from 8 orders) (Figure 1).

Figure 1: Electron micrographs of recent marine dinoflagellates from the coastal zone of the southern Gulf of Mexico (A, B, E, and H are from the Yucatán Peninsula; C, D, F and G are from the state of Veracruz): A – Pyrodinium bahamense L. Plate is highly toxic producer of paralytic shellfish poisoning (PSP) in humans, distributed in tropical waters; B – Gonyaulax polygramma F. Stein is a non-toxic species causing harmful algal blooms in subtropical waters; C – Ceratocorys horrida F. Stein is distributed mainly in the open sea in the tropical zone, possesses membraned spines characteristic of warm waters; D – Blixaea quinquecornis (T.H. Abé) Gottschling in Gottschling et al. is one of the few benthic-planktonic dinoflagellates with a tropical-boreal distribution, inhabiting both marine and estuarine brackish waters, non-toxic, causing intense blooms in the southern Gulf of Mexico; E – Scrippsiella spinifera G. Honsell & M. Cabrini is a non-toxic species described from the Mediterranean Sea in 1991, recently found in the southern Gulf of Mexico together with the blooming species Scrippsiella acuminata (Ehrenberg) Kretschmann, Elbrächter, Zinssmeister, S. Soehner, Kirsch, Kusber & Gottschling, capable of producing calcareous resting cysts unlike most dinoflagellates; F – Phalacroma rapa F. Stein is a non-toxic species; G – Dinophysis caudata Kent is a toxic neritic tropical-boreal species, producing okadaic acid and causing diarrhetic shellfish poisoning (DSP) in humans; H – Prorocentrum cordatum (Ostenfeld) J.D. Dodge is a toxic bloom-forming species, causing hepatotoxicity in mice and adverse effects on invertebrates, inhabiting both marine and brackish waters.

Other major microalgal groups are Bacillariophyceae (31 species), Haptophyta (8), Raphidophyceae (4) and Dictyochophyceae (3) [23]. Sixty-seven species and the taxa identified to the generic level are harmful non-toxic, not considering 20 species in the so-called grey list. It is worth considering that about one half of the recent dinoflagellate species are heterotrophs, and the other half are photosynthetic, most of which are mixotrophs. Strictly speaking, heterotrophic species are not phytoplankton if we define it as composed of photosynthetic organisms. Furthermore, ciliates, which also belong to the clade Alveolata as do dinoflagellates, include many photosynthetic species (also known as plastidic ciliates; facultative or obligate autotrophs); e.g., Mesodinium rubrum (Lohmann) (= Myrionecta rubra Lohmann) is an obligate autotroph that contains endosymbiotic cryptophytes [26].

“Marine Snow” and Its Composition

Microalgal cells and, in particular, dinoflagellates cells, can be part of the phenomenon known as “marine snow”, organic aggregates, particle flux, detrital material, pelagic sediment(s) or particulate organic matter [27-37]. “The scientists used the term “marine snow” for the abundant, readily visible, suspended particles in the water.” [37: 6]. “Material exported from the euphotic zone leaves as large, fast-sinking particles, and constitutes a source of food for pelagic and benthic organisms.” [35: 565]. Marine snow consists of detritus (organic debris including fecal pellets), living organisms (phytoplankton, protozoans, zooplankton) and inorganic matter, with associated microbial communities [30]. The history of marine snow research, emphasizing the major discoveries prior to the end of the 20th century, was presented by Silver (2015) [37]. In addition, marine snow-like particles, such as fecal pellets (mainly of the crustacean zooplankton) and amorphous aggregations, are distinguished [34]. It is also worth noting that microaggregates found in the water column [38] are different from large or macroscopic aggregates [31,32], which are synonymous with marine snow. The composition of marine snow has been actively studied during several decades hitherto. Both prokaryotic and eukaryotic microalgae are part of it. In earlier studies, as components of sinking particles, diatoms, dinoflagellates, coccolithophorids and unidentified (micro)flagellates [35,36] have been commonly mentioned. Apart from those, the following taxonomic groups of unicellular eukaryotic organisms have been mentioned more frequently: radiolarians and foraminiferans [34]. It is logical to suggest that, considering the advances in the field of taxonomy of major microalgal groups (discussed below), most become a part of marine snow or microaggregates and participate in vertical carbon flux. Based on the data obtained with the use of sediment trap deployments in all oceans with a sampling duration of more than one year, it was concluded that at some locations the particle flux maxima are the results of bloom periods of individual phytoplankton species [33]; the highest primary production values up to more than 100 g m^-2 yr^-1 were related to the blooming species of dinoflagellates, diatoms and coccolithophorids [39,40].

The Origin of Petroleum

A historical overview of the origin of petroleum was given by Walters (2006) and Höök et al. (2010) [40,41]. All theories of petroleum origin can be divided into abiotic (= abiogenic or non- biogenic) and biotic (= biogenetic) (Figure 2).

Figure 2: The importance of phytoplankton and zooplankton in the formation of petroleum in marine systems.

Previously, it was concluded that the origin of petroleum is duplex, organic and inorganic, stating that petroleum was laid down in Cambrian and Ordovician strata during the last 500 million years [42] for the distribution of the land masses and water basins on the Earth 500 Mya, see Figure 3).

Figure 3: Distribution of the land masses and oceans on the Earth in the Late Jurassic; modified from Scotese (2001), and Encyclopædia Britannica.

Paleontologists indicate a possible Precambrian origin of dinoflagellates more than 570 years ago [44]. Ehrenberg (1836) [45] was the first to recognize fossil dinoflagellate cysts. At least 2,294 dinoflagellate species have been described [46], and ca. 13-16% of them produce resting cysts also called dinocysts [47]. Resting cyst are the result of sexual reproduction, and they are diploid hypnozygotes (zygotic resting cysts), unlike haploid vegetative cells (except for Noctilucophyceae that have diplontic life-cycle, i.e. with the predominant diplontic stage). However, sexual reproduction has been known only for 1% of recent dinoflagellate species, but it is suggested that it could be a universal phenomenon for this taxonomic group; the zygotic nature of most fossil cysts remains unproven [44].

Recent Advances in the Taxonomy of Major Phytoplankton Groups and Their Relative Abundances

During the last decades a number of new classes of microalgae were described, including previously described taxa of the lower taxonomic level: Dictyochophyceae Silva 1980; Coleochaetophyceae Jeffrey 1982; Pavlovophyceae Cavalier-Smith 1986, emend. Green & Medlin 2000; Pedinophyceae Moestrup 1991, emend. Fawley, Zechman & Buchheim in Adl et al. 2012; Pelagophyceae Andersen & Saunders 1993; Noctilucophyceae Fensome et al. 1993; Klebsormidiophyceae van den Hoek et al. 1995; Trebouxiophyceae Friedl 1995; Zygnematophyceae van den Hoek et al. 1995, emend. Hall et al. 1999; Bolidophyceae Guillou et al. 1999; Pinguiophyceae Kawachi et al. 2003; Chlorokybophyceae Lewis & McCourt 2004; Mesostigmatophyceae Marin & Melkonian 1999, emend. Lewis & McCourt 2004; Mediophyceae Jouse & Proshkina-Lavrenko in Medlin & Kaczmarska 2004; Mamiellophyceae Marin & Melkonian 2010; Palmophyllophyceae Lelaert et al. 2016; Chloropicophyceae Lopes dos Santos & Eikrem 2017, Picocystophyceae Lopes dos Santos & Eikrem 2017; Biddulphiophyceae D.G. Mann in Adl et al. 2019; Leprocylindrophyceae D.G. Mann in Adl et al. 2019; Corethrophyceae D.G. Mann in Adl et al. 2019 [48]. Most major microalgal taxa have been described since 1990s based on transmission electron and light microscopy observations, and some were also based on biochemical data (e.g., Pelagophyceae and Bolidophyceae). Recently, metagenomic data of the relative abundances of different taxonomic groups in marine phytoplankton of all the oceans, based on the Tara Oceans Project in 2009-2013, have been published. Although dinoflagellates and diatoms remain the dominant groups in terms of the cell abundance, there are other groups (the picocyanobacterial genera Prochlorococcus Chisholm, Frankel, Goericke, Olson, Palenik, Waterbury, West- Johnsrud & Zettler ex Komárek et al. and Synechococcus Nägeli, Haptophyta (= Prymnesiophyta), Chlorophyta and Pelagophyta [49,50].

