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Simple Summary: A tumor tissue is composed of not only cancer cells but also other cell types and microorganisms that communicate among themselves in a three-dimensional (3D) space to support cancer cell growth. Using two different gynecologic tumor tissue samples, we integrated multiple new techniques using a suite of newly developed analytical methods to simultaneously identify expression of genes, metabolites, and proteins in single tissue 'voxels'. These tissue voxels contain cells, genes from those cells and microorganisms, and the stromal context of proteins (collagen), glycans, metabolites, and peptides, all identified in the 3D space within a tumor tissue. We have successfully demonstrated different arrays of analytes expressed by cancer cells and their neighboring cells in different regions of the tumor tissue. Understanding how cancer cells communicate with other cell types in the 3D space of the tumor tissue will allow for the identification of new therapeutic targets for the treatment of these diseases. Most platforms used for the molecular reconstruction of the tumor–immune microenvironment (TIME) of a solid tumor fail to explore the spatial context of the three-dimensional (3D) space of the tumor at a single-cell resolution, and thus lack information about cell–cell or cell–extracellular matrix (ECM) interactions. To address this issue, a pipeline which integrated multiplex spatially resolved multi-omics platforms was developed to identify crosstalk signaling networks among various cell types and the ECM in the 3D TIME of two FFPE (formalin-fixed paraffin embedded) gynecologic tumor samples. These platforms include non-targeted mass spectrometry imaging (glycans, metabolites, and peptides) and Stereo-seq (spatial transcriptomics) and targeted seqIF (IHC proteomics). The spatially resolved imaging data in a two- and three-dimensional space demonstrated various cellular neighborhoods in both samples. The collection of spatially resolved analytes in a voxel (3D pixel) across serial sections of the tissue was also demonstrated. Data collected from this analytical pipeline were used to construct spatial 3D maps with single-cell resolution, which revealed cell identity, activation, and energized status. These maps will provide not only insights into the molecular basis of spatial cell heterogeneity in the TIME, but also novel predictive biomarkers and therapeutic targets, which can improve patient survival rates. [ABSTRACT FROM AUTHOR]

Simple Summary: Intrahepatic cholangiocarcinoma (ICC) is a primary liver cancer that currently has limited treatment options. As a result, patients with this disease generally have a poor prognosis. Previous studies have demonstrated that mutations in KRAS and loss of BRCA-1-associated protein 1 (BAP1) are frequently found in ICC. We developed a mouse model for ICC that incorporates KRAS and BAP1 mutations in a liver-dependent fashion to aid in the improvement of our understanding of this devastating disease. Our findings suggest that complete loss of BAP1 function combined with mutant KRAS appears to be a requirement for inducing ICC formation within the liver. Intrahepatic cholangiocarcinoma (ICC) is a primary biliary malignancy that harbors a dismal prognosis. Oncogenic mutations of KRAS and loss-of-function mutations of BRCA1-associated protein 1 (BAP1) have been identified as recurrent somatic alterations in ICC. However, an autochthonous genetically engineered mouse model of ICC that genocopies the co-occurrence of these mutations has never been developed. By crossing Albumin-Cre mice bearing conditional alleles of mutant Kras and/or floxed Bap1, Cre-mediated recombination within the liver was induced. Mice with hepatic expression of mutant KrasG12D alone (KA), bi-allelic loss of hepatic Bap1 (BhomoA), and heterozygous loss of Bap1 in conjunction with mutant KrasG12D expression (BhetKA) developed primary hepatocellular carcinoma (HCC), but no discernible ICC. In contrast, mice with homozygous loss of Bap1 in conjunction with mutant KrasG12D expression (BhomoKA) developed discrete foci of HCC and ICC. Further, the median survival of BhomoKA mice was significantly shorter at 24 weeks when compared to the median survival of ≥40 weeks in BhetKA mice and approximately 50 weeks in BhomoA and KA mice (p < 0.001). Microarray analysis performed on liver tissue from KA and BhomoKA mice identified differentially expressed genes in the setting of BAP1 loss and suggests that deregulation of ferroptosis might be one mechanism by which loss of BAP1 cooperates with oncogenic Ras in hepato-biliary carcinogenesis. Our autochthonous model provides an in vivo platform to further study this lethal class of neoplasm. [ABSTRACT FROM AUTHOR]

