Survival Data Of Cancer Patients Research Articles

The reliability of multi‐source data linkage for population‐based cancer survival estimates: A study in a metropolitan cancer registry of China

AbstractBackgroundPopulation‐based cancer survival is a key metric in evaluating the overall effectiveness of health services and cancer control activities. Advancement in information technology enables accurate vital status tracking through multi‐source data linkage. However, its reliability for survival estimates in China is unclear.MethodsWe analyzed data from Dalian Cancer Registry to evaluate the reliability of multi‐source data linkage for population‐based cancer survival estimates in China. Newly diagnosed cancer patients in 2015 were included and followed until June 2021. We conducted single‐source data linkage by linking patients to Dalian Vital Statistics System, and multi‐source data linkage by further linking to Dalian Household Registration System and the hospital medical records. Patient vital status was subsequently determined through active follow‐up via telephone calls, referred to as comprehensive follow‐up, which served as the gold standard. Using the cohort method, we calculated 5‐year observed survival and age‐standardized relative survival for 20 cancer types and all cancers combined.ResultsCompared to comprehensive follow‐up, single‐source data linkage overestimated 5‐year observed survival by 3.2% for all cancers combined, ranging from 0.1% to 8.6% across 20 cancer types. Multi‐source data linkage provided a relatively complete patient vital status, with an observed survival estimate of only 0.3% higher for all cancers, ranging from 0% to 1.5% across 20 cancer types.ConclusionMulti‐source data linkage contributes to reliable population‐based cancer survival estimates in China. Linkage of multiple databases might be of great value in improving the efficiency of follow‐up and the quality of survival data for cancer patients in developing countries.

The role of CRYAB in tumor prognosis and immune infiltration: A Pan-cancer analysis.

There is evidence that the Crystallin Alpha B (CRYAB) gene is involved in the regulation of the tumor microenvironment and influences tumor prognosis in some cancers. However, the role of CRYAB gene in prognosis and immunology in pan-cancer is still unclear. In this study, we analyzed the transcriptional profiles and survival data of cancer patients from The Cancer Genome Atlas (TCGA) database. CRYAB gene and its relationships with pan-cancer were analyzed using R packages, TIMER2.0, GEPIA2, Sangerbox, UALCAN, cBioPortal, ESTIMATE algorithm, and STRING. Besides, real-time fluorescence quantitative polymerase chain reaction (RT-qPCR) was utilized to detect CRYAB expression in KIRC and a human KIRC cell line (Caki-1). We found that CRYAB expression was different in tumors and adjacent tumors in human cancers, affecting patients' prognosis in 15 cancer types. Additionally, CRYAB expression significantly correlated with tumor microenvironment (TME), immune checkpoints (ICP), tumor mutational burden (TMB), and microsatellite instability (MSI) in human cancers. Besides, CRYAB expression was positively associated with the immune infiltration of cancer-associated fibroblasts (CAFs) and endothelial cells in most human cancers. Based on enrichment analysis, the most prevalent CRYAB gene mechanism in malignant tumors may be through anti-apoptotic activity. Moreover, some FDA-approved drugs were found to be associated with CRYAB and might be potential cancer therapeutic candidates. CRYAB is a crucial component of the TME and influences immune cell infiltration, making it a promising biomarker to assess immune infiltration and prognosis in many malignancies.

MLsurvival: an automated machine learning tool for survival analysis of cancer patients.

