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Supplementary Table Part 2 - PDF file 334K, Proteins Altered by Progesterone and Calcitiriol. The supplementary table contains the full list of proteins that were found to exhibit differential expression in cancer cell lines following exposure to progesterone and calcitriol

Supplementary Figure 1. TNF-alpha induced apoptosis. Cells were treated with 2.5 ng/ml TNF-alpha and 250 ng/ml CHX, ethanol fixed, and stained with PI. The percentage of cells containing sub-G/G1 DNA content was assessed by flow cytometry at time 0, 60, 90, and 120 minutes after exposure to TNF-alpha.

A subset of acute promyelocytic leukemia (APL) cases has been characterized by the t(5;17)(q35;q21) translocation variant, which fuses nucleophosmin (NPM) to retinoic acid receptor α (RARA). The resultant NPM-RAR fusion protein blocks myeloid differentiation and leads to a leukemic phenotype similar to that caused by the t(15;17)(q22;q21) PML-RAR fusion. The contribution of the N-terminal 117 amino acids of NPM contained within NPM-RAR has not been well studied. As a molecular chaperone, NPM interacts with a variety of proteins implicated in leukemogenesis. Therefore, a proteomic analysis was conducted to identify novel NPM-RAR–associated proteins. TNF receptor type I–associated DEATH domain protein (TRADD) was identified as a relevant binding partner for NPM-RAR. This interaction was validated by coprecipitation and colocalization analysis. Biologic assessment found that NPM-RAR expression impaired TNF-induced signaling through TRADD, blunting TNF-mediated activation of caspase-3 (CASP3) and caspase-8 (CASP8), to ultimately block apoptosis.Implications: This study identifies a novel mechanism through which NPM-RAR affects leukemogenesis. Mol Cancer Res; 12(9); 1283–91. ©2014 AACR.

Human studies suggest that progesterone and calcitriol may prove beneficial in preventing or inhibiting oncogenesis, but the underlying mechanism is not fully understood. The current study investigates the effects of progesterone, calcitriol, and their combination on immortalized human endometrial epithelial cells and endometrial cancer cells and identifies their targets of action. Combination treatment with both agents enhanced vitamin D receptor expression and inhibited cell proliferation through caspase-3 activation and induction of G0–G1 cell-cycle arrest with associated downregulation of cyclins D1 and D3 and p27 induction. We used mass spectrometry–based proteomics to measure protein abundance differences between calcitriol-, progesterone-, or combination-exposed endometrial cells. A total of 117 proteins showed differential expression among these three treatments. Four proteins were then selected for validation studies: histone H1.4 (HIST1H1E), histidine triad nucleotide-binding protein 2 (HINT2), IFN-induced, double-stranded RNA-activated protein kinase (EIF2AK2), and Bcl-2–associated X protein (BAX). Abundance levels of selected candidates were low in endometrial cancer cell lines versus the immortalized endometrial epithelial cell line. All four proteins displayed elevated expression in cancer cells upon exposure to calcitriol, progesterone, or the combination. Further BAX analysis through gain- or loss-of-function experiments revealed that upregulation of BAX decreased cell proliferation by changing the BAX:BCL-2 ratio. Knockdown of BAX attenuated progesterone- and calcitriol-induced cell growth inhibition. Our results showed that progesterone and calcitriol upregulate the expression of BAX along with other apoptosis-related proteins, which induce inhibition of endometrial cancer cell growth by apoptosis and cell-cycle arrest. Cancer Prev Res; 6(7); 731–43. ©2013 AACR.

Supplementary Table Part 1 - PDF file 319K, Proteins Altered by Progesterone and Calcitiriol. The supplementary table contains the full list of proteins that were found to exhibit differential expression in cancer cell lines following exposure to progesterone and calcitriol

PDF file - 61KB, Antibodies used to evaluate candidate biomarkers in the TMA.