Simple Summary: High-grade serous ovarian cancer (HGSC) caused more than 13,000 deaths annually in the United States. A critically important component that influences the HGSC patient survival is the tumor microenvironment. However, how different cells interact to influence HGSC patients' survival remains largely unknown. To investigate this, we developed a pipeline that combines imaging mass cytometry (IMC), location-specific transcriptomics, and deep learning to identify the distribution of various stromal, tumor and immune cells as well as their spatial relationship. Our pipeline automatically and accurately segments cells and extracts salient cellular features to identify biomarkers, and multiple nearest-neighbor interactions among different cells that coordinate to influence overall survival rates in HGSC patients. In addition, we integrated IMC data with microdissected tumor and stromal transcriptomes to identify novel signaling networks. These results may lead to the discovery of novel survival rate-modulating mechanisms in HGSC patients. Stromal and immune cells in the tumor microenvironment (TME) have been shown to directly affect high-grade serous ovarian cancer (HGSC) malignant phenotypes, however, how these cells interact to influence HGSC patients' survival remains largely unknown. To investigate the cell-cell communication in such a complex TME, we developed a SpatioImageOmics (SIO) pipeline that combines imaging mass cytometry (IMC), location-specific transcriptomics, and deep learning to identify the distribution of various stromal, tumor and immune cells as well as their spatial relationship in TME. The SIO pipeline automatically and accurately segments cells and extracts salient cellular features to identify biomarkers, and multiple nearest-neighbor interactions among tumor, immune, and stromal cells that coordinate to influence overall survival rates in HGSC patients. In addition, SIO integrates IMC data with microdissected tumor and stromal transcriptomes from the same patients to identify novel signaling networks, which would lead to the discovery of novel survival rate-modulating mechanisms in HGSC patients. [ABSTRACT FROM AUTHOR]

Simple Summary: Genes with deleterious mutations in tumors are widely accepted as tumor suppressors, since, loss of their normal expression often promotes tumor development. However, most tumors develop over a long period, with gradual accumulation of tumor-promoting events. Thus, to comprehend the role of individual genes in this evolving process of tumorigenesis, it is critical to investigate their role in both early precursors and established tumor cells. Despite recurrent mutations in ARID1A in genomes of human cancer, including pancreatic cancer, its role in tumorigenesis is not clear. We aim to understand the role of Arid1a in pancreatic cancer development and maintenance by investigating its role in both early pancreatic precursor cells and established pancreatic cancer cells. Besides novel understanding of context-dependent role of Arid1a in pancreatic cancer, this study will also enable development of therapeutic strategies for pancreatic cancers patients with ARID1A mutations, which is currently a critical unmet need in clinic. Background & Aims: ARID1A is postulated to be a tumor suppressor gene owing to loss-of-function mutations in human pancreatic ductal adenocarcinomas (PDAC). However, its role in pancreatic pathogenesis is not clear despite recent studies using genetically engineered mouse (GEM) models. We aimed at further understanding of its direct functional role in PDAC, using a combination of GEM model and PDAC cell lines. Methods: Pancreas-specific mutant Arid1a-driven GEM model (Ptf1a-Cre; KrasG12D; Arid1af/f or "KAC") was generated by crossing Ptf1a-Cre; KrasG12D ("KC") mice with Arid1af/f mice and characterized histologically with timed necropsies. Arid1a was also deleted using CRISPR-Cas9 system in established human and murine PDAC cell lines to study the immediate effects of Arid1a loss in isogenic models. Cell lines with or without Arid1a expression were developed from respective autochthonous PDAC GEM models, compared functionally using various culture assays, and subjected to RNA-sequencing for comparative gene expression analysis. DNA damage repair was analyzed in cultured cells using immunofluorescence and COMET assay. Results: Retention of Arid1a is critical for early progression of mutant Kras-driven pre-malignant lesions into PDAC, as evident by lower Ki-67 and higher apoptosis staining in "KAC" as compared to "KC" mice. Enforced deletion of Arid1a in established PDAC cell lines caused suppression of cellular growth and migration, accompanied by compromised DNA damage repair. Despite early development of relatively indolent cystic precursor lesions called intraductal papillary mucinous neoplasms (IPMNs), a subset of "KAC" mice developed aggressive PDAC in later ages. PDAC cells obtained from older autochthonous "KAC" mice revealed various compensatory ("escaper") mechanisms to overcome the growth suppressive effects of Arid1a loss. Conclusions: Arid1a is an essential survival gene whose loss impairs cellular growth, and thus, its expression is critical during early stages of pancreatic tumorigenesis in mouse models. In tumors that arise in the setting of ARID1A loss, a multitude of "escaper" mechanisms drive progression. [ABSTRACT FROM AUTHOR]

Uterine serous cancer (USC) is an aggressive subtype of endometrial cancer, with poor survival and high recurrence rates. The development of novel and effective therapies specific to USC would aid in its management. However, few studies have focused solely on this rare subtype. The current study demonstrated that the orally bioavailable, investigational new drug and novel imipridone ONC206 suppressed USC cell proliferation and induced apoptosis both in vitro and in vivo. Disruption of the DRD2-mediated p38MAPK/ERK/PGC-1α network by ONC206 led to metabolic reprogramming and suppression of both glycolysis and oxidative phosphorylation. ONC206 also synergized with paclitaxel in reducing USC cell viability. In addition, DRD2 overexpression correlated with poor overall survival in patients. This study provides the first evidence that ONC206 induced metabolic reprogramming in USC cells and is a promising therapeutic agent for USC treatment. These findings support further development of ONC206 as a promising therapeutic agent and improves survival rates in patients with USC. [ABSTRACT FROM AUTHOR]