e13555 Background: Survival analysis is used to establish a connection between covariates and the time of event with censored data. Compared with traditional statistical methods, machine learning approaches based on sophisticated and effective computational algorithms are more capable for handling complex multi-dimensional medical data. Methods: We developed an automated machine learning tool MLsurvival to analyze survival data of cancer patients, algorithms of which include the statistical cox regression and machine learning based on linear model (elastic net), ensemble model (gradient boosting with least squares or regression trees and random forest) and support vector kernel (linear and non-linear). The workflow of MLsurvival is comprised with four modules: preprocessing (missing data remove or imputation and feature standardization), feature selection (unsupervised multi-statistics and supervised machine recursive feature elimination with cross-validation), modeling (hyperparameter and performance evaluation) and prediction. To evaluate the performance of this tool, we analyzed medical data for 222 hepatocellular carcinoma (HCC) patients at stage II-III who underwent surgical resection and developed five machine learning approach based estimation models for overall survival (OS). Models were trained on 155 patients with 300 features, including clinical information, somatic mutation and copy number variation, and independently validated on the rest 67 patients. Results: The ensemble model of gradient boosting fitted by MLsurvival using 48 selected features for the data of 155 HCC patients possessed the highest mean AUC and C-Index value. For 67 patients in validation set, this model predicted half year mortality of patients with an AUC of 0.9 (95% CI, 0.771-1.029) and one year mortality with an AUC of 0.897 (95% CI, 0.816-0.978). In addition to that, this model was also predictive for the time of recurrence (pvalue < 0.0001). Furthermore, we also utilized this tool in survival analysis for extensive real data from patients with breast, lung, and esophagus cancers, while most of results showed superior accuracy and stable performance. Conclusions: MLsurvial is an automate tool for survival analysis of cancer patients with well performance. The risk scoring system implemented in this tool offers a novel strategy for incorporating multi-dimensional risk factors to predict clinical outcome, contributes to the better understanding of disease background and helps to optimize the clinical follow-up and therapeutic treatment for cancer patients.

Inferences of Individual Drug Response-Related Long Non-coding RNAs Based on Integrating Multi-omics Data in Breast Cancer

Differences in individual drug responses are obstacles in breast cancer (BRCA) treatment, so predicting responses would help to plan treatment strategies. The accumulation of cancer molecular profiling and drug response data provide opportunities and challenges to identify novel molecular signatures and mechanisms of tumor responsiveness to drugs in BRCA. This study evaluated drug responses with a multi-omics integrated system that depended on long non-coding RNAs (lncRNAs). We identified drug response-related lncRNAs (DRlncs) by combining expression data of lncRNA, microRNA, messenger RNA, methylation levels, somatic mutations, and the survival data of cancer patients treated with drugs. We constructed an integrated and computational multi-omics approach to identify DRlncs for diverse chemotherapeutic drugs in BRCA. Some DRlncs were identified with Adriamycin, Cytoxan, Tamoxifen, and all samples for BRCA patients. These DRlncs showed specific features regarding both expression and computational accuracies. The DRlnc-gene co-expression networks were constructed and analyzed. Key DRlncs, such as HOXA-AS2 (Ensembl: ENSG00000253552), in the drug Adriamycin were characterized. The experimental analysis also suggested that HOXA-AS2 (Ensembl: ENSG00000253552) was a key DRlnc in Adriamycin drug resistance in BRCA patients. Some DRlncs were associated with survival and some specific functions. A possible mechanism of DRlnc HOXA-AS2 (Ensembl: ENSG00000253552) in the Adriamycin drug response for BRCA resistance was inferred. In summary, this study provides a framework for lncRNA-based evaluation of clinical drug responses in BRCA. Understanding the underlying molecular mechanisms of drug responses will facilitate improved responses to chemotherapy and outcomes of BRCA treatment.

MutEx: a multifaceted gateway for exploring integrative pan-cancer genomic data.

Somatic mutation and gene expression dysregulation are considered two major tumorigenesis factors. While independent investigations of either factor pervade, studies of associations between somatic mutations and gene expression changes have been sporadic and nonsystematic. Utilizing genomic data collected from 11315 subjects of 33 distinct cancer types, we constructed MutEx, a pan-cancer integrative genomic database. This database records the relationships among gene expression, somatic mutation and survival data for cancer patients. MutEx can be used to swiftly explore the relationship between these genomic/clinic features within and across cancer types and, more importantly, search for corroborating evidence for hypothesis inception. Our database also incorporated Gene Ontology and several pathway databases to enhance functional annotation, and elastic net and a gene expression composite score to aid in survival analysis. To demonstrate the usability of MutEx, we provide several application examples, including top somatic mutations associated with the most extensive expression dysregulation in breast cancer, differential mutational burden downstream of DNA mismatch repair gene mutations and composite gene expression score-based survival difference in breast cancer. MutEx can be accessed at http://www.innovebioinfo.com/Databases/Mutationdb_About.php.

Correction: Epistasis in genomic and survival data of cancer patients.

