Network-based Survival Analysis Reveals Subnetwork Signatures for Predicting Outcomes of Ovarian Cancer Treatment

Cox regression is commonly used to predict the outcome by the time to an event of interest and in addition, identify relevant features for survival analysis in cancer genomics. Due to the high-dimensionality of high-throughput genomic data, existing Cox models trained on any particular dataset usually generalize poorly to other independent datasets. In this paper, we propose a network-based Cox regression model called Net-Cox and applied Net-Cox for a large-scale survival analysis across multiple ovarian cancer datasets. Net-Cox integrates gene network information into the Cox''s proportional hazard model to explore the co-expression or functional relation among high-dimensional gene expression features in the gene network. Net-Cox was applied to analyze three independent gene expression datasets including the TCGA ovarian cancer dataset and two other public ovarian cancer datasets. Net-Cox with the network information from gene co-expression or functional relations identified highly consistent signature genes across the three datasets, and because of the better generalization across the datasets, Net-Cox also consistently improved the accuracy of survival prediction over the Cox models regularized by or . This study focused on analyzing the death and recurrence outcomes in the treatment of ovarian carcinoma to identify signature genes that can more reliably predict the events. The signature genes comprise dense protein-protein interaction subnetworks, enriched by extracellular matrix receptors and modulators or by nuclear signaling components downstream of extracellular signal-regulated kinases. In the laboratory validation of the signature genes, a tumor array experiment by protein staining on an independent patient cohort from Mayo Clinic showed that the protein expression of the signature gene FBN1 is a biomarker significantly associated with the early recurrence after 12 months of the treatment in the ovarian cancer patients who are initially sensitive to chemotherapy. Net-Cox toolbox is available at http://compbio.cs.umn.edu/Net-Cox/.     

Association between Common Germline Genetic Variation in 94 Candidate Genes or Regions and Risks of Invasive Epithelial Ovarian Cancer

Background

Recent studies have identified several single nucleotide polymorphisms (SNPs) in the population that are associated with variations in the risks of many different diseases including cancers such as breast, prostate and colorectal. For ovarian cancer, the known highly penetrant susceptibility genes (BRCA1 and BRCA2) are probably responsible for only 40% of the excess familial ovarian cancer risks, suggesting that other susceptibility genes of lower penetrance exist.

Methods

We have taken a candidate approach to identifying moderate risk susceptibility alleles for ovarian cancer. To date, we have genotyped 340 SNPs from 94 candidate genes or regions, in up to 1,491 invasive epithelial ovarian cancer cases and 3,145 unaffected controls from three different population based studies from the UK, Denmark and USA.

Results

After adjusting for population stratification by genomic control, 18 SNPs (5.3%) were significant at the 5% level, and 5 SNPs (1.5%) were significant at the 1% level. The most significant association was for the SNP rs2107425, located on chromosome 11p15.5, which has previously been identified as a susceptibility allele for breast cancer from a genome wide association study (P-trend = 0.0012). When SNPs/genes were stratified into 7 different pathways or groups of validation SNPs, the breast cancer associated SNPs were the only group of SNPs that were significantly associated with ovarian cancer risk (P-heterogeneity = 0.0003; P-trend = 0.0028; adjusted (for population stratification) P-trend = 0.006). We did not find statistically significant associations when the combined data for all SNPs were analysed using an admixture maximum likelihood (AML) experiment-wise test for association (P-heterogeneity = 0.051; P-trend = 0.068).

Conclusion

These data suggest that a proportion of the SNPs we evaluated were associated with ovarian cancer risk, but that the effect sizes were too small to detect associations with individual SNPs.     

Quantitative Immunohistochemical Analysis Reveals Association between Sodium Iodide Symporter and Estrogen Receptor Expression in Breast Cancer

Background

Human sodium iodide symporter (hNIS) gene over-expression is under active consideration worldwide as an alternative target molecule for breast cancer (BC) diagnosis and targeted radio-iodine treatment. However, the field demands better stratified analysis of endogenous hNIS expression across major BC subtypes. Therefore, we have analyzed subtype-specific variation of hNIS overexpression in breast tumor tissue samples by immunohistochemistry (IHC) and also report the development of a homogeneous, quantitative analysis method of digital IHC images.

