Biomedical subjects

Jayastu Senapati

Publications and source records attributed to Jayastu Senapati.

3 recordsLinked to original sources

PubMed2026

Acute Myeloid Leukemia With KMT2A Amplification: A TP53-Alteration-Enriched Subgroup Associated With Chromoanagenesis and Poor Prognosis.

KMT2A amplification (KMT2A-amp) is a rare but aggressive genomic abnormality in acute myeloid leukemia (AML), with limited characterization in prior studies. We retrospectively analyzed 96 patients with AML harboring KMT2A-amp, including 56 newly diagnosed (ND) and 40 relapsed/refractory (RR) cases, with a median age of 68 years. Approximately half of the cases had therapy-related or secondary AML. All cases demonstrated highly complex karyotypes, with frequent -5/del(5q), -7/del(7q), and -17/del(17p). TP53 alteration was present in 93% of patients, whereas other recurrent AML-associated mutations were uncommon, and no AML-defining gene fusions or mutations were identified. In cases evaluated by optical genome mapping, all showed chromoanagenesis involving chromosome 11q23 region. Clinical outcomes were poor, with a median overall survival of 5.5 months in ND and 2.3 months in RR patients. Intensive chemotherapy did not improve survival compared with lower-intensity therapy, whereas venetoclax-based regimens were associated with improved overall survival (7.1 vs 4.6 months; p = 0.04) and event-free survival (6.7 vs 0.17 months; p < 0.01). We conclude that KMT2A-amp AML represents an extremely high-risk subgroup occurring in the context of TP53-associated genomic instability and chromoanagenesis. Its refractoriness to conventional chemotherapy highlights the urgent need for more effective, targeted therapeutic strategies.

KMT2A amplification↗

PubMed2026

Risk Prognostication After Hypomethylating Agents Combined With Venetoclax in AML: The PRISM Risk Model.

PURPOSE: As risk stratification for patients with AML treated with lower-intensity venetoclax-based therapy remains suboptimal, we developed and validated a prognostic model integrating clinical, cytogenetic, and molecular features. METHODS: We assembled a multinational data set comprising 2,092 adults with newly diagnosed AML treated with hypomethylating agents plus venetoclax (HMA + VEN). One thousand nine hundred eighteen patients with complete data were randomly divided into training (70%) and internal validation (30%) cohorts. Two independent external validation cohorts were assembled (n = 500 and n = 222). Modeling overall survival (OS), Elastic Net regression was applied in 1,000 bootstrap samples from the training cohort to select variables for a Ridge regression, which generated a continuous Prognostic Risk Integration for Survival Modeling (PRISM) score and risk categories based on tertiles (PRISM-3: low, moderate, high). These PRISM indices were then computed for the validation cohorts and compared with the 4-gene classifier (based on mutations in FLT3-ITD, N/KRAS, and TP53). RESULTS: PRISM integrated 17 clinical and genomic variables and demonstrated a linear association with OS. PRISM-3 stratified survival consistently across all cohorts (median OS: 25.1-28.8 months for low risk, 12.5-14.7 months for moderate risk, and 5.8-6.7 months for high risk; P < .001). Compared with the 4-gene classifier, PRISM-3 reassigned approximately 40% of patients (and >50% of those with favorable risk) and demonstrated significantly better discrimination in validation cohorts (C-index 0.63-0.65 v 0.59-0.61; P < .05). CONCLUSION: PRISM is a validated prognostic model for patients with AML receiving HMA + VEN that improves survival risk stratification beyond current standard tools and supports individualized, risk-adapted clinical decision making. The model, the PRISM-AML Risk Calculator, is publicly available.

Humans↗

PubMed2026

Inclusion of Multi-Omic Biomarkers Improves Prediction Accuracy of Response, Relapse, and Overall Survival in Acute Myeloid Leukemia Patients Receiving High-Intensity Induction Chemotherapy.

BACKGROUND: Despite advancements in genetic markers for acute myeloid leukemia (AML) risk stratification, outcome prediction remains challenging due to disease heterogeneity and dynamic genetic changes, highlighting the need for reliable biomarkers to improve AML treatment strategies and patient outcomes. To refine outcome predictions, we investigated the use of microbial-derived biomarkers to predict composite complete remission (CRc), relapse, and survival for patients on high- and low-intensity regimens, and to integrate those variables into the widely clinically utilized European Leukemia Network (ELN-2022) genetic risk classification model for high-intensity-treated patients. METHODS: We first developed machine learning models that integrate baseline fecal metabolomics, 16S rRNA-based stool microbiome features, and clinical metadata (sex, antibiotic administration, AML somatic mutations, and cytogenetics) from two cohorts of AML patients (n = 83) undergoing remission induction chemotherapy. Univariate tests and sparse canonical correlation analysis were employed for variable selection and to explore fecal metabolite-microbe relationships. A robust machine learning approach using XGBoost was employed, with 100 stratified data splits (80% training, 20% testing) and coarse-to-fine hyperparameter optimization. Variable importance was aggregated across all models to select key predictors. RESULTS: For high-intensity-treated patients, XGBoost models achieved aggregated AUROC scores of 0.719, 0.729, and 0.65 for CRc, relapse, and overall survival, respectively. For low-intensity-treated patients, these models achieved aggregate AUROC scores of 0.945, 0.724, and 0.768 for these same outcomes, respectively. Integrating the biomarkers identified in the high-intensity machine-learning models with the current ELN-2022 AML risk stratification system effectively stratified patients into risk categories, which obtained higher concordance indices and likelihood ratios, demonstrating improved prognostic accuracy for each outcome compared to ELN-2022 alone. CONCLUSIONS: The inclusion of microbial-derived biomarkers serves as a robust prognostic tool to improve outcome prediction in AML patients, highlighting the potential of its integration into AML risk assessment and paving the way for personalized treatment strategies and improved patient outcomes.

Humans↗