Publications by authors named "Kelly Conrads"

A central theme in cancer research is to increase our understanding of the cancer tissue microenvironment, which is comprised of a complex and spatially heterogeneous ecosystem of malignant and non-malignant cells, both of which actively contribute to an intervening extracellular matrix. Laser microdissection (LMD) enables histology selective harvest of cellular subpopulations from the tissue microenvironment for their independent molecular investigation, such as by high-throughput DNA and RNA sequencing. Although enabling, LMD often requires a labor-intensive investment to harvest enough cells to achieve the necessary DNA and/or RNA input requirements for conventional next-generation sequencing workflows.

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Background: Black women are at an increased risk of developing uterine leiomyomas and experiencing worse disease prognosis than White women. Epidemiologic and molecular factors have been identified as underlying these disparities, but there remains a paucity of deep, multiomic analysis investigating molecular differences in uterine leiomyomas from Black and White patients.

Objective: To identify molecular alterations within uterine leiomyoma tissues correlating with patient race by multiomic analyses of uterine leiomyomas collected from cohorts of Black and White women.

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We performed a deep proteogenomic analysis of bulk tumor and laser microdissection enriched tumor cell populations from high-grade serous ovarian cancer (HGSOC) tissue specimens spanning a broad spectrum of purity. We identified patients with longer progression-free survival had increased immune-related signatures and validated proteins correlating with tumor-infiltrating lymphocytes in 65 tumors from an independent cohort of HGSOC patients, as well as with overall survival in an additional 126 HGSOC patient cohort. We identified that homologous recombination deficient (HRD) tumors are enriched in pathways associated with metabolism and oxidative phosphorylation that we validated in independent patient cohorts.

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Numerous multi-omic investigations of cancer tissue have documented varying and poor pairwise transcript:protein quantitative correlations, and most deconvolution tools aiming to predict cell type proportions (cell admixture) have been developed and credentialed using transcript-level data alone. To estimate cell admixture using protein abundance data, we analyzed proteome and transcriptome data generated from contrived admixtures of tumor, stroma, and immune cell models or those selectively harvested from the tissue microenvironment by laser microdissection from high grade serous ovarian cancer (HGSOC) tumors. Co-quantified transcripts and proteins performed similarly to estimate stroma and immune cell admixture (r ≥ 0.

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Article Synopsis
  • Uterine serous carcinoma (USC) is a rare but aggressive type of uterine cancer that shows high resistance to chemotherapy and recurrence, leading to significant mortality rates.
  • This study aimed to explore the complex tumor microenvironment of USC through advanced techniques like multi-region microsampling and quantitative proteomics from tumor samples of nine patients.
  • Results revealed significant heterogeneity within the tumors, showing that different cell populations vary considerably, highlighting the importance of analyzing specific cellular subgroups instead of just bulk tumor tissue for better understanding of disease mechanics.
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Breast cancer in young patients is known to exhibit more aggressive biological behavior and is associated with a less favorable prognosis than the same disease in older patients, owing in part to an increased incidence of brain metastases. The mechanistic explanations behind these findings remain poorly understood. We recently reported that young mice, in comparison to older mice, developed significantly greater brain metastases in four mouse models of triple-negative and luminal B breast cancer.

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Alcohol use disorder (AUD) affects transcriptomic, epigenetic and proteomic expression in several organs, including the brain. There has not been a comprehensive analysis of altered protein abundance focusing on the multiple brain regions that undergo neuroadaptations occurring in AUD. We performed a quantitative proteomic analysis using a liquid chromatography-tandem mass spectrometry (LC-MS/MS) analysis of human postmortem tissue from brain regions that play key roles in the development and maintenance of AUD, the amygdala (AMG), hippocampus (HIPP), hypothalamus (HYP), nucleus accumbens (NAc), prefrontal cortex (PFC) and ventral tegmental area (VTA).

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Objective: ATR kinase inhibitors promote cell killing by inducing replication stress and through potentiation of genotoxic agents in gynecologic cancer cells. To explore mechanisms of acquired resistance to ATRi in ovarian cancer, we characterized ATRi-resistant ovarian cancer cells generated by metronomic dosing with the clinical ATR inhibitor AZD6738.

