-Author name in bold denotes the presenting author
-Asterisk * with author name denotes a Non-ASH member
Clinically Relevant Abstract denotes an abstract that is clinically relevant.

PhD Trainee denotes that this is a recommended PHD Trainee Session.

Ticketed Session denotes that this is a ticketed session.

569 DNA Liquid Biopsies for Cancer-Associated Venous Thromboembolism Prediction

Program: Oral and Poster Abstracts
Type: Oral
Session: 332. Thrombosis and Anticoagulation: Clinical and Epidemiological: Clonal Hematopoiesis of Indeterminate Potential (CHIP) and Cancer Associated Thrombosis (CAT): Understanding Risk as a Potential Means to Improve Patient Care
Hematology Disease Topics & Pathways:
Research, Bleeding and Clotting, Translational Research, Clinical Research, genomics, thromboembolism, Diseases, real-world evidence, Biological Processes, Study Population, Human
Sunday, December 10, 2023: 5:30 PM

Justin Jee1*, Angela Rose Brannon1*, Chris Fong1*, Adrian Lee2*, Lauren Gray2*, Karl Pichotta1*, Anisha Luthra1*, Monica Diosdado1*, Mohammad Haque1*, Jiannan Guo3*, Jennifer Hernandez3*, Kavita Garg3*, Andriy Derkach, PhD4*, Clare Wilhelm1*, Rohan Singh1*, Maria E. Arcila, M.D.1*, Nick Pavlakis2*, Stephen Clarke2*, Sohrab P Shah1*, Pedram Razavi1*, Jorge Reis-Filho1*, Marc Ladanyi1*, Nikolaus Schultz1*, Jeffrey I. Zwicker, MD5, Michael F. Berger, PhD6*, Bob T Li1* and Simon Mantha, MD, MPH1

1Memorial Sloan Kettering Cancer Center, New York, NY
2GenesisCare, Sydney, Australia
3Resolution Bioscience, Agilent Technologies, Kirkland, WA
4Department of Biostatistics and Epidemiology, Memorial Sloan Kettering Cancer Center, New York, NY
5Beth Israel Deaconess Medical Center Harvard Medical School, Boston, MA
6Memorial Sloan-Kettering Cancer Center, New York, NY


Cancer-associated venous thromboembolism (VTE) is a major source of oncologic cost, morbidity and mortality. Identifying an optimal population for prophylactic anticoagulation is challenging and adds to clinician overhead. Circulating tumor (ct)DNA sequencing assays (“liquid biopsies”) are increasingly deployed in clinic, with multiple US FDA approvals for matching to molecularly targeted therapy. Preliminary data suggest that cell-free (cf)DNA, which is also measured as part of liquid biopsies and may consist of tumor or wild-type DNA, is thrombogenic due to its association with neutrophil extracellular traps. However, clinical validation of cfDNA for VTE prediction is lacking. ctDNA detection is associated with aggressive tumor biology and worse survival; however, whether it is associated with VTE is unknown. To address these gaps, we studied the clinical utility of ctDNA and cfDNA from liquid biopsies for predicting VTE.


We conducted an observational study in two cohorts: 1) Patients with any cancer type at Memorial Sloan Kettering Cancer Center (MSK, New York USA), an academic medical center, with plasma ctDNA sequencing using MSK-ACCESS, a NY State-approved, 129-gene assay (N=4,141). 2) A nonoverlapping cohort of patients at MSK or GenesisCare (Sydney, Australia), a community oncology setting, with stage IV or recurrent non-small cell lung cancer and ctDNA sequencing using a separate commercial assay (Agilent ctDx Lung, N=463). We studied the independent contribution of ctDNA, cfDNA and related variables for predicting VTE using Cox proportional hazards. We studied the potential utility of DNA liquid biopsies for predicting VTE within six months using machine learning models, comparing their dynamic area under the receiver operating curve, precision, and recall to those of the Khorana score, the most widely implemented risk metric. We tested the impact of ongoing anticoagulation for non-VTE indications on future VTE in patients with and without detectable ctDNA using nonrandomized, real-world evidence.