Supplementary Data from Proteomic Analysis of Laser-Captured Paraffin-Embedded Tissues: A Molecular Portrait of Head and Neck Cancer Progression

PDF file - 148KB, Exploratory Baseline Analytes.

Purpose: The EGF receptor (EGFR) and COX2 pathways are upregulated in head and neck squamous cell carcinoma (HNSCC). Preclinical models indicate synergistic antitumor activity from dual blockade. We conducted a randomized, double-blind, placebo-controlled window trial of erlotinib, an EGFR inhibitor; erlotinib plus sulindac, a nonselective COX inhibitor; versus placebo.Experimental Design: Patients with untreated, operable stage II-IVb HNSCC were randomized 5:5:3 to erlotinib, erlotinib–sulindac, or placebo. Tumor specimens were collected before and after seven to 14 days of treatment. The primary endpoint was change in Ki67 proliferation index. We hypothesized an ordering effect in Ki67 reduction: erlotinib–sulindac > erlotinib > placebo. We evaluated tissue microarrays by immunohistochemistry for pharmacodynamic modulation of EGFR and COX2 signaling intermediates.Results: From 2005–2009, 47 patients were randomized for the target 39 evaluable patients. Thirty-four tumor pairs were of sufficient quality to assess biomarker modulation. Ki67 was significantly decreased by erlotinib or erlotinib–sulindac (omnibus comparison, two-sided Kruskal–Wallis, P = 0.04). Wilcoxon pairwise contrasts confirmed greater Ki67 effect in both erlotinib groups (erlotinib–sulindac vs. placebo, P = 0.043; erlotinib vs. placebo, P = 0.027). There was a significant trend in ordering of Ki67 reduction: erlotinib–sulindac > erlotinib > placebo (two-sided exact Jonckheere–Terpstra, P = 0.0185). Low baseline pSrc correlated with greater Ki67 reduction (R2 = 0.312, P = 0.024).Conclusions: Brief treatment with erlotinib significantly decreased proliferation in HNSCC, with additive effect from sulindac. Efficacy studies of dual EGFR–COX inhibition are justified. pSrc is a potential resistance biomarker for anti-EGFR therapy, and warrants investigation as a molecular target. Clin Cancer Res; 20(12); 3289–98. ©2014 AACR.

PDF file - 176KB, Assessing the Adequacy of Randomization.

Purpose: Squamous cell carcinoma of the head and neck (HNSCC), the sixth most prevalent cancer among men worldwide, is associated with poor prognosis, which has improved only marginally over the past three decades. A proteomic analysis of HNSCC lesions may help identify novel molecular targets for the early detection, prevention, and treatment of HNSCC.Experimental Design: Laser capture microdissection was combined with recently developed techniques for protein extraction from formalin-fixed paraffin-embedded (FFPE) tissues and a novel proteomics platform. Approximately 20,000 cells procured from FFPE tissue sections of normal oral epithelium and well, moderately, and poorly differentiated HNSCC were processed for mass spectrometry and bioinformatic analysis.Results: A large number of proteins expressed in normal oral epithelium and HNSCC, including cytokeratins, intermediate filaments, differentiation markers, and proteins involved in stem cell maintenance, signal transduction, migration, cell cycle regulation, growth and angiogenesis, matrix degradation, and proteins with tumor suppressive and oncogenic potential, were readily detected. Of interest, the relative expression of many of these molecules followed a distinct pattern in normal squamous epithelia and well, moderately, and poorly differentiated HNSCC tumor tissues. Representative proteins were further validated using immunohistochemical studies in HNSCC tissue sections and tissue microarrays.Conclusions: The ability to combine laser capture microdissection and in-depth proteomic analysis of FFPE tissues provided a wealth of information regarding the nature of the proteins expressed in normal squamous epithelium and during HNSCC progression, which may allow the development of novel biomarkers of diagnostic and prognostic value and the identification of novel targets for therapeutic intervention in HNSCC.

