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CURATOR
A pinboard by
Elizabeth Lightbody

PhD Candidate , Queen's University

PINBOARD SUMMARY

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.

3 ITEMS PINNED

Meta-analysis of the association between three microRNA polymorphisms and breast cancer susceptibility.

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