Molecular Subsets in Renal Cancer Determine Outcome to Checkpoint and Angiogenesis Blockade

Motzer, Robert J.; Banchereau, Romain; Hamidi, Habib; Powles, Thomas; McDermott, David F.; Atkins, Michael B.; Escudier, Bernard; Liu, Lifen; Leng, Ning; Abbas, Alexander R.; Fan, Jinzhen; Koeppen, Hartmut; Lin, Jennifer; Carroll, Susheela; Hashimoto, Kenji; Mariathasan, Sanjeev; Green, Marjorie; Tayama, Darren; Hegde, Priti S.; Schiff, Christina; Huseni, Mahrukh; Rini, Brian I.

doi:10.1016/j.ccell.2020.10.011

Cited by 350 publications

(346 citation statements)

References 63 publications

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“…In IMotion150, treatmentnaïve patients with high T effector combined with high myeloid-suppressive signatures tended not to do as well with single-agent PD-L1 blockade [10]. In this cohort, low angiogenesis and high T effector signatures were more likely to respond, consistent with previously published studies [10,23]. While results between this study and IMotion150 were discrepant regarding ability to respond with a high myeloid signature, this may be due to differences in patient population and line of therapy.…”

Section: Discussionsupporting

confidence: 77%

“…Molecular studies have shed light on the biological response behind anti-PD-1 monotherapy, such as the presence of endogenous retroviruses [19,20] and differential expression of gene signatures, including T cell effector function [10], interferon (IFN) or tumor necrosis factor (TNFα) signaling [21], and metabolic gene signatures [22]. In the randomized trials IMmotion150 and IMmotion151, molecular signatures of response were assessed in treatment-naïve patients who received sunitinib, the combination of atezolizumab (PD-L1 antibody) and bevacizumab (vascular endothelial growth factor (VEGF) antibody), or, in the case of IMmotion150, atezolizumab monotherapy [10,23]. A T effector signature correlated with response in both ICI monotherapy and combination therapy arms, while the angiogenic signature correlated with response in anti-VEGF monotherapy and combination therapy.…”

Section: Introductionmentioning

confidence: 99%

“…Furthermore, responders to atezolizumab monotherapy generally had lower inflammatory myeloid signatures and higher T effector signatures, while patients with high suppressive myeloid signatures were more likely to achieve response if VEGF inhibition was combined with checkpoint inhibition [10]. More in-depth analysis from IMmotion150 further categorized RCC patients based on integration of various molecular parameters into seven molecular subsets, which appeared to have differential clinical outcomes to sunitinib versus atezolizumab plus bevacizumab [23].…”

Section: Introductionmentioning

confidence: 99%

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Clinical Features and Multiplatform Molecular Analysis Assist in Understanding Patient Response to Anti-PD-1/PD-L1 in Renal Cell Carcinoma

Shiuan

Reddy

Dudzinski

et al. 2021

Cancers

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Predicting response to ICI therapy among patients with renal cell carcinoma (RCC) has been uniquely challenging. We analyzed patient characteristics and clinical correlates from a retrospective single-site cohort of advanced RCC patients receiving anti-PD-1/PD-L1 monotherapy (N = 97), as well as molecular parameters in a subset of patients, including multiplexed immunofluorescence (mIF), whole exome sequencing (WES), T cell receptor (TCR) sequencing, and RNA sequencing (RNA-seq). Clinical factors such as the development of immune-related adverse events (odds ratio (OR) = 2.50, 95% confidence interval (CI) = 1.05–5.91) and immunological prognostic parameters, including a higher percentage of circulating lymphocytes (23.4% vs. 17.4%, p = 0.0015) and a lower percentage of circulating neutrophils (61.8% vs. 68.5%, p = 0.0045), correlated with response. Previously identified gene expression signatures representing pathways of angiogenesis, myeloid inflammation, T effector presence, and clear cell signatures also correlated with response. High PD-L1 expression (>10% cells) as well as low TCR diversity (≤644 clonotypes) were associated with improved progression-free survival (PFS). We corroborate previously published findings and provide preliminary evidence of T cell clonality impacting the outcome of RCC patients. To further biomarker development in RCC, future studies will benefit from integrated analysis of multiple molecular platforms and prospective validation.

