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Purpose: We recently reported that the transcription factor NFATC4, in response to chemotherapy, drives cellular quiescence to increase ovarian cancer chemoresistance. The goal of this work was to better understand the mechanisms of NFATC4-driven ovarian cancer chemoresistance. Experimental Design: We used RNA sequencing to identify NFATC4-mediated differential gene expression. CRISPR-Cas9 and FST (follistatin)-neutralizing antibodies were used to assess impact of loss of FST function on cell proliferation and chemoresistance. ELISA was used to quantify FST induction in patient samples and in vitro in response to chemotherapy. Results: We found that NFATC4 upregulates FST mRNA and protein expression predominantly in quiescent cells and FST is further upregulated following chemotherapy treatment. FST acts in at least a paracrine manner to induce a p-ATF2–dependent quiescent phenotype and chemoresistance in non-quiescent cells. Consistent with this, CRISPR knockout (KO) of FST in ovarian cancer cells or antibody-mediated neutralization of FST sensitizes ovarian cancer cells to chemotherapy treatment. Similarly, CRISPR KO of FST in tumors increased chemotherapy-mediated tumor eradication in an otherwise chemotherapy-resistant tumor model. Suggesting a role for FST in chemoresistance in patients, FST protein in the abdominal fluid of patients with ovarian cancer significantly increases within 24 hours of chemotherapy exposure. FST levels decline to baseline levels in patients no longer receiving chemotherapy with no evidence of disease. Furthermore, elevated FST expression in patient tumors is correlated with poor progression-free, post–progression-free, and overall survival. Conclusions: FST is a novel therapeutic target to improve ovarian cancer response to chemotherapy and potentially reduce recurrence rates.

Supplementary Figure 2. RNA-seq analysis. A. Schematic of the RNA-seq experimental design. B. Waterfall plot of RNA-seq data showing the 141 genes which were DE at all time points compared to luciferase control in HEY1 and SKOV3 cell lines. C. Unsupervised clustering analysis of the 141 DE genes. D. Box plots demonstrating MYC expression following NFATC4 activation. E. STRING analysis of the 141 DE genes.

Supplementary Figure 1. Histological analysis of PT340 cell line. PT340 cells were injected into NSG mice, and the resulting tumors were harvested for histological analysis, H&E staining showed features of Clear Cell Ovarian Carcinoma.

Purpose:We recently reported that the transcription factor NFATC4, in response to chemotherapy, drives cellular quiescence to increase ovarian cancer chemoresistance. The goal of this work was to better understand the mechanisms of NFATC4-driven ovarian cancer chemoresistance.Experimental Design:We used RNA sequencing to identify NFATC4-mediated differential gene expression. CRISPR-Cas9 and FST (follistatin)-neutralizing antibodies were used to assess impact of loss of FST function on cell proliferation and chemoresistance. ELISA was used to quantify FST induction in patient samples and in vitro in response to chemotherapy.Results:We found that NFATC4 upregulates FST mRNA and protein expression predominantly in quiescent cells and FST is further upregulated following chemotherapy treatment. FST acts in at least a paracrine manner to induce a p-ATF2–dependent quiescent phenotype and chemoresistance in non-quiescent cells. Consistent with this, CRISPR knockout (KO) of FST in ovarian cancer cells or antibody-mediated neutralization of FST sensitizes ovarian cancer cells to chemotherapy treatment. Similarly, CRISPR KO of FST in tumors increased chemotherapy-mediated tumor eradication in an otherwise chemotherapy-resistant tumor model. Suggesting a role for FST in chemoresistance in patients, FST protein in the abdominal fluid of patients with ovarian cancer significantly increases within 24 hours of chemotherapy exposure. FST levels decline to baseline levels in patients no longer receiving chemotherapy with no evidence of disease. Furthermore, elevated FST expression in patient tumors is correlated with poor progression-free, post–progression-free, and overall survival.Conclusions:FST is a novel therapeutic target to improve ovarian cancer response to chemotherapy and potentially reduce recurrence rates.