Methods: ATRi-resistant ovarian cancer cells (OVCAR3 and OV90) were generated by dosing with AZD6738 and assessed for sensitivity to Chk1i (LY2603618), PARPi (Olaparib) and combination with cisplatin or a CDK4/6 inhibitor (Palbociclib).

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High-grade serous ovarian cancer (HGSC) is frequently characterized by homologous recombination (HR) DNA repair deficiency and, while most such tumors are sensitive to initial treatment, acquired resistance is common. We undertook a multiomics approach to interrogate molecular diversity in end-stage disease, using multiple autopsy samples collected from 15 women with HR-deficient HGSC. Patients had polyclonal disease, and several resistance mechanisms were identified within most patients, including reversion mutations and HR restoration by other means.

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Background: Low-grade serous ovarian cancer (LGSOC) is a rare disease that occurs more frequently in younger women than those with high-grade disease. The current treatment is suboptimal and a better understanding of the molecular pathogenesis of this disease is required. In this study, we compared the proteogenomic analyses of LGSOCs from short- and long-term survivors (defined as < 40 and > 60 months, respectively).

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Importance: Despite similar histologic appearance among high-grade serous ovarian cancers (HGSOCs), clinical observations suggest vast differences in gross appearance. There is currently no systematic framework by which to classify HGSOCs according to their gross morphologic characteristics.

Objective: To develop and characterize a gross morphologic classification system for HGSOC.

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Background: Optimal cytoreduction to no residual disease (R0) correlates with improved disease outcome for high-grade serous ovarian cancer (HGSOC) patients. Treatment of HGSOC patients with neoadjuvant chemotherapy, however, may select for tumor cells harboring alterations in hallmark cancer pathways including metastatic potential. This study assessed this hypothesis by performing proteomic analysis of matched, chemotherapy naïve and neoadjuvant chemotherapy (NACT)-treated HGSOC tumors obtained from patients who had suboptimal (R1, n = 6) versus optimal (R0, n = 14) debulking at interval debulking surgery (IDS).

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The tumor microenvironment (TME) represents a complex ecosystem comprised of dozens of distinct cell types, including tumor, stroma, and immune cell populations. To characterize proteome-level variation and tumor heterogeneity at scale, high-throughput methods are needed to selectively isolate discrete cellular populations in solid tumor malignancies. This protocol describes a high-throughput workflow, enabled by artificial intelligence (AI), that segments images of hematoxylin and eosin (H&E)-stained, thin tissue sections into pathology-confirmed regions of interest for selective harvest of histology-resolved cell populations using laser microdissection (LMD).

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Background: The incidence of venous thromboembolism (VTE) in patients with ovarian cancer is higher than most solid tumors, ranging between 10-30%, and a diagnosis of VTE in this patient population is associated with worse oncologic outcomes. The tumor-specific molecular factors that may lead to the development of VTE are not well understood.

Objectives: The aim of this study was to identify molecular features present in ovarian tumors of patients with VTE compared to those without.

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Characterization of ancestry-linked peptide variants in disease-relevant patient tissues represents a foundational step to connect patient ancestry with disease pathogenesis. Nonsynonymous single-nucleotide polymorphisms encoding missense substitutions within tryptic peptides exhibiting high allele frequencies in European, African, and East Asian populations, termed peptide ancestry informative markers (pAIMs), were prioritized from 1000 genomes. analysis identified that as few as 20 pAIMs can determine ancestry proportions similarly to >260K SNPs (R = 0.

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Enriched tumor epithelium, tumor-associated stroma, and whole tissue were collected by laser microdissection from thin sections across spatially separated levels of ten high-grade serous ovarian carcinomas (HGSOCs) and analyzed by mass spectrometry, reverse phase protein arrays, and RNA sequencing. Unsupervised analyses of protein abundance data revealed independent clustering of an enriched stroma and enriched tumor epithelium, with whole tumor tissue clustering driven by overall tumor "purity." Comparing these data to previously defined prognostic HGSOC molecular subtypes revealed protein and transcript expression from tumor epithelium correlated with the differentiated subtype, whereas stromal proteins (and transcripts) correlated with the mesenchymal subtype.

