Experimental and computational modeling for signature and biomarker discovery of renal cell carcinoma progression

BACKGROUND

Renal Cell Carcinoma (RCC) is difficult to treat with 5-year survival rate of 10% in metastatic patients. Main reasons of therapy failure are lack of validated biomarkers and scarce knowledge of the biological processes occurring during RCC progression. Thus, the investigation of mechanisms regulating RCC progression is fundamental to improve RCC therapy.

METHODS

In order to identify molecular markers and gene processes involved in the steps of RCC progression, we generated several cell lines of higher aggressiveness by serially passaging mouse renal cancer RENCA cells in mice and, concomitantly, performed functional genomics analysis of the cells. Multiple cell lines depicting the major steps of tumor progression (including primary tumor growth, survival in the blood circulation and metastatic spread) were generated and analyzed by large-scale transcriptome, genome and methylome analyses. Furthermore, we performed clinical correlations of our datasets. Finally we conducted a computational analysis for predicting the time to relapse based on our molecular data.

RESULTS

Through in vivo passaging, RENCA cells showed increased aggressiveness by reducing mice survival, enhancing primary tumor growth and lung metastases formation. In addition, transcriptome and methylome analyses showed distinct clustering of the cell lines without genomic variation. Distinct signatures of tumor aggressiveness were revealed and validated in different patient cohorts. In particular, we identified SAA2 and CFB as soluble prognostic and predictive biomarkers of the therapeutic response. Machine learning and mathematical modeling confirmed the importance of CFB and SAA2 together, which had the highest impact on distant metastasis-free survival. From these data sets, a computational model predicting tumor progression and relapse was developed and validated. These results are of great translational significance.

CONCLUSION

A combination of experimental and mathematical modeling was able to generate meaningful data for the prediction of the clinical evolution of RCC.

Plk1, upregulated by HIF-2, mediates metastasis and drug resistance of clear cell renal cell carcinoma

Polo-like kinase 1 (Plk1) expression is inversely correlated with survival advantages in many cancers. However, molecular mechanisms that underlie Plk1 expression are poorly understood. Here, we uncover a hypoxia-regulated mechanism of Plk1-mediated cancer metastasis and drug resistance. We demonstrated that a HIF-2-dependent regulatory pathway drives Plk1 expression in clear cell renal cell carcinoma (ccRCC). Mechanistically, HIF-2 transcriptionally targets the hypoxia response element of the Plk1 promoter. In ccRCC patients, high expression of Plk1 was correlated to poor disease-free survival and overall survival. Loss-of-function of Plk1 in vivo markedly attenuated ccRCC growth and metastasis. High Plk1 expression conferred a resistant phenotype of ccRCC to targeted therapeutics such as sunitinib, in vitro, in vivo, and in metastatic ccRCC patients. Importantly, high Plk1 expression was defined in a subpopulation of ccRCC patients that are refractory to current therapies. Hence, we propose a therapeutic paradigm for improving outcomes of ccRCC patients.

Impact of Extracellular Matrix Components to Renal Cell Carcinoma Behavior

Renal cell carcinoma (RCC) represents the main renal tumors and are highly metastatic. They are heterogeneous tumors and are subdivided in 12 different subtypes where clear cell RCC (ccRCC) represents the main subtype. Tumor extracellular matrix (ECM) is composed, in RCC, mainly of different fibrillar collagens, fibronectin, and components of the basement membrane such as laminin, collagen IV, and heparan sulfate proteoglycan. Little is known about the role of these ECM components on RCC cell behavior. Analysis from The Human Protein Atlas dataset shows that high collagen 1 or 4A2, fibronectin, entactin, or syndecan 3 expression is associated with poor prognosis whereas high collagen 4A3, syndecan 4, or glypican 4 expression is associated with increased patient survival. We then analyzed the impact of collagen 1, fibronectin 1 or Matrigel on three different RCC cell lines (Renca, 786-O and Caki-2) in vitro. We found that all the different matrices have little effect on RCC cell proliferation. The three cell lines adhere differently on the three matrices, suggesting the involvement of a different set of integrins. Among the 3 matrices tested, collagen 1 is the only component able to increase migration in the three cell lines as well as MMP-2 and 9 activity. Moreover, collagen 1 induces MMP-2 mRNA expression and is implicated in the epithelial to mesenchymal transition of two RCC cell lines via Zeb2 (Renca) or Snail 2 (Caki-2) mRNA expression. Taken together, our results show that collagen 1 is the main component of the ECM that enhances tumor cell invasion in RCC, which is important for the metastasic process.