Induction of apoptosis underlies a mechanism for inhibiting tumorigenesis by phenethyl isothiocyanate (PEITC) and sulforaphane (SFN). However, the upstream events by which isothiocyanates (ITC) induce apoptosis have not been fully investigated. As electrophiles, ITCs could trigger apoptosis by binding to DNA or proteins or by inducing oxidative stress. To better understand the molecular mechanisms of apoptosis by ITCs, we examined, as a first step, the role of these events in human non–small lung cancer A549 cells. PEITC was a more potent inducer than SFN; it induced apoptosis at 20 μmol/L, whereas SFN induced at 40 μmol/L but not at 20 μmol/L. To study binding with cellular proteins and DNA, cells were treated with 14C-ITCs; the initial protein binding by PEITC was almost 3-fold than that of SFN. The binding by PEITC increased with time, whereas binding by SFN remained low. Therefore, 4 h after incubation proteins became the predominant targets for PEITC with a 6-fold binding than that of SFN. To characterize the chemical nature of binding by the ITCs, we used bovine serum albumin (BSA) as a surrogate protein. PEITC also modified BSA covalently to a greater extent than SFN occurring exclusively at cysteine residues. Surprisingly, neither PEITC nor SFN bound to DNA or RNA at detectable levels or caused significant DNA strand breakage. The levels of oxidative damage in cells, measured as reactive oxygen species, 8-oxo-deoxyguanosine, and protein carbonyls formation, were greater in cells treated with SFN than PEITC. Because PEITC is a stronger inducer of apoptosis than SFN, these results indicate that direct covalent binding to cellular proteins is an important early event in the induction of apoptosis by the ITCs. [Cancer Res 2007;67(13):6409–16]

Supplementary Methods and Materials from The Role of Protein Binding in Induction of Apoptosis by Phenethyl Isothiocyanate and Sulforaphane in Human Non–Small Lung Cancer Cells

ImportanceDespite 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.ObjectiveTo develop and characterize a gross morphologic classification system for HGSOC.Design, Setting, and ParticipantsThis cohort study included patients with suspected advanced-stage ovarian cancer who presented between April 1, 2013, and August 5, 2016, to the University of Texas MD Anderson Cancer Center, a large referral center. Patients underwent laparoscopic assessment of disease burden before treatment and received a histopathologic diagnosis of HGSOC. Researchers assigning morphologic subtype and performing molecular analyses were blinded to clinical outcomes. Data analysis was performed between April 2020 and November 2021.ExposuresGross tumor morphologic characteristics.Main Outcomes and MeasuresClinical outcomes and multiomic profiles of representative tumor samples of type I or type II morphologic subtypes were compared.ResultsOf 112 women (mean [SD] age 62.7 [9.7] years) included in the study, most patients (84% [94]) exhibited a predominant morphologic subtype and many (63% [71]) had a uniform morphologic subtype at all involved sites. Compared with those with uniform type I morphologic subtype, patients with uniform type II morphologic subtype were more likely to have a favorable Fagotti score (83% [19 of 23] vs 46% [22 of 48]; P = .004) and thus to be triaged to primary tumor reductive surgery. Similarly, patients with uniform type II morphologic subtype also had significantly higher mean (SD) estimated blood loss (639 [559; 95% CI, 391-887] mL vs 415 [527; 95% CI, 253-577] mL; P = .006) and longer mean (SD) operative time (408 [130; 95% CI, 350-466] minutes vs 333 [113; 95% CI, 298-367] minutes; P = .03) during tumor reductive surgery. Type I tumors had enrichment of epithelial-mesenchymal transition (false discovery rate [FDR] q-value, 3.10 × 10−24), hypoxia (FDR q-value, 1.52 × 10−5), and angiogenesis pathways (FDR q-value, 2.11 × 10−2), whereas type II tumors had enrichment of pathways related to MYC signaling (FDR q-value, 2.04 × 10−9) and cell cycle progression (FDR q-value, 1.10 × 10−5) by integrated proteomic and transcriptomic analysis. Abundances of metabolites and lipids also differed between the 2 morphologic subtypes.Conclusions and RelevanceThis study identified 2 novel, gross morphologic subtypes of HGSOC, each with unique clinical features and molecular signatures. The findings may have implications for triaging patients to surgery or chemotherapy, identifying outcomes, and developing tailored therapeutic strategies.

