Extended Data Fig. 6: Expansion of UQCR11-MTHFD2 collateral lethality and machine-learning based evaluation of targeting MTHFD2 in endometrial cancer.
(a) Correlation between MTHFD2 and UQCR11 mRNA expression only for the TCGA uterine corpus endometrial carcinoma (UCEC) (Pan Cancer, 2018) dataset corresponding to the pan-cancer analysis results in Fig. 2i. Correlations estimated using Spearman’s rank correlation test. (b) Most significant focal deletions in chromosomes 1-22, identified by GISTIC2.0 within deleted regions for endometrial cancer samples from the TCGA UCEC and uterine carcinosarcoma (UCS) datasets combined (n=584). Correlation of mRNA expression and copy-number alteration of UQCR11 in the same set of samples for which data was available (n=568). Correlations estimated using Spearman’s rank correlation test. (c) Two-layer machine learning model trained using 54 mutations observed in endometrial cancers, select gene-expression panel of 112 mitochondrial genes, and fraction genome altered as input. Inputs for both layers are chosen a priori using linear support vector classification (SVC) for recursive feature elimination, and predictive output of 19p13.3 copy-loss from Layer #1 is used as input to Layer #2. Layer #1 is trained with data available for samples from TCGA and GENIE datasets. Layer #2 is trained using samples from the TCGA dataset. 20% is reserved as test data and not used for training either of the two ML layers. (d) Precision-recall curves for test and training data for Layer #1 of the multi-layer machine learning model that predicts 19p13.3 copy-loss in endometrial cancers. (e) Summary performance metrics and precision-recall curves for training and test data for layer #2 for the multi-layer model that predicts response to MTHFD2 inhibition in endometrial cancers.
