Session: 618. Acute Myeloid Leukemias: Biomarkers and Molecular Markers in Diagnosis and Prognosis: Poster II
Hematology Disease Topics & Pathways:
Research, Acute Myeloid Malignancies, AML, Adult, Translational Research, Genomics, Bioinformatics, Diseases, Immune mechanism, Computational biology, Myeloid Malignancies, Biological Processes, Molecular biology, Technology and Procedures, Profiling, Study Population, Human, Molecular testing, Omics technologies
Methods: We initially re-analyzed legacy RNA-seq data from the Beat-AML Master Trial, which includes a FLT3-mut (FLT3-pos) and a FLT3-wt (FLT3-neg) cohort of samples. Published gene signatures related to T-cell dysfunction in cancer (Chu et al, Nature Medicine 2023) and AML stem cell hierarchies (Zeng et al, Nat. Med. 2023 and Long et al, Cancer Cell 2022) were used as input to compute gene set enrichment scores. To determine the potential prognostic impact of transcriptomic differences related to FLT3 mutational status, we applied the least absolute shrinkage and selection operator (LASSO)/Cox PH statistical method on DEGs between FLT3-pos and FLT3-neg and we built a prognostic score (PS) which was then used for validation purposes in the entire BEAT-AML2 and TCGA cohorts. The Kaplan-Meier method was used to estimate overall survival (OS). We then profiled a wet-lab cohort of 37 FLT3-mut AML patients treated either with Midostaurin plus chemotherapy (M) or Gilteritinib (G) at Bologna Hematology Institute by using the PanCancer IO 360 Panel (NanoString Technologies, San Diego, CA). Data were collected in accordance with GCP and Helsinki declaration. A public sc-RNA-seq dataset of newly-diagnosed (ND) AML (Dufva et al. Cancer Cell 2020) was used to map DEGs between Responders (R) and Non-Responders (NR) to FLT3i to well annotated BM cell populations. The MV411 and THP1 cell lines were used for in-vitro validation.
Results: We initially compared transcriptomic data from FLT3-pos (n= 173) and a randomly selected subgroup of FLT3-neg (n=185) Beat-AML samples. FLT3-pos cases had a lower ssGSEA score for CD8+ and CD4+ T-Cell functional signatures (e.g. T-cell Activation/Effector function, Exhaustion and Interferon Response). Furthermore, FLT3-pos cases had higher stemness scores, whereas FLT3-neg cases showed a more differentiated state (Monocytic/Erythroid). Using LASSO-penalized regression for feature selection, while also mitigating data collinearity, on the DEGs between FLT3-pos and FLT3-neg cases, we identified 46 genes with non-zero coefficients and we computed a transcriptomic prognostic score (FLT3-PS) for OS (p<0.0001). By applying the same FLT3-PS to the entire BEAT-AML2 and TCGA cohorts, the score retained its prognostic impact irrespective of FLT3 mutational status. The 16 immune-related genes in the PS (34.8 %) maintained the ability to parse patient survival (p<0.0001).
We then quantified immune gene expression in BM aspirates from 17 ND and 20 Relapsed/Refractory FLT3-mut AMLs who received M and G therapy, respectively. Significant differences in immune profiles were observed comparing R with NR, including a higher expression of genes involved in T/NK function in G-R. By projecting the top 30 DEGs genes between G-R and G-NR onto a sc-RNA-seq AML-BM map, we found that they were primarily expressed by effector and central memory CD8+ T cells and NK cells. Conversely, by projecting the top 30 DEGs between M-R and M-NR a wider contribution from different cell-types emerged, with a higher expression of innate immunity genes among M-R.
Finally, our in-vitro experiments supported the in-vivo data by showing that IFN type I-based immune responses are modulated by G in FLT3-mut cell lines.
Conclusion: This study provides evidence that FLT3 mutational status is associated with a unique T-cell activation profile and AML maturation state, and that T/NK cell-associated genes correlate with response to FLT3i. Our RNA metric of FLT3 mutational status could be utilized to refine patient stratification in future clinical trials.
S.R. & A.C. equally contributed
Disclosures: Vadakekolathu: Wugen: Research Funding. Sartor: Amgen: Honoraria; Novartis: Honoraria; Abbvie: Honoraria. Soverini: Istituto Gentili: Honoraria, Research Funding; Blueprint Medicines: Honoraria; Incyte Biosciences: Consultancy. Papayannidis: Pfizer: Honoraria, Membership on an entity's Board of Directors or advisory committees; Amgen: Honoraria; Abbvie: Honoraria, Membership on an entity's Board of Directors or advisory committees; Astellas: Honoraria, Membership on an entity's Board of Directors or advisory committees; Servier: Honoraria; Menarini/Stemline: Honoraria; BMS: Honoraria; Janssen: Honoraria, Membership on an entity's Board of Directors or advisory committees; Incyte: Honoraria; Novartis: Honoraria, Membership on an entity's Board of Directors or advisory committees; GSK: Membership on an entity's Board of Directors or advisory committees; Blueprint: Membership on an entity's Board of Directors or advisory committees; Jazz Pharmaceuticals: Membership on an entity's Board of Directors or advisory committees; Delbert Laboratories: Membership on an entity's Board of Directors or advisory committees. Curti: Menarini stemline: Honoraria; Jazz Pharmaceutics: Honoraria; Abbvie: Honoraria; Pfizer: Honoraria, Research Funding.