As new generations of targeted therapies emerge and tumor genome sequencing discovers increasingly comprehensive mutation repertoires, the functional relationships of mutations to tumor phenotypes remain largely unknown. Here, we measured ex vivo sensitivity of 246 blood cancers to 63 drugs alongside genome, transcriptome, and DNA methylome analysis to understand determinants of drug response. We assembled a primary blood cancer cell encyclopedia data set that revealed disease-specific sensitivities for each cancer. Within chronic lymphocytic leukemia (CLL), responses to 62% of drugs were associated with 2 or more mutations, and linked the B cell receptor (BCR) pathway to trisomy 12, an important driver of CLL. Based on drug responses, the disease could be organized into phenotypic subgroups characterized by exploitable dependencies on BCR, mTOR, or MEK signaling and associated with mutations, gene expression, and DNA methylation. Fourteen percent of CLLs were driven by mTOR signaling in a non–BCR-dependent manner. Multivariate modeling revealed immunoglobulin heavy chain variable gene (IGHV) mutation status and trisomy 12 as the most important modulators of response to kinase inhibitors in CLL. Ex vivo drug responses were associated with outcome. This study overcomes the perception that most mutations do not influence drug response of cancer, and points to an updated approach to understanding tumor biology, with implications for biomarker discovery and cancer care.

Here we walk through an end-to-end gene-level RNA-Seq differential expression workflow using Bioconductor packages. We will start from the FASTQ files, show how these were aligned to the reference genome, and prepare a count matrix which tallies the number of RNA-seq reads/fragments within each gene for each sample. We will perform exploratory data analysis (EDA) for quality assessment and to explore the relationship between samples, perform differential gene expression analysis, and visually explore the results.

Here we walk through an end-to-end gene-level RNA-Seq differential expression workflow using Bioconductor packages. We will start from the FASTQ files, show how these were aligned to the reference genome, and prepare a count matrix which tallies the number of RNA-seq reads/fragments within each gene for each sample.We will perform exploratory data analysis (EDA) for quality assessment and to explore the relationship between samples, perform differential gene expression analysis, and visually explore the results.

Morphological profiling is a powerful technology that enables unbiased characterization of cellular states through image-based screening. Inspired by recent progress in self-supervised learning (SSL), we sought to explore the potential benefits of using SSL in this domain and conducted a comprehensive benchmark study of recent SSL methods for learning representations from Cell Painting images without segmentation. We trained DINO, MAE, and SimCLR on subsets of the JUMP-CP consortium data, one of the largest publicly available Cell Painting image sets, and observed improved model performance with larger and more heterogeneous training sets. Our best model (DINO) surpassed the widely used profiling tool CellProfiler by 29% in mean average precision (mAP) on classifying chemical perturbations and significantly accelerated feature extraction by 50x, at a lower cost. Moreover, DINO outperformed CellProfiler in clustering gene families on an independent gene overexpression dataset. Our findings indicate that SSL methods can improve the efficiency and performance of morphological profiling, offering the potential to expedite drug discovery and reduce compute costs.

Bispecific antibodies (BsAb) can induce long-term responses in refractory and relapsed B cell lymphoma patients. Nevertheless, response rates across patients are heterogenous and the factors determining quality and duration of responses are poorly understood. In order to identify key determinants of response to BsAb, we established a primary, autologous culture model allowing us to mimic treatment with CD3xCD19 and CD3xCD20 BsAb within the lymph node microenvironment ex vivo. T cell-mediated killing of lymphoma cells and proliferation of T cells varied significantly among patients but highly correlated between BsAb targeting CD20 or CD19. Ex vivo response to BsAb was significantly associated with expansion of T cells and secretion of effector molecules, such as granzyme B and perforin, but not with expression of T cell exhaustion (e.g. PD1, TIM3) or activation markers (e.g. CD25, CD69) or formation of intercellular contacts. In addition, we identified a distinct phenotype of regulatory T cells that was linked to ex vivo response independently from T cell frequency at baseline. High expression levels of Aiolos (IKZF1), ICOS and CXCR5 were positively associated with ex vivo response, whereas strong expression of Helios (IKZF2) had unfavorable impact on ex vivo response to BsAb. Furthermore, we demonstrated that lenalidomide, nivolumab and atezolizumab improved ex vivo response to BsAb by potentiating T cell effector functions. In summary, our ex vivo study identifies a distinct regulatory T cell phenotype as potential contributor to treatment failure of BsAb, and suggests drug combinations of high clinical relevance that could improve the efficacy of BsAb.