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Pathogenic mutations in fumarate hydratase (FH) drive hereditary leiomyomatosis and renal cell cancer (HLRCC) and increase the risk of developing uterine leiomyomas (ULMs). An integrated proteogenomic analysis of ULMs from HLRCC (n = 16; FH-mutation confirmed) and non-syndromic (NS) patients (n = 12) identified a significantly higher protein:transcript correlation in HLRCC (R = 0.35) vs.

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Cancer has no borders: Generation and analysis of molecular data across multiple centers worldwide is necessary to gain statistically significant clinical insights for the benefit of patients. Here we conceived and standardized a proteotype data generation and analysis workflow enabling distributed data generation and evaluated the quantitative data generated across laboratories of the international Cancer Moonshot consortium. Using harmonized mass spectrometry (MS) instrument platforms and standardized data acquisition procedures, we demonstrate robust, sensitive, and reproducible data generation across eleven international sites on seven consecutive days in a 24/7 operation mode.

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The diversity and heterogeneity within high-grade serous ovarian cancer (HGSC), which is the most lethal gynecologic malignancy, is not well understood. Here, we perform comprehensive multi-platform omics analyses, including integrated analysis, and immune monitoring on primary and metastatic sites from highly clinically annotated HGSC samples based on a laparoscopic triage algorithm from patients who underwent complete gross resection (R0) or received neoadjuvant chemotherapy (NACT) with excellent or poor response. We identify significant distinct molecular abnormalities and cellular changes and immune cell repertoire alterations between the groups, including a higher rate of NF1 copy number loss, and reduced chromothripsis-like patterns, higher levels of strong-binding neoantigens, and a higher number of infiltrated T cells in the R0 versus the NACT groups.

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Preoperative use of metformin in obese women with endometrioid endometrial cancer (EEC) reduces tumor proliferation and inhibits the mammalian target of rapamycin pathway, though is only effective in select cases. This study sought to identify a predictive and/or pharmacodynamic proteomic signature of metformin response to tailor its pharmacologic use. Matched pre- and post-metformin-treated tumor tissues from a recently completed preoperative window trial of metformin in EEC patients (ClinicalTrials.

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Background: Endometrial cancer is the most common gynecological cancer in the United States. However, no early detection test exists for asymptomatic women at average risk for endometrial cancer.

Objective: We sought to identify early detection biomarkers for endometrial cancer using prediagnostic serum.

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High grade serous ovarian cancer (HGSOC) patients have a high recurrence rate after surgery and adjuvant chemotherapy due to inherent or acquired drug resistance. Cell lines derived from HGSOC tumors that are resistant to chemotherapeutic agents represent useful pre-clinical models for drug discovery. Here, we describe establishment of a human ovarian carcinoma cell line, which we term WHIRC01, from a patient-derived mouse xenograft established from a chemorefractory HGSOC patient who did not respond to carboplatin and paclitaxel therapy.

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Background And Objective: Nua kinase 1 (NUAK1) was identified in multigene signatures of survival and suboptimal debulking in high-grade serous ovarian cancer (HGSOC). This study investigates the individual clinical and biologic contributions of NUAK1 in HGSOC patients and cell lines.

Methods: Public transcript expression, clinical, and outcome data were used to interrogate the relationship between NUAK1 and clinicopathologic factors and patient outcomes including progression-free survival (PFS) and molecular subtypes using logistic and Cox modeling.

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AT-rich interactive domain-containing protein 1A (ARID1A) is a recently identified nuclear tumor suppressor frequently altered in solid tumor malignancies. We have identified a bipartite-like nuclear localization sequence (NLS) that contributes to nuclear import of ARID1A not previously described. We functionally confirm activity using GFP constructs fused with wild-type or mutant NLS sequences.

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