DOI: 10.31038/CST.2019434

Commentary

Research studies aimed at advancing cancer prevention, diagnosis, and treatment depend on a number of key resources, including a ready supply of high-quality annotated biospecimens that can be used to test new drugs, assess the validity of prognostic biomarkers, and develop tailor-made therapies. The development of more effective interventions against cancer requires a better understanding of its molecular basis and a more rapid translation of laboratory findings into improved patient care. One of the most precious resources for patient-directed cancer research is the collection of biospecimens that are appropriately stored in a biobank or biorepository. When donated with informed consent, thereby respecting patient confidentiality and privacy, such samples enable examination of the molecular basis of disease in addition to the identification of novel biological targets. In order to achieve essential added value, research data must be correlated to clinic-pathological and survival data. As the area of molecular diagnostics and personalized medicine is rapidly increasing and is central to identifying new targeted therapies for lung cancer patients, the participation of patients in the biobanking process is of important significance. This broad definition therefore includes all collections that are associated with research projects, studies, clinical trials or formal infrastructure projects. Patients and the general public are both key to the success of such initiatives. Patients consent to the use of biological samples on which current and future research is based, and importantly play at key role in relation to influencing public and government opinions. Therefore, one of the essential roles of a biobank is to ensure that people understand how their contributions, together with the development of research excellence, will be of benefit to them and future generations.

Biospecimens are obtained through biobanking which can encompass many steps including patient enrolment and consent, biospecimen collection, processing, annotation, storage, and distribution. The ways in which these are conducted continue to change as research advances and new assays and technologies become available [1]. However, advances in technology have meant that the value of biospecimens, such as frozen tissue, are diminishing, in contrast to access of formalin-fixed and paraffin-embedded cell and tissue blocks in pathology archives which are growing [2]. Pathology archives themselves are becoming a highly valued biobank resource for discovery phase research in addition to other phases. A current and novel theme in translational phase research for example, is the exploration of blood plasma factors such as circulating tumor DNA in gene mutation analysis and targeted therapies [3].

More personalized decisions together with greater access to targeted therapies which are guided by more informative biomarkers, have brought the era of personalised medicine to the forefront of cancer medicine in which biobanks have played, and continue to play, a role in this process [4]. In a study by Castillo-Pelayo et al [5], grants received (2010–2011) by investigators from the Cancer Research Society (CRS), a Canadian organization that funds studies across the spectrum of cancer research were selected. Publications arising from these grants between 2010 and 2014 were analysed and categorized by a number of factors such as research area, the acknowledged source of funding, specific scientific focus and the presence of any data that involved specific indicators. These incorporated human biospecimens, cell lines, animal models, advanced microscopy, flow cell sorters, and next generation sequencing. Publications involving biobanking were classified by biospecimen provenance and the type of biospecimen used. The authors reported that biorepositories that coordinate the activity of biobanking rank amongst the most important of established health research infrastructures as contributors to research publications. Furthermore, the study suggested that biospecimen-derived data was obtained directly from biorepositories in approximately 30% of publications. Of interest, biorepositories that coordinated the use of biobanks, as indicated by the use of human biospecimens, ranked second only to cell culture facilities and had a similar level of importance to the use of animal care facilities when considered relative to these and other better recognized forms of health research infrastructures. The Biobanking and BioMolecular Resources Research Infrastructure-European Research Infrastructure (BBMRI-ERIC) consortium provides fair access to quality-controlled human biological samples and associated biomedical and biomolecular data, thereby enabling the investigation of basic mechanisms underlying diseases such as cancer. Such consortia are indispensable for the development of new biomarkers and drugs.

In the last decade, the importance of biobanks in the field of cancer research has increased with the emergence of big data collection [6]. Maintaining privacy and confidentiality while protecting and conserving personal data are all fundamental duties of a biobank. Impacting on biobanks within Europe is the European General Data Protection Regulation (GDPR) which came into force in May 2018. While this directive aspires to providing a high level of protection to safeguard individuals’ personal data, this in turn has the potential to incur considerable constraints on scientific and clinical research involving biobanks. Some of the more restrictive impositions of local regulations in specific European countries are the issues surrounding re-consenting, which in the long term, could pose a serious threat to health research progression and subsequent treatments for patients affected by a wide variety of health conditions such as cancer [7].

If human biospecimens are as commonly used and important to the generation of data in translational cancer research as are animal models and cell lines, one would envisage biobanking to be governed in such a way that facilitates improved access, utilization, standardization and quality of samples while biorepositories should become a central component of the health research infrastructure across all medical and research institutions.

References

1. Cole A, Cheah S, Dee S, Hughes S, Watson PH. Biospecimen use correlates with emerging techniques in cancer research: Impact on planning future biobanks. Biopreserv Biobank 2012; 10: 518–525.
2. Gaffney EF, Riegman PH, Grizzle WE, Watson PH. Factors that drive the increasing use of FFPE tissue in basic and translational cancer research. Biotech Histochem 2018; 93: 373–386.
3. Wan JCM, Massie C, Garcia-Corbacho J, Mouliere F, Brenton JD, Caldas C, Pacey S, Baird R, Rosenfeld N. Liquid biopsies come of age: Towards implementation of circulating tumour DNA. Nat Rev Cancer 2017; 17: 223–238.
4. Rush A, Matzke L, Cooper S, Gedye C, Byrne JA, Watson PH. Research Perspective on Utilizing and Valuing Tumor Biobanks. Biopreserv Biobank. 2018; 1–11.
5. Castillo-Pelayo T, Babinszky S, LeBlanc J, Watson PH. The importance of biobanking in cancer research. Biopreserv Biobank. 2015; 13: 172–177.
6. Hofman P, Dagher G, Laurent-Puig P, Marquette CH, Barlesi F, Bibeau F, Clément B. Tumor banks and complex data management: Current and future challenges. Ann Pathol. 2019; 39(2): 137–143.
7. Clarke N, Vale G, Reeves EP, Kirwan M, Smith D, Farrell M, Hurl G, McElvaney NG. GDPR: an impediment to research? Ir J Med Sci. 2019; doi.org/10.1007/s11845-019-01980-2.

