In a new study published in Leukemia titled, “Spatial remodeling of bone marrow architecture defines tissue-state signatures of disease activity and therapeutic response in myelodysplastic neoplasm,“ researchers from Weill Cornell Medicine have developed a new AI-based method to assess patients with a form of blood cancer called myelodysplastic neoplasms (MDS). The approach compares the size and shape characteristics of hematopoietic cells in the patient’s bone marrow sample with those in healthy bone marrow to generate a score that reflects disease severity.
MDS is a form of blood cancer that usually affects older adults. About one-third of patients progress to a more aggressive cancer called acute myeloid leukemia. MDS patients must undergo frequent biopsies to identify signs of remission or worsening disease.
“It’s fundamentally a chronic and progressive disease,” said Sanjay Patel, MD, clinical chief of hematopathology, and an associate professor of pathology and laboratory medicine at Weill Cornell Medicine. “With the current methods pathologists use to assess MDS samples, there are some clear-cut cases and a lot of gray area.”
The researchers developed a new method using fully deidentified patient samples that may provide more clarity for patients with MDS and their physicians.
“Using AI to assist us in looking at the spatial architecture of the bone marrow using widely available laboratory assays allows us to improve our capability to assess patients’ prognosis and perhaps triage them for precision therapies,” said David Redmond, PhD, assistant professor of computational biology research in medicine at Weill Cornell Medicine.
The MDS-Microarchitectural Perturbation Score (MDS-MAPS) score ranks patient samples based on 82 features associated with normal tissue and different genetic subtypes of MDS. The tool uses routinely collected samples, tissue staining protocols, and imaging technologies that could be implemented in most hospital pathology departments and laboratories.
“We can generate an MDS-MAPS value for a patient at diagnosis and track how it changes over time,” said Patel.
The next step will be to validate the method in larger cohorts of patient samples. The researchers will collaborate with Pinkal Desai, MD, associate professor of medicine at Weill Cornell Medicine and a hematologist/oncologist at NewYork-Presbyterian/Weill Cornell Medical Center, to study how the tool performs on other forms of MDS, and increasingly recognized precursor conditions.
“Patients with MDS and related precursor conditions have many mutations as part of the disease biology and each patient’s molecular signature is different,” said Desai. “We have always wondered if these mutations signal a different spatial pattern and whether these patterns have an impact in predicting how patients progress and respond to treatments. Together we are poised to harness technology to answer real-world clinical questions.”
