Pathologists have made much progress in understanding the diversity — and to some extent, the aggressiveness — of different renal cell carcinoma (RCC) tumors. But a key gap remains the ability to reliably and objectively predict patients’ responses to treatment and their survival.

In an effort towards filling that knowledge gap, a collaboration of researchers recently published an extensive transcriptomic and genomic analysis of clear-cell or sarcomatoid RCC tumors that had been collected prior to the phase 3 IMmotion151 trial ( Identifier: NCT02420821). Results reported last year demonstrated prolonged progression-free survival (PFS) in patients with metastatic disease who received a combination of the vascular endothelial growth factor (VEGF)-targeting monoclonal antibody (mAb) bevacizumab and the programmed death ligand 1 (PD-L1)–targeting checkpoint inhibitor atezolizumab vs patients who only received the antiangiogenesis treatment sunitinib, a VEGF-targeting tyrosine kinase inhibitor (TKI).1

Now, the new research, published in November 2020 in Cancer Cell, identified 7 molecular subsets of clear cell RCC with distinct angiogenesis, cell-cycle, immune, metabolism, and stromal programs, which are often also associated with somatic gene mutations. These subsets exhibited different clinical responses to the mAb alone or with the checkpoint inhibitor, providing a molecular explanation of patients’ responses to treatment and aiding the development of prognostic and predictive biomarkers in RCC.

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“It’s an incredibly valuable dataset that will have important implications for the development of predictive biomarkers for the 2 therapies that they examined,” said Marcin Cieslik, PhD, assistant professor of computational medicine & bioinformatics and assistant professor of pathology in the department of pathology at Michigan Medicine, who wasn’t involved in the new research.

The study focused on tumor samples — mostly from nephrectomies — collected from 823 of the 915 patients who participated in the trial no longer than 2 years before enrollment. The tumors were either of primary (625) or metastatic (198) origin at collection (although the trial focused on metastatic disease). And, 688 were of clear cell histology, though the remainder had varying degrees of sarcomatoid components.

After generating whole-transcriptome profiles of the samples through RNA sequencing, the researchers used a machine learning algorithm to cluster the samples into discrete groups based on the top 10% most variable genes.

This revealed 7 biologically distinct tumor groups. Clusters 1 and 2 (representing 12% and 30% of the tumors, respectively) were enriched for angiogenesis-related genes, including genes involved in the VEGF pathway. Tumors assigned to cluster 3 (19%) tended to have low expression of angiogenesis and immune-related genes but moderate expression of ones involved in cell cycle regulation. Clusters 4, 5, and 6 (14%, 9%, and 13%) had notably low expression of angiogenesis-related genes but pronounced cell cycle- and anabolic metabolism-related transcriptional signatures. Cluster 4 also exhibited a notably immunogenic, inflammatory kind of signature. Tumors in cluster 7 (3% of samples) were enriched with small nucleolar RNAs — a class of RNAs with unclear biological significance in this context.

Interestingly, tumors in the first 2 angiogenesis-related clusters tended to fall into favorable prognostic risk categories according to the Memorial Sloan Kettering Cancer Center (MSKCC) classification. Patients with these tumors had longer PFS in both treatment arms of the trial, irrespective of treatment.

Notably, the more “proliferative,” cell cycle–focused phenotypes in clusters 4, 5, and 6 occurred more frequently in poor-risk MSKCC groups.

For those in the low-angiogenesis clusters — such as the inflammatory-type cluster 4 and the proliferative cluster 5 — the atezolizumab-bevacizumab combination significantly improved PFS as well as overall response (OR) compared with sunitinib. This underscored the importance of PD-L1 blockade in low-angiogenesis subgroups, as well as the potential of using immune and angiogenesis elements as biomarkers of response to the checkpoint and angiogenesis blockade in advanced RCC patients, the researchers wrote.