Maximizing the Robustness of Fragmentomics-based Cancer Biomarkers
Advances in circulating tumor DNA (ctDNA) analysis are rapidly expanding the insights that can be obtained about a patient’s cancer from a blood draw. A new frontier of ctDNA analysis is the field of fragmentomics. Fragmentomics uses computational approaches to identify patterns of ctDNA fragmentation that correlate with local epigenetic features. Recently reported fragmentomics methods classify cancer types based on ctDNA fragment size, end locations, end nucleotide sequence, or abundance at regulatory elements. While promising, these methods were developed on small sample sets, often with confounding between case/controls and batches, raising the risk of classifier overfitting. This proposal will systematically benchmark published fragmentomics algorithms, identify the most robust and generalizable features of DNA fragmentation in cancer, and use this information to develop a fragmentomics-based method for inferring estrogen receptor (ER) status from breast cancer ctDNA. The results of this project will provide critical, lacking knowledge about the relative performance and information content of these methods. This knowledge will accelerate the translation of fragmentomic assays into clinically useful biomarkers. Further, the method we propose for assessing ER status form blood could provide a minimally invasive means of guiding therapy selection for patients with breast cancer.
Final Report:
When cancer cells die, they release fragments of DNA into the bloodstream, known as circulating tumor DNA (ctDNA). In patients with cancer, a simple blood draw allows researchers to isolate ctDNA and analyze it, providing a window into the molecular landscape of a patient’s cancer. Most ctDNA tests have focused on detecting mutations, or changes in the DNA sequence. Recently, however, researchers have found that ctDNA also contains detailed information about epigenetic properties of cancer, including what genes are turned on and off in a patient’s cancer. A promising approach for extracting this information from ctDNA is the emerging field of fragmentomics. Fragmentomics uses computational methods to analyze the fragmentation patterns of ctDNA. Our laboratory and others have found that the sizes and locations of ctDNA fragments contain a great deal of information about which genes are turned on in cancer and how the expression of these genes is regulated.
Our study supported by The Fund for Innovation in Cancer Informatics extended the field of fragmentomics by systematically analyzing how ctDNA fragmentation is influenced by the transcription of genes and the regulatory elements that control gene expression. We found consistent patterns of ctDNA fragmentation that mark expressed genes and active enhancers that fine-tune gene expression. Based on these findings, we developed a “transcriptional activation score” that accurately infers enhancer activity and gene expression in cancer from a blood draw. We uncovered a biological explanation for these patterns: active regions of the genome contain more tightly packed nucleosomes and less of the H1 linker histone, producing distinctive fragmentation signatures. Using these insights, we demonstrated that sequencing ctDNA and measuring fragmentation patterns can identify cancer-specific regulatory programs, distinguish cancer subtypes, and detect expression of clinically actionable targets such as DLL3, HER2, and the androgen receptor enhancer involved in treatment resistance. A paper describing these results is under review in Nature Genetics. This work will advance precision oncology by enabling non-invasive, genome-wide monitoring of tumor gene regulation, improving diagnosis, treatment selection, and real-time tracking of therapeutic response.
The support of The Fund for Innovation in Cancer Informatics has been instrumental in enabling not only this research but also the development of the Baca laboratory in its critical early years. The discoveries enabled by this project will also provide preliminary data for applications for additional federal grants, which will allow us to translate these findings to benefit patients. In addition to the work described above, two offshoots of this study focused on additional applications of our findings are described in manuscripts under review in Nature Communications and accepted at the Journal of Clinical Oncology. We are deeply grateful for the support of The Fund for Innovation in Cancer Informatics, which will significantly enhance our ability to study cancer and guide cancer treatment using a blood test, enabling a new level of precision care to patients with cancer.
Learn More About Their Work:
Journal of Clinical Oncology: Precision Oncology 2.0: Guiding Magic Bullets With Expression-Based Liquid Biopsy