Tumor functional mutation and epitope loads as improved predictive biomarkers for immunotherapy response

Abstract

A method (100, 200, 400) for predicting a response of a tumor to immunotherapy, comprising: analyzing (120) a tumor sample; analyzing (130) a non-tumor sample obtained from the patient; identifying (140) one or more tumor-specific mutations; analyzing (150) the genetic information from the tumor sample to determine a variant allele frequency for the identified tumor- specific mutations; analyzing(160) genetic information to determine a tumor purity of the patient's tumor; determining (210) a pathogenicity for the identified tumor-specific mutations; calculating (220), from: (i) the determinedvariant allele frequency and / or a determined allele-specific expression, exon expression, or gene expression of the one or more tumor-specific mutations; (ii) the determined tumor purity; and (iii) the determined pathogenicity, a tumor functional mutation load score; predicting (410), based on the score, a response of the patient's tumor to an immunotherapy treatment; and determining (420), basedon said prediction, a treatment for the patient.

Description

technical field [0001] The present disclosure generally relates to methods and systems for predicting tumor response to immunotherapy. Background technique [0002] If cancer cells respond to a particular immunotherapy treatment, that immunotherapy may be an effective treatment for cancer. If the cancer cells do not respond to a particular immunotherapy, the treatment can lead to toxicity and unwanted side effects for the patient without any benefit. Therefore, when managing a patient, it may be very beneficial to determine or estimate the responsiveness of a tumor to a particular immunotherapeutic treatment. [0003] Tumor mutational burden and tumor neoantigen burden are examples of predictive biomarkers of response to immunotherapy. Tumor mutation burden (TML), also known as tumor mutation burden (TMB), can be defined as the total number of somatic, nonsynonymous, exonic mutations in the tumor genome. This information can be obtained, for example, by sequencing such as...

AI Technical Summary

Problems solved by technology

[0006] However, current methods and systems for predicting tumor response to immunotherapy do not take into account the full complement of available information and therefore cannot provide a complete prediction

For example, while tumor mutational burden and tumor neoantigen burden are known to be effective biomarkers of response to immunotherapy, these approaches still treat all mutations equally, despite the fact that mutations are present in varying proportions in tumors and have different functional impacts

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