[This corrects the article DOI: 10.1371/journal.pcbi.1005626.].

Inferences of individual drug responses across diverse cancer types using a novel competing endogenous RNA network

Differences in individual drug responses are an obstacle to progression in cancer treatment, and predicting responses would help to plan treatment. The accumulation of cancer molecular profiling and drug response data provides opportunities and challenges to identify novel molecular signatures and mechanisms of tumor responsiveness to drugs. This study evaluated drug responses with a competing endogenous RNA (ceRNA) system that depended on competition between diverse RNA species. We identified drug response‐related ceRNA (DRCEs) by combining the sequence and expression data of long noncoding RNA (lncRNA), microRNA (miRNA), and messenger RNA (mRNA), and the survival data of cancer patients treated with drugs. We constructed a patient–drug two‐layer integrated network and used a linear weighting method to predict individual drug responses. DRCEs were found to be significantly enriched in known cancer and drug‐associated data resources, involved in biological processes known to mediate drug responses, and correlated to drug activity in cancer cell lines. The dysregulation of DRCE expression influenced drug response‐associated functions and pathways, suggesting DRCEs as potential therapeutic targets affecting drug responses. A further case study in breast invasive carcinoma (BRCA) found that DRCE expression was consistent with the drug response pattern and the aberrant expression of the two NEAT1‐related DRCEs may lead to poor response to tamoxifen therapy for patients with TP53 mutations. In summary, this study provides a framework for ceRNA‐based evaluation of clinical drug responses across multiple cancer types. Understanding the underlying molecular mechanisms of drug responses will allow improved response to chemotherapy and outcomes of cancer treatment.

Epistasis in genomic and survival data of cancer patients.

Cancer aggressiveness and its effect on patient survival depends on mutations in the tumor genome. Epistatic interactions between the mutated genes may guide the choice of anticancer therapy and set predictive factors of its success. Inhibitors targeting synthetic lethal partners of genes mutated in tumors are already utilized for efficient and specific treatment in the clinic. The space of possible epistatic interactions, however, is overwhelming, and computational methods are needed to limit the experimental effort of validating the interactions for therapy and characterizing their biomarkers. Here, we introduce SurvLRT, a statistical likelihood ratio test for identifying epistatic gene pairs and triplets from cancer patient genomic and survival data. Compared to established approaches, SurvLRT performed favorable in predicting known, experimentally verified synthetic lethal partners of PARP1 from TCGA data. Our approach is the first to test for epistasis between triplets of genes to identify biomarkers of synthetic lethality-based therapy. SurvLRT proved successful in identifying the known gene TP53BP1 as the biomarker of success of the therapy targeting PARP in BRCA1 deficient tumors. Search for other biomarkers for the same interaction revealed a region whose deletion was a more significant biomarker than deletion of TP53BP1. With the ability to detect not only pairwise but twelve different types of triple epistasis, applicability of SurvLRT goes beyond cancer therapy, to the level of characterization of shapes of fitness landscapes.

Abstract 2621: Disruption of mucin synthesis by targeting GCNT3 inhibits pancreatic cancer progression

