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Using complex networks for refining survival prognosis in prostate cancer patient.

ABSTRACT: Complex network theory has been used, during the last decade, to understand the structures behind complex biological problems, yielding new knowledge in a large number of situations. Nevertheless, such knowledge has remained mostly qualitative. In this contribution, I show how information extracted from a network representation can be used in a quantitative way, to improve the score of a classification task. As a test bed, I consider a dataset corresponding to patients suffering from prostate cancer, and the task of successfully prognosing their survival. When information from a complex network representation is added on top of a simple classification model, the error is reduced from 27.9% to 23.8%. This confirms that network theory can be used to synthesize information that may not readily be accessible by standard data mining algorithms.

SUBMITTER: Zanin M

PROVIDER: S-EPMC5333606 | biostudies-other | 2016

REPOSITORIES: biostudies-other

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Using complex networks for refining survival prognosis in prostate cancer patient.

Zanin Massimiliano M

F1000Research 20161116

Complex network theory has been used, during the last decade, to understand the structures behind complex biological problems, yielding new knowledge in a large number of situations. Nevertheless, such knowledge has remained mostly qualitative. In this contribution, I show how information extracted from a network representation can be used in a quantitative way, to improve the score of a classification task. As a test bed, I consider a dataset corresponding to patients suffering from prostate ca ...[more]

PMID: 28344772

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Project description:Background: Liver cirrhosis involves multiple organ systems and has a high mortality. A network approach to complex diseases often reveals the collective system behaviours and intrinsic interactions between organ systems. However, mapping the functional connectivity for each individual patient has been challenging due to the lack of suitable analytical methods for assessment of physiological networks. In the present study we applied a parenclitic approach to assess the physiological network of each individual patient from routine clinical/laboratory data available. We aimed to assess the value of the parenclitic networks to predict survival in patients with cirrhosis. Methods: Parenclitic approach creates a network from the perspective of an individual subject in a population. In this study such an approach was used to measure the deviation of each individual patient from the existing network of physiological interactions in a reference population of patients with cirrhosis. 106 patients with cirrhosis were retrospectively enrolled and followed up for 12 months. Network construction and analysis were performed using data from seven clinical/laboratory variables (serum albumin, bilirubin, creatinine, ammonia, sodium, prothrombin time and hepatic encephalopathy) for calculation of parenclitic deviations. Cox regression was used for survival analysis. Result: Initial network analysis indicated that correlation between five clinical/laboratory variables can distinguish between survivors and non-survivors in this cohort. Parenclitic deviations along albumin-bilirubin (Hazard ratio = 1.063, p < 0.05) and albumin-prothrombin time (Hazard ratio = 1.138, p < 0.05) predicted 12-month survival independent of model for end-stage liver disease (MELD). Combination of MELD with the parenclitic measures could predict survival better than MELD alone. Conclusion: The parenclitic network approach can predict survival of patients with cirrhosis and provides pathophysiologic insight on network disruption in chronic liver disease.

Project description:IntroductionGenetic and molecular signatures have been incorporated into cancer prognosis prediction and treatment decisions with good success over the past decade. Clinically, these signatures are usually used in early-stage cancers to evaluate whether they require adjuvant therapy following surgical resection. A molecular signature that is prognostic across more clinical contexts would be a useful addition to current signatures.MethodsWe defined a signature for the ubiquitous tissue factor, E2F4, based on its shared target genes in multiple tissues. These target genes were identified by chromatin immunoprecipitation sequencing (ChIP-seq) experiments using a probabilistic method. We then computationally calculated the regulatory activity score (RAS) of E2F4 in cancer tissues, and examined how E2F4 RAS correlates with patient survival.ResultsGenes in our E2F4 signature were 21-fold more likely to be correlated with breast cancer patient survival time compared to randomly selected genes. Using eight independent breast cancer datasets containing over 1,900 unique samples, we stratified patients into low and high E2F4 RAS groups. E2F4 activity stratification was highly predictive of patient outcome, and our results remained robust even when controlling for many factors including patient age, tumor size, grade, estrogen receptor (ER) status, lymph node (LN) status, whether the patient received adjuvant therapy, and the patient's other prognostic indices such as Adjuvant! and the Nottingham Prognostic Index scores. Furthermore, the fractions of samples with positive E2F4 RAS vary in different intrinsic breast cancer subtypes, consistent with the different survival profiles of these subtypes.ConclusionsWe defined a prognostic signature, the E2F4 regulatory activity score, and showed it to be significantly predictive of patient outcome in breast cancer regardless of treatment status and the states of many other clinicopathological variables. It can be used in conjunction with other breast cancer classification methods such as Oncotype DX to improve clinical outcome prediction.

Dataset Information

Using complex networks for refining survival prognosis in prostate cancer patient.

Publications

Using complex networks for refining survival prognosis in prostate cancer patient.

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