Protecting Marine Ecosystems Where Phytoplankton Live Could Ensure Petroleum Resources in the Future

Phytoplankton are a group of unicellular and colonial photosynthetic organisms capable of inhabiting diverse marine and freshwater bodies [51]. In aquatic ecosystems, phytoplankton comprises a network of biological interactions that have enabled successful conversion of light energy to chemical energy through biogeochemical cycles. Therefore, along with higher plants, phytoplankton provides a solid foundation of ecosystem services in both terrestrial and marine environments [52]. These services include supporting, regulating, provisioning and cultural services. It is estimated that 45% of global net primary production or more (≈108 pg C yr-¹) comes from phytoplankton [53,54]. In particular, groups such as diatoms and dinoflagellates are the primary source of the organic matter that makes up kerogen types I and II. These microorganisms synthesize lipid compounds that, when attached to sediments, possess the necessary energy density to transform into liquid petroleum [17]. Therefore, the protection of marine systems should be treated globally as a matter of energy security. This will ensure phytoplankton biodiversity, which, in turn, guarantees that the chemical quality of the organic matter reaching the seabed is optimal for hydrocarbon generation [55].

Acknowledgments

We thank Marcia M. Gowing (Seattle, WA, USA) for improving the English style and for valuable advice, and Dora A. Huerta- Quintanilla (CINVESTAV-IPN, Mérida, Yucatán) for her help with the scanning electron microscope. Financial support to the FOMIX CONACYT-Yucatán project “Análisis de las causas, dispersión y consecuencias ambientales de la marea roja en Yucatán” (No. 108897; 2009–2012) given to Jorge A. Herrera-Silveira (CINVESTAV-IPN), and the FOMIX CONACYT-Yucatán (No. 108160) and CONACYT LAB-2009-01 (No. 123913) projects of the Laboratorio Nacional de Nano y Biomateriales (CINVESTAV-IPN) given to Patricia Quintana- Owen is much appreciated.

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Pathologic Complete Response as a Prognostic and Surrogate Endpoint in Neoadjuvant Therapy for Solid Tumors: A Comprehensive Review Beyond Breast Cancer

1 Reply

DOI: 10.31038/CST.20261111

Abstract

Background: Neoadjuvant therapy is increasingly used across solid tumors to improve surgical outcome, assess systemic treatment sensitivity, and reduce early metastatic risk. Pathologic complete response (pCR), defined as the absence of residual invasive cancer following neoadjuvant treatment, has been accepted by the U.S. Food and Drug Administration (FDA) as a surrogate endpoint to support accelerated approval in high-risk early-stage breast cancer. However, the strength and generalizability of the association between pCR and long-term clinical outcomes, such as event-free survival (EFS) and overall survival (OS), vary substantially across tumor types and disease subgroups.

Methods: We evaluated recent meta-analyses, randomized trials, and large retrospective studies examining the association between pCR and survival outcomes, including EFS, OS, and recurrence-free survival (RFS), across multiple tumor types (ie. breast cancer, non-small cell lung cancer, melanoma, gastrointestinal malignancies, head and neck cancer, ovarian cancer, bladder cancer, and Merkel cell carcinoma).

Results: In breast cancer, pCR strongly predicts improved EFS and OS, particularly in triple-negative and HER2-positive subtypes, while its predictive value is less pronounced in HR+/HER2- disease. Emerging evidence supports pCR as a prognostic marker in melanoma, NSCLC, gastrointestinal, ovarian, head and neck squamous cell carcinoma, and muscle-invasive bladder cancers, though trial-level correlations vary and data remain limited in several settings. Associations are generally stronger in patient-level analyses than trial-level surrogacy assessments.

Conclusions: These findings highlight both the clinical value and the limitations of pCR as an endpoint. Continued tumor-specific evaluation of pCR using rigorous patient- and trial-level statistical frameworks is warranted to inform regulatory decision-making and optimize neoadjuvant drug development beyond breast cancer.

Keywords

Pathologic complete response, Breast cancer, Head and neck cancer, Melanoma, NSCLC, Esophageal cancer, Gastrointestinal cancer, Ovarian, Bladder cancer, Merkel cell carcinoma

Introduction

Neoadjuvant therapies havebecome a cornerstone in themanagement of multiple solid tumors, with potential to downstage disease, improve surgical resectability, provide early assessment of treatment sensitivity, and eradicate micrometastatic progression before surgery. With the expansion of neoadjuvant approaches to include targeted therapies and immunotherapies, the identification of robust intermediate endpoints that predict long-term clinical benefit has gained critical importance.

pCR, defined as the absence of residual invasive cancer cells in the resected primary tumor and sampled regional lymph nodes following neoadjuvant treatment (typically ypT0/is ypN0), has been recognized by the FDA as a surrogate endpoint to support accelerated approval specifically in high-risk early-stage breast cancer. Its use in other tumors is exploratory and not validated broadly for regular approval. This position is articulated in the current FDA guidance, Pathological Complete Response in Neoadjuvant Treatment of High-Risk Early- Stage Breast Cancer: Use as an Endpoint to Support Accelerated Approval [1]. The 2020 revision reflects FDA’s updated thinking and provides recommendations for trial design, statistical considerations, and acceptable pCR definitions in the neoadjuvant breast cancer setting. This regulatory precedent has motivated growing interest in evaluating pCR as a potential surrogate endpoint in additional tumor types, although its predictive value varies considerably across diseases.

Clinical practice guidelines also reflect the evolving role of treatment response in neoadjuvant care. Recent versions of the National Comprehensive Cancer Network (NCCN) Clinical Practice Guidelines in Oncology incorporate neoadjuvant systemic therapy pathways and acknowledge that the degree of treatment response—including clinical and pathological response—can inform postoperative management in specific settings. In breast cancer (NCCN Breast Cancer Version 5.2025), neoadjuvant therapy is strongly recommended for HER2- positive and triple-negative subtypes, with the extent of response (including pCR) refining adjuvant treatment decisions, consistent with evidence linking pCR to improved long-term outcomes. In rectal cancer (NCCN Rectal Cancer Version 4.2025), guidelines endorse consideration of nonoperative “watch-and-wait” management for carefully selected patients demonstrating a clinical complete response after neoadjuvant chemoradiation, underscoring the prognostic importance of response assessment even when not strictly defined pathologically [2,3]. For esophageal and gastroesophageal junction cancers (NCCN Esophageal/EGJ Cancers Version 1.2026), pathologic evaluation after neoadjuvant chemoradiotherapy is recommended as part of postoperative planning. NCCN Bladder Cancer (v3.2025) recognizes pCR post-neoadjuvant chemotherapy as prognostic but not a formal decision criterion. NCCN Cutaneous Melanoma (v2.2025) notes pCR/MPR as prognostic, informing de-escalation in responders but not standardized. NCCN NSCLC (v3.2026) emphasizes neoadjuvant chemo-immunotherapy with pCR/MPR as prognostic (e.g., CheckMate-816: pCR patients 95.3% (95% CI 82.7–98.8) 5-year OS) but not mandatory for decisions.

