Germline determinants of aberrant signaling pathways in cancer.

Cancer is a complex disease influenced by a heterogeneous landscape of both germline genetic variants and somatic aberrations. While there is growing evidence suggesting an interplay between germline and somatic variants, and a substantial number of somatic aberrations in specific pathways are now recognized as hallmarks in many well-known forms of cancer, the interaction landscape between germline variants and the aberration of those pathways in cancer remains largely unexplored. Utilizing over 8500 human samples across 33 cancer types characterized by TCGA and considering binary traits defined using a large collection of somatic aberration profiles across ten well-known oncogenic signaling pathways, we conducted a series of GWAS and identified genome-wide and suggestive associations involving 276 SNPs. Among these, 94 SNPs revealed cis-eQTL links with cancer-related genes or with genes functionally correlated with the corresponding traits' oncogenic pathways. GWAS summary statistics for all tested traits were then used to construct a set of polygenic scores employing a customized computational strategy. Polygenic scores for 24 traits demonstrated significant performance and were validated using data from PCAWG and CCLE datasets. These scores showed prognostic value for clinical variables and exhibited significant effectiveness in classifying patients into specific cancer subtypes or stratifying patients with cancer-specific aggressive phenotypes. Overall, we demonstrate that germline genetics can describe patients' genetic liability to develop specific cancer molecular and clinical profiles.

MIMESIS: minimal DNA-methylation signatures to quantify and classify tumor signals in tissue and cell-free DNA samples.

DNA-methylation alterations are common in cancer and display unique characteristics that make them ideal markers for tumor quantification and classification. Here we present MIMESIS, a computational framework exploiting minimal DNA-methylation signatures composed by a few dozen informative DNA-methylation sites to quantify and classify tumor signals in tissue and cell-free DNA samples. Extensive analyses of multiple independent and heterogenous datasets including >7200 samples demonstrate the capability of MIMESIS to provide precise estimations of tumor content and to enable accurate classification of tumor type and molecular subtype. To assess our framework for clinical applications, we designed a MIMESIS-informed assay incorporating the minimal signatures for breast cancer. Using both artificial samples and clinical serial cell-free DNA samples from patients with metastatic breast cancer, we show that our approach provides accurate estimations of tumor content, sensitive detection of tumor signal and the ability to capture clinically relevant molecular subtype in patients' circulation. This study provides evidence that our extremely parsimonious approach can be used to develop cost-effective and highly scalable DNA-methylation assays that could support and facilitate the implementation of precision oncology in clinical practice.

PIK3CA co-occurring mutations and copy-number gain in hormone receptor positive and HER2 negative breast cancer.

We aim to elucidate the prognostic value of PIK3CA mutations and copy number (CN) gain (PIK3CA-mut/gain) in hormone receptor-positive and HER2-negative (HR + /HER2-) breast cancer (BC). We analyzed primary HR + /HER2- BC from three publicly available datasets comprising over 2000 samples and assessed the associations with tumoral and clinical characteristics and outcome. Clinical benefit (CB) in alpelisib-treated patients from two studies including 46 patients was analyzed. About 8-10% of HR + /HER2- primary BC had PIK3CA-mut/gain. In two of the datasets analyzed, among patients with PIK3CA mutant tumors, those with mut/gain had significantly worse outcome compared to those with CN neutral (PIK3CA-mut/neut) and PIK3CA-mut/gain remained an independent prognostic factor. CB of alpelisib-treated patients with PIK3CA-mut/gain and PIK3CA-mut/neut tumors was comparable. PIK3CA CN might help clarifying the prognostic and predictive role of PIK3CA mutations. Further studies are warranted.

Genomic and Transcriptomic Analyses of Breast Cancer Primaries and Matched Metastases in AURORA, the Breast International Group (BIG) Molecular Screening Initiative.

AURORA aims to study the processes of relapse in metastatic breast cancer (MBC) by performing multi-omics profiling on paired primary tumors and early-course metastases. Among 381 patients (primary tumor and metastasis pairs: 252 targeted gene sequencing, 152 RNA sequencing, 67 single nucleotide polymorphism arrays), we found a driver role for GATA1 and MEN1 somatic mutations. Metastases were enriched in ESR1, PTEN, CDH1, PIK3CA, and RB1 mutations; MDM4 and MYC amplifications; and ARID1A deletions. An increase in clonality was observed in driver genes such as ERBB2 and RB1. Intrinsic subtype switching occurred in 36% of cases. Luminal A/B to HER2-enriched switching was associated with TP53 and/or PIK3CA mutations. Metastases had lower immune score and increased immune-permissive cells. High tumor mutational burden correlated to shorter time to relapse in HR+/HER2- cancers. ESCAT tier I/II alterations were detected in 51% of patients and matched therapy was used in 7%. Integration of multi-omics analyses in clinical practice could affect treatment strategies in MBC. SIGNIFICANCE: The AURORA program, through the genomic and transcriptomic analyses of matched primary and metastatic samples from 381 patients with breast cancer, coupled with prospectively collected clinical data, identified genomic alterations enriched in metastases and prognostic biomarkers. ESCAT tier I/II alterations were detected in more than half of the patients.This article is highlighted in the In This Issue feature, p. 2659.