Supplementary Figure 4. The effect of ATF2 siRNA on gene expression and PT340 FST induced chemoresistance. A. Schematic detailing the transwell experiments. CellTrace Violet cells were sorted for rapidly dividing (Dim) or quiescent cells (Bright) which were then plated in transwell chambers either as Dim:Dim or Bright:Dim. Cells were untreated or treated with taxol alone or in combination with IgG or anti-FST antibody. Cell counts were performed on the dim cells and data was expressed as fold chnage. B. Relative ATF2 gene expression of PT412 and PT340 treated with scrambled siRNA or two independent ATF2 siRNAs (ATF2 siRNA #1 and #2). C. Viable cell number of PT340 cells treated with control scrambled siRNA or knocked down with ATF2 siRNA and treated with cisplatin +/- FST. *P

Maintenance of CTL-, Th1-, and NK cell–mediated type-1 immunity is essential for effective antitumor responses. Unexpectedly, we observed that the critical soluble mediators of type-1 immune effector cells, IFNγ and TNFα, synergize in the induction of cyclooxygenase 2 (COX2), the key enzyme in prostaglandin (PG)E2 synthesis, and the subsequent hyperactivation of myeloid-derived suppressor cells (MDSC) within the tumor microenvironment (TME) of ovarian cancer patients. MDSC hyperactivation by type-1 immunity and the resultant overexpression of indoleamine 2,3-dioxygenase (IDO), inducible nitric oxide synthase (iNOS/NOS2), IL10, and additional COX2 result in strong feedback suppression of type-1 immune responses. This paradoxical immune suppression driven by type-1 immune cell activation was found to depend on the synergistic action of IFNγ and TNFα, and could not be reproduced by either of these factors alone. Importantly, from a therapeutic standpoint, these negative feedback limiting type-1 responses could be eliminated by COX2 blockade, allowing amplification of type-1 immunity in the ovarian cancer TME. Our data demonstrate a new mechanism underlying the self-limiting nature of type-1 immunity in the human TME, driven by the synergistic induction of COX2 by IFNγ and TNFα, and provide a rationale for targeting the COX2–PGE2 axis to enhance the effectiveness of cancer immunotherapies. Cancer Immunol Res; 4(4); 303–11. ©2016 AACR.

Supplemental Table 1 provides the information about the clinical status of patients(donors of ovarian cancer ascites). Supplemental Figures S1-S6 provide the characterization of the immune cells and experimental systems involved in the study and additional data from complementary assays and individual donors. Figure legends are provided under each Figure.

Interplay between IFNα and COX-2/PGE2 system in the regulation of and suppressive inflammatory mediators.

Presence of cytotoxic CD8+ T cells (CTL) in tumor microenvironments (TME) is critical for the effectiveness of immune therapies and patients' outcome, whereas regulatory T(reg) cells promote cancer progression. Immune adjuvants, including double-stranded (ds)RNAs, which signal via Toll-like receptor-3 (TLR3) and helicase (RIG-I/MDA5) pathways, all induce intratumoral production of CTL-attractants, but also Treg attractants and suppressive factors, raising the question of whether induction of these opposing groups of immune mediators can be separated. Here, we use human tumor explant cultures and cell culture models to show that the (ds) RNA Sendai Virus (SeV), poly-I:C, and rintatolimod (poly-I:C12U) all activate the TLR3 pathway involving TRAF3 and IRF3, and induce IFNα, ISG-60, and CXCL10 to promote CTL chemotaxis to ex vivo–treated tumors. However, in contrast with SeV and poly I:C, rintatolimod did not activate the MAVS/helicase pathway, thus avoiding NFκB– and TNFα-dependent induction of COX2, COX2/PGE2-dependent induction of IDO, IL10, CCL22, and CXCL12, and eliminating Treg attraction. Induction of CTL-attractants by either poly I:C or rintatolimod was further enhanced by exogenous IFNα (enhancer of TLR3 expression), whereas COX2 inhibition enhanced the response to poly-I:C only. Our data identify the helicase/NFκB/TNFα/COX2 axis as the key suppressive pathway of dsRNA signaling in human TME and suggest that selective targeting of TLR3 or elimination of NFκB/TNFα/COX2-driven suppression may allow for selective enhancement of type-1 immunity.Significance: This study characterizes two different poly-I:C-induced signaling pathways in their induction of immunostimulatory and suppressive factors and suggests improved ways to reprogram the TME to enhance the antitumor efficacy of immunotherapies. Cancer Res; 78(15); 4292–302. ©2018 AACR.

