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.

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.

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.

Abstract 3377: Integrated molecular analysis of ovarian cancer cells identifies the ATR signaling axis central to cisplatin resistance

Ovarian cancer is the most lethal gynecological malignancy due primarily to the high percentage of women diagnosed at advanced stage. Although most patients initially respond to treatment, recurrence is common and second-line treatment is less effective, particularly in platinum-resistant disease. An integrated molecular analysis of an ovarian cancer cell line, A2780, and its isogenic cisplatin resistant line, A2780-CP20, was conducted utilizing next generation sequencing of the exome and the transcriptome (RNA-seq), along with a comprehensive analysis of the proteome and the phosphoproteome. Integration of these data revealed a number of differentially expressed candidates at the level of the proteome, the mechanistic nature of which could be explained by mutational status or differential splicing. We focused our attention on the ataxia telangiectasia mutated and Rad3-related (ATR) kinase. ATR activity is induced by DNA damage when single strand DNA results from persistently stalled replication forks. The gene and transcript for ATR were observed as mutated, alternatively spliced or altered at the transcript level in A2780-CP20 versus A2780 cells. The gene product was identified and validated to be ∼50-fold elevated in A2780-CP20 relative to A2780 cells. We hypothesized that inhibition of ATR would sensitize ovarian cancer cells to cisplatin-induced DNA lesions. To test this hypothesis undifferentiated (A2780, A2780-CP20) and papillary serous ovarian cancer cells (OV90 and OVCAR3) were challenged with cisplatin in the presence and absence of a selective ATR small molecule inhibitor (5 μM ATRi, ETP46464). The results demonstrate significantly increased cisplatin sensitivity in all cell lines tested by 54-99%. We further evaluated the ATR signaling axis, and repeated the cisplatin challenge experiments with a selective small molecule inhibitor against the serine/threonine kinase Chk1 (LY2603618), a downstream target that is phosphorylated by activated ATR. Similarly to what we observed with inhibition of ATR, these data reveal significant sensitization of ovarian cancer cells to cisplatin when co-cultured with the Chk1 inhibitor. To evaluate the selectivity of this sensitization response, the ovarian cancer cells were co-cultured with a selective inhibitor to the serine/threonine kinase Chk2 (2-(4-(4-Chlorophenoxy)phenyl)-1H-benzimidazole-5-carboxamide), which revealed no change in cisplatin sensitivity. We conclude that selective inhibition of ATR or Chk1, but not Chk2, significantly sensitizes ovarian carcinoma cell lines to cisplatin. Furthermore, we suggest that ATR and Chk1 represent key signaling nodes that are centrally involved in cisplatin resistance in ovarian cancer, and therefore represent attractive candidates for molecularly targeted therapy in the setting of resistant/recurrent ovarian cancer.

Citation Format: Pang-Ning Teng, Guisong Wang, Tracy J. Litzi, Brian L. Hood, Brian Blanton, Kelly A. Conrads, Kathleen M. Darcy, Chad A. Hamilton, William P. Mcguire, G. Larry Maxwell, Chris J. Bakkenist, Thomas P. Conrads. Integrated molecular analysis of ovarian cancer cells identifies the ATR signaling axis central to cisplatin resistance. [abstract]. In: Proceedings of the 104th Annual Meeting of the American Association for Cancer Research; 2013 Apr 6-10; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2013;73(8 Suppl):Abstract nr 3377. doi:10.1158/1538-7445.AM2013-3377

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.

Targeted disruption of the Kvlqt1 gene causes deafness and gastric hyperplasia in mice

The KvLQT1 gene encodes a voltage-gated potassium channel. Mutations in KvLQT1 underlie the dominantly transmitted Ward-Romano long QT syndrome, which causes cardiac arrhythmia, and the recessively transmitted Jervell and Lange-Nielsen syndrome, which causes both cardiac arrhythmia and congenital deafness. KvLQT1 is also disrupted by balanced germline chromosomal rearrangements in patients with Beckwith-Wiedemann syndrome (BWS), which causes prenatal overgrowth and cancer. Because of the diverse human disorders and organ systems affected by this gene, we developed an animal model by inactivating the murine Kvlqt1. No electrocardiographic abnormalities were observed. However, homozygous mice exhibited complete deafness, as well as circular movement and repetitive falling, suggesting imbalance. Histochemical study revealed severe anatomic disruption of the cochlear and vestibular end organs, suggesting that Kvlqt1 is essential for normal development of the inner ear. Surprisingly, homozygous mice also displayed threefold enlargement by weight of the stomach resulting from mucous neck cell hyperplasia. Finally, there were no features of BWS, suggesting that Kvlqt1 is not responsible for BWS.