Methods

hNIS expression was analyzed from 108 BC tissue samples by IHC. Sub-cellular localization of hNIS protein was analyzed by dual immunofluorescence (IF) staining method using hNIS and HER2 antibodies. An ImageJ based two-step digital analysis method was developed and applied for the bias-free analysis of the images.

Results

Staining of the tumor samples show 70% cases are hNIS positive indicating high incidence of hNIS positive cases in BC. More importantly, a subtype specific analysis done for the first time shows that hNIS expression is overly dominated in estrogen receptor (ER) positive cases than the receptor negative cases. Further, 56% of the ER+ve, PgR+ve, HER2-ve and 36% of ER+ve, PgR+ve, HER2+ve cases show highest intensity staining equivalent to the thyroid tissue. A significant positive correlation is also observed between hNIS and estrogen receptor expression (p = 0.0033, CI = 95%) suggesting hNIS mediated targeted radio-iodine therapy procedures may benefit both ER+ve, PgR+ve, HER2–ve as well as HER2+ve cases. Further, in a few cases, hNIS and HER2 protein localization is demonstrated by overlapping membrane co-expression. ImageJ based image analysis method shows over 70% match with manual pathological scoring method.

Conclusion

The study indicates a positive link between hNIS and ER expression in BC. The quantitative IHC image analysis method reported here will further help in patient stratification and potentially benefit global clinical assessment where hNIS mediated targeted ¹³¹I radio-ablative therapy is aimed.     

The Association of Statin Use after Cancer Diagnosis with Survival in Pancreatic Cancer Patients: A SEER-Medicare Analysis

Background

Pancreatic cancer has poor prognosis and existing interventions provide a modest benefit. Statin has anti-cancer properties that might enhance survival in pancreatic cancer patients. We sought to determine whether statin treatment after cancer diagnosis is associated with longer survival in those with pancreatic ductal adenocarcinoma (PDAC).

Methods

We analyzed data on 7813 elderly patients with PDAC using the linked Surveillance, Epidemiology, and End Results (SEER) - Medicare claims files. Information on the type, intensity and duration of statin use after cancer diagnosis was extracted from Medicare Part D. We treated statin as a time-dependent variable in a Cox regression model to determine the association with overall survival adjusting for follow-up, age, sex, race, neighborhood income, stage, grade, tumor size, pancreatectomy, chemotherapy, radiation, obesity, dyslipidemia, diabetes, chronic pancreatitis and chronic obstructive pulmonary disease (COPD).

Results

Overall, statin use after cancer diagnosis was not significantly associated with survival when all PDAC patients were considered (HR = 0.94, 95%CI 0.89, 1.01). However, statin use after cancer diagnosis was associated with a 21% reduced hazard of death (Hazard ratio = 0.79, 95% confidence interval (CI) 0.67, 0.93) in those with grade I or II PDAC and to a similar extent in those who had undergone a pancreatectomy, in those with chronic pancreatitis and in those who had not been treated with statin prior to cancer diagnosis.

Conclusions

We found that statin treatment after cancer diagnosis is associated with enhanced survival in patients with low-grade, resectable PDAC.     

Differences in Survival between Colon and Rectal Cancer from SEER Data

Background

Little is known about colorectal cancer or colon and rectal cancer. Are they the same disease or different diseases?

Objectives

The aim of this epidemiology study was to compare the features of colon and rectal cancer by using recent national cancer surveillance data.

Design and setting

Data included colorectal cancer (1995–2008) from the Surveillance, Epidemiology, and End Results Program (SEER) database. Only adenocarcinoma was included for analysis.

Patients

A total of 372,130 patients with a median follow-up of 32 months were analyzed.

Main outcome measures

Mean survival of patients with the same stage of colon and rectal cancer was evaluated.