This review synthesizes the available evidence assessing the association between pCR and long-term survival outcomes across multiple solid tumors. We examine both patient-level prognostic associations, where pCR consistently correlates with favorable outcomes, and trial-level surrogacy analyses, where correlations between treatment effects on pCR and survival endpoints are more variable. By integrating data across tumor types, our objective is to provide a balanced and rigorous framework for evaluating the strengths, limitations, and future potential of pCR as an endpoint to guide neoadjuvant drug development, clinical trial design, and regulatory strategies beyond breast cancer.

Material and Methods

A comprehensive systematic literature review was conducted using databases including PubMed, EMBASE, and the Cochrane Library from database inception through December 2025. The literature consists of meta-analyses, randomized clinical trials, and clinical studies evaluating associations between pCR and survival outcomes. Both patient-level and trial-level associations were included if reported.

Results

Breast Cancer

In breast cancer, achieving pCR is strongly associated with improved long-term outcomes, including EFS, disease-free survival (DFS), and OS. A large pooled meta-analysis of 52 studies involving 27,895 patients treated with neoadjuvant therapy demonstrated a robust association between pCR and improved EFS across major subtypes—triple-negative breast cancer (TNBC), HER2-positive disease, and hormone receptor-positive/HER2-negative (HR+/ HER2-) disease. Across the entire population, 5-year EFS was 88% in patients achieving pCR compared with 67% in those without pCR, underscoring the prognostic significance of pCR irrespective of subtype (Table 1) [4]. While pCR is a strong patient-level prognostic marker, trial-level surrogacy is weak in some analyses; for example, a meta-regression across 29 randomized trials showed only modest correlation between treatment effects on pCR and DFS (R² = 0.08) or OS (R² = 0.09), highlighting variability by subtype and regimen [5].

Table 1: Breast Cancer

Study	Number of Patients	Number of Trials	Summary of Findings
von Minckwitz et al., 2012 [8]	6,377	7	ypT0 ypN0 is associated with better DFS compared to ypTis ypN0 (HR = 0.57) and better OS (HR = 0.71) ypT0 ypN0 is associated with better DFS (HR = 0.31) and OS (HR = 0..25) than ypT0/is ypN+ Prognostic impact strongest in HER2+ and TNBC; absent in luminal A
Berruti et al., 2014 [5]	14,641	29	Weak trial-level association between pCR and DFS (R² = 0.08) or OS (R² = 0.09)
Broglio et al., 2016 [9]	5,768	36	HER2+ Breast Cancer: EFS for pCR vs non-pCR was significant (HR = 0.37). Hormone receptor-negative (HR = 0.29); hormone receptor-positive (HR = 0.52).
Cortazar et al., 2014 [7]	11,955	12	For pCR vs non-PCR: Overall population: EFS: HR 0.48; OS: HR 0.36 HR-pos HER2-neg: EFS: HR 0.49; OS: HR 0.43 HER2-pos: EFS: HR 0.39; OS: HR 0.34 HR-neg: EFS HR 0.25, OS HR 0.19. TNBC: EFS HR 0.24; R² = 0.03 and 0.24 for EFS and OS.
Spring et al., 2020 [4]	27,895	52	5-yr EFS: 88% (pCR) vs 67% (no pCR) Patients with a pCR after NAT had significantly better EFS (HR = 0.31), particularly for TNBC (HR = 0.18) and HER2- pos (HR = 0.32) disease. pCR after NAT was also associated with improved survival (HR = 0.22).
Huang et al., 2020 [10]	4,330	25	TNBC: pCR was associated with improved EFS (HR = 0.24) and OS (HR = 0.19). The 5-year EFS was 86% vs 50% for pCR vs no pCR. The 5-year OS was 92% vs 58% for pCR vs no pCR.
Davey et al., 2022 [6]	25,150	78	HER2-pos: pCR predicted better EFS (HR 0.67; 41 studies), RFS (HR 0.69; 18 studies) and OS (HR 0.63; 29 studies).

Subtype-specific analyses further reinforce this relationship. In HER2-positive early breast cancer, a systematic review and meta- analysis of 78 studies (25,150 patients) showed that pCR was associated with significant improvements in survival outcomes, with hazard ratios of 0.67 (95% CI 0.60–0.74) for EFS and 0.63 (95% CI 0.56–0.70) for OS. These benefits were consistent across various HER2-targeted regimens, though the magnitude was greatest in hormone receptor- negative HER2-positive disease [6].

A landmark systematic review and pooled analysis of approximately 12,000 patients from 12 international trials (CTNeoBC) demonstrated that pCR rates vary substantially by subtype: highest in HER2-positive (particularly HR-/HER2+) and TNBC, and lowest in HR+/HER2- disease [7]. Although the association between pCR and long-term outcomes was weaker in HR+/HER2- tumors overall, achievement of pCR in this subgroup still correlated with significantly better EFS and OS, particularly among high-grade tumors. In HER2-positive disease, pCR strongly predicted improved EFS and OS regardless of hormone receptor status, with a more pronounced prognostic effect in HR-/ HER2+ tumors than in HR+/HER2+ tumors. Across all subtypes, the collective evidence positions pCR as a strong prognostic marker, with the greatest clinical relevance in TNBC and HER2-positive disease, where both absolute pCR rates and survival correlations are highest.

Non-Small Cell Lung Cancer (NSCLC)

In resectable NSCLC, pCR is a strong patient-level prognostic marker. A meta-analysis of 7,011 patients treated with neoadjuvant chemotherapy (± radiotherapy) showed that pCR was associated with significantly improved outcomes (EFS HR 0.46; OS HR 0.50) [11]. With the introduction of neoadjuvant chemo-immunotherapy, a meta- analysis of eight randomized trials (3,387 patients) demonstrated higher pCR rates (RR 5.58) and improved 2-year EFS (HR 0.57) compared with chemotherapy alone (Table 2) [12].

Table 2: Non-small cell lung cancer.

