Dataset Information

The added value of genetic information in colorectal cancer risk prediction models: development and evaluation in the UK Biobank prospective cohort study.

ABSTRACT: Colorectal cancer (CRC) risk prediction models could be used to risk-stratify the population to provide individually tailored screening provision. Using participants from the UK Biobank prospective cohort study, we evaluated whether the addition of a genetic risk score (GRS) could improve the performance of two previously validated models. Inclusion of the GRS did not appreciably improve discrimination of either model, and led to substantial miscalibration. Following recalibration the discrimination did not change, but good calibration for models incorporating the GRS was recovered. Comparing predictions between models with and without the GRS, 5% of participants or fewer changed their absolute risk by ±0.3% or more in either model. In summary, addition of a GRS did not meaningfully improve the performance of validated CRC-risk prediction models. At present, provision of genetic information is not useful for risk stratification for CRC.

SUBMITTER: Smith T

PROVIDER: S-EPMC6203780 | biostudies-literature | 2018 Oct

REPOSITORIES: biostudies-literature

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The added value of genetic information in colorectal cancer risk prediction models: development and evaluation in the UK Biobank prospective cohort study.

Smith Todd T Gunter Marc J MJ Tzoulaki Ioanna I Muller David C DC

British journal of cancer 20181016 8

Colorectal cancer (CRC) risk prediction models could be used to risk-stratify the population to provide individually tailored screening provision. Using participants from the UK Biobank prospective cohort study, we evaluated whether the addition of a genetic risk score (GRS) could improve the performance of two previously validated models. Inclusion of the GRS did not appreciably improve discrimination of either model, and led to substantial miscalibration. Following recalibration the discrimina ...[more]

PMID: 30323197

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Project description:In Rspondin-based three-dimensional cultures, Lgr5 stem cells from multiple organs form ever-expanding epithelial organoids that retain their tissue identity. Here we report the establishment of tumor organoid cultures from 20 consecutive colorectal carcinoma (CRC) patients. For most, organoids were also generated from adjacent normal tissue. Organoids closely resemble the original tumor. The spectrum of genetic changes within the 'living biobank' agrees well with previous large-scale mutational analyses of CRC. Gene expression analysis indicates that the major CRC molecular subtypes are represented. Tumor organoids are amenable to high-throughput drug screens allowing detection of gene-drug associations. As an example, a single organoid culture was exquisitely sensitive to Wnt secretion (porcupine) inhibitors and carried a mutation in the negative Wnt feedback regulator RNF43, rather than in APC. Organoid technology may fill the gap between cancer genetics and patient trials, complement cell line- and xenograft-based drug studies and allow personalized therapy design. Self-renewal of the intestinal epithelium is driven by Lgr5 stem cells located in crypts. We have recently developed a long-term culture system that maintains basic crypt physiology. Wnt signals are required for the maintenance of active crypt stem cells. Indeed, the Wnt agonist R-spondin1 induces dramatic crypt hyperplasia in vivo. R-spondin-1 is the ligand for Lgr5. Epidermal growth factor (EGF) signaling is associated with intestinal proliferation, while transgenic expression of Noggin induces a dramatic increase in crypt numbers. The combination of R-spondin-1, EGF, and Noggin in Matrigel sustains ever-expanding small intestinal organoids, which display all hallmarks of the original tissue in terms of architecture, cell type composition, and self-renewal dynamics. We adapted the culture condition for long-term expansion of human colonic epithelium and primary colonic adenocarcinoma, by adding nicotinamide, A83-01 (Alk inhibitor), Prostaglandin E2 and the p38 inhibitor SB202190. Of note, a two-dimensional culture method for cells from normal and malignant primary tissue has been described by Schlegel and colleagues. Here, we explore organoid technology to routinely establish and phenotypically annotate ‘paired organoids’ derived from adjacent tumor and healthy epithelium from CRC patients.

Project description:BackgroundA previously developed risk prediction model needs to be validated before being used in a new population. The finite size of the validation sample entails that there is uncertainty around model performance. We apply value-of-information (VoI) methodology to quantify the consequence of uncertainty in terms of net benefit (NB).MethodsWe define the expected value of perfect information (EVPI) for model validation as the expected loss in NB due to not confidently knowing which of the alternative decisions confers the highest NB. We propose bootstrap-based and asymptotic methods for EVPI computations and conduct simulation studies to compare their performance. In a case study, we use the non-US subsets of a clinical trial as the development sample for predicting mortality after myocardial infarction and calculate the validation EVPI for the US subsample.ResultsThe computation methods generated similar EVPI values in simulation studies. EVPI generally declined with larger samples. In the case study, at the prespecified threshold of 0.02, the best decision with current information would be to use the model, with an incremental NB of 0.0020 over treating all. At this threshold, the EVPI was 0.0005 (relative EVPI = 25%). When scaled to the annual number of heart attacks in the US, the expected NB loss due to uncertainty was equal to 400 true positives or 19,600 false positives, indicating the value of further model validation.ConclusionVoI methods can be applied to the NB calculated during external validation of clinical prediction models. While uncertainty does not directly affect the clinical implications of NB findings, validation EVPI provides an objective perspective to the need for further validation and can be reported alongside NB in external validation studies.HighlightsExternal validation is a critical step when transporting a risk prediction model to a new setting, but the finite size of the validation sample creates uncertainty about the performance of the model.In decision theory, such uncertainty is associated with loss of net benefit because it can prevent one from identifying whether the use of the model is beneficial over alternative strategies.We define the expected value of perfect information for external validation as the expected loss in net benefit by not confidently knowing if the use of the model is net beneficial.The adoption of a model for a new population should be based on its expected net benefit; independently, value-of-information methods can be used to decide whether further validation studies are warranted.

Dataset Information

The added value of genetic information in colorectal cancer risk prediction models: development and evaluation in the UK Biobank prospective cohort study.

Publications

The added value of genetic information in colorectal cancer risk prediction models: development and evaluation in the UK Biobank prospective cohort study.

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