PhD Candidate , Queen's University
Identify and validate miRNA profiles that will predict disease recurrence and survival outcomes
Breast cancer is the second leading cause of cancer-related death among women. One subtype of this disease is triple negative breast cancer (TNBC), characterized by the lack of estrogen receptor, progesterone receptor, and human epidermal growth factor receptor 2, and accounts for 10-24% of all breast cancer cases, with 70% of these further characterized as aggressive basal-like tumours. TNBC patients have a poor prognosis and treatment options limited to standard chemotherapy; not surprisingly, metastasis and recurrence present often, and the majority of deaths occur within five years from diagnosis. Thus, research in this area is needed. Small RNA molecules called microRNAs (miRs) regulate many genes critical for tumourigenesis. A few miRs are reportedly prognostic for TNBC outcomes, but, global TNBC miR expression profiles are largely unknown.
We hypothesize that there is a miR signature profile for TNBC patients that is prognostic/predictive for breast tumour metastasis, recurrence and overall survival.
My studies are based on a breast tumour cohort known as the South Eastern Ontario Breast Cancer Predictive Oncology (SEOBC) Tumour Bank and Database. This cohort is derived from 485 consenting breast cancer patients, linked to a rich clinical outcome database with a 15-year patient follow, drawn from routine practice at the Cancer Center of South Eastern Ontario and Kingston General Hospital (KGH). I extracted quality assured RNA from formalin-fixed paraffin-embedded (FFPE) tumour core samples from a subset of n=51 TNBC patients, stratified based on their basal (n=32) or non-basal (n=19) status, and completed global miR expression profiling for each patient using next generation small RNA sequencing (RNA-seq). Bioinformatic analyses using feature selection algorithms with an ensemble of five different methods, along with univariate and multivariate analyses were used to identify miRs that correlate with patient metastasis, recurrence, survival and treatment response.
Overall, this study will identify and validate miR profiles as new predictive biomarkers for TNBC clinical outcomes. The study will also identify novel miR-regulated proteins that may serve as future therapeutic targets for personalized treatment to reduce deaths among TNBC patients.
Abstract: Breast cancer is a major health problem that affects one in eight women worldwide. As such, detecting breast cancer at an early stage anticipates better disease outcome and prolonged patient survival. Extensive research has shown that microRNA (miRNA) are dysregulated at all stages of breast cancer. miRNA are a class of small noncoding RNA molecules that can modulate gene expression and are easily accessible and quantifiable. This review highlights miRNA as diagnostic, prognostic and therapy predictive biomarkers for early breast cancer with an emphasis on the latter. It also examines the challenges that lie ahead in their use as biomarkers. Noteworthy, this review addresses miRNAs reported in patients with early breast cancer prior to chemotherapy, radiotherapy, surgical procedures or distant metastasis (unless indicated otherwise). In this context, miRNA that are mentioned in this review were significantly modulated using more than one statistical test and/or validated by at least two studies. A standardized protocol for miRNA assessment is proposed starting from sample collection to data analysis that ensures comparative analysis of data and reproducibility of results.
Pub.: 01 Dec '16, Pinned: 29 Aug '17
Abstract: Single nucleotide polymorphisms (SNPs) in three microRNAs (miRNAs), rs2910164 in miR-146a, rs11614913 in miR-196a2, and rs3746444 in miR-499, have been associated with breast cancer (BC) susceptibility, but the evidence is conflicting. To obtain a more robust assessment of the association between these miRNA variants and BC risk, we carried out a meta-analysis through systematic literature retrieval from the PubMed and Embase databases. A total of 9 case-control studies on rs2910164, 12 on rs11614913, and 7 on rs3746444 were included. Pooled odds ratios and 95% confidence intervals were used to evaluate associations with BC risk. Overall analysis showed that rs2910164 was not associated with BC susceptibility in any genetic model, whereas rs11614913 was associated with a decreased risk in both the allelic contrast and recessive models, and rs3746444 imparted an increased risk in all genetic models. Stratified analyses showed that rs11614913 may decrease the risk of BC in the heterozygote model in Asians, and in all genetic models, except the heterozygote model, when the sample size is ≥ 500. Subgroup analysis indicated that rs3746444 was associated with increased risk of BC in Asians, but not Caucasians, at all sample sizes. This meta-analysis suggests that rs11614913 in miR-196a2 may decrease the risk of BC, while rs3746444 in miR-499 may increase it, especially in Asians when the sample size is large. We propose that rs11614913(C > T) and rs3746444 (A > G) may be useful biomarkers predictive of BC risk.
Pub.: 22 Jun '17, Pinned: 29 Aug '17
Abstract: Pancreatic cancer is a dismal disease with a mortality rate almost similar to its incidence rate. To date, there are neither validated predictive nor prognostic biomarkers for this lethal disease. Thus, the aim of the present study was to retrospectively investigate the capability of biochemical parameters and molecular profiles to predict survival of patients with metastatic pancreatic ductal adenocarcinoma (mPDAC) who participated in a phase II clinical trial to test the safety and efficacy of the combination treatment of capecitabine plus nab-paclitaxel. Herein, we investigated the association of 18 biochemical parameters obtained from routine diagnosis and the clinical outcome of the 30 patients enrolled in the clinical trial. Furthermore, we analysed formalin-fixed paraffin-embedded (FFPE) tumour tissue to identify molecular biomarkers via RNA seq and the Illumina TruSeq Amplicon Cancer panel which covers 48 hotspot genes. Our analysis identified SERPINB7 as a novel transcript and a DNA mutation signature that might predict a poor outcome of disease. Moreover, we identified the bilirubin basal level as an independent predictive factor for overall survival in our study cohort.
Pub.: 09 Jul '17, Pinned: 29 Aug '17
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