ctDNA detection was associated with an increased risk of VTE in a dose-dependent manner (Figure 1). This association was observed across assays and treatment sites and regardless of genomic subtype. ctDNA was associated with VTE risk in non-small cell lung, breast, pancreatic, prostate, and less represented cancers as well as melanoma but not in bladder, hepatobiliary, or colorectal cancer. By contrast, cfDNA concentration was associated with VTE risk in all cancer types. In multivariate analysis, ctDNA detection, cfDNA concentration, Khorana score, chemotherapy treatment within 30 days, and number of organ sites of metastasis were all independently associated with VTE. In a cohort of patients with treatment naive lung adenocarcinoma with segmented positron emission tomography scans, ctDNA was an independent predictor of VTE after controlling for metabolic tumor volume. A random survival forest machine learning model to predict VTE within six months of plasma draw using liquid biopsy data (i.e. cfDNA and ctDNA levels as well as genomic content), cancer type, and chemotherapy receipt (“LB+”) outperformed the Khorana score in 5-fold cross-validation and was similarly superior in the external lung cohort (Table 1). The superiority of the model was observed in patients treated or not treated with chemotherapy. Additional clinical and radiologic data, including demographics, metastatic sites of disease, and Khorana score components (“All”), did not improve the ctDNA assay-based LB+ model. Patients with anticoagulation had lower VTE rates if ctDNA was detectable (adjusted HR 0.54, 95%CI 0.33-0.87) but not if ctDNA was undetectable.


In patients with cancer, ctDNA and cfDNA are independent biomarkers for VTE. Risk models incorporating liquid biopsy data are feasible in academic or community settings and, if prospectively validated, would provide a means of VTE risk stratification at scale.

Disclosures: Fong: Various biotech exchange traded funds: Patents & Royalties: US20150181840A1. Guo: Agilent: Current Employment. Hernandez: Agilent: Current Employment. Garg: Agilent: Current Employment. Arcila: Janssen Global Services, Bristol-Myers Squibb, AstraZeneca, Roche, Biocartis: Consultancy; Biocartis, Invivoscribe, physician educational resources , Peerview institute for medical education, clinical care options, RMEI medical education: Speakers Bureau. Pavlakis: Boehringer Ingelheim, MSD, Merck, Bristol-Myers Squib, Astra Zeneca, Takeda, Pfizer, Roche, Novartis, Ipsen, and Bayer: Honoraria; Bayer, Pfizer, and Roche: Research Funding. Shah: Canesia Health Inc: Current equity holder in private company. Razavi: Grail, Illumina, Novartis, Epic Sciences, ArcherDx: Research Funding; Novartis, Foundation Medicine, AstraZeneca, Epic Sciences, Inivata, Natera, and Tempus: Consultancy. Reis-Filho: Roche, Genetech, Roche Tissue Diagnostics, Ventana, Novartis, InVicro, GRAIL, Goldman Sachs, Paige.AI and Volition RX: Membership on an entity's Board of Directors or advisory committees; Goldman Sachs, Paige.AI and REPARE Therapeutics: Consultancy. Ladanyi: Merck, Astra-Zeneca, Bristol Myers Squibb, Blueprint Medicines, Janssen Pharmaceuticals, Takeda Pharmaceuticals, Lilly Oncology, LOXO Oncology, Bayer, ADC Therapeutics, Riken Genesis, and Paige AI: Honoraria; LOXO Oncology, Merus, and Helsinn Therapeutics: Research Funding. Zwicker: Incyte Corporation, Quercegen: Research Funding; CSL Behring: Consultancy; Pfizer/BMS, Portola, Daiichi: Honoraria; Sanofi, CSL, Parexel: Consultancy; calyx: Consultancy; Sanofi: Consultancy; Janssen: Consultancy. Berger: Grail: Research Funding; PetDx and Eli Lilly: Consultancy. Li: Amgen, Genentech, AstraZeneca, Daiichi Sankyo, Lilly, Illumina, GRAIL, Guardant Health, Hengrui Therapeutics, MORE Health and Bolt Biotherapeutics: Research Funding. Mantha: Janssen Pharmaceuticals: Membership on an entity's Board of Directors or advisory committees.

*signifies non-member of ASH