The Role of cfDNA in Early Cancer Detection

Cancer has been shown by sufficient clinical studies to have a better prognosis and survival rate when diagnosed and treated earlier, thus early detection of cancers and their markers serves as a pivotal role in cancer intervention and treatment. Cell-free DNA (cfDNA) provides a non-invasive and easily accessible way for multi-cancer early detection and monitoring due to the presence of circulating tumor DNA (ctDNA), which reflects tumor-specific genetic and epigenetic alternations, and are physically shorter than normal cfDNAs. However, such an early detection by cfDNA-based liquid biopsy method still remains as a challenge due to its yet to be improved sensitivity when comparing with late-stage detection.

Enhancing Cancer Prognosis with Integrative Modeling

To tackle this issue and further enhanced cancer prognosis, a cornerstone work on integrative modeling Bae et al., Nat Commun, 2023 of tumor genomes and epigenomes cfDNA has been recently published in Nature Communication, showing its algorithm detects early-stage cancers with high sensitivity that is comparable to that of late-stage detection. Moreover, the model interpretation reveals the contribution of cancer type-specific genomic and epigenomic features. A total of 3366 samples were split into 2 cohorts sequenced on MGI DNBSEQ G400 and Illumina Novaseq-6000 using PE100, respectively.

Superior Performance of MGI Data and Future Directions

At 95% specificity, the combined model had a higher sensitivity of 91.1% on the MGI data for stage I cancers than that of 79.6% from Illumina data. Moreover, per tumor type evaluation was performed to reveal the accuracy of localizing the tissue of origin. The genome and epigenome models by both the MGI and Illumina cohorts outperformed the existing methods, and further improvements were achieved when combing the two models. Overall speaking, a better performance is achieved on the MGI data compared with the Illumina data implies that superior data quality and higher read depths achieved by MGI DNBSEQ platform together its cfDNA library kit contribute to prediction accuracy. The methodologies proposed in this study lay the foundation for accurate cfDNA-based cancer diagnosis, especially at early stages. The results could be further improved when coupling with integrative modeling based on tumor biology, an increasing amount of reference and cfDNA analysis training data to accomplish ultrasensitive early cancer detection and accurate cancer diagnosis.

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