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Ovarian cancer mortality ranks highest among all gynecologic cancers with growth factor pathways playing an integral role in tumorigenesis, metastatic dissemination, and therapeutic resistance. The HER and VEGF receptor (VEGFR) are both overexpressed and/or aberrantly activated in subsets of ovarian tumors. While agents targeting either the HER or VEGF pathways alone have been investigated, the impact of these agents have not led to overall survival benefit in ovarian cancer. We tested the hypothesis that cotargeting HER and VEGFR would maximize antitumor efficacy at tolerable doses. To this end, ovarian cancer xenografts grown intraperitoneally in athymic nude mice were tested in response to AC480 (pan-HER inhibitor, “HERi”), cediranib (pan-VEGFR inhibitor “VEGFRi”), or BMS-690514 (combined HER/VEGFR inhibitor “EVRi”). EVRi was superior to both HERi and VEGFRi in terms of tumor growth, final tumor weight, and progression-free survival. Correlative tumor studies employing phosphoproteomic antibody arrays revealed distinct agent-specific alterations, with EVRi inducing the greatest overall effect on growth factor signaling. These data suggest that simultaneous inhibition of HER and VEGFR may benefit select subsets of ovarian cancer tumors. To this end, we derived a novel HER/VEGF signature that correlated with poor overall survival in high-grade, late stage, serous ovarian cancer patient tumors. Mol Cancer Ther; 12(12); 2909–16. ©2013 AACR.

PDF file - 157K, Supplemental Table 1. Regulatory T cell genes included in this study (N=25). Supplemental Table 2. Regulatory T cell SNPs included in this study. Supplemental Table 3. Participating invasive epithelial ovarian cancer studies. Supplemental Table 4. Association between clinical variables and overall survival.

PDF - 32KB, No significant change in body weight was observed throughout the experimental duration.

The presence of regulatory T cells (Treg) in solid tumors is known to play a role in patient survival in ovarian cancer and other malignancies. We assessed inherited genetic variations via 749 tag single-nucleotide polymorphisms (SNP) in 25 Treg-associated genes (CD28, CTLA4, FOXP3, IDO1, IL10, IL10RA, IL15, 1L17RA, IL23A, IL23R, IL2RA, IL6, IL6R, IL8, LGALS1, LGALS9, MAP3K8, STAT5A, STAT5B, TGFB1, TGFB2, TGFB3, TGFBR1, TGRBR2, and TGFBR3) in relation to ovarian cancer survival. We analyzed genotype and overall survival in 10,084 women with invasive epithelial ovarian cancer, including 5,248 high-grade serous, 1,452 endometrioid, 795 clear cell, and 661 mucinous carcinoma cases of European descent across 28 studies from the Ovarian Cancer Association Consortium (OCAC). The strongest associations were found for endometrioid carcinoma and IL2RA SNPs rs11256497 [HR, 1.42; 95% confidence interval (CI), 1.22–1.64; P = 5.7 × 10−6], rs791587 (HR, 1.36; 95% CI, 1.17–1.57; P = 6.2 × 10−5), rs2476491 (HR, = 1.40; 95% CI, 1.19–1.64; P = 5.6 × 10−5), and rs10795763 (HR, 1.35; 95% CI, 1.17–1.57; P = 7.9 × 10−5), and for clear cell carcinoma and CTLA4 SNP rs231775 (HR, 0.67; 95% CI, 0.54–0.82; P = 9.3 × 10−5) after adjustment for age, study site, population stratification, stage, grade, and oral contraceptive use. The rs231775 allele associated with improved survival in our study also results in an amino acid change in CTLA4 and previously has been reported to be associated with autoimmune conditions. Thus, we found evidence that SNPs in genes related to Tregs seem to play a role in ovarian cancer survival, particularly in patients with clear cell and endometrioid epithelial ovarian cancer. Cancer Immunol Res; 2(4); 332–40. ©2014 AACR.

