A deep learning system accurately classifies primary and metastatic cancers using passenger mutation patterns.

In cancer, the primary tumour's organ of origin and histopathology are the strongest determinants of its clinical behaviour, but in 3% of cases a patient presents with a metastatic tumour and no obvious primary. Here, as part of the ICGC/TCGA Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium, we train a deep learning classifier to predict cancer type based on patterns of somatic passenger mutations detected in whole genome sequencing (WGS) of 2606 tumours representing 24 common cancer types produced by the PCAWG Consortium. Our classifier achieves an accuracy of 91% on held-out tumor samples and 88% and 83% respectively on independent primary and metastatic samples, roughly double the accuracy of trained pathologists when presented with a metastatic tumour without knowledge of the primary. Surprisingly, adding information on driver mutations reduced accuracy. Our results have clinical applicability, underscore how patterns of somatic passenger mutations encode the state of the cell of origin, and can inform future strategies to detect the source of circulating tumour DNA

Tumor Intrinsic Subtypes and Gene Expression Signatures in Early-Stage ERBB2/HER2 -Positive Breast Cancer: A Pooled Analysis of CALGB 40601, NeoALTTO, and NSABP B-41 Trials

Importance: Biologic features may affect pathologic complete response (pCR) and event-free survival (EFS) after neoadjuvant chemotherapy plus ERBB2/HER2 blockade in ERBB2/HER2-positive early breast cancer (EBC). Objective: To define the quantitative association between pCR and EFS by intrinsic subtype and by other gene expression signatures in a pooled analysis of 3 phase 3 trials: CALGB 40601, NeoALTTO, and NSABP B-41. Design, Setting, and Participants: In this retrospective pooled analysis, 1289 patients with EBC received chemotherapy plus either trastuzumab, lapatinib, or the combination, with a combined median follow-up of 5.5 years. Gene expression profiling by RNA sequencing was obtained from 758 samples, and intrinsic subtypes and 618 gene expression signatures were calculated. Data analyses were performed from June 1, 2020, to January 1, 2023. Main Outcomes and Measures: The association of clinical variables and gene expression biomarkers with pCR and EFS were studied by logistic regression and Cox analyses. Results: In the pooled analysis, of 758 women, median age was 49 years, 12% were Asian, 6% Black, and 75% were White. Overall, pCR results were associated with EFS in the ERBB2-enriched (hazard ratio [HR], 0.45; 95% CI, 0.29-0.70; P <.001) and basal-like (HR, 0.19; 95% CI, 0.04-0.86; P =.03) subtypes but not in luminal A or B tumors. Dual trastuzumab plus lapatinib blockade over trastuzumab alone had a trend toward EFS benefit in the intention-to-treat population; however, in the ERBB2-enriched subtype there was a significant and independent EFS benefit of trastuzumab plus lapatinib vs trastuzumab alone (HR, 0.47; 95% CI, 0.27-0.83; P =.009). Overall, 275 of 618 gene expression signatures (44.5%) were significantly associated with pCR and 9 of 618 (1.5%) with EFS. The ERBB2/HER2 amplicon and multiple immune signatures were significantly associated with pCR. Luminal-related signatures were associated with lower pCR rates but better EFS, especially among patients with residual disease and independent of hormone receptor status. There was significant adjusted HR for pCR ranging from 0.45 to 0.81 (higher pCR) and 1.21-1.94 (lower pCR rate); significant adjusted HR for EFS ranged from 0.71 to 0.94. Conclusions and relevance: In patients with ERBB2/HER2-positive EBC, the association between pCR and EFS differed by tumor intrinsic subtype, and the benefit of dual ERBB2/HER2 blockade was limited to ERBB2-enriched tumors. Immune-activated signatures were concordantly associated with higher pCR rates and better EFS, whereas luminal signatures were associated with lower pCR rates..SCOPUS: ar.jinfo:eu-repo/semantics/publishe