A novel molecular signature identifies mixed subtypes in renal cell carcinoma with poor prognosis and independent response to immunotherapy

Abstract

Background Renal cell carcinoma (RCC) is a heterogeneous disease comprising histologically defined subtypes. For therapy selection, precise subtype identification and individualized prognosis are mandatory, but currently limited. Our aim was to refine subtyping and outcome prediction across main subtypes, assuming that a tumor is composed of molecular features present in distinct pathological subtypes. Methods Individual RCC samples were modeled as linear combination of the main subtypes (clear cell (ccRCC), papillary (pRCC), chromophobe (chRCC)) using computational gene expression deconvolution. The new molecular subtyping was compared with histological classification of RCC using the Cancer Genome Atlas (TCGA) cohort ( n = 864; ccRCC: 512; pRCC: 287; chRCC: 65) as well as 92 independent histopathologically well-characterized RCC. Predicted continuous subtypes were correlated to cancer-specific survival (CSS) in the TCGA cohort and validated in 242 independent RCC. Association with treatment-related progression-free survival (PFS) was studied in the JAVELIN Renal 101 ( n = 726) and IMmotion151 trials ( n = 823). CSS and PFS were analyzed using the Kaplan–Meier and Cox regression analysis. Results One hundred seventy-four signature genes enabled reference-free molecular classification of individual RCC. We unambiguously assign tumors to either ccRCC, pRCC, or chRCC and uncover molecularly heterogeneous tumors (e.g., with ccRCC and pRCC features), which are at risk of worse outcome. Assigned proportions of molecular subtype-features significantly correlated with CSS (ccRCC ( P = 4.1E − 10), pRCC ( P = 6.5E − 10), chRCC ( P = 8.6E − 06)) in TCGA. Translation into a numerical RCC-R(isk) score enabled prognosis in TCGA ( P = 9.5E − 11). Survival modeling based on the RCC-R score compared to pathological categories was significantly improved ( P = 3.6E − 11). The RCC-R score was validated in univariate ( P = 3.2E − 05; HR = 3.02, 95% CI: 1.8–5.08) and multivariate analyses including clinicopathological factors ( P = 0.018; HR = 2.14, 95% CI: 1.14–4.04). Heterogeneous PD-L1-positive RCC determined by molecular subtyping showed increased PFS with checkpoint inhibition versus sunitinib in the JAVELIN Renal 101 ( P = 3.3E − 04; HR = 0.52, 95% CI: 0.36 − 0.75) and IMmotion151 trials ( P = 0.047; HR = 0.69, 95% CI: 0.48 − 1). The prediction of PFS significantly benefits from classification into heterogeneous and unambiguous subtypes in both cohorts ( P = 0.013 and P = 0.032). Conclusion Switching from categorical to continuous subtype classification ...

Multi-omics features-based machine learning method improve immunotherapy response in clear cell renal cell carcinoma

“…2a, b). We further evaluated the aforementioned signatures in the IMmotion151 dataset 44 , where 400+ previously untreated ccRCC patients were given atezolizumab (an anti-PD-L1 inhibitor) plus bevacizumab (an anti-VEGF inhibitor), and found 329 genes relevant to response and survival (Fig. 2c).…”

“…For the immune datasets, we collected transcriptome and genomic data with clinical annotations from three ccRCC cohorts: IMmotion151 44 (atezolizumab + bevacizumab versus Sunitinib; first-line) from the European Genome-phenome Archive (EGA) dataset, CheckMate (nivolumab versus everolimus; latter-line) from the supplementary materials provided by Braun et al 21 , and JAVELIN (avelumab + axitinib versus sunitinib; first-line) from the supplementary materials provided by Motzer et al 18 .We also gathered 4 clinically annotated transcriptome immune cohorts of urothelial carcinoma (UC) (IMvigor210 61 , treated by atezolizumab, n = 208), NSCLC (POPLAR 62 , treated by atezolizumab, n = 81), RCC (IMmotion150 19 , treated by atezolizumab alone or in combination with bevacizumab, n = 162), multi-cancer (PCD4989g 56 , treated by atezolizumab, n = 206). Patients with clearly 4 treatment outcomes of “CR, PR, PD, SD” were enrolled except JEVELIN which with known survival time records were enrolled.…”

“…For the immune datasets, we collected transcriptome and genomic data with clinical annotations from three ccRCC cohorts: IMmotion151 44 (https://www.cbioportal.org/study/summary?id=kirc_tcga). This study generates no new data, so no ethics approval is needed.…”

Synchronous ipsilateral papillary renal cell carcinoma and urothelial carcinoma: A case report

“…RCC is one of the 10 most commonly detected malignancies worldwide, and especially in Western countries, its incidence is rising. RCC comprises of histologically defined subtypes that differ in pathophysiology, clinical course, therapeutic response and prognosis ( 15 ). Approximately 40% of patients with localized RCC have distant metastases after surgery ( 6 ).…”

Effect of the m6ARNA gene on the prognosis of thyroid cancer, immune infiltration, and promising immunotherapy

“…In addition, thyroid cancer is usually asymptomatic ( 6 ), and at autopsy, 35.6% of the Finnish population had occult thyroid cancer ( 7 ). Currently, most thyroid cancers are curable with conventional treatments such as surgery, radioactive iodide (RAI) therapy,thyroid stimulating hormone (TSH) suppression therapy for local or localized disease ( 8 ) and bioinformatics is also a useful tool to analyze sequencing results and clinical data, construct a prognostic model and seek involved pathways or therapeutic targets, particularly in tumor research ( 9 – 11 ). Therefore, using a panel of susceptible genes with RNA/DNA sequencing techniques to identify high-risk patients is advocated for the early diagnosis of high-risk patients and precision medicine ( 12 , 13 ).…”