Purpose: Due to variation of outcome among cases, we sought to examine whether overall survival in ovarian cancer was associated with common inherited variants in 227 candidate genes from ovarian cancer–related pathways including angiogenesis, inflammation, detoxification, glycosylation, one-carbon transfer, apoptosis, cell cycle regulation, and cellular senescence.Experimental Design: Blood samples were obtained from 325 women with invasive epithelial ovarian cancer diagnosed at the Mayo Clinic from 1999 to 2006. During a median follow-up of 3.8 years (range, 0.1-8.6 years), 157 deaths were observed. Germline DNA was analyzed at 1,416 single nucleotide polymorphisms (SNP). For all patients, and for 203 with serous subtype, we assessed the overall significance of each gene and pathway, and estimated risk of death via hazard ratios (HR) and 95% confidence intervals (CI), adjusting for known prognostic factors.Results: Variation within angiogenesis was most strongly associated with survival time overall (P = 0.03) and among patients with serous cancer (P = 0.05), particularly for EIF2B5 rs4912474 (all patients HR, 0.69; 95% CI, 0.54-0.89; P = 0.004), VEGFC rs17697305 (serous subtype HR, 2.29; 95% CI, 1.34-3.92; P = 0.003), and four SNPs in VHL. Variation within the inflammation pathway was borderline significant (all patients, P = 0.09), and SNPs in CCR3, IL1B, IL18, CCL2, and ALOX5 which correlated with survival time are worthy of follow-up.Conclusion: An extensive multiple-pathway assessment found evidence that inherited differences may play a role in outcome of ovarian cancer patients, particularly in genes within the angiogenesis and inflammation pathways. Our work supports efforts to target such mediators for therapeutic gain. Clin Cancer Res; 16(3); 995–1007

Supplementary Table 1. Key clinical characteristics of public cohort with respect to individual 14 studies Supplementary Table 2. Key clinical characteristics of Mayo Clinic HGSOC cohort Supplementary Table 3. Expression centroids of five de novo subtypes Supplementary Table 4. Expression centroids of four Tothill's HGSOC subtypes (C1/C2/C4/C5) Supplementary Table 5. Cross-tab between de novo subtypes and two other subtyping systems in public cohort Supplementary Table 6. The differential expression analysis results between s1.MES and rest de novo subtypes in public cohort Supplementary Table 7. The differential expression analysis results between s2.IMM and rest de novo subtypes in public cohort Supplementary Table 8. The differential expression analysis results between s3.RPO and rest de novo subtypes in public cohort Supplementary Table 9. The differential expression analysis results between s4.DIF and rest de novo subtypes in public cohort Supplementary Table 10. The differential expression analysis results between s5.ANM and rest de novo subtypes in public cohort Supplementary Table 11. Distribution tables of HGSOC molecular subtypes vs. surgical outcomes (optimal- and sub-optimal debulking) in Public and Mayo Clinic cohorts Supplementary Table 12. Previously reported debulking-predictive genes and their differential expressions related to s1.MES subtype in HGSOC public cases Supplementary Table 13. Enriched pathways (q-value

PDF file - 298K, 1) Ki67 IHC staining of patient & Avatars; 2) pan-CK, CD45, vimentin staining of Avatars; 3) graph of gains/losses by aCGH in Avatars; 4) Ca125 levels in serum of patients/Avatars.

