This is an early access version, the complete PDF, HTML, and XML versions will be available soon.
A Nomogram Prediction Model and Scoring System for Resistance in Acute Myeloid Leukemia Patients Treated with Venetoclax Combined with Hypomethylating Agents
by
Qingqing Fan
1,†, 
Yujiao Guo
1,†,
Xiang Hui
1,
Yu Zhang
1,
Jianrui Li
1,
Jinhua Liang
2,* and
Yongqing Wang
1,3,4,* 1
Department of Pharmacy, The First Affiliated Hospital with Nanjing Medical University, Nanjing 210029, China
2
Department of Hematology, The First Affiliated Hospital with Nanjing Medical University, Nanjing 210029, China
3
Research Division of Clinical Pharmacology, The First Affiliated Hospital with Nanjing Medical University, Nanjing 210029, China
4
School of Pharmacy, Nanjing Medical University, Nanjing 211166, China
*
Authors to whom correspondence should be addressed.
†
These authors contributed equally to this work.
Curr. Oncol. 2026, 33(6), 357; https://doi.org/10.3390/curroncol33060357 (registering DOI)
Submission received: 8 April 2026
/
Revised: 24 May 2026
/
Accepted: 3 June 2026
/
Published: 13 June 2026
(This article belongs to the Special Issue Deep Insights into Acute Myeloid Leukemia: Molecular Targets and Evolving Treatment Paradigms)
Simple Summary
Acute myeloid leukemia (AML) is a type of blood cancer. In this study, we looked at patients with AML who were treated with a combination of two medicines (called VEN and HMAs). We wanted to find out which factors might predict that the cancer does not respond to these medicines. We studied 74 patients and analyzed their medical data. We found that three specific features—certain changes in two genes (KIT and TP53) and a specific subtype of AML (called FAB-M5)—were linked to a higher chance of the medicines not working. Using this information, we created a simple scoring tool that divides patients into low, medium, or high risk for treatment resistance. This tool may help doctors predict whether a patient is likely to benefit from this medicine combination, allowing for better treatment decisions in the future.
Abstract
To investigate the predictive factors for resistance to VEN combined with HMAs in the treatment of AML, construct a drug resistance prediction model, and visualize the model. A retrospective analysis was conducted on 74 AML patients. Multivariate logistic regression was used to identify independent predictors of primary resistance, based on which a nomogram model and a risk scoring system for drug resistance were constructed. The results showed that KIT (p = 0.012), TP53 (p = 0.010), and FAB-M5 (p = 0.059) were significantly associated with primary resistance to VEN. A nomogram prediction model incorporating FAB-M5, KIT, and TP53 was established. Based on the nomogram model, a drug resistance prediction scoring tool comprising three variables was developed, categorizing patients into high-risk (6–10 points), intermediate-risk (3–5 points), and low-risk (0–2 points) groups. Significant differences in NR rates were observed among the three risk groups (p < 0.001). KIT, TP53, and FAB-M5 are independent factors influencing VEN resistance. The constructed nomogram prediction model and scoring system may provide valuable references for predicting primary resistance to VEN.
Keywords:
venetoclax; BCL2; drug resistance; acute myeloid leukemia; prediction model
