Yanshuo Chu, Enyu Dai, Yating Li, Guangchun Han, Guangsheng Pei, Davis R Ingram, Krupa Thakkar, Jiang-Jiang Qin, Minghao Dang, Xiuning Le, Can Hu, Qing Deng, Ansam Sinjab, Pravesh Gupta, Ruiping Wang, Dapeng Hao, Fuduan Peng, Xinmiao Yan, Yunhe Liu, Shumei Song, Shaojun Zhang, John V Heymach, Alexandre Reuben, Yasir Y Elamin, Melissa P Pizzi, Yang Lu, Rossana Lazcano, Jian Hu, Mingyao Li, Michael Curran, Andrew Futreal, Anirban Maitra, Amir A Jazaeri, Jaffer A Ajani, Charles Swanton, Xiang-Dong Cheng, Hussein A Abbas, Maura Gillison, Krishna Bhat, Alexander J Lazar, Michael Green, Kevin Litchfield, Humam Kadara, Cassian Yee, Linghua Wang <<<

Tumor-infiltrating T cells offer a promising avenue for cancer treatment, yet their states remain to be fully characterized. Here we present a single-cell atlas of T cells from 308,048 transcriptomes across 16 cancer types, uncovering previously undescribed T cell states and heterogeneous subpopulations of follicular helper, regulatory and proliferative T cells. We identified a unique stress response state, T, characterized by heat shock gene expression. T cells are detectable in situ in the tumor microenvironment across various cancer types, mostly within lymphocyte aggregates or potential tertiary lymphoid structures in tumor beds or surrounding tumor edges. T cell states/compositions correlated with genomic, pathological and clinical features in 375 patients from 23 cohorts, including 171 patients who received immune checkpoint blockade therapy. We also found significantly upregulated heat shock gene expression in intratumoral CD4/CD8 cells following immune checkpoint blockade treatment, particularly in nonresponsive tumors, suggesting a potential role of T cells in immunotherapy resistance. Our well-annotated T cell reference maps, web portal and automatic alignment/annotation tool could provide valuable resources for T cell therapy optimization and biomarker discovery. <<<

Somatic mutations in cancer cells can generate tumour-specific neoepitopes, which are recognized by autologous T cells in the host. As neoepitopes are not subject to central immune tolerance and are not expressed in healthy tissues, they are attractive targets for therapeutic cancer vaccines. Because the vast majority of cancer mutations are unique to the individual patient, harnessing the full potential of this rich source of targets requires individualized treatment approaches. Many computational algorithms and machine-learning tools have been developed to identify mutations in sequence data, to prioritize those that are more likely to be recognized by T cells and to design tailored vaccines for every patient. In this Review, we fill the gaps between the understanding of basic mechanisms of T cell recognition of neoantigens and the computational approaches for discovery of somatic mutations and neoantigen prediction for cancer immunotherapy. We present a new classification of neoantigens, distinguishing between guarding, restrained and ignored neoantigens, based on how they confer proficient antitumour immunity in a given clinical context. Such context-based differentiation will contribute to a framework that connects neoantigen biology to the clinical setting and medical peculiarities of cancer, and will enable future neoantigen-based therapies to provide greater clinical benefit. <<<

Somatic mutations in cancer cells can generate tumour-specific neoepitopes, which are recognized by autologous T cells in the host. As neoepitopes are not subject to central immune tolerance and are not expressed in healthy tissues, they are attractive targets for therapeutic cancer vaccines. Because the vast majority of cancer mutations are unique to the individual patient, harnessing the full potential of this rich source of targets requires individualized treatment approaches. Many computational algorithms and machine-learning tools have been developed to identify mutations in sequence data, to prioritize those that are more likely to be recognized by T cells and to design tailored vaccines for every patient. In this Review, we fill the gaps between the understanding of basic mechanisms of T cell recognition of neoantigens and the computational approaches for discovery of somatic mutations and neoantigen prediction for cancer immunotherapy. We present a new classification of neoantigens, distinguishing between guarding, restrained and ignored neoantigens, based on how they confer proficient antitumour immunity in a given clinical context. Such context-based differentiation will contribute to a framework that connects neoantigen biology to the clinical setting and medical peculiarities of cancer, and will enable future neoantigen-based therapies to provide greater clinical benefit. <<<