Study	Number of Patients	Number of Trials	Summary of Findings
Rosner et al., 2022 [11]	7,011	28	pCR vs non-pCR: OS HR 0.50; EFS HR 0.46
Banna et al., 2024 [12]	3,387	8	Neoadjuvant chemo-immunotherapy compared to chemo: improved 2-year EFS (HR 0.57) and increased pCR rate (RR 5.58). No reports were provided about the comparison of EFS for pCR vs non-pCR patients.
Hines et al. 2024 [13]	2,385	7	At the patient level, the R² of pCR with 2-year EFS was 0.82. The odds ratio of 2-year EFS rates by response status was 0.12 (0.07–0.19). At the trial level, the R² for the association of odds ratio of response and HR of EFS was 0.58.
Waser et al., 2024 [14]	6,530	20	pCR vs no pCR: HR = 0.49 for OS MPR vs no MPR: HR = 0.36 for OS pCR vs no pCR: HR = 0.49 for EFS (11 studies, n=2,156) Trial-level analyses did not show a strong correlation between pCR and OS (R² = 0.045) or EFS (R² = 0.319).
CheckMate 816 5-yrupdate [15]	358	1	pCR vs non-pCR: 5-yr OS 95.3% (95% CI 82.7-98.8) vs 55.7% (95% CI 46.9-63.7) (exploratory) Pre-surgery ctDNA clearance vs others: 5-yr OS 75.0% vs 52.6%

A recent surrogacy analysis of seven immune checkpoint inhibitor trials (2,385 patients) found a strong patient-level correlation between pCR and 2-year EFS (R²=0.82), although trial-level surrogacy was only moderate and imprecise (R²=0.82) [13]. A larger pooled analysis reported weaker trial-level correlations (R²=0.045 for OS; 0.319 for EFS) [14].

Long-term observational data also support pCR as a prognostic marker. In a 403-patient stage IIIA–IIIC cohort treated with induction chemotherapy, concurrent chemoradiation, and surgery, pCR occurred in 34% and independently predicted improved survival (OS HR 0.27; EFS HR 0.35) [15]. pCR rates were higher in squamous tumors (46%) than non-squamous (27%).

Updated results from major perioperative trials further reinforce this association. In the final 5-year analysis of CheckMate 816, overall 5-year OS was 65.4% in the nivolumab-chemotherapy arm versus 55.0% with chemotherapy alone (HR 0.72, P=0.048). In exploratory subgroup analyses, patients achieving pCR had a 5-year OS of 95.3% (95% CI 82.7–98.8) compared with 55.7% (95% CI 46.9–63.7) in those without pCR [16]. Similar patterns have been observed in AEGEAN and KEYNOTE-671, where higher pCR/MPR rates parallel sustained EFS benefits [17,18].

Ongoing IASLC initiatives continue to standardize pCR/MPR assessment and evaluate their potential as surrogate endpoints for regulatory use in resectable NSCLC.

Melanoma

In resectable stage III melanoma, neoadjuvant immunotherapy— particularly dual checkpoint blockade with PD-1 and CTLA-4 inhibitors—induces high pathologic response rates with strong prognostic value. In the OpACIN-neo trial, interobserver agreement for pathologic response assessment was excellent (κ = 0.879; ICC = 0.965), and patients achieving a pathologic response (<50% viable tumor) had markedly improved outcomes, with 3-year recurrence-free survival (RFS) of 95% versus 37% in non-responders (P < 0.001). An immune- active fibrosis-rich subtype further predicted absence of recurrence and prolonged RFS. Long-term follow-up from the combined OpACIN and OpACIN-neo trials confirmed durable benefit, with pathologic response remaining the strongest predictor of low relapse risk [19,20].

Single-agent PD-1 therapy demonstrates similar prognostic patterns. In a phase Ib trial of neoadjuvant pembrolizumab followed by adjuvant pembrolizumab, no deaths occurred among patients achieving major pathologic response (MPR) or pCR, yielding a 5-year OS of 100% compared with 72.8% in non-MPR patients [21].

Pooled analyses from the International Neoadjuvant Melanoma Consortium (INMC; n = 818) further confirm the prognostic value of pathologic response. In the updated INMC pooled dataset presented at ESMO 2024, patients achieving major or complete pathologic response had 3-year recurrence-free survival of 89%, demonstrating durable disease control across neoadjuvant regimens [22] (Table 3).

Table 3: Melanoma and Merkel Cell Carcinoma.

Study	Number of Patients	Number of Trials	Summary of Findings
OpACIN-neo trial [20]	86	1	Melanoma: Pathologic response (<50% viable tumor, including pCR + near-pCR/MPR) achieved in ~74–78% (regimen-dependent; 77% in optimal arm). pCR (0% viable tumor) in substantial subset (~20–47% across arms). Estimated 3-year RFS 95% for pathologic responders vs 37% for non-responders (P < 0.001) after median 47-monthfollow-up.
Study NCT02434354 [21]	30	1	Melanoma: 5-yr OS 100% in MPR/pCR vs 72.8% in non-MPR; no deaths in MPR/pCR
NADINA [23]	423	1	Melanoma: Neoadjuvant nivo+ipi: MPR 59%; 12-mo RFS 95.1% MPR vs 57% non-response; 2-yr EFS 77.3% (HR 0.32–0.40 vs adjuvant nivo) pCR associated with 5-year EFS of 83% vs. 53% for non-pCR; 5-year OS of 89% vs. 64% for non-pCR Updated 2-year data (ESMO 2025) show 24-month EFS 77.3% (HR 0.40; 95% CI 0.28-0.57; P<0.001) and DMFS 82.8% (HR0.43; 95% CI 0.29-0.64; P<0.001) for neoadjuvant vs adjuvant. MPR (59%) strongly predicted 12-month RFS (95.1% vs 57%).
INMC pooled (ESMO 2024) [22]	818	1	Melanoma: 633 (77%) trial pts and 185 (23%) real-world pts MPR rates 46% (PD-1 mono), 62% (PD-1+CTLA-4), 67% (PD-1+LAG-3); 3-year RFS 89% in MPR achievers (highest prognostic marker). 3-year EFS 64% (PD-1 alone), 76–77% (PD-1+CTLA-4), 82% (PD-1+LAG-3).
PRADO [24]	99	1	Melanoma: MPR in 61/99 (61.6%), including pCR in 41/99 (41.4%). MPR patients underwent de-escalated surgery and omitted adjuvanttherapy. 2-year RFS was 93% (95% CI 86–100%) in MPR patients.
SWOG S1801 [25]	313	1	Melanoma: 2-year EFS 72% with neoadjuvant-adjuvant pembrolizumab vs 49% with adjuvant-only pembrolizumab (HR 0.58; 95% CI 0.39–0.87; P = 0.004).
CheckMate 358 [63]	36	1	Merkel Cell Carcinoma: Neoadjuvant nivolumab: pCR 47.2% (17/36); no relapses in pCR patients at 19.3-month median post-op follow-up. RFS at 12 months: 100% vs 59.6% (pCR vs non-pCR); at 24 months: 88.9% vs 52.2%; RFS HR 0.12.

Recent phase 3 data further validate neoadjuvant immunotherapy. In the NADINA trial, neoadjuvant nivolumab plus ipilimumab followed by response-adapted adjuvant therapy significantly improved EFS (HR 0.32–0.40 vs adjuvant nivolumab) with an MPR rate of 59%, which strongly predicted outcomes (12-month RFS 95.1% vs 57%) [23]. The PRADO extension showed that MPR enabled de-escalated surgery, with 2-year RFS of 93% in responders [24]. SWOG S1801 similarly demonstrated EFS superiority for neoadjuvant-adjuvant pembrolizumab over adjuvant-only therapy [25].

NCCN Cutaneous Melanoma Guidelines (Version 2.2025) now endorse neoadjuvant immune checkpoint inhibition for resectable stage IIIB–IIID disease and recognize pCR/MPR as important prognostic markers informing postoperative treatment intensity, though not yet validated surrogate endpoints.

Overall, achieving MPR or pCR after neoadjuvant immunotherapy predicts exceptional long-term RFS and OS in melanoma, supporting ongoing shifts toward response-adapted neoadjuvant strategies.

