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Abstract Background The acquisition of oncogenic drivers is a critical feature of cancer progression. For some carcinomas, it is clear that certain genetic drivers occur early in neoplasia and others late. Why these drivers are selected and how these changes alter the neoplasia’s fitness is less understood. Methods Here we use spatially oriented genomic approaches to identify transcriptomic and genetic changes at the single-duct level within precursor neoplasia associated with invasive breast cancer. We study HER2 amplification in ductal carcinoma in situ (DCIS) as an event that can be both quantified and spatially located via fluorescence in situ hybridization (FISH) and immunohistochemistry on fixed paraffin-embedded tissue. Results By combining the HER2-FISH with the laser capture microdissection (LCM) Smart-3SEQ method, we found that HER2 amplification in DCIS alters the transcriptomic profiles and increases diversity of copy number variations (CNVs). Particularly, interferon signaling pathway is activated by HER2 amplification in DCIS, which may provide a prolonged interferon signaling activation in HER2-positive breast cancer. Multiple subclones of HER2-amplified DCIS with distinct CNV profiles are observed, suggesting that multiple events occurred for the acquisition of HER2 amplification. Notably, DCIS acquires key transcriptomic changes and CNV events prior to HER2 amplification, suggesting that pre-amplified DCIS may create a cellular state primed to gain HER2 amplification for growth advantage. Conclusion By using genomic methods that are spatially oriented, this study identifies several features that appear to generate insights into neoplastic progression in precancer lesions at a single-duct level.

Abstract Metabolic reprogramming of tumor cells and the increase of glucose uptake is one of the hallmarks of cancer. In order to identify metabolic pathways activated in leiomyosarcoma (LMS), we analyzed transcriptomic profiles of distinct subtypes of LMS in several datasets. Primary, recurrent and metastatic tumors in the subtype 2 of LMS showed consistent enrichment of genes involved in hexosamine biosynthesis pathway (HBP). We demonstrated that glutamine-fructose-6-phosphate transaminase 2 (GFPT2), the rate-limiting enzyme in HBP, is expressed on protein level in a subset of LMS and the expression of this enzyme is frequently retained in patient-matched primary and metastatic tumors. In a new independent cohort of 327 patients, we showed that GFPT2 is associated with poor outcome of uterine LMS but not extra-uterine LMS. Based on the analysis of a small group of patients studied by 18F-FDG-PET imaging, we propose that strong expression of GFPT2 in primary LMS may be associated with high metabolic activity. Our data suggest that HBP is a potential new therapeutic target in one of the subtypes of LMS.

Abstract Background Tumors comprise a complex microenvironment of interacting malignant and stromal cell types. Much of our understanding of the tumor microenvironment comes from in vitro studies isolating the interactions between malignant cells and a single stromal cell type, often along a single pathway. Result To develop a deeper understanding of the interactions between cells within human lung tumors, we perform RNA-seq profiling of flow-sorted malignant cells, endothelial cells, immune cells, fibroblasts, and bulk cells from freshly resected human primary non-small-cell lung tumors. We map the cell-specific differential expression of prognostically associated secreted factors and cell surface genes, and computationally reconstruct cross-talk between these cell types to generate a novel resource called the Lung Tumor Microenvironment Interactome (LTMI). Using this resource, we identify and validate a prognostically unfavorable influence of Gremlin-1 production by fibroblasts on proliferation of malignant lung adenocarcinoma cells. We also find a prognostically favorable association between infiltration of mast cells and less aggressive tumor cell behavior. Conclusion These results illustrate the utility of the LTMI as a resource for generating hypotheses concerning tumor-microenvironment interactions that may have prognostic and therapeutic relevance.

Cellular composition and structural organization of cells in the tissue determine effective antitumor response and can predict patient outcome and therapy response. Here we present Seg-SOM, a method for dimensionality reduction of cell morphology in H&E-stained tissue images. Seg-SOM resolves cellular tissue heterogeneity and reveals complex tissue architecture. We leverage a self-organizing map (SOM) artificial neural network to group cells based on morphological features like shape and size. Seg-SOM allows for cell segmentation, systematic classification, and in silico cell labeling. We apply the Seg-SOM to a dataset of breast cancer progression images and find that clustering of SOM classes reveals groups of cells corresponding to fibroblasts, epithelial cells, and lymphocytes. We show that labeling the Lymphocyte SOM class on the breast tissue images accurately estimates lymphocytic infiltration. We further demonstrate how to use Seq-SOM in combination with non-negative matrix factorization to statistically describe the interaction of cell subtypes and use the interaction information as highly interpretable features for a histological classifier. Our work provides a framework for use of SOM in human pathology to resolve cellular composition of complex human tissues. We provide a python implementation and an easy-to-use docker deployment, enabling researchers to effortlessly featurize digitalized H&E-stained tissue.