DDR1 and DDR2 physical interaction leads to signaling interconnection but with possible distinct functions

Discoidin domain receptors 1 and 2 (DDR1 and DDR2) are members of the tyrosine kinase receptors activated after binding with collagen. DDRs are implicated in numerous physiological and pathological functions such as proliferation, adhesion and migration. Little is known about the expression of the two receptors in normal and cancer cells and most of studies focus only on one receptor. Western blot analysis of DDR1 and DDR2 expression in different tumor cell lines shows an absence of high co-expression of the two receptors suggesting a deleterious effect of their presence at high amount. To study the consequences of high DDR1 and DDR2 co-expression in cells, we over-express the two receptors in HEK 293T cells and compare biological effects to HEK cells over-expressing DDR1 or DDR2. To distinguish between the intracellular dependent and independent activities of the two receptors we over-express an intracellular truncated dominant-negative DDR1 or DDR2 protein (DDR1DN and DDR2DN). No major differences of Erk or Jak2 activation are found after collagen I stimulation, nevertheless Erk activation is higher in cells co-expressing DDR1 and DDR2. DDR1 increases cell proliferation but co-expression of DDR1 and DDR2 is inhibitory. DDR1 but not DDR2 is implicated in cell adhesion to a collagen I matrix. DDR1, and DDR1 and DDR2 co-expression inhibit cell migration. Moreover a DDR1/DDR2 physical interaction is found by co-immunoprecipitation assays. Taken together, our results show a deleterious effect of high co-expression of DDR1 and DDR2 and a physical interaction between the two receptors.

Practical identifiability analysis of a mechanistic model for the time to distant metastatic relapse and its application to renal cell carcinoma

Distant metastasis-free survival (DMFS) curves are widely used in oncology. They are classically analyzed using the Kaplan-Meier estimator or agnostic statistical models from survival analysis. Here we report on a method to extract more information from DMFS curves using a mathematical model of primary tumor growth and metastatic dissemination. The model depends on two parameters, α and μ, respectively quantifying tumor growth and dissemination. We assumed these to be lognormally distributed in a patient population. We propose a method for identification of the parameters of these distributions based on least-squares minimization between the data and the simulated survival curve. We studied the practical identifiability of these parameters and found that including the percentage of patients with metastasis at diagnosis was critical to ensure robust estimation. We also studied the impact and identifiability of covariates and their coefficients in α and μ, either categorical or continuous, including various functional forms for the latter (threshold, linear or a combination of both). We found that both the functional form and the coefficients could be determined from DMFS curves. We then applied our model to a clinical dataset of metastatic relapse from kidney cancer with individual data of 105 patients. We show that the model was able to describe the data and illustrate our method to disentangle the impact of three covariates on DMFS: a categorical one (Führman grade) and two continuous ones (gene expressions of the macrophage mannose receptor 1 (MMR) and the G Protein-Coupled Receptor Class C Group 5 Member A (GPRC5a) gene). We found that all had an influence in metastasis dissemination (μ), but not on growth (α).

Understanding biological mechanisms leading to metastasis development is a major challenge in order to prevent distant relapse of cancer. Classical methods to study associations of biomarkers with subsequent metastatic relapse rely on the analysis of metastasis free survival curves by means of statistical models such as proportional hazards Cox regression. These models act as black boxes and don’t provide detailed information about the specific mechanism involved. In our study, we propose to use a method based on mechanistic modeling of the metastatic development, that is, a mathematical model that simulates the biological process. The main challenge for these models is to implement the right level of complexity, because if too many parameters are included, these cannot be precisely identified from the data. We reduced the metastatic process to two main aspects: growth and dissemination. We then proposed a theoretical study of the identifiability of the two associated parameters from metastasis-free survival curves. Eventually, we applied our method to a clinical dataset in kidney cancer and illustrated how we could gain biological insights about the role of some diagnosis markers.

