Data-driven design of targeted gene panels for estimating immunotherapy biomarkers

TL;DR

This paper presents a data-driven framework for designing targeted gene panels to estimate cancer immunotherapy biomarkers accurately, enabling optimized panel selection and improved biomarker prediction across multiple cancer types.

Contribution

It introduces a generative mutation profile model and a novel procedure for selecting gene panels to estimate biomarkers, advancing personalized cancer immunotherapy diagnostics.

Findings

High accuracy in biomarker estimation across multiple cancer types

Effective gene panel augmentation and selection methods

Demonstrated performance on non-small cell lung cancer data

Abstract

We introduce a novel data-driven framework for the design of targeted gene panels for estimating exome-wide biomarkers in cancer immunotherapy. Our first goal is to develop a generative model for the profile of mutation across the exome, which allows for gene- and variant type-dependent mutation rates. Based on this model, we then propose a new procedure for estimating biomarkers such as tumour mutation burden and tumour indel nurden. Our approach allows the practitioner to select a targeted gene panel of a prespecified size, and then construct an estimator that only depends on the selected genes. Alternatively, the practitioner may apply our method to make predictions based on an existing gene panel, or to augment a gene panel to a given size. We demonstrate the excellent performance of our proposal using data from three non-small cell lung cancer studies, as well as data from six other cancer types.