DOI: 10.31038/CST.2019433

Abstract

Objective: To determine if tumor habitat perfusion and diffusion characteristics are related to treatment response in soft tissue sarcoma patients.

Methods: Eight patients (58.6 ± 10.2 y/o) with soft tissue sarcomas underwent pre-treatment Dynamic Contrast Enhanced- (DCE-) and Diffusion Weighted-MRI and post-treatment resection allowing pathology to determine treatment response. Tumors were manually segmented from T1-weighted post-contrast images. Tumor habitats, classified as well-perfused, hypoxic and necrotic, were determined from DCE-MRI using a pattern recognition technique. Each habitat was characterized with pharmacokinetic parameters, k_ep and K^trans, calculated from the DCE-MRI sequence and the apparent diffusion coefficients from the co-registered Diffusion Weighted-MRI. The quantitative imaging features were examined for associations with treatment response. Patients were classified as responders and nonresponders based on histopathology.

Results: Using unsupervised clustering on the imaging features, the patients were divided accurately between responders and nonresponders. Out of all determined features, k_ep (p = 0.04), K^trans (p < 0.01), and percent volume (p = 0.02) for well-perfused habitats were significantly lower in nonresponders, whereas volume (p = 0.04) and percent volume (p < 0.01) for necrotic habitats were significantly higher in responders.

Conclusion: Prediction of treatment response in soft tissue sarcoma patients yielded promising results when utilizing differences in Dynamic Contrast Enhanced- and Diffusion Weighted-MRI features between unique tumor habitats.

Keywords

Pattern Recognition; DCE-MRI; DW-MRI; mpMRI; Soft Tissue Sarcoma

Introduction

Soft tissue sarcomas are a rare but diverse form of cancer with over 50 different subtypes [1]. Due to the heterogeneity of sarcomas, patients often require personalized treatment plans involving radiation, chemotherapy, and surgery, but treatment often is ineffective with a five year survival rate of 64% [2]. Methods to evaluate and monitor soft tissue sarcomas are needed to improve management of the disease. Among potential prognostic factors, the amount of necrosis in soft tissue sarcoma tumors has been found to be predictive of local outcome and recurrence based on resected tumor samples [3]. An in vivo technique to identify tumor composition would potentially allow prediction of patient response to treatment. In imaging, the concept of “habitats” was introduced to map tumor heterogeneity and quantify amounts of each different microenvironment [4–6].

Several studies have explored the use of Dynamic Contrast Enhanced-MRI (DCE-MRI) for prediction of treatment response in soft tissue sarcoma patients [7–10]. DCE-MRI measures perfusion by observing the flow of contrast through tissue over time, producing signal-versus-time curves. Pharmacokinetic fitting of signal-versus-time curves in tumor voxels revealed parameters that allowed prediction of treatment response with promising results [7, 9, 10]. Another functional technique, Diffusion Weighted Imaging (DWI), may provide additional predictive parameters. DWI offers a measure of cellularity by observing water diffusion patterns in tissue, producing Apparent Diffusion Coefficient (ADC) values. DWI has never been demonstrated on pre-treatment prediction of response in sarcoma patients but has shown promise in treatment monitoring [11–15]. However, analyses of both DCE-MRI and DWI datasets in these studies are still performed on the whole tumor ROI, considering the tumor as a homogenous region. The presence of heterogeneous habitats could potentially be masked by an analysis averaging over the entire area of the tumor.

Consideration of signal-versus-time curves and ADC values from different tumor habitats would offer additional features for prediction while providing a more accurate depiction of tumor state than grouping curves from different habitats in the same analyses. Using pattern recognition techniques, a method to determine the location and distribution of tumor habitats from DCE-MRI datasets has been previously demonstrated [5]. Well-perfused, hypoxic, and necrotic habitats were determined in pre-clinical prostate and brain tumor models, as well as in clinical sarcoma and prostate datasets. Habitat quantitative features, extracted from DCE-MRI and DWI sequences, could potentially be associated with the treatment outcomes of soft tissue sarcomas through a radiomics approach. Radiomics data have proven capable for building descriptive and predictive models relating image features to outcomes, including in sarcomas [16].

In this paper, we utilize knowledge of the location and volumes of tumor habitats in soft tissue sarcomas to generate habitat-specific features for use in prediction of treatment response. Features include pharmacokinetic parameters from modeling of signal-versus-time curves from DCE-MRI and ADC values. The use of multiple features and features native to individual habitats is explored to provide more accurate prediction that may facilitate effective treatment management for soft tissue sarcoma patients.

