Evaluation of tumor microenvironment and biomarkers of immune checkpoint inhibitor response in metastatic renal cell carcinoma

Abstract

BackgroundImmunotherapy combinations including ipilimumab and nivolumab are now the standard of care for untreated metastatic renal cell carcinoma (mRCC). Biomarkers of response are lacking to predict patients who will have a favorable or unfavorable response to immunotherapy. This study aimed to use the OmniSeq transcriptome-based platform to develop biomarkers of response to immunotherapy.MethodsTwo cohorts of patients were retrospectively collected. These included an investigational cohort of patients with mRCC treated with immune checkpoint inhibitor therapy from five institutions, and a subsequent validation cohort of patients with mRCC treated with combination ipilimumab and nivolumab from two institutions (Duke Cancer Institute and Cleveland Clinic Taussig Cancer Center). Tissue-based RNA sequencing was performed using the OmniSeq Immune Report Card on banked specimens to identify gene signatures and immune checkpoints associated with differential clinical outcomes. A 5-gene expression panel was developed based on the investigational cohort and was subsequently evaluated in the validation cohort. Clinical outcomes including progression-free survival (PFS) and overall survival (OS) were extracted by retrospective chart review. Objective response rate (ORR) was assessed by Response Evaluation Criteria in Solid Tumors (RECIST) V.1.1.ResultsThe initial investigation cohort identified 86 patients with mRCC who received nivolumab (80%, 69/86), ipilimumab/nivolumab (14%, 12/86), or pembrolizumab (6%, 5/86). A gene expression score was created using the top five genes found in responders versus non-responders (FOXP3, CCR4, KLRK1, ITK, TIGIT). The ORR in patients with high gene expression (GEhigh) on the 5-gene panel was 29% (14/48), compared with low gene expression (GElow) 3% (1/38, χ2p=0.001). The validation cohort was comprised of 62 patients who received ipilimumab/nivolumab. There was no difference between GEhighand GElowin terms of ORR (44% vs 38.5%), PFS (HR 1.5, 95% CI 0.58 to 3.89), or OS (HR 0.96, 95% CI 0.51 to 1.83). Similarly, no differences in ORR, PFS or OS were observed when patients were stratified by tumor mutational burden (high=top 20%), PD-L1 (programmed death-ligand 1) expression by immunohistochemistry or RNA expression, or CTLA-4 (cytotoxic T-lymphocytes-associated protein 4) RNA expression. The International Metastatic RCC Database Consortium (IMDC) risk score was prognostic for OS but not PFS.ConclusionA 5-gene panel that was associated with improved ORR in a predominantly nivolumab monotherapy population of patients with mRCC was not predictive for radiographic response, PFS, or OS among patients with mRCC treated with ipilimumab and nivolumab.

MOICS, a novel classier deciphering immune heterogeneity and aid precise management of clear cell renal cell carcinoma at multiomics level

“… 48 This correlation aligns with findings by Brown et al, who identified distinct expression patterns of immune markers differentiating responders from non-responders to ICIs. 49 These patterns are potentially predictive of treatment outcomes, suggesting that a nuanced understanding of tumor-infiltrating lymphocytes (TILs) could substantially refine the personalization of immunotherapy in RCC. Further emphasizing the complexity of the immune landscape in ccRCC, the comprehensive analyses by de Vries-Brilland et al highlighted how the density and type of immune infiltrates might dictate therapeutic strategies.…”

Identification of aneuploidy-related gene signature to predict survival in head and neck squamous cell carcinomas

“…The human CC chemokine receptors such as CCR4 and CCR7 involve in T cell trafficking [ 39 ]. Over expressed CCR4 appeared to be treated as a biomarker for immune checkpoint inhibitor therapeutic response in renal cancer patients [ 40 ]. Similar to our findings, a high-level of CCR4 was also correlated with good prognosis in HNSC suffers through joining in immune infiltration [ 41 ].…”

The Tumor Immune Microenvironment in Clear Cell Renal Cell Carcinoma

“…In addition, other checkpoint inhibitors, such as antibodies targeting T cell immunoglobulin (VISTA), mucin domain 3 (TIM-3), lymphocyte activation gene 3 (LAG-3), and TIGIT, are also being evaluated in clinical trials for the treatment of ccRCC [63].…”