Dataset Information

Genomic Biomarkers for Breast Cancer Risk.

ABSTRACT: Clinical risk assessment for cancer predisposition includes a three-generation pedigree and physical examination to identify inherited syndromes. Additionally genetic and genomic biomarkers may identify individuals with a constitutional basis for their disease that may not be evident clinically. Genomic biomarker testing may detect molecular variations in single genes, panels of genes, or entire genomes. The strength of evidence for the association of a genomic biomarker with disease risk may be weak or strong. The factors contributing to clinical validity and utility of genomic biomarkers include functional laboratory analyses and genetic epidemiologic evidence. Genomic biomarkers may be further classified as low, moderate or highly penetrant based on the likelihood of disease. Genomic biomarkers for breast cancer are comprised of rare highly penetrant mutations of genes such as BRCA1 or BRCA2, moderately penetrant mutations of genes such as CHEK2, as well as more common genomic variants, including single nucleotide polymorphisms, associated with modest effect sizes. When applied in the context of appropriate counseling and interpretation, identification of genomic biomarkers of inherited risk for breast cancer may decrease morbidity and mortality, allow for definitive prevention through assisted reproduction, and serve as a guide to targeted therapy .

SUBMITTER: Walsh MF

PROVIDER: S-EPMC5016023 | biostudies-literature | 2016

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Genomic Biomarkers for Breast Cancer Risk.

Walsh Michael F MF Nathanson Katherine L KL Couch Fergus J FJ Offit Kenneth K

Advances in experimental medicine and biology 20160101

Clinical risk assessment for cancer predisposition includes a three-generation pedigree and physical examination to identify inherited syndromes. Additionally genetic and genomic biomarkers may identify individuals with a constitutional basis for their disease that may not be evident clinically. Genomic biomarker testing may detect molecular variations in single genes, panels of genes, or entire genomes. The strength of evidence for the association of a genomic biomarker with disease risk may be ...[more]

PMID: 26987529

Similar Datasets

Project description:In this pilot study, the authors examined associations between image-based phenotypes and genomic biomarkers. The potential genetic contribution of UGT2B genes to interindividual variation in breast density and mammographic parenchymal patterns is demonstrated by performing an association study between image-based phenotypes and genomic biomarkers [single-nucleotide polymorphism (SNP) genotypes].This candidate-gene approach study included 179 subjects for whom both mammograms and blood DNA samples had been obtained. The full-field digital mammograms were acquired using a GE Senographe 2000D FFDM system (12-bit; 0.1 mm-pixel size). Regions-of-interest, 256 × 256 pixels in size, selected from the central breast region behind the nipple underwent computerized image analysis to yield image-based phenotypes of mammographic density and parenchymal texture patterns. SNP genotyping was performed using a Sequenom MassArray System. One hundred twenty three SNPs with minor allele frequency above 5% were genotyped for the UGT2B gene clusters, and used in the study. The association between the image-based phenotypes and genomic biomarkers was assessed with the Pearson correlation coefficient via thePLINK software, and included permutation and correction for multiple SNP comparisons.From the phenotype-genotype association analysis, a parenchyma texture coarseness feature was found to be correlated with SNP rs451632 after multiple test correction for the multiple SNPs (p = 0.022). The power law ?, which is used to characterize the frequency component of texture patterns, was found to be correlated with SNP rs4148298 (p = 0.035).The authors' results indicate that UGT2B gene variation may contribute to interindividual variation in mammographic parenchymal patterns and breast density. Understanding the relationship between image-based phenotypes and genomic biomarkers may help understand the biologic mechanism for image-based biomarkers and yield a future role in personalized medicine.