Methods

Study Protocol

All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards. Eight patients (58.6 ± 10.2 y/o) diagnosed with soft tissue sarcomas were enrolled in an Institutional Review Board-approved study. For this retrospective study, informed consent was waved. DCE- and DWI sequences were acquired for each patient, prior to receiving neoadjuvant chemo/radiation therapy. Pathology was performed upon resection of tumors, revealing percent necrosis of sample by which response was determined and by which results from MRI analysis could be compared. Patients’ clinical information and treatment response are described in Table 1.

Table 1. Patient clinical information and treatment response.

MRI Protocol

Imaging parameters for DW and DCE-MRI scans for each patient are listed in detail in Table 2. Briefly, were carried out either on a 3T Skyra or 1.5T Symphony MR Scanner (Siemens, Erlangen, Germany).

Table 2. Patient imaging parameters.

Abbreviations: TR = Repetition time; TE = Echo time.

DCE-MRI analysis

Tumor Volumes of Interest (VOIs) for each patient were contoured manually in MIM (MIM, Cleveland, Ohio) by an experienced skeletomuscular radiologist (T.S.). DCE-MRI signal-versus-time curves from VOIs were analyzed using Constrained Non-Negative Matrix Factorization (cNMF), as detailed in previous publications [5, 17], to reveal the distribution of habitats. The method uncovers the main patterns underlying the signal-versus-time curves and the weights of these constituent patterns in each voxel. The number of these patterns, associated with different tumor habitats was set to three to account for well-perfused, hypoxic, and necrotic habitats.

To assign voxels to specific habitats, a threshold of 60% was applied to the cNMF weights, whereby voxels containing a given habitat at a greater fraction than the threshold were assigned to that habitat. For instance, if for a given voxel the fraction of hypoxic, necrotic and well-perfused habitats were 10%, 20% and 70%, the voxel was considered part of the well-perfused habitat. Conversely, if the contribution of the habitats were 30%, 30% and 40%, the voxel was not assigned to any habitat. The signal-versus-time curves for the voxels corresponding to a given habitat were averaged together, producing a representative curve for each habitat. Pharmacokinetic modeling was performed on these representative curves using a Tofts model [18, 19]. Using the synthetic Parker fixed population average Arterial Input Function (AIF) [20] the volume transfer constant between plasma and Extracellular Extravascular Space (EES), K^trans (related to perfusion and permeability per unit volume of tissue), the fractional volume v_e of the EES and k_ep, the ratio of K^trans and v_e, also known as the efflux rate constant, were quantified. In addition, the volume of each habitat was determined and also divided against the overall tumor volume to obtain percent volume of habitats.

ADC analysis

DCE-MRI and ADC maps were co-registered in MIM. The same tumor VOIs and tumor habitats determined from the DCE-MRI analysis were applied to the ADC. Average ADC values for voxels corresponding to well-perfused, hypoxic, and necrotic habitats were determined.

Prediction of patient response

The association between (i) the imaging features for each habitat: K^trans, k_ep, volume, percent from tumor volume, and ADC and (ii) patient response to neoadjuvant therapy was investigated using hierarchical clustering of patient features. Features involving habitats that were not present in a given tumor were set to 0. The final two clusters at the top level of the clustergram were determined to be the groups of patients corresponding to patients predicted to be responders and nonresponders.

Results

Quantitative measures, including habitat-specific K^trans, k_ep, volume, percent volume, and mean ADC for well-perfused, hypoxic, and necrotic habitats are listed in Table 3. Three habitats were identified in 7 patients and 2 habitats in one patient. Roughly, similar fractions of necrotic habitats were determined between the present approach and pathology. An example of habitat locations and signal-versus-time curves in a patient is shown in Figure 1. K^trans showed an increasing trend from necrotic to hypoxic to well-perfused habitats, which generally contained voxels with curves with the highest K^trans. Similarly, k_ep showed a decreasing trend from necrotic to hypoxic to well-perfused habitats, which generally contained voxels with curves with the lowest k_ep. Volume, percent volume, and mean ADC did not have an apparent trend with habitat type.

Table 3. Quantitative measures for patients based on analysis of DCE-MRI data.

Abbreviations: P = Well-perfused; H = Hypoxic; N = Necrotic; Vol = Volume; Per = Percent Volume.

Figure 1. (A) Axial DCE-MRI of responding 56 year-old extraskeletal osteosarcoma female patient post-treatment. Well-perfused, hypoxic, and necrotic habitats determined in the patient are overlaid with red, green, and blue respectively. Early enhancing image from DCE-MRI (left) and ADC (right) are shown in inset from left to right. (B) Axial DCE-MRI of nonresponding 53 year-old synovial sarcoma female patient post-treatment. Well-perfused, hypoxic, and necrotic habitats determined in the patient are overlaid with red, green, and blue respectively. Early enhancing image from DCE-MRI (left) and ADC (right) are shown in inset from left to right. (C) Corresponding average signal-versus-time curves for each habitat are shown below for Fig. 1a. (D) Corresponding average signal-versus-time curves for each habitat are shown below for Fig. 1b.

Hierarchical clustering was performed on subject data using the quantitative measures described above (Figure 2). Two clusters were readily apparent, separating responding from non-responding patients. Parameters which were significantly correlated with response after correction for multiple comparison with Tukey’s honest significant difference test were k_ep well-perfused (p = 0.04), K^trans well-perfused (p < 0.01), volume necrotic (p = 0.04), percent well-perfused (p = 0.02), and percent necrotic (p < 0.01). The well-perfused habitat’s characteristics: k_ep, K^trans, and percent volume were lower in responders whereas volume necrotic and percent necrotic were higher in responders.