Background: Genome-wide association studies (GWAS) have so far reported 12 loci associated with serous epithelial ovarian cancer (EOC) risk. We hypothesized that some of these loci function through nearby transcription factor (TF) genes and that putative target genes of these TFs as identified by coexpression may also be enriched for additional EOC risk associations.Methods: We selected TF genes within 1 Mb of the top signal at the 12 genome-wide significant risk loci. Mutual information, a form of correlation, was used to build networks of genes strongly coexpressed with each selected TF gene in the unified microarray dataset of 489 serous EOC tumors from The Cancer Genome Atlas. Genes represented in this dataset were subsequently ranked using a gene-level test based on results for germline SNPs from a serous EOC GWAS meta-analysis (2,196 cases/4,396 controls).Results: Gene set enrichment analysis identified six networks centered on TF genes (HOXB2, HOXB5, HOXB6, HOXB7 at 17q21.32 and HOXD1, HOXD3 at 2q31) that were significantly enriched for genes from the risk-associated end of the ranked list (P < 0.05 and FDR < 0.05). These results were replicated (P < 0.05) using an independent association study (7,035 cases/21,693 controls). Genes underlying enrichment in the six networks were pooled into a combined network.Conclusion: We identified a HOX-centric network associated with serous EOC risk containing several genes with known or emerging roles in serous EOC development.Impact: Network analysis integrating large, context-specific datasets has the potential to offer mechanistic insights into cancer susceptibility and prioritize genes for experimental characterization. Cancer Epidemiol Biomarkers Prev; 24(10); 1574–84. ©2015 AACR.

Purpose: High-grade serous ovarian cancer (HGSOC) is an aggressive disease with few available targeted therapies. Despite high expression of estrogen receptor-alpha (ERα) in approximately 80% of HGSOC and some small but promising clinical trials of endocrine therapy, ERα has been understudied as a target in this disease. We sought to identify hormone-responsive, ERα-dependent HGSOC.Experimental Design: We characterized endocrine response in HGSOC cells across culture conditions [ two-dimensional (2D), three-dimensional (3D), forced suspension] and in patient-derived xenograft (PDX) explants, assessing proliferation and gene expression. Estrogen-regulated transcriptome data were overlapped with public datasets to develop a comprehensive panel of ERα target genes. Expression of this panel and ERα H-score were assessed in HGSOC samples from patients who received endocrine therapy. Time on endocrine therapy was used as a surrogate for clinical response.Results: Proliferation is ERα-regulated in HGSOC cells in vitro and in vivo, and is partly dependent on 3D context. Transcriptomic studies identified genes shared by cell lines and PDX explants as ERα targets. The selective ERα downregulator (SERD) fulvestrant is more effective than tamoxifen in blocking ERα action. ERα H-score is predictive of efficacy of endocrine therapy, and this prediction is further improved by inclusion of target gene expression, particularly IGFBP3.Conclusions: Laboratory models corroborate intertumor heterogeneity of endocrine response in HGSOC but identify features associated with functional ERα and endocrine responsiveness. Assessing ERα function (e.g., IGFBP3 expression) in conjunction with H-score may help select patients who would benefit from endocrine therapy. Preclinical data suggest that SERDs might be more effective than tamoxifen. Clin Cancer Res; 23(14); 3802–12. ©2017 AACR.