Project description:BackgroundWe examined the current biomarker landscape in breast cancer when programmed death-ligand 1 (PD-L1) testing is integrated with comprehensive genomic profiling (CGP).Material and methodsWe analyzed data from samples of 312 consecutive patients with breast carcinoma tested with both CGP and PD-L1 (SP142) immunohistochemistry (IHC) during routine clinical care. These samples were stratified into hormone receptor positive (HR+)/human epidermal growth factor receptor negative (HER2-; n = 159), HER2-positive (n = 32), and triple-negative breast cancer (TNBC) cohorts (n = 121).ResultsWe found that in the TNBC cohort, 43% (52/121) were immunocyte PD-L1-positive, and in the HR+/HER2- cohort, 30% (48/159) had PIK3CA companion diagnostics mutations, and hence were potentially eligible for atezolizumab plus nab-paclitaxel or alpelisib plus fulvestrant, respectively. Of the remaining 212 patients, 10.4% (22/212) had a BRCA1/2 mutation, which, if confirmed by germline testing, would allow olaparib plus talazoparib therapy. Of the remaining 190 patients, 169 (88.9%) were positive for another therapy-associated marker or a marker that would potentially qualify the patient for a clinical trial. In addition, we examined the relationship between immunocyte PD-L1 positivity and different tumor mutation burden (TMB) cutoffs and found that when a TMB cutoff of ≥9 mutations per Mb was applied (cutoff determined based on prior publication), 11.6% (14/121) patients were TMB ≥9 mutations/Mb and of these, TMB ≥9 mutations per Mb, 71.4% (10/14) were also positive for PD-L1 IHC.ConclusionOur integrated PD-L1 and CGP methodology identified 32% of the tested patients as potentially eligible for at least one of the two new Food and Drug Administration approved therapies, atezolizumab or alpelisib, and an additional 61.2% (191/312) had other biomarker-guided potential therapeutic options.Implications for practiceThis integrated programmed death-ligand 1 immunohistochemistry and comprehensive genomic profiling methodology identified 32% of the tested patients as eligible for at least one of the two new Food and Drug Administration-approved therapies, atezolizumab or alpelisib, and an additional 61.2% (191/312) had other biomarker-guided potential therapeutic options. These findings suggest new research opportunities to evaluate the predictive utility of other commonly seen PIK3CA mutations in hormone receptor-positive breast cancers and to standardize tumor mutation burden cutoffs to evaluate its potentially predictive role in triple-negative breast cancer.

Project description:PurposeIn addition to impacting incidence, risk factors for breast cancer may also influence recurrence and survival from the disease. However, it is unclear how these factors affect combinatorial biomarkers for aiding treatment decision-making in breast cancer.MethodsPatients were 8179 women with histologically confirmed invasive breast cancer, diagnosed and treated in a large cancer hospital in Beijing, China. Individual clinicopathological (tumor size, grade, lymph nodes) and immunohistochemical (IHC: ER, PR, HER2, KI67) markers were used to define clinically relevant combinatorial prognostic biomarkers, including the Nottingham Prognostic Index (NPI: combining size, grade, nodes) and IHC4 score (combining ER, PR, HER2, KI67). Odds ratios (ORs) and 95% confidence intervals (CIs) for associations between breast cancer risk factors and quartiles (Q1-Q4) of NPI and IHC4 were assessed in multivariable polytomous logistic regression models.ResultsOverall, increasing parity (ORtrend(95% CI) = 1.20(1.05-1.37);Ptrend = 0.007), overweight (OR(95% CI)vs normal = 1.60(1.29-1.98)), and obesity (OR(95% CI) vs normal = 2.12(1.43-3.14)) were associated with higher likelihood of developing tumors with high (Q4) versus low (Q1) NPI score. Conversely, increasing age (ORtrend(95% CI) = 0.75(0.66-0.84);Ptrend < 0.001) and positive family history of breast cancer (FHBC) (OR(95% CI) = 0.66(0.45-0.95)) were inversely associated with NPI. Only body mass index (BMI) was associated with IHC4, with overweight (OR(95% CI) vs normal = 0.82(0.66-1.02)) and obese (OR(95% CI) vs normal = 0.52(0.36-0.76)) women less likely to develop high IHC4 tumors. Notably, elevated BMI was associated with higher NPI irrespective of hormone receptor-expression status.ConclusionsOur findings indicate that factors affecting breast cancer incidence, particularly age, parity, FHBC, and BMI, may impact clinically relevant prognostic biomarkers with implications for surveillance, prognostication, and counseling.

Dataset Information

Genomic Biomarkers for Breast Cancer Risk.

Publications

Genomic Biomarkers for Breast Cancer Risk.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets