Fig. 3

Development and validation of a BRCA-defect type-specific HRD transcriptional signature. a Workflow for transcriptional signature development. Data is split into training and testing cohorts. The training data undergoes expression deconvolution to extract a cancer cell-specific signal, using the Qian et al. single-cell RNA-seq cohort as a reference, and genes that are lowly expressed in this dataset are removed. Processed training data undergoes 1000 iterations of tenfold cross validation of elastic net regression, and a signature is formed from the 228 genes selected in every iteration. Centroid templates are formed for HRD/HR-proficient and BRCA-type HRD groups from the 228 genes across the training cohort, and scores for testing and validation cohorts are calculated by correlating the new sample against each template. b Summary of the 228-gene HRD transcriptional signature profiles across the TCGA training set. The HRD status assignment is annotated along with BRCA1/2 defects. c,d Comparison of HRD scores calculated using the transcriptional signature between c HRD vs HR-proficient and d HRD/BRCA-defect groups. e Comparison of HRD transcriptional signatures and gene expression markers for predicting HRD status in the TCGA testing set, measured by AUC. ‘Elastic net’ refers to the 228-gene transcriptional signature presented in this study. ‘Peng’, ‘CIN70’, ‘Severson’, and ‘PARPi7’ refer to alternative transcriptional signatures as described in the Methods. POLQ, BRCA1, PARP1, and BRCA2 are gene expression markers. f Comparison of HRD/BRCA-defect scores across HRD/BRCA-defect groups in the TCGA testing cohort. Each panel corresponds to a specific HRD/BRCA-defect signature, with the y-axis representing correlation with the respective centroid model. Each box refers to the samples within the respective group