Motivation: Molecular subtypes of cancers and autoimmune disease, defined by transcriptomic profiling, have provided insight into disease pathogenesis, molecular heterogeneity and therapeutic responses. However, technical biases inherent to different gene expression profiling platforms present a unique problem when analyzing data generated from different studies. Currently, there is a lack of effective methods designed to eliminate platform-based bias. We present a method to normalize and classify RNA-seq data using machine learning classifiers trained on DNA microarray data and molecular subtypes in two datasets: breast invasive carcinoma (BRCA) and colorectal cancer (CRC).Results: Multiple analyses show that feature specific quantile normalization (FSQN) successfully removes platform-based bias from RNA-seq data, regardless of feature scaling or machine learning algorithm. We achieve up to 98% accuracy for BRCA data and 97% accuracy for CRC data in assigning molecular subtypes to RNA-seq data normalized using FSQN and a support vector machine trained exclusively on DNA microarray data. We find that maximum accuracy was achieved when normalizing RNA-seq datasets that contain at least 25 samples. FSQN allows comparison of RNA-seq data to existing DNA microarray datasets. Using these techniques, we can successfully leverage information from existing gene expression data in new analyses despite different platforms used for gene expression profiling.Availability and implementation: FSQN has been submitted as an R package to CRAN. All code used for this study is available on Github (https://github.com/jenniferfranks/FSQN).Contact: michael.l.whitfield@dartmouth.edu.Supplementary information: Supplementary data are available at Bioinformatics online. <<<

Braydon Meyer, Samuel Clifton, Warwick Locke, Phuc-Loi Luu, Qian Du, Dilys Lam, Nicola J Armstrong, Beena Kumar, Niantao Deng, Kate Harvey, Alex Swarbrick, Vinod Ganju, Susan J Clark, Ruth Pidsley, Clare Stirzaker <<<

Neoadjuvant chemotherapy (NAC) is used to treat triple-negative breast cancer (TNBC) prior to resection. Biomarkers that accurately predict a patient's response to NAC are needed to individualise therapy and avoid chemotoxicity from unnecessary chemotherapy. We performed whole-genome DNA methylation profiling on diagnostic TNBC biopsy samples from the Sequential Evaluation of Tumours Undergoing Preoperative (SETUP) NAC study. We found 9 significantly differentially methylated regions (DMRs) at diagnosis which were associated with response to NAC. We show that 4 of these DMRs are associated with TNBC overall survival (P < 0.05). Our results highlight the potential of DNA methylation biomarkers for predicting NAC response in TNBC. <<<

Historically, the primary focus of cancer research has been molecular and clinical studies of a few essential pathways and genes. Recent years have seen the rapid accumulation of large-scale cancer omics data catalysed by breakthroughs in high-throughput technologies. This fast data growth has given rise to an evolving concept of 'big data' in cancer, whose analysis demands large computational resources and can potentially bring novel insights into essential questions. Indeed, the combination of big data, bioinformatics and artificial intelligence has led to notable advances in our basic understanding of cancer biology and to translational advancements. Further advances will require a concerted effort among data scientists, clinicians, biologists and policymakers. Here, we review the current state of the art and future challenges for harnessing big data to advance cancer research and treatment. <<<

Recent breakthroughs in cancer immunotherapy and decreasing costs of high-throughput technologies have sparked intensive research into tumour-immune cell interactions using genomic tools. The wealth of the generated data and the added complexity pose considerable challenges and require computational tools to process, to analyse and to visualize the data. Recently, various tools have been developed and used to mine tumour immunologic and genomic data effectively and to provide novel mechanistic insights. Here, we review computational genomics tools for cancer immunology and provide information on the requirements and functionality in order to assist in the selection of tools and assembly of analytical pipelines. <<<

Bulk-tissue DNA methylomes represent an average over many different cell types, hampering our understanding of cell-type-specific contributions to disease development. As single-cell methylomics is not scalable to large cohorts of individuals, cost-effective computational solutions are needed, yet current methods are limited to tissues such as blood. Here we leverage the high-resolution nature of tissue-specific single-cell RNA-sequencing datasets to construct a DNA methylation atlas defined for 13 solid tissue types and 40 cell types. We comprehensively validate this atlas in independent bulk and single-nucleus DNA methylation datasets. We demonstrate that it correctly predicts the cell of origin of diverse cancer types and discovers new prognostic associations in olfactory neuroblastoma and stage 2 melanoma. In brain, the atlas predicts a neuronal origin for schizophrenia, with neuron-specific differential DNA methylation enriched for corresponding genome-wide association study risk loci. In summary, the DNA methylation atlas enables the decomposition of 13 different human tissue types at a high cellular resolution, paving the way for an improved interpretation of epigenetic data. <<<

The success of checkpoint inhibitors has accelerated the clinical implementation of a vast mosaic of single agents and combination immunotherapies. However, the lack of clinical translation for a number of immunotherapies as monotherapies or in combination with checkpoint inhibitors has clarified that new strategies must be employed to advance the field. The next chapter of immunotherapy should examine the immuno-oncology therapeutic failures, and consider the complexity of immune cell-cancer cell interactions to better design more effective anticancer drugs. Herein, we briefly review the history of immunotherapy and checkpoint blockade, highlighting important clinical failures. We discuss the critical aspects - beyond T cell co-receptors - of immune processes within the tumour microenvironment (TME) that may serve as avenues along which new therapeutic strategies in immuno-oncology can be forged. Emerging insights into tumour biology suggest that successful future therapeutics will focus on two key factors: rescuing T cell homing and dysfunction in the TME, and reappropriating mononuclear phagocyte function for TME inflammatory remodelling. New drugs will need to consider the complex cell networks that exist within tumours and among cancer types. <<<