Methodological and practical challenges for personalized cancer therapies.
ABSTRACT Many experts agree that personalized cancer medicine, defined here as treatment based on the molecular characteristics of a tumor from an individual patient, has great potential in the therapy of many types of cancer. Although targeted therapy agents are increasingly available for clinical applications, many of these promising drugs have produced disappointing results when tested in clinical trials, indicating that there are many challenges that must be addressed to advance this field. We propose that a new generation of clinical trials requiring biopsies to obtain relevant tumor specimens, as well as novel statistical designs, will be essential to improve treatment outcomes. However, these novel clinical trials will only be successful if appropriate biomarkers are identified to help guide the selection of the most beneficial treatments for the participating patients. Although biomarkers based on single gene mutations are the most commonly used in clinical applications today, gene-expression or protein-expression 'signatures' and new imaging technologies have the potential to play important roles as biomarkers in the future. Therefore, it is of crucial importance that we identify and resolve existing challenges that may impede the rapid identification and translation of validated biomarkers with acceptable sensitivity and specificity from the laboratory to the clinic. These challenges include limitations of current biomarker development methodologies and regulatory and reimbursement policies and practices.
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ABSTRACT: The development of molecular signatures for the prediction of time-to-event outcomes is a methodologically challenging task in bioinformatics and biostatistics. Although there are numerous approaches for the derivation of marker combinations and their evaluation, the underlying methodology often suffers from the problem that different optimization criteria are mixed during the feature selection, estimation and evaluation steps. This might result in marker combinations that are suboptimal regarding the evaluation criterion of interest. To address this issue, we propose a unified framework to derive and evaluate biomarker combinations. Our approach is based on the concordance index for time-to-event data, which is a non-parametric measure to quantify the discriminatory power of a prediction rule. Specifically, we propose a gradient boosting algorithm that results in linear biomarker combinations that are optimal with respect to a smoothed version of the concordance index. We investigate the performance of our algorithm in a large-scale simulation study and in two molecular data sets for the prediction of survival in breast cancer patients. Our numerical results show that the new approach is not only methodologically sound but can also lead to a higher discriminatory power than traditional approaches for the derivation of gene signatures.PLoS ONE 01/2014; 9(1):e84483. DOI:10.1371/journal.pone.0084483 · 3.53 Impact Factor
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ABSTRACT: Advances in the treatment of metastatic colorectal cancer have led to an improvement in survival from 12 months with fluorouracil monotherapy to approximately 2 years. This is partly as a result of the addition of irinotecan and oxaliplatin, but is also due to the use of monoclonal antibodies against the epidermal growth factor receptor (EGFR) and antiangiogenic drugs such as bevacizumab. However, there are significant molecular differences between tumours which can affect both prognosis and response to treatment. Personalized medicine aims to tailor treatment according to the characteristics of the individual patient and is now a clinical reality as testing for KRAS mutations to guide treatment with the anti-EGFR monoclonal antibodies cetuximab and panitumumab is now part of routine clinical practice. However, not all patients who are KRAS wild type respond to anti-EGFR therapy and a validated biomarker for antiangiogenic therapy is still lacking. Therefore, other biomarkers are needed to assist with predicting response to both existing drugs as well as to drugs currently under investigation. This review summarizes the molecular biology of colorectal cancer, focusing on the genetic features that are currently most clinically relevant. Current and emerging biomarkers are reviewed along with their roles in selecting patients for targeted treatment with currently licensed therapies and drugs being evaluated in clinical trials. The value of predictive biomarkers of chemosensitivity and potential future treatment strategies are also discussed.Therapeutic Advances in Gastroenterology 09/2013; 6(5):381-95. DOI:10.1177/1756283X13491797
- Nature Reviews Clinical Oncology 03/2011; 8(3):133-4. DOI:10.1038/nrclinonc.2010.230 · 15.70 Impact Factor