Abstract 155P
Background
Breast cancer is a public health issue both in the world and in Mexico, where 70% of patients with the disease are diagnosed in later stages. Because of this, at Instituto Nacional de Cancerología (The National Cancer Institute), neoadjuvant chemotherapy has become the standard for managing locally advanced breast cancer. However, studies show that less than 50% of patients present a complete pathologic response to this regime, which is why is essential to identify molecular biomarkers capable of predicting resistance to this therapy. Thus, the use of machine learning techniques, together with transcriptome analyses could contribute to the management of oncological treatments for this stage of the disease.
Methods
We included 118 samples corresponding to patients classified in the Luminal A, Luminal B, HER2, and Triple Negative subtypes in locally advanced stages who did not present metastasis or had not received treatment at the time of biopsy in the period 2012 - 2015. RNA was extracted from fresh tumor biopsies prior to the administration of neoadjuvant chemotherapy. After obtaining the results of the clinical monitoring of response to the treatment, the patient samples were grouped as "control" if they presented complete pathological response to the treatment, and "case" if they presented resistance. Subsequently, the total RNA of the samples was processed by massive RNA sequencing (RNA-seq) and a differential expression analysis was performed using DESeq2. Finally, machine learning algorithms were used to select a group of genes capable of classifying patients into "resistant" and "non-resistant" and a panel of response biomarkers was proposed.
Results
A set of mRNAs and lncRNAs capable of discriminating the outcome of breast cancer patients to neoadjuvant chemotherapy as well as the enriched pathways associsted with them was identified.
Conclusions
We propose a preliminary biomarker panel, wich will later be tested and validated in large, well-designed, prospective clinical trials. The validation of molecular biomarkers and their eventual incorporation into clinical practice will improve personalized cancer treatments and the management of advanced stages.
Editorial acknowledgement
Clinical trial identification
Legal entity responsible for the study
Instituto Nacional de Cancerología INCan (018/055/DII) (CEI/1302/18).
Funding
Instituto Nacional de Cancerología INCan (018/055/DII) (CEI/1302/18).
Disclosure
All authors have declared no conflicts of interest.
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