Supplemental Figure S 3

Dose-dependent response to rintatolimod (RINT) and poly-I:C (pIC) in macrophage cultures.

Supplemental Figure S 6B

Suppl Table S1. Differentially expressed genes from different group comparisons

Supplementary Materials and Methods

IFNα enhances the response to TLR3 triggering by either poly-I:C or rintatolimod, but prostaglandin inhibition is selectively needed to optimize the pattern of inflammation induced by poly-I:C.

Background: While numerous susceptibility loci for epithelial ovarian cancer (EOC) have been identified, few associations have been reported with overall survival. In the absence of common prognostic genetic markers, we hypothesize that rare coding variants may be associated with overall EOC survival and assessed their contribution in two exome-based genotyping projects of the Ovarian Cancer Association Consortium (OCAC).Methods: The primary patient set (Set 1) included 14 independent EOC studies (4,293 patients) and 227,892 variants, and a secondary patient set (Set 2) included six additional EOC studies (1,744 patients) and 114,620 variants. Because power to detect rare variants individually is reduced, gene-level tests were conducted. Sets were analyzed separately at individual variants and by gene, and then combined with meta-analyses (73,203 variants and 13,163 genes overlapped).Results: No individual variant reached genome-wide statistical significance. A SNP previously implicated to be associated with EOC risk and, to a lesser extent, survival, rs8170, showed the strongest evidence of association with survival and similar effect size estimates across sets (Pmeta = 1.1E−6, HRSet1 = 1.17, HRSet2 = 1.14). Rare variants in ATG2B, an autophagy gene important for apoptosis, were significantly associated with survival after multiple testing correction (Pmeta = 1.1E−6; Pcorrected = 0.01).Conclusions: Common variant rs8170 and rare variants in ATG2B may be associated with EOC overall survival, although further study is needed.Impact: This study represents the first exome-wide association study of EOC survival to include rare variant analyses, and suggests that complementary single variant and gene-level analyses in large studies are needed to identify rare variants that warrant follow-up study. Cancer Epidemiol Biomarkers Prev; 25(3); 446–54. ©2016 AACR.

Background: Despite a large body of literature evaluating the association between recreational physical activity and epithelial ovarian cancer (EOC) risk, the extant evidence is inconclusive, and little is known about the independent association between recreational physical inactivity and EOC risk. We conducted a pooled analysis of nine studies from the Ovarian Cancer Association Consortium to investigate the association between chronic recreational physical inactivity and EOC risk.Methods: In accordance with the 2008 Physical Activity Guidelines for Americans, women reporting no regular, weekly recreational physical activity were classified as inactive. Multivariable logistic regression was utilized to estimate the ORs and 95% confidence intervals (CI) for the association between inactivity and EOC risk overall and by subgroups based upon histotype, menopausal status, race, and body mass index.Results: The current analysis included data from 8,309 EOC patients and 12,612 controls. We observed a significant positive association between inactivity and EOC risk (OR = 1.34; 95% CI, 1.14–1.57), and similar associations were observed for each histotype.Conclusions: In this large pooled analysis examining the association between recreational physical inactivity and EOC risk, we observed consistent evidence of an association between chronic inactivity and all EOC histotypes.Impact: These data add to the growing body of evidence suggesting that inactivity is an independent risk factor for cancer. If the apparent association between inactivity and EOC risk is substantiated, additional work via targeted interventions should be pursued to characterize the dose of activity required to mitigate the risk of this highly fatal disease. Cancer Epidemiol Biomarkers Prev; 25(7); 1114–24. ©2016 AACR.

Potential hormonal mechanism to motivate investigation of multi-factor obesity-hormone related risk factors - SNP interactions.

