Molecular Risk Model Better Predicts Outcomes in Rare Blood Cancer
Chronic myelomonocytic leukemia (CMML) is a rare type of blood cancer that starts in the stem cells in bone marrow and eventually leads to ineffective production of blood. It generally affects older adults and occurs in four out of every million people in the United States. Because the disease shares features with other types of blood cancers, CMML is sometimes misdiagnosed as a myelodysplastic syndrome.
To improve the generally poor treatment outcomes for CMML, researchers have developed various models based on molecular information to better predict the prognosis of the disease.
One of the newer models, called the Molecular International Prognostic Scoring System Risk Stratification Model (IPSS-M), has shown improved accuracy in predicting outcomes. Most recently, this model was introduced for myelodysplastic syndrome.
Dr. Luis E. Aguirre, Malignant Hematology Program
Dr. Luis E. Aguirre, a fellow in the Malignant Hematology Program at Moffitt Cancer Center, recently presented findings at the 2023 American Society of Clinical Oncology Annual Meeting that assessed the applicability and effectiveness of the IPSS-M when applied to patients with CMML.
Aguirre’s team, under the direction of Dr. Rami Komrokji, had previously conducted one of the largest external validation studies of this model in patients with myelodysplastic syndrome and presented this data at the 64th American Society of Hematology Annual Meeting.
In Aguirre’s study, researchers analyzed data from 367 CMML patients who were treated at Moffitt Cancer Center. They looked at both clinical and molecular information to understand the disease better. The IPSS-M score was used as a reference point to compare different models: CMML-specific prognostic scores model (CPSS-Mol), Mayo Molecular model (MMM) and Groupe Francophone des Myélodysplasies model (GFM). They also looked at how long patients survived and whether they developed other complications.
The researchers found that certain gene mutations were more common in CMML patients. By using the IPSS-M model, they categorized patients into different risk groups, ranging from very low to very high. This information helped predict how long patients would survive.
The study showed that both CPSS-Mol and IPSS-M models were better at predicting overall survival compared to MMM and GFM. The accuracy of these models improved by 11% and 6%, respectively. Both models also outperformed their predecessors.
By using IPSS-M for risk stratification, the researchers were able to reclassify a significant number of patients from equivalent CPSS-Mol risk categories. Some patients were reclassified into higher-risk groups, while others were moved to lower-risk groups.
The study also looked at how well the models predicted outcomes in patients who received a specific type of treatment called hypomethylating agent. The IPSS-M, CPSS-Mol and MMM models showed good accuracy in predicting outcomes for these patients.
Ultimately, Aguirre determined that the IPSS-M model can be confidently used in patients with CMML and provides similar accuracy in predicting prognosis compared to the CPSS-Mol.
This is especially important in community health care settings where CMML is often misdiagnosed as myelodysplastic syndrome. In such cases, using the IPSS-M model for risk stratification is unlikely to have negative effects and can help dictate treatment decisions.
Aguirre’s work was honored with the 2023 Conquer Cancer ASCO Annual Meeting Merit Award.
Dr. Luis Aguirre (@LeafOnc) joined President and CEO @PatrickHwuMD at #ASCO23 for a discussion on refining risk stratification in CMML.
— Moffitt Cancer Center (@MoffittNews) June 5, 2023
Learn more ➡️ https://t.co/YTrwTSFWQX#MoffittASCO23 @ASCO pic.twitter.com/hWIlPyGGn8
