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126 Epigenetic Age Acceleration in Hematological Malignancies: Beyond Chronological Age, Clinical Implications, and Therapeutic Perspectives

Program: Oral and Poster Abstracts
Type: Oral
Session: 803. Emerging Tools, Techniques and Artificial Intelligence in Hematology: Reading the Blood: Generative and Discriminative AI in Hematology
Hematology Disease Topics & Pathways:
Research, epidemiology, Clinical Research
Saturday, December 9, 2023: 10:45 AM

Vikram Dhillon, DO, MBA1,2, Jeff Aguilar, MD, MBA3 and Suresh Kumar Balasubramanian, MD4,5

1Department of Hematology/Oncology, Neal Cancer Center/Houston Methodist Hospital, Detroit, MI
2Department of Oncology, Karmanos Cancer Center/Wayne State University, Detroit, MI
3Wayne State University/Karmanos Cancer Center, DETROIT, MI
4Karmanos Cancer Institute/Department of Oncology, Wayne State University, Detroit, MI
5Department of Translational Hematology and Oncology Research, Taussig Cancer Institute, Cleveland Clinic, Cleveland, OH

Background: Age is important in prognosticating hematological malignancies. Biological based on DNA methylome changes offers new insights into the complex interplay between epigenetics, genetics, environment, and cancer. DNA methylation levels at specific CpG sites evolve in a predictable fashion with aging and can be used to develop biomarkers that estimate the epigenetic age (EA) of a tissue. Such biomarkers are called epigenetic clocks (Fig 1-B). Based on the EA, we can determine Epigenetic Age Acceleration (EAA), a tool to identify patients aging faster biologically than their chronological age. We sought to build a blood-based clock to predict epigenetic age in healthy young and old adults and subsequently assess the degree of EAA in AML patients. Finally, among multiple planned downstream applications, we propose to use EAA as a biomarker for monitoring treatment responses in targeted therapies (Fig 1-A).

Methods: We used ClockBase and NCBI's Gene Expression Omnibus (GEO) repositories to collect methylation data based on Illumina’s 450k array for healthy adults and patients with AML/MDS. Pre-processing was done using the minifi BioConductor package. We used a cohort of healthy young adults (YA, aged 18-30 ys.) to train our epigenetic clock and calibrate it with three well-studied epigenetic clocks: Horvath clock (HC), Hannum clock (HAC), and PhenoAge (PA). We hypothesized that EA and chronological ages (CA) should approximate each other in younger patients as they do not generally carry comorbidities or have a higher CHIP prevalence which could influence EA. We compared our clock’s performance to known epigenetic clocks, and after fine-tuning, all 4 models were validated in a cohort of healthy older adults (OA, aged 70-85 ys.) with a higher risk of CHIP. Then, we tested our clock in an AML patient cohort and illustrated how EAA can predict therapy responses in AML patients treated with IDH1/2 inhibitors (Published methylation data from Wang et al 2021, Nat Comm.)

Results: We designed the clock using an Elastic-Net regression scheme. We screened 130,000 patient samples from GEO and 1,813 eligible samples were divided into 3 cohorts: 800 healthy younger adults (aged 18-30 ys.), 800 healthy elderly adults (aged 70-85 ys.), and 213 patients with AML.

To train and test our clock, we used the YA cohort (n=800) with a median age of 20 ys. (SD 3.168 ys) and our epigenetic clock predicted a median EA (MEA) of 19.1 ys. (SD 3.4 ys). HC, HAC and PA predicted MEA of 21.6 ys. (SD 3.9 ys.), 19.4 ys. (SD 4.1 ys.) and 21.5 ys. (SD 3.9 ys.).

After fine-tuning of weights, we tested our clock in OA cohort (n=800) with a median age of 78 ys. (SD 4.8 ys.). Our clock predicted a MEA of 82.3 ys. (SD 4.9 ys.). HC, HAC and PA predicted a MEA of 83.7 ys. (SD 5.7 ys.), 85.2 ys. (SD 6.0 ys.) and 84.9 ys. (SD of 5.1 ys.) resp. The AML cohort (n=213) had a median age of 80.9 ys. (SD 2.5 ys.). We expected a significantly higher EA due to the mutation burden along with genomic and epigenomic changes induced by the disease, our clock predicted a MEA of 84.7 ys. (SD 3.0 ys.).

EAA was calculated from the difference between EA and CA. YA cohort had a median EAA of 0.9 ys. (SD 1.8 ys.) and our clock predicted a median EAA of 1.1 ys. (SD 2.0 ys.). HC, HAC, and PA predicted a median EAA of 1.4 ys. (SD 2.3 ys.), 1.4 ys. (SD 2.9 ys.), and 1.5 ys. (SD 2.4 ys.). In the OA cohort, we expected higher EAA due to age-associated cardiometabolic disease and CHIP; our clock predicted a median EAA of 4.2 (SD 2.1). HC, HAC, and PA predicted a median EAA of 6.0 ys. (SD 2.7), 5.6 ys. (SD 3.0 ys.), and 5.1 ys. (SD 2.2 ys.). For the AML cohort, our clock predicted a median EAA of 8.0 ys. (SD 5.6).

We then applied our clock to a cohort of 60 AML patients treated with IDH inhibitors to predict whether a patient cleared the IDH clone after therapy. We discovered that patients who failed to clear the IDH-clone (n=14) had a higher EAA (3.23 vs 7.91 ys., p=0.012) and shorter RFS compared to patients who cleared the IDH-clone (n=7) [RFS 20.6 vs 6.1 mo., p=0.0086].

Conclusion: We built an epigenetic clock that outperforms three standard clocks in predicting epigenetic age and applied it to AML patients demonstrating a significant increase in epigenetic age. We also illustrated how EAA can be used as a biomarker for targeted therapies. Ongoing work at Karmanos Cancer Center is focused on improving the underlying regression model for more sensitive epigenetic age predictions and implementing novel pace-of-aging algorithms for EAA calculations.

Disclosures: Balasubramanian: Karyopharm Therapeutics: Other: Drug supply for research; Kura Oncology: Research Funding.

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