Background: Many epithelial ovarian cancer (EOC) risk factors relate to hormone exposure and elevated estrogen levels are associated with obesity in postmenopausal women. Therefore, we hypothesized that gene–environment interactions related to hormone-related risk factors could differ between obese and non-obese women.Methods: We considered interactions between 11,441 SNPs within 80 candidate genes related to hormone biosynthesis and metabolism and insulin-like growth factors with six hormone-related factors (oral contraceptive use, parity, endometriosis, tubal ligation, hormone replacement therapy, and estrogen use) and assessed whether these interactions differed between obese and non-obese women. Interactions were assessed using logistic regression models and data from 14 case–control studies (6,247 cases; 10,379 controls). Histotype-specific analyses were also completed.Results: SNPs in the following candidate genes showed notable interaction: IGF1R (rs41497346, estrogen plus progesterone hormone therapy, histology = all, P = 4.9 × 10−6) and ESR1 (rs12661437, endometriosis, histology = all, P = 1.5 × 10−5). The most notable obesity–gene–hormone risk factor interaction was within INSR (rs113759408, parity, histology = endometrioid, P = 8.8 × 10−6).Conclusions: We have demonstrated the feasibility of assessing multifactor interactions in large genetic epidemiology studies. Follow-up studies are necessary to assess the robustness of our findings for ESR1, CYP11A1, IGF1R, CYP11B1, INSR, and IGFBP2. Future work is needed to develop powerful statistical methods able to detect these complex interactions.Impact: Assessment of multifactor interaction is feasible, and, here, suggests that the relationship between genetic variants within candidate genes and hormone-related risk factors may vary EOC susceptibility. Cancer Epidemiol Biomarkers Prev; 25(5); 780–90. ©2016 AACR.

Supplementary Figure 3. The effect of FST or taxol treatment ovarian cancer proliferation and FST secretion, and the ability of a FST neutralization antibody to inhibit FST's effect on proliferation. A. PT340 and PT412 cells were treated with 200 ng/mL FST for 72h before cell counts were performed. B (i) IHC images and (ii) FST+ cell bodies in OVSAHO tumor xenografts treated with or without taxol and stained with both Ki67 (Forangi Blue) and FST (Fast Red). C. Titer of the ability of the anti-FST neutralizing antibody to inhibit the effect of FST on cell growth.

PDF file - 488K, Supplementary Figure 1: Experimental design. Supplementary Figure 2. Reliability of RNA extraction and reproducibility of RTqPCR techniques. Supplementary Figure 3. Reliability of RT-qPCR assays. Supplementary Figure 4. miR-132 demonstrates consistent CT values (ranging from 27 to 29.5.) across all categories of plasma samples (healthy 0=Normal Healthy Endo1=mEnedtoriosis 2 = SSOerCous 3E=EAAOOCC controls, n=20; endometriosis, n=33; EAOC, n=14; and SOC, n=21), and was used for normalization of RT-qPCR results in this study. Supplementary Figure 5. Unsupervised hierarchical clustering analysis of samples based on the 23-miRNA expression profiles. Supplementary Figure 6. Box plots of top three differentially expressed miRNAs in pair-wise comparisons. Supplementary Figure 7. Lack of correlation for miRNA expression between matching tumor tissue and plasma samples from five tumor-bearing LSLKrasG12D/+/ Ptenloxp/loxp mice.

Supplemental Figure S 5

Supplemental Table S1: Number of invasive EOC patients by study site. Supplemental Table S2: Results of Set 1 single variant analysis, for variants with P

PDF file - 36K, Supplementary Table 1. Demographic and clinical characteristics of the study cohort. Supplementary Table 2. List of the 24 miRNAs identified by genome-wide plasma miRNA expression profiling from healthy controls (n=6), endometriosis patients (n=7), and EAOC patients (n=7). Supplementary Table 3. The 10 most differentially expressed miRNAs in pair-wise comparisons between SOC patients and healthy controls, endometriosis, or EAOC samples.

