Mapping three-dimensional intratumor proteomic heterogeneity in uterine serous carcinoma by multiregion microsampling.

BACKGROUND: Although uterine serous carcinoma (USC) represents a small proportion of all uterine cancer cases, patients with this aggressive subtype typically have high rates of chemotherapy resistance and disease recurrence that collectively result in a disproportionately high death rate. The goal of this study was to provide a deeper view of the tumor microenvironment of this poorly characterized uterine cancer variant through multi-region microsampling and quantitative proteomics. METHODS: Tumor epithelium, tumor-involved stroma, and whole "bulk" tissue were harvested by laser microdissection (LMD) from spatially resolved levels from nine USC patient tumor specimens and underwent proteomic analysis by mass spectrometry and reverse phase protein arrays, as well as transcriptomic analysis by RNA-sequencing for one patient's tumor. RESULTS: LMD enriched cell subpopulations demonstrated varying degrees of relatedness, indicating substantial intratumor heterogeneity emphasizing the necessity for enrichment of cellular subpopulations prior to molecular analysis. Known prognostic biomarkers were quantified with stable levels in both LMD enriched tumor and stroma, which were shown to be highly variable in bulk tissue. These USC data were further used in a comparative analysis with a data generated from another serous gynecologic malignancy, high grade serous ovarian carcinoma, and have been added to our publicly available data analysis tool, the Heterogeneity Analysis Portal ( https://lmdomics.org/ ). CONCLUSIONS: Here we identified extensive three-dimensional heterogeneity within the USC tumor microenvironment, with disease-relevant biomarkers present in both the tumor and the stroma. These data underscore the critical need for upfront enrichment of cellular subpopulations from tissue specimens for spatial proteogenomic analysis.

ProteoMixture: A cell type deconvolution tool for bulk tissue proteomic data.

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 ≥ 0.63) in two commonly used deconvolution algorithms, ESTIMATE or ConsensusTME. We further developed and optimized protein-based signatures estimating cell admixture proportions and benchmarked these using bulk tumor proteomic data from over 150 patients with HGSOC. The optimized protein signatures supporting cell type proportion estimates from bulk tissue proteomic data are available at https://lmdomics.org/ProteoMixture/.

Proteogenomic analysis of enriched HGSOC tumor epithelium identifies prognostic signatures and therapeutic vulnerabilities.

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. We further identified that polycomb complex protein BMI-1 is elevated in HR proficient (HRP) tumors, that elevated BMI-1 correlates with poor overall survival in HRP but not HRD HGSOC patients, and that HRP HGSOC cells are uniquely sensitive to BMI-1 inhibition.

Extensive three-dimensional intratumor proteomic heterogeneity revealed by multiregion sampling in high-grade serous ovarian tumor specimens.

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.

Prediction of lymph node metastasis in patients with endometrioid endometrial cancer using expression microarray.

PURPOSE: To characterize the gene expression profiles of endometrioid endometrial cancers associated with lymph node metastasis in an effort to identify genes associated with metastatic spread. EXPERIMENTAL DESIGN: Tumors from 41 patients with endometrioid endometrial cancer grossly confined to the uterine cavity were evaluated. Positive lymph nodes were noted in 12 of 41 patients. RNA was analyzed for gene expression using the Affymetrix HG133A and HG133B GeneChip set, representing 45,000 array features covering >28,000 UniGene clusters. Data analysis was done using multidimensional scaling, binary comparison, and hierarchical clustering. Gene expression for several differentially expressed genes was examined using quantitative PCR. RESULTS: Gene expression data was obtained from 30,964 genes that were detected in at least 5% of the cases. Supervised analysis of node-positive versus node-negative cases indicated that 450 genes were significantly differentially expressed between the two classes at P < 0.005, 81 of which were differentially expressed by at least 2-fold at P < 0.005. Overexpressed genes included two cell cycle checkpoint genes, CDC2 and MAD2L1, which have previously been described in association with lymph node metastasis in other cancer types. The ZIC2 zinc finger gene was overexpressed in endometrial cancers with positive nodes versus those with negative nodes. CONCLUSION: Gene expression profiling of the primary tumors in patients with endometrioid endometrial cancers seems promising for identifying genes associated with lymph node metastasis. Future studies should address whether the status of nodal metastasis can be determined from the expression profiles of preoperative tissue specimens.

Microarray analysis of endometrial carcinomas and mixed mullerian tumors reveals distinct gene expression profiles associated with different histologic types of uterine cancer.

Previous studies using cDNA microarray have indicated that distinct gene expression profiles characterize endometrioid and papillary serous carcinomas of the endometrium. Molecular studies have observed that mixed mullerian tumors, characterized by both carcinomatous and sarcomatous components, share features that are characteristic of endometrial carcinomas. The objective of this analysis was to more precisely define gene expression patterns that distinguish endometrioid and papillary serous histologies of endometrial carcinoma and mixed mullerian tumors of the uterus. One hundred nineteen pathologically confirmed uterine cancer samples were studied (66 endometrioid, 24 papillary serous, and 29 mixed mullerian tumors). Gene expressions were analyzed using the Affymetrix Human Genome Arrays U133A and U133B Genechip set. Unsupervised analysis revealed distinct global gene expression patterns of endometrioid, papillary serous, mixed mullerian tumors, and normal tissues as grossly separated clusters. Two-sample t tests comparing endometrioid and papillary serous, endometrioid and mixed mullerian tumor, and papillary serous and mixed mullerian tumor pairs identified 1,055, 5,212, and 1,208 differentially expressed genes at P < 0.001, respectively. These data revealed that distinct patterns of gene expression characterize various histologic types of uterine cancer. Gene expression profiles for select genes were confirmed using quantitative PCR. An understanding of the molecular heterogeneity of various histologic types of endometrial cancer has the potential to lead to better individualization of treatment in the future.