Figure 2. Heatmap showing hierachical clustering of 8 patients using quantitative imaging features. Nonresponders, patients 5, 7, and 8, are shown to be in a separate cluster from responders, patients 1, 2, 3, 4, and 6.

Discussion

In this feasibility study, a radiomics approach utilizing perfusion, diffusion and volumetric information from unique tumor habitats was applied to the prediction of treatment response in soft tissue sarcoma patients. Patients from a small sample set were correctly classified into responders and nonresponders, and habitat-related features were identified that had differentiating potential for the two populations. The success of the approach suggests that consideration of tumor heterogeneity is important for treatment prediction and that the state of the tumor may not be accurately represented by analyses of the whole tumor.

This importance of analysis of unique habitats to prognosis has primarily been recognized in breast cancer tumors. DCE-MRI studies of breast cancer have shown tumors with a larger proportion of voxels with fast washin and a steady plateau of washout, corresponding to the shape of curves in well-perfused habitats, are more likely to be malignant [21]. Numerous studies on breast cancer tumors have focused analyses on these ‘hot spots’ in tumors [22–27], determined by looking for contiguous ROIs of high enhancement in DCE-MRI datasets. Similar analyses have been performed on soft tissue sarcoma tumors [9, 28]. However, although ‘hot spot’ methods begin to consider the heterogeneity in tumors, they only focus on well-perfused habitats, whereas identification and analysis of additional tumor habitats can provide additional discriminatory features for treatment prediction as in the present study and offer a broader understanding of circumstances in the tumor. In addition, common problems with analysis of ‘hot spots’ include the selected ROIs are often manually contoured [22–26, 28] although semi-automated [29, 30] and automated methods [27] have been proposed. Manual contours produce significant variability in measurements derived from selected ROIs [30]. Analysis of ‘hot spots’ also often precludes the inclusion of anatomical features as only the highest enhancing voxels are selected, which may not extend to the whole extent of the habitat. The present approach has the benefit of addressing the two issues by automating delineation of tumor habitats, avoiding variability with manual selection of ROIs. The approach also allows detection of habitats at a subpixel resolution [5], leading to more accurate determination of habitat locations and allowing the analysis of both habitat-specific pharmacokinetic and anatomical parameters. Specifically, in sarcoma, a similar pattern recognition approach on DCE-MRI data has been applied with good agreement between determined habitats and histopathology [31].

Significant differences were seen in perfusion, diffusion and volumetric parameters from necrotic and well-perfused habitats between responders and nonresponders. The relation of treatment response with features in necrotic habitats may be expected due to the correlation of response to necrosis in pathological samples [3]. Percent volume and volume of necrosis directly relate to the amount of necrosis in tumors, whereas k_ep and K^trans of necrotic habitats may represent how ‘necrotic’ those areas are, with lower values representing greater extent of cell death. Similarly, features associated with well-perfused regions may be expected to correlate with response based on past DCE-MRI findings. As mentioned, in breast cancer tumors, a greater proportion of well-perfused habitats have correlated with a greater chance of malignancy [21]. Since vascularity is necessary for growing tumors, well-perfused habitats would offer a window into the ‘health’ of tumors. In the case of sarcomas, lower k_ep and K^trans in well-perfused habitats indicate slower diffusion from vessels into the extracellular space, suggesting a tumor lagging in growth and potentially less active. In the present study, no significant differences in parameters from DW-MRI, namely ADC, were found between responders and responders. Given prior work linking ADC to treatment monitoring in soft tissue sarcoma patients [11–15], the lack of discriminatory ability for ADC was unexpected. A possible explanation may the use of multiple b-values across multiple MR systems in DW-MRI acquisition. Different patients would potentially have datasets with different diffusion weighting and signal-to-noise confounding ADC values between responders and nonresponders. Standardized imaging parameters would allow more definitive assessment of the value of ADC to prediction of treatment response.

The study has several limitations that are typical for such pilot investigations. It is retrospective and includes a small number of patients. The analyzed patients had different types of sarcoma and varying treatment plans and imaging exam parameters. However, despite the heterogeneity in the dataset, results are promising on the limited samples. The nature of the approach provides quantitative measures robust to the variety of cases likely to be seen in sarcoma patients. Both ADC and pharmacokinetic parameters are derived to measure true physiological characteristics of the tumor (diffusion, contrast-to-tissue exchange rates) and thus can be assumed to be relatively independent from the imaging sequences.

The appeal of the proposed approach is the non-invasive way it can identify tumor habitats in vivo. For this purpose, a pattern recognition method was utilized that was previously validated on pre-clinical [5, 32] and clinical data [5]. The method assumed the presence of three habitats in tumors which corresponded well with results seen in the pre-clinical model [5, 32] as well as with literature where three shapes of curves have been observed in tumors [21]. Finally, patients enrolled in the study had heterogeneous presentations of sarcomas, including location, tissue types, and stage of sarcomas. Although differences in the patient population could provide a confounding factor in prediction of treatment response, these differences should serve to negatively impact prediction as prediction would be more difficult on a diverse set of patients.

In conclusion, in this pilot study the feasibility for prediction of treatment response in soft tissue sarcoma patients was demonstrated based on identification and analysis of tumor heterogeneity from mpMRI. Pharmacokinetic and anatomical information extracted from unique tumor habitats provided features that could separate responders and nonresponders, the combination of which provided more discriminatory ability than a single feature. A more extensive study will be needed in the future to validate the performance of this approach. For soft tissue sarcomas which encompass a broad range of tumors, additional information derived from the state and characteristics of tumor habitats may lead to a more accurate prediction of treatment response in patients.

