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Figure S1 is related to Figure 1 and shows image cytometry gating strategies used in multiplex IHC analysis

File contains supplementary Materials & Methods, supplementary References, and Supplementary Tables S1-S10.

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Unconventional T-lymphocyte populations are emerging as important regulators of tumor immunity. Despite this, the role of TCRαβ+CD4−CD8−NK1.1− innate αβ T cells (iαβT) in pancreatic ductal adenocarcinoma (PDA) has not been explored. We found that iαβTs represent ∼10% of T lymphocytes infiltrating PDA in mice and humans. Intratumoral iαβTs express a distinct T-cell receptor repertoire and profoundly immunogenic phenotype compared with their peripheral counterparts and conventional lymphocytes. iαβTs comprised ∼75% of the total intratumoral IL17+ cells. Moreover, iαβT-cell adoptive transfer is protective in both murine models of PDA and human organotypic systems. We show that iαβT cells induce a CCR5-dependent immunogenic macrophage reprogramming, thereby enabling marked CD4+ and CD8+ T-cell expansion/activation and tumor protection. Collectively, iαβTs govern fundamental intratumoral cross-talk between innate and adaptive immune populations and are attractive therapeutic targets.Significance:We found that iαβTs are a profoundly activated T-cell subset in PDA that slow tumor growth in murine and human models of disease. iαβTs induce a CCR5-dependent immunogenic tumor-associated macrophage program, T-cell activation and expansion, and should be considered as novel targets for immunotherapy.See related commentary by Banerjee et al., p. 1164.This article is highlighted in the In This Issue feature, p. 1143

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Immunotherapies targeting aspects of T cell functionality are efficacious in many solid tumors, but pancreatic ductal adenocarcinoma (PDAC) remains refractory to these treatments. Deeper understanding of the PDAC immune ecosystem is needed to identify additional therapeutic targets and predictive biomarkers for therapeutic response and resistance monitoring. To address these needs, we quantitatively evaluated leukocyte contexture in 135 human PDACs at single-cell resolution by profiling density and spatial distribution of myeloid and lymphoid cells within histopathologically defined regions of surgical resections from treatment-naive and presurgically (neoadjuvant)–treated patients and biopsy specimens from metastatic PDAC. Resultant data establish an immune atlas of PDAC heterogeneity, identify leukocyte features correlating with clinical outcomes, and, through an in silico study, provide guidance for use of PDAC tissue microarrays to optimally measure intratumoral immune heterogeneity. Atlas data have direct applicability as a reference for evaluating immune responses to investigational neoadjuvant PDAC therapeutics where pretherapy baseline specimens are not available. Significance: We provide a phenotypic and spatial immune atlas of human PDAC identifying leukocyte composition at steady state and following standard neoadjuvant therapies. These data have broad utility as a resource that can inform on leukocyte responses to emerging therapies where baseline tissues were not acquired. This article is highlighted in the In This Issue feature, p. 1861

Crucial transitions in cancer-including tumor initiation, local expansion, metastasis, and therapeutic resistance-involve complex interactions between cells within the dynamic tumor ecosystem. Transformative single-cell genomics technologies and spatial multiplex in situ methods now provide an opportunity to interrogate this complexity at unprecedented resolution. The Human Tumor Atlas Network (HTAN), part of the National Cancer Institute (NCI) Cancer Moonshot Initiative, will establish a clinical, experimental, computational, and organizational framework to generate informative and accessible three-dimensional atlases of cancer transitions for a diverse set of tumor types. This effort complements both ongoing efforts to map healthy organs and previous large-scale cancer genomics approaches focused on bulk sequencing at a single point in time. Generating single-cell, multiparametric, longitudinal atlases and integrating them with clinical outcomes should help identify novel predictive biomarkers and features as well as therapeutically relevant cell types, cell states, and cellular interactions across transitions. The resulting tumor atlases should have a profound impact on our understanding of cancer biology and have the potential to improve cancer detection, prevention, and therapeutic discovery for better precision-medicine treatments of cancer patients and those at risk for cancer.