Abstract P012: Investigation of interleukin-34 dependent regulation of renal carcinoma tumor microenvironment

In 2020, Renal Carcinoma (RC) accounted for around 179,000 worldwide deaths and its mortality is predicted to double in the next 20 years. The major issue for RC patients is the absence of an efficient therapeutic option, especially for metastatic forms of the disease where the 5-years survival rate is close to 10%. The therapy of RC mainly targets the angiogenic and immunosuppressive tumor microenvironment (TME), but it is rarely curative and drug resistance is almost inevitable. Lack of validated biomarkers and scarce knowledge of the biological processes occurring during RC progression are main reasons of therapy failure. In our unpublished work, using a syngeneic murine model of RC, we identified interleukin-34 (IL34) as potential biomarker of RC progression. In particular, we observed that increased IL34 expression was associated with enhanced cancer cell aggressiveness and reduced survival both in mice and in RC patients from two different cohorts (i.e. KIRC-TCGA, and UroCCR local cohort). IL34 exerts pleiotropic functions in different biological processes, including immunity regulation, cell proliferation and monocytes survival and differentiation into macrophages. The expression of IL34 in the TME is heterogeneous between cancer types, and high IL34 levels can represent either a poor (i.e. in brain and lung cancers) or a favorable (i.e. in neck and breast cancers) prognostic factor. Regarding to RC, IL34 role in the TME still remains elusive and has never been described. For this reason, using the KIRC-TCGA database, we performed Gene Ontology enrichment analysis using a list of IL34 co-expressed genes to predict potential IL34 regulatory functions in the TME of RC patients. This analysis revealed that such genes are involved in different immune system related processes, including positive regulation of leukocyte activation, IL-10 production and adaptive immune response. Subsequently, using IL34-overexpressing murine renal carcinoma RENCA cells implanted in BALB/c mice, we investigated the IL34-dependent alteration of the tumor immune microenvironment. In particular, we observed that, both in generated primary tumors and lung metastases, IL34 increased the density of tumor associated macrophages (TAM), which expressed M2-type markers (e.g. Cd206 and Cd163). Furthermore, in these samples, qPCR analysis showed an increase of interleukin-10 gene expression suggesting that IL34 could induce an immunosuppressive microenvironment. Conversely, when we blocked IL34 activity in lung metastases using an inhibitor of CSF1R, the main receptor of IL34, we observed a significant reduction in TAM accumulation and Il10 expression. The major aim of this project is to investigate whether IL34 can sustain immunosuppression and, consequently, chemoresistance by accumulating TAM in the TME. Furthermore, the IL34-dependent regulation of other immune cell populations (e.g. T-reg or myeloid-derived suppressor cells) is under investigation. The study of IL34 role in the TME of RC can be fundamental to improve the current therapy.

Citation Format: Andrea Emanuelli, Wilfried Souleyreau, Lindsay Cooley, Tiffanie Chouleur, Marie-Alix Derieppe, Jean-Christophe Bernhard, Andreas Bikfalvi. Investigation of interleukin-34 dependent regulation of renal carcinoma tumor microenvironment [abstract]. In: Abstracts: AACR Virtual Special Conference: Tumor Immunology and Immunotherapy; 2021 Oct 5-6. Philadelphia (PA): AACR; Cancer Immunol Res 2022;10(1 Suppl):Abstract nr P012.

Inhibition of the membrane repair protein annexin-A2 prevents tumor invasion and metastasis

Cancer cells are exposed to major compressive and shearing forces during invasion and metastasis, leading to extensive plasma membrane damage. To survive this mechanical stress, they need to repair membrane injury efficiently. Targeting the membrane repair machinery is thus potentially a new way to prevent invasion and metastasis. We show here that annexin-A2 (ANXA2) is required for membrane repair in invasive breast and pancreatic cancer cells. Mechanistically, we show by fluorescence and electron microscopy that cells fail to reseal shear-stress damaged membrane when ANXA2 is silenced or the protein is inhibited with neutralizing antibody. Silencing of ANXA2 has no effect on proliferation in vitro, and may even accelerate migration in wound healing assays, but reduces tumor cell dissemination in both mice and zebrafish. We expect that inhibiting membrane repair will be particularly effective in aggressive, poor prognosis tumors because they rely on the membrane repair machinery to survive membrane damage during tumor invasion and metastasis. This could be achieved either with anti-ANXA2 antibodies, which have been shown to inhibit metastasis of breast and pancreatic cancer cells, or with small molecule drugs.