Funding: NEI F30-EY027162 (sponsor had no role in the study design; in the collection, analysis and interpretation of the data; in the writing of the report; and in the decision to submit the paper for publication).

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DOI: 10.31038/CST.2019432

Abstract

Triple-negative breast cancers (TNBC) lack estrogen receptor (ER), progesterone receptor and epidermal growth factor receptor HER2 and share many features of basal-like breast cancer (BLBC). Therefore, these patients have to rely on chemotherapy. The mechanistic target of rapamycin complex 1 (mTORC1) as well as its downstream targets p70 S6 kinase (S6K1) and S6K2 have been implicated in breast cancer. The 40S ribosomal protein S6 kinase 2 (S6K2) has been associated with endocrine resistance. We have previously shown that S6K2 protects against apoptotic cell death in ER-positive breast cancer cells. In the present study, we investigated if S6K2 could serve as a potential target for TNBC. Our analysis of TCGA dataset as well as immunohistochemistry of patient samples revealed that S6K2 is overexpressed not only in ER-positive but also in TN breast tumors compared to normal breast tissues. Silencing of S6K2 by siRNA enhanced sensitivity of BLBC MCF10CA1d cells to chemotherapeutic drugs cisplatin and doxorubicin. S6K2 knockdown also increased sensitivity of BLBC MCF10CA1a and TNBC HCC1395 cells to TRAIL. While S6K2 knockdown alone had little effect on apoptosis, it enhanced TRAIL-induced apoptosis as judged by the increase in caspase-3 activity, PARP cleavage and annexin V/PI staining. Overexpression of constitutively-active S6K2 construct in MDA-MB-231 cells protected against TRAIL-induced apoptosis. These results suggest that S6K2 also promotes survival of TNBC. Therefore, targeting S6K2 in combination with chemotherapeutic agents could improve therapy of TNBC.

Keywords

Triple-negative breast cancer, basal-like breast cancer, S6K2, chemotherapeutic drugs, apoptosis

Introduction

Breast cancer is the second leading cause of cancer-related death among women in the United States. The major breast cancer subtypes include hormone receptor-positive, human epidermal growth factor receptor 2 (HER2)-enriched and triple-negative or basal-like breast cancer [1]. Triple-negative breast cancers (TNBC), which account for approximately 20% of all breast cancers, are a highly aggressive form of breast cancer with high mortality and poor prognosis [2]. They share many features of basal-like breast cancer (BLBC) subtype and they are often used synonymously [3]. No targeted therapy exists to treat majority of patients with TNBC because they lack estrogen receptor (ER), progesterone receptor (PR) and HER2/neu [4, 5].

The PI3K/Akt/mTOR pathway is most frequently altered in breast cancers and has been associated with drug resistance [6, 7]. The mechanistic target of rapamycin (mTOR), which acts downstream of PI3K/Akt, is an important target for breast cancer therapy since it is frequently deregulated in breast cancers and plays a critical role in tumorigenesis [8]. mTOR forms complexes with either raptor (mTORC1) or rictor (mTORC2). mTORC1 mediates its function via its downstream targets ribosomal S6 kinase (S6K1 and S6K2) and 4E-binding protein 1 (4E-BP1) [9].

40S ribosomal protein S6 kinase 2 or S6K2 encoded by RPS6KB2 is localized on chromosome 11q13 [10, 11], which constitutes a high-risk subgroup of ER-positive breast cancers [12]. RPS6KB2 gains/amplifications have been associated with poor prognosis and resistance to endocrine therapy [10, 11]. A recent study showed high levels of S6K2 was associated with ER-positive breast cancers [13]. We have shown that S6K2 promotes survival of ER-positive breast cancers to apoptotic stimuli [14].

 The PI3K/Akt/mTOR pathway is also frequently deregulated in TNBC [2]. Since chemotherapy continues to be the standard-of-care treatment for TNBC but unacceptable side effects are major problems in the treatment, we examined if targeting S6K2 could enhance sensitivity of TNBCs to chemotherapeutic agents. Our results show that S6K2 is overexpressed in TNBC and depletion of S6K2 sensitized TNBC cells to apoptotic stimuli.

Methods and Materials

Cell Culture

The MCF-10A series developed by Dr. Fred Miller and colleagues [15], was purchased from the Barbara Ann Karmanos Cancer Institute (Detroit, MI). MCF10A, MCF10AT and MCF10AT3G cells were cultured as described previously [16]. MCF-10CA1a and MCF10-CA1d cells were maintained in DMEM-F12 medium supplemented with 5% horse serum. MDA-MB-231 and HCC1395 (obtained from Drs. Adi Gazdar and John Minna, UT Southwestern Medical Center) cells were maintained in RPMI 1640 medium supplemented with 5% fetal bovine serum and 2 mM glutamine. Cells were kept in a humidified incubator at 37⁰C with 95% air and 5% CO₂.

Transfection

Cells were transfected with control non-targeting or target-specific siRNAs (GE Dharmacon, Chicago, IL) using Lipofectamine RNAiMax transfection reagent (Invitrogen, Carlsbad, CA) as described before [17]. We overexpressed S6K2 in MDA-MB-231 cells. We purchased pcDNA3 S6K2 E388 D3E (a gift from Dr. John Blenis) from Addgene plasmid#17731; http: //n2t.net/addgene: 17731; RRID: Addgene_17731) [18] and cloned into pLVX-mCherry-C1 lentiviral vector (Clontech). MDA-MB-231 cells were infected with either the empty vector pLVX or vector containing S6K2 construct. The extent of gene knockdown or overexpression was determined by Western blot analysis.