Supplementary Figure 1. Analytical workflow of the present study. Supplementary Figure 2. Principal component analysis plots of public expression datasets before (A) and after (B) batch-correction Supplementary Figure 3. Consensus clustering results of non-negative matrix factorization from K= 2 to K = 9. The stable clustering relationships can be observed from K=2 to K=5. Supplementary Figure 4. Overall survival plots according to late-stage HGSOC cases in public cohort, n=1946. Supplementary Figure 5. Overall survival plots according to late-stage HGSOC cases with optimal debulking surgeries, in (A) public cohort, n=936; (B) Mayo Clinic cohort, n=250. Supplementary Figure 6. Overall survival plots according to late-stage HGSOC cases with sub-optimal debulking surgeries, in (A) public cohort, n=660; (B) Mayo Clinic cohort, n=88. Supplementary Figure 7. Overall survival plots according to surgical outcomes in Mayo clinic cohorts, for (A) all the late-stage HGSOC patients, (B-F) for late-stage patients belonging to each subtype. Supplementary Figure 8. Volcano plot of S1.MES subtype vs. other molecular subtypes. X-axis is the log-scaled expression fold-change (FC) and Y-axis is the significance level of differential expression analysis, defined as -log10(q-value). Two identical volcano plots were presented, where the larger one plotting according to gene symbols, and the smaller one plotting using dots. Red-color and green-color are used to indicate significantly up-regulated (FC{greater than or equal to}0.5 & q-value{less than or equal to}0.01) and down-regulated genes (FC{less than or equal to} -0.5 & q-value{less than or equal to}0.01), respectively. Supplementary Figure 9. Volcano plot of S2.IMM subtype vs. other molecular subtypes. X-axis is the log-scaled expression fold-change (FC) and Y-axis is the significance level of differential expression analysis, defined as -log10(q-value). Two identical volcano plots were presented, where the larger one plotting according to gene symbols, and the smaller one plotting using dots. Red-color and green-color are used to indicate significantly up-regulated (FC{greater than or equal to}0.5 & q-value{less than or equal to}0.01) and down-regulated genes (FC{less than or equal to} -0.5 & q-value{less than or equal to}0.01), respectively. Supplementary Figure 10. Volcano plot of S3.PRO subtype vs. other molecular subtypes. X-axis is the log-scaled expression fold-change (FC) and Y-axis is the significance level of differential expression analysis, defined as -log10(q-value). Two identical volcano plots were presented, where the larger one plotting according to gene symbols, and the smaller one plotting using dots. Red-color and green-color are used to indicate significantly up-regulated (FC{greater than or equal to}0.5 & q-value{less than or equal to}0.01) and down-regulated genes (FC{less than or equal to} -0.5 & q-value{less than or equal to}0.01), respectively. Supplementary Figure 11. Volcano plot of S4.DIF subtype vs. other molecular subtypes. X-axis is the log-scaled expression fold-change (FC) and Y-axis is the significance level of differential expression analysis, defined as -log10(q-value). Two identical volcano plots were presented, where the larger one plotting according to gene symbols, and the smaller one plotting using dots. Red-color and green-color are used to indicate significantly up-regulated (FC{greater than or equal to}0.5 & q-value{less than or equal to}0.01) and down-regulated genes (FC{less than or equal to} -0.5 & q-value{less than or equal to}0.01), respectively. Supplementary Figure 12. Volcano plot of S5.ANM subtype vs. other molecular subtypes. X-axis is the log-scaled expression fold-change (FC) and Y-axis is the significance level of differential expression analysis, defined as -log10(q-value). Two identical volcano plots were presented, where the larger one plotting according to gene symbols, and the smaller one plotting using dots. Red-color and green-color are used to indicate significantly up-regulated (FC{greater than or equal to}0.5 & q-value{less than or equal to}0.01) and down-regulated genes (FC{less than or equal to} -0.5 & q-value{less than or equal to}0.01), respectively. Supplementary Figure 13. Enriched pathways (q-value {less than or equal to}0.1) for S1.MES subtype vs. other molecular subtypes. Green and red colors are used to represent significantly down- and up-regulated pathways, respectively. Supplementary Figure 14. Enriched pathways (q-value {less than or equal to}0.1) for S2.IMM subtype vs. other molecular subtypes. Green and red colors are used to represent significantly down- and up-regulated pathways, respectively. Supplementary Figure 15. Enriched pathways (q-value {less than or equal to}0.1) for S3.PRO subtype vs. other molecular subtypes. Green and red colors are used to represent significantly down- and up-regulated pathways, respectively. Supplementary Figure 16. Enriched pathways (q-value {less than or equal to}0.1) for S4.DIF subtype vs. other molecular subtypes. Green and red colors are used to represent significantly down- and up-regulated pathways, respectively. Supplementary Figure 17. Enriched pathways (q-value {less than or equal to}0.1) for S5.ANM subtype vs. other molecular subtypes. Green and red colors are used to represent significantly down- and up-regulated pathways, respectively. Supplementary Figure 18. Expression scatterplot between PD-L1 and TAP1 genes; the figure was generated using cBioPortal (http://www.cbioportal.org/) according to TCGA HGSOC data. Supplementary Figure 19. Distribution boxplots of four genomics scores with respect to five de novo subtypes for TCGA HGSOC cases.

Expression of PD-1 on BMDCs and T cells after exposure to IL-6 and IL-10

Purpose: APOBEC3 DNA cytosine deaminase family members normally defend against viruses and transposons. However, deregulated APOBEC3 activity causes mutations in cancer. Because of broad expression profiles and varying mixtures of normal and cancer cells in tumors, including immune cell infiltration, it is difficult to determine where different APOBEC3s are expressed. Here, we ask whether correlations exist between APOBEC3 expression and T-cell infiltration in high-grade serous ovarian cancer (HGSOC), and assess whether these correlations have prognostic value.Experimental Design: Transcripts for APOBEC3G, APOBEC3B, and the T-cell markers, CD3D, CD4, CD8A, GZMB, PRF1, and RNF128 were quantified by RT-qPCR for a cohort of 354 HGSOC patients. Expression values were correlated with each other and clinical parameters. Two additional cohorts were used to extend HGSOC clinical results. Immunoimaging was used to colocalize APOBEC3G and the T-cell marker CD3. TCGA data extended expression analyses to additional cancer types.Results: A surprising positive correlation was found for expression of APOBEC3G and several T cell genes in HGSOC. Immunohistochemistry and immunofluorescent imaging showed protein colocalization in tumor-infiltrating T lymphocytes. High APOBEC3G expression correlated with improved outcomes in multiple HGSOC cohorts. TCGA data analyses revealed that expression of APOBEC3D and APOBEC3H also correlates with CD3D across multiple cancer types.Conclusions: Our results identify APOBEC3G as a new candidate biomarker for tumor-infiltrating T lymphocytes and favorable prognoses for HGSOC. Our data also highlight the complexity of the tumor environment with respect to differential APOBEC family gene expression in both tumor and surrounding normal cell types. Clin Cancer Res; 22(18); 4746–55. ©2016 AACR.