This Perspective explores the application of machine learning toward improved diagnosis and treatment. We outline a vision for how machine learning can transform three broad areas of biomedicine: clinical diagnostics, precision treatments, and health monitoring, where the goal is to maintain health through a range of diseases and the normal aging process. For each area, early instances of successful machine learning applications are discussed, as well as opportunities and challenges for machine learning. When these challenges are met, machine learning promises a future of rigorous, outcomes-based medicine with detection, diagnosis, and treatment strategies that are continuously adapted to individual and environmental differences.

Background and AimsMetastatic pancreatic adenocarcinoma (mPDAC) is lethal, yet a subset of patients who have metastatic disease that spreads only to the lung have better outcomes. We identified unique transcriptomic and immune features that distinguish patients who develop metastases in the liver (liver cohort) versus those with lung-avid but liver-averse mPDAC (lung cohort).MethodsWe used clinical data from the Oregon Pancreas Tissue Registry to identify PDAC patients with liver and/or lung metastases. Gene expression and genomic alteration data from 290 PDAC tumors were used to identify features unique to patients from the liver and lung cohorts. In parallel, T cell receptor sequencing data from 289 patients were used to identify immune features unique to patients in the lung cohort.ResultsLung cohort patients had better survival outcomes than liver cohort patients. Primary tumors from patients in the liver cohort expressed a novel gene signature associated with ongoing replication stress (RS) response predictive of poor patient outcome independent from known subtypes. In contrast, patients with tumors lacking the RS response signature survived longer, especially if their tumors had alterations in DNA damage repair genes. A subset of patients in the lung cohort demonstrated new T cell clonal development in their primary and metastatic tumors leading to diverse peripheral blood TCR repertoires.ConclusionLiver-avid metastatic PDAC is associated with an ongoing RS response, whereas tumors lacking the RS response with ongoing T cell clonal responses may have unique vulnerabilities allowing long-term survival in patients with lung-avid, liver-averse metastatic PDAC.

Supervised machine learning is an essential but difficult to use approach in biomedical data analysis. The Galaxy-ML toolkit (https://galaxyproject.org/community/machine-learning/) makes supervised machine learning more accessible to biomedical scientists by enabling them to perform end-to-end reproducible machine learning analyses at large scale using only a web browser. Galaxy-ML extends Galaxy (https://galaxyproject.org), a biomedical computational workbench used by tens of thousands of scientists across the world, with a suite of tools for all aspects of supervised machine learning.

There is increasing evidence that the spatial organization of cells within the tumor-immune microenvironment (TiME) of solid tumors influences survival and response to therapy in numerous cancer types. Here, we report results and demonstrate the applicability of quantitative single-cell spatial proteomics analyses in the TiME of primary and recurrent human papillomavirus (HPV)-negative head and neck squamous cell carcinoma (HNSCC) tumors. Single-cell compositions of a nine patient, primary and recurrent (n = 18), HNSCC cohort is presented, followed by deeper investigation into the spatial architecture of the TiME and its relationship with clinical variables and progression free survival (PFS). Multiple spatial algorithms were used to quantify the spatial landscapes of immune cells within TiMEs and demonstrate that neoplastic tumor-immune cell spatial compartmentalization, rather than mixing, is associated with longer PFS. Mesenchymal (αSMA

The traditional model of genomic data analysis - downloading data from centralized warehouses for analysis with local computing resources - is increasingly unsustainable. Not only are transfers slow and cost prohibitive, but this approach also leads to redundant and siloed compute infrastructure that makes it difficult to ensure security and compliance of protected data. The NHGRI Genomic Data Science Analysis, Visualization, and Informatics Lab-space (AnVIL; https://anvilproject.org) inverts this model, providing a unified cloud computing environment for data storage, management, and analysis. AnVIL eliminates the need for data movement, allows for active threat detection and monitoring, and provides scalable, shared computing resources that can be acquired by researchers as needed. This presents many new opportunities for collaboration and data sharing that will ultimately lead to scientific discoveries at scales not previously possible.

