Why Your New Cancer Biomarker May Never Work: Recurrent Patterns and Remarkable Diversity in Biomarker Failures
ABSTRACT Less than 1% of published cancer biomarkers actually enter clinical practice. Although best practices for biomarker development are published, optimistic investigators may not appreciate the statistical near-certainty and diverse modes by which the other 99% (likely including your favorite new marker) do indeed fail. Here, patterns of failure were abstracted for classification from publications and an online database detailing marker failures. Failure patterns formed a hierarchical logical structure, or outline, of an emerging, deeply complex, and arguably fascinating science of biomarker failure. A new cancer biomarker under development is likely to have already encountered one or more of the following fatal features encountered by prior markers: lack of clinical significance, hidden structure in the source data, a technically inadequate assay, inappropriate statistical methods, unmanageable domination of the data by normal variation, implausibility, deficiencies in the studied population or in the investigator system, and its disproof or abandonment for cause by others. A greater recognition of the science of biomarker failure and its near-complete ubiquity is constructive and celebrates a seemingly perpetual richness of biologic, technical, and philosophical complexity, the full appreciation of which could improve the management of scarce research resources. Cancer Res; 72(23); 1-5. ©2012 AACR.
- SourceAvailable from: Victor Trevino
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- "A miRNA signature trial has been recently announced (MRX34 trial). A key issue for miRNA signatures for cancer is their validation in diverse cohorts (Kern, 2012). Such task generates difficulties even though many public cohorts are available. "
ABSTRACT: MicroRNAs (miRNAs) play a key role in post-transcriptional regulation of RNAm levels. Their function in cancer has been studied by high throughput methods generating valuable sources of public information. Thus miRNA signatures predicting cancer clinical outcomes are emerging. An important step to propose miRNA-based biomarkers before clinical validation is their evaluation in independent cohorts. Although it can be carried out using public data, such task is time consuming, and requires a specialized analysis. Therefore to aid and simplify the evaluation of prognostic miRNA signatures in cancer, we developed SurvMicro, a freely and easy to use web tool that assesses miRNA signatures from publicly available miRNA profiles using multivariate survival analysis. SurvMicro is composed of a wide and updated database of more than 40 cohorts in different tissues and a web tool where survival analysis can be done in minutes. We presented evaluations to portrait the straightforward functionality of SurvMicro in Liver and Lung cancer. To our knowledge SurvMicro is the only bioinformatic tool that aids the evaluation of multivariate prognostic miRNA signatures in cancer. SurvMicro and its tutorial are freely available at http://bioinformatica.mty.itesm.mx/SurvMicro.Bioinformatics 02/2014; 30(11). DOI:10.1093/bioinformatics/btu087 · 4.62 Impact Factor
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- "In one study, for example, samples from spatially separated sites within glioblastoma multiforme (GBM) tumors found that multiple molecular subtypes were present in all of the examined tumors . It is clear that this molecular heterogeneity may significantly limit efforts to personalize cancer treatment based on the use of molecular profiling to identify druggable targets   . However, there has, thus far, been little effort to relate the spatial heterogeneity observed in clinical imaging with the genetic heterogeneity found in molecular studies. "
ABSTRACT: We examined pretreatment magnetic resonance imaging (MRI) examinations from 32 patients with glioblastoma multiforme (GBM) enrolled in The Cancer Genome Atlas (TCGA). Spatial variations in T1 post-gadolinium and either T2-weighted or fluid attenuated inversion recovery sequences from each tumor MRI study were used to characterize each small region of the tumor by its local contrast enhancement and edema/cellularity ("habitat"). The patient cohort was divided into group 1 (survival < 400 days, n = 16) and group 2 (survival > 400 days, n = 16). Histograms of relative values in each sequence demonstrated that the tumor regions were consistently divided into high and low blood contrast enhancement, each of which could be subdivided into regions of high, low, and intermediate cell density/interstitial edema. Group 1 tumors contained greater volumes of habitats with low contrast enhancement but intermediate and high cell density (not fully necrotic) than group 2. Both leave-one-out and 10-fold cross-validation schemes demonstrated that individual patients could be correctly assigned to the short or long survival group with 81.25% accuracy. We demonstrate that novel image analytic techniques can characterize regional habitat variations in GBMs using combinations of MRI sequences. A preliminary study of 32 patients from the TCGA database found that the distribution of MRI-defined habitats varied significantly among the different survival groups. Radiologically defined ecological tumor analysis may provide valuable prognostic and predictive biomarkers in GBM and other tumors.Translational oncology 02/2014; 7(1):5-13. DOI:10.1593/tlo.13730 · 3.40 Impact Factor
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ABSTRACT: We investigated the efficacy and underlying molecular mechanism of a novel chemopreventive strategy combining EGF receptor (EGFR) tyrosine kinase inhibitor (TKI) with cyclooxygenase-2 inhibitor (COX-2I). We examined the inhibition of tumor cell growth by combined EGFR-TKI (erlotinib) and COX-2I (celecoxib) treatment using head and neck cancer cell lines and a preventive xenograft model. We studied the antiangiogenic activity of these agents and examined the affected signaling pathways by immunoblotting analysis in tumor cell lysates and immunohistochemistry (IHC) and enzyme immunoassay (EIA) analyses on the mouse xenograft tissues and blood, respectively. Biomarkers in these signaling pathways were studied by IHC, EIA, and an antibody array analysis in samples collected from participants in a phase I chemoprevention trial of erlotinib and celecoxib. The combined treatment inhibited head and neck cancer cell growth significantly more potently than either single agent alone in cell line and xenograft models, and resulted in greater inhibition of cell-cycle progression at G phase than either single drug. The combined treatment modulated the EGFR and mTOR signaling pathways. A phase I chemoprevention trial of combined erlotinib and celecoxib revealed an overall pathologic response rate of 71% at time of data analysis. Analysis of tissue samples from participants consistently showed downregulation of EGFR, pERK, and pS6 levels after treatment, which correlated with clinical response. Treatment with erlotinib combined with celecoxib offers an effective chemopreventive approach through inhibition of EGFR and mTOR pathways, which may serve as potential biomarkers to monitor the intervention of this combination in the clinic. Clin Cancer Res; 19(5); 1244-56. ©2013 AACR.Clinical Cancer Research 03/2013; 19(5):1244-56. DOI:10.1158/1078-0432.CCR-12-3149 · 8.19 Impact Factor