Results

Around 35% of patients had stage information. Among them, colon cancer patients had better survival than those with rectal cancer, by a margin of 4 months in stage IIB. In stage IIIC and stage IV, rectal cancer patients had better survival than colon cancer patients, by about 3 months. Stage IIB colorectal cancer patients had a poorer prognosis than those with stage IIIA and IIIB colorectal cancer. After adjustment of age, sex and race, colon cancer patients had better survival than rectal cancer of stage IIB, but in stage IIIC and IV, rectal cancer patients had better survival than colon cancer.

Limitations

The study is limited by its retrospective nature.

Conclusion

This was a population-based study. The prognosis of rectal cancer was not worse than that of colon cancer. Local advanced colorectal cancer had a poorer prognosis than local regional lymph node metastasis. Stage IIB might require more aggressive chemotherapy, and no less than that for stage III.     

Study of Integrated Heterogeneous Data Reveals Prognostic Power of Gene Expression for Breast Cancer Survival

BackgroundStudies show that thousands of genes are associated with prognosis of breast cancer. Towards utilizing available genetic data, efforts have been made to predict outcomes using gene expression data, and a number of commercial products have been developed. These products have the following shortcomings: 1) They use the Cox model for prediction. However, the RSF model has been shown to significantly outperform the Cox model. 2) Testing was not done to see if a complete set of clinical predictors could predict as well as the gene expression signatures.Methodology/FindingsWe address these shortcomings. The METABRIC data set concerns 1981 breast cancer tumors. Features include 21 clinical features, expression levels for 16,384 genes, and survival. We compare the survival prediction performance of the Cox model and the RSF model using the clinical data and the gene expression data to their performance using only the clinical data. We obtain significantly better results when we used both clinical data and gene expression data for 5 year, 10 year, and 15 year survival prediction. When we replace the gene expression data by PAM50 subtype, our results are significant only for 5 year and 15 year prediction. We obtain significantly better results using the RSF model over the Cox model. Finally, our results indicate that gene expression data alone may predict long-term survival.Conclusions/SignificanceOur results indicate that we can obtain improved survival prediction using clinical data and gene expression data compared to prediction using only clinical data. We further conclude that we can obtain improved survival prediction using the RSF model instead of the Cox model. These results are significant because by incorporating more gene expression data with clinical features and using the RSF model, we could develop decision support systems that better utilize heterogeneous information to improve outcome prediction and decision making.     

Statin Therapy Is Associated with Improved Survival in Patients with Non-Serous-Papillary Epithelial Ovarian Cancer: A Retrospective Cohort Analysis

Aim

To determine whether statin use is associated with improved epithelial ovarian cancer (OvCa) survival.

Methods

This is a single-institution retrospective cohort review of patients treated for OvCa between 1992 and 2013. Inclusion criteria were International Federation of Gynecology and Obstetrics (FIGO) stage I–IV OvCa. The primary exposures analyzed were hyperlipidemia and statin use. The primary outcomes were progression-free survival (PFS) and disease-specific survival (DSS).

Results

442 patients met inclusion criteria. The cohort was divided into three groups: patients with hyperlipidemia who used statins (n = 68), patients with hyperlipidemia who did not use statins (n = 28), and patients without hyperlipidemia (n = 346). OvCa outcomes were evaluated. When we analyzed the entire cohort, we found no significant differences in PFS or DSS among the groups. The median PFS for hyperlipidemics using statins, hyperlipidemics not using statins, and non-hyperlipidemics was 21.7, 13.6, and 14.7 months, respectively (p = 0.69). Median DSS for hyperlipidemics using statins, hyperlipidemics not using statins, and non-hyperlipidemics was 44.2, 75.7, and 41.5 months, respectively (p = 0.43). These findings did not change after controlling for confounders. However, a secondary analysis revealed that, among patients with non-serous-papillary subtypes of OvCa, statin use was associated with a decrease in hazards of both disease recurrence (adjusted HR = 0.23, p = 0.02) and disease-specific death (adjusted HR = 0.23, p = 0.04). To augment the findings in the retrospective cohort, the histology-specific effects of statins were also evaluated in vitro using proliferation assays. Here, statin treatment of cell lines resulted in a variable level of cytotoxicity.

