Identification of gene signature for treatment response to guide precision oncology in clear-cell renal cell carcinoma

Established and emerging biomarkers of immunotherapy in renal cell carcinoma

Immunotherapies, such as immune checkpoint inhibitors, have heralded impressive progress for patient care in renal cell carcinoma (RCC). Despite this success, some patients' disease fails to respond, and other patients experience significant side effects. Thus, development of biomarkers is needed to ensure that patients can be selected to maximize benefit from immunotherapies. Improving clinicians' ability to predict which patients will respond to immunotherapy and which are most at risk of adverse events - namely through clinical biomarkers - is indispensable for patient safety and therapeutic efficacy. Accordingly, an evolving suite of therapeutic biomarkers continues to be investigated. This review discusses biomarkers for immunotherapy in RCC, highlighting current practices and emerging innovations, aiming to contribute to improved outcomes for patients with RCC.

The genomic and transcriptomic landscape of advanced renal cell cancer for individualized treatment strategies

Differences in the clinical course and treatment responses in individual patients with advanced renal cell carcinoma (RCC) can largely be explained by the different genomics of this disease. To improve the personalized treatment strategy and survival outcomes for patients with advanced RCC, the genomic make-up in patients with advanced RCC was investigated to identify putative actionable variants and signatures. In this prospective multicenter study (NCT01855477), whole-genome sequencing (WGS) data of locally advanced and metastatic tissue biopsies and matched whole-blood samples were collected from 91 patients with histopathologically confirmed RCC. WGS data were analyzed for small somatic variants, copy-number alterations and structural variants. For a subgroup of patients, RNA sequencing (RNA-Seq) data could be analyzed. RNA-Seq data were clustered on immunogenic and angiogenic gene expression patterns according to a previously developed angio-immunogenic gene signature. In all patients with papillary and clear cell RCC, putative actionable drug targets were detected by WGS, of which 94% were on-label available. RNA-Seq data of clear cell and papillary RCC were clustered using a previously developed angio-immunogenic gene signature. Analyses of driver mutations and RNA-Seq data revealed clear differences among different RCC subtypes, showing the added value of WGS and RNA-Seq over clinicopathological data. By improving both histological subtyping and the selection of treatment according to actionable targets and immune signatures, WGS and RNA-Seq may improve therapeutic decision making for most patients with advanced RCC, including patients with non-clear cell RCC for whom no standard treatment is available to data. Prospective clinical trials are needed to evaluate the impact of genomic and transcriptomic diagnostics on survival outcome for advanced RCC patients.

Prognostic and predictive biomarkers for immunotherapy in advanced renal cell carcinoma

The therapeutic algorithm of renal cell carcinoma has been revolutionized by the approval of immunotherapy agents by regulatory agencies. However, objective and durable responses are still not observed in a large number of patients, and prognostic and predictive biomarkers for immunotherapy response are urgently needed. Prognostic models used in clinical practice are based on clinical and laboratory factors (such as hypercalcaemia, neutrophil count or Karnofsky Performance Status), but, with progress in molecular biology and genome sequencing techniques, new renal cell carcinoma molecular features that might improve disease course and outcomes prediction have been highlighted. An implementation of current models is needed to improve the accuracy of prognosis in the immuno-oncology era. Moreover, several potential biomarkers are currently under evaluation, but effective markers to select patients who might benefit from immunotherapy and to guide therapeutic strategies are still far from validation.