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). Methods Tumor epithelium was harvested by laser microdissection from formalin-fixed, paraffin-embedded tissues from matched, pre- and post-NACT treated tumors for twenty HGSOC patients and analyzed by quantitative mass spectrometry-based proteomics. Results Differential analysis of patient matched pre- and post-NACT treated tumors revealed proteins associated with cell survival and metabolic signaling to be significantly altered in post-NACT treated tumor cells. Comparison of pre-NACT treated tumors from suboptimal (R1) versus optimally (R0) debulked patients identified proteins associated with tumor cell viability and invasion signaling enriched in R1 patients. We identified five proteins altered between R1 and R0 patients in pre- NACT treated tumors that significantly correlated with PFS in an independent cohort of HGSOC patients, including Fermitin family homolog 2 (FERMT2), a protein elevated in R1 that correlated with disease progression in HGSOC patients (multivariate Cox HR = 1.65, Wald p = 0.022) and increased metastatic potential in solid-tumor malignancies. Conclusions This study identified distinct proteome profiles in patient matched pre- and post-NACT HGSOC tumors that correlate with NACT resistance and that may predict residual disease status at IDS that collectively warrant further pre-clinical investigation.

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 (HE)-stained, thin tissue sections into pathology-confirmed regions of interest for selective harvest of histology-resolved cell populations using laser microdissection (LMD). This strategy includes a novel algorithm enabling the transfer of regions denoting cell populations of interest, annotated using digital image software, directly to laser microscopes, thus enabling more facile collections. Successful implementation of this workflow was performed, demonstrating the utility of this harmonized method to selectively harvest tumor cell populations from the TME for quantitative, multiplexed proteomic analysis by high-resolution mass spectrometry. This strategy fully integrates with routine histopathology review, leveraging digital image analysis to support enrichment of cellular populations of interest and is fully generalizable, enabling harmonized harvests of cell populations from the TME for multiomic analyses.

We present a deep proteogenomic profiling study of 87 lung adenocarcinoma (LUAD) tumors from the United States, integrating whole-genome sequencing, transcriptome sequencing, proteomics and phosphoproteomics by mass spectrometry, and reverse-phase protein arrays. We identify three subtypes from somatic genome signature analysis, including a transition-high subtype enriched with never smokers, a transversion-high subtype enriched with current smokers, and a structurally altered subtype enriched with former smokers, TP53 alterations, and genome-wide structural alterations. We show that within-tumor correlations of RNA and protein expression associate with tumor purity and immune cell profiles. We detect and independently validate expression signatures of RNA and protein that predict patient survival. Additionally, among co-measured genes, we found that protein expression is more often associated with patient survival than RNA. Finally, integrative analysis characterizes three expression subtypes with divergent mutations, proteomic regulatory networks, and therapeutic vulnerabilities. This proteogenomic characterization provides a foundation for molecularly informed medicine in LUAD.

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.gov: NCT01911247) were analyzed by mass spectrometry (MS)‐based proteomic and immunohistochemical analyses. Jupiter microtubule‐associated homolog 1 (JPT1) was significantly elevated in metformin responders (n = 13) vs nonresponders (n = 7), and found to decrease in abundance in metformin responders following treatment; observations that were verified by immunohistochemical staining for JPT1. Metformin response and loss of JPT1 were assessed in RL95‐2 and ACI‐181 endometrial cancer (EC) cell lines. We further identified that silencing of JPT1 abundance does not alter cellular response to metformin or basal cell proliferation, but that JPT1 abundance does decrease in response to metformin treatment in RL95‐2 and ACI‐181 EC cell lines. These data suggest that JPT1 represents a predictive and pharmacodynamic biomarker of metformin response that, if validated in larger patient populations, may enable preoperative EEC patient stratification to metformin treatment and the ability to monitor patient response., We report JPT1 (Jupiter microtubule‐associated homolog 1) as a predictive and pharmacodynamic biomarker candidate of metformin response in endometrial cancers.

