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The cost and cost trajectory of whole-genome analysis guiding treatment of patients with advanced cancers.

ABSTRACT: Limited data exist on the real-world costs of applying whole-genome analysis (WGA) in a clinical setting. We estimated the costs of applying WGA to guide treatments for patients with advanced cancers and characterized how costs evolve over time.The setting is the British Columbia Cancer Agency Personalized OncoGenomics (POG) program in British Columbia, Canada. Cost data were obtained for patients who enrolled in the program from 2012 to 2015. We estimated mean WGA costs using bootstrapping. We applied time series analysis and produced 10-year forecasts to determine when costs are expected to reach critical thresholds.The mean cost of WGA over the study period was CDN$34,886 per patient (95% CI: $34,051, $35,721). Over time, WGA costs decreased, driven by a reduction in costs of sequencing. Yet, costs of other components of WGA increased. Forecasting showed WGA costs may not reach critical thresholds within the next 10 years.WGA costs decreased over the studied time horizon, but expenditures needed to realize WGA remain significant. Future research exploring costs and benefits of WGA-guided cancer care are crucial to guide health policy.

SUBMITTER: Weymann D

PROVIDER: S-EPMC5441418 | biostudies-literature | 2017 May

REPOSITORIES: biostudies-literature

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The cost and cost trajectory of whole-genome analysis guiding treatment of patients with advanced cancers.

Weymann Deirdre D Laskin Janessa J Roscoe Robyn R Schrader Kasmintan A KA Chia Stephen S Yip Stephen S Cheung Winson Y WY Gelmon Karen A KA Karsan Aly A Renouf Daniel J DJ Marra Marco M Regier Dean A DA

Molecular genetics & genomic medicine 20170312 3

<h4>Background</h4>Limited data exist on the real-world costs of applying whole-genome analysis (WGA) in a clinical setting. We estimated the costs of applying WGA to guide treatments for patients with advanced cancers and characterized how costs evolve over time.<h4>Methods</h4>The setting is the British Columbia Cancer Agency Personalized OncoGenomics (POG) program in British Columbia, Canada. Cost data were obtained for patients who enrolled in the program from 2012 to 2015. We estimated mean ...[more]

PMID: 28546995

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Project description:We present a radiomics-based model for predicting response to pembrolizumab in patients with advanced rare cancers. The study included 57 patients with advanced rare cancers who were enrolled in our phase II clinical trial of pembrolizumab. Tumor response was evaluated using Response Evaluation Criteria in Solid Tumors (RECIST) 1.1 and immune-related RECIST (irRECIST). Patients were categorized as 20 "controlled disease" (stable disease, partial response, or complete response) or 37 progressive disease). We used 3D-slicer to segment target lesions on standard-of-care, pretreatment contrast enhanced CT scans. We extracted 610 features (10 histogram-based features and 600 second-order texture features) from each volume of interest. Least absolute shrinkage and selection operator logistic regression was used to detect the most discriminatory features. Selected features were used to create a classification model, using XGBoost, for the prediction of tumor response to pembrolizumab. Leave-one-out cross-validation was performed to assess model performance. The 10 most relevant radiomics features were selected; XGBoost-based classification successfully differentiated between controlled disease (complete response, partial response, stable disease) and progressive disease with high accuracy, sensitivity, and specificity in patients assessed by RECIST (94.7%, 97.3%, and 90%, respectively; p<0.001) and in patients assessed by irRECIST (94.7%, 93.9%, and 95.8%, respectively; p<0.001). Additionally, the common features of the RECIST and irRECIST groups also highly predicted pembrolizumab response with accuracy, sensitivity, specificity, and p value of 94.7%, 97%, 90%, p<0.001% and 96%, 96%, 95%, p<0.001, respectively. Our radiomics-based signature identified imaging differences that predicted pembrolizumab response in patients with advanced rare cancer. Our radiomics-based signature identified imaging differences that predicted pembrolizumab response in patients with advanced rare cancer.

Dataset Information

The cost and cost trajectory of whole-genome analysis guiding treatment of patients with advanced cancers.

Publications

The cost and cost trajectory of whole-genome analysis guiding treatment of patients with advanced cancers.

Similar Datasets

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