Tumor Mutational Burden Estimation Using Synonymous and Non-Synonymous Mutations
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Solution Overview
Problem
Current methods for calculating tumor mutational burden (TMB) face inconsistencies due to arbitrary thresholds and overestimation of mutation rates in targeted panels, lacking clear cutoffs for clinical decision-making, especially in identifying responsive patients for immunotherapy.
Innovation Solution
A computer-based system that estimates TMB using both non-synonymous and synonymous mutations, applying data transformation and Gaussian mixture modeling to identify clear cancer subtypes without increasing computational burden, thereby providing consistent and accurate TMB estimations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If arbitrary thresholds are used to calculate TMB in targeted panels, then the calculation process is simple, but the measurement precision and reliability are poor due to overestimation of mutation rates
Solution Approach 1:
The patent changes the parameters used in TMB calculation by incorporating both synonymous and non-synonymous mutations, applying data transformation (log2 scaling), and using Gaussian mixture modeling to determine optimal thresholds. This resolves the contradiction by maintaining calculation feasibility while significantly improving measurement precision and eliminating overestimation issues associated with arbitrary thresholds.
Solution Approach 2:
The patent replaces the simple counting-based mechanical calculation method with a statistical modeling approach (Gaussian mixture model) that automatically determines thresholds and weights. This substitution maintains ease of implementation through computational algorithms while dramatically improving the reliability and precision of TMB estimates by removing arbitrary threshold biases.
2Ease of operation
If clear cutoffs for TMB are established, then clinical decision-making is improved, but the device complexity and computational burden increase
Solution Approach 1:
The patent performs preliminary computational work by training the Gaussian mixture model on reference datasets to pre-determine optimal thresholds and weighting parameters. These pre-calculated values are then applied to patient samples, providing clear clinical cutoffs without requiring complex real-time computations during clinical decision-making, thus resolving the contradiction between clarity and complexity.
Solution Approach 2:
The patent introduces an intermediary computational layer (the Gaussian mixture model) that translates raw mutation data into standardized TMB scores with clear clinical thresholds. This intermediary processing step simplifies clinical decision-making by providing interpretable results while containing the computational complexity within the analysis pipeline rather than requiring complex clinical workflows.
3Ease of manufacture
If only non-synonymous mutations are counted for TMB, then the calculation is straightforward, but the reliability is reduced due to driver mutation effects and overestimation
Solution Approach 1:
The patent changes the composition of mutations included in TMB calculation by incorporating both synonymous and non-synonymous mutations with different weights determined through Gaussian mixture modeling. This resolves the contradiction by improving reliability through more comprehensive mutation analysis while maintaining calculation simplicity through automated statistical methods that account for driver mutation effects.
Solution Approach 2:
The patent converts the previously harmful effect of driver mutations (which caused overestimation) into a beneficial feature by using the pattern of synonymous and non-synonymous mutations as informative signals. The Gaussian mixture model learns to weight these mutation types appropriately, transforming what was previously noise or bias into a reliable indicator of true tumor mutational burden.
Data Source
AI summary
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.