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

Abstract

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.

Keywords: Intratumor heterogeneity; Laser microdissection; Proteogenomics; Proteomics; Spatial proteomics; Tumor microenvironment; Uterine serous carcinoma.

Fig. 1

Study workflow. A Illustration of histological tissue specimen and LMD enriched sample preparation followed by quantitative proteomic [high-resolution liquid chromatography-tandem mass spectrometry (TMT LC–MS/MS) and reverse phase protein microarray (RPPA)] and transcriptomic (RNA-seq) analyses. Frozen tissue specimens from 9 USC patients were sectioned into ~ 200 consecutive thin tissue sections which were divided into 5 evenly distributed sampling levels (quintiles). Tissue sections within each level were laser microdissected for harvest of ET, ES, and BT to support each downstream analytical workflow. B Representative pre- and post-LMD micrographs from the top and bottom levels of tissue from case 343WC. The number in the bottom right corner of each micrograph indicates the section number. The scale bar in the bottom left corner of each micrograph indicates a length of 4 or 5 mm, as specified

Fig. 2

Unsupervised hierarchical cluster analysis of differentially abundant proteins and transcripts. A 351 variably abundant proteins (MAD > 1) and B 464 variably abundant transcripts (MAD > 0.5) co-quantified in both the RNA-seq and mass spectrometry datasets. A Protein abundances are represented across 118 samples derived from n = 9 patients consisting of ET (n = 44 total; 4–5 levels/patient), ES (n = 29 total; 2–5 levels/patient), and BT (n = 45 total; 5 levels/patient). The color gradient inset below the heatmap depicts median tumor purity estimates per level, determined by manual pathology review for each of the BT collections

Fig. 3

Protein abundance of cell type-specific markers and cellular admixture analyses (xCell [21]). A Boxplots depicting relative protein abundances of classical tumor (KRT19 and CDH1) and stroma (FAP and VCAN) markers. B Cell type signature scores as determined by xCell. Wilcox p-values with (*) indicate statistically significant differential expression (p < 0.0001) between ET and ES

Fig. 4

Correlation matrices of samples using proteins co-quantified by LC–MS/MS and RPPA. Spearman correlation analysis of samples using 160 proteins that were co-measured by TMT LC–MS/MS and RPPA for A ET, B ES, and C BT

Fig. 5

Patient-specific phylogenetic analyses. Dendrograms were constructed using Spearman correlations based on differentially expressed proteins between ET and ES. Spearman correlations were calculated within ET and ES for cases 343VR, 343VV, 343VW, 343WC, 343WE, and 343WF using the abundances of 106, 145, 1,061, 263, 175, and 278 proteins with MAD > 1, respectively. Statistically significant differences between LMD collection types are shown with (*) for p < 0.05 and (**) for p < 0.01. A Cases with higher Spearman correlations in ET. B Cases with higher Spearman correlations in ES

Fig. 6

Disease-specific alterations in USC versus HGSOC tissue specimens. Comparative analysis was performed using differentially expressed proteins in ET versus ES from USC and HGSOC specimens. Proteins passing limma adjusted p < 0.05 with the same pattern of enrichment (i.e., positive or negative LogFC values) across all patients within the respective USC (n = 455 proteins from Additional file 8: Table S3) and HGSOC (n = 796 proteins from Hunt et al. Additional file 8: Table S7 [13]) datasets were prioritized for comparative analyses between serous carcinoma types. The top 5 significantly altered canonical pathways identified by Ingenuity Pathway Analysis (IPA) are highlighted. Drug targets identified by IPA were further crossed with those that are FDA-approved [20]. Canonical pathways and drug targets with positive z-scores (highlighted blue) are elevated in ET, while those with negative z-scores (highlighted red) are elevated in ES

Fig. 7

Comparison of proteins altered between ET and ES in USC and stromal admixture scores with proteomic data generated from BT collections of USC tumors reported by Dou Y et al. 2020 [28]. A Proteins significantly co-altered (limma adj. p < 0.05) between ET and ES for n = 9 USC tumors (from Additional file 8: Tables S16 & S18; n = 455 proteins total) were correlated with global proteome data from bulk tissues collections for n = 9 USC tumors reported by Dou Y et al. Among 7908 total proteins quantified in all samples by Dou Y et al., 442 proteins were co-quantified in our cohort with tumor and stroma alterations. Correlation of the quantitative abundances for these 442 proteins were directly compared to metrics of tumor purity derived from analysis of methylation data (Purity_Cancer) as reported in Dou Y et al. B Stroma scores were calculated for global proteome data reported by Dou Y et al. using ProteoMixture [25] and directly compared to stroma purity metrics derived from methylation data (Purity_Stroma) reported by Dou Y et al.

Fig. 8

Representative case (343VW) depicting variable molecular expression by LMD sampling level. Representative micrographs of H&E-stained tissue sections mounted on glass slides from the top of each sampling level. The tissue section number is notated in the bottom right corner. The scale bar in the bottom left corner of each micrograph indicates a length of 5 mm. The median tumor cellularity (%), as determined by manual pathology review, per level with relative standard deviation (%CV) is shown (middle table). The proteomic abundances of representative tumor/epithelial (CDH1 and KRT19) and stroma (FAP and VCAN) markers, and xCell cell type enrichment scores for epithelial cells and fibroblasts [21] are shown in the heatmap