Soluble CD163: a novel independent prognostic biomarker in patients with metastatic renal cell carcinoma

The hemoglobin-haptoglobin scavenger receptor CD163 is present in both a membrane-bound form on monocytes and macrophages (mCD163) and a shed soluble circulating form (sCD163). CD163 is a well-described marker of M2-like tumor-associated macrophages, but in patients with metastatic renal cell carcinoma (mRCC), monocyte mCD163 and serum sCD163 levels have not previously been investigated and associated with patient overall survival (OS). Here, we report mCD163 expression on peripheral blood monocytes, as well as sCD163 serum levels, in samples from 89 patients newly diagnosed with mRCC and 20 healthy controls. We found that in mRCC patients, compared to healthy controls, monocyte mCD163 levels were reduced (P < 0.001) whereas serum sCD163 levels were increased (P = 0.004). Moreover, an inverse correlation between mCD163 and sCD163 levels (P = 0.04) was shown. In survival analyses, intermediary levels of monocyte mCD163 were associated with longest OS, compared to both lower and higher mCD163 levels, which were both associated with worse outcomes (P < 0.01). Further, higher levels of sCD163 at diagnosis were associated with poor OS in both univariate (P < 0.001) and multivariate analysis (HR = 1.28; 95%CI 1.09-1.50, P = 0.002). Importantly, stratification by low vs. high sCD163 was able to separate patients with International Metastatic RCC Database Consortium (IMDC) intermediate risk (IMDC_INT) into two subgroups with different OS (P = 0.03): IMDC_INT-sCD163_LOW showed survival similar to IMDC_FAV patients, and IMDC_INT-sCD163_HIGH showed survival similar to IMDC_POOR patients. Thus, baseline sCD163 is a novel independent biomarker of OS in mRCC, and using sCD163 as an add-on biomarker may improve prognostic value for patients in the heterogenous IMDC intermediate group.

A Molecularly Characterized Preclinical Platform of Subcutaneous Renal Cell Carcinoma (RCC) Patient-Derived Xenograft Models to Evaluate Novel Treatment Strategies

Renal cell carcinoma (RCC) is a kidney cancer with an onset mainly during the sixth or seventh decade of the patient’s life. Patients with advanced, metastasized RCC have a poor prognosis. The majority of patients develop treatment resistance towards Standard of Care (SoC) drugs within months. Tyrosine kinase inhibitors (TKIs) are the backbone of first-line therapy and have been partnered with an immune checkpoint inhibitor (ICI) recently. Despite the most recent progress, the development of novel therapies targeting acquired TKI resistance mechanisms in advanced and metastatic RCC remains a high medical need. Preclinical models with high translational relevance can significantly support the development of novel personalized therapies. It has been demonstrated that patient-derived xenograft (PDX) models represent an essential tool for the preclinical evaluation of novel targeted therapies and their combinations. In the present project, we established and molecularly characterized a comprehensive panel of subcutaneous RCC PDX models with well-conserved molecular and pathological features over multiple passages. Drug screening towards four SoC drugs targeting the vascular endothelial growth factor (VEGF) and PI3K/mTOR pathway revealed individual and heterogeneous response profiles in those models, very similar to observations in patients. As unique features, our cohort includes PDX models from metastatic disease and multi-tumor regions from one patient, allowing extended studies on intra-tumor heterogeneity (ITH). The PDX models are further used as basis for developing corresponding in vitro cell culture models enabling advanced high-throughput drug screening in a personalized context. PDX models were subjected to next-generation sequencing (NGS). Characterization of cancer-relevant features including driver mutations or cellular processes was performed using mutational and gene expression data in order to identify potential biomarker or treatment targets in RCC. In summary, we report a newly established and molecularly characterized panel of RCC PDX models with high relevance for translational preclinical research.

A Novel Machine Learning 13-Gene Signature: Improving Risk Analysis and Survival Prediction for Clear Cell Renal Cell Carcinoma Patients

Simple Summary

Clear cell renal cell carcinoma is a type of kidney cancer which comprises the majority of all renal cell carcinomas. Many efforts have been made to identify biomarkers which could help healthcare professionals better treat this kind of cancer. With extensive public data available, we conducted a machine learning study to determine a gene signature that could indicate patient survival with high accuracy. Through the min-Redundancy and Max-Relevance algorithm we generated a signature of 13 genes highly correlated with patient outcomes. These findings reveal potential strategies for personalized medicine in the clinical practice.

