AuthorCitation

High-Throughput Processing to Preserve Viable Cells: A Precision Medicine Initiative Cohort Program Workshop

Biopreservation and Biobanking | April 25, 2017 | Volume: 15 | Issue: 4 | Pages: 341-343

10.1089/bio.2017.0016 | PMID 28441039

The Japanese Journal of Urology | April 6, 2017 | Volume: 91 | Issue: 3 | Pages: 308

10.5980/jpnjurol.91.308_2

DNA ploidy is a stronger prognostic variable compared to microsatellite instability or 18q allelic loss in patients with stages II and III colon cancer

Journal of Clinical Oncology | February 23, 2017 | Volume: 23 | Issue: 16_suppl | Pages: 9523-9523

10.1200/jco.2005.23.16_suppl.9523

A comprehensive analysis of clinical and tumor characteristics with BRAF and KRAS mutations status in adjuvant colon cancer trial N0147.

Journal of Clinical Oncology | February 23, 2017 | Volume: 30 | Issue: 4_suppl | Pages: 446-446

10.1200/jco.2012.30.4_suppl.446

446 Background: MutKRAS and mutBRAF are important predictive and prognostic markers, respectively, in colorectal cancer (CRC). However, little is known about why some tumors harbor these mutations. N0147 is a phase III adjuvant trial for stage III CRC patients (N=3397) in which patients completed questionnaires and tumor variables and KRAS/BRAF mutation status was assessed. Methods: 2,323/3,397 patients who were enrolled prior to a 2008 amendment requiring KRAS testing completed a Patient Questionnaire. Primary tumors were assessed for mutKRAS and mutBRAF using standard methods; MMR status was determined using IHC. Multivariate analysis was performed to identify predictors of mutation status. Results: 60% and 86% of mutKRAS and mutBRAF tumors were right-sided, respectively. MutKRAS was less likely if there was a family history of CRC and in ever-smokers, and was nearly mutually exclusive with mutBRAF. MutBRAF was associated with deficient MMR (dMMR), high grade histology, and 4+ positive lymph nodes at surgery. MutBRAF was more likely in patients age > 70, retired persons, ever-smokers, but was less likely in non-whites and men. Conclusions: Smoking, family history of CRC, and dMMR status are associated with lower frequency of mutKRAS , but higher mutBRAF. Both mutations tend to be right sided, and mutBRAF is more common in the elderly, females, and non-Hispanic whites. [Table: see text]

gsSKAT: Rapid gene set analysis and multiple testing correction for rare‐variant association studies using weighted linear kernels

Genetic Epidemiology | February 17, 2017 | Volume: 41 | Issue: 4 | Pages: 297-308

10.1002/gepi.22036 | PMID 28211093

ABSTRACTNext‐generation sequencing technologies have afforded unprecedented characterization of low‐frequency and rare genetic variation. Due to low power for single‐variant testing, aggregative methods are commonly used to combine observed rare variation within a single gene. Causal variation may also aggregate across multiple genes within relevant biomolecular pathways. Kernel‐machine regression and adaptive testing methods for aggregative rare‐variant association testing have been demonstrated to be powerful approaches for pathway‐level analysis, although these methods tend to be computationally intensive at high‐variant dimensionality and require access to complete data. An additional analytical issue in scans of large pathway definition sets is multiple testing correction. Gene set definitions may exhibit substantial genic overlap, and the impact of the resultant correlation in test statistics on Type I error rate control for large agnostic gene set scans has not been fully explored. Herein, we first outline a statistical strategy for aggregative rare‐variant analysis using component gene‐level linear kernel score test summary statistics as well as derive simple estimators of the effective number of tests for family‐wise error rate control. We then conduct extensive simulation studies to characterize the behavior of our approach relative to direct application of kernel and adaptive methods under a variety of conditions. We also apply our method to two case‐control studies, respectively, evaluating rare variation in hereditary prostate cancer and schizophrenia. Finally, we provide open‐source R code for public use to facilitate easy application of our methods to existing rare‐variant analysis results.