Purpose:Increased prevalence of cytotoxic T lymphocytes (CTL) in the tumor microenvironment (TME) predicts positive outcomes in patients with epithelial ovarian cancer (EOC), whereas the regulatory T cells (Treg) predict poor outcomes. Guided by the synergistic activity of TLR3 ligands, IFNα, and COX-2 blockers in selectively enhancing CTL-attractants but suppressing Treg-attractants, we tested a novel intraperitoneal chemoimmunotherapy combination (CITC), to assess its tolerability and TME-modulatory impact in patients with recurrent EOC.Patients and Methods:Twelve patients were enrolled in phase I portion of the trial NCT02432378, and treated with intraperitoneal cisplatin, intraperitoneal rintatolimod (dsRNA, TLR3 ligand), and oral celecoxib (COX-2 blocker). Patients in cohorts 2, 3, and 4 also received intraperitoneal IFNα at 2, 6, and 18 million units (MU), respectively. Primary objectives were to evaluate safety, identify phase 2 recommended dose (P2RD), and characterize changes in the immune TME. Peritoneal resident cells and intraperitoneal wash fluid were profiled via NanoString and Meso Scale Discovery (MSD) multiplex assay, respectively.Results:The P2RD of IFNα was 6 MU. Median progression-free survival and overall survival were 8.4 and 30 months, respectively. Longitudinal sampling of the peritoneal cavity via intraperitoneal washes demonstrated local upregulation of IFN-stimulated genes (ISG), including CTL-attracting chemokines (CXCL-9, -10, -11), MHC I/II, perforin, and granzymes. These changes were present 2 days after chemokine modulation and subsided within 1 week.Conclusions:The chemokine-modulating intraperitoneal-CITC is safe, tolerable, and associated with ISG changes that favor CTL chemoattraction and function. This combination (plus DC vaccine) will be tested in a phase II trial.See related commentary by Aranda et al., p. 1993

Purpose: Endometriosis, a largely benign, chronic inflammatory disease, is an independent risk factor for endometrioid and clear cell epithelial ovarian tumors. We aimed to identify plasma miRNAs that can be used to differentiate patients with endometriosis and ovarian cancer from healthy individuals.Experimental Design: We conducted a two-stage exploratory study to investigate the use of plasma miRNA profiling to differentiate between patients with endometriosis, patients with endometriosis-associated ovarian cancer (EAOC), and healthy individuals. In the first stage, using global profiling of more than 1,000 miRNAs via reverse transcriptase quantitative PCR (RT-qPCR) in a 20-patient initial screening cohort, we identified 23 candidate miRNAs, which are differentially expressed between healthy controls (n = 6), patients with endometriosis (n = 7), and patients with EAOC (n = 7) based on the fold changes. In the second stage, the 23 miRNAs were further tested in an expanded cohort (n = 88) of healthy individuals (n = 20), endometriosis (n = 33), EAOC (n = 14), and serous ovarian cancer cases (SOC; n = 21, included as controls).Results: We identified three distinct miRNA signatures with reliable differential expression between healthy individuals, patients with endometriosis, and patients with EAOC. When profiled against the control SOC category, our results revealed different miRNAs, suggesting that the identified signatures are reflective of disease-specific pathogenic mechanisms. This was further supported by the fact that the majority of miRNAs differentially expressed in human EAOCs were mirrored in a double transgenic mouse EAOC model.Conclusion: Our study reports for the first time that distinct plasma miRNA expression patterns may serve as highly specific and sensitive diagnostic biomarkers to discriminate between healthy, endometriosis, and EAOC cases. Clin Cancer Res; 19(5); 1213–24. ©2013 AACR.

Suppl Fig S1. Venn diagram of DE genes from 3 group comparisons

Supplementary File 3 contains the available pre- and post-treatment CA-125 data for our patient cohort.

Suppl Fig S4. Complement and non-target siRNA

Supplemental Figure Legends

Supplementary File 1 contains the genes included in the "EndoRx" NanoString code set.

The model: Molecular separation of pro-inflammatory and suppressive pathways of dsRNA signaling in cancer TME.

