Complement pathway is frequently altered in endometriosis and endometriosis-associated ovarian cancer

Abstract

Purpose: Mechanisms of immune dysregulation associated with advanced tumors are relatively well understood. Much less is known about the role of immune effectors against cancer precursor lesions. Endometrioid and clear-cell ovarian tumors partly derive from endometriosis, a commonly diagnosed chronic inflammatory disease. We performed here a comprehensive immune gene expression analysis of pelvic inflammation in endometriosis and endometriosis-associated ovarian cancer (EAOC).

Experimental design: RNA was extracted from 120 paraffin tissue blocks comprising of normal endometrium (n = 32), benign endometriosis (n = 30), atypical endometriosis (n = 15), and EAOC (n = 43). Serous tumors (n = 15) were included as nonendometriosis-associated controls. The immune microenvironment was profiled using Nanostring and the nCounter GX Human Immunology Kit, comprising probes for a total of 511 immune genes.

Results: One third of the patients with endometriosis revealed a tumor-like inflammation profile, suggesting that cancer-like immune signatures may develop earlier, in patients classified as clinically benign. Gene expression analyses revealed the complement pathway as most prominently involved in both endometriosis and EAOC. Complement proteins are abundantly present in epithelial cells in both benign and malignant lesions. Mechanistic studies in ovarian surface epithelial cells from mice with conditional (Cre-loxP) mutations show intrinsic production of complement in epithelia and demonstrate an early link between Kras- and Pten-driven pathways and complement upregulation. Downregulation of complement in these cells interferes with cell proliferation.

Conclusions: These findings reveal new characteristics of inflammation in precursor lesions and point to previously unknown roles of complement in endometriosis and EAOC.

Fig. 1

Global gene expression across disease categories. A–B. Multidimensional scaling plot showing separation of disease categories with for differentially expressed (DE) genes filtered with 50-quantile cutoff of mean and standard deviation. Endometriosis-associated ovarian cancer (EAOC) cases are shown in red and controls in green. Endometriosis (E) and atypical endometriosis (AE) are shown in blue (panel A and B, respectively). C–D. Unsupervised clustering of 100 filtered genes. EAOC cases are listed in red and controls in green; E and AE are listed in blue (panel C and D, respectively).

Fig. 2

Differentially expressed genes across disease categories. A. Heatmaps of DE genes in five different two-group comparisons: control vs. E (n=39), endometriosis vs. EAOC (n=73), normal vs. EAOC (n=99), control vs. AE (n=95), AE vs. EAOC (n=75). B. Venn diagram showing number DE genes that are differentially expressed in controls, E and EAOC. Accompanying table lists the nine common DE genes (common intersection, arrow), fold change and P value. Complement genes are in bold. C. Heat map of 7 complement genes (C4A, CFH, CFB, C3, C7, CFD, MASP1) shows a differentiating pattern among the four different disease categories (controls, E, AE and EAOC). D. Individual gene expression profile of five common complement DE genes (C7, CFB, CFD, CFH, MASP1) across the four cohorts (controls, E, AE and EAOC). All genes are significantly different (false discovery rate FDR < 0.05 and absolute log FC greater than 1) for any of the two group comparisons (normal, endometriosis and EAOC).

Fig. 3

Tissue validation of complement proteins via IHC. A. Tissue deposition of complement C7 protein. B. Intensity staining for C7 was scored as follows: 0-no staining, 1-weak staining, 2-moderate staining and 3-strong staining. Y-axis: average score, plus standard deviation. Each of the three groups (controls, endometriosis and EAOC) contained five representative samples. One way ANOVA, p=0.001. C. Tissue expression of CFB, CFD, CFH and MASP1 proteins in endometriosis and EAOC FFPE tissues. At least five cases in each disease categories were stained for each marker. Representative images for each marker are shown.

Fig. 4

Complement biology in an EAOC preclinical mouse model. A. Tissue deposition of complement in mouse ovarian tumors from MUC1KrasPten mice. B. Changes in complement gene expression measured by qPCR in MKPOSE cells treated with either empty vector (MKPOSE-EV, control) or Cre-encoding adenovirus (MKPOSE-AdCre). C. MKPOSE intracellular staining for C7 by immunocytochemistry. D. Quantitation of C7 by flow cytometry. Positive cells (percentages) were gated outside of the negative control gate. E. Antibody-mediated complement mediated cell killing assay using MKPOSE-EV and MKPOSE-AdCre cells. Cell death was assessed via flow cytometry, using propidium iodide staining. Positive staining measures cells death.

Fig. 5

Complement inhibition and tumor cell growth. A. Changes in C7 gene expression in C7 siRNA (25 nM) treated cells as compared to untreated cells. B. C7 siRNA treated cells show decreased cell growth as compared to untreated cells (*, p<0.01). C. Survival curve of MKPOSE AdCre cells treated with Kras and Pten pathway inhibitor drugs AZD6244 (80 nM) or BEZ235 (25 nM) respectively, along with C7 siRNA. Assays were run in triplicate. Average values and standard errors are shown. Results from one of two experiments shown. Student t test. * p<0.05; **p<0.005.