Purpose: Folate receptor alpha (FR) is overexpressed in several cancers. Endogenous immunity to the FR has been demonstrated in patients and suggests the feasibility of targeting FR with vaccine or other immune therapies. CD4 helper T cells are central to the development of coordinated immunity, and prior work shows their importance in protecting against relapse. Our previous identification of degenerate HLA-class II epitopes from human FR led to the development of a broad coverage epitope pool potentially useful in augmenting antigen-specific immune responses in most patients.Patients and Methods: We conducted a phase I clinical trial testing safety and immunogenicity of this vaccine, enrolling patients with ovarian cancer or breast cancer who completed conventional treatment and who showed no evidence of disease. Patients were initially treated with low-dose cyclophosphamide and then vaccinated 6 times, monthly. Immunity and safety were examined during the vaccine period and up to 1 year later.Results: Vaccination was well tolerated in all patients. Vaccine elicited or augmented immunity in more than 90% of patients examined. Unlike recall immunity to tetanus toxoid (TT), FR T-cell responses developed slowly over the course of vaccination with a median time to maximal immunity in 5 months. Despite slow development of immunity, responsiveness appeared to persist for at least 12 months.Conclusions: The results demonstrate that it is safe to augment immunity to the FR tumor antigen, and the developed vaccine is testable for therapeutic activity in most patients whose tumors express FR, regardless of HLA genotype. Clin Cancer Res; 24(13); 3014–25. ©2018 AACR.

Survival in epithelial ovarian cancer (EOC) is influenced by the host immune response, yet the key genetic determinants of inflammation and immunity that affect prognosis are not known. The nuclear factor-κB (NF-κB) transcription factor family plays an important role in many immune and inflammatory responses, including the response to cancer. We studied common inherited variation in 210 genes in the NF-κB family in 10,084 patients with invasive EOC (5,248 high-grade serous, 1,452 endometrioid, 795 clear cell, and 661 mucinous) from the Ovarian Cancer Association Consortium. Associations between genotype and overall survival were assessed using Cox regression for all patients and by major histology, adjusting for known prognostic factors and correcting for multiple testing (threshold for statistical significance, P < 2.5 × 10−5). Results were statistically significant when assessed for patients of a single histology. Key associations were with caspase recruitment domain family, member 11 (CARD11) rs41324349 in patients with mucinous EOC [HR, 1.82; 95% confidence interval (CI), 1.41–2.35; P = 4.13 × 10−6] and tumor necrosis factor receptor superfamily, member 13B (TNFRSF13B) rs7501462 in patients with endometrioid EOC (HR, 0.68; 95% CI, 0.56–0.82; P = 2.33 × 10−5). Other associations of note included TNF receptor–associated factor 2 (TRAF2) rs17250239 in patients with high-grade serous EOC (HR, 0.84; 95% CI, 0.77–0.92; P = 6.49 × 10−5) and phospholipase C, gamma 1 (PLCG1) rs11696662 in patients with clear cell EOC (HR, 0.43; 95% CI, 0.26–0.73; P = 4.56 × 10−4). These associations highlight the potential importance of genes associated with host inflammation and immunity in modulating clinical outcomes in distinct EOC histologies. Cancer Epidemiol Biomarkers Prev; 23(7); 1421–7. ©2014 AACR.

Supplementary Figure S1; Supplementary Table S1; Supplementary Tables S3-S4.