Merkel Cell Carcinoma

In Merkel cell carcinoma (MCC), a rare and aggressive neuroendocrine skin cancer, pCR after neoadjuvant immunotherapy is a strong patient-level prognostic marker, associated with prolonged RFS and excellent long-term outcomes in responders (Table 3).

Although data are limited by disease rarity, emerging evidence from neoadjuvant immune checkpoint inhibitor trials supports high pCR rates and favorable prognosis. In the phase I/II CheckMate 358 trial, neoadjuvant nivolumab (two doses) achieved a pCR rate of 47.2% (17/36 evaluable patients), with additional major pathologic responses observed. No patient achieving pCR experienced relapse at a median postoperative follow-up of 19.3 months, and RFS was markedly improved in responders (12- and 24-month RFS 100% and 88.9%, respectively) [63] (Table 3).

Supporting evidence from case reports and small series demonstrates similar findings with neoadjuvant avelumab, including complete pathologic responses and absence of recurrence at short- term follow-up [64].

Collectively, these findings highlight pCR/MPR as a robust prognostic indicator in resectable MCC treated with neoadjuvant immunotherapy, consistent with the tumor’s high immunogenicity. However, trial-level surrogacy remains unestablished due to small cohorts, lack of randomized data, and limited long-term follow- up. Ongoing studies evaluating neoadjuvant combinations and response-adapted strategies may further clarify the clinical utility of pCR, including potential treatment de-escalation in complete responders.

Gastrointestinal Malignancies

In gastric and gastroesophageal junction (GEJ) cancers, pCR after neoadjuvant or perioperative therapy is associated with improved survival at the patient level, but trial-level surrogacy remains limited. Foundational perioperative chemotherapy trials first established modern treatment paradigms. The MAGIC trial (ISRCTN93793971) demonstrated improved 5-year overall survival (OS) with perioperative ECF (epirubicin, cisplatin, and 5-fluorouracil) versus surgery alone (36% vs 23%; HR 0.75; 95% CI 0.60–0.93; P = 0.009), showing pathologic downstaging but without pCR as a primary endpoint [26]. The FLOT4 trial established perioperative FLOT (5-fluorouracil, leucovorin, oxaliplatin, and docetaxel) as superior to ECF/ECX (epirubicin, cisplatin, and 5-fluorouracil or capecitabine), improving median OS (50 vs 35 months; HR 0.77; 95% CI 0.63–0.94; P = 0.012) and increasing pCR rates (16% vs 6%) (Table 4) [27].

Table 4: Gastrointestinal Cancers.

Study	Number of Patients	Number of Trials	Summary of Findings
MAGIC trial [26]	503	1	Gastroesophageal: Improved 5-year OS 36% vs 23%; HR 0.75 (95% CI 0.60–0.93), P = 0.009. Significant pathologic T- and N-stage downstaging. pCR not prospectively assessed
FLOT4 Trial [27]	716	1	Gastric: pCR rate: 16% (Becker Ia) with FLOT vs 6% with ECF/ECX (P<0.001 in phase 2 part; consistent in full trial). Higher major regression with FLOT.
MATTERHORN Trial [28]	948	1	Gastric: pCR rate: 19.2% (95% CI 15.7-23.0) with durvalumab + FLOT vs 7.2% (95% CI 5.0-9.9) with FLOT alone (P<0.001). Supports perioperative IO addition; linked to EFS/OS benefits
KEYNOTE-585 (2025final update) [29]	804	1	Gastric: pCR rate: 12.9% (95% CI 9.8-16.6) with perioperative pembrolizumab + chemo vs 2.0% (95% CI 0.9-3.9) with chemo alone (P<0.00001). Final OS showed numerical improvement but non-significant EFS benefit (44.4 vs 25.7 mo; HR 0.81, 95% CI 0.67- 0.98; did not meet prespecified threshold) and OS (71.8 vs 55.7 mo; HR 0.86, 95% CI 0.71-1.06; not significant); pCR prognostic but not strong trial-level surrogate.
Sugumar et al., 2025 [31]	11,882	25	Rectal: No trial-level correlation. On meta-regression analysis, pCR was not correlated with OS (β = 0.37; 95% CI, -0.98 to 1.71; P = .57). Similarly, pCR was not correlated with DFS (β = -0.84; 95% CI, -2.55 to 0.87; P = .32).
Smyth et al., 2016 [32]	330	1	Gastroesophageal: Mandard TRG 1–2 vs 3–5: 5-year OS 58.8% vs 28.9%; HR 1.94, P = 0.021. Demonstrates prognostic value of histologic regression (retrospective).
Soro et al 2018 [33]	56		Esophageal: pCR vs non-pCR: Median OS 4.1 vs 1.7 yrs; Median DFS 3.1 vs 1.1 yrs (P=0.04)
Murphy et al 2017 [34]	911	1	Esophageal: pCR was associated with better OS (median 71.28 vs 35.87 mo) and higher 5-year OS rate 52% vs 41% compared to non-pCR; was also associated with better RFS (median 70.75 vs 26.07 mo).
Lin et al. 2018 [35]	68	1	Esophageal: pCR is associated with higher 2-year OS rate 81.3% vs 58.3% (P = 0.025).
Wan et al 2019 [36]	6,780	21	Esophageal, esophagogastric junction AC, gastric AC, rectal cancer and pancreatic cancer: pCR vs non-pCR: OS (HR = 0.50, P < 0.001) and DFS (HR = 0.49, P < 0.001). In EGJAC/GAC, the correlation of pCR with OS was significant (HR = 0.38, p = 0.02).
Li et al. 2018 [37]	1,143	7	Gastric: RR of pCR vs non-pCR is 0.5(p<0.0001), 0.34(p<0.0001), and 0.44 (p<0.0001) for 1, 3, 5-year-OS, respectively. RR for 3-year DFS was 0.43 (p = 0.002)
Petrelli et al. 2017 [38]	10,050	22	Rectal: Patient level: change of pCR is correlated weakly with change of OS 5-year rate (R² = 0.28). 3-year DFS rate and OS was similarly (R²=0.37). Trial level: R² = 0.41 and 0.04 respectively.
Sun et al. 2025 [40]	8,040	38	Esophageal: pCR vs non-pCR (esophageal, post-NCRT): OS HR 0.54 (95% CI 0.52-0.57); DFS HR 0.51 (95% CI 0.46-0.57). Strong patient-level prognostic association
RAPIDO Trial [41]	912	1	Rectal: pCR rate: 28% with TNT vs 14% with standard CRT (OR 2.37; P < 0.001) Disease-related treatment failure: 23.7% vs 30.4% (HR 0.75; P = 0.019) TNT significantly increased pCR, but pCR showed limited trial-level surrogacy for long-term outcomes

Perioperative chemo-immunotherapy further increases pCR but demonstrates variable impact on survival. In MATTERHORN (NCT04592913), durvalumab plus FLOT doubled pCR rates (19.2% vs 7.2%; OR 3.12; 95% CI 2.23–4.37; P < 0.00001) and significantly improved event-free survival (EFS HR 0.71; 95% CI 0.58–0.86; P < 0.001), with final OS HR 0.78 (95% CI 0.63–0.96; P = 0.021) and FDA approval in November 2025 [28].

