In metastatic cancer, the role of heterogeneity at the tumor-immune microenvironment, its molecular underpinnings and clinical relevance remain largely unexplored. To understand tumor-immune dynamics at baseline and upon chemotherapy treatment, we performed unbiased pathway and cell type-specific immunogenomics analysis of treatment-naive (38 5 samples from 8 patients) and paired chemotherapy treated (80 paired samples from 40 patients) high-grade serous ovarian cancer (HGSOC) samples. Whole transcriptome analysis and imagebased quantification of T cells from treatment-naive tumors revealed ubiquitous variability in immune signaling and distinct immune microenvironments co-existing within the same individuals and within tumor deposits at diagnosis. To systematically explore cell type composition of the tumor microenvironment using bulk mRNA, we derived consensus immune and stromal cell gene signatures by intersecting state-of-the-art deconvolution methods, providing improved accuracy and sensitivity when compared to HGSOC immunostaining and leukocyte methylation data sets. Cell-type deconvolution and pathway analyses revealed that Myc and Wnt signaling associate with immune cell exclusion in untreated HGSOC. To evaluate the effect of chemotherapy on the intrinsic tumor-immune heterogeneity, we compared sitematched and site-unmatched tumors before and after neoadjuvant chemotherapy.Transcriptomic and T-cell receptor sequencing analyses showed that site-matched samples had increased cytotoxic immune activation and oligoclonal expansion of T cells after chemotherapy, which was not seen in site-unmatched samples where heterogeneity could not be accounted for. These results demonstrate that the tumor-immune interface in advanced HGSOC is intrinsically heterogeneous, and thus requires site-specific analysis to reliably unmask the impact of therapy on the tumor-immune microenvironment..

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In metastatic cancer, the role of heterogeneity at the tumor-immune microenvironment, its molecular underpinnings and clinical relevance remain largely unexplored. To understand tumor-immune dynamics at baseline and upon chemotherapy treatment, we performed unbiased pathway and cell type-specific immunogenomics analysis of treatment-naive (38 5 samples from 8 patients) and paired chemotherapy treated (80 paired samples from 40 patients) high-grade serous ovarian cancer (HGSOC) samples. Whole transcriptome analysis and imagebased quantification of T cells from treatment-naive tumors revealed ubiquitous variability in immune signaling and distinct immune microenvironments co-existing within the same individuals and within tumor deposits at diagnosis. To systematically explore cell type 10 composition of the tumor microenvironment using bulk mRNA, we derived consensus immune and stromal cell gene signatures by intersecting state-of-the-art deconvolution methods, providing improved accuracy and sensitivity when compared to HGSOC immunostaining and leukocyte methylation data sets. Cell-type deconvolution and pathway analyses revealed that Myc and Wnt signaling associate with immune cell exclusion in untreated HGSOC. To evaluate 15 the effect of chemotherapy on the intrinsic tumor-immune heterogeneity, we compared sitematched and site-unmatched tumors before and after neoadjuvant chemotherapy.Transcriptomic and T-cell receptor sequencing analyses showed that site-matched samples had increased cytotoxic immune activation and oligoclonal expansion of T cells after chemotherapy, which was not seen in site-unmatched samples where heterogeneity could not be accounted 20for. These results demonstrate that the tumor-immune interface in advanced HGSOC is intrinsically heterogeneous, and thus requires site-specific analysis to reliably unmask the impact of therapy on the tumor-immune microenvironment. 1050 Conceptualization [Ideas; formulation or evolution of overarching research goals and aims] AS, AJS, MLM, ES Data curation [Management activities to annotate (produce metadata), scrub data and maintain research data (including software code, where necessary for interpreting the data itself) for 1055 initial use and later re-use] AJS, KL, PC Formal analysis [Application of statistical, mathematical, computational, or other formal techniques to analyse or synthesize study data] 1060 AJS Funding acquisition [Acquisition of the financial support for the project leading to this publication] AV, ES, AS, MLM 1065 Investigation [Conducting a research and investigation process, specifically performing the experiments, or data/evidence collection] PC, KL, TW, YM, IN, BW, DC, ES 1070 50 Methodology [Development or design of methodology; creation of models] ES Project administration [Management and coordination responsibility for the research activity planning and execution] 1075 AS, MLM, ES Resources [Provision of study materials, reagents, materials, patients, laboratory samples, animals, instrumentation, computing resources, or other a...

