Identification of a candidate single-nucleotide polymorphism related to chemotherapeutic response through a combination of knowledge-based algorithm and hypothesis-free genomic data

The impact of ERBB-family germline single nucleotide polymorphisms on survival response to adjuvant trastuzumab treatment in HER2-positive breast cancer

Background

Trastuzumab treatment for women with HER2-positive breast cancer (BC) resulted in the significant improvement of both relapse free survival (RFS) and overall survival (OS). However, many women who are classified as HER2-positive do not respond. Many studies have focused on the role of somatic mutations rather than germline polymorphisms in trastuzumab resistance.

Results

We completed an Agena MassArray screen of 10 ERBB-family single nucleotide polymorphisms (SNPs) in 194 adjuvant trastuzumab treated HER2-positive BC patients. SNPs in EGFR genes have a significant association with RFS and OS. Patients with the minor allele of EGFR N158N had significantly worse OS (hazard ratio (HR) = 4.01, (confidence interval (CI) = 1.53– 10.69), p = 0.05) relative to those with either the heterozygous or wild-type (WT) allele. Patients with the minor allele of EGFR T903T (HR = 3.52, (CI = 1.38– 8.97), p = 0.05) had worse RFS relative to those with either the heterozygous or WT allele.

Patients and methods

Using next generation sequencing (NGS) we identified ERBB-family (EGFR, HER2, HER3 and HER4) single nucleotide polymorphisms (SNPs) that occurred in 2 or more patients of a 32 HER2-positive BC patient cohort. Agena MassArray analysis confirmed the frequency of these SNPs in 194 women with HER2-positive BC who received trastuzumab in the adjuvant setting. Using Kaplan-Meier estimates and Cox regression analysis we correlated the presence of ERBB-family SNPs with both RFS and OS.

Conclusions

The presence of germline ERBB-family SNPs may play an important role in how a patient responds to adjuvant trastuzumab, and clinical assessment of these SNPs by targeted genetic screening of patients' blood may be important to stratify patients for treatment.

Confidentiality in Biobanking Research: A Comparison of Donor and Nondonor Families' Understanding of Risks

AIMS

Confidentiality of personal identifiers potentially linking the genetic results from biobanking participants back to the donor and donor relatives is a concern. The risks associated with a breach of confidentiality should be ascertained when biobanks collect samples requiring the consent of a family decision maker (FDM) from deceased organ and tissue donors. This article explores FDM knowledge and opinions regarding risks associated with participation in biobanking research in the context of the Genotype-Tissue Expression (GTEx) Project.

METHODS

Data collection included a survey completed by organ procurement organization requesters (n = 37) and semistructured telephone interviews with the FDMs (n = 85).

RESULTS

Donor families were more likely to know that there was a risk that a patient's identity could be revealed through a breach of confidentiality (p < 0.05). They also were more likely to understand that researchers using biobanked tissue would not have access to the patient's exact identity (p < 0.05). FDMs who refused donation were more concerned about risks than donors and reported lower levels of support for medical research in general. Finally, families were frequently interested in the return of results and willing to trade absolute confidentiality for participation.

CONCLUSIONS

Clear discussion of the risk of breach of confidentiality is needed during the consent process. The risk and benefit equation could be equalized if studies such as GTEx offered genomic results to interested participants.

Construction of possible integrated predictive index based on EGFR and ANXA3 polymorphisms for chemotherapy response in fluoropyrimidine-treated Japanese gastric cancer patients using a bioinformatic method

Background

Variability in drug response between individual patients is a serious concern in medicine. To identify single-nucleotide polymorphisms (SNPs) related to drug response variability, many genome-wide association studies have been conducted.

Methods

We previously applied a knowledge-based bioinformatic approach to a pharmacogenomics study in which 119 fluoropyrimidine-treated gastric cancer patients were genotyped at 109,365 SNPs using the Illumina Human-1 BeadChip. We identified the SNP rs2293347 in the human epidermal growth factor receptor (EGFR) gene as a novel genetic factor related to chemotherapeutic response. In the present study, we reanalyzed these hypothesis-free genomic data using extended knowledge.

Results

We identified rs2867461 in annexin A3 (ANXA3) gene as another candidate. Using logistic regression, we confirmed that the performance of the rs2867461 + rs2293347 model was superior to those of the single factor models. Furthermore, we propose a novel integrated predictive index (iEA) based on these two polymorphisms in EGFR and ANXA3. The p value for iEA was 1.47 × 10⁻⁸ by Fisher’s exact test. Recent studies showed that the mutations in EGFR is associated with high expression of dihydropyrimidine dehydrogenase, which is an inactivating and rate-limiting enzyme for fluoropyrimidine, and suggested that the combination of chemotherapy with fluoropyrimidine and EGFR-targeting agents is effective against EGFR-overexpressing gastric tumors, while ANXA3 overexpression confers resistance to tyrosine kinase inhibitors targeting the EGFR pathway.

Conclusions

These results suggest that the iEA index or a combination of polymorphisms in EGFR and ANXA3 may serve as predictive factors of drug response, and therefore could be useful for optimal selection of chemotherapy regimens.