Highly multiplexed tissue imaging makes detailed molecular analysis of single cells possible in a preserved spatial context. However, reproducible analysis of large multichannel images poses a substantial computational challenge. Here, we describe a modular and open-source computational pipeline, MCMICRO, for performing the sequential steps needed to transform whole-slide images into single-cell data. We demonstrate the use of MCMICRO on tissue and tumor images acquired using multiple imaging platforms, thereby providing a solid foundation for the continued development of tissue imaging software. MCMICRO is a modular and open-source computational pipeline for transforming highly multiplexed whole-slide images of tissues into single-cell data. MCMICRO is versatile and can be used with CODEX, mxIF, CyCIF, mIHC and H&E staining data.

There is increasing evidence that the spatial organization of cells within the tumor-immune microenvironment (TiME) of solid tumors influences survival and response to therapy in numerous cancer types. Here, we report results and demonstrate the applicability of quantitative single-cell spatial proteomics analyses in the TiME of primary and recurrent human papillomavirus (HPV)-negative head and neck squamous cell carcinoma (HNSCC) tumors. Single-cell compositions of a nine patient, primary and recurrent (n=18), HNSCC cohort is presented, followed by deeper investigation into the spatial architecture of the TiME and its relationship with clinical variables and progression free survival (PFS). Multiple spatial algorithms were used to quantify the spatial landscapes of immune cells within TiMEs and demonstrate that neoplastic tumor-immune cell spatial compartmentalization, rather than mixing, is associated with longer PFS. Mesenchymal (αSMA+) cellular neighborhoods describe distinct immune landscapes associated with neoplastic tumor-immune compartmentalization and improved patient outcomes. Results from this investigation are concordant with studies in other tumor types, suggesting that trends in TiME cellular heterogeneity and spatial organization may be shared across cancers and may provide prognostic value in multiple cancer types.

SummaryMechanisms of therapeutic resistance manifest in metastatic cancers as tumor cell intrinsic alterations and extrinsic microenvironmental influences that can change during treatment. To support the development of methods for the identification of these mechanisms in individual patients, we present here an Omic and Multidimensional Spatial (OMS) Atlas generated from four serial biopsies of a metastatic breast cancer patient during 3.5 years of therapy. This resource links detailed, longitudinal clinical metadata including treatment times and doses, anatomic imaging, and blood-based response measurements to exploratory analytics including comprehensive DNA, RNA, and protein profiles, images of multiplexed immunostaining, and 2- and 3-dimensional scanning electron micrographs. These data reveal aspects of therapy-associated heterogeneity and evolution of the cancer’s genome, signaling pathways, immune microenvironment, cellular composition and organization, and ultrastructure. We present illustrative examples showing how integrative analyses of these data provide insights into potential mechanisms of response and resistance, and suggest novel therapeutic vulnerabilities.

A growing number of biomedical methods and protocols are being disseminated as open-source software packages. When put in concert with other packages, they can execute in-depth and comprehensive computational pipelines. Therefore, their integration with other software packages plays a prominent role in their adoption in addition to their availability. Accordingly, package management systems are developed to standardize the discovery and integration of software packages. Here we study the impact of package management systems on software dissemination and their scholarly recognition. We study the citation pattern of more than 18,000 scholarly papers referenced by more than 23,000 software packages hosted by Bioconda, Bioconductor, BioTools, and ToolShed—the package management systems primarily used by the Bioinformatics community. Our results suggest that there is significant evidence that the scholarly papers’ citation count increases after their respective software was published to package management systems. Additionally, our results show that the impact of different package management systems on the scholarly papers’ recognition is of the same magnitude. These results may motivate scientists to distribute their software via package management systems, facilitating the composition of computational pipelines and helping reduce redundancy in package development.Significance StatementSoftware packages are the building blocks of computational pipelines. A myriad of packages are developed; however, the lack of integration and discovery standards hinders their adoption, leaving most scientists’ scholarly contributions unrecognized. Package management systems are developed to facilitate software dissemination and integration. However, developing software to meet their code and packaging standards is an involved process. Therefore, our study results on the significant impact of the package management systems on scholarly paper’s recognition can motivate scientists to invest in disseminating their software via package management systems. Dissemination of more software via package management systems will lead to a more straightforward composition of computational pipelines and less redundancy in software packages.

