Novel antibody-drug conjugates against HER2 are showing high activity in HER2-negative breast cancer (BC) with low HER2 expression (i.e., 1+ or 2+ and lack of ERBB2 amplification). However, the clinical and molecular features of HER2-low BC are yet to be elucidated. Here, we collected retrospective clinicopathological and PAM50 data from 3,689 patients with HER2-negative disease and made the following observations. First, the proportion of HER2-low was higher in HR-positive disease (65.4%) than triple-negative BC (TNBC, 36.6%). Second, within HR-positive disease, ERBB2 and luminal-related genes were more expressed in HER2-low than HER2 0. In contrast, no gene was found differentially expressed in TNBC according to HER2 expression. Third, within HER2-low, ERBB2 levels were higher in HR-positive disease than TNBC. Fourth, HER2-low was not associated with overall survival in HR-positive disease and TNBC. Finally, the reproducibility of HER2-low among pathologists was suboptimal. This study emphasizes the large biological heterogeneity of HER2-low BC, and the need to implement reproducible and sensitive assays to measure low HER2 expression.

Purpose: The therascreen PIK3CA mutation assay and the alpha-specific PI3K inhibitor alpelisib are FDA-approved for identifying and treating patients with advanced PIK3CA-mutated (PIK3CAmut) breast cancer (BC). However, it is currently unknown to what extend this assay detects most PIK3CA mutations in BC. This information is critical as patients and clinicians are using this and other genomic assays to indicate alpelisib. Methods: Data from 6338 patients with BC was explored across 10 publicly available studies. The primary objective was to evaluate the proportion and distribution of PIK3CA mutations in BC. Secondary objectives were (1) to evaluate in silico the spectrum of PIK3CA mutations in BC that would be captured by the therascreen panel; (2) to evaluate the proportion and distribution of PIK3CA mutations in hormone receptor-positive/HER2-negative (HR+/ HER2−), HER2+, and triple-negative BC (TNBC); and (3) to explore the identification of PIK3CA mutations in a cohort of 48 HR+/HER2− advanced BC patients by the Guardant B360 circulating tumor DNA (ctDNA) assay. Results: Patients with PIK3CAmut tumors represented 35.7% (2261/6338). Five PIK3CA mutations comprised 73% of all PIK3CA mutations: H1047R (35%), E545K (17%), E542K (11%), N345K (6%), and H1047L (4%). Therascreen gene list would capture 72% of all PIK3CA mutations and 80% of patients with a known PIK3CAmut BC. Among patients with double PIK3CAmut tumors (12% of all PIK3CAmut), the therascreen panel would capture 78% as harboring 1 single PIK3CA mutation, 17% as PIK3CAmut undetected, and 5% as PIK3CA double-mut. PIK3CA mutation rates were lower in TNBC (16%) compared to HR+/HER2 (42%) and HER2+ (31%) BC; however, the distribution of the 4 main PIK3CA mutations across subtypes was similar. Finally, 28% of PIK3CA mutations identified in ctDNA in 48 patients with advanced HR+/HER2− BC were not part of the therascreen panel.

Genome studies of diffuse large B-cell lymphoma (DLBCL) have revealed a large number of somatic mutations and structural alterations. However, the clinical significance of these alterations is still not well defined. In this study, we have integrated the analysis of targeted next-generation sequencing of 106 genes and genomic copy number alterations (CNA) in 150 DLBCL. The clinically significant findings were validated in an independent cohort of 111 patients. Germinal center B-cell and activated B-cell DLBCL had a differential profile of mutations, altered pathogenic pathways and CNA. Mutations in genes of the NOTCH pathway and tumor suppressor genes (TP53/CDKN2A), but not individual genes, conferred an unfavorable prognosis, confirmed in the independent validation cohort. A gene expression profiling analysis showed that tumors with NOTCH pathway mutations had a significant modulation of downstream target genes, emphasizing the relevance of this pathway in DLBCL. An in silico drug discovery analysis recognized 69 (46%) cases carrying at least one genomic alteration considered a potential target of drug response according to early clinical trials or preclinical assays in DLBCL or other lymphomas. In conclusion, this study identifies relevant pathways and mutated genes in DLBCL and recognizes potential targets for new intervention strategies.

Pediatric large B-cell lymphomas (LBCLs) share morphological and phenotypic features with adult types but have better prognosis. The higher frequency of some subtypes such as LBCL with IRF4 rearrangement (LBCL-IRF4) in children suggests that some age-related biological differences may exist. To characterize the genetic and molecular heterogeneity of these tumors, we studied 31 diffuse LBCLs (DLBCLs), not otherwise specified (NOS); 20 LBCL-IRF4 cases; and 12 cases of high-grade B-cell lymphoma (HGBCL), NOS in patients ≤25 years using an integrated approach, including targeted gene sequencing, copy-number arrays, and gene expression profiling. Each subgroup displayed different molecular profiles. LBCL-IRF4 had frequent mutations in IRF4 and NF-κB pathway genes (CARD11, CD79B, and MYD88), losses of 17p13 and gains of chromosome 7, 11q12.3-q25, whereas DLBCL, NOS was predominantly of germinal center B-cell (GCB) subtype and carried gene mutations similar to the adult counterpart (eg, SOCS1 and KMT2D), gains of 2p16/REL, and losses of 19p13/CD70. A subset of HGBCL, NOS displayed recurrent alterations of Burkitt lymphoma–related genes such as MYC, ID3, and DDX3X and homozygous deletions of 9p21/CDKN2A, whereas other cases were genetically closer to GCB DLBCL. Factors related to unfavorable outcome were age >18 years; activated B-cell (ABC) DLBCL profile, HGBCL, NOS, high genetic complexity, 1q21-q44 gains, 2p16/REL gains/amplifications, 19p13/CD70 homozygous deletions, and TP53 and MYC mutations. In conclusion, these findings further unravel the molecular heterogeneity of pediatric and young adult LBCL, improve the classification of this group of tumors, and provide new parameters for risk stratification.