Electronic supplementary material

The online version of this article (doi:10.1186/s12885-015-1721-z) contains supplementary material, which is available to authorized users.

Analysis of gene expression profiles of soft tissue sarcoma using a combination of knowledge-based filtering with integration of multiple statistics

The diagnosis and treatment of soft tissue sarcomas (STS) have been difficult. Of the diverse histological subtypes, undifferentiated pleomorphic sarcoma (UPS) is particularly difficult to diagnose accurately, and its classification per se is still controversial. Recent advances in genomic technologies provide an excellent way to address such problems. However, it is often difficult, if not impossible, to identify definitive disease-associated genes using genome-wide analysis alone, primarily because of multiple testing problems. In the present study, we analyzed microarray data from 88 STS patients using a combination method that used knowledge-based filtering and a simulation based on the integration of multiple statistics to reduce multiple testing problems. We identified 25 genes, including hypoxia-related genes (e.g., MIF, SCD1, P4HA1, ENO1, and STAT1) and cell cycle- and DNA repair-related genes (e.g., TACC3, PRDX1, PRKDC, and H2AFY). These genes showed significant differential expression among histological subtypes, including UPS, and showed associations with overall survival. STAT1 showed a strong association with overall survival in UPS patients (logrank p = 1.84 × 10(-6) and adjusted p value 2.99 × 10(-3) after the permutation test). According to the literature, the 25 genes selected are useful not only as markers of differential diagnosis but also as prognostic/predictive markers and/or therapeutic targets for STS. Our combination method can identify genes that are potential prognostic/predictive factors and/or therapeutic targets in STS and possibly in other cancers. These disease-associated genes deserve further preclinical and clinical validation.

Application of a combination of a knowledge-based algorithm and 2-stage screening to hypothesis-free genomic data on irinotecan-treated patients for identification of a candidate single nucleotide polymorphism related to an adverse effect

Interindividual variation in a drug response among patients is known to cause serious problems in medicine. Genomic information has been proposed as the basis for “personalized” health care. The genome-wide association study (GWAS) is a powerful technique for examining single nucleotide polymorphisms (SNPs) and their relationship with drug response variation; however, when using only GWAS, it often happens that no useful SNPs are identified due to multiple testing problems. Therefore, in a previous study, we proposed a combined method consisting of a knowledge-based algorithm, 2 stages of screening, and a permutation test for identifying SNPs. In the present study, we applied this method to a pharmacogenomics study where 109,365 SNPs were genotyped using Illumina Human-1 BeadChip in 168 cancer patients treated with irinotecan chemotherapy. We identified the SNP rs9351963 in potassium voltage-gated channel subfamily KQT member 5 (KCNQ5) as a candidate factor related to incidence of irinotecan-induced diarrhea. The p value for rs9351963 was 3.31×10⁻⁵ in Fisher's exact test and 0.0289 in the permutation test (when multiple testing problems were corrected). Additionally, rs9351963 was clearly superior to the clinical parameters and the model involving rs9351963 showed sensitivity of 77.8% and specificity of 57.6% in the evaluation by means of logistic regression. Recent studies showed that KCNQ4 and KCNQ5 genes encode members of the M channel expressed in gastrointestinal smooth muscle and suggested that these genes are associated with irritable bowel syndrome and similar peristalsis diseases. These results suggest that rs9351963 in KCNQ5 is a possible predictive factor of incidence of diarrhea in cancer patients treated with irinotecan chemotherapy and for selecting chemotherapy regimens, such as irinotecan alone or a combination of irinotecan with a KCNQ5 opener. Nonetheless, clinical importance of rs9351963 should be further elucidated.

Macrophage migration inhibitory factor and stearoyl-CoA desaturase 1: potential prognostic markers for soft tissue sarcomas based on bioinformatics analyses

The diagnosis and treatment of soft tissue sarcomas (STSs) has been particularly difficult, because STSs are a group of highly heterogeneous tumors in terms of histopathology, histological grade, and primary site. Recent advances in genome technologies have provided an excellent opportunity to determine the complete biological characteristics of neoplastic tissues, resulting in improved diagnosis, treatment selection, and investigation of therapeutic targets. We had previously developed a novel bioinformatics method for marker gene selection and applied this method to gene expression data from STS patients. This previous analysis revealed that the extracted gene combination of macrophage migration inhibitory factor (MIF) and stearoyl-CoA desaturase 1 (SCD1) is an effective diagnostic marker to discriminate between subtypes of STSs with highly different outcomes. In the present study, we hypothesize that the combination of MIF and SCD1 is also a prognostic marker for the overall outcome of STSs. To prove this hypothesis, we first analyzed microarray data from 88 STS patients and their outcomes. Our results show that the survival rates for MIF- and SCD1-positive groups were lower than those for negative groups, and the p values of the log-rank test are 0.0146 and 0.00606, respectively. In addition, survival rates are more significantly different (p = 0.000116) between groups that are double-positive and double-negative for MIF and SCD1. Furthermore, in vitro cell growth inhibition experiments by MIF and SCD1 inhibitors support the hypothesis. These results suggest that the gene set is useful as a prognostic marker associated with tumor progression.