EClinicalMedicine. 2026 Oct;100
104238
Riya Nandi,
Kun Zhao,
Yash Pershad,
Nabi Nazmoon Khondoker,
Caitlyn Vlasschaert,
Md Mesbah Uddin,
Pradeep Natarajan,
Daniel J DeAngelo,
Marlise R Luskin,
R Coleman Lindsley,
Hajime Uno,
Shai Shimony,
Alexander G Bick,
Lachelle D Weeks.
Background: Clonal haematopoiesis is a precursor of myeloid neoplasia and common in older adults, Precursor risk stratification is critical to early cancer detection, guiding surveillance and prevention efforts to high-risk populations. Existing clonal haematopoiesis risk scores use molecular features to estimate risk of myeloid neoplasia, but only around 1% of adults with detectable clonal haematopoiesis are at high risk of myeloid neoplasia transformation. Moreover, sequencing based molecular diagnostics are expensive with limited global availability highlighting the need for pre-sequencing strategies which identify populations enriched for individuals with high-risk clonal haematopoiesis.
Methods: We used blood count parameters, age, and sex as input features to and develop a multi-class machine learning model that pre-classifies individuals based on their probability of having high-risk clonal haematopoiesis or developing future myeloid neoplasia. Stratified splitting was used to divide 461,576 eligible UK Biobank participants (recruited October 1, 2006 to March 13, 2010) into a training cohort for model derivation (n = 369,260) and a test cohort for internal validation (n = 92,316). Ground truth output class assignments were defined based on documented progression to incident myeloid neoplasia and presence of clonal haematopoiesis with high-risk features defined using molecular parameters of the clonal haematopoiesis risk score. Individuals were labelled positive if they had high-risk clonal haematopoiesis and progressed to myeloid neoplasia, negative if they lacked clonal haematopoiesis with high-risk features, and equivocal if they had higher risk features but had no documented incident myeloid neoplasia event. We then compared model predictions against ground truth labels for validation in the UKB test cohort as well as in independent clinical and population cohorts from Dana-Farber Cancer Institute Hematologic Malignancy Data Repository (HMDR; n = 2233; March 26, 2010, to June 5, 2024) and the All of US database (n = 14,674; May 31, 2017 to October 1, 2023), respectively.
Findings: The resulting pre-sequencing risk model (PRISM) categorises all cases of high-risk clonal haematopoiesis as positive and pre-classifies 40% (2158 of 5383) of low-risk clonal haematopoiesis and 55% (47,894 of 86,429) of clonal haematopoiesis-negative cases as negative. Validation of PRISM revealed an area under the curve of 0.89 (95% CI 0.85-0.93) in the UK Biobank test cohort, 0.74 (0.61-0.84) in HMDR, and 0.87 (0.77-0.96) in All of US. Negative predictive values for negative class assignments were 0.993 (95% CI 0.992-0.993) in the UKB test cohort, 0.968 (0.943-0.981) in HMDR, and 0.998 (0.997-0.999) in All of US.
Interpretation: PRISM positive class assignment defines a population most enriched for high-risk clonal haematopoiesis, while negative class predictions, mark individuals without high-risk clonal haematopoiesis. PRISM therefore provides a framework for reproducibly assigning blood count-based risk thresholds to prioritise clonal haematopoiesis screening in populations at risk for myeloid neoplasia.
Funding: Damon Runyon Cancer Research Foundation, Edward P Evans Foundation for MDS, and the National Heart Lung and Blood Institute.
Keywords: Blood counts; Clonal haematopoiesis; High-risk clonal haematopoiesis; Myeloid neoplasia; PRISM; Pre-sequencing risk model