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 largescale 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.Cancer forms and progresses through a series of critical transitions-from pre-malignant to malignant states, from locally contained to metastatic disease, and from treatment-responsive to treatment-resistant tumors (Figure 1). Although specifics differ across tumor types and patients, all transitions involve complex dynamic interactions between diverse pre-malignant, malignant, and non-malignant cells (e.g., stroma cells and immune cells), often organized in specific patterns within the tumor

No abstract

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.

Normal epithelium exists within a dynamic extracellular matrix (ECM) that is tuned to regulate tissue specific epithelial cell function. As such, ECM contributes to tissue homeostasis, differentiation, and disease, including cancer. Though it is now recognized that the functional unit of normal and transformed epithelium is the epithelial cell and its adjacent ECM, we lack a basic understanding of tissue-specific ECM composition and abundance, as well as how physiologic changes in ECM impact cancer risk and outcomes. While traditional proteomic techniques have advanced to robustly identify ECM proteins within tissues, methods to determine absolute abundance have lagged. Here, with a focus on tissues relevant to breast cancer, we utilize mass spectrometry methods optimized for absolute quantitative ECM analysis. Employing an extensive protein extraction and digestion method, combined with stable isotope labeled Quantitative conCATamer (QconCAT) peptides that serve as internal standards for absolute quantification of protein, we quantify 98 ECM, ECM-associated, and cellular proteins in a single analytical run. In rodent models, we applied this approach to the primary site of breast cancer, the normal mammary gland, as well as a common and particularly deadly site of breast cancer metastasis, the liver. We find that mammary gland and liver have distinct ECM abundance and relative composition. Further, we show mammary gland ECM abundance and relative compositions differ across the reproductive cycle, with the most dramatic changes occurring during the pro-tumorigenic window of weaning-induced involution. Combined, this work suggests ECM candidates for investigation of breast cancer progression and metastasis, particularly in postpartum breast cancers that are characterized by high metastatic rates. Finally, we suggest that with use of absolute quantitative ECM proteomics to characterize tissues of interest, it will be possible to reconstruct more relevant in vitro models to investigate tumor-ECM dynamics at higher resolution.

No abstract

The mammary gland is not classically considered a mucosal organ, although it exhibits some features common to mucosal tissues. Notably, the mammary epithelium is contiguous with the external environment, is exposed to bacteria during lactation, and displays antimicrobial features. Nonetheless, immunological hallmarks predictive of mucosal function have not been demonstrated in the mammary gland, including immune tolerance to foreign Ags under homeostasis. This inquiry is important, as mucosal immunity in the mammary gland may assure infant and women's health during lactation. Further, such mucosal immune programs may protect mammary function at the expense of breast cancer promotion via decreased immune surveillance. In this study, using murine models, we evaluated mammary specific mucosal attributes focusing on two reproductive states at increased risk for foreign and self-antigen exposure: lactation and weaning-induced involution. We find a baseline mucosal program of RORγT CD4 T cells that is elevated within lactating and involuting mammary glands and is extended during involution to include tolerogenic dendritic cell phenotypes, barrier-supportive antimicrobials, and immunosuppressive Foxp3 CD4 T cells. Further, we demonstrate suppression of Ag-dependent CD4 T cell activation, data consistent with immune tolerance. We also find Ag-independent accumulation of memory RORγT Foxp3 CD4 T cells specifically within the involution mammary gland consistent with an active immune process. Overall, these data elucidate strong mucosal immune programs within lactating and involuting mammary glands. Our findings support the classification of the mammary gland as a temporal mucosal organ and open new avenues for exploration into breast pathologic conditions, including compromised lactation and breast cancer.

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

Fig. 1. Immune cell infiltration of lung carcinoma-in-situ lesions. (a-b) Immunohistochemistry images of (a) progressive CIS lesion and (b) regressive CIS lesion with CD4+ cells stained in brown, CD8+ cells in red and FOXP3+ in blue. Immune cells are separately quantified within the CIS lesion and in the surrounding stroma. c) Combined quantitative immunohistochemistry data of CD4, CD8 and FOXP3 staining (n=44; 28 progressive, 16 regressive) with total lymphocyte quantification from H&E images (n=116; 69 progressive, 47 regressive) shown. We observe increased lymphocytes (p=0.023) and CD8+ cells (p=0.037) per unit area of epithelium within regressive CIS lesions compared to progressive. Stromal regions adjacent to CIS lesions showed no significant differences in immune cells between progressive and regressive lesions. p-values are calculated using linear mixed effects models to account for samples from the same patient; *p<0.05. 2 | bioRχiv Pennycuick et al. | Immune surveillance in clinical regression of pre-invasive squamous cell lung cancer .