Abstract 3140: Interleukin-34 regulates tumor immune microenvironment of renal cell carcinoma

Renal Cell Carcinoma (RCC) is a type of kidney cancer which derives from renal tubular epithelial cells and is responsible for approximately 90-95% of cases in adults. Incidence and prevalence of RCC are rising, and 5-year survival of metastatic disease is close to 10%. The therapy of RCC mainly targets the angiogenic and immunosuppressive tumor microenvironment (TME), but it is rarely curative and drug resistance is almost inevitable. Lack of validated biomarkers and scarce knowledge of the biological processes occurring during RCC progression are main reasons of therapy failure. Using an immunocompetent murine model of RCC, we identified Interleukin-34 (IL34) as potential biomarker of RCC progression, whose expression is associated with advanced tumor stage and reduced survival in RCC patients. In cancer, IL34 was demonstrated to regulate cancer cell proliferation and monocytes survival and differentiation into macrophages. Regarding to RCC, IL34 role in the TME still remains elusive and has never been described. To elucidate the function of IL34 in RCC biology, we undertook different approaches: Gene Ontology enrichment analysis of IL34 co-expressed genes, using the KIRC-TCGA database, which revealed that such genes are involved in different immune system related processes; orthotopic implantation of IL34 over-expressing murine renal cancer cells (i.e. RENCA cell line) in BALB/c mice, where we observed, both in primary tumors and lung metastases, an accumulation of tumor associated macrophages (TAM) expressing M2-type macrophage markers (e.g. Cd206 and Cd163). Subsequently, to deeply investigate how IL34 can finely tune the tumor immune microenvironment of RCC, we also employed an experiment of Single Nuclei Sequencing using murine samples over-expressing IL34 (or control empty vector). Briefly, RENCA cancer cells were implanted in BALB/c mice using two different injection modalities: orthotopic implantation in the kidney, to generate primary tumors; 2) tail vein injection, for lung metastases formation. After tumor or metastases formation, samples were snap-frozen in liquid nitrogen and processed for cDNA library preparation for next generation sequencing. Our project aims to elucidate how IL34 can be implicated in the regulation of TME in RCC and, in particular, of the phenotype of the accumulated TAM. As TAM are known to promote cancer progression by enhancing tumor angiogenesis and immunosuppression, the study of IL34 role in the TME can reveal novel paradigms in RCC biology, and may be fundamental to improve the current therapy (e.g. using drugs targeting IL34 activity).

Citation Format: Andrea Emanuelli, Wilfried Souleyreau, Tiffanie Chouleur, Marie-Alix Derieppe, Andreas Bikfalvi. Interleukin-34 regulates tumor immune microenvironment of renal cell carcinoma [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 3140.

Deep learning model for automatic segmentation of lungs and pulmonary metastasis in small animal MR images

Lungs are the most frequent site of metastases growth. The amount and size of pulmonary metastases acquired from MRI imaging data are the important criteria to assess the efficacy of new drugs in preclinical models. While efficient solutions both for MR imaging and the downstream automatic segmentation have been proposed for human patients, both MRI lung imaging and segmentation in preclinical animal models remains challenging due to the physiological motion (respiratory and cardiac movements), to the low amount of protons in this organ and to the particular challenge of precise segmentation of metastases. As a consequence post-mortem analysis is currently required to obtain information on metastatic volume. In this work, we have developed a complete methodological pipeline for automated analysis of lungs and metastases in mice, consisting of an MR sequence for image acquisition and a deep learning method for automatic segmentation of both lungs and metastases. On one hand, we optimized an MR sequence for mouse lung imaging with high contrast for high detection sensitivity. On the other hand we developed DeepMeta, a multiclass U-Net 3+ deep learning model to automatically segment the images. To assess if the proposed deep learning pipeline is able to provide an accurate segmentation of both lungs and pulmonary metastases, we have longitudinally imaged mice with fast- and slow-growing metastasis. Fifty-five balb/c mice were injected with two different derivatives of renal carcinoma cells. Mice were imaged with a SG-bSSFP (self-gated balanced steady state free precession) sequence at different time points after the injection of cancer cells. Both lung and metastases segmentations were manually performed by experts. DeepMeta was trained to perform lung and metastases segmentation based on the resulting ground truth annotations. Volumes of lungs and of pulmonary metastases as well as the number of metastases per mouse were measured on a separate test dataset of MR images. Thanks to the SG method, the 3D bSSFP images of lungs were artifact-free, enabling the downstream detection and serial follow-up of metastases. Moreover, both lungs and metastases segmentation was accurately performed by DeepMeta as soon as they reached the volume of ∼ 0.02 m m 3 . Thus we were able to distinguish two groups of mice in terms of number and volume of pulmonary metastases as well as in terms of the slow versus fast patterns of growth of metastases. We have shown that our methodology combining SG-bSSFP with deep learning, enables processing of the whole animal lungs and is thus a viable alternative to histology alone.