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. Methods: We performed a multiplatform omics analysis incorporating RNA and DNA sequencing, quantitative proteomics, as well as immune cell profiling of high-grade serous ovarian carcinoma (HGSC) samples from a cohort of 32 patients with or without VTE. Results: Pathway analyses revealed upregulation of both inflammatory and coagulation pathways in the VTE group. While DNA whole-exome sequencing failed to identify significant coding alterations between the groups, the results of an integrated proteomic and RNA sequencing analysis indicated that there is a relationship between VTE and the expression of platelet-derived growth factor subunit B (PDGFB) and extracellular proteins in tumor cells, namely collagens, that are correlated with the formation of thrombosis. Conclusions: In this comprehensive analysis of HGSC tumor tissues from patients with and without VTE, we identified markers unique to the VTE group that could contribute to development of thrombosis. Our findings provide additional insights into the molecular alterations underlying the development of VTE in ovarian cancer patients and invite further investigation into potential predictive biomarkers of VTE in ovarian cancer.

Summary 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. Protein and transcript abundance in the tumor epithelium and stroma exhibited decreased correlation in samples collected just hundreds of microns apart. These data reveal substantial tumor microenvironment protein heterogeneity that directly bears on prognostic signatures, biomarker discovery, and cancer pathophysiology and underscore the need to enrich cellular subpopulations for expression profiling., Graphical abstract, Highlights • LMD was used to investigate 3-D molecular heterogeneity in ovarian cancer tissue • Diverse molecular profiles were identified from 3-D spatially separated samples • Molecular heterogeneity impacts HGSOC prognostic sub-type assignment • Proteomic heterogeneity analysis web portal deployed at www.lmdomics.org, Oncology; Cancer systems biology; Proteomics; Transcriptomics

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. The data presented from the high-resolution MS1-based quantitative data-independent acquisition (HRMS1-DIA) workflow shows that coordinated proteotype data acquisition is feasible from clinical specimens using such standardized strategies. This work paves the way for the distributed multi-omic digitization of large clinical specimen cohorts across multiple sites as a prerequisite for turning molecular precision medicine into reality., Distributed multi-omic digitization of clinical specimen across multiple sites is a prerequisite for turning molecular precision medicine into reality. Here, the authors show that coordinated proteotype data acquisition is feasible using standardized MS data acquisition and analysis strategies.

