biomodal’s Exhibitor Spotlight Theater and Poster Sessions

Patient-derived pancreatic cancer organoids showed significant growth inhibition when treated with decitabine, an epigenetic inhibitor. Noticeably, the combination of decitabine with a PARP inhibitor showed pronounced synergism in inhibiting tumor organoid growth. By employing 6-base genome multiomics and duet bioinformatic pipeline, we have identified differentially methylated genes that may be responsible for therapy resistance and tumor aggression in pancreatic cancer.

*This Exhibitor Spotlight Theater is a promotional activity and is not approved for continuing education credit. The content of this Exhibitor Spotlight Theater and opinions expressed by presenters are those of the sponsor or presenter and are not of the American Association for Cancer Research® (AACR).

Metastatic castration-resistant prostate cancer (mCRPC) exhibits high mortality due to the emergence of therapy resistance phenotypes. We recently tested a novel combination therapy in patients with mCRPC who had progressed on androgen receptor (AR)-targeted therapies, targeting PD-L1 and PARP1 with durvalumab and olaparib, respectively. While a subset of patients exhibited partial responses, most patients experienced disease progression within a year. To obtain mechanistic insights about the treatment failures, plasma cell-free DNA (cfDNA) was harvested from patients at baseline, after two months of treatment, and upon disease progression. Whole-genome sequencing identified oncogenic mutations capable of predicting therapy response or failures. To infer epigenetic regulation from cfDNA, whole-genome 5-hydroxymethylcytosine and 5-methylcytosine (5hmC and 5mC, respectively) sequencing was performed using the Biomodal evoC platform. Global loss of DNA methylation, which was often prominent in cancers, was observed in cfDNA. More importantly, AR signaling genes, which mark the presence of prostate cancer, were enriched for 5hmCs. Similarly, AR-associated transcription factor binding sites were enriched for 5hmCs and depleted for 5mCs. Notably, genes regulating immune activities and cell adhesion were progressively decorated with 5hmCs from a patient with CDK12 deficiency after treatment. Genetic activities suggestive of distinct tumor phenotypes and treatment failures were also discovered and will be discussed during the presentation. 

Authors: Chennan Li1, Anna Baj1, Clara C. Y. Seo1, Nicholas T. Terrigino1, John R. Bright1, S. Thomas Hennigan1, Isaiah M. King1, Scott Wilkinson1, Shana Y. Trostel1, William D. Figg1, William L. Dahut1, Jung-Min Lee2, David Y. Takeda1, Fatima Karzai1, and Adam G. Sowalsky1 

Affiliations: 1Genitourinary Malignancies Branch, National Cancer Institute, Bethesda, Maryland, USA 

2Women’s Malignancies Branch, National Cancer Institute, Bethesda, Maryland, USA 

*This Exhibitor Spotlight Theater is a promotional activity and is not approved for continuing education credit. The content of this Exhibitor Spotlight Theater and opinions expressed by presenters are those of the sponsor or presenter and are not of the American Association for Cancer Research® (AACR).

*This Exhibitor Spotlight Theater is a promotional activity and is not approved for continuing education credit. The content of this Exhibitor Spotlight Theater and opinions expressed by presenters are those of the sponsor or presenter and are not of the American Association for Cancer Research® (AACR).

Early cancer detection has the potential to significantly improve treatment outcomes and survival rates. Epigenetic biomarkers in cell-free DNA, including DNA methylation, have been shown to differentiate between cancer and non-cancer and are already being integrated into liquid biopsy development programs. Traditional DNA methylation sequencing provides a conflated modified Cytosine (modC) readout, measuring CpGs that are 5-methylcytosine (5mC) or 5-hydroxymethylcytosine (5hmC) but not discriminating between the two states. Dynamic DNA demethylation occurs through TET enzyme activity, with conversion of 5mC to 5hmC preceding eventual loss of methylation.  Hence, we hypothesized that obtaining separate measurements of 5mC and 5hmC would improve the ability to detect the development of colorectal cancer at the earliest stage. 

We have therefore applied a technology which provides the 6-base genome (the complete genetic sequence whilst simultaneously distinguishing 5mC and 5hmC) from low nanogram input quantities of cfDNA. We generated whole genome 6-base data from cfDNA extracted from plasma of 32 healthy volunteers and 37 patients with colorectal cancer (CRC) at stages I and IV. We used machine learning approaches to build classifiers with features based on modC, 5mC alone, 5hmC alone, and both 5mC and 5hmC, to differentiate between cfDNA from patients with cancer and individuals without cancer, as well as between stage I CRC and Stage IV CRC.  

Our findings indicate that separate measurements of 5mC and 5hmC significantly enhance diagnostic accuracy for the detection of stage I CRC (AUC = 0.95) compared to traditional approaches that conflate these markers (modified C, AUC = 0.66). Notably, most regions with an increase in 5hmC in stage I CRC cfDNA also decreased in 5mC in stage IV CRC, suggesting that 5hmC can effectively track regions undergoing demethylation during tumor development. These results support the hypothesis that distinguishing between 5mC and 5hmC can improve the sensitivity of liquid biopsy tests for early cancer detection.  

We feel there is merit in applying 6-base sequencing to larger clinical cohorts, across different indications, to more broadly evaluate the potential of 6-base data to improve the earlier detection, diagnosis, and treatment of disease.  

Jean Teyssandier,  Nicholas Harding, Sabri Jamal, Michael J. Wilson, Gary Frewin, Nicola Wong, William Stark, Mark S. Hill, Páidí Creed1 

1biomodal Ltd, Cambridge, United Kingdom 

We present analysis software optimised to analyse 5mC and 5hmC data at scale and describe its performance on a novel liquid biopsy dataset.  

Methylation data has diverse applications in cancer, including early-stage diagnosis through liquid biopsy, classification to guide treatment pathways, and prognosis. However, analyzing methylation data poses significant challenges, as it is constrained by scalability and usability issues. 

Our recently introduced technology, duet multiomics solution evoC, enables the reading of 6-base information (A, T, G, C, 5-mC and 5-hmC) from DNA, further amplifying the complexity and scale of datasets generated in a single sequencing experiment. To address this, we present a fast and scalable software package for the analysis of 6-base genomes, using multi-core out-of-memory processing to enable extremely efficient computation, even for datasets that are too large to fit into memory. 

Our approach enables the analysis of large datasets, scaling to thousands of samples. Unlike existing tools that exceed typical laptop memory with ~10 samples, the analysis software can efficiently process a colorectal cancer liquid biopsy dataset of over 100 samples in minutes on a standard laptop. Furthermore, it runs a complex DMR model (logistic regression with covariates) genome-wide in under an hour, a task infeasible with current tools. 

The analysis software combines efficient computation with tools for exploratory (e.g., plotting, correlation) and downstream analyses (e.g., DMR identification, PCA). Designed for efficiency and ease of use, it enables users to rapidly transition from raw data to actionable insights and publication-ready results.  As multiomic data become the standard in cancer research, our data structure supports the integration of additional data types, allowing us to handle combined genomic and epigenomic data from solutions like duet evoC. This will enable streamlined and efficient multiomic analysis to uncover deeper biological insights.