Tumor classification based on predicted tumor mutational burden

Abstract

The present disclosure provides systems and methods of classifying and / or identifying a cancer subtype. The present disclosure also provides methods of enhancing the prediction of a tumor mutational burden by using both synonymous and non-synonymous somatic mutations in the computation method. It is believed that by increasing the number of mutations in the computation of the tumor mutational burden, a comparatively more consistent tumor mutational burden may be derived, especially for targeted-panel sequencing. It is believed that the consistent computation of the tumor mutational burden from targeted panels allows for computationally quicker and less costly analysis of sequencing data as compared with a tumor mutational burden computed from whole exome sequencing data.

Description

Background technique [0001] The study of human genetic variation using DNA sequencing has undergone extraordinary developments since its introduction into the state of the art 40 years ago, allowing the human genome to be sequenced and analyzed in a matter of days. The release of the first "next-generation sequencing" (NGS) instruments in the mid-2000s sparked a revolution in disease research, dramatically increasing speed at dramatically reduced costs, allowing the generation of whole human genome sequences in just a few weeks. In addition to price and performance, new sequencing technologies are proving to compensate for some of the technical shortcomings of older sequencing and genotyping technologies, allowing genome-wide detection of variants, including novel variants, at low cost. A further breakthrough in NGS in human genomics has been through the introduction of targeted enrichment methods, which allow selective sequencing of regions of interest, significantly reducing...

AI Technical Summary

Problems solved by technology

It is believed that some tumors with higher numbers of mutations may be more susceptible to immune responses

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