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PURPOSE The FMS-related tyrosine kinase 3 (FLT3) inhibitor gilteritinib is standard therapy for relapsed/refractory FLT3-mutated ( FLT3 mut) acute myeloid leukemia (AML) but seldom reduces FLT3 mut burden or induces sustained efficacy. Gilteritinib combines synergistically with the BCL-2 inhibitor venetoclax in preclinical models of FLT3 mut AML. METHODS This phase Ib open-label, dose-escalation/dose-expansion study (ClinicalTrials.gov identifier: NCT03625505 ) enrolled patients with FLT3 wild-type and FLT3 mut (escalation) or FLT3 mut (expansion) relapsed/refractory AML. Patients received 400 mg oral venetoclax once daily and 80 mg or 120 mg oral gilteritinib once daily. The primary objectives were safety, identification of the recommended phase II dose, and the modified composite complete response (mCRc) rate (complete response [CR] + CR with incomplete blood count recovery + CR with incomplete platelet recovery + morphologic leukemia-free state) using ADMIRAL phase III–defined response criteria. RESULTS Sixty-one patients were enrolled (n = 56 FLT3 mut); 64% (n = 36 of 56) of FLT3 mut patients had received prior FLT3 inhibitor therapy. The recommended phase II dose was 400 mg venetoclax once daily and 120 mg gilteritinib once daily. The most common grade 3/4 adverse events were cytopenias (n = 49; 80%). Adverse events prompted venetoclax and gilteritinib dose interruptions in 51% and 48%, respectively. The mCRc rate for FLT3 mut patients was 75% (CR, 18%; CR with incomplete blood count recovery, 4%; CR with incomplete platelet recovery, 18%; and morphologic leukemia-free state, 36%) and was similar among patients with or without prior FLT3 inhibitor therapy (80% v 67%, respectively). The median follow-up was 17.5 months. The median time to response was 0.9 months, and the median remission duration was 4.9 months (95% CI, 3.4 to 6.6). FLT3 molecular response (< 10−2) was achieved in 60% of evaluable mCRc patients (n = 15 of 25). The median overall survival for FLT3 mut patients was 10.0 months. CONCLUSION The combination of venetoclax and gilteritinib was associated with high mCRc and FLT3 molecular response rates regardless of prior FLT3 inhibitor exposure. Dose interruptions were needed to mitigate myelosuppression.

398868 Background: The standard of care in patients (pts) with newly diagnosed (dx) Philadelphia chromosome positive (Ph+) acute lymphoblastic leukemia (ALL) is BCR::ABL1 tyrosine kinase inhibitors (TKIs) in combination with chemotherapy (chemo) or steroids. Treated with 1st- or 2nd-generation TKIs, pts eventually progress due to emergence of resistance. Multiple studies have reported promising minimal residual disease (MRD) negativity (neg) rates and survival outcomes with pon in combination with chemo or chemo-free regimens. PhALLCON (NCT03589326), the first randomized study comparing TKIs in pts with Ph+ALL, evaluates pon vs im in combination with reduced-intensity chemo. Methods: This phase 3 open-label trial randomized adult newly dx Ph+ALL pts 2:1 to receive pon (30 mg once daily [QD]) or im (600 mg QD) with reduced-intensity chemo through end of induction (EOI; Cycles 1–3), consolidation (Cycles 4–9), and post-consolidation (Cycles 10–20). After Cycle 20, pts received single-agent pon or im until disease progression or unacceptable toxicity. The composite primary endpoint was MRD-neg ( BCR:: ABL1 ≤0.01% ) complete remission (CR) for 4 weeks at EOI. Event-free survival (EFS: any cause death, failure to achieve CR by EOI, relapse from CR) was a key secondary endpoint. Results: 245 pts were randomized to pon (n=164) or im (n=81); median age was 54 y (37% ≥60 y). At data cutoff (Aug 2022), 78 pts (pon vs im: 42% vs 12%) were on study treatment; the top 3 reasons for discontinuation were hematopoietic stem cell transplantation (31% vs 37%), adverse events (12% vs 12%), and lack of efficacy (7% vs 26%). Median follow-up was 20 mo vs 18 mo (pon vs im). The primary endpoint was met (Table) by significantly higher MRD-neg CR rate for pon vs im (34.4% vs 16.7%; p=0.0021). Survival data were not mature; however, the median EFS was reached in im and not in pon, with a trend toward improvement (HR=0.652, 95% CI 0.385–1.104). Time to treatment failure reported an improvement as well (HR=0.455). The treatment-emergent adverse event (TEAE) rates (any grade [Gr] and Gr3/4/5) were comparable between treatment arms. Arterial occlusive events (AOEs) were infrequent and similar between the arms (Table). Conclusions: Pon was superior to im in combination with reduced-intensity chemo in pts with newly dx Ph+ALL, with a significantly higher MRD-neg CR rate at the EOI. Pon was associated with deeper and more durable responses, with a trend toward improved EFS and comparable safety vs im. Clinical trial information: NCT03589326 . [Table: see text]