High-grade serous ovarian cancer (HGSC) is the leading cause of morbidity and mortality from gynecologic malignant tumors. Overall survival remains low because of the nearly ubiquitous emergence of platinum resistance and the paucity of effective next-line treatments. Current cell culture-based models show limited similarity to HGSC and are therefore unreliable predictive models for preclinical evaluation of investigational drugs. This deficiency could help explain the low overall rate of successful drug development and the decades of largely unchanged approaches to HGSC treatment. We used gene expression, copy number variation, and exome sequencing analyses to credential HGSC patient-derived xenografts (PDXs) as effective preclinical models that recapitulate the features of human HGSC. Mice bearing PDXs were also treated with standard-of-care carboplatin therapy. PDXs showed similar sensitivity to carboplatin as the patient's tumor at the time of sampling. PDXs also recapitulated the diversity of genomic alterations (copy number variation and mutation profiles) previously described in large data sets that profiled HGSC. Furthermore, mRNA profiling showed that the PDXs represent all HGSC subtypes with the exception of the immunoreactive group. Credentialing of PDX models of HGSC should aid progress in HGSC research by providing improved preclinical models of HGSC that can be used to test novel targets and more accurately evaluate their likelihood of success.

Anemia, which is highly prevalent in oncology patients, is one of the most established negative prognostic factors for several gynecologic malignancies. Multiple factors can cause or contribute to the development of anemia in patients with gynecologic cancers; these factors include blood loss (during surgery or directly from the tumor), renal impairment (caused by platinum-based chemotherapy), and marrow dysfunction (from metastases, chemotherapy, and/or radiation therapy). Several peri- and intra-operative strategies can be used to optimize patient management and minimize blood loss related to surgery. Blood transfusions are routinely employed as corrective measures against anemia; however, blood transfusions are one of the most overused healthcare interventions. There are safe and effective evidence-based blood transfusion strategies used in other patient populations that warrant further investigation in the surgical oncology setting. Blood is a valuable healthcare resource, and clinicians can learn to use it more judiciously through knowledge of the potential risks and complications of blood interventions, as well as the ability to properly identify the patients most likely to benefit from such interventions.

AUTHOR CONTRIBUTIONS Marina Stasenko: Conception and design; analysis and interpretation of data; drafting of article; revising article critically for important intellectual content; final approval of version to be published; agrees to be accountable for all aspects of the work. Paulina Cybulska: Analysis and interpretation of data; drafting of article; final approval of version to be published; agrees to be accountable for all aspects of the work. Noah Feit: Analysis and interpretation of data; revising article critically; final approval of version to be published; agrees to be accountable for all aspects of the work. Vicky Makker: Interpretation of data; revising article critically; final approval of version to be published; agrees to be accountable for all aspects of the work. Jason Konner: Interpretation of data; revising article critically; final approval of version to be published; agrees to be accountable for all aspects of the work. Roisin E. O'Cearbhaill: Interpretation of data; revising article critically; final approval of version to be published; agrees to be accountable for all aspects of the work. Kaled M. Alektiar: Interpretation of data; revising article critically; final approval of version to be published; agrees to be accountable for all aspects of the work. Kathryn Beal: Interpretation of data; revising article critically; final approval of version to be published; agrees to be accountable for all aspects of the work. Ginger J. Gardner: Interpretation of data; revising article critically; final approval of version to be published; agrees to be accountable for all aspects of the work. Kara C. Long Roche: Interpretation of data; revising article critically; final approval of version to be published; agrees to be accountable for all aspects of the work. Yukio Sonoda: Interpretation of data; revising article critically; final approval of version to be published; agrees to be accountable for all aspects of the work. Dennis S. Chi: Interpretation of data; revising article critically; final approval of version to be published; agrees to be accountable for all aspects of the work. Oliver Zivanovic: Interpretation of data; revising article critically; final approval of version to be published; agrees to be accountable for all aspects of the work. Mario M. Leitao Jr.: Interpretation of data; revising article critically; final approval of version to be published; agrees to be accountable for all aspects of the work. Karen A. Cadoo: Interpretation of data; revising article critically; final approval of version to be published; agrees to be accountable for all aspects of the work. William P. Tew: Conception and design; analysis and interpretation of data; drafting of article; revising article critically for important intellectual content; final approval of version to be published; agrees to be accountable for all aspects of the work.

BRCA1 and BRCA2 are essential for the repair of double-strand DNA breaks, and alterations in these genes are a hallmark of breast and ovarian carcinomas. Other functionally related genes may also play important roles in carcinogenesis. Amplification of EMSY, a putative BRCAness gene, has been suggested to impair DNA damage repair by suppressing BRCA2 function. We employed direct repeat GFP (DR-GFP) and RAD51 foci formation assays to show that EMSY overexpression impairs the repair of damaged DNA, suggesting that EMSY belongs to the family of BRCAness proteins. We also identified a novel phospho-site at threonine 207 (T207) and demonstrated its role in EMSY-driven suppression of DNA damage repair. In vitro kinase assays established that protein kinase A (PKA) directly phosphorylates the T207 phospho-site. Immunoprecipitation experiments suggest that EMSY-driven suppression of DNA damage repair is a BRCA2-independent process. The data also suggest that EMSY amplification is a BRCAness feature, and may help to expand the population of patients who could benefit from targeted therapies that are also effective in BRCA1/2-mutant cancers.

Canadian botanicals represent a potential source of novel compounds which inhibit Ng, including isolates resistant to antibiotics.