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Current genomics methods are designed to handle tens to thousands of samples but will need to scale to millions to match the pace of data and hypothesis generation in biomedical science. Here, we show that high efficiency at low cost can be achieved by leveraging general-purpose libraries for computing using graphics processing units (GPUs), such as PyTorch and TensorFlow. We demonstrate > 200-fold decreases in runtime and ~ 5–10-fold reductions in cost relative to CPUs. We anticipate that the accessibility of these libraries will lead to a widespread adoption of GPUs in computational genomics.

Somatic cells can accumulate mutations over the course of an individual's lifetime. This generates cells that differ genetically at specific loci within the genome. To explore how this genetic diversity in individuals contributes to disease, Yizhak et al. developed a method to detect mutations from RNA sequencing data (see the Perspective by Tomasetti). Applying this method to Cancer Genome Atlas samples and normal samples from the Genotype-Tissue Expression (GTEx) project generated a tissue-specific study of mutation accumulation. Somatic mutations were detected in nearly all individuals and across many normal human tissues in genomic regions called cancer hotspots and in genes that play a role in cancer. Interestingly, the skin, lung, and esophagus exhibited the most mutations, suggesting that the environment generates many human mutations. Science , this issue p. eaaw0726 ; see also p. 938 “Normal” skin and other human tissues include macroscopic clonal expansions that contain genes associated with cancer risk. How somatic mutations accumulate in normal cells is poorly understood. A comprehensive analysis of RNA sequencing data from ~6700 samples across 29 normal tissues revealed multiple somatic variants, demonstrating that macroscopic clones can be found in many normal tissues. We found that sun-exposed skin, esophagus, and lung have a higher mutation burden than other tested tissues, which suggests that environmental factors can promote somatic mosaicism. Mutation burden was associated with both age and tissue-specific cell proliferation rate, highlighting that mutations accumulate over both time and number of cell divisions. Finally, normal tissues were found to harbor mutations in known cancer genes and hotspots. This study provides a broad view of macroscopic clonal expansion in human tissues, thus serving as a foundation for associating clonal expansion with environmental factors, aging, and risk of disease.

Multiple myeloma is a plasma cell malignancy almost always preceded by precursor conditions, but low tumor burden of these early stages has hindered the study of their molecular programs through bulk sequencing technologies. Here, we generate and analyze single cell RNA-sequencing of plasma cells from 26 patients at varying disease stages and 9 healthy donors. In silico dissection and comparison of normal and transformed plasma cells from the same bone marrow biopsy enables discovery of patient-specific transcriptional changes. Using Non-Negative Matrix Factorization, we discover 15 gene expression signatures which represent transcriptional modules relevant to myeloma biology, and identify a signature that is uniformly lost in abnormal cells across disease stages. Finally, we demonstrate that tumors contain heterogeneous subpopulations expressing distinct transcriptional patterns. Our findings characterize transcriptomic alterations present at the earliest stages of myeloma, providing insight into the molecular underpinnings of disease initiation. Development of multiple myeloma is preceded by precursor conditions. Here, the authors use single cell RNA-sequencing of plasma cells from patients across disease stages to identify genomic signatures present even at the earliest stages of disease.

New computational tools reveal the contribution of microsatellite insertions and deletions to the mutational landscape in human cancer. Microsatellites (MSs) are tracts of variable-length repeats of short DNA motifs that exhibit high rates of mutation in the form of insertions or deletions (indels) of the repeated motif. Despite their prevalence, the contribution of somatic MS indels to cancer has been largely unexplored, owing to difficulties in detecting them in short-read sequencing data. Here we present two tools: MSMuTect, for accurate detection of somatic MS indels, and MSMutSig, for identification of genes containing MS indels at a higher frequency than expected by chance. Applying MSMuTect to whole-exome data from 6,747 human tumors representing 20 tumor types, we identified >1,000 previously undescribed MS indels in cancer genes. Additionally, we demonstrate that the number and pattern of MS indels can accurately distinguish microsatellite-stable tumors from tumors with microsatellite instability, thus potentially improving classification of clinically relevant subgroups. Finally, we identified seven MS indel driver hotspots: four in known cancer genes ( ACVR2A , RNF43 , JAK1 , and MSH3 ) and three in genes not previously implicated as cancer drivers ( ESRP1 , PRDM2 , and DOCK3 ).