The transcriptional regulator YAP is regarded as the universal mechanotransducer, largely from 2D culture studies. Here the authors show that in breast cancer patient tissues and cells in 3D culture, mechanical signals are transduced independently of YAP, questioning YAP as a therapeutic target.

Pathogenic gene fusions have been identified in several histologic types of salivary gland neoplasia, but not previously in acinic cell carcinoma (AcCC). To discover novel gene fusions, we performed whole-transcriptome sequencing surveys of three AcCC archival cases. In one specimen we identified a novel HTN3-MSANTD3 gene fusion, and in another a novel PRB3-ZNF217 gene fusion. The structure of both fusions was consistent with the promoter of the 5' partner (HTN3 or PRB3), both highly expressed salivary gland genes, driving overexpression of full-length MSANTD3 or ZNF217. By fluorescence in situ hybridization of an expanded AcCC case series, we observed MSANTD3 rearrangements altogether in 3 of 20 evaluable cases (15%), but found no additional ZNF217 rearrangements. MSANTD3 encodes a previously uncharacterized Myb/SANT domain-containing protein. Immunohistochemical staining demonstrated diffuse nuclear MSANTD3 expression in 8 of 27 AcCC cases (30%), including the three cases with MSANTD3 rearrangement. MSANTD3 displayed heterogeneous expression in normal salivary ductal epithelium, as well as among other histologic types of salivary gland cancer though without evidence of translocation. In a broader survey, MSANTD3 showed variable expression across a wide range of normal and neoplastic human tissue specimens. In preliminary functional studies, engineered MSANTD3 overexpression in rodent salivary gland epithelial cells did not enhance cell proliferation, but led to significant upregulation of gene sets involved in protein synthesis. Our findings newly identify MSANTD3 rearrangement as a recurrent event in salivary gland AcCC, providing new insight into disease pathogenesis, and identifying a putative novel human oncogene.

Characterization of the molecular attributes and spatial arrangements of cells and features within complex human tissues provides a critical basis for understanding processes involved in development and disease. Moreover, the ability to automate steps in the analysis and interpretation of histological images that currently require manual inspection by pathologists could revolutionize medical diagnostics. Toward this end, we developed a new imaging approach called multidimensional microscopic molecular profiling (MMMP) that can measure several independent molecular properties in situ at subcellular resolution for the same tissue specimen. MMMP involves repeated cycles of antibody or histochemical staining, imaging, and signal removal, which ultimately can generate information analogous to a multidimensional flow cytometry analysis on intact tissue sections. We performed a MMMP analysis on a tissue microarray containing a diverse set of 102 human tissues using a panel of 15 informative antibody and 5 histochemical stains plus DAPI. Large-scale unsupervised analysis of MMMP data, and visualization of the resulting classifications, identified molecular profiles that were associated with functional tissue features. We then directly annotated H&E images from this MMMP series such that canonical histological features of interest (e.g. blood vessels, epithelium, red blood cells) were individually labeled. By integrating image annotation data, we identified molecular signatures that were associated with specific histological annotations and we developed statistical models for automatically classifying these features. The classification accuracy for automated histology labeling was objectively evaluated using a cross-validation strategy, and significant accuracy (with a median per-pixel rate of 77% per feature from 15 annotated samples) for de novo feature prediction was obtained. These results suggest that high-dimensional profiling may advance the development of computer-based systems for automatically parsing relevant histological and cellular features from molecular imaging data of arbitrary human tissue samples, and can provide a framework and resource to spur the optimization of these technologies.

Gene fusions, like BCR/ABL1 in chronic myelogenous leukemia, have long been recognized in hematologic and mesenchymal malignancies. The recent finding of gene fusions in prostate and lung cancers has motivated the search for pathogenic gene fusions in other malignancies. Here, we developed a "breakpoint analysis" pipeline to discover candidate gene fusions by tell-tale transcript level or genomic DNA copy number transitions occurring within genes. Mining data from 974 diverse cancer samples, we identified 198 candidate fusions involving annotated cancer genes. From these, we validated and further characterized novel gene fusions involving ROS1 tyrosine kinase in angiosarcoma (CEP85L/ROS1), SLC1A2 glutamate transporter in colon cancer (APIP/SLC1A2), RAF1 kinase in pancreatic cancer (ATG7/RAF1) and anaplastic astrocytoma (BCL6/RAF1), EWSR1 in melanoma (EWSR1/CREM), CDK6 kinase in T-cell acute lymphoblastic leukemia (FAM133B/CDK6), and CLTC in breast cancer (CLTC/VMP1). Notably, while these fusions involved known cancer genes, all occurred with novel fusion partners and in previously unreported cancer types. Moreover, several constituted druggable targets (including kinases), with therapeutic implications for their respective malignancies. Lastly, breakpoint analysis identified new cell line models for known rearrangements, including EGFRvIII and FIP1L1/PDGFRA. Taken together, we provide a robust approach for gene fusion discovery, and our results highlight a more widespread role of fusion genes in cancer pathogenesis.