Immunoblot Analysis

Cells were harvested by trypsinization, washed twice with PBS and lysed in buffer containing 20 mM Tris-HCl, pH 7.4, 0.15 M NaCl, 1 mM EGTA, 1 mM EDTA, 1.0% Triton X-100, 0.5% Nonidet-40, 10 mM β-glycerophosphate, protease inhibitor cocktail and phosphatase inhibitor cocktail (Calbiochem/EMD-Millipore, Bedford, MA). Equivalent amounts of total proteins were electrophoresed by SDS-PAGE and transferred electrophoretically to polyvinylidene difluoride membrane (EMD Millipore, Bedford, MA). Immunoblot analyses were performed with 1: 1, 000 dilution of S6K1, P-S6 (Cell Signaling Technology, Danvers, MA) and S6K2 (R&D Systems, Minneapolis, MN) or 1: 5, 000 dilution of PARP (Pharmingen, San Diego, CA) and actin antibodies (Sigma, St. Louis, MO) as described before [19]. 1: 10, 000 dilution of horseradish-peroxidase-conjugated donkey anti-rabbit or goat anti-mouse secondary antibodies (Jackson ImmunoResearch Laboratories, Inc., West Grove, PA) were used. The blots were visualized using the enhanced chemiluminescence detection kit (Amersham, Arlington Heights, IL) and the manufacturer’s protocol. The intensities of immunoreactive proteins were quantified using ImageJ software. The blots were probed with actin to control for equal loading.

Immunohistochemistry (IHC)

Formalin-fixed paraffin-embedded tissue microarrays (TMA) obtained from US BioMax, Inc. were deparaffinized with successive washes of xylene and ethanol, and rehydrated. The endogenous peroxidase was blocked by incubation with 0.3% H₂O₂ and antigen retrieval was performed by heating in citrate buffer (pH 6.0). Immunohistochemistry was performed with anti-S6K2 antibody and immunodetection was performed using a Vecstatin ABC Elite kit and a DAB peroxidase substrate kit from Vector laboratories according to the manufacturer’s protocol.

Caspase activity assay

Cells transfected with control non-targeting or S6K2 siRNA were treated with or without TRAIL. DEVDase activity was determined at 37⁰C using an Ac-DEVD-AFC assay kit (BioVision, Palo Alto, CA, USA) and the manufacturer’s protocol [19]. The fluorescence liberated from DEVD-AFC was measured using a SpectraMax GeminiXS fluorometer and SOFTmax PRO 3.1.1 software (Molecular Devices, Sunnyvale, CA, USA) with an excitation wavelength of 400-nm and emission wavelength of 505 nm.

Annexin V/Propidium Iodide Binding Assay

Cells were treated with or without 0.1 nM TRAIL (R&D Systems, Minneapolis, MN) for 14 h. At the end of the incubation, both detached and attached cells were collected and washed with phosphate-buffered saline. Cells were then stained with annexin V-Alexa 488 conjugate and propidium iodide (Molecular Probes, Eugene, OR) according to the manufacturer’s protocol and analyzed using a flow cytometer (Coulter Epics) [14].

Statistical analysis

Statistical significance was determined by paired Student’s t-test using Microsoft Excel. A p-value < 0.05 was considered statistically significant.

Results

Comparison of S6K2 expression in ER-positive and triple-negative breast cancers

Both S6K1 and S6K2 have been implicated in breast cancer [20, 21]. Therefore, we compared the expression of S6K1 and S6K2 in breast tumor tissues. We analyzed 106 normal and 813 breast tumor tissues from TCGA dataset. The expression of S6K2 but not S6K1 is increased by approximately 2-fold in breast tumor tissues (Fig. 1A), including both ER-positive (n=575) and triple-negative (TN) (n=118) subtypes (Fig 1B). This correlation holds true when tumor tissues are compared with matched normal tissues (Fig. 1C).

Figure 1. Analysis of S6K1 and S6K2 expression in the TCGA dataset. mRNA expression was measured by RNA-seq in TCGA breast tissue samples. A, S6K1 and S6K2 in all breast tumors (789) and normal (103) samples. B, S6K2 in either ER+ (ER) or triple-negative (TN) subtypes (ER+, N=575, TN, N=118) comparing with those in the normal samples from ER+ (73) or TN (12) patients. C, S6K2 in matched tumor-normal sample pairs of ER+ (73) and TN (12) patients. The error-bar shows the standard deviation of mRNA level. Except for the comparison of S6K1 between normal and tumors samples, all of the comparison for S6K2 levels between tumor and normal samples are significant (p-value < 0.0005), with the fold-change near two (1.74~1.92).

We also analyzed 112 breast tumor specimens and 12 normal tissues in a tissue microarray (TMA) by immunohistochemistry (IHC) (Fig. 2). S6K2 staining is low in normal mammary epithelial cells (Fig. 2A). The staining of S6K2 was intense in both ER-positive (Fig. 2B) and TN (Fig. 2C) breast cancer cells. Thus, analysis of both TCGA and IHC show that S6K2 level is elevated not only in ER-positive breast cancer but also in TNBC.

