biomodal’s Exhibitor Spotlight Theater and Poster Sessions

Understanding biological changes in health and disease requires a comprehensive approach integrating epigenetics and genetics. Whilst a high number of genetic associations have been linked to disease, we understand that this only accounts for one-third of our health risks. The remaining two-thirds necessitate exploration through alternative modalities, to uncover mechanisms that are not solely driven by genetics. DNA methylation, particularly 5mC and 5hmC modifications, play distinct roles in genome regulation, with 5hmC serving as a crucial marker between inactive, methylated and active, unmethylated regions. 

Here we introduce the duet evoC multiomics solution, which delivers the complete 6-base genome from a single sample, and as little as 5ng DNA, through a single workflow. This approach provides the four canonical genetic bases (A, C, G and T) alongside 5mC and 5hmC calls at single-base resolution. The technology maintains base calling accuracy of WGS whilst overcoming challenges with conventional epigenetic sequencing approaches such as DNA input requirements and necessity for separate genetic and epigenetic workflows.  

The 6-base genome obtained with the duet evoC multiomics solution, facilitates mechanistic insight into genome dynamics, enabling efficient identification, classification, and monitoring of disease progression, thereby advancing our understanding of complex biological processes. 

*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).

The talk will highlight how collaboration between the Cancer Epigenetics Institute at Fox Chase Cancer Center and biomodal resulted in novel research opportunities. Furthermore, we will highlight how our studies have uncovered unappreciated mechanisms controlling DNA amplification and rearrangements, and offered new strategies for epigenome modification with potential therapeutic implications.

*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).

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).

Background: Patients with pancreatic ductal adenocarcinoma (PDAC) have a 5-year survival rate of 13%. In those with unresectable disease, standard chemotherapy offers limited benefit, with a median survival between 8 and 12 months. Approximately 17% of patients with PDAC carry a germline or somatic mutation in the BRCA or PALB2 genes. These patients may benefit from poly (ADP-ribose) polymerase inhibitors (PARPi), which induce synthetic lethality in tumors with homologous recombination deficiency. The POLO trial showed that maintenance olaparib – a PARPi – doubled progression-free survival in patients with germline BRCA mutations and metastatic PDAC. We’ve previously reported that DNA methyltransferase inhibitors (DNMTi) can induce a “BRCAness” phenotype in other types of cancer, thus sensitizing BRCA-proficient cancer cells to PARPi. Here we explore the synergistic potential of PARPi talazoparib (TAL) and DNMTi decitabine (DAC) in pancreatic cancer cell lines and patient-derived pancreatic tumor organoids (PTOs).

Methods: Synergy studies with TAL and DAC were performed in two murine (Panc02 and KPC) and three human (Capan1, SW1990, and MIA PaCa-2) pancreatic cancer cell lines with diverse genetic profiles. Of note, Capan1 harbored a BRCA2 mutation. Combination TAL+DAC treatment was performed in a dose-dependent and fixed ratio scheme, and the various degrees of synergy were determined using Chou-Talalay mathematical modeling (Compusyn). Clonogenic assays were performed with murine cell lines. Three patient-derived PTOs were treated with single agent (TAL or DAC) or combination (TAL+DAC) regimens and response was determined by MTS assay. Methylomic profiles including gene level methylation and hydroxymethylation fraction in treated and untreated PTOs were acquired using Biomodal’s Duet Multiomics Assay. Statistical analyses for clonogenic and MTS assays were performed using GraphPad Prism.

Results: A synergistic inhibitory effect was observed in all pancreatic cancer cell lines when treated with TAL+DAC. The Capan1 cell line was more sensitive to TAL, as expected. Clonogenic survival assays showed a statistically significant decrease in colony numbers after treatment with the combination regimen compared to single-agent treatment. All PTOs exhibited growth inhibition when treated with DAC alone, whereas TAL alone had limited effects. The combination of TAL and DAC at low doses was more effective than either agent alone. Methylomic analysis revealed decreased methylation of key genes in DAC-treated PTOs, validating DAC’s mechanistic effects.

Conclusions: The combination of TAL and DAC demonstrates synergistic cytotoxicity in pancreatic cancer cell lines and tumor organoids, suggesting potential for this treatment strategy in BRCA-mutated and BRCA-proficient PDAC.

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.  