Background While the quantities and types of immune, tumor, and structure-related cells present in the tumor-immune microenvironment (TiME) are important for understanding aspects of cancer progression and potential responses to therapy, spatial locations and relationships of these cells also play a critical role. Emerging single-cell imaging modalities, such as multiplex immunohistochemistry (mIHC), provide phenotypic and functional state information for each cell present in the TiME while maintaining the spatial context of tissue architecture. We performed a quantitative analysis of mIHC images to characterize the cellular composition and spatial organization of human head and neck squamous cell carcinomas (HNSCC) and identified features correlated with patient survival. Methods mIHC is an immunoassay-based imaging platform that evaluates sequentially stained immune lineage epitope-specific antibodies for immunodetection on FFPE tissue sections to phenotype single cells as tumor, stromal (mesenchymal), or one of more than 20 different immune cell lineages, all while maintaining the Cartesian coordinates of each cell.1 2 Matched primary and recurrent HNSCC tumors from nine patients were assayed via mIHC. Using unsupervised hierarchical clustering and principal component analysis, we interrogated the heterogeneity in cellular composition of each tumor section. We further quantified the spatial organization of tumors and identified prognostic tumor and immune cell architectures,3 as well as cellular neighborhoods that clustered together based on similar compositions and physically grouped together to reveal common spatial features across tumors. Results Regions from the same tumor and tumors from the same patient clustered together more in their cellular composition than tumors from different patients. We also observed a decrease in the fraction of B cells present in recurrent tumors following therapy for all patients (p=0.024). While common biomarkers for HNSCC, such as CD8+ T cell density and tumor cell abundance were not associated with outcome, the tumor-immune spatial relationship was prognostic. Tissue regions of compartmentalization between immune and tumor cells were associated with higher fractions of αSMA+ stromal cells and had a greater proportion of Ki-67+ lymphocytes present, as compared to mixed regions. Patients with more compartmentalization in their primary tumors demonstrated longer progression free survival than those with more mixing between these cell types (p=0.027). Conclusions Our results provide insight into the spatial organization of HNSCCs, highlighted by the result that compartmentalization between immune and tumor cells is associated with improved outcomes. This study provides spatial analysis methods and hypotheses that can be used as a framework for analysis of larger cohorts. Ethics Approval This study was approved by Oregon Health and Science University’s IRB (protocol #809 and #3609), and written informed consent was obtained. References Tsujikawa T, et al. Quantitative multiplex immunohistochemistry reveals myeloid-inflamed tumor-immune complexity associated with poor prognosis. Cell Rep 2017;19:203–217. Banik G, et al. High-dimensional multiplexed immunohistochemical characterization of immune contexture in human cancers. Methods Enzymol 2020;635:1–20. Keren L, et al. A structured tumor-immune microenvironment in triple negative breast cancer revealed by multiplexed ion beam imaging. Cell 2018;174:1373–1387.

In a pilot study, we evaluated the feasibility of real-time deep analysis of serial tumor samples from triple negative breast cancer patients to identify mechanisms of resistance and treatment opportunities as they emerge under therapeutic stress engendered by poly-ADP-ribose polymerase (PARP) inhibitors (PARPi). In a BRCA-mutant basal breast cancer exceptional long-term survivor, a striking tumor destruction was accompanied by a marked infiltration of immune cells containing CD8 effector cells, consistent with pre-clinical evidence for association between STING mediated immune activation and benefit from PARPi and immunotherapy. Tumor cells in the exceptional responder underwent extensive protein network rewiring in response to PARP inhibition. In contrast, there were minimal changes in the ecosystem of a luminal androgen receptor rapid progressor, likely due to indifference to the effects of PARP inhibition. Together, identification of PARPi-induced emergent changes could be used to select patient specific combination therapies, based on tumor and immune state changes.

