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Predicting risk of chemotherapy-induced side effects in patients with colon cancer with single-nucleotide polymorphism (SNP) Bayesian networks (BNs).
Abstract
Background: Patients undergoing chemotherapy for colon cancer are at significant risk for developing moderate-to-severe side effects as a result of their treatment regimens. These side effects can be debilitating to the patient and are often associated with a number of negative health and economic consequences. There is currently no accurate method to identify which patients are at risk for chemotherapy-induced side effects. If such a prediction tool were available, it would provide opportunities for directed prophylactic interventions. This study adopted a novel approach to filling the unmet clinical need for an accurate risk prediction tool. It capitalized on the growing body of evidence that genetic factors (in particular, networks of interacting genes) play a role in determining the likelihood of a patient’s risk for developing side effects. Specifically, the study was designed to assess the feasibility of identifying SNP-BNs 1 that could accurately predict the risk for 6 common chemotherapy-induced side effects: chemotherapyinduced nausea and vomiting (CINV), diarrhea, oral mucositis (OM) 2 , cognitive dysfunction (CD), peripheral neuropathy (PN), and fatigue. Methods: Patients (n=57) with colon cancer who received at least 3 cycles of FOLFOX6 +/bevacizumab (along with standard supportive care strategies) were enrolled. After informed consent, saliva samples were collected, DNA was isolated, and SNPs were analyzed on Illumina Omni microarrays (2.5 x 10 6 SNPs). Side effects under consideration were observed using Patient Care Monitor © , a validated patient-reported symptom assessment instrument. BNs were developed for each of the 6 side effects and cross-validated using robust statistical analyses. Results: The percentage of patients who experienced moderate-to-severe side effects was notable despite supportive care measures. Side effects included the following: CINV (32%), diarrhea (16%), OM (26%), CD (21%), PN (26%), and fatigue (56%). SNP-BNs were defined for each of the 6 side effects and were found to predict risk with a high degree of accuracy (>90%) and receiver operating characteristic (ROC) curves (>0.920).
... This theory is supported by the finding that clusters of SNPs, discovered by Bayesian network analysis, have been reported to be associated with CTRF risk in patients being treated with cycled chemotherapy for breast and colorectal cancers. 11,12 In the current study, we evaluated an alternative analytical method in which genes were identified using a series of hierarchical filters and nearest-neighbor (NN) analysis to identify a group of genes that predicted CTRF in men being irradiated for prostate cancer. This proof-of-concept investigation not only demonstrated the utility of the analysis, but also confirmed the observation that focal radiation therapy is capable of inducing gene expression changes in peripheral white blood cell RNA. ...
... 31 Our approach differed in that we proposed that the risk of a complex disease, such as CTRF, could well be more easily defined by identifying groups of simultaneously expressed, synergistically functioning genes. While this hypothesis is supported by studies in which Bayesian network development was used to identify SNP clusters predictive of chemotherapyrelated side effects, [11][12][13] we sought to accelerate and simplify the analytical process through the use of a novel method in which we used a sequence of supervised and learned (unsupervised) "filters" to identify the most predictive cluster of genes for CTRF. Our finding that the gene cluster so identified was then able to predict CTRF risk with an accuracy of .75% suggests that the approach has validity. ...
Fatigue is a common side effect of cancer (CA) treatment. We used a novel analytical method to identify and validate a specific gene cluster that is predictive of fatigue risk in prostate cancer patients (PCP) treated with radiotherapy (RT).
A total of 44 PCP were categorized into high-fatigue (HF) and low-fatigue (LF) cohorts based on fatigue score change from baseline to RT completion. Fold-change differential and Fisher's linear discriminant analyses (LDA) from 27 subjects with gene expression data at baseline and RT completion generated a reduced base of most discriminatory genes (learning phase). A nearest-neighbor risk (k-NN) prediction model was developed based on small-scale prognostic signatures. The predictive model validity was tested in another 17 subjects using baseline gene expression data (validation phase).
The model generated in the learning phase predicted HF classification at RT completion in the validation phase with 76.5% accuracy.
The results suggest that a novel analytical algorithm that incorporates fold-change differential analysis, LDA, and a k-NN may have applicability in predicting regimen-related toxicity in cancer patients with high reliability, if we take into account these results and the limited amount of data that we had at disposal. It is expected that the accuracy will be improved by increasing data sampling in the learning phase.
... Schwartzber et al. [22] found that 15 % of breast cancer patients experienced moderate to severe diarrhoea while receiving dose-dense doxorubicin/ cyclophosphamide plus paclitaxel. Another study by Sonis et al. [23] cited a 16 % incidence rate of moderate to severe diarrhoea in patients with colon cancer who received at least three cycles of FOLFOX6 ± bevacizumab. ...
... In contrast, when Mittmann et al. [19] analyzed two specific breast cancer chemotherapy regimens, they noted incidences of 7.1 and 2.0 % in patients receiving TAC and FAC, respectively. In recent studies, the incidence rates of moderate to severe patient-reported mucositis have been 49 % in breast cancer patients treated with dose-dense doxorubicin/cyclophosphamide plus paclitaxel [22] and 26 % in patients with colon cancer receiving at least three cycles of FOLFOX6 ± bevacizumab [23]. ...
Side effects or toxicities are frequent, undesirable companions of almost all forms of non-surgical cancer therapy. It is unusual for patients to complete treatment with radiation or chemotherapy without experiencing at least one form of therapy-associated tissue injury or systemic side effect. Often, toxicities do not occur as solitary events; rather, they result in clusters of symptoms that share a common biological aetiology. Like any disease, cancer treatment-related toxicities (CTRTs) vary in their severity. But, in contrast to most diseases in which incidence is described as being present or absent, the current approach to CTRT typically limits reporting to severe cases only. Not only does this dilute the frequency with which CTRTs occur, but it also undermines our ability to determine the full burden of their impact and to accurately assess the cost effectiveness of potential toxicity interventions. In this article, we report the results of a directed literature review for the years 2000-2012, in which we studied and compared three tissue-based toxicities (nausea and vomiting, diarrhoea, and oral mucositis) and one systemic toxicity (fatigue). Our results confirm the heavy burden of resource use and cost associated with CTRTs. The inclusion of fatigue in our analysis provided an opportunity to compare and contrast a toxicity in which there are both acute and chronic consequences. Our findings also demonstrate a number of challenges to, and opportunities for, future study. Among the most obvious are the lack of provider consistency in diagnosis and grading, especially when there is no global agreement on severity scales. Compounding this inconsistency is the disconnect between healthcare providers and patients that exists when describing toxicity severity and impact. In many cases, cancer can be thought of as a chronic disease that requires prolonged but episodic treatment once the acute disease is eradicated. This change reflects increasing treatment successes, but it also implies that the burden of CTRTs will be expanded and prolonged. Creation of hierarchical attribution of costs in the presence of simultaneous CTRTs, accurate coding, and consistent tracking tools for toxicities will be imperative for effective appraisal of the costs associated with cancer treatment regimen toxicities.
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