Background: Epithelial ovarian cancer (EOC) is the fifth leading cause of cancer-related death in females and leading gynecologic cause of cancer-related death. Despite the identification of a number of serum biomarkers, methods to identify early-stage disease and predict prognosis remain scarce. We have evaluated two biologically connected serum biomarkers, serum leukocyte protease inhibitor (SLPI) and progranulin (PGRN).Methods: Two-hundred frozen plasma samples were acquired from the Mayo Clinic Biospecimen Repository for Ovarian Cancer Research. Samples were obtained from 50 patients with benign conditions, 50 with American Joint Committee on Cancer (AJCC) stage I and II EOC, and 100 with AJCC stage III and IV EOC. Samples were obtained before surgical resection of a mass and were analyzed for absolute levels of SLPI and PGRN using ELISA assays. Receiver-operator characteristic curves were generated for SLPI and PGRN. Median follow-up was 48 months.Results: Absolute levels of SLPI were significantly elevated in patients with EOC compared with benign disease and predicted the presence of EOC (AUC of 0.812; P = 0.04); SLPI remained elevated in the subset of patients with normal CA-125. PGRN levels were not significantly increased in patients with early-stage or late-stage EOC as a whole, but an increase in PGRN levels was associated with decreased overall survival in advanced EOC.Conclusions: SLPI levels are elevated in EOC, and SLPI shows promise as a diagnostic biomarker for patients with both elevated and normal CA-125 levels. An increase in PGRN is associated with decreased overall survival.Impact: SLPI is elevated in EOC and warrants investigation in a screening study in women at risk for EOC. Cancer Epidemiol Biomarkers Prev; 22(10); 1730–5. ©2013 AACR.

PDF - 93K, Receiver-operator curve for SLPI in early stage EOC versus control patients (A) and advanced stage EOC versus control patients (B).

PDF - 8738K, Figure S1 H&E staining from three high-grade serous (A, B, C) and three endometrioid carcinomas (D, E, F)

PDF - 149K, Supplemental Table 1. Participating invasive epithelial ovarian cancer studies. Supplementary Table 2. NF-κB genes studied. Supplemental Table 3. Association between clinical variables and overall survival.

Purpose: Ovarian cancer has a high recurrence and mortality rate. A barrier to improved outcomes includes a lack of accurate models for preclinical testing of novel therapeutics.Experimental Design: Clinically relevant, patient-derived tumorgraft models were generated from sequential patients and the first 168 engrafted models are described. Fresh ovarian, primary peritoneal, and fallopian tube carcinomas were collected at the time of debulking surgery and injected intraperitoneally into severe combined immunodeficient mice.Results: Tumorgrafts demonstrated a 74% engraftment rate with microscopic fidelity of primary tumor characteristics. Low-passage tumorgrafts also showed comparable genomic aberrations with the corresponding primary tumor and exhibit gene set enrichment of multiple ovarian cancer molecular subtypes, similar to patient tumors. Importantly, each of these tumorgraft models is annotated with clinical data and for those that have been tested, response to platinum chemotherapy correlates with the source patient.Conclusions: Presented herein is the largest known living tumor bank of patient-derived, ovarian tumorgraft models that can be applied to the development of personalized cancer treatment. Clin Cancer Res; 20(5); 1288–97. ©2014 AACR.

PDF - 59K, Legend for Supplementary Figure 1.

Background: Invasive ovarian cancer is a significant cause of gynecologic cancer mortality.Methods: We examined whether this mortality was associated with inherited variation in approximately 170 candidate genes/regions [993 single-nucleotide polymorphisms (SNPs)] in a multistage analysis based initially on 312 Mayo Clinic cases (172 deaths). Additional analyses used The Cancer Genome Atlas (TCGA; 127 cases, 62 deaths). For the most compelling gene, we immunostained Mayo Clinic tissue microarrays (TMA, 326 cases) and conducted consortium-based SNP replication analysis (2,560 cases, 1,046 deaths).Results: The strongest initial mortality association was in HGF (hepatocyte growth factor) at rs1800793 (HR = 1.7, 95% CI = 1.3–2.2, P = 2.0 × 10−5) and with overall variation in HGF (gene-level test, P = 3.7 × 10−4). Analysis of TCGA data revealed consistent associations [e.g., rs5745709 (r2 = 0.96 with rs1800793): TCGA HR = 2.4, CI = 1.4–4.1, P = 2.2 × 10−3; Mayo Clinic + TCGA HR = 1.6, CI = 1.3–1.9, P = 7.0 × 10−5] and suggested genotype correlation with reduced HGF mRNA levels (P = 0.01). In Mayo Clinic TMAs, protein levels of HGF, its receptor MET (C-MET), and phospho-MET were not associated with genotype and did not serve as an intermediate phenotype; however, phospho-MET was associated with reduced mortality (P = 0.01) likely due to higher expression in early-stage disease. In eight additional ovarian cancer case series, HGF rs5745709 was not associated with mortality (HR = 1.0, CI = 0.9–1.1, P = 0.87).Conclusions: We conclude that although HGF signaling is critical to migration, invasion, and apoptosis, it is unlikely that HGF genetic variation plays a major role in ovarian cancer mortality. Furthermore, any minor role is not related to genetically-determined expression.Impact: Our study shows the utility of multiple data types and multiple data sets in observational studies. Cancer Epidemiol Biomarkers Prev; 20(8); 1638–48. ©2011 AACR.

