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. We observe an increase in systemic and intratumoral eosinophils in patients and mice responding to ICB treatment. Mechanistically, ICB increased IL-5 production by CD4+ T cells, stimulating elevated eosinophil production from the bone marrow, leading to systemic eosinophil expansion. Additional induction of IL-33 by ICB-cisplatin combination or recombinant IL-33 promotes intratumoral eosinophil infiltration and eosinophil-dependent CD8+ T cell activation to enhance ICB response. This work demonstrates the critical role of eosinophils in ICB response and provides proof-of-principle for eosinophil engagement to enhance ICB efficacy.

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. In this paper, we present VDJdb, a database that stores and aggregates the results of published T-cell specificity assays and provides a universal platform that couples antigen specificities with TCR sequences. We demonstrate that VDJdb is a versatile instrument for the annotation of TCR repertoire data, enabling a concatenated view of antigen-specific TCR sequence motifs. VDJdb can be accessed at https://vdjdb.cdr3.net and https://github.com/antigenomics/vdjdb-db.

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. We find that cell lines faithfully recapitulate oncogenic alterations identified in tumors, find that many of these associate with drug sensitivity/resistance, and highlight the importance of tissue lineage in mediating drug response. Logic-based modeling uncovers combinations of alterations that sensitize to drugs, while machine learning demonstrates the relative importance of different data types in predicting drug response. Our analysis and datasets are rich resources to link genotypes with cellular phenotypes and to identify therapeutic options for selected cancer sub-populations.

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. This change of perspective allows for a simplified approach as recursive peak splitting procedures and repeated re-estimation of the background model are avoided. Furthermore, we control the false discovery rate on the level of called regions, rather than at the probe level, as in competing algorithms. We benchmark RUBIC against GISTIC2 (a state-of-the-art approach) and RAIG (a recently proposed approach) on simulated copy number data and on three SNP6 and NGS copy number data sets from TCGA. We show that RUBIC calls more focal recurrent regions and identifies a much larger fraction of known cancer genes.

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. This classification is indicative of patients' response to high dose alkylating and platinum containing chemotherapy regimens, which targets the inability of BRCA1 deficient cells to repair DNA double strand breaks. We investigated whether this classification can be reliably obtained with next generation sequencing and copy number platforms other than the bacterial artificial chromosome (BAC) array Comparative Genomic Hybridization (aCGH) on which it was originally developed. We investigated samples from 230 breast cancer patients for which a CN profile had been generated on two to five platforms, comprising low coverage CN sequencing, CN extraction from targeted sequencing panels (CopywriteR), Affymetrix SNP6.0, 135K/720K oligonucleotide aCGH, Affymetrix Oncoscan FFPE (MIP) technology, 3K BAC and 32K BAC aCGH. Pairwise comparison of genomic position-mapped profiles from the original aCGH platform and other platforms revealed concordance. For most cases, biological differences between samples exceeded the differences between platforms within one sample. We observed the same classification across different platforms in over 80% of the patients and kappa values of at least 0.36. Differential classification could be attributed to CN profiles that were not strongly associated to one class. In conclusion, we have shown that the genomic regions that define our BRCA1-like classifier are robustly measured by different CN profiling technologies, providing the possibility to retro- and prospectively investigate BRCA1-like classification across a wide range of CN platforms.

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. RESULTS: Genome wide copy number profiles of the BRCA1-mutated breast carcinomas in our data appeared heterogeneous. Gene expression analyses identified varying amounts of tumor infiltrating lymphocytes (TILs) as a major cause for this heterogeneity. Indeed, selecting tumors with relative low amounts of TILs, resulted in the identification of three known but also five previously unrecognized BRCA1-associated copy number aberrations. Moreover, these aberrations occurred with high frequencies in the BRCA1-mutated tumor samples. Using these regions it was possible to discriminate BRCA1-mutated from BRCAX breast carcinomas, and they were validated in two independent cohorts. To further substantiate our findings, we used flow cytometry to isolate cancer cells from formalin-fixed, paraffin-embedded, BRCA1-mutated triple negative breast carcinomas with estimated TIL percentages of 40% and higher. Genomic profiles of sorted and unsorted fractions were compared by shallow whole genome sequencing and confirm our findings. CONCLUSION: This study shows that genomic profiling of in particular basal-like, and thus BRCA1-mutated, breast carcinomas is severely affected by the presence of high numbers of TILs. Previous reports on genomic profiling of BRCA1-mutated breast carcinomas have largely neglected this. Therefore, our findings have direct consequences on the interpretation of published genomic data. Also, these findings could prove valuable in light of currently used genomic tools for assessing BRCA1-involvement in breast cancer patients and pathogenicity assessment of BRCA1 variants of unknown significance. The BRCA1-associated genomic aberrations identified in this study provide possible leads to a better understanding of BRCA1-associated oncogenesis.

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. The BAC array technology has been replaced with oligonucleotide arrays. To continue clinical use of existing classifiers, we mapped oligonucleotide aCGH data to the BAC domain, such that the oligonucleotide profiles can be employed as in the BAC classifier. We demonstrate that segmented profiles derived from oligonucleotide aCGH show high correlation with BAC aCGH profiles. Furthermore, we trained a support vector machine score to objectify aCGH profile quality. Using the mapped oligonucleotide aCGH data, we show equivalence in classification of biologically relevant cases between BAC and oligonucleotide data. Furthermore, the predicted benefit of DSB inducing chemotherapy due to a homologous recombination defect is retained. We conclude that oligonucleotide aCGH data can be mapped to and used in the previously developed and validated BAC aCGH classifiers. Our findings suggest that it is possible to map copy number data from any other technology in a similar way.

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. However, GISTIC performs false discovery rate control on probes instead of events. Here we propose Analytical Multi-scale Identification of Recurrent Events, a multi-scale Gaussian smoothing approach, for the detection of both broad and focal (potentially overlapping) recurring copy number alterations. Importantly, false discovery rate control is performed analytically (no need for permutations) on events rather than probes. The method does not require segmentation or calling on the input dataset and therefore reduces the potential loss of information due to discretization. An important characteristic of the approach is that the error rate is controlled across all scales and that the algorithm outputs a single profile of significant events selected from the appropriate scales. We perform extensive simulations and showcase its utility on a glioblastoma SNP array dataset. Importantly, ADMIRE detects focal events that are missed by GISTIC, including two events involving known glioma tumor-suppressor genes: CDKN2C and NF1.