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CUPiD, a cfDNA methylation-based tissue-of-origin classifier for Cancers of Unknown Primary

ABSTRACT: Cancers of Unknown Primary (CUP) remains a diagnostic and therapeutic challenge due to biological heterogeneity and poor responses to standard chemotherapy. Predicting tissue-of-origin (TOO) molecularly could help refine this diagnosis, with tissue acquisition barriers mitigated via liquid biopsies. However, TOO liquid biopsies have yet to be explored in CUP cohorts. Using publicly available DNA methylation data, we developed a machine learning classifier termed CUPiD with accurate TOO predictions across 29 tumour classes. We tested CUPiD on 143 cfDNA samples from patients with 13 cancer types alongside 27 non-cancer controls, with overall sensitivity of 87.1% and specificity of 97.9%. CUPiD predictions for a further 41 patients with CUP were made for 78.0% of cases, of which 71.9% were clinically consistent with a subsequent or suspected primary tumour diagnosis. Combining CUPiD with cfDNA mutation data demonstrated potential diagnosis re-classification and/or treatment change in this hard-to-treat cancer group.

PROVIDER: EGAS00001007445 | EGA |

REPOSITORIES: EGA

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Project description:Background: Liquid biopsy based on cell free DNA (cfDNA) analysis holds significant promise as a minimally invasive approach for the diagnosis, genotyping and monitoring of solid malignancies. Human tumors release cfDNA in the bloodstream through a combination of events, including cell death, active and passive release. However, the precise mechanisms leading to cfDNA shedding remain to be characterized. Addressing this question in patients is confounded by several factors, such as tumor burden extent, anatomical and vasculature barriers, release of nucleic acids from normal cells. In this work, we exploited cancer models to dissect basic mechanisms of DNA release. Methods: We measured cell loss ratio, doubling time and cfDNA release in the supernatant of a colorectal cancer (CRC) cell line collection (N=76) representative of the molecular subtypes previously identified in cancer patients. Association analyses between quantitative parameters of cfDNA release, cell proliferation and molecular features were evaluated. Functional experiments were performed to test the impact of modulating DNA methylation on cfDNA release. Results: Higher levels of supernatant cfDNA were significantly associated with slower cell cycling and increased cellular crashes. In addition, a higher cfDNA shedding was found in non-CpG Island Methylator Phenotype (CIMP) models. These results indicate a positive correlation between lower methylation and increased cfDNA levels. To explore this further, we exploited methylation microarrays to identify a subset of probes significantly associated with cfDNA shedding and derive a methylation signature capable of discriminating high from low cfDNA releasers. We applied this signature to an independent set of 176 CRC cell lines and patient derived organoids to select 14 models predicted to be low or high releasers. The methylation profile successfully predicted the amount of cfDNA released in the supernatant. At the functional level, genetic ablation of DNA methyl-transferases increased chromatin accessibility and DNA fragmentation, leading to increased cfDNA release in isogenic CRC cell lines. Furthermore, in vitro treatment of five low releaser CRC cells with a demethylating agent was able to induce a significant increase in cfDNA shedding. Conclusion: Methylation status of cancer cell lines contributes to the variability of cfDNA shedding in vitro. Changes in methylation pattern are associated with cfDNA release levels and might be exploited to increase sensitivity of liquid biopsy assays.

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CUPiD, a cfDNA methylation-based tissue-of-origin classifier for Cancers of Unknown Primary

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