By contrast, KEYNOTE-585 (NCT03221426) significantly increased pCR with pembrolizumab plus chemotherapy (12.9% vs 2%) but did not achieve statistically significant EFS or OS improvement in final analyses. Final analysis (JCO 2025) showed non-significant EFS benefit (44.4 vs 25.7 mo; HR 0.81, 95% CI 0.67-0.98) and OS (71.8 vs 55.7 mo; HR 0.86, 95% CI 0.71-1.06), neither reaching protocol defined significance [29]. The DANTE/FLOT8 trial (NCT03421288) reported enhanced histopathologic regression and downstaging with atezolizumab + FLOT versus FLOT alone, though survival follow-up remains ongoing [30].

In rectal cancer, pCR following neoadjuvant chemoradiotherapy (CRT) or total neoadjuvant therapy (TNT) strongly predicts individual patient outcomes—including improved DFS, OS, and reduced recurrence—but does not function as a reliable trial-level surrogate. A comprehensive meta-analysis of 25 randomized trials (11,882 patients) found no significant correlation between treatment- related increases in pCR and improvements in OS or DFS, indicating that pCR does not function as a validated surrogate endpoint in rectal cancer trials [31].

pCR after CRT or total TNT is a strong patient-level prognostic marker in locally advanced rectal cancer, associated with lower rates of local and distant recurrence and superior OS and DFS in responders compared with non-responders.

In high-risk disease, TNT regimens improve both pCR rates and long-term outcomes. The RAPIDO trial (short-course radiotherapy followed by consolidation chemotherapy vs long-course CRT) reported pCR occurred in 28% of patients in the TNT arm versus 14% in the standard CRT arm (OR 2.37; P < 0.001), and disease-related treatment failure was reduced to 23.7% versus 30.4% (HR 0.75; P = 0.019) [58]. The UNICANCER-PRODIGE 23 trial (neoadjuvant mFOLFIRINOX followed by CRT vs standard CRT) showed improved 3-year DFS (76% vs 69%; HR 0.69; P = 0.034) and higher pCR rates in the experimental arm [59]. These data support TNT as a standard of care for high-risk locally advanced rectal cancer.

pCR also has important clinical implications beyond prognosis. Patients achieving complete response may be candidates for organ- preservation strategies, including watch-and-wait approaches, which have been associated with favorable oncologic outcomes and avoidance of permanent stomas in carefully selected patients [2,3].

However, a systematic review and meta-analysis of 25 randomized trials (11,882 patients) found no significant trial-level association between treatment-related differences in pCR and OS or DFS, indicating that pCR does not function as a reliable surrogate endpoint for survival at the trial level [31]. Overall, pCR remains clinically valuable for individual risk stratification and for guiding organ- preservation strategies in selected patients, but current evidence does not support its validation as a surrogate endpoint for survival in rectal cancer trials.

Achievement of pCR after CRT is strongly associated with superior survival in esophageal cancer. In a CRT cohort of 56 patients, Soror et al. reported a median OS of 4.1 years in pCR patients versus 1.7 years in non-pCR patients, and median DFS of 3.1 years versus 1.1 years, respectively (P = 0.04) [33]. In a large trimodality series of 911 patients, Blum Murphy et al. observed significantly longer survival, with median OS of 71.3 months vs 35.9 months and median RFS of 70.8 months vs 26.1 months in pCR versus non-pCR patients (both P < 0.01) [34]. Similarly reported superior outcomes in patients achieving pCR, with 2-year OS of 81.3% vs 58.3% in non-pCR patients (P = 0.025) [35].

Earlier studies by Davies et al. [36], Meredith et al. [37], Donahue et al. [38], and Berger et al. [39] consistently demonstrated markedly improved long-term survival in patients achieving pCR. A 2025 meta- analysis confirmed strong patient-level associations between pCR and survival, with OS HR 0.54 and DFS HR 0.51, consistent across histologic subtypes [40]. A 2024 pooled analysis further showed that overall pCR rates after neoadjuvant therapy were below 30%, with higher pCR rates in squamous cell carcinoma (SCC) than adenocarcinoma [41].

Neoadjuvant immunochemotherapy (PD-1/PD-L1 inhibitors + chemotherapy ± RT) increases pCR and major pathologic response rates, particularly in SCC, although trial-level surrogacy for OS remains weak (R² = 0.07) [42]. Recent comparative cohorts report pCR rates of 20–30% and improved 3-year OS compared with standard CRT in selected stage III SCC [43].

Overall, pCR remains a meaningful patient-level prognostic marker across gastrointestinal malignancies, but its trial-level surrogacy is weak, supporting its use for risk stratification but not yet as a validated regulatory surrogate.

Head and Neck Cancer

In resectable head and neck squamous cell carcinoma (HNSCC), pCR or MPR after neoadjuvant immunotherapy is associated with substantially improved long-term outcomes at the patient level. Across early-phase trials of neoadjuvant anti-PD-1 agents, reported pCR rates range from10 to15%, and MPR rates range from 20% to 50% with strong prognostic implications [44-46]. In a neoadjuvant pembrolizumab study, pCR occurred in 10% of patients and MPR in 44%, and no recurrences were observed among patients achieving MPR, whereas recurrences occurred predominantly among non-responders [44]. In a phase II trial of nivolumab ± ipilimumab, 2-year DFS was 71% in responders vs 51% in non-responders, with MPR independently predicting better outcomes [45]. A 2021 meta-analysis of neoadjuvant ICI confirmed pCR/MPR linked to superior EFS and OS [46] (Table 5).

Table 5: Neoadjuvant Immunotherapy in Head and Neck Squamous Cell Carcinoma (HNSCC).

Study	Number of Patients	Number of Trials	Summary of Findings
NCT02296684 [44]	36	1	Pembrolizumab Neoadjuvant–Adjuvant Trial: pCR 10%; MPR 44%. No recurrences among MPR patients; recurrences confined to non-responders.
NCT02919683 [45]	29	1	Nivolumab ± Ipilimumab Neoadjuvant Trial: MPR 45%. 2-year DFS 71% (responders) vs 51% (non-responders). MPR independently predicted DFS and OS.
Shibata et al. 2021 [46]	382	17	Neoadjuvant ICI Systematic Review: Pooled pCR 12%; MPR 37%. pCR/MPR significantly associated with improved EFS and OS.
KEYNOTE-689 (NCT03765918) [47]	714	1	3-year EFS 57.6% vs 46.4% (perioperative pembrolizumab vs SOC); HR 0.73 (95% CI 0.58–0.92; P = 0.008). Neoadjuvant pembrolizumab did not compromise surgery.
Zandberg et al. 2025 [48]	624	9	Pooled IO Combination Analysis (2024–2025): MPR/pCR 20%–67% depending on regimen (highest with chemo-IO). Responders showed markedly longer DFS and OS; grade ≥3 toxicity higher with chemo-IO.

Recent phase III data (KEYNOTE-689, 2025) demonstrate improved EFS with perioperative pembrolizumab + standard care (surgery + adjuvant RT ± cisplatin) significantly improves EFS (3- year rates 57.6% vs 46.4%; HR reduction) versus standard care alone, with neoadjuvant pembrolizumab safe and not compromising surgery [47]). Updated meta-analyses (2024–2025) report pooled MPR or pCR rates ranging from 20% to 67% with ICI combinations (higher with chemoimmunotherapy), correlating with improved DFS/OS in responders, though chemoimmunotherapy increases grade 3–4 toxicity [48].

