Back

Single-cell transcriptomics-guided development of flow cytometric tests predicting chronic myeloid leukemia blast crisis transformation at chronic phase diagnosis

Yu, M.; Krishnan, V.; Shyamsunder, P.; Lee, K. L.; Schmidt, F.; Ren, X.; Chuah, C.; Prabhakar, S.; Saunders, V.; Yeung, D.; Shanmuganathan, N.; Yong, A.; Hughes, T.; Ong, S. T.

2025-01-15 cancer biology
10.1101/2025.01.09.632094 bioRxiv
Show abstract

Clinical risk scores in chronic myeloid leukemia (CML) are inadequate for identifying chronic phase (CP) patients at high-risk of blast crisis (BC) progression. The lack of accurate predictive tests hamper timely interventions, including stem cell transplants, that are more effective in early disease. By interrogating a single cell atlas of primary imatinib resistance for a BC-like gene expression signature, we identify aberrant CD42A+ megakaryocytic and CD10+CD19+ lymphoid progenitor expansion, as well as STAT1- and IFN{gamma}-related inflammatory programs, as consistent features in the bone marrow and peripheral blood of CP patients at high-risk of BC transformation. We develop multi-color flow cytometry-based tests (MFC) to detect these features in CP patients at the time of diagnosis. Validating our MFC panels on a combined Australia-Singapore cohort comprising 28 CP patients, including 14 who underwent transformation, we demonstrate their ability to detect 100% of CP patients who transform with no false positives. Our findings highlight small populations of inflamed hematopoietic stem and progenitor cells, present at CP diagnosis, as powerful harbingers of future BC transformation. The ability of MFC panels to detect these cells at diagnosis support their inclusion as accurate risk-assessment tools to improve management of high-risk patients.

Matching journals

The top 4 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.