Purpose: PD-0332991 is a selective inhibitor of the CDK4/6 kinases with the ability to block retinoblastoma (Rb) phosphorylation in the low nanomolar range. Here we investigate the role of CDK4/6 inhibition in human ovarian cancer.Experimental Design: We examined the effects of PD-0332991 on proliferation, cell-cycle, apoptosis, and Rb phosphorylation using a panel of 40 established human ovarian cancer cell lines. Molecular markers for response prediction, including p16 and Rb, were studied using gene expression profiling, Western blot, and array CGH. Multiple drug effect analysis was used to study interactions with chemotherapeutic drugs. Expression of p16 and Rb was studied using immunohistochemistry in a large clinical cohort of ovarian cancer patients.Results: Concentration-dependent antiproliferative effects of PD-0332991 were seen in all ovarian cancer cell lines, but varied significantly between individual lines. Rb-proficient cell lines with low p16 expression were most responsive to CDK4/6 inhibition. Copy number variations of CDKN2A, RB, CCNE1, and CCND1 were associated with response to PD-0332991. CDK4/6 inhibition induced G0/G1 cell cycle arrest, blocked Rb phosphorylation in a concentration-and time-dependent manner, and enhanced the effects of chemotherapy. Rb-proficiency with low p16 expression was seen in 97/262 (37%) of ovarian cancer patients and was independently associated with poor progression-free survival (adjusted relative risk 1.49, 95% CI 1.00–2.24, P = 0.052).Conclusions: PD-0332991 shows promising biologic activity in ovarian cancer cell lines. Assessment of Rb and p16 expression may help select patients most likely to benefit from CDK4/6 inhibition in ovarian cancer. Clin Cancer Res; 17(6); 1591–602. ©2011 AACR.

Supplementary Tables 1-7 from Assessment of Hepatocyte Growth Factor in Ovarian Cancer Mortality

PDF - 118K, Scatter plot comparing CA-125 concentrations and SLPI concentrations in control, early EOC, and advanced EOC patients. The dashed line shows the institutional upper limit of normal for CA-125 (35 units/ml).

Figure S1: Vaccination generates T cell immunity to FR following immunization.

Ligation of programmed cell death-1 (PD-1) in the tumor microenvironment is known to inhibit effective adaptive antitumor immunity. Blockade of PD-1 in humans has resulted in impressive, durable regression responses in select tumor types. However, durable responses have been elusive in ovarian cancer patients. PD-1 was recently shown to be expressed on and thereby impair the functions of tumor-infiltrating murine and human myeloid dendritic cells (TIDC) in ovarian cancer. In the present work, we characterize the regulation of PD-1 expression and the effects of PD-1 blockade on TIDC. Treatment of TIDC and bone marrow–derived dendritic cells (DC) with IL10 led to increased PD-1 expression. Both groups of DCs also responded to PD-1 blockade by increasing production of IL10. Similarly, treatment of ovarian tumor–bearing mice with PD-1 blocking antibody resulted in an increase in IL10 levels in both serum and ascites. While PD-1 blockade or IL10 neutralization as monotherapies were inefficient, combination of these two led to improved survival and delayed tumor growth; this was accompanied by augmented antitumor T- and B-cell responses and decreased infiltration of immunosuppressive MDSC. Taken together, our findings implicate compensatory release of IL10 as one of the adaptive resistance mechanisms that undermine the efficacy of anti–PD-1 (or anti–PD-L1) monotherapies and prompt further studies aimed at identifying such resistance mechanisms. Cancer Res; 77(23); 6667–78. ©2017 AACR.

Supplementary Figures 1-4 from Assessment of Hepatocyte Growth Factor in Ovarian Cancer Mortality

PDF file - 76K

PDF - 100K, Table S1 Demographics from the OTTA cohorts representing the test set and excluded cases Table S2 Antibodies, details of immunohistochemical protocols and scoring cut-off Table S3 Training set revision of the COSP model - Areas under the curve (AUC) by histology and model Table S4 Test for heterogeneity for marker expression between training and testing set Table S5 Pairwise agreement of histological types in the testing set by classification method for A_COSP Table S6 Univariate Cox model in the testing set comparing A_COPS and TB_COSPv2. Table S7 Five-year survival estimates for four different type assignments as defined in the text within histological types Table S8 Demographics of endometrioid carcinomas in the test set classified by different methods Table S9 Prediction of type in test set among cases with unclear original diagnosis (N=71).

Additional materials.

