Abstract 858: Predicting cancer patient treatment outcomes from recurrent somatic mutations: identifying and mitigating some critical data biases
Authors: Friedman Jonathan Malcolm 
Published: April 2023

Abstract
Prior work (Friedman 2021) had suggested a way to predict cancer patients' outcomes based on their cancer type, somatic mutations, and drug treatment; and it yielded AUC values that were nearly 1.00 for most combinations of a cancer type and treatment (CANxDRG) with &gt; 25 patients in TCGA. Each "Predicted" Test Patient had been classified as having a successful (HIT) or failed (MISS) treatment using a "classification measure" value that was calculated without regard to the Test Patient's own (actual known) outcome. However, a big caveat that remained was that each Test Patient's classification measure was being compared to an analogous measure for each other Test Patient and derivation of these other "comparison" patients' individual measures had each used the "unknown" Test Patient's known outcome during the calculation. Here, it was found that the caveat was in fact critical and caused the initial method largely to fail when each Test Patient's true outcome was omitted from the classification measures of the comparison Test Patients. A computationally more intensive extension of the previous methods was found, though, that circumvents this earlier caveat. Although this method maintains consistently high predictiveness as before, for the few tested cases, it appears to require about a doubling of the minimum learning set size and also results in having about 10-20% of all patients classified as being "not predictable" from the given data. The method entails patient-to-patient comparison of patient-specific networks of somatic gene mutations, comparisons that lead to a patient-to-patient similarity measure for each patient. This similarity measure is calculated without using the "Unknown" Test Patient outcome information for any of the patients to whom that Test Patient is being compared. This similarity measure – on a scale of -1.00 to 1.00, with 0.00 being indeterminate – does not require additional scaling for comparisons between patients. The networks of gene mutations (and of patient-to patient similarity) are determined using an extension of spectral graph theory to connect statistically interconnected pairs of genes (or pairs of patients) into larger groupings of possibly interrelated genes (or groupings of patients exhibiting similar individualized networks of interrelated significant gene mutations). The need to maintain separate calculations for each Test Patient and their Comparison Patients required a 2-3 orders of magnitude greater number of calculations compared to the original calculation method, but the original method's unoptimized code could be modified to run the analogous calculations correspondingly faster.
Friedman JM. Probabilistic analysis of somatic mutations indicates individual survival outcome classes with AUC near 1.00 ... In: Proc. 102nd Annual Mtg. AACR; 2021 Apr 10-15; Philadelphia PA (virtual): AACR, 2021. Abstract nr. 191.
Citation Format: Jonathan Malcolm Friedman. Predicting cancer patient treatment outcomes from recurrent somatic mutations: identifying and mitigating some critical data biases [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 858.