Figure 2. S6K2 expression in normal versus breast tumor tissues. IHC was performed with tissue microarrays. Representative images are shown. A, Cancer adjacent normal breast tissue; B, ER-positive breast tumor (Stage IIb); C, TNBC (Stage IIb). We did not counterstain the slides with hematoxylin since it may obscure S6K2 staining.

We then examined the status of S6K2 in several breast cancer cell lines. We have used progressive basal-like MCF10A series, which include immortalized but non-tumorigenic MCF10A cells as well as highly aggressive metastatic MCF10CA1a and MCF10CA1d variants. These cells do not express hormone receptors. We also included TNBC MDA-MB-231 and ER-positive MCF-7 cells in our comparison. S6K2 but not S6K1 level was increased modestly in MCF10CA1a and MCF10CA1d cells (Fig. 3). In contrast, S6K2 level was low in MDA-MB-231 cells. p70S6K1 gene is amplified in MCF-7 cells and the upper band represents the p85 form of S6K1. Both Akt which acts upstream of mTOR as well as P-S6 which acts downstream of mTORC1/S6K were elevated in MCF10CA1a and MCF10CA1d cells compared to non-tumorigenic MCF10A cells, suggesting that mTORC1 signaling is activated in these cells. In subsequent studies, we depleted S6K2 from MCF10CA1a and MCF10CA1d cells and overexpressed S6K2 in MDA-MB-231 cells.

Figure 3. Comparison of Akt and S6K levels in breast cancer cells. Western blot analysis was performed with the indicated antibodies with total cell lysates. The upper band in S6K1 blot represents the p85 form of S6K1.

Effect of S6K2 knockdown on the sensitivity of TNBC cells to apoptotic stimuli

We examined if depletion of S6K2 enhances cell death in TNBC cells by monitoring the cleavage of PARP, a substrate for caspase-3 and -7. Figure 4 shows that while silencing of S6K2 by siRNA alone had little effect on PARP cleavage, it increased the levels of cleaved PARP in response to chemotherapeutic drugs cisplatin and doxorubicin.

Figure 4. Depletion of S6K2 enhanced cisplatin- and doxorubicin-induced PARP cleavage in MCF10CA1d breast cancer cells. MCF10CA1d cells transfected with control non-targeting or S6K2 siRNAs were treated with or without indicated concentrations of cisplatin or doxorubicin and Western blot analysis was performed with indicated antibodies.

We then examined if depletion of S6K2 increases sensitivity of MCF10CA1a cells to another apoptotic stimulus TRAIL (TNF-related apoptosis-inducing ligand). Knockdown of S6K2 increased TRAIL-induced PARP cleavage as well as the level of cleaved caspase-9 (Fig. 5A), suggesting that S6K2 depletion increased TRAIL-induced apoptosis. To directly determine the effect of S6K2 knockdown on apoptosis, we monitored caspase-3 activity by using fluorogenic DEVD-AFC substrate. Cells transfected with control non-targeting or S6K2 siRNA were treated with TRAIL and the time-course of caspase-3 activation was monitored. As shown in Figure 5B, S6K2 knockdown alone (unfilled triangle) caused a slight increase in caspase-3 activity as compared to control siRNA-transfected cells (unfilled circle). While TRAIL treatment had little effect on caspase-3 activity in control siRNA-transfected cells (filled circle), it dramatically enhanced time-dependent increase in caspase-3 activity in S6K2 siRNA-transfected cells (filled triangle).

Figure 5. Depletion of S6K2 enhanced TRAIL-induced apoptosis in MCF10CA1a cells. A, MCF10CA1a cells were transfected with control non-targeting or S6K2 siRNA and treated with or without TRAIL. Western blot analysis was performed with indicated antibodies. B, MCF-10CA1a cells transfected with control non-targeting (circle) or S6K2 (triangle) siRNA were treated without (unfilled) or with (filled) TRAIL. Caspase activity assay was performed with DEVD-AFC as the substrate. Fluorescent intensity was calculated for equal amount of protein.

To further examine the effect of S6K2 knockdown on apoptosis, we examined the consequence of S6K2 knockdown on TRAIL-induced apoptosis in HCC1395 cells, which were derived from patients with TNBC. Figure 6 shows that knockdown of S6K2 caused a modest increase in TRAIL-induced PARP cleavage. Similar results were obtained when we monitored apoptosis by Annexin V/PI dye binding assay (Fig. 5B).

Figure 6. Depletion of S6K2 enhanced TRAIL-induced apoptosis in HCC1395 cells. HCC1395 cells were transfected with control non-targeting or S6K2 siRNA and treated with indicated concentrations of TRAIL. A, Western blot analysis was performed with indicated antibodies. B, Annexin V/PI staining was performed to determine cell death by flow cytometry.

Effect of S6K2 overexpression on the sensitivity of TNBC cells to apoptotic stimuli

To further demonstrate the effect of S6K2 on apoptosis, we overexpressed S6K2 in MDA-MB-231 cells, which express low levels of S6K2. We used an S6K2 construct in which Thr388 was mutated to Glu, and Ser residues at 410, 417 and 423 were mutated to Asp. This phosphomimicking constitutively active form of S6K2 is resistant to mTORC1 inhibitor rapamycin. As shown in Figure 7A, overexpression of S6K2 attenuated TRAIL-induced PARP cleavage. Moreover, while treatment of MDA-MB-231 cells with TRAIL caused a substantial increase in caspase-3 activity, overexpression of S6K2 blunted this increase. These results suggest that S6K2 protects MDA-MB-231 cells against apoptotic stimuli.