Deep proteogenomic analyses of diverse populations will improve our understanding of molecular drivers of disease, such as those underlying uterine neoplasms which exhibit marked racial disparities. The characterization of ancestry-linked peptide variants in disease-relevant patient tissues represents a foundational step towards investigating relationships linking patient ancestry with the proteome, genome and disease pathogenesis. We selected 1,037 nonsynonymous single nucleotide polymorphisms (nsSNPs) encoding missense amino acid substitutions occurring within putative tryptic peptides exhibiting high allele frequencies (≥ 50%) in European, African, and East Asian populations, termed peptide ancestry informative markers (pAIMs) and investigated these in cell line models and tissues of uterine neoplasms collected from women representing diverse ancestries. We quantified 62 pAIMs in total across >100 patient-derived samples by multiplexed, quantitative proteomic analyses. Our analyses of endometrial cancer cell lines established from European (n=7), African (n=3), and East Asian (n=3) women resulted in the quantitation of 43 pAIMs that significantly correlated with population-level allele frequencies consistent with global ancestry estimates defined by genotype analyses. We next investigated uterine leiomyoma (ULM) tissues from self-described African-American (n=31) and European-American (n=35) women and quantified 33 pAIMs that correlated with patient race. We validated 10 of these in an independent cohort of ULM tissues collected from women determined to be of European (n=16) and African (n=11) ancestry by genotype analyses. This validated subset was predominated by candidates of undetermined clinical significance (ClinVar) and included a substitution in GC vitamin D-binding protein, i.e. (D451E, rs7041), that exhibits a higher allele frequency in European (58%) versus African (7.3%) populations and correlates with altered GC protein and vitamin D levels. Comparison of cell line and tissue analyses revealed co-quantitation of pAIMs encoded within Protein-L-isoaspartate(D-aspartate) O-methyltransferase (V178I, >80% AF in African and East Asians), Serine/threonine-protein phosphatase CPPED1 (A19D, >75% AF in Africans) and AH receptor-interacting protein (Q228K, >99% AF in European and East Asians). We further assessed the performance of pAIMs to determine patient ancestry in silico relative to standard genotype estimates by random sampling analyses and find that as few as 20 population-specific pAIMs can determine ancestry proportions similarly to >260K SNPs (R2=0.9905). These efforts have quantified and validated pAIMs in diverse uterine neoplasms that represent a foundational set of ancestry-linked variant peptides observable by multiplexed proteomics and provides proof of concept that estimates of European, African and East Asian ancestry can be determined from routine proteomic analysis of disease-relevant patient cell lines and tissues. This work will support ongoing efforts focused on linking the proteome and genome with patient ancestry to better understand relationships with disease mechanisms driving racial disparities in the pathogenesis of uterine neoplasms.

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. NS ULMs (R = 0.242, MWU p = 0.0015). Co-altered proteins and transcripts (228) included antioxidant response element (ARE) target genes, such as thioredoxin reductase 1 (TXNRD1), and correlated with activation of NRF2-mediated oxidative stress response signaling in HLRCC ULMs. We confirm 185 transcripts previously described as altered between HLRCC and NS ULMs, 51 co-altered at the protein level and several elevated in HLRCC ULMs are involved in regulating cellular metabolism and glycolysis signaling. Furthermore, 367 S-(2-succino)cysteine peptides were identified in HLRCC ULMs, of which sixty were significantly elevated in HLRCC vs. NS ULMs (LogFC = 1.86, MWU p FH tumor suppressor gene.

Cell cycle regulatory molecules including cyclin-dependent kinases can be recruited into non-nuclear pathways to coordinate cell cycling with the energetic state of the cell or with functions such as motility. Little is known about the role of cell cycle regulators in anucleate cells such as platelets. We report that cyclin-dependent kinase (cdk2) is robustly expressed in human platelets, is activated by thrombin and is required for platelet activation. Cdk2 activation required Src signaling downstream of the platelet thrombin receptor PAR1. Kinase-active cdk2 promoted the activation of downstream platelet kinases by phosphorylating and inactivating the catalytic subunit of protein phosphatase 1 (PP1). Erk was bound to PP1 in a complex with the PP1 regulator PPP1R12a (MYPT1) in platelets, and cdk2 inhibited the phosphatase activity of PP1 and PPP1R12a bound complexes. The requirement for cdk2 in Erk activation could be replaced by the phosphatase inhibitor calyculin if cdk2 was inhibited. Blockade of cdk2 kinase with chemical and peptide cdk2 inhibitors resulted in suppression of thrombin-induced platelet aggregation, and partially inhibited GPIIb/IIIa integrin activation as well as platelet secretion of P-Selectin and ATP. Together, these data indicate a requirement for cdk2 in platelet activation.

