At the time of diagnosis, about one-third of patients with Renal Cell Carcinoma (RCC) have metastasis, while one-third of those with complex diseases definitely experience disease recurrence. Currently, the US Food and Drug Administration have approved four immunotherapy drug combinations as first-line treatments for metastatic RCC (mRCC): pembrolizumab (anti-Programmed Cell Death protein 1 [anti-PD-1] antibody] plus axitinib [VEGF-R Tyrosine Kinase Inhibitor [TKI]), avelumab (anti-PD-L1 antibody] plus axitinib, nivolumab.

How to determine the optimum first-line regimen for mRCC patients who have never received treatment is thus one of the most important unmet needs. The Heng score has been validated as a valuable prognostic score and has been used to stratify treatment-naive mRCC patients enrolled in clinical trials, with low-score patients typically treated with anti-angiogenic TKIs and intermediate/high-score ones with combination therapy. However, it could not be useful on its own for deciding between Immuno and Immuno-TKI combination therapies. Contradictory findings have been found when genetic changes, such as PBRM1 mutations, have been investigated as predictors for immunotherapy.

In this situation, it is essential to create biomarkers that can forecast whether a patient will respond to existing medicines or not. In the JAVELIN Renal 101 clinical study (avelumab+axitinib vs. sunitinib), Motzer examined Gene Expression Patterns (GEPs) on tumour specimens from patients and connected them to Progression-Free Survival (PFS) in both therapy groups. Only patients in the combo arm could be stratified using the 26-gene Renal 101 Immuno-signature, which regulates innate and adaptive immune responses, cell trafficking, and inflammation. Patients who achieved PFS improvements had median expression levels of this signature or higher. A 26-gene angiogenesis distinguished patients in the sunitinib arm-but not in the combination one-with a prolonged PFS in a comparable but opposite manner.

The GEPs from the IMmotion150 phase 2 trials, which compared sunitinib and atezolizumab (an anti-PD-L1 antibody) as first-line therapies for mRCC, identified three key signatures: angiogenesis, T-effector, and myeloid. Interestingly, atezolizumab showed lower responses in cases of myeloid inflammation (high myeloid signature), whereas sunitinib efficacy was greater in highly angiogenic tumours (high angio-signature), even though the addition of bevacizumab in such cases may overcome immune checkpoint inhibitor resistance.

A 66-gene signature was found by D'Costa and colleagues after they analysed the GEPs of 469 clear cell RCC patients from The Cancer Genome Atlas. This signature categorises patients into three groups: angiogenesis, T-effector, and mixed. This 66-gene signature was able to stratify patients with a better overall survival and disease-free survival for angio-signature compared to the others, in contrast to the usage of IMmotion150 32-gene signature applied to the same group. According to Barata, the 66-gene signature was also applied to 316 RCC patients, revealing noticeably distinct immunological profiles and mutational profiles between the angiogenesis and T-effector signatures.

Sequencings of bulk and individual RNA transcripts are critical research tools for investigating tumour parameters, the tumour microenvironment, and the biology of particular cancer types, revealing subgroups that may differ from one another in their responses to treatments. However, the studies so far provided have not been able to confirm a particular signature (i.e., immunological vs. angiogenic ones) as a predictive biomarker of response to therapy in mRCC patients. Contrarily, the angio-signature would not shed light on the function of VEGF-R targeting medicines given that patients were treated with TKIs in both of the study's arms, even if the T-effector signature predicted high responses in patients who received immunotherapy in combination. Clinical trials should be created employing GEPs as stratification criteria in order to clinically evaluate and investigate the prognostication of GEPs in order to identify a certain subset of patients who benefit from a particular medication. They would then be able to evaluate the precise impact of those signatures in connection to the care the patients got.

In conclusion, GEPs are a potentially helpful tool to predict response to specific therapies and could help clinicians choose between various therapeutic options, but their use is currently challenging due to the lack of clinical validations of "real world" patient stratification based on GEPs data and GEPs-led clinical trials.