Ascending the learning curve – MSI testing experience of a six-laboratory consortium

Cancer Biomarkers | January 31, 2017 | Volume: 2 | Issue: 1-2 | Pages: 5-9

10.3233/cbm-2006-21-202 | PMID 17192055

Association of DNA Mismatch Repair and Mutations in BRAF and KRAS With Survival After Recurrence in Stage III Colon Cancers

JAMA Oncology | December 22, 2016 | Volume: 3 | Issue: 4 | Pages: 472

10.1001/jamaoncol.2016.5469 | PMID 28006055

Cytotoxic T Cells and Granzyme B Associated with Improved Colorectal Cancer Survival in a Prospective Cohort of Older Women

Cancer Epidemiology, Biomarkers & Prevention | December 15, 2016 | Volume: 26 | Issue: 4 | Pages: 622-631

10.1158/1055-9965.epi-16-0641 | PMID 27979806

Abstract Background: Host immune response may predict the course of colorectal cancer. We examined the survival of 468 colorectal cancer patients associated with two tumor-infiltrating immune biomarkers, the number of cytotoxic T lymphocytes (CTLs), and the activated CTLs, as reflected by the number of cells expressing granzyme B (GZMB) in the prospective Iowa Women's Health Study. Methods: Using paraffin-embedded tissue samples, we constructed and immunostained tumor microarrays with CD8 (for CTL) and GZMB antibodies. We scored CTL and GZMB densities in tumor epithelial and stromal tissues and also created a composite score for each biomarker (sum of the scores across tissue compartments). Cox regression estimated the HR and 95% confidence intervals (CI) for all-cause and colorectal cancer–specific death associated with each composite score. Results: CTL and GZMB composite scores were positively correlated (r = 0.65) and each biomarker was inversely correlated with stage at diagnosis. Both composite scores were higher in proximal colon tumors and tumors characterized by MSI-high, CIMP-high, or BRAF mutation status. HRs (95% CI) were 0.53 (0.38–0.75; Ptrend = 0.0004) and 0.66 (0.51–0.86; Ptrend = 0.002) for all-cause death, respectively, and 0.30 (0.18–0.51; Ptrend < 0.0001) and 0.41 (0.27–0.63; Ptrend < 0.0001) for colorectal cancer–related death, respectively. Including CTL and GZMB scores simultaneously in the model significantly improved the predictive performance of the models for all-cause and colorectal cancer–related death. Conclusions: Higher tumor infiltration with CTL and GZMB cells is associated with improved all-cause and cancer-specific survival of colorectal cancer patients. Impact: Both the number of CTLs and GZMB appear to be useful prognostic factors in colorectal cancer, irrespective of stage. Cancer Epidemiol Biomarkers Prev; 26(4); 622–31. ©2016 AACR.

SNP interaction pattern identifier (SIPI): an intensive search for SNP–SNP interaction patterns

Bioinformatics | November 29, 2016 | Volume: 33 | Issue: 6 | Pages: 822-833

10.1093/bioinformatics/btw762 | PMID 28039167

Abstract Motivation Testing SNP–SNP interactions is considered as a key for overcoming bottlenecks of genetic association studies. However, related statistical methods for testing SNP–SNP interactions are underdeveloped. Results We propose the SNP Interaction Pattern Identifier (SIPI), which tests 45 biologically meaningful interaction patterns for a binary outcome. SIPI takes non-hierarchical models, inheritance modes and mode coding direction into consideration. The simulation results show that SIPI has higher power than MDR (Multifactor Dimensionality Reduction), AA_Full, Geno_Full (full interaction model with additive or genotypic mode) and SNPassoc in detecting interactions. Applying SIPI to the prostate cancer PRACTICAL consortium data with approximately 21 000 patients, the four SNP pairs in EGFR-EGFR, EGFR-MMP16 and EGFR-CSF1 were found to be associated with prostate cancer aggressiveness with the exact or similar pattern in the discovery and validation sets. A similar match for external validation of SNP–SNP interaction studies is suggested. We demonstrated that SIPI not only searches for more meaningful interaction patterns but can also overcome the unstable nature of interaction patterns. Availability and Implementation The SIPI software is freely available at http://publichealth.lsuhsc.edu/LinSoftware/. Supplementary information Supplementary data are available at Bioinformatics online.

Whole exome sequencing in 75 high-risk families with validation and replication in independent case-control studies identifies TANGO2, OR5H14, and CHAD as new prostate cancer susceptibility genes

Oncotarget | November 26, 2016 | Volume: 8 | Issue: 1 | Pages: 1495-1507

10.18632/oncotarget.13646 | PMID 27902461

Thibodeau, Stephen N.

Citations 169