These data position pCR/MPR as a meaningful patient-level prognostic marker in HNSCC with neoadjuvant immunotherapy. However, trial-level surrogacy for OS remains unvalidated, and longer follow-up from randomized studies will be required before pathologic response can be accepted as a regulatory surrogate endpoint in this disease.

Ovarian Cancer

In advanced high-grade serous ovarian cancer, pCR after neoadjuvant chemotherapy (NACT) is uncommon (<5–10%) but, when achieved, is strongly associated with improved survival. Because true pCR is rare, most studies use the validated three-tier Chemotherapy Response Score (CRS), with CRS3 indicating complete or near-complete histologic response in the omentum and is considered the pathologic surrogate of chemosensitivity [49] (Table 6).

Table 6: Ovarian Cancer and Bladder Cancer.

Study	Number of Patients	Number of Trials	Summary of Findings
Bohm et al., 2015 [49]	98	1	Ovarian: Development and validation of 3-tier CRS in high-grade serous ovarian carcinoma post-NACT. CRS3 (complete/near-complete response) vs CRS1-2: prolonged PFS and OS (strong prognostic value, especially in omentum).
Marsh et al., 2025 [50]	133	1	Ovarian (FIGO III–IV Multicenter CRS Study): CRS3 vs CRS1-2: median PFS 24.8 months vs shorter (p < 0.001); improved OS (p = 0.011). Pathologic response to NACT significantly associated with better PFS/OS.
Choe et al. 2023 [55]	4,287	12	Ovarian (CRS Meta-analysis): CRS3 and pCR: OS HR 0.48; PFS HR 0.52
Kim et al. 2024 [56]	5,231	15	Ovarian (CRS Meta-analysis): CRS3 vs CRS1–2: OS HR 0.46; PFS HR 0.51
Bhandoria et al. 2024 [57]	3,942	10	Ovarian: pCR vs residual disease: OS HR 0.41; RFS HR 0.44
SWOG 8710 (Grossman et al. 2003) [60]	307	1	MIBC (MVAC vs surgery): Neoadjuvant MVAC vs surgery alone: pCR (ypT0) 38% vs 15%; median OS 77 vs 46 months. Established pCR as a strong prognostic marker after cisplatin-based NAC.
PURE-01 (NCT02736266) [61]	155	1	MIBC: Neoadjuvant pembrolizumab (3 cycles): pCR (ypT0N0) 36.8%. 36-month EFS 74.4%, OS 83.8%. 36-month RFS in ypT0N0: 96.3%, demonstrating near-curative outcomes in pCR patients.
NIAGARA [62]	1063	1	MIBC: Durvalumab + gemcitabine-cisplatin vs GC alone: 24-mo EFS 67.8% vs 59.8% (HR 0.68; P<0.001); 24-mo OS 82.2% vs 75.2% (HR 0.75; P=0.01). First phase III trial showing that increasing pCR translates into improved survival.

Across multiple cohorts, CRS3 consistently demonstrates robust prognostic value. In a 133-patient FIGO III/IV cohort, CRS3 was associated with significantly longer progression-free survival (median 24.8 months vs 16.7 months; P < 0.001) and improved overall survival (P = 0.011) compared with CRS1–2 [50]. Foundational work by Böhm et al first established CRS3 as a marker of favorable prognosis [49], and external validation cohorts-including Lee et al. [51], Singh et al. [52], Rajkumar et al. [53] and Cohen et al. [54] -consistently confirmed that CRS3 is associated with significantly prolonged PFS and OS relative to CRS1-2.

Although rare, pCR is likewise associated with substantially longer recurrence-free and overall survival (often by 12–24 months) compared with patients with residual disease. Recent systematic reviews and meta-analyses [55-57] reaffirm the strong patient-level prognostic value of CRS3 and pCR, but highlight persistent limitations for trial-level surrogacy, including low pCR incidence and variability in histopathologic scoring.

Overall, pCR/CRS3 represents a robust patient-level prognostic marker following NACT in advanced ovarian cancer and may help refine postoperative treatment strategies, while broader validation as a regulatory surrogate endpoint is still evolving.

Bladder Cancer

In muscle-invasive bladder cancer (MIBC), pCR after NAC is a strong patient-level prognostic marker, associated with markedly improved RFS and OS. Patients achieving pCR (ypT0N0) at radical cystectomy experience significantly lower recurrence and mortality risks compared with those with residual disease, reflecting highly chemo-sensitive and immunosensitive tumor biology.

Cisplatin-based neoadjuvant chemotherapy (NAC) established this paradigm. In SWOG 8710, neoadjuvant MVAC increased pCR rates to 38% compared with 15% with surgery alone and improved median overall survival (77 vs 46 months) [60]. Subsequent pooled series and meta-analyses consistently demonstrate 5-year OS exceeding 70–80% in ypT0 patients, compared with ~40–50% in patients with residual disease, confirming pCR as a robust patient- level prognostic marker [61] (Table 6).

Neoadjuvant immune checkpoint blockade has confirmed that immunotherapy-induced pCR also translates into durable survival benefit. In PURE-01, three cycles of pembrolizumab before cystectomy produced a pCR rate of 36.8%. At 3-year median follow-up, event- free survival was 74.4% and overall survival was 83.8%. Importantly, among patients achieving ypT0N0, 36-month recurrence-free survival was 96.3%, and 96.1% (95% CI, 89-100) for ypT1/a/isN0, 74.9% (95% CI, 60.2-93) for ypT2-4N0, and 58.3% (95% CI, 36.2-94.1) for ypTanyN1-3 response, demonstrating near-curative outcomes associated with immunotherapy-mediated tumor eradication [61]

Definitive evidence that increasing pathologic response translates into improved survival now comes from the phase III NIAGARA trial. In this perioperative study of durvalumab plus gemcitabine–cisplatin versus chemotherapy alone, the dual primary endpoints of pCR and EFS were met. At 24 months, EFS was 67.8% with durvalumab versus 59.8% with chemotherapy (HR 0.68; P < 0.001), and overall survival was 82.2% versus 75.2% (HR 0.75; P = 0.01), establishing perioperative immunochemotherapy as a new standard of care [62].

Taken together, pCR is a validated patient-level prognostic biomarker in MIBC across chemotherapy and immunotherapy platforms. PURE-01 demonstrates the durability of immunotherapy- induced pCR, while NIAGARA provides the first randomized evidence that increasing pCR translates into improved EFS and OS, strengthening the regulatory case for perioperative immunochemotherapy in bladder cancer.

Discussion

This review synthesizes the evidence supporting pCR as an intermediate endpoint in neoadjuvant therapy across solid tumors, with a focus on its prognostic significance and potential role as a surrogate for long-term clinical outcomes. At the patient level, pCR is consistently associated with improved outcomes across tumors. However, trial-level surrogacy is weak or absent in rectal cancer (no correlation per Sugumar 2025), moderate but imprecise in NSCLC (R²=0.58 per Hines 2024; weaker per Waser 2024), and limited elsewhere due to data immaturity. Regulatory use beyond breast cancer requires tumor-specific validation. The association is most robust and reproducible in breast cancer, particularly in triple- negative and HER2-positive subtypes, and is also well supported in melanoma, NSCLC, muscle-invasive bladder cancer, and head and HNSCC. Emerging evidence suggests comparable prognostic relevance in ovarian cancer using near-complete response metrics such as Chemotherapy Response Score 3 [CRS3]), as well as in Merkel cell carcinoma, and selected gastrointestinal malignancies including gastric, gastroesophageal junction, rectal, esophageal. Notably, the strength of the association between pCR and long-term outcomes varies substantially by tumor biology, molecular subtype, and treatment modality, with the most pronounced effects observed in immunogenic tumors treated with immune checkpoint inhibitors.