Figure 7. Overexpression of S6K2 attenuated TRAIL-induced apoptosis in MDA-MB-231 cells. MDA-MB-231 breast cancer cells expressing either control vector or rapamycin-resistant (RR) S6K2 were treated with or without TRAIL. A, Western blot analysis was performed with indicated antibodies. B, Caspase activity assay was performed with DEVD-AFC as the substrate.

Discussion

There has been significant advancement in the treatment of breast cancer. The presence of hormone receptors in ER-positive breast cancers suggests a favorable prognosis since the patients can be treated with anti-estrogens or aromatase inhibitors [22]. Similarly, HER2-enriched breast cancers could be treated with herceptin (tratuzumab), a monoclonal antibody against HER2 receptors. TNBCs remain a clinical challenge due to lack of appropriate FDA-approved targeted therapies for most patients with TNBC [3, 4]. Chemotherapy continues to be the standard-of-care treatment for systemic therapy in TNBC patients [23]. However, progression-free survival is short and patients often advance to aggressive metastatic disease [3]. In addition, unacceptable toxicity of cytotoxic chemotherapeutic drugs is a serious problem in the management of the disease.

The PI3K/Akt/mTOR pathway is frequently deregulated in breast cancers and there have been numerous attempts to target this pathway albeit with limited efficacy [24, 25]. While mTORC1 and its downstream target S6K1 have been studied extensively, little attention has been paid to S6K2 [26, 27]. Because of the high degree of homology between S6K1 and S6K2, it was generally believed that the function of these two homologs is similar [26, 27]. Emerging studies identified S6K2 as a novel candidate oncogene and overexpression of S6K2 was associated with poor prognosis and resistance to endocrine therapy [10, 11]. We have shown that S6K1 and S6K2 have opposite effect on the survival of ER-positive breast cancers [14]. Knockdown of S6K2 but not S6K1 enhanced cell death in response to apoptotic stimuli in ER-positive breast cancer cells [17].

The results of our present study show that S6K2 also plays an important role in promoting the survival of TNBC. Based on the analysis of the TCGA dataset, the expression of S6K2 but not S6K1 is increased in both ER-positive and TN breast cancers. Consistent with the TCGA data, immunohistochemical staining of patient samples also revealed that S6K2 protein expression was higher in both ER-positive and TN subtypes. Moreover, in the progressive MCF10A breast cancer model, S6K2 levels appear to be higher in aggressive cell lines compared to non-malignant cells. However, S6K2 level was low in MDA-MB-231 cells, which may rely on other oncogenic signaling pathways for survival.

We found that depletion of S6K2 alone had little effect on cell death but S6K2 knockdown enhanced cell death in MCF10ACA1a, MCF10CA1d and HCC1395 cells that lack ER, PR and HER2/neu. We have used chemotherapeutic drugs, such as doxorubicin and cisplatin that act via the mitochondrial pathway as well as TRAIL, which acts via the receptor-initiated pathway. S6K2 knockdown enhanced cell death by apoptosis as evident by the cleavage of PARP, caspase-3 activity and annexin V/PI staining. Furthermore, overexpression of S6K2 in MDA-MB-231 cells attenuated TRAIL-induced apoptosis reinforcing our notion that S6K2 promotes survival in TNBC cells.

The PI3K/Akt and Ras/MAPK pathways are often deregulated in breast cancers [22, 30]. We have previously shown that S6K2 promotes survival of ER-positive breast cancer MCF-7 cells partly via activation of the Akt signaling pathway [14]. It remains to be seen if S6K2 promotes survival of TNBC cells via Akt-dependent or -independent pathway. P-Akt could be detected in malignant MCF10ACA1a and MCF10CA1d cells but not in non-malignant MCF10A cells. During the generation of MCF-10CA1a cells, MCF10A cells were transfected with T24 Ha-Ras [15]. Therefore, ERK signaling pathway is also activated in these cells. Thus, the observation that S6K2 KD induced cell death in MCF10CA1a and MCF10CA1d cells with activated Akt/mTOR and Ras/Raf/MAPK pathway [16] is significant.

Given that chemotherapy continues to be the major treatment option for TNBC patients and S6K2 knockout mice are viable and exhibit normal development and homeostasis [26, 28–30], intervention with S6K2 in combination with other chemo- or biological therapeutic agents is expected to provide a novel non-toxic therapy for TNBCs.

Author Contributions: AB and SS designed and performed experiments, ZX analyzed TCGA dataset, AB wrote the manuscript and SS edited the manuscript.

Abbreviations:

BC, Breast cancer;

BLBC, Basal-like breast cancer;

4E-BP1, Eukaryotic translation initiation factor 4E (eIF4E)-binding protein 1;

ERK, Extracellular signal-regulated kinase;

ER, Estrogen receptor;

HER2, Human epidermal growth factor receptor 2;

IHC, Immunohistochemistry;

mTORC1, Mechanistic target of rapamycin complex 1;

PARP, Poly (ADP-ribose) polymerase;

PI, Propidium iodide;

PI3K, Phosphatidylinositol-3-kinase;

PR Progesterone receptor;

p70 S6 kinase 1 (S6K1);

S6K2, 40S ribosomal protein S6 kinase 2;

TCGA, The cancer genome atlas;

TNBC, Triple-negative breast cancer;

TMA, Tissue microarray;

TNF, Tumor necrosis factor-α;

TRAIL, TNF-related apoptosis-inducing ligand;

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