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PURPOSE The combination of the Bruton tyrosine kinase inhibitor ibrutinib with bendamustine-rituximab for first-line treatment of mantle cell lymphoma (MCL) prolonged progression-free survival (PFS), but without improvement in overall survival (OS), likely because of toxicity. Acalabrutinib was shown to be efficacious and less toxic than ibrutinib in a head-to-head trial in chronic lymphocytic leukemia and therefore might lead to better outcomes in MCL. METHODS Patients 65 years and older with previously untreated MCL received acalabrutinib (100 mg twice daily) or placebo (until disease progression or unacceptable toxicity), plus six cycles of bendamustine (90 mg/m 2 once daily; days 1 and 2) and rituximab (375 mg/m 2 as a single dose; day 1) followed by rituximab maintenance in responding patients for 2 years. Crossover to acalabrutinib at disease progression was permitted. The primary end point was PFS per the independent review committee; overall response rate and OS were secondary end points. RESULTS In total, 598 patients were randomly assigned, with 299 in each arm. At a median follow-up of 49.8 months using the reverse Kaplan-Meier method, the median PFS was 66.4 months in the acalabrutinib arm and 49.6 months in the placebo arm (hazard ratio [HR], 0.73 [95% CI, 0.57 to 0.94]; P = .0160). Benefit was seen across all subgroups, including those with high-risk features. Overall response/complete response rates were 91.0%/66.6% and 88.0%/53.5% in the acalabrutinib and placebo arms, respectively. OS was not significantly different (HR, 0.86 [95% CI, 0.65 to 1.13]; P = .27). Grade 3 or greater adverse events were reported in 88.9% and 88.2% in the acalabrutinib and placebo arms, respectively. CONCLUSION The combination of acalabrutinib with bendamustine-rituximab significantly improved PFS. Clinical benefit of acalabrutinib with bendamustine-rituximab was achieved with manageable toxicity.

Abstract The ACE-536-MF-001 trial enrolled patients with myelofibrosis (n = 95) into 4 cohorts: patients in cohorts 1 and 3A were non–transfusion dependent (NTD) and had anemia; patients in cohorts 2 and 3B were transfusion dependent (TD); and patients in cohort 3A/3B had stable ruxolitinib treatment before and during the study. All patients received luspatercept (1.0-1.75 mg/kg, 21-day cycles). Treatment was extended if clinical benefit was observed at day 169. The primary end point was anemia response rate (NTD, ≥1.5 g/dL hemoglobin increase from baseline; TD, transfusion-independence) over any 12-week period during the primary treatment period (weeks 1-24). Overall, 14% of patients in cohorts 1 and 3A, 10% in cohort 2, and 26% in cohort 3B met the primary end point. In cohorts 1 and 3A (NTD), 27% and 50% of patients, respectively, had mean hemoglobin increase of ≥1.5 g/dL from baseline. Among TD patients, ∼50% had ≥50% reduction in transfusion burden. Reduction in total symptom score was observed in all cohorts, with the greatest response rate seen in cohort 3A. Overall, 94% of patients had ≥1 adverse event (AE); 47% had ≥1 treatment-related AE (TRAE; 11% grade ≥3), most frequently hypertension (18%), managed with medical intervention. One patient had a serious TRAE leading to luspatercept discontinuation. Nine patients died on treatment (unrelated to study drug). In most patients, ruxolitinib dose and spleen size remained stable. In patients with myelofibrosis, luspatercept improved anemia and transfusion burden across cohorts; the safety profile was consistent with previous studies. This trial was registered at www.ClinicalTrials.gov as #NCT03194542.

