Distinguishing cancer-associated missense mutations from common polymorphisms.

Missense variants are commonly identified in genomic sequence but only a small fraction directly contribute to oncogenesis. The ability to distinguish those missense changes that contribute to cancer progression from those that do not is a difficult problem usually only accomplished through functional in vivo analyses. Using two computational algorithms, Sorting Intolerant from Tolerant (SIFT) and the Pfam-based LogR.E-value method, we have identified features that distinguish cancer-associated missense mutations from other classes of missense change. Our data reveal that cancer mutants behave similarly to Mendelian disease mutations, but are clearly distinct from either complex disease mutations or common single-nucleotide polymorphisms. We show that both activating and inactivating oncogenic mutations are predicted to be deleterious, although activating changes are likely to increase protein activity. Using the Gene Ontology and data from the SIFT and LogR.E-value metrics, a classifier was built that predicts cancer-associated missense mutations with a very low false-positive rate. The classifier does remarkably well in a number of different experiments designed to distinguish polymorphisms from true cancer-associated mutations. We also show that recurrently observed mutations are much more likely to be predicted to be cancer-associated than rare mutations, suggesting that our classifier will be useful in distinguishing causal from passenger mutations. In addition, from an expressed sequence tag-based screen, we identified a previously unknown germ line change (P1104A) in tumor tissues that is predicted to disrupt the function of the TYK2 protein. The data presented here show that this novel bioinformatics approach to classifying cancer-associated variants is robust and can be used for large-scale analyses.

Mutations in the epidermal growth factor receptor and in KRAS are predictive and prognostic indicators in patients with non-small-cell lung cancer treated with chemotherapy alone and in combination with erlotinib.

PURPOSE: Epidermal growth factor receptor (EGFR) mutations have been associated with tumor response to treatment with single-agent EGFR inhibitors in patients with relapsed non-small-cell lung cancer (NSCLC). The implications of EGFR mutations in patients treated with EGFR inhibitors plus first-line chemotherapy are unknown. KRAS is frequently activated in NSCLC. The relationship of KRAS mutations to outcome after EGFR inhibitor treatment has not been described. PATIENTS AND METHODS: Previously untreated patients with advanced NSCLC in the phase III TRIBUTE study who were randomly assigned to carboplatin and paclitaxel with erlotinib or placebo were assessed for survival, response, and time to progression (TTP). EGFR exons 18 through 21 and KRAS exon 2 were sequenced in tumors from 274 patients. Outcomes were correlated with EGFR and KRAS mutations in retrospective subset analyses. RESULTS: EGFR mutations were detected in 13% of tumors and were associated with longer survival, irrespective of treatment (P < .001). Among erlotinib-treated patients, EGFR mutations were associated with improved response rate (P < .05) and there was a trend toward an erlotinib benefit on TTP (P = .092), but not improved survival (P = .96). KRAS mutations (21% of tumors) were associated with significantly decreased TTP and survival in erlotinib plus chemotherapy-treated patients. CONCLUSION: EGFR mutations may be a positive prognostic factor for survival in advanced NSCLC patients treated with chemotherapy with or without erlotinib, and may predict greater likelihood of response. Patients with KRAS-mutant NSCLC showed poorer clinical outcomes when treated with erlotinib and chemotherapy. Further studies are needed to confirm the findings of this retrospective subset analysis.

Analytical and Clinical Validation of a Digital Sequencing Panel for Quantitative, Highly Accurate Evaluation of Cell-Free Circulating Tumor DNA.

Next-generation sequencing of cell-free circulating solid tumor DNA addresses two challenges in contemporary cancer care. First this method of massively parallel and deep sequencing enables assessment of a comprehensive panel of genomic targets from a single sample, and second, it obviates the need for repeat invasive tissue biopsies. Digital Sequencing™ is a novel method for high-quality sequencing of circulating tumor DNA simultaneously across a comprehensive panel of over 50 cancer-related genes with a simple blood test. Here we report the analytic and clinical validation of the gene panel. Analytic sensitivity down to 0.1% mutant allele fraction is demonstrated via serial dilution studies of known samples. Near-perfect analytic specificity (> 99.9999%) enables complete coverage of many genes without the false positives typically seen with traditional sequencing assays at mutant allele frequencies or fractions below 5%. We compared digital sequencing of plasma-derived cell-free DNA to tissue-based sequencing on 165 consecutive matched samples from five outside centers in patients with stage III-IV solid tumor cancers. Clinical sensitivity of plasma-derived NGS was 85.0%, comparable to 80.7% sensitivity for tissue. The assay success rate on 1,000 consecutive samples in clinical practice was 99.8%. Digital sequencing of plasma-derived DNA is indicated in advanced cancer patients to prevent repeated invasive biopsies when the initial biopsy is inadequate, unobtainable for genomic testing, or uninformative, or when the patient's cancer has progressed despite treatment. Its clinical utility is derived from reduction in the costs, complications and delays associated with invasive tissue biopsies for genomic testing.

Cell-Free DNA Next-Generation Sequencing in Pancreatobiliary Carcinomas.

UNLABELLED: Patients with pancreatic and biliary carcinomas lack personalized treatment options, in part because biopsies are often inadequate for molecular characterization. Cell-free DNA (cfDNA) sequencing may enable a precision oncology approach in this setting. We attempted to prospectively analyze 54 genes in tumor and cfDNA for 26 patients. Tumor sequencing failed in 9 patients (35%). In the remaining 17, 90.3% (95% confidence interval, 73.1%-97.5%) of mutations detected in tumor biopsies were also detected in cfDNA. The diagnostic accuracy of cfDNA sequencing was 97.7%, with 92.3% average sensitivity and 100% specificity across five informative genes. Changes in cfDNA correlated well with tumor marker dynamics in serial sampling (r = 0.93). We demonstrate that cfDNA sequencing is feasible, accurate, and sensitive in identifying tumor-derived mutations without prior knowledge of tumor genotype or the abundance of circulating tumor DNA. cfDNA sequencing should be considered in pancreatobiliary cancer trials where tissue sampling is unsafe, infeasible, or otherwise unsuccessful. SIGNIFICANCE: Precision medicine efforts in biliary and pancreatic cancers have been frustrated by difficulties in obtaining adequate tumor tissue for next-generation sequencing. cfDNA sequencing reliably and accurately detects tumor-derived mutations, paving the way for precision oncology approaches in these deadly diseases.

Highly efficient somatic-mutation identification using Escherichia coli mismatch-repair detection.

The discovery of somatic mutations in cancer tissue is extremely laborious, time-consuming and costly. In an evaluation comparing mismatch repair detection (MRD) against Sanger sequencing for somatic-mutation detection, we found that MRD had a specificity of 96% and a sensitivity of 92%. Our results showed that MRD is a robust and cost-effective alternative to Sanger sequencing for identifying somatic mutations in human tumors.