Abstract

Patients with clear cell renal cell carcinoma (ccRCC) have poor survival outcomes, especially if it has metastasized. It is of paramount importance to identify biomarkers in genomic data that could help predict the aggressiveness of ccRCC and its resistance to drugs. Thus, we conducted a study with the aims of evaluating gene signatures and proposing a novel one with higher predictive power and generalization in comparison to the former signatures. Using ccRCC cohorts of the Cancer Genome Atlas (TCGA-KIRC) and International Cancer Genome Consortium (ICGC-RECA), we evaluated linear survival models of Cox regression with 14 signatures and six methods of feature selection, and performed functional analysis and differential gene expression approaches. In this study, we established a 13-gene signature (AR, AL353637.1, DPP6, FOXJ1, GNB3, HHLA2, IL4, LIMCH1, LINC01732, OTX1, SAA1, SEMA3G, ZIC2) whose expression levels are able to predict distinct outcomes of patients with ccRCC. Moreover, we performed a comparison between our signature and others from the literature. The best-performing gene signature was achieved using the ensemble method Min-Redundancy and Max-Relevance (mRMR). This signature comprises unique features in comparison to the others, such as generalization through different cohorts and being functionally enriched in significant pathways: Urothelial Carcinoma, Chronic Kidney disease, and Transitional cell carcinoma, Nephrolithiasis. From the 13 genes in our signature, eight are known to be correlated with ccRCC patient survival and four are immune-related. Our model showed a performance of 0.82 using the Receiver Operator Characteristic (ROC) Area Under Curve (AUC) metric and it generalized well between the cohorts. Our findings revealed two clusters of genes with high expression (SAA1, OTX1, ZIC2, LINC01732, GNB3 and IL4) and low expression (AL353637.1, AR, HHLA2, LIMCH1, SEMA3G, DPP6, and FOXJ1) which are both correlated with poor prognosis. This signature can potentially be used in clinical practice to support patient treatment care and follow-up.

Dual role of endothelial Myct1 in tumor angiogenesis and tumor immunity

The cross-talk between angiogenesis and immunity within the tumor microenvironment (TME) is critical for tumor prognosis. While pro-angiogenic and immunosuppressive TME promote tumor growth, anti-angiogenic and immune stimulatory TME inhibit tumor progression. Therefore, there is a great interest in achieving vascular normalization to improve drug delivery and enhance antitumor immunity. However, anti-vascular endothelial growth factor (VEGF) mechanisms to normalize tumor vessels have offered limited therapeutic efficacies for patients with cancer. Here, we report that Myct1, a direct target of ETV2, was nearly exclusively expressed in endothelial cells. In preclinical mouse tumor models, Myct1 deficiency reduced angiogenesis, enhanced high endothelial venule formation, and promoted antitumor immunity, leading to restricted tumor progression. Analysis of The Cancer Genome Atlas (TCGA) datasets revealed a significant (P < 0.05) correlation between MYCT1 expression, angiogenesis, and antitumor immunity in human cancers, as suggested by decreased FOXP3 expression and increased antitumor macrophages in patients with low MYCT1 expression. Mechanistically, MYCT1 interacted with tight junction protein Zona Occludens 1 and regulated Rho GTPase-mediated actin cytoskeleton dynamics, thereby promoting endothelial motility in the angiogenic environment. Myct1-deficient endothelial cells facilitated trans-endothelial migration of cytotoxic T lymphocytes and polarization of M1 macrophages. Myct1 targeting combined with anti-PD1 treatment significantly (P < 0.05) increased complete tumor regression and long-term survival in anti-PD1-responsive and -refractory tumor models in mice. Our data collectively support a critical role for Myct1 in controlling tumor angiogenesis and reprogramming tumor immunity. Myct1-targeted vascular control, in combination with immunotherapy, may become an exciting therapeutic strategy.

