TRIple Negative Breast Cancer Markers In Liquid Biopsies Using Artificial Intelligence

Official Title

Triple Negative Breast Cancer Markers in Liquid Biopsies Using Artificial Intelligence (TRICIA Study)


Triple negative breast cancer (TNBC) is the most aggressive of breast cancers and it is usually treated with chemotherapy even before surgery. In many cases, the chemotherapy completely "melts" the tumour and these patients do well. When the tumour is not eliminated by the chemotherapy, the patient receive more chemotherapy after surgery to decrease the chances of it coming back. Yet many of these patients don't need that extra chemotherapy and will do well in any case. One of the most exciting recent developments in cancer is the use of "liquid biopsies". It turns out that the tumour's DNA, RNA and proteins can be detected in small vesicles found in the patient's blood. Thanks to advances in Artificial Intelligence, there is now informatics tools to integrate many types of molecular information. Our industrial partner, MIMs, will apply novel informatics tools to generate a test using all the molecular information obtained from blood vesicles and tissue that will be able to find out early if tumour has spread outside of the breast, and how much tumour is left after surgery. The goal is hope to develop a multi-dimensional test for TNBC patients that can be used to decide how much treatment they need and if treatment given after surgery is working.

Trial Description

Primary Outcome:

  • Develop signatures of good and poor outcome as well of tumour response to chemotherapy in TNBCs by integrating multidimensional profiling of both tumour and liquid biopsies making use of Artificial Intelligence (AI) tools
Rationale: The most aggressive form of breast cancer is triple negative breast cancer (TNBC), so called because these tumours do not express hormone receptors or HER2 receptor, and therefore have no readily targetable molecules. Chemotherapy is the only treatment, with chemoresistance signaling a very poor outcome even in early TNBC. The presence of residual tumour at surgery (non-pathological complete response or non-pCR) signals chemoresistance and poor prognosis, with about 30-40% of these patients dying of TNBC within the first 5 years after surgery. A recent clinical trial showed that the addition of further chemotherapy (Capecitabine) results in improved survival in these patients with non-pCR, although only about 15% of such patients do benefit. One of the most urgent unmet needs is to identify patients who will do well despite non-pCR (so as to avoid extra chemotherapy) and who will do poorly despite it, and also to identify factors of poor prognosis that may lead to novel therapeutic strategies in this group.

Current state of advancement of the technology: Until now, no biomarker except BRCA1/2 mutations has demonstrated clinical utility in the treatment of TNBC, likely due to the complex biology and heterogeneity of the disease. With the recent advances in Artificial Intelligence methodology, combining and integrating several layers of molecular data to predict outcome, until now challenging, becomes a reality. The hypothesize is that combining multi-dimensional data of tumour and plasma EVs can facilitate the development of prognostic and predictive signatures in this very aggressive disease.

Preliminary data: Thanks to our Q-CROC-03 biopsy driven clinical trial where tumour and plasma from patients with TNBC resistant to chemotherapy were collected. Whole exome seq data were translated to generate personalized circulating tumour DNA (ctDNA) assays. Our data shows a potential prognostic value to the detection of ctDNA after pre-operative chemotherapy. There is a collaboration established with Rodney Ouellette (ACRI) to isolate and profile extracellular vesicles (EVs) from plasma.

Objectives: The objective of the present study is to develop signatures of good and poor outcome as well of tumour response to chemotherapy in TNBCs by integrating multidimensional profiling of both tumour and liquid biopsies making use of Artificial Intelligence (AI) tools.

Experimental approach: EVs profiling from plasma collected in the Q-CROC-03 trial and the JGH biobank (prior, during and after chemotherapy treatment) will be performed. Profiling will include Whole Genome Sequencing (GWS), proteomics, transcriptomics and miRNA analysis. In collaboration with our industrial partner, My Intelligent Machines (MIMs), experts in bioinformatics and AI, machine-learning algorithms will be developed to integrate OMICs data from resistant tumours with matched plasma EVs data and generate a tumour/plasma signature associated with poor outcome. In parallel, in collaboration with the EXACTIS Innovation Network, patients recruitment, collection of residual tumours post chemotherapy and matched serial plasma samples during capecitabine treatment after surgery to perform the validation of the signature identified, the tumour/EV signature will be associated with patient survival.

Milestones of the proposed project: 1. Profiling of EVs from plasma. 2. Profiling of chemoresistant tumours 3. Development of algorithms to integrate multidimensional data from tumour and EVs.

The developed signatures will be IP protected. Academic and industrial partners will have shared IP (respective % to be determined). Prognostic tests will be developed on identified biomarkers and distributed through MIMsOmic Platform. MIMsOmic is an AI-powered platform commercialized by MIMs and enabling an easy, efficient and cost-effective delivery of clinical tests involving Omic data analysis.

The present project will develop a biomarker signature of poor prognosis for the most aggressive type of breast cancer. This signature will allow the identification of patients who should not be treated with post-surgery chemotherapy, and avoid unnecessary exposure to the toxicity associated with this drug.

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