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 demonstrated robust, sensitive, and reproducible data generation across eleven sites in nine countries on seven consecutive days in a 24/7 operation mode. The data presented from the high-resolution MS1-based quantitative data-independent acquisition (HRMS1-DIA) workflow shows that coordinated proteotype data acquisition is feasible from clinical specimens using such standardized strategies. This work paves the way for the distributed multi-omic digitization of large clinical specimen cohorts across multiple sites as a prerequisite for turning molecular precision medicine into reality.

Objectives: Endometrial cancer (EC) cell lines models established from diverse ancestral backgrounds and reflective of in situ disease characteristics are necessary to support preclinical investigations with broad generalizability. We performed multi-omic analyses of EC cell line models established from women representing diverse ancestries and compared these with clinical and molecular profiling data from the TCGA UCEC patient cohort to assess the relevance of models to in situ clinical and molecular disease characteristics. Methods: Whole genome sequencing (WGS), transcriptome (RNAseq) and proteome (LC-MS/MS and RPPA) analyses were performed for primary cell line models established from endometrioid EC patients (NCI-EC1, ACI-80, ACI-181, ACI-52, ACI-61 and ACI-68) as well as commercial EC models (MFE-296, SNG-M, HEC1A, RL95-2, Ishikawa, AN3-CA, KLE). Microsatellite instability (MSI) was determined from WGS data using MSISensor2 and standard ancestry estimates by comparison with reference populations from the 1000 Genomes Project. Hierarchical cluster analyses was performed using Pvclust and differential analyses using LIMMA. TCGA UCEC clinical and transcriptome data (n=371 patients) were downloaded from the TCGA UCEC manuscript supplement or cbioportal.org and odds ratio significance was assessed using Fisher's exact test (p Results: EC cell line models are MSI-high except ACI-61, ACI-68 and KLE cells which are microsatellite stable. Standard ancestry estimates showed most cell lines are from women of European ancestry except Ishikawa, HEC1A and SNG-M which are of East Asian ancestry and NCI-EC1, ACI-181 and ACI-80 which are of African ancestry. Unsupervised hierarchical analysis of proteome data revealed distinct clusters comprising MFE-296, AN3-CA, ACI-68, ACI-61, NCI-EC1 cells (cluster 1) and ACI-80, ACI-52, HEC1A, SNG-M, RL95-2, KLE, ACI-181 and Ishikawa cells (cluster 2). Differential analyses showed elevated levels of mesenchymal markers (eg, vimentin) and lower levels of epithelial markers (eg, E-cadherin) in cluster 1 versus 2 cells. Correlation of transcript-level data with TCGA UCEC patients showed all cell lines are significantly correlated with endometrioid versus mixed/ uterine serous carcinoma tumors except NCI-EC1, ACI-52, HEC1A and KLE cells. Most cell lines significantly correlated with MSI-H patient tumors and largely trended as being correlated with tumors harboring high mutational loads, except ACI-68 and KLE cells. Cluster 2 cell lines are largely correlated with tumors exhibiting low somatic copy-number alterations except KLE, Ishikawa and HEC1A cells and are significantly correlated with tumors classified as hormonal versus mitotic or immunoreactive molecular subtypes except KLE cells. Conclusions: We describe EC cell line models established from women of diverse patient ancestries including those from women of African descent (i.e. NCI-EC1, ACI-181 and ACI-80 cells) that represent novel preclinical resources to investigate ancestry-linked molecular alterations underlying racial disparities between European and African-American women diagnosed with EC. We further define EC cell line models correlating with endometrioid tumors that exhibit more epithelial-like (elevation of E-Cadherin) as well as hormonal characteristics that will benefit preclinical investigations of endometrioid EC disease biology.