Subgroup-Independent Mapping of Renal Cell Carcinoma-Machine Learning Reveals Prognostic Mitochondrial Gene Signature Beyond Histopathologic Boundaries

Background: Renal cell carcinoma (RCC) is divided into three major histopathologic groups—clear cell (ccRCC), papillary (pRCC) and chromophobe RCC (chRCC). We performed a comprehensive re-analysis of publicly available RCC datasets from the TCGA (The Cancer Genome Atlas) database, thereby combining samples from all three subgroups, for an exploratory transcriptome profiling of RCC subgroups.

Materials and Methods: We used FPKM (fragments per kilobase per million) files derived from the ccRCC, pRCC and chRCC cohorts of the TCGA database, representing transcriptomic data of 891 patients. Using principal component analysis, we visualized datasets as t-SNE plot for cluster detection. Clusters were characterized by machine learning, resulting gene signatures were validated by correlation analyses in the TCGA dataset and three external datasets (ICGC RECA-EU, CPTAC-3-Kidney, and GSE157256).

Results: Many RCC samples co-clustered according to histopathology. However, a substantial number of samples clustered independently from histopathologic origin (mixed subgroup)—demonstrating divergence between histopathology and transcriptomic data. Further analyses of mixed subgroup via machine learning revealed a predominant mitochondrial gene signature—a trait previously known for chRCC—across all histopathologic subgroups. Additionally, ccRCC samples from mixed subgroup presented an inverse correlation of mitochondrial and angiogenesis-related genes in the TCGA and in three external validation cohorts. Moreover, mixed subgroup affiliation was associated with a highly significant shorter overall survival for patients with ccRCC—and a highly significant longer overall survival for chRCC patients.

Conclusions: Pan-RCC clustering according to RNA-sequencing data revealed a distinct histology-independent subgroup characterized by strengthened mitochondrial and weakened angiogenesis-related gene signatures. Moreover, affiliation to mixed subgroup went along with a significantly shorter overall survival for ccRCC and a longer overall survival for chRCC patients. Further research could offer a therapy stratification by specifically addressing the mitochondrial metabolism of such tumors and its microenvironment.

Tissue Based Biomarkers for Metastatic Clear Cell Renal Carcinoma: A Systematic Review

Background: Treatments for metastatic clear cell renal carcinoma (mccRCC) are evolving with multiple targeted and immune therapy drugs currently approved by regulatory agencies as single agents or in combination. Developing predictive biomarkers to determine which patients derive a differential benefit from a particular treatment is an area of ongoing clinical research. Objective: We sought to systematically evaluate the role of tumour tissue-based biomarkers that assist in selection of therapy for mccRCC. Methods: Literature addressing the role of biomarkers in mccRCC was identified through a search of the electronic databases MEDLINE, Embase, and the Web of Science and a hand search of major conference abstracts (from Jan 2010 –Sep 2020). Abstracts were screened to identify papers meriting full-text review. Studies with a comparison arm were included to assess biomarker relevance. A narrative review of studies was performed. Results: The literature search yielded 6784 potentially relevant articles. 133 articles met criteria for full text review, and 10 articles were identified by scanning bibliographies of relevant studies. A total of 33 articles (involving 13 studies) were selected for data extraction and subsequent review. Conclusions: Predictive biomarkers for immediate use in the clinic are lacking, and embedding their evaluation and validation in future clinical trials is needed to refine practice and patient selection.

Biomarker Development for Metastatic Renal Cell Carcinoma: Omics, Antigens, T-cells, and Beyond

The treatment of metastatic renal cell carcinoma has evolved quickly over the last few years from a disease managed primarily with sequential oral tyrosine kinase inhibitors (TKIs) targeting the vascular endothelial growth factor (VEGF) pathway, to now with a combination of therapies incorporating immune checkpoint blockade (ICB). Patient outcomes have improved with these innovations, however, controversy persists regarding optimal sequence and patient selection amongst the available combinations. Ideally, predictive biomarkers would aid in guiding treatment decisions and personalizing care. However, clinically-actionable biomarkers have remained elusive. We aim to review the available evidence regarding biomarkers for both TKIs and ICB and will present where the field may be headed in the years to come.