REVEAL GENOMICS´ Breakthrough Technology Enters the Liquid Biopsy Field in Oncology

REVEAL GENOMICS, S.L., a Barcelona-based biotechnology start-up seeking to revolutionize precision oncology through biomarker innovation, announced  a new addition to its pipeline consisting of a novel biomarker approach in liquid biopsy for patients with advanced cancer. The main findings of this development were published in Nature Communications this month, in collaboration with several academic institutions, including the Hospital Clinic/IDIBAPS de Barcelona (Spain), University of Barcelona (Spain), Vall d’Hebrón Institute of Oncology (Barcelona, Spain), Hospital Universitario 12 de Octubre (Madrid, Spain), the Catalan Institute of Oncology (Badalona, Spain), and the University of North Carolina (Chapel Hill, USA).

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“Our proprietary and novel supervised learning computational approach predicts complex tumor features including gene expression, protein and tumor histology using one data source: DNA sequencing. Here, we demonstrate that our approach is feasible in plasma cfDNA and that it provides clinically relevant information.”

Analysis of cell-free DNA (cfDNA), present in body fluids such as plasma, is broadly known as liquid biopsy. In oncological patients, cfDNA contains a fraction of tumor-derived genomic material (ctDNA), that has been proven as a valuable resource to identify tumor-borne genetic alterations. However, cancer is highly complex, and additional biological information is needed to refine the prediction of patient outcome and/or benefit from treatment.

Dr. Aleix Prat, Co-Founder and CSO of REVEAL GENOMICS and the lead author of the study, explained: “Our proprietary and novel supervised learning computational approach predicts complex tumor features including gene expression, protein and tumor histology using one data source: DNA sequencing. Here, we demonstrate that our approach is feasible in plasma cfDNA and that it provides clinically relevant information.”

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New product in the pipeline
Single DNA alterations in metastatic breast cancer, such as ESR1 or PIK3CA mutations, are valuable information but are not enough to predict the patient outcome. To address this issue, investigators from REVEAL GENOMICS sequenced cfDNA from 459 patients with metastatic breast cancer, including 245 patients with hormone receptor-positive/HER2-negative (HR+/HER2-) treated with endocrine therapy and a CDK4/6 inhibitor

Firstly, REVEAL GENOMICS’ proprietary machine-learning multi-gene signatures were applied in plasma and linked to DNA/RNA/protein data from tumor tissue (paired samples). The results showed, for the first time, that multi-gene signatures tracking complex biological features can be successfully identified in ctDNA. These complex biological features include, among others, measures of tumor proliferation and estrogen receptor signaling, similar to what is accomplished using direct tumor tissue DNA or RNA profiling. For example, one of REVEAL GENOMICS´ signatures in plasma ctDNA, known as the estrogen receptor (ER) signaling signature, predicted the ER status of the tumor tissue as determined by standard immunohistochemistry.

Secondly, the authors discovered that the combination of 150 biologically relevant signatures identified 4 new DNA-based molecular subtypes of breast cancer. More importantly, these 4 biological entities were strongly associated with survival outcome in patients with early-stage and/or metastatic HR+/HER2- breast cancer.

Finally, the investigators focused on a particular genomic signature called retinoblastoma loss-of-heterozygosity (RB-LOH), which was designed to capture tumor biological status induced by the loss of RB – a known key player in tumor cell division needed for CDK4/6 inhibitors to work. In patients with metastatic HR+/HER2- breast cancer treated with endocrine therapy and a CDK4/6 inhibitor, REVEAL GENOMICS’ ctDNA RB-LOH signature identified the 20-30% of patients who do not respond well to this treatment.

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