IL-5-producing CD4 T cells and eosinophils cooperate to enhance response to immune checkpoint blockade in breast cancer.

Immune checkpoint blockade (ICB) has heralded a new era in cancer therapy. Research into the mechanisms underlying response to ICB has predominantly focused on T cells; however, effective immune responses require tightly regulated crosstalk between innate and adaptive immune cells. Here, we combine unbiased analysis of blood and tumors from metastatic breast cancer patients treated with ICB with mechanistic studies in mouse models of breast cancer.

VDJdb: a curated database of T-cell receptor sequences with known antigen specificity.

The ability to decode antigen specificities encapsulated in the sequences of rearranged T-cell receptor (TCR) genes is critical for our understanding of the adaptive immune system and promises significant advances in the field of translational medicine. Recent developments in high-throughput sequencing methods (immune repertoire sequencing technology, or RepSeq) and single-cell RNA sequencing technology have allowed us to obtain huge numbers of TCR sequences from donor samples and link them to T-cell phenotypes. However, our ability to annotate these TCR sequences still lags behind, owing to the enormous diversity of the TCR repertoire and the scarcity of available data on T-cell specificities.

A Landscape of Pharmacogenomic Interactions in Cancer.

Systematic studies of cancer genomes have provided unprecedented insights into the molecular nature of cancer. Using this information to guide the development and application of therapies in the clinic is challenging. Here, we report how cancer-driven alterations identified in 11,289 tumors from 29 tissues (integrating somatic mutations, copy number alterations, DNA methylation, and gene expression) can be mapped onto 1,001 molecularly annotated human cancer cell lines and correlated with sensitivity to 265 drugs.

RUBIC identifies driver genes by detecting recurrent DNA copy number breaks.

The frequent recurrence of copy number aberrations across tumour samples is a reliable hallmark of certain cancer driver genes. However, state-of-the-art algorithms for detecting recurrent aberrations fail to detect several known drivers. In this study, we propose RUBIC, an approach that detects recurrent copy number breaks, rather than recurrently amplified or deleted regions.

Robust BRCA1-like classification of copy number profiles of samples repeated across different datasets and platforms.

Breast cancers with BRCA1 germline mutation have a characteristic DNA copy number (CN) pattern. We developed a test that assigns CN profiles to be 'BRCA1-like' or 'non-BRCA1-like', which refers to resembling a BRCA1-mutated tumor or resembling a tumor without a BRCA1 mutation, respectively. Approximately one third of the BRCA1-like breast cancers have a BRCA1 mutation, one third has hypermethylation of the BRCA1 promoter and one third has an unknown reason for being BRCA1-like.

Proper genomic profiling of (BRCA1-mutated) basal-like breast carcinomas requires prior removal of tumor infiltrating lymphocytes.

Introduction: BRCA1-mutated breast carcinomas may have distinct biological features, suggesting the involvement of specific oncogenic pathways in tumor development. The identification of genomic aberrations characteristic for BRCA1-mutated breast carcinomas could lead to a better understanding of BRCA1-associated oncogenic events and could prove valuable in clinical testing for BRCA1-involvement in patients.

Methods: For this purpose, genomic and gene expression profiles of basal-like BRCA1-mutated breast tumors (n = 27) were compared with basal-like familial BRCAX (non-BRCA1/2/CHEK2*1100delC) tumors (n = 14) in a familial cohort of 120 breast carcinomas.

Platform comparisons for identification of breast cancers with a BRCA-like copy number profile.

Previously, we employed bacterial artificial chromosome (BAC) array comparative genomic hybridization (aCGH) profiles from BRCA1 and -2 mutation carriers and sporadic tumours to construct classifiers that identify tumour samples most likely to harbour BRCA1 and -2 mutations, designated 'BRCA1 and -2-like' tumours, respectively. The classifiers are used in clinical genetics to evaluate unclassified variants, and patients for which no good quality germline DNA is available. Furthermore, we have shown that breast cancer patients with BRCA-like tumour aCGH profiles benefit substantially from platinum-based chemotherapy, potentially due to their inability to repair DNA double strand breaks (DSB), providing a further important clinical application for the classifiers.

A scale-space method for detecting recurrent DNA copy number changes with analytical false discovery rate control.

Tumor formation is partially driven by DNA copy number changes, which are typically measured using array comparative genomic hybridization, SNP arrays and DNA sequencing platforms. Many techniques are available for detecting recurring aberrations across multiple tumor samples, including CMAR, STAC, GISTIC and KC-SMART. GISTIC is widely used and detects both broad and focal (potentially overlapping) recurring events.