Background: Small cell lung cancer (SCLC) is the most lethal form of lung cancer. This poor prognosis is due in large part to SCLC’s recalcitrance to therapy. The standard of care for extensive-stage SCLC (ES-SCLC) is chemotherapy with immunotherapy. While most tumors initially respond, responses are transient and >80% of tumors progress within one year of treatment. Seminal studies have shown that sequencing aggressive tumors before and after therapy identifies drivers of resistance. Unfortunately, the aggressive clinical course of ES-SCLC has precluded collection of patient-matched naïve and recurrent tumor tissue traditionally required to study evolution. In lieu of this, other groups have used circulating tumor DNA (ctDNA) to study SCLC evolution. However, these studies used sequencing panels that analyze <0.004% of the SCLC genome. Using new methods to comprehensively trace how SCLCs evolve to therapy resistance is a significant unmet need.

Methods: Medical records were reviewed to identify treatment naïve ES-SCLC patients with longitudinal liquid biopsies collected pre- and post-therapy using IRB-approved protocols. ctDNA was isolated from plasma using NucleoSnap cfDNA kits. Germline DNA was extracted from buffy coats using NucleoSpin Blood kits. Whole genome sequencing (WGS) libraries were prepared using Twist cfDNA Library Preparation with UMI Adapter kits. Deep WGS of ctDNA (>100X) and germline DNA (>40X) was performed using an Illumina NovaSeqX sequencer. Reads were aligned to the hg38 reference genome with BWA-mem. Somatic mutations were called using a two-of-three consensus approach and Mutect2, Strelka2, and MuSE. Structural variants were called using a consensus approach with GRIDSS, SvABA, and Manta. Subclonal copy number alterations were identified with Battenberg. Pyclone was used to reconstruct clonal populations.

Results: As expected, we identified deleterious genomic alterations impacting TP53 and RB1 tumor suppressor genes, known drivers of SCLC development. COSMIC mutational signature analysis identified high activity of tobacco carcinogen, APOBEC, and replicative-based processes across tumors. We found highly varied patterns of genomic evolution following frontline chemoimmunotherapy across patients. Most ES-SCLC tumors exhibited genomic evolution at recurrence, with evidence of focal amplifications and deletions or whole genome duplication. Some tumors exhibited minimal copy number evolution at relapse, suggesting non-genomic mechanisms driving resistance. We also found that second line therapies forced additional genomic evolution following initial recurrence.

Conclusions: Our data demonstrates that deep WGS of ctDNA recapitulates known genetics of ES-SCLC and enables sensitive characterization of resistant clones. Our data highlights the under-appreciated role of genomic evolution in ES-SCLC frontline therapy resistance.

Background:

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. The goal of the current study is to perform comprehensive profiling of circulating tumor DNA (ctDNA) and buffy coat to assess the molecularcharacteristics of progressive vs. responsive prostate tumors.

Methods:

We obtained plasma cell-free DNA (cfDNA), a mixture of ctDNA and other tissue-derived DNA, from 38 individuals treated with the PD-L1/PARP1-targeted combination therapy (NCT02484404) at baseline, after two months of treatment, and upon disease progression. Whole-genome sequencing (WGS, median coverage: 139×) was performed using cfDNA and buffy coat DNA as germline control. Germline and somatic mutations including small mutations and structural rearrangements were individually curated to determine cfDNA tumor fraction. To infer epigenetic regulation, whole-genome 5-hydroxymethylcytosine and 5-methylcytosine (5hmC and 5mC, respectively) sequencing was performed using the Biomodal evoCplatform. T/B cell receptor repertoire was inferred from buffy coat sequencing using MiXCR.

Results:

A negative association between baseline cfDNA tumor fraction and time-to-progression was observed. BRCA2 alterations were associated with durable responses, whereas oncogenic mutations in TP53 and MAPK signaling pathway were associated with intrinsic resistance or rapid progression. CDK12 deficiency, which is associated with a more immunogenic phenotype, was observed in three patients whose genomes harbored an abundance of tandem duplications, and demonstrated initial responses to therapy prior to eventual progression. Greater baseline clonotype diversity of the alpha/beta chains of the T cell receptor correlated with favorable response. Finally, using cfDNA methylation sequencing, we identified disease-specific AR signaling genes 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.

Conclusions:

Baseline genomic deficiencies associated with DNA damage repair and baseline T cell clonotype diversitywere differentially correlated with PD-L1/PARP1-targeted therapy response. Time-course evaluation of global patterns of 5hmCs and 5mCs identified distinct epigenetic mechanisms associated with therapy response or failure in each patient.

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

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.  

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