June 12, 2024

Companies partner to identify biomarkers for renal cell carcinoma

Original source here.

OncoHost and Dana-Farber Cancer Institute have launched a new study to identify biomarkers for renal cell carcinoma (RCC).

Immune-checkpoint inhibitor (ICI) therapy has transformed treatment outcomes for RCC. However, ICIs can also trigger immune-related adverse events and may not benefit all patients.

The collaboration will leverage Dana-Farber’s repository of patient plasma samples and corresponding clinical data, providing essential materials for OncoHost to create a proteomic plasma profile.

Ofer Sharon, CEO of OncoHost, commented: “As we enter the era of precision medicine, validated biomarkers are increasingly used to guide treatment decisions and uncover resistance pathways in various types of tumours. However, RCC lacks reliable biomarkers to predict ICI response. Our goal is to overcome this challenge to improve clinical decision-making in the first line setting and, ultimately, patient outcomes.”

The primary objectives of the study include creating a proteomic plasma profile of metastatic RCC patients before treatment, thereby associating protein expression changes with treatment response metrics like best response, overall survival, and progression-free survival, and evaluating differential response predictions to specific treatment combinations.

The research project will also explore proteomic pathways associated with treatment response and immune-related adverse events (irAEs), offering potential insights into treatment options and biological patterns of resistance. Comparisons with pathways identified in other cancers, as part of the PROPHETIC trial, will further enhance the understanding of RCC’s unique biomolecular landscape.

Wenxin Xu, Physician, Dana-Farber Cancer Institute, Assistant Professor of Medicine, Harvard Medical School, and Principal Investigator of the research study, said: “This study explores whether measuring blood levels of thousands of cancer related proteins can be used to build personalised, data-driven predictions. If successful, the data we generate could help us learn more about the biology of renal cell carcinoma and its treatments.”