Enriched tumor epithelium, tumor-associated stroma, and whole tissue were collected by laser microdissection from thin sections across spatially separated levels of ten primary high-grade serous ovarian tumors and analyzed using proteomics (mass spectrometry and reverse phase protein microarray) and RNA-sequencing analyses. Comparative analyses of transcript and protein abundances revealed independent clustering of enriched stroma and enriched tumor epithelium, with whole tumor tissue clustering between purified collections, driven by overall tumor purity. Comparison of historic prognostic molecular subtypes for HGSOC revealed protein and transcript expression from tumor epithelium correlated most strongly with the differentiated molecular subtype, whereas stromal proteins and transcripts most strongly correlated with mesenchymal subtype. Protein and transcript abundance in tumor epithelium and stromal collections from neighboring sections exhibited decreased correlation in samples collected just hundreds of microns apart. These data reveal substantial protein and transcript expression heterogeneity within the tumor microenvironment that directly bears on prognostic signatures and underscore the need to enrich cellular subpopulations for expression profiling.

Preoperative use of metformin in obese women with endometrioid endometrial cancer (EEC) reduces tumor proliferation and inhibits the mammalian target of rapamycin (mTOR) 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 phase 0 window trial of metformin in EEC patients (ClinicalTrials.gov:NCT01911247), were analyzed by mass spectrometry (MS)-based proteomic and immunohistochemical analyses. Hematological and neurological expressed 1 (HN1) was significantly elevated in metformin responders (n=13) versus non-responders (n=7), which was also found to decrease in abundance in metformin responders following treatment; observations that were verified by immunohistochemical staining for HN1. Metformin response and loss of HN1 was assessed in RL95-2 and ACI-181 endometrial cancer cell lines. We further identified that silencing of HN1 abundance does not alter cellular response to metformin or basal cell proliferation, but that HN1 abundance does decrease in response to metformin treatment in RL95-2 and ACI-181 endometrial cancer cell lines. These data suggest that HN1 represents a predictive and pharmacodynamic biomarker of metformin response that, if validated in larger patient populations, may enable pre-operative EEC patient stratification to metformin treatment and the ability to monitor patient response.Novelty and ImpactHematological and Neurological Expressed 1 (HN1) is elevated in endometrioid endometrial cancer (EEC) patients that respond to metformin vs non-responders and decreases after treatment. HN1 decreases after metformin treatment of EEC cells in vitro, but is not necessary for metformin response or proliferation. We report HN1 as a novel predictive and pharmacodynamic biomarker of metformin response in EEC that may enable pre-operative EEC patient stratification to metformin treatment and the ability to monitor patient response.

Prognostic and predictive biomarkers of disease and treatment outcome are needed to ensure optimal treatment of patients with triple-negative breast cancer (TNBC). In a mass spectrometry-based global proteomic study of 44 formalin-fixed, paraffin-embedded (FFPE) primary TNBC tumors and 10 corresponding metastases, we found that Cytochrome P450 reductase (CYPOR) expression correlated with patient outcome. The correlation between CYPOR expression and outcome was further evaluated in a Danish cohort of 113 TNBC patients using immunohistochemistry and publicly available gene expression data from two cohorts of TNBC and basal-like breast cancer patients, respectively (N = 249 and N = 580). A significant correlation between high CYPOR gene expression and shorter recurrence-free survival (RFS), but not overall survival, was found in the cohort of 249 TNBC patients (p = 0.018, HR = 1.77, 95% CI 1.1–2.85), and this correlation was recapitulated in a cohort of 580 basal-like breast cancer patients (p = 0.018, HR = 1.4, 95% CI 1.06–1.86). High CYPOR protein expression was also associated with shorter RFS in the cohort of 113 TNBC patients (p = 0.017, HR = 2.73, 95% CI 1.20–6.19), particularly those who were lymph node tumor-negative (p = 0.029, HR = 5.22). Multivariate Cox regression analysis identified CYPOR as an independent prognostic factor for shorter RFS in TNBC patients (p = 0.032, HR = 2.19, 95% CI 1.07–4.47). Together, these data suggest high expression of CYPOR as an independent prognostic biomarker of shorter RFS, which could be used to identify patients who should receive more extensive adjuvant treatment and more aggressive surveillance.