Due to complexity of advanced epithelial cancers, it is necessary to implement patient specific combination therapies if we are to markedly improve patient outcomes. However, our ability to select and implement patient specific combination therapies based on dynamic molecular changes in the tumor and tumor ecosystem in response to therapy remains extremely limited. In a pilot study, we evaluated the feasibility of real-time deep analysis of serial tumor samples from triple negative breast cancer patients to identify mechanisms of resistance and treatment opportunities as they emerge under therapeutic stress engendered by poly-ADP-ribose polymerase (PARP) inhibitors (PARPi). Although PARP inhibition was consistently observed in all patients, deep molecular analysis of the tumor and its ecosystem revealed insights into potentially effective therapeutic PARPi combinations. In a BRCA-mutant basal breast cancer exceptional long-term survivor, we noted striking PARPi-induced tumor destruction accompanied by a marked infiltration of immune cells containing CD8 effector cells, consistent with pre-clinical evidence for association between STING mediated immune activation and benefit from PARPi and immunotherapy. Tumor cells in the exceptional responder underwent extensive protein network rewiring in response to PARP inhibition. In contrast, there were minimal changes in the ecosystem of a luminal androgen receptor (LAR) rapid progressor in response to PARPi likely due to indifference to the effects of PARP inhibition. In this rapid progressor, there was minimal evidence of immune activation or protein network rewiring in response to PARPi, despite PARP being inhibited, and no clinical benefit was noted for this participant. Together, deep real-time analysis of longitudinal biopsies identified a suite of PARPi-induced emergent changes including immune activation, DNA damage checkpoint activation, apoptosis and signaling pathways including RTK, PI3K-AKT and RAS-MAPK, that could be used to select patient specific combination therapies, based on tumor and immune state changes that are likely to benefit specific patients.HighlightsLongitudinal analysis of serial tumor samples in real-time identifies adaptive mechanisms of resistance to PARPi therapies.Deep molecular analysis of the tumor reveals insights into potentially effective therapeutic PARPi combinations.Extensive protein network rewiring, microenvironment and immune state changes are assessable factors for patient specific combination therapies.

RNA-seq is widely used for studying gene expression, but commonly used sequencing platforms produce short reads that only span up to two exon junctions per read. This makes it difficult to accurately determine the composition and phasing of exons within transcripts. Although long-read sequencing improves this issue, it is not amenable to precise quantitation, which limits its utility for differential expression studies. We used long-read isoform sequencing combined with a novel analysis approach to compare alternative splicing of large, repetitive structural genes in muscles. Analysis of muscle structural genes that produce medium (Nrap: 5 kb), large (Neb: 22 kb), and very large (Ttn: 106 kb) transcripts in cardiac muscle, and fast and slow skeletal muscles identified unannotated exons for each of these ubiquitous muscle genes. This also identified differential exon usage and phasing for these genes between the different muscle types. By mapping the in-phase transcript structures to known annotations, we also identified and quantified previously unannotated transcripts. Results were confirmed by endpoint PCR and Sanger sequencing, which revealed muscle-type-specific differential expression of these novel transcripts. The improved transcript identification and quantification shown by our approach removes previous impediments to studies aimed at quantitative differential expression of ultralong transcripts.

Precision oncology relies on accurate discovery and interpretation of genomic variants, enabling individualized diagnosis, prognosis and therapy selection. We found that six prominent somatic cancer variant knowledgebases were highly disparate in content, structure and supporting primary literature, impeding consensus when evaluating variants and their relevance in a clinical setting. We developed a framework for harmonizing variant interpretations to produce a meta-knowledgebase of 12,856 aggregate interpretations. We demonstrated large gains in overlap between resources across variants, diseases and drugs as a result of this harmonization. We subsequently demonstrated improved matching between a patient cohort and harmonized interpretations of potential clinical significance, observing an increase from an average of 33% per individual knowledgebase to 57% in aggregate. Our analyses illuminate the need for open, interoperable sharing of variant interpretation data. We also provide a freely available web interface (search.cancervariants.org) for exploring the harmonized interpretations from these six knowledgebases., This analysis presents a harmonized meta-knowledgebase to facilitate clinical interpretation of somatic genomic variants in cancer. This community-based project highlights the need for cooperative efforts to curate clinical interpretations of somatic variants for robust practice of precision oncology.