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Section: Discussionsupporting

confidence: 77%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Clinical Features and Multiplatform Molecular Analysis Assist in Understanding Patient Response to Anti-PD-1/PD-L1 in Renal Cell Carcinoma

Shiuan

Reddy

Dudzinski

et al. 2021

Cancers

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“…Some critics think that a single biomarker is not enough to guide the choice of treatment, we need a comprehensive combination of biomarkers. Therefore, mRNA panel signatures or molecular subsets, reflecting the tumor as well as its microenvironment and the host, was given particular attention (35,36). In this study, we found that there were significant differences in TKI related genes and immune checkpoint genes between high-risk and low-risk groups, suggesting that the model has the potential to predict TKI and immunotherapy response.…”

Section: Discussionmentioning

confidence: 77%

Identification of a Novel Glycolysis-Related Gene Signature Correlates With the Prognosis and Therapeutic Responses in Patients With Clear Cell Renal Cell Carcinoma

Lin

et al. 2021

Front. Oncol.

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BackgroundAccumulating evidences indicate significant alterations in the aerobic glycolysis in clear cell renal cell carcinoma (ccRCC). We aim to develop and validate a glycolysis-related genes signature for predicting the clinical outcomes of patients with ccRCC.MethodsmRNA expression profiling of ccRCC was obtained from The Cancer Genome Atlas database. Univariate Cox regression analysis and lasso Cox regression model were performed to identify and construct the prognostic gene signature. The protein expression levels of the core genes were obtained from the Human Protein Atlas database. We used four external independent data sets to verify the predictive power of the model for prognosis, tyrosine kinase inhibitor (TKI) therapy, and immunotherapy responses, respectively. Finally, we explored the potential mechanism of this signature through gene set enrichment analysis (GSEA).ResultsThrough the GSEA, glycolysis-related gene sets were significantly different between ccRCC tissues and normal tissues. Next, we identified and constructed a seven-mRNA signature (GALM, TGFA, RBCK1, CD44, HK3, KIF20A, and IDUA), which was significantly correlated with worse survival outcome and was an independent prognostic indicator for ccRCC patients. Furthermore, the expression levels of hub genes were validated based on the Human Protein Atlas databases. More importantly, the model can predict patients’ response to TKI therapy and immunotherapy. These findings were successfully validated in the external independent ccRCC cohorts. The mechanism exploration showed that the model may influence the prognosis by influencing tumor proliferation, base mismatch repair system and immune status of patients.ConclusionsOur study has built up a robust glycolysis-based molecular signature that predicts the prognosis and TKI therapy and immunotherapy responses of patients with ccRCC with high accuracy, which might provide important guidance for clinical assessment. Also, clinical investigations in large ccRCC cohorts are greatly needed to validate our findings.

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“…However, fsINDEL burden has so far not been shown to predict benefit from CPI in patients with ccRCC 20, 21 , again in contrast to other tumour types 22, 23 . Mutations in PBRM1 are reported to be enriched in responders to CPI in ccRCC 19, 24, 25 , though this has not been observed consistently 20, 21, 26, 27 .…”

Section: Introductionmentioning

confidence: 94%

Determinants of anti-PD1 response and resistance in clear cell renal cell carcinoma

Hatipoglu

Massy

et al. 2021

Preprint

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Antigen recognition and T-cell mediated cytotoxicity in clear-cell renal cell carcinoma (ccRCC) remains incompletely understood. To address this knowledge gap, we analysed 115 multiregion tumour samples collected from 15 treatment-naive patients pre- and post-nivolumab therapy, and at autopsy in three patients. We performed whole-exome sequencing, RNAseq, TCRseq, multiplex immunofluorescence and flow cytometry analyses and correlated with clinical response. We observed pre-treatment intratumoural TCR clonal expansions suggesting pre-existing immunity. Nivolumab maintained pre-treatment expanded, clustered TCR clones in responders, suggesting ongoing antigen-driven stimulation of T-cells. T-cells in responders were enriched for expanded TCF7+CD8+ T-cells and upregulated GZMK/B upon nivolumab-binding. By contrast, nivolumab promoted accumulation of new TCR clones in non-responders, replacing pre-treatment expanded clonotypes. In this dataset, mutational features did not correlate with response to nivolumab and human endogenous retrovirus expression correlated indirectly. Our data suggests that nivolumab potentiates clinical responses in ccRCC by binding pre-existing expanded CD8+ T-cells to enhance cytotoxicity.

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Molecular Subsets in Renal Cancer Determine Outcome to Checkpoint and Angiogenesis Blockade

Cited by 350 publications

References 63 publications

Clinical Features and Multiplatform Molecular Analysis Assist in Understanding Patient Response to Anti-PD-1/PD-L1 in Renal Cell Carcinoma

Clinical Features and Multiplatform Molecular Analysis Assist in Understanding Patient Response to Anti-PD-1/PD-L1 in Renal Cell Carcinoma

Identification of a Novel Glycolysis-Related Gene Signature Correlates With the Prognosis and Therapeutic Responses in Patients With Clear Cell Renal Cell Carcinoma

Determinants of anti-PD1 response and resistance in clear cell renal cell carcinoma

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