Abstract Pancreatic cancer (PC) is a lethal disease, and its management is an ongoing challenge. PC is the fourth leading cause of deaths due to cancer in the United States. It is a highly aggressive cancer that is usually diagnosed at an advanced stage, and has the worst prognosis of any malignancy, with a five year survival of <7% due to high chemoresistance. This chemoresistance is due in part to altered expressions of mucins, which form a mesh that makes target sites inaccessible to drugs. Clinical and preclinical studies have shown aberrant expression of mucins during PC development. The mucins may prevent drugs from accessing their sites of action. Although several mucins that lead to chemoresistance have been targeted, to date, no mucin synthesizing genes have been identified as targets. Using human pancreatic cancer patient survival data (90 cases of tumor and matched normal adjacent tissue), next-generation sequencing (NGS) of genetically engineered Kras mouse pancreatic tumors (N = 6/group), human PC cells, we identified novel core mucin synthesizing gene target GCNT3 (core 2 beta 1,6 N-acetylglucosaminyltransferase). NGS revealed that GCNT3 upregulation (103-fold; p<0.0001) was correlated with increased mucins Muc4 (50-fold; p<0.04), Muc5ac (87-fold; p<0.01), Muc6 (67-fold; p<0.008), Muc1 (5-fold; p<0.009), Muc16 (5-fold; p<0.0003) and Muc20 (17-fold; p<0.007)]. Aberrant GCNT3 expression was associated with increased mucin production and aggressive tumorigenesis and reduced patient survival. Patients with low expression of GCNT3 had a longer survival time than patients with high expression of GCNT3 (median survival: 17.5 vs. 10.5 months, p = 0.036). Further, using in-silico approaches of small molecular docking simulations, we identified talniflumate as a novel inhibitor that specifically binds to GCNT3. Our blind docking simulations reveal that talniflumate binds to GNTC3 with a docking affinity of -8.3 kcal/mol and deeper in the pocket of GCNT3. The docking predictions suggest three notable hydrogen bonds between talniflumate and GCNT3: Arg192 (3.0 Angstroms), Try288 (3.5 Angstroms), and Ala287 (2.9 Angstroms). Pancreata from 6-week-old Kras mice treated with talniflumate for 1 week showed a significant decrease in GCNT3 and mucin expression in PanIN lesions. mRNA expression of GCNT3 was also observed to be lower in pancreatic tissues from talniflumate-treated mice. CRISPR knock-out of GCNT3 in PC cells reduced proliferation and spheroid formation. Further, talniflumate alone and in combination with low-dose gefitinib reduced GCNT3 leading to disruption of mucins in vivo and in vitro. Hence, mucin disruption might enhance targeted therapy. These findings suggest a prominent role for Kras activation and aberrant mucin synthesis leading to PC pathogenesis, and warrant consideration of GCNT3 and EGFR inhibitors as a combination treatment for PC. (Grant Support: COMAA, Kerley-Cade Endowed Fund). Citation Format: Altaf Mohammed, Naveena B. Janakiram, Venkateshwar Madka, Gaurav Kumar, Scott Edgar, Gopal Pathuri, Taylor Bryant, Hannah Kutsche, Yuting Zhang, Laura Biddick, Hariprasad Gali, Yan Daniel Zhao, Stan Lightfoot, Chinthalapally V. Rao. Disruption of mucin synthesis by targeting GCNT3 inhibits pancreatic cancer progression. [abstract]. In: Proceedings of the 107th Annual Meeting of the American Association for Cancer Research; 2016 Apr 16-20; New Orleans, LA. Philadelphia (PA): AACR; Cancer Res 2016;76(14 Suppl):Abstract nr 2621.

Re-analysis of survival data of cancer patients utilizing additive homeopathy

In this short communication we present a re-analysis of homeopathic patient data in comparison to control patient data from the same Outpatient´s Unit “Homeopathy in malignant diseases” of the Medical University of Vienna. In this analysis we took account of a probable immortal time bias. For patients suffering from advanced stages of cancer and surviving the first 6 or 12 months after diagnosis, respectively, the results show that utilizing homeopathy gives a statistically significant (p<0.001) advantage over control patients regarding survival time. In conclusion, bearing in mind all limitations, the results of this retrospective study suggest that patients with advanced stages of cancer might benefit from additional homeopathic treatment until a survival time of up to 12 months after diagnosis.

Front-line intraperitoneal versus intravenous chemotherapy in stage III-IV epithelial ovarian, tubal, and peritoneal cancer with minimal residual disease: a competing risk analysis.