7016 Background: Many patients (pts) with myelofibrosis (MF) experience anemia resulting from disease or JAK inhibitor therapy. The open-label, phase 2 ACE-536-MF-001 study (NCT03194542) evaluated the safety and efficacy of luspatercept (LUSPA) for the treatment of MF-related anemia. Methods: Eligible pts comprised 4 cohorts based on transfusion dependence (TD, defined as receiving 4–12 red blood cell [RBC] units/84 days [d] immediately up to cycle 1 d 1) and stable ruxolitinib (RUX) treatment; cohort 1: no TD, no RUX; cohort 2: TD, no RUX; cohort 3A: no TD, RUX; cohort 3B: TD, RUX. Pts received subcutaneous LUSPA 1.0 mg/kg (1.33 mg/kg for pts in cohort 3B expansion) with titration up to 1.75 mg/kg in 21-d cycles. Disease response was assessed at d 169 and treatment extended for pts showing clinical benefit; pts were followed for ≥ 3 years (y) post last dose. The primary endpoint was anemia response (defined as 84 consecutive d: ≥ 1.5 g/dL hemoglobin [Hgb] from baseline without transfusion [cohorts 1 and 3A] or RBC transfusion-free [cohorts 2 and 3B]); secondary endpoints included duration of response (DOR), transfusion frequency, change in Hgb level, and symptom response. Results: Of 95 pts in the ITT group, the most common reason for treatment discontinuation was lack of clinical benefit at d 169 (n = 28). Median (range) age was 71.0 (50–89) y. Most (54.7%) pts had primary MF, intermediate-risk 2 disease (75.8%), and > 2 y since initial diagnosis (74.7%). Median prior RUX therapy was 18.1 months (cohorts 3A/3B) and 84.7% of pts had baseline transfusion burden 6–12 units/84 d (cohorts 2/3B). Mean treatment duration was 42.8 weeks. Cohort 3B had the highest anemia response rate with 26.3% (95% CI, 13.4–43.1) of pts transfusion independent during the primary treatment period and 31.6% (95% CI, 17.5–48.7) transfusion independent during the entire treatment period; median (range) DOR was 448 d (85–1582). In cohort 3B: 19 (50.0%) pts had reduced transfusion burden by ≥ 50% during the primary treatment period; 8 (21.1%) pts achieved mean Hgb increase ≥ 1.5 g/dL from baseline throughout the entire treatment period and 6 (15.8%) achieved a mean ≥ 50% reduction in symptom score from baseline. Overall, 47.4% of pts had ≥ 1 treatment-related adverse event (TRAE), most frequently hypertension (17.9%, 5.3% grade ≥ 3); 3.2% of pts had a serious TRAE. One TRAE led to LUSPA discontinuation. Two (2.1%) pts had transformation to acute myeloid leukemia. Nine pts died on treatment from: post-procedural hemorrhage, pneumonia, intracranial hemorrhage, septic shock, ischemic stroke, multiple organ dysfunction syndrome, renal failure, pneumonitis, and myelofibrosis (n = 1 each); no deaths were considered related to the study drug. Conclusions: In pts with MF, the safety profile of LUSPA was consistent with previous studies and efficacy results showed promising improvements in anemia and transfusion burden in all cohorts. Clinical trial information: NCT03194542 .

As the most lethal major cancer, pancreatic cancer is a global healthcare challenge. Personalized medicine utilizing cutting-edge multi-omics data holds potential for major breakthroughs in tackling this critical problem. Radiomics and deep learning, two trendy quantitative imaging methods that take advantage of data science and modern medical imaging, have shown increasing promise in advancing the precision management of pancreatic cancer via diagnosing of precursor diseases, early detection, accurate diagnosis, and treatment personalization and optimization. Radiomics employs manually-crafted features, while deep learning applies computer-generated automatic features. These two methods aim to mine hidden information in medical images that is missed by conventional radiology and gain insights by systematically comparing the quantitative image information across different patients in order to characterize unique imaging phenotypes. Both methods have been studied and applied in various pancreatic cancer clinical applications. In this review, we begin with an introduction to the clinical problems and the technology. After providing technical overviews of the two methods, this review focuses on the current progress of clinical applications in precancerous lesion diagnosis, pancreatic cancer detection and diagnosis, prognosis prediction, treatment stratification, and radiogenomics. The limitations of current studies and methods are discussed, along with future directions. With better standardization and optimization of the workflow from image acquisition to analysis and with larger and especially prospective high-quality datasets, radiomics and deep learning methods could show real hope in the battle against pancreatic cancer through big data-based high-precision personalization.