Molecular characterization of HER2-negative breast cancers reveals a distinct patient subgroup with 17q12 deletion and heterozygous loss of ERBB2.

Background: The approval of trastuzumab deruxtecan has prompted the subgrouping of human epidermal growth factor receptor 2-negative (HER2-) breast cancers (BCs) to HER2 0 and HER2 low on the basis of immunohistochemistry, although the biological significance of these subgroups remains uncertain. This study is aimed to better understand the molecular and genetic differences among HER2- tumors stratified by quantitative levels of HER2.

Patients And Methods: We analyzed the transcriptomic and genomic data from the Molecular Taxonomy of BC International Consortium (discovery cohort) and The Cancer Genome Atlas (independent validation cohort).

Classification of non-TCGA cancer samples to TCGA molecular subtypes using compact feature sets.

Molecular subtypes, such as defined by The Cancer Genome Atlas (TCGA), delineate a cancer's underlying biology, bringing hope to inform a patient's prognosis and treatment plan. However, most approaches used in the discovery of subtypes are not suitable for assigning subtype labels to new cancer specimens from other studies or clinical trials. Here, we address this barrier by applying five different machine learning approaches to multi-omic data from 8,791 TCGA tumor samples comprising 106 subtypes from 26 different cancer cohorts to build models based upon small numbers of features that can classify new samples into previously defined TCGA molecular subtypes-a step toward molecular subtype application in the clinic.