BackgroundIn the analysis of survival data for cancer patients, the problem of competing risks is often ignored. Competing risks have been recognized as a special case of time-to-event analysis. The conventional techniques for time-to-event analysis applied in the presence of competing risks often give biased or uninterpretable results.MethodsUsing a prospectively collected administrative health care database in a single institution, we identified patients diagnosed with stage III or IV primary epithelial ovarian, tubal, and peritoneal cancers with minimal residual disease after primary cytoreductive surgery between 1995 and 2012. Here, we sought to evaluate whether intraperitoneal chemotherapy outperforms intravenous chemotherapy in the presence of competing risks. Unadjusted and multivariable subdistribution hazards models were applied to this database with two types of competing risks (cancer-specific mortality and other-cause mortality) coded to measure the relative effects of intraperitoneal chemotherapy.ResultsA total of 1263 patients were recruited as the initial cohort. After propensity score matching, 381 patients in each arm entered into final competing risk analysis. Cumulative incidence estimates for cancer-specific mortality were statistically significantly lower (p = 0.017, Gray test) in patients receiving intraperitoneal chemotherapy (5-year estimates, 34.5 %; 95 % confidence interval [CI], 29.5–39.6 %, and 10-year estimates, 60.7 %; 95 % CI, 52.2–68.0 %) versus intravenous chemotherapy (5-year estimates, 41.3 %; 95 % CI, 36.2–46.3 %, and 10-year estimates, 67.5 %, 95 % CI, 61.6–72.7 %). In subdistribution hazards analysis, for cancer-specific mortality, intraperitoneal chemotherapy outperforms intravenous chemotherapy (Subdistribution hazard ratio, 0.82; 95 % CI, 0.70–0.96) after correcting other covariates.ConclusionsIn conclusion, results from this comparative effectiveness study provide supportive evidence for previous published randomized trials that intraperitoneal chemotherapy outperforms intravenous chemotherapy even eliminating the confounding of competing risks. We suggest that implementation of competing risk analysis should be highly considered for the investigation of cancer patients who have medium to long-term follow-up period.Electronic supplementary materialThe online version of this article (doi:10.1186/s12885-016-2279-0) contains supplementary material, which is available to authorized users.

Synthetic sickness or lethality points at candidate combination therapy targets in glioblastoma

Synthetic lethal interactions in cancer hold the potential for successful combined therapies, which would avoid the difficulties of single molecule-targeted treatment. Identification of interactions that are specific for human tumors is an open problem in cancer research. This work aims at deciphering synthetic sick or lethal interactions directly from somatic alteration, expression and survival data of cancer patients. To this end, we look for pairs of genes and their alterations or expression levels that are "avoided" by tumors and "beneficial" for patients. Thus, candidates for synthetic sickness or lethality (SSL) interaction are identified as such gene pairs whose combination of states is under-represented in the data. Our main methodological contribution is a quantitative score that allows ranking of the candidate SSL interactions according to evidence found in patient survival. Applying this analysis to glioblastoma data, we collect 1,956 synthetic sick or lethal partners for 85 abundantly altered genes, most of which show extensive copy number variation across the patient cohort. We rediscover and interpret known interaction between TP53 and PLK1, as well as provide insight into the mechanism behind EGFR interacting with AKT2, but not AKT1 nor AKT3. Cox model analysis determines 274 of identified interactions as having significant impact on overall survival in glioblastoma, which is more informative than a standard survival predictor based on patient's age.

Estimating relative survival among people registered with cancer in England and Wales.

Because routinely collected survival data for cancer patients in England and Wales do not typically specify cause of death, conventional estimates of survival in cancer patients based on such data are a measure of their mortality from all causes rather than their mortality due to cancer. As a result, trends in survival over time are difficult to interpret because changes in overall survival may well reflect changes in the risk of death from other causes, rather than from the cancer of interest. One way of overcoming this problem is to use some form of ‘relative survival’ defined as a measure of survival corrected for the effect of other independent causes of death. Since this concept was first introduced, various methods for calculating relative survival have been proposed and this had led to some confusion as to the most appropriate choice of estimate. This paper aims to provide an introduction to the concept of relative survival and reviews some of the suggested methods of estimation. In addition, a particularly simple, but robust approach, is highlighted based on expected and observed mortality. This method is illustrated using preliminary data from the Office for National Statistics on cancer survival in patients born after 1939 and diagnosed with cancer during 1972–84. The examples presented, although limited to analyses on a small number of selected sites, highlight some encouraging trends in survival in people aged under 35 diagnosed with leukaemia, Hodgkin's disease and testicular cancer during this period. © 1999 Cancer Research Campaign

Maximum utilization of the life table method in analyzing survival

We have illustrated the life table method for computing survival rates with 5-year survival data for cancer patients, emphasizing the advantage gained by including survival information on cases which entered the series too late to have had the opportunity to survive a full 5 years. The advantage is measured in terms of reduction in standard error of the survival rate. For the five series of patients in this paper, the reduction in standard error ranged from one-third to two-thirds.