Multiomics Approach for Patients Stratification and Novel Target Identification in Metastatic Clear Renal Cell Carcnoma

IMPACT In the last ten years the systemic treatment of metastatic renal cell carcinoma has been revolutioned with the introduction of at least ten active drugs. With the advent of novel immuno-based and tyrosine kinase inhibitors (TKIs)-based combinations, the choice of the best strategy in treatment-naive metastatic clear-cell renal cell carcinoma (mccRCC) patients (pts) is becoming an issue, since no biomarkers are available to guide the treatment allocation strategy . In recent clinical trials, combination therapies including nivolumab plus ipilimumab, pembrolizumab plus axitinib, atezolizumab plus bevacizumab, avelumab plus axitinib, pembrolizumab plus lenvatinib and nivolumab plus cabozantinib exhibited significant benefits in terms of overall survival (OS) and/or progression-free survival (PFS) for mRCC compared with sunitinib as a standard first-line treatment for mRCC . However, there is a clear need for clinical predictive biomarkers to guide optimal treatment decisions. Through the above research the investigators are confident to provide proof of concept that combine the informations from genomics and radiomics using computational approaches such as machine learning, will provide an opportunity for a molecularly driven patient's stratification.

RATIONALE AND FEASIBILITY

BIOMARKERS Risk stratification models based on gene expression pattern (both messenger and long non-coding RNA) in ccRCC have proven to have strong prognostic values. Hence, there is an interest in the identification and development of treatment predictive biomarkers to enable precision oncology increasing drug response. Multiple candidates for predictive biomarkers from plasma, tumor, and host tissues have been explored in patients with metastatic renal-cell carcinoma who are receiving systemic therapies, but, as yet, none have entered clinical practice and all require prospective validation in clinical trials.

In the era of VEGF inhibitors, the investigators counted on IMDC (International Metastatic RCC Database Consortium) model, considering Karnofsky performance status <80, time to initiation of therapy <1 year, hemoglobin < lower level of normal, serum calcium, neutrophil count, and platelet count > upper limit of normal. CheckMate 214 study showed that OS and ORR were significantly higher with nivolumab plus ipilimumab than with sunitinib among intermediate- and poor-risk pts. Extended study follow-up of KEYNOTE-426 study demonstrated that the benefit in OS and PFS is consistent in this class of patients's risk also with the IO/TKI combination therapy. The IMDC score is confirmed to be prognostic in every combos study.

PD-L1 has also been demonstrated to be a prognostic marker for poor prognosis in RCC, regardless of the type of treatment used. More recently PD-L1 expression has also been evaluated for its predictive role that is only partially confirmed in the CheckMate-214 population that received IO-IO combo and considered a poor marker for targeted therapies. Gene signatures from ImMotion 150 and 151 evidenced that two different signatures (angiogenesis versus immune signature) in RCC patients could predict the response to combo treatment.