Development and validation of risk models and molecular diagnostics to permit personalized management of cancer

Abstract

Despite the advances made in cancer management over the past few decades, improvements in cancer diagnosis and prognosis are still poor, highlighting the need for individualized strategies. Toward this goal, risk prediction models and molecular diagnostic tools have been developed, tailoring each step of risk assessment from diagnosis to treatment and clinical outcomes based on the individual's clinical, epidemiological, and molecular profiles. These approaches hold increasing promise for delivering a new paradigm to maximize the efficiency of cancer surveillance and efficacy of treatment. However, they require stringent study design, methodology development, comprehensive assessment of biomarkers and risk factors, and extensive validation to ensure their overall usefulness for clinical translation. In the current study, the authors conducted a systematic review using breast cancer as an example and provide general guidelines for risk prediction models and molecular diagnostic tools, including development, assessment, and validation. Cancer 2014;120:11-9. V C 2013 American Cancer Society.KEYWORDS: cancer, risk prediction model, molecular diagnosis, personalized management, model assessment. INTRODUCTIONWe are entering an era of personalized medicine, whereby the development of risk prediction models and molecular diagnostics are emerging to provide guidelines for clinical decision-making and the personalized management of cancer. After Gail et al published what to our knowledge is the first risk prediction model for the absolute risk of breast cancer, 1 many risk prediction models were developed, including those for bladder cancer, 2 breast cancer, 3,4 colorectal cancer, 5,6 liver cancer, 7,8 lung cancer, 9,10 and melanoma. 11,12 With advances in technology, several molecular biomarkers and corresponding diagnostic assays have been developed. 13 In this review, we summarize the development, evaluation, and validation of risk prediction models and contributions from modern molecular diagnostics, and discuss some of the challenges for clinical translation.

Prediction of three lipid derivatives for postoperative gastric cancer mortality: the Fujian prospective investigation of cancer (FIESTA) study

“…The C-index ranges from 0.0 to 1.0, with the higher value indicating a higher accuracy. It is generally accepted that the C-index of less than 0.7 suggests no improvement in model performance [ 24 ]. In calibration curve, the 45-degree line represents optimal predictions, and it illustrates how far the predicted probabilities of this nomogram are from actual observations.…”

COL12A1, a novel potential prognostic factor and therapeutic target in gastric cancer

“…Although progress has been made in therapeutic strategies, and early detection and clinical management have improved its prognosis, GC remains a complex clinical challenge, with the majority of patients being diagnosed during the later stages of the disease (2,3). Previously, it was reported that the aberrant expression of specific biomarkers was related to cancer development and a worse prognosis, and that some biomarkers affected the sensitivity of cancer to chemotherapeutic drugs (4). Therefore, it is of great benefit to identify novel biomarkers characterized by high sensitivity as well as high specificity that can be used as predictors for GC diagnosis and precise treatment.…”

GPRC5A: An Emerging Biomarker in Human Cancer

“…As cancer is the result of the accumulation of adverse disease-related molecular events, it is reasonable to stratify patients according to genetic alterations in one or more genes. This has become an important factor in clinical intervention [ 1 , 2 ]. However, while a number of cancer biomarkers for molecular diagnosis have been described recently, the specificity and diagnostic capacity of currently available biomarkers are limited [ 3 – 5 ].…”