Waldenstrom’s Macroglobulinemia (WM) is an IgM-secreting bone marrow (BM) lymphoma that is preceded by an asymptomatic state (AWM). To dissect tumor-intrinsic and immune mechanisms of progression, we perform single-cell RNA-sequencing on 294,206 BM tumor and immune cells from 30 patients with AWM/WM, 26 patients with Smoldering Myeloma, and 23 healthy donors. Despite their early stage, patients with AWM present extensive immune dysregulation, including in normal B cells, with disease-specific immune hallmarks. Patient T and NK cells show systemic hypo-responsiveness to interferon, which improves with interferon administration and may represent a therapeutic vulnerability. MYD88 -mutant tumors show transcriptional heterogeneity, which can be distilled in a molecular classification, including a DUSP22 / CD9 -positive subtype, and progression signatures which differentiate IgM MGUS from overt WM and can help advance WM research and clinical practice. The impact of tumor intrinsic and immune alterations on disease progression in patients with Waldenstrom’s Macroglobulinemia (WM) remains to be characterized. Here, the authors perform single-cell RNA-sequencing and identify distinct tumor subtypes, tumour microenvironment features and potential therapeutic vulnerabilities in patients with WM.

Claudin 18.2 (CLDN18.2)-targeted CAR-T cell therapies have shown promising clinical efficacy in gastric cancer. However, early-phase trials have reported gastrointestinal adverse events due to on-target off-tumor recognition of CLDN18.2 in the gastric mucosa. By leveraging shared CLDN18.2 epitopes and expression in humans and mice, we establish an in vivo model that replicates the on-target off-tumor toxicity of CLDN18.2 CAR-T. Our findings confirm that this toxicity is independent of the CAR construct’s design, co-stimulatory domain, and tumor model. Additionally, we demonstrate the utility of this model in testing strategies to mitigate on-target toxicity, such as Boolean-logic AND-gate approaches. Our results offer insights into the use of mouse models that recapitulate on-target off-tumor toxicities, with the caveat that although we are often concerned that models will undercall toxicities in humans, they may also overcall the incidence and severity of toxicities, prematurely discarding promising therapeutic agents from further clinical development. Promising clinical activity of Claudin (CLDN) 18.2-directed CAR-T cell therapy in patients with gastric cancer has been recently reported, however gastrointestinal toxicities have also been described. Here the authors recapitulate the on-target off-tumor toxicity of CLDN18.2-directed CAR-T cells due to gastric mucosa damage in preclinical models, suggesting an AND-gate strategy targeting CLDN18.2 and mesothelin to overcome CAR-T cell toxicity

Reports of secondary malignancies after chimeric antigen receptor (CAR)-T and possible CAR-T derived malignant transformation necessitate caution. Here we describe a patient with diffuse large B-cell lymphoma who developed new lymphadenopathy 2.5 years after CAR-T in the context of COVID-19 infection with histopathologic features consistent with T-cell lymphoma (TCL). Deep molecular interrogation with genomic sequencing and single-cell spatial transcriptomics reveals a highly proliferative clonal T-cell population co-expressing CD4 and CD8 with biallelic TCR rearrangement and no evidence of the CAR construct. The expanded clonotype displayed T follicular helper (TFH) cell transcriptomic programs and occupies immune-excluded spatial niches within the lymph node, supportive of TFH-like neoplastic T cell behavior. Remarkably, the lymphadenopathy spontaneously resolved on interval imaging. Our data underscore the need for better understanding of post-CAR-T clonal T-cell lymphoproliferative disorders to avoid unnecessary treatment and higher specificity in diagnostic methods for TCL. Secondary malignancies and chimeric antigen receptor (CAR)-T-derived malignant T cell transformation have been reported after CAR-T therapy. Here, the authors describe a patient with diffuse large B-cell lymphoma (DLBCL) who developed new lymphadenopathy 2.5 years after CAR-T in the context of COVID-19 infection with histopathologic features consistent with T-cell lymphoma (TCL).

