Identification of novel therapeutic targets in microdissected clear cell ovarian cancers

Abstract

Clear cell ovarian cancer is an epithelial ovarian cancer histotype that is less responsive to chemotherapy and carries poorer prognosis than serous and endometrioid histotypes. Despite this, patients with these tumors are treated in a similar fashion as all other ovarian cancers. Previous genomic analysis has suggested that clear cell cancers represent a unique tumor subtype. Here we generated the first whole genomic expression profiling using epithelial component of clear cell ovarian cancers and normal ovarian surface specimens isolated by laser capture microdissection. All the arrays were analyzed using BRB ArrayTools and PathwayStudio software to identify the signaling pathways. Identified pathways validated using serous, clear cell cancer cell lines and RNAi technology. In vivo validations carried out using an orthotopic mouse model and liposomal encapsulated siRNA. Patient-derived clear cell and serous ovarian tumors were grafted under the renal capsule of NOD-SCID mice to evaluate the therapeutic potential of the identified pathway. We identified major activated pathways in clear cells involving in hypoxic cell growth, angiogenesis, and glucose metabolism not seen in other histotypes. Knockdown of key genes in these pathways sensitized clear cell ovarian cancer cell lines to hypoxia/glucose deprivation. In vivo experiments using patient derived tumors demonstrate that clear cell tumors are exquisitely sensitive to antiangiogenesis therapy (i.e. sunitinib) compared with serous tumors. We generated a histotype specific, gene signature associated with clear cell ovarian cancer which identifies important activated pathways critical for their clinicopathologic characteristics. These results provide a rational basis for a radically different treatment for ovarian clear cell patients.

Conflict of interest statement

Competing Interests: Pfizer Canada is a healthcare company. SLE and YZW received Investigator-initiated project funding from Pfizer Canada. This does not alter the authors' adherence to all the PLoS ONE policies on sharing data and materials.

Figures

Figure 1. Whole genome expression profiling of clear cell ovarian tumors.

(a) Graphic representation of whole genome expression profiling of the Clear Cell Ovarian Cancer Specimen (CCOC) and Ovarian Surface Epithelium (OSE). (b) and (c) Comparison of qRT-PCR data and microarray data of the six overexpressed and four underexpressed genes used for validation (d) Pathway analysis of differentially regulated genes identified in the clear cell ovarian cancer microarray. Genes included in the analysis were required to have a fold change ≥1.5. Multiple probe sets were averaged for each gene. Red, gene is up-regulated. Blue, gene is down-regulated.

Figure 2. Clear cell ovarian cancer cell lines were more resistant to hypoxia/glucose deprivation than serous ovarian cancer cell lines.

(a) Cellular proliferation assays of ovarian cancer cell lines of clear cell and serous origin under different oxygen and glucose conditions. The doubling times of the cell lines were compared, and the fold change in doubling time between the conditions of normal oxygen/normal glucose (NN) and hypoxia/glucose deprivation (HG) were calculated. (b) The bar graph demonstrates a statistically significant difference when the fold change in doubling times is averaged by histotype, showing that the three clear cell ovarian cancer cell lines were less affected by hypoxia/glucose deprivation than the six serous cell lines (p = 0.0037). (c)Trypan blue exclusion assay of OVCA-420 and ES2 cells grown in normal oxygen/normal glucose (NN) and hypoxia/ glucose deprivation (HG) for 72 hours. (d) Proliferation assay of OVCA-420 cells incubated in NN, HG, and HG with Z-VAD-FAK. The addition of Z-VAD-FAK did not alter the growth inhibition of HG. (e) Necrosis assay of OVCA-420 cells demonstrated a statistically significant increase in necrosis when incubated in HG (p = 0.003).

Figure 3. Knockdown of HIF1α and ENO1 in three clear cell ovarian cancer cell lines.

(a) Knockdown efficiency of siRNA molecules targeting HIF1α and ENO1 in three clear cell ovarian cancer cell lines, assessed by quantitative real-time PCR. (b) The percent of growth inhibition after transfection of siRNA molecules targeting HIF1α and ENO1. Growth was assessed after 24 hours of hypoxia/glucose deprivation *p = 0.12, **p<0.05,***p = <0.0001. (c, d) Knockdown efficiency of siRNA molecules targeting HIF1α and ENO1 in OVCA429 and OVCA420 and the percent of growth inhibition after transfection of siRNA molecules targeting HIF1α and ENO1. (e, f) Effect of HIF1α and ENO1 knockdown on proliferation of ES-2 and TOV-21G clear cell cell lines (***p = <0.0001).

Figure 4. Effect of Enolase or HIF1 α siRNA-DOPC ± sunitinib on ovarian clear cell tumor progression in the female athymic nude mouse model.

(a) Tumor weight: ES2 cells (1×106 cells/mouse) were injected intraperitoneal into mice. The mice were treated with control, Enolase or HIF1 α siRNA-DOPC ± sunitinib (n = 10/group). Immunohistochemical staining for CD31 antigen was performed on frozen slides of tumor to evaluate the number of tumor nodules (b) and tumor microvessel density (MVD) (c). (See Materials and Methods section for details).

Figure 5. Effect of sunitinib on the growth of patient-derived CCOC tissue xenografts.

(a.) Effect of a two-week treatment with sunitinib on growth of subrenal capsule xenografts in NOD-SCID mice (6 mice/group; 2 grafts per kidney) of transplantable serous (LTL237, 247 and 259) and clear cell (LTL175) ovarian carcinoma tissue lines derived from patients' cancers. Growth of the xenografts is expressed as % tumor volume determined at necropsy by measurement with calipers. Data are presented as means ± S.E.M. (b, c) Effect of sunitinib on VEGFR2 and PDGFRβ tyrosine phosphorylation as shown by Western blot analysis. Subrenal capsule xenografts of ovarian LTL247 serous carcinoma tissue and LTL175 clear cell carcinoma tissue in mice, treated for 2 weeks with sunitinib or vehicle (control), were lysed and processed for Western blot analysis of VEGFR2 (b) and PDGFRβ (c) tyrosine residues. The results are representative of 3 experiments. (d) Representative H&E-stained tumor sections of control (d.a), and sunitinib-treated (d.b) LTL175 tissue. (e) Effect of sunitinib on microvessel density of subrenal capsule xenografts in NOD-SCID mice of serous (LTL237, 247, 259) and clear cell (LTL175) ovarian carcinoma tissue lines. Data are presented as the average number of blood vessels per ×400 microscopic field ± S.E.M. (f) Effect of sunitinib on apoptosis in LTL175 xenografts in NOD-SCID mice treated with sunitinib for 2 weeks. Representative tissue sections of control (f. a) and sunitinib-treated (f. c) TUNEL-stained tumor tissue and counterstained with DAPI (f. b, control; f. d, sunitinib-treated); (g), percentage of apoptotic cells determined via microscopic analysis using a 400× microscopic field; data presented as means ± S.E.M.