Purpose: Here we assess whether molecular subtyping identifies biological features of tumors that correlate with survival and surgical outcomes of high-grade serous ovarian cancer (HGSOC).Experimental Design: Consensus clustering of pooled mRNA expression data from over 2,000 HGSOC cases was used to define molecular subtypes of HGSOCs. This de novo classification scheme was then applied to 381 Mayo Clinic HGSOC patients with detailed survival and surgical outcome information.Results: Five molecular subtypes of HGSOC were identified. In the pooled dataset, three subtypes were largely concordant with prior studies describing proliferative, mesenchymal, and immunoreactive tumors (concordance > 70%), and the group of tumors previously described as differentiated type was segregated into two new types, one of which (anti-mesenchymal) had downregulation of genes that were typically upregulated in the mesenchymal subtype. Molecular subtypes were significantly associated with overall survival (P < 0.001) and with rate of optimal surgical debulking (≤1 cm, P = 1.9E−4) in the pooled dataset. Among stage III-C or IV Mayo Clinic patients, molecular subtypes were also significantly associated with overall survival (P = 0.001), as well as rate of complete surgical debulking (no residual disease; 16% in mesenchymal tumors compared with >28% in other subtypes; P = 0.02).Conclusions: HGSOC tumors may be categorized into five molecular subtypes that associate with overall survival and the extent of residual disease following debulking surgery. Because mesenchymal tumors may have features that were associated with less favorable surgical outcome, molecular subtyping may have future utility in guiding neoadjuvant treatment decisions for women with HGSOC. Clin Cancer Res; 23(15); 4077–85. ©2017 AACR.

Supplementary Table S2.

Table S1 has RTqPCR primers and probe information. Table S2 has clinical information for the Dutch cohort. Table S3 has a summary of TCGA samples used in our analyses. Figure S1 has RTqPCR assay validation data. Figure S2 shows clinical correlations of APOBEC3G expression in independent cohorts.

The PD-1:PD-L1 immune signaling axis mediates suppression of T-cell–dependent tumor immunity. PD-1 expression was recently found to be upregulated on tumor-infiltrating murine (CD11c+CD11b+CD8−CD209a+) and human (CD1c+CD19−) myeloid dendritic cells (TIDC), an innate immune cell type also implicated in immune escape. However, there is little knowledge concerning how PD-1 regulates innate immune cells. In this study, we examined the role of PD-1 in TIDCs derived from mice bearing ovarian tumors. Similar to lymphocytes, TIDC expression of PD-1 was associated with expression of the adapter protein SHP-2, which signals to NF-κB; however, in contrast to its role in lymphocytes, we found that expression of PD-1 in TIDC tonically paralyzed NF-κB activation. Further mechanistic investigations showed that PD-1 blocked NF-κB–dependent cytokine release in a SHP-2–dependent manner. Conversely, inhibition of NF-κB–mediated antigen presentation by PD-1 occurred independently of SHP-2. Collectively, our findings revealed that PD-1 acts in a distinct manner in innate immune cells compared with adaptive immune cells, prompting further investigations of the signaling pathways controlled by this central mediator of immune escape in cancer. Cancer Res; 76(2); 239–50. ©2015 AACR.

PDF file - 481K, Table S1: Cell line information Table S2: Quantitative PCR primer and probe sequences. Table S3: Non-malignant tissues tested Table S4: Early stage serous ovarian tumors used in sequence analyses Table S5: Additional ovarian tumor specimens analyzed Table S6: Microarray analysis of APOBEC3 and select control gene probe sets in ovarian TCGA data Figure S1: Polynucleotide cytosine deaminase expression in ovarian cell lines. Figure S2: Endogenous DNA deaminase activity in APOBEC3B high and low cell lines. Figure S3: Endogenous APOBEC3B activity on alternate dinucleotide substrates. Figure S4: Polynucleotide cytosine deaminase expression in ovarian primary samples. Figure S5: Polynucleotide cytosine deaminase expression in ovarian TCGA samples by RNAseq.

Supplementary Figure 5 from Immune-Induced Epithelial to Mesenchymal Transition In vivo Generates Breast Cancer Stem Cells

Supplementary Figure 1 from Immune-Induced Epithelial to Mesenchymal Transition In vivo Generates Breast Cancer Stem Cells

PDF file - 418KB, Supplementary Methods and Results Fig. S1. In vitro blockade of PD-1 on TUBO cells does not inhibit cell growth. Fig. S2. CD4 T cells and CD4 Tregs in TUBO TILs. Fig. S3. Combination therapy enhances the antigen-specific function of tumor-infiltrating lymphocytes. Fig. S4. Antigen-specific production IL-5 and IL-4 by tumor-infiltrating CD8 T cells. Fig. S5. Combination therapy decreased the number of tumor-infiltrating myeloid derived suppressor cells (MDSCs). Fig S6. PD-1 expression on tumor DCs and splenic CD8 T cells in co-culture.