3047 Background: Expressed RNA can capture mutations, changes in expression levels due to methylation, and provide information on cell of origin, growth, and proliferation status. We developed an approach to isolate fragmented RNA from peripheral blood plasma and explored its potential to be used in liquid biopsy. Methods: Peripheral blood cfRNA was extracted from patients with neoplasms in B-cell (#105), T-cell (#16), Myeloid (#73), and from solid tumors (#44), Normal individuals (#51), and reactive post-transplant (#137). RNA was sequenced using a 1459-gene panel. Expression profile was generated using Cufflinks. Results: cfRNA levels of various solid tumor biomarkers (CA-125, CA-15-3, CEA 8, Keratin19, Keratin6A...) were significantly higher (P < 0.0001) in samples from solid tumors as compared with normal control. Similarly, cfRNA lymphoid markers (CD19, CD22, CD79A, and CD79B...) and cfRNA myeloid markers (CD33, CD14, CD117, CD56...) were all higher in B-cell lymphoid neoplasms and myeloid neoplasms, respectively (P < 0.0001), as compared with control. In evaluating the host immune system, cfRNA CD4:CD8B and CD3D:CD19 ratios in normal controls were as expected (median: 5.92 and 6.87, respectively) and were significantly lower in solid tumors (median 3.40 and 2.23, respectively, P < 0.0002). Solid tumor cfRNA showed CTLA4:CD8B ratio significantly higher in tumors than in normal (median 0.74 vs 0.19, P = 0.0001), while there was no difference in cfRNA PD-L1:CD8B ratio (median 1.45 vs 1.77, P = 0.96). Similar distinct patterns are noted for various cytokine and chemokines. cfRNA was highly predictive of diagnosis (AUC > 0.98) of solid tumors, B-cell lymphoid neoplasms, T-cell lymphoid neoplasms, and myeloid neoplasms as compared with normal control. When a specific neoplastic disease was considered against all cases including control and other neoplasms, the AUC varied between 0.77 and 0.949. Conclusions: This data shows that liquid biopsy using targeted sequencing of cfRNA in patients with various types of cancer provides comprehensive and reliable information on the neoplastic disease as well as the host. [Table: see text]

3018 Background: Diagnosis and classification of tumors is becoming increasingly dependent on biological and molecular biomarkers. RNA expression profiling using next generation sequencing (NGS) provides information on various biological and molecular changes in the cancer and in the microenvironment. We explored the potential of using targeted transcriptome and artificial intelligence (AI) in the differential diagnosis and classification of various hematologic and solid tumors. Methods: RNA from hematologic neoplasms (N = 2606) and solid tumors (N = 2038) as well as normal bone marrow and lymph node control (N = 806) were sequenced by NGS using a targeted 1408-gene panel. The hematologic neoplasms included 20 different subtypes. Solid tumors included 24 different subtypes. Machine learning is used for comparing two classes at a time. Geometric Mean Naïve Bayesian (GMNB) classifier is used to provide differential diagnosis across 45 diagnostic entities with assigned ranking. Results: Machine learning showed high accuracy in distinguishing between two diagnoses with AUC varied between 1 (Sarcoma vs GIST) and 0.841 (MDS vs normal control) (examples in Table). For differential diagnosis between all 45 different diagnoses, we used 3045 samples for training the GMNB algorithm and 1415 samples for testing. Correct first choice diagnosis was obtained in 100% of ALL, 88% of AML, 85% of DLBCL, 82% of colorectal cancer, 88% of lung cancer, 72% of CLL, and 72% of follicular lymphoma. The algorithm had difficulty in typically overlapping diagnoses and diagnosed as first choice 19% of MDS, 46% of normal, and 12% of MPN. Diagnosis improved significantly when second choice was considered. Conclusions: Targeted RNA profiling with proper AI can provide highly useful tools for the pathologic diagnosis and classification of various cancers. Additional information such as mutation profile and clinical information can improve these algorithms, reduce subjectivity, and minimize errors in pathologic diagnoses. [Table: see text]