Conclusion

Statin use among patients with non-serous-papillary OvCa was associated with improvement in both PFS and DSS.     

Impact of Human Cytomegalovirus Infection and its Immune Response on Survival of Patients with Ovarian Cancer

Human cytomegalovirus (HCMV) has been detected in various types of tumors. We studied the prevalence of HCMV in ovarian cancer and its relation to clinical outcome. Paraffin-embedded tissues obtained prospectively from 45 patients with ovarian cancer and 30 patients with benign ovarian cystadenoma were analyzed for expression of HCMV immediate-early protein (IE) and HCMV tegument protein (pp65) by immunohistochemistry. Plasma was analyzed for HCMV serology. HCMV-IgG levels were higher in patients with ovarian cancer or benign cystadenoma than in age-matched controls (P?=?.002, P?<?.0001, respectively). HCMV IgM was detected in 12% of ovarian cancer patients and 3% of patients with benign tumors but was absent in controls. In patients with ovarian cancer, higher IgG levels were associated with better outcomes (P?=?.04). Extensive HCMV-IE protein expression was detected in 75% of ovarian cancers and 26% of benign tumors; pp65 was detected in 67% of ovarian cancers and 14% of benign tumors. A higher grade of HCMV infection was associated with higher stage of disease. Extensive HCMV-pp65 expression was associated with shorter median overall survival than focal expression (39 versus 42.5?months, P?=?.03). At study closure, 58% of ovarian cancer patients with focal pp65 expression were alive versus 27% of patients with extensive pp65 expression (P?=?.03). Thus, HCMV proteins are detected at different levels in ovarian tumors and benign cystadenomas. Ovarian cancer patients with focal HCMV-pp65 expression in their tumors and high IgG levels against HCMV lived longer, highlighting a need for in-depth studies of the oncomodulatory role of HCMV in ovarian cancer.     

Association of Germline Variation in CCNE1 and CDK2 with Breast Cancer Risk,Progression and Survival among Chinese Han Women

Background

Somatic alterations of cyclin-dependent kinase 2 (CDK2)-cyclin E complex have been shown to contribute to breast cancer (BC) development and progression. This study aimed to explore the effects of single nucleotide polymorphisms (SNPs) in CDK2 and CCNE1 (a gene encoding G1/S specific cyclin E1 protein, formerly called cyclin E) on BC risk, progression and survival in a Chinese Han population.

Methodology/Principal Findings

We herein genotyped 6 haplotype-tagging SNPs (htSNPs) of CCNE1 and 2 htSNPs of CDK2 in 1207 BC cases and 1207 age-matched controls among Chinese Han women, and then reconstructed haplotype blocks according to our genotyping data and linkage disequilibrium status of these htSNPs. For CCNE1, the minor allele homozygotes of three htSNPs were associated with BC risk (rs3218035: adjusted odds ratio [aOR] = 3.35, 95% confidence interval [CI] = 1.69–6.67; rs3218038: aOR = 1.81, 95% CI = 1.22–2.70; rs3218042: aOR = 2.64, 95% CI = 1.31–5.34), and these three loci showed a dose-dependent manner in increasing BC risk (P _trend = 0.0001). Moreover, the 5-SNP haplotype CCGTC, which carried none of minor alleles of the 3 at-risk SNPs, was associated with a favorable event-free survival (hazard ratio [HR] = 0.53, 95% CI = 0.32–0.90). Stratified analysis suggested that the minor-allele homozygote carriers of rs3218038 had a worse event-free survival among patients with aggressive tumours (in tumour size>2 cm group: HR = 2.06, 95% CI = 1.06–3.99; in positive lymph node metastasis group: HR = 2.41, 95% CI = 1.15–5.03; in stage II–IV group: HR = 2.03, 95% CI = 1.09–3.79). For CDK2, no significant association was found.

Conclusions/Significance

This study indicates that genetic variants in CCNE1 may contribute to BC risk and survival in Chinese Han population. They may become molecular markers for individual evaluation of BC susceptibility and prognosis. Nevertheless, further validation studies are needed.     