Supplementary Figure 4 from Immune-Induced Epithelial to Mesenchymal Transition In vivo Generates Breast Cancer Stem Cells

A missense single-nucleotide polymorphism (SNP) in the immune modulatory gene IL1A has been associated with ovarian cancer risk (rs17561). Although the exact mechanism through which this SNP alters risk of ovarian cancer is not clearly understood, rs17561 has also been associated with risk of endometriosis, an epidemiologic risk factor for ovarian cancer. Interleukin-1α (IL1A) is both regulated by and able to activate NF-κB, a transcription factor family that induces transcription of many proinflammatory genes and may be an important mediator in carcinogenesis. We therefore tagged SNPs in more than 200 genes in the NF-κB pathway for a total of 2,282 SNPs (including rs17561) for genotype analysis of 15,604 cases of ovarian cancer in patients of European descent, including 6,179 of high-grade serous (HGS), 2,100 endometrioid, 1,591 mucinous, 1,034 clear cell, and 1,016 low-grade serous, including 23,235 control cases spanning 40 studies in the Ovarian Cancer Association Consortium. In this large population, we confirmed the association between rs17561 and clear cell ovarian cancer [OR, 0.84; 95% confidence interval (CI), 0.76–0.93; P = 0.00075], which remained intact even after excluding participants in the prior study (OR, 0.85; 95% CI, 0.75–0.95; P = 0.006). Considering a multiple-testing–corrected significance threshold of P < 2.5 × 10−5, only one other variant, the TNFSF10 SNP rs6785617, was associated significantly with a risk of ovarian cancer (low malignant potential tumors OR, 0.85; 95% CI, 0.79–0.91; P = 0.00002). Our results extend the evidence that borderline tumors may have a distinct genetic etiology. Further investigation of how these SNPs might modify ovarian cancer associations with other inflammation-related risk factors is warranted. Cancer Res; 74(3); 852–61. ©2013 AACR.

PDF file - 145K, Supplementary Table 1. Studies in the Ovarian Cancer Association Consortium (OCAC) are listed, as well as the abbreviation, location, type, and number of cases and controls for each study. Supplementary Table 2. The number of cases included in the analysis from each OCAC study site is listed by histologic subtype. Supplementary Table 3. Gene symbols and IDs are listed for NF-κB pathway genes that were tagged in this study, as well as chromosome position, number of SNPs tagged, and bin coverage of each gene using Hapmap or 1000 genomes as a reference. Supplementary Table 4. Reasons and numbers of samples excluded from the analysis following the sample quality control are described. Supplementary Table 5. We evaluated rs17561 and rs6785617 for interactions with known epidemiologic risk factors for risk of clear cell and LMP tumors, respectively, and report interaction p-values in the table below.

Supplementary Figure 2 from Immune-Induced Epithelial to Mesenchymal Transition In vivo Generates Breast Cancer Stem Cells

Supplementary Figure 3 from Immune-Induced Epithelial to Mesenchymal Transition In vivo Generates Breast Cancer Stem Cells

Supplementary methods and figures. Fig. 1:TIDCs obtained from the ascites from ovarian cancer patients express PD-1 mRNA. Fig. 2: TIDCs obtained from the ascites of ID8 tumor bearing mice express PD-1 mRNA.

Supplementary Figure Legends 1-5, Methods from Immune-Induced Epithelial to Mesenchymal Transition In vivo Generates Breast Cancer Stem Cells

The breast cancer stem cell (BCSC) hypotheses suggest that breast cancer is derived from a single tumor-initiating cell with stem-like properties, but the source of these cells is unclear. We previously observed that induction of an immune response against an epithelial breast cancer led in vivo to the T-cell–dependent outgrowth of a tumor, the cells of which had undergone epithelial to mesenchymal transition (EMT). The resulting mesenchymal tumor cells had a CD24−/loCD44+ phenotype, consistent with BCSCs. In the present study, we found that EMT was induced by CD8 T cells and the resulting tumors had characteristics of BCSCs, including potent tumorigenicity, ability to reestablish an epithelial tumor, and enhanced resistance to drugs and radiation. In contrast to the hierarchal cancer stem cell hypothesis, which suggests that breast cancer arises from the transformation of a resident tissue stem cell, our results show that EMT can produce the BCSC phenotype. These findings have several important implications related to disease progression and relapse. [Cancer Res 2009;69(7):2887–95]