Type: Oral
Session: 618. Acute Myeloid Leukemias: Biomarkers and Molecular Marker in Diagnosis and Prognosis: Refining Diagnostic Risk Assessment
Hematology Disease Topics & Pathways:
AML, Acute Myeloid Malignancies, Genomics, Diseases, Myeloid Malignancies, Biological Processes, Study Population, Human
Here, we aim to analyze pts with MDS/AML overlap to highlight differences in molecular features and clinical outcomes using MDS-based IPSS-M and AML-based ELN risk stratifications. Also, we identified distinct molecular signatures of MDS/AML cases based on our previously reported machine learning unsupervised molecular clustering approach (Kewan & Durmaz, et al. Nat Comm. 2023).
We analyzed 3,598 pts with myeloid neoplasms from a multicenter cohort. Pts with bone marrow (BM) blasts <10% were excluded. Cases were classified into MDS/AML (blasts: 10-19%), oligoblastic sAML (OB-sAML, blasts: 20-29%) and sAML (blasts ≥30%). Only pts with available molecular data were included and IPSS-M was calculated for pts with BM blasts ≤30%. Overall survival (OS) was assessed from the time of diagnosis. The performance of IPSS-M and ELN risk groups on OS prediction was assessed using the Harrell’s C-index. Pts were assigned to one of the 14 identified molecularly distinct clusters (C).
Overall, 898 pts were included, of whom 367 (41%) had MDS/AML, 213 (24%) had OB-sAML, and 318 (35%) had sAML. Only 80 (9%) pts received allogenic BM transplantation. The median age was 71 (IQR: 65-77) years. Normal karyotype was observed in 424 (47%) of the cases. Compared to pts with OB-sAML, pts with MDS/AML had significantly higher median age (73 vs. 71 years, p=0.01), higher hemoglobin level (9.9 vs. 9.4 g/dL, p=0.001), and higher platelet count (91 vs. 50 x109/L, p=<0.001). MDS/AML cases had a significantly higher frequency of normal karyotype vs OB-sAML/sAML (55% vs. 42%, p=0.006). Trisomy 8 (15% vs. 7%) was more often encountered in sAML (BM blasts ≥30%) compared to MDS/AML (p= 0.002). Complex karyotype was not different between the two groups (20% vs. 24%, P=0.297).
For all cases, TET2 (24%), ASXL1 (24%), RUNX1 (23%), and SRSF2 (22%) were the most common identified MT. Pts with MDS/AML had more ASXL1MT (31% vs. 19%, p=0.002), SRSF2MT (31% vs. 17%, p=0.003), TET2MT (38% vs. 13%, p<0.001) and U2AF1 MT (10% vs. 5%, p=0.03). DNMT3AMT (11% vs. 6%) was significantly higher in sAML cases with BM blasts ≥30% compared to MDS/AML (p=0.02).
According to our molecular clustering model, more MDS/AML cases were classified to C6 (normal karyotype, SRSF2MT, and RASMT, 17% vs. 3%) and C9 (normal karyotype, SRSF2MT, ASXL1MTand RUNX1MT, 11% vs. 6%). While more OB-sAML pts were classified to C2 (normal karyotype, DNMT3AMT, and RASMT 27% vs. 15%) and C14 (Del-Y and SF3B1MT, 11% vs 3%). In reverse analysis, MDS/AML cases constituted most of C3 (Del-Y, TET2MT, ZRSR2MT, and ASXL1MT, 88%), C6 (91%), C7 (other MTs, 79%), C9 (76%), C10 (normal karyotype, SF3B1MT, DNMT3AMT, and TET2MT, 14%), and C12 (normal karyotype, TET2MT, ASXL1MT, SRSF2MT, and RUNX1MT, 81%). OB-sAML cases composed most of C5 (Del-20q, U2AF1MT, and ASXL1MT, 55%) and C14 (72%).
Most of the MDS/AML cases were assigned to adverse ELN risk (82% vs. 62%) and 34% had high risk IPSS-M. IPSS-M very-high risk was more frequent among OB-sAML cases (68% vs. 37%). The median OS for MDS/AML pts (25.5, 95% CI: 20.1-34.2 mo.) was significantly higher than OB-sAML (14, 95%CI: 11-22 mo.) and sAML with BM blasts ≥30% (10, 95%CI: 8-13 mo.) pts, p<0.001. Compared to ELN risk groups, IPSS-M had better C-index (95% CI) for OS in MDS/AML (0.724 vs. 0.546) pts and OB-sAML pts (0.608 vs. 0.526).
In summary based on our machine learning model, pts with MDS/AML had unique clinical and molecular features compared to pts with OB-sAML and sAML. ASXL1, SRSF2, TET2 and U2AF1 genes were more frequently mutated in MDS/AML pts. OB-sAML and sAML generally had worse OS, which reflects the advanced stage of the disease. Despite being validated in cases with blasts up to 19%, the IPSS-M provided better OS predictions for both MDS/AML and OB-sAML cases compared to the ELN risk groups, highlighting the urgent need for improved prognostic models for sAML cases.
Disclosures: Carraway: Novartis: Membership on an entity's Board of Directors or advisory committees; Abbvie: Membership on an entity's Board of Directors or advisory committees; Celgene: Research Funding; BMS: Membership on an entity's Board of Directors or advisory committees; Stemline: Membership on an entity's Board of Directors or advisory committees; Daiichi: Membership on an entity's Board of Directors or advisory committees; Jazz: Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Servier: Membership on an entity's Board of Directors or advisory committees. Balasubramanian: Alexion AstraZeneca: Speakers Bureau; Kura Oncology: Research Funding. Bat: Recordati Rare Diseases: Other: Advisory Board; Novartis: Other: Advisory Board; Alexion: Other: Advisory Board; Sanofi: Other: Advisory Board. Madanat: OncLive, MD Education, Sierra Oncology, Stemline, MorphoSys: Consultancy; Blueprint Medicines, MD Education, and Morphosys: Other: travel; Taiho Oncology, Rigel Pharmaceuticals, Novartis: Consultancy; Sierra Oncology, Stemline Therapeutics, Blueprint Medicines, Morphosys, Taiho Oncology, SOBI, Rigel Pharmaceuticals, Geron, Cogent Biosciences and Novartis: Other: Advisory Board; BMS, Kura Oncology, BluePrint Medicines, Geron: Consultancy.