Association between RASSF1A Promoter Methylation and Ovarian Cancer: A Meta-Analysis

Background

The RAS association domain family protein 1a gene (RASSF1A) is one of the tumor suppressor genes (TSG). Inactivation of RASSF1A is critical to the pathogenesis of cancer. Aberrant TSG methylation was considered an important epigenetic silencing mechanism in the progression of ovarian cancer. A number of studies have discussed association between RASSF1A promoter methylation and ovarian cancer. However, they were mostly based on a small number of samples and showed inconsist results, Therefore, we conducted a meta-analysis to better identify the association.

Methods

Eligible studies were identified by searching the PubMed, EMBASE, Web of Science, and CNKI databases using a systematic searching strategy. We pooled the odds ratio (ORs) from individual studies using a fixed-effects model. We performed heterogeneity and publication bias analysis simultaneously.

Results

Thirteen studies, with 763 ovarian cancer patients and 438 controls were included in the meta-analysis. The frequencies of RASSF1A promoter methylation ranged from 30% to 58% (median is 48%) in the cancer group and 0 to 21% (median is 0) in the control group. The frequencies of RASSF1A promoter methylation in the cancer group were significantly higher than those in the control group. The pooled odds ratio was 11.17 (95% CI = 7.51–16.61) in the cancer group versus the corresponding control group under the fixed-effects model.

Conclusion

The results suggested that RASSF1A promoter methylation had a strong association with ovarian cancer.     

71例Ⅲ-N2期非小细胞肺癌综合治疗病例的生存分析

目的:对71例Ⅲ-N2期非小细胞肺癌综合治疗的病例进行生存分析。方法:我院2007年3月至2014年3月诊断为Ⅲ-N2期的非小细胞肺癌患者71例,所有患者均接受手术、诱导或辅助化疗及术后放疗。采用Kaplan-Meier法对以上患者的总生存期(overall survival,OS)和无病生存期(disease free survival,DFS)进行分析。结果:71例患者的中位生存期为32个月,1年、3年和5年生存率分别为84.5%、49.6%和35.5%;1年、3年和5年无病生存率分别为70.4%、41.8%和27.4%;9例患者(12.6%)出现局部复发;鳞状细胞癌患者的中位生存期为43个月,而腺癌患者的中位生存期为21个月。鳞癌患者1年、3年、5年的OS和DFS分别为85%、60%、42.5%和80.0%、55.0%、34.2%;腺癌患者1年、3年、5年的OS和DFS分别为77.1%、42.0%、36.0%和60.0%、28.6%、28.6%,两组OS和DFS均无显著性差异。结论:不同细胞类型非小细胞肺癌综合治疗生存期存在差异     

Integrated Proteomic Analysis of Human Cancer Cells and Plasma from Tumor Bearing Mice for Ovarian Cancer Biomarker Discovery

Background

The complexity of the human plasma proteome represents a substantial challenge for biomarker discovery. Proteomic analysis of genetically engineered mouse models of cancer and isolated cancer cells and cell lines provide alternative methods for identification of potential cancer markers that would be detectable in human blood using sensitive assays. The goal of this work is to evaluate the utility of an integrative strategy using these two approaches for biomarker discovery.

Methodology/Principal Findings

We investigated a strategy that combined quantitative plasma proteomics of an ovarian cancer mouse model with analysis of proteins secreted or shed by human ovarian cancer cells. Of 106 plasma proteins identified with increased levels in tumor bearing mice, 58 were also secreted or shed from ovarian cancer cells. The remainder consisted primarily of host-response proteins. Of 25 proteins identified in the study that were assayed, 8 mostly secreted proteins common to mouse plasma and human cancer cells were significantly upregulated in a set of plasmas from ovarian cancer patients. Five of the eight proteins were confirmed to be upregulated in a second independent set of ovarian cancer plasmas, including in early stage disease.

Conclusions/Significance

Integrated proteomic analysis of cancer mouse models and human cancer cell populations provides an effective approach to identify potential circulating protein biomarkers.