Chimeric antigen receptor (CAR) therapy has had a transformative effect on the treatment of haematologic malignancies 1 – 6 , but it has shown limited efficacy against solid tumours. Solid tumours may have cell-intrinsic resistance mechanisms to CAR T cell cytotoxicity. Here, to systematically identify potential resistance pathways in an unbiased manner, we conducted a genome-wide CRISPR knockout screen in glioblastoma, a disease in which CAR T cells have had limited efficacy 7 , 8 . We found that the loss of genes in the interferon-γ receptor (IFNγR) signalling pathway ( IFNGR1 , JAK1 or JAK2 ) rendered glioblastoma and other solid tumours more resistant to killing by CAR T cells both in vitro and in vivo. However, loss of this pathway did not render leukaemia or lymphoma cell lines insensitive to CAR T cells. Using transcriptional profiling, we determined that glioblastoma cells lacking IFNγR1 had lower upregulation of cell-adhesion pathways after exposure to CAR T cells. We found that loss of IFNγR1 in glioblastoma cells reduced overall CAR T cell binding duration and avidity. The critical role of IFNγR signalling in susceptibility of solid tumours to CAR T cells is surprising, given that CAR T cells do not require traditional antigen-presentation pathways. Instead, in glioblastoma tumours, IFNγR signalling was required for sufficient adhesion of CAR T cells to mediate productive cytotoxicity. Our work demonstrates that liquid and solid tumours differ in their interactions with CAR T cells and suggests that enhancing binding interactions between T cells and tumour cells may yield improved responses in solid tumours. A genome-wide CRISPR knockout screen in a model of glioblastoma shows that killing by chimeric antigen receptor T cells requires interferon-γ receptor-dependent adhesion to tumour cells, but cytotoxicity of liquid tumours does not rely on this pathway.

Chimeric antigen receptor (CAR)-T cell therapy has revolutionized the treatment of hematologic malignancies. Approximately half of patients with refractory large B cell lymphomas achieve durable responses from CD19-targeting CAR-T treatment; however, failure mechanisms are identified in only a fraction of cases. To gain new insights into the basis of clinical response, we performed single-cell transcriptome sequencing of 105 pretreatment and post-treatment peripheral blood mononuclear cell samples, and infusion products collected from 32 individuals with large B cell lymphoma treated with either of two CD19 CAR-T products: axicabtagene ciloleucel (axi-cel) or tisagenlecleucel (tisa-cel). Expansion of proliferative memory-like CD8 clones was a hallmark of tisa-cel response, whereas axi-cel responders displayed more heterogeneous populations. Elevations in CAR-T regulatory cells among nonresponders to axi-cel were detected, and these populations were capable of suppressing conventional CAR-T cell expansion and driving late relapses in an in vivo model. Our analyses reveal the temporal dynamics of effective responses to CAR-T therapy, the distinct molecular phenotypes of CAR-T cells with differing designs, and the capacity for even small increases in CAR-T regulatory cells to drive relapse. Single-cell transcriptomics analyses of pretreatment and post-treatment peripheral blood mononuclear cells from patients treated with CD19 chimeric antigen receptor (CAR)-T products reveal a role for CAR-T regulatory cells in treatment relapse.

Design of nucleic acid-based viral diagnostics typically follows heuristic rules and, to contend with viral variation, focuses on a genome’s conserved regions. A design process could, instead, directly optimize diagnostic effectiveness using a learned model of sensitivity for targets and their variants. Toward that goal, we screen 19,209 diagnostic–target pairs, concentrated on CRISPR-based diagnostics, and train a deep neural network to accurately predict diagnostic readout. We join this model with combinatorial optimization to maximize sensitivity over the full spectrum of a virus’s genomic variation. We introduce Activity-informed Design with All-inclusive Patrolling of Targets (ADAPT), a system for automated design, and use it to design diagnostics for 1,933 vertebrate-infecting viral species within 2 hours for most species and within 24 hours for all but three. We experimentally show that ADAPT’s designs are sensitive and specific to the lineage level and permit lower limits of detection, across a virus’s variation, than the outputs of standard design techniques. Our strategy could facilitate a proactive resource of assays for detecting pathogens. Viral diagnostics with maximum sensitivity are designed using machine learning and combinatorial optimization.