Circulating DNA (cfDNA) has emerged as a potential biomarker in cancer, and is the subject of extensive studies in translational and clinical research. Our group has been interested in its implication and clinical significance in the field of oncology for many years, and is now focused on evaluating its potential for early cancer detection.
We recently developed a screening test (MNR: Multi Normalized Ratio), based on various cfDNA parameters determined by a specific q-PCR based method, targeting both nuclear and mitochondrial sequences.
When applied to the supernatant of cell culture, the MNR had a discriminative potential of 100% between normal and cancer cell lines. An extensive evaluation of this test was carried out in plasma samples of 289 healthy subjects and 987 cancer patients (CRC, breast, liver, pancreatic, ovarian) of all stages. Preliminary results revealed a high potential with an AUC of 0.81 (0.78-0.84, 95% CI), a 70% sensitivity (Se) and 77% specificity (Sp). In breast cancer (N = 169), an AUC of 0.82 (0.78-0.86, 95% CI) with 72% Se and 80% Sp were observed. In all stages CRC patients (N = 795), the results showed an AUC of 0.80 (0.78-0.84, 95%, CI), 75% Se and 70% Sp; for CRC stages 0/I/II (N = 426), an AUC of 0.79 (0.75-0.82, 95% CI), 70% Se and 72% Sp; and for CRC stage IV (N = 186), a 0.86 AUC (0.82-0.89, 95% CI) with 75% Se and 80% Sp. When combining the MNR to a total cfDNA concentration threshold value (AUC = 0.81 (0.79-0.83, 95% CI), 72% Se and 76% Sp for all stage cancers (N = 987)), in a test cohort of 173 stages 0/I/II CRC patients and 132 healthy individuals, we increased the sensitivity and specificity to 74% and 95% respectively. Furthermore we recently discovered that cfDNA fragmentation, as determined by Whole Genome Sequencing using either double or single strand library, is also a parameter enabling discrimination between healthy and cancer individuals.
The implementation of a multi-parametric test combining total cfDNA quantification, MNR and fragmentation biomarkers, with help of a decision tree in machine learning, is currently on-going. Our data suggest that our strategy in targeting cfDNA structural features might be powerful for early cancer detection, and appears as an alternative or a synergistic combination to the detection of mutations.
Alain R. Thierry - INSERM (Institut national de la santé et de la recherche médicale).
MSDAvenir - Mitest / Alain R. Thierry is supported by INSERM (Institut national de la santé et de la recherche médicale).
All authors have declared no conflicts of interest.