Precision therapy of cancer is the ultimate best treatment sought by doctors for their patients. This new article in Cancer Discovery describes a comprehensive resource of precision combination therapies tailored to specific patient groups.

Targeted cancer therapies have already improved clinical outcomes for many patients. However, monotherapy against a single target often leads to treatment resistance. This is because cancer cells frequently rely on co-occurring alterations, such as mutations in two signaling pathways, to drive tumor progression.  In this case, targeting both alterations simultaneously could enhance the treatment outcome and constitutes a precision therapy.

The Researchers developed a new bioinformatics platform that predicts optimal treatment combinations for a given group of patients based on co-occurring tumor alterations. Their goal was to facilitate both the discovery and the selection of combination therapies matched to the molecular composition of tumors. For this, they developed Recurrent Features Leveraged for Combination Therapy (REFLECT), which integrates machine learning and cancer informatics algorithms.

They analyzed cancer datasets, including pre-treatment patient tumor samples, cell lines and patient-derived xenografts (PDXs), representing more than 10,000 patients and 33 cancer types. This generated 201 patient cohorts, each defined by a single therapeutically targetable biomarker, such as epidermal growth factor receptor (EGFR), HER2 and DNA repair aberrations.

Within each cohort, the team generated REFLECT signatures of additional alterations that may act as therapeutic targets. Thus, they identified a total of 2,166 sub-cohorts that may benefit from specific combination therapies.

The researchers validated their new platform retrospectively. They found that REFLECT-matched combination therapies in preclinical and clinical studies had the best results.

In the future, multi-omic profiles from pre-treatment patient samples could be loaded to the REFLECT pipeline to generate co-alteration signatures, allowing physicians to consider precision combination therapies tailored to molecular profiles of those patients. Additionally, the researchers plan to expand their study to better address and predict toxicity from matched drug combinations.

Reference:  Li, X., et al. (2022) Precision combination therapies based on recurrent oncogenic co-alterations. Cancer Discovery. doi.org/10.1158/2159-8290.CD-21-0832.

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