Abstract 3853: Investigating PRL2/PTP4A2 as a new target for glioblastoma treatment

Glioblastoma (GBM) is the most aggressive primary brain tumor. The standard treatment consists in tumor resection followed by chemo- and radio- therapy. However, tumor recurrence still remains inevitable, and the average lifespan of patients is only 15 months. Over the past decades, targeted therapies such as anti-angiogenic therapy, were proposed but failed to improve overall survival. In this context, the identification of new therapeutic targets is fundamental to implement current treatments. Protein Tyrosine Phosphatases (PTPs) are known to be involved in oncogenesis in several types of cancer, including GBM. Oncogenic properties of PRL2 has been demonstrated in different tumors (e.g. leukemia, breast, lung and nasopharyngeal cancer), but no evidence of PRL2 involvement in GBM has been reported so far. The aim of this project is to understand the role of PRL2 in GBM development, in order to evaluate the potential of PRL2 inhibition as a new therapeutic strategy. Analysis of TCGA (The Cancer Genome Atlas) dataset revealed that PRL2 was a poor prognostic factor in gliomas, and its expression correlated GBM aggressiveness. PTP4A2 expression also correlated with expression of genes involved in immune responses, reactive oxygen species, actin cytoskeleton and trafficking. Thus, we oriented our work towards these directions. To study PRL2 effects both in brain tumor cells and microenvironment, we used spheroids of patient-derived GBM cells, in which PTP4A2 expression was modulated. In vitro assays showed that migration, invasion and adhesion abilities were increased in PTP4A2-KO cells but cell proliferation was not affected. Next, in orthotopic xenografts experiments, PRL2 over-expression promoted tumor growth and reduced mouse survival rate. In addition, to overcome PRLs functional compensation and to use a clinically relevant strategy, we targeted all PRLs (PRL1, 2 and 3) activity with an inhibitory compound. Inhibiting all PRLs drastically reduced viability of GBM cells. Our project aims at discovering how PRL2 is involved in the progression and tumor microenvironment of GBM. Our results indicate that PRL2 promotes GBM growth in response to microenvironmental pressure and its inhibition improves mouse outcomes. However, the precise mechanisms of this regulation are still under investigation. Targeting PRLs and particularly PRL2 open avenue for therapeutic strategy in GBM treatment.

Citation Format: Tiffanie Chouleur, Marie-Alix Derieppe, Wilfried Souleyreau, Michel L. Tremblay, Andreas Bikfalvi. Investigating PRL2/PTP4A2 as a new target for glioblastoma treatment [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 3853.

Protocol for open-source automated universal high-content multiplex fluorescence for RNA in situ analysis

In situ hybridization visualizes RNA in cells, but image analysis is complex. We present a protocol based on open-source software for automated high-content multiplex fluorescence in situ transcriptomics analysis. Steps include nuclei segmentation with a Fiji macro and quantification of up to 14 mRNA probes per image. We describe procedures for storing raw data, quality control images, and the use of a Python app to summarize all the results in one spreadsheet detailing the number of single or co-positive cells.

PTP4A2 Promotes Glioblastoma Progression and Macrophage Polarization under Microenvironmental Pressure

Phosphatase of regenerating liver 2 (also known as PTP4A2) has been linked to cancer progression. Still, its exact role in glioblastoma (GBM), the most aggressive type of primary brain tumor, remains elusive. In this study, we report that pharmacologic treatment using JMS-053, a pan-phosphatase of regenerating liver inhibitor, inhibits GBM cell viability and spheroid growth. We also show that PTP4A2 is associated with a poor prognosis in gliomas, and its expression correlates with GBM aggressiveness. Using a GBM orthotopic xenograft model, we show that PTP4A2 overexpression promotes tumor growth and reduces mouse survival. Furthermore, PTP4A2 deletion leads to increased apoptosis and proinflammatory signals. Using a syngeneic GBM model, we show that depletion of PTP4A2 reduces tumor growth and induces a shift in the tumor microenvironment (TME) toward an immunosuppressive state. In vitro assays show that cell proliferation is not affected in PTP4A2-deficient or -overexpressing cells, highlighting the importance of the microenvironment in PTP4A2 functions. Collectively, our results indicate that PTP4A2 promotes GBM growth in response to microenvironmental pressure and support the rationale for targeting PTP4A2 as a therapeutic strategy against GBM.

SIGNIFICANCE

High levels of PTP4A2 are associated with poor outcomes in patients with glioma and in mouse models. PTP4A2 depletion increases apoptosis and proinflammatory signals in GBM xenograft models, significantly impacts tumor growth, and rewires the TME in an immunocompetent host. PTP4A2 effects in GBM are dependent on the presence of the TME.