Supplemental Table S1. Pooled Odds Ratios and 95% Confidence Intervals Representing the Association Between Physical Inactivity and Epithelial Ovarian Cancer by Menopause Status. Supplemental Table S2. Age-Adjusted And Multivariable-Adjusted Pooled Odds Ratios And 95% Confidence Intervals (CI) Representing The Association Between Physical Inactivity And Epithelial Ovarian Cancer By Race. Supplemental Table S3. Pooled odds ratios and 95% confidence intervals representing the association between physical inactivity and epithelial ovarian cancer by standard BMI classification. Supplemental Table S4. Pooled odds ratios and 95% confidence intervals representing the association between physical inactivity and epithelial ovarian cancer by dichotomous BMI classification

Supplementary Table from Phase I Trial Combining Chemokine-Targeting with Loco-Regional Chemoimmunotherapy for Recurrent, Platinum-Sensitive Ovarian Cancer Shows Induction of CXCR3 Ligands and Markers of Type 1 Immunity

Supplemental Figure S 4

Supplementary Tables S1-6, Figures S1-2. Supplementary Table S1: Summary of serous EOC GWAS data sets; Supplementary Table S2: Genes in the six significant co-expression networks; Supplementary Table S3: GSEA results after LD-based clumping of SNPs; Supplementary Table S4: GSEA results for the replication data set (COGS); Supplementary Table S5: GSEA results for all networks with > 10 genes; Supplementary Table S6: Number of intragenic SNPs and genes covered by SNPs in the different analyses; Supplementary Figure S1: Q-Q plots of the minimum P-value among all SNPs in each gene with and without the modified Sidak correction; Supplementary Figure S2: Disease Association Protein-Protein Link Evaluator (DAPPLE) protein-protein interaction network

Uniform activation of immunostimulatory and suppressive inflammatory mediators, including COX-2, by different TLR ligands.

Suppl Table S3. Top five networks from different group comparisons, identified via Ingenuity Pathway Analyses

Supplemental Figure S1

Supplementary Table 1. Primers used in this study.

Ex vivo tumor tissue explant culture system

Suppl Table S2. Top five canonical pathways from different group comparisons, identified via Ingenuity Pathway Analyses

Supplementary Figure from Phase I Trial Combining Chemokine-Targeting with Loco-Regional Chemoimmunotherapy for Recurrent, Platinum-Sensitive Ovarian Cancer Shows Induction of CXCR3 Ligands and Markers of Type 1 Immunity

This document contains the supplementary figures for the manuscript.

Suppl Fig S2. Kras and Pten pathways in relationship to complement gene expression

Suppl Fig S3. Antibody-mediated, complement-induced cytotoxicity

High-risk susceptibility genes explain CCND1, CCND2, CCND3, CCNE1, CDK2, CDK4, CDK6, CDKN1A, CDKN1B, CDKN2A, CDKN2B, CDKN2C, and CDKN2D—and risk of invasive epithelial ovarian cancer. We used a two-stage, multicenter, case-control study. In stage 1, 88 SNPs that tag common variation in these genes were genotyped in three studies from the United Kingdom, United States, and Denmark (∼1,500 cases and 2,500 controls). Genotype frequencies in cases and controls were compared using logistic regression. In stage 2, eight other studies from Australia, Poland, and the United States (∼2,000 cases and ∼3,200 controls) were genotyped for the five most significant SNPs from stage 1. No SNP was significant in the stage 2 data alone. Using the combined stages 1 and 2 data set, CDKN2A rs3731257 and CDKN1B rs2066827 were associated with disease risk (unadjusted P trend = 0.008 and 0.036, respectively), but these were not significant after adjusting for multiple testing. Carrying the minor allele of these SNPs was found to be associated with reduced risk [OR, 0.91 (0.85–0.98) for rs3731257; and OR, 0.93 (0.87–0.995) for rs2066827]. In conclusion, we have found evidence that a single tagged SNP in both the CDKN2A and CDKN1B genes may be associated with reduced ovarian cancer risk. This study highlights the need for multicenter collaborations for genetic association studies. [Cancer Res 2007;67(7):3027–35]

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