From a regulatory standpoint, the FDA’s acceptance of pCR as an endpoint reasonably likely to predict clinical benefit—most notably in high-risk, early-stage breast cancer—rests on a combination of strong patient-level prognostic associations and supportive trial- level surrogacy. Outside this validated setting, however, pCR does not yet meet the evidentiary standard required for broad regulatory reliance. Although patient-level correlations between pCR and survival outcomes remain consistent across many tumor types, trial- level surrogacy-defined as the extent to which treatment-induced improvements in pCR reliably translate into proportional gains in long-term survival has been weak, inconsistent, or absent in several malignancies. For example, meta-regression analyses have failed to demonstrate significant trial-level correlations in rectal cancer, have yielded modest and imprecise estimates in NSCLC, and remain severely limited in rare tumor types due to small sample size and a paucity of randomized neoadjuvant trials.

This fundamental distinction between prognostic utility and surrogate validity carries critical implications for both trial design and regulatory decision-making. pCR is clinically valuable for risk stratification, response-adapted management (e.g., watch-and-wait in rectal cancer, de-escalation in melanoma or Merkel cell carcinoma), and organ preservation paradigms. However, in most settings, pCR alone is insufficient to support claims of clinical benefit or to justify accelerated approval outside of contexts in which trial-level surrogacy has been rigorously established. Consistent with guidance from EMA/CHMP, broader regulatory acceptance of pCR will require tumor-specific validation that demonstrates reproducible trial- level surrogacy, standardized pathologic assessment (e.g., immune- modified neoadjuvant criteria in melanoma and Böhm CRS in ovarian cancer), and confirmation of durable clinical benefit in adequately powered prospective studies.

In conclusion, pCR is a clinically meaningful and prognostically powerful endpoint in neoadjuvant therapy across diverse solid tumors. Its role is firmly established in breast cancer and shows strong patient-level promise in multiple other malignancies, particularly in the context of immunotherapy. However, the use of pCR as a regulatory surrogate beyond breast cancer must be determined on a tumor-specific and context-dependent basis. Continued generation of high-quality neoadjuvant trial data, extended follow-up for survival outcomes, harmonization of pathologic response criteria, and robust trial-level validation will be essential to define the appropriate role of pCR in future drug development and regulatory frameworks.

Conflict of Interest

SC is an employee of Nektar Therapeutics and may own its stocks. PH is an employee of Daiichi Sankyo Inc and may own its stocks. JF is an employee of TransThera Sciences Inc and may own its stocks and may own AstraZeneca stocks. NR declares no competing interests related to this work.

Funding

The authors received no fund for this research.

Disclaimer

Please note the views and opinions expressed are those of the authors and are not intended to reflect the views and/or opinions of their employer(s).

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Variable	Mean	SD	n	%
Age			219	100
18-25			21	9.6
25-30			45	20.5
30-35			72	32.9
35 or Older			81	37
Race/Ethnicity
African			15	6.8
Black American			104	47.5
Afro-Black Caribbean			48	21.9
Afro-Black Latin X			38	17.4
Afro Latino			14	6.4
Region
Northeast			19	8.7
Southwest			47	21.6
West			52	23.9
Southeast			63	28.9
Midwest			37	17
Missing			1
Enrollment Status
Part-Time			9	4.1
Full-Time			209	95.9
Missing			1
Education
High-School Diploma/GED			16	7.3
Freshman			1	.5
Sophomore			5	2.3
Junior			8	3.7
Senior			14	6.4
Bachelor’s Degree			93	42.5
Continuing Education Student			14	6.4
Masters/Doctoral/Professional			22	10
Non-Degree Seeking			3	1.4
Associates degree			43	19.6
Marital Status
Single			13	5.9
In a Relationship			81	37
Married			74	33.8
Separated			25	11.4
Divorced			20	9.1
Widowed			6	2.7
Annual Income
$15,000 or less			6	2.7
$15,001 – $30,000			19	8.7
$30,001 – $45,000			45	20.5
$45,000 – $60,000			76	34.7
$60,001 – $80,000			50	22.8
$80,000 – $100,000			11	5
Over $100,000			12	5.5
Number of Children
1			24	11
2			56	25.6
3			83	37.9
4			42	19.2
5+			14	6.4
Number of Children in the last Five years
1			119	54.8
2			75	34.6
3			19	8.8
4			3	1.4
5+			1	.5

Missing	2
Prenatal Care During Pregnancy
No	11	5
Yes	208	95
Pregnancy Complications
No	19	8.7
Yes	199	91.3
Missing	1
Mental Health Services During Pregnancy
No	196	88.1
Yes	26	11.9
Missing	1
Mental Health Services After Pregnancy
No	195	89
Yes	24	11
Financial Difficulties During Pregnancy
No	17	7.8
Yes	201	92.2
Missing	1
Average Work Hours/ Week
5 hours or less	8	3.7
5-10 hours	16	7.3
10-15 hours	102	46.6
15+ hours	93	42.6
Number of Jobs
None	4	1.8
One	83	38.1
Two	95	43.6
Three	32	14.7
More than Three	4	1.8
Missing	1
Primary Caregiver
No	20	9.1
Yes	199	90.9
Number of Family/Friends That Help
1-2	35	16
3-4	110	50.2
4+	74	33.8
Media on Maternal Health
Very Often	17	7.8
Often	86	39.3
Sometimes	99	45.2
Not at All	17	7.8
Maternal Health discussed. By Family/Friends
Very Often	25	11.4
Often	72	32.9
Sometimes	86	39.3
Not at All	36	16.4
Maternal Health Support Groups
Very Often	42	19.2
Often	47	21.5
Sometimes	67	30.6
Not at All	63	28.8
N=219

Variables	*Mdn*	*IQR*
Perceived Stress (PSS)	29	14-40
Perceived Racism in Medical Settings (PRS) Postpartum Depression (PND)	49 14	12-60 4-37
Perceptions of the Medical Health Care Community (DMS)	26	7-35

Variables	API	PND	DMS	PSS	PRS
Annual Pre-Tax Income (API)
Postpartum Depression (PND)	-.017
Discrimination in Medical Settings (DMS)	-.028	-.110
Perceived Stress (PSS)	.026	-.141*	.752**
Perceived Racism (PRS)	.017	-.130	.671**	.675**

Community across levels of Stress
Stress Group	DMS
1 Median	21
2 Median	29

	β	p	R²	∆R²
Model 1 Perceived Racism (PRS)	.70	.001***	.49	.49
Model 2 Perceptions of the Medical Health Care Community (DMS)	.53	.001***	.62	.13
Model 3 PRS x DMS	.49	.001***	.63	.01

Source	df	SS	MS	F	p	N
Between Groups	6	1369.321	228.220	6.79	<.001***	218
Within Groups	212	7129.237	33.628			218
Total	218	8498.557

	β	p	R²
Model 1 Perceptions of the Medical Health Care Community (DMS)	-.157	.020*	.025
Model 2 Annual Pre-Tax Income	-.034	.620	.001
Model 3 DMS + Income	-.204	.001***	.018