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Monitoring of measurable residual disease (MRD) in patients with advanced myelodysplastic syndromes (MDS) or acute myeloid leukaemia (AML) who achieve a morphological complete remission can predict haematological relapse. In this prospective study, we aimed to determine whether MRD-guided pre-emptive treatment with azacitidine could prevent relapse in these patients. The relapse prevention with azacitidine (RELAZA2) study is an open-label, multicentre, phase 2 trial done at nine university health centres in Germany. Patients aged 18 years or older with advanced MDS or AML, who had achieved a complete remission after conventional chemotherapy or allogeneic haemopoietic stem-cell transplantation, were prospectively screened for MRD during 24 months from baseline by either quantitative PCR for mutant NPM1, leukaemia-specific fusion genes (DEK–NUP214, RUNX1–RUNX1T1, CBFb–MYH11), or analysis of donor-chimaerism in flow cytometry-sorted CD34-positive cells in patients who received allogeneic haemopoietic stem-cell transplantation. MRD-positive patients in confirmed complete remission received azacitidine 75 mg/m2 per day subcutaneously on days 1–7 of a 29-day cycle for 24 cycles. After six cycles, MRD status was reassessed and patients with major responses (MRD negativity) were eligible for a treatment de-escalation. The primary endpoint was the proportion of patients who were relapse-free and alive 6 months after the start of pre-emptive treatment. Analyses were done per protocol. This trial is registered with ClincialTrials.gov, number NCT01462578, and finished recruitment on Aug 21, 2018. Between Oct 10, 2011, and Aug 20, 2015, we screened 198 patients with advanced MDS (n=26) or AML (n=172), of whom 60 (30%) developed MRD during the 24-month screening period and 53 (88%) were eligible to start study treatment. 6 months after initiation of azacitidine, 31 (58%, 95% CI 44–72) of 53 patients were relapse-free and alive (p<0·0001; one-sided binomial test for null hypothesis pexp≤0·3). With a median follow-up of 13 months (IQR 8·5–22·8) after the start of MRD-guided treatment, relapse-free survival at 12 months was 46% (95% CI 32–59) in the 53 patients who were MRD-positive and received azacitidine. In MRD-negative patients, 12-month relapse-free survival was 88% (95% CI 82–94; hazard ratio 6·6 [95% CI 3·7–11·8], p<0·0001). The most common (grade 3–4) adverse event was neutropenia, occurring in 45 (85%) of 53 patients. One patient with neutropenia died because of an infection considered possibly related to study treatment. Pre-emptive therapy with azacitidine can prevent or substantially delay haematological relapse in MRD-positive patients with MDS or AML who are at high risk of relapse. Our study also suggests that continuous MRD negativity during regular MRD monitoring might be prognostic for patient outcomes. Celgene Pharma, José Carreras Leukaemia Foundation, National Center for Tumor Diseases (NCT), and German Cancer Consortium (DKTK) Foundation.

Standard treatment for GVHD is glucocorticoids, but for glucocorticoid-refractory GVHD, no intervention has emerged as standard second-line treatment. This trial with 329 patients compared ruxolitinib with control (chosen from among 10 possible therapies) in patients with glucocorticoid-refractory chronic GVHD. Response at week 24 was 50% with ruxolitinib as compared with 26% with control therapy.

The evaluation of bone marrow morphology by experienced hematopathologists is essential in the diagnosis of acute myeloid leukemia (AML); however, it suffers from a lack of standardization and inter-observer variability. Deep learning (DL) can process medical image data and provides data-driven class predictions. Here, we apply a multi-step DL approach to automatically segment cells from bone marrow images, distinguish between AML samples and healthy controls with an area under the receiver operating characteristic (AUROC) of 0.9699, and predict the mutation status of Nucleophosmin 1 (NPM1)—one of the most common mutations in AML—with an AUROC of 0.92 using only image data from bone marrow smears. Utilizing occlusion sensitivity maps, we observed so far unreported morphologic cell features such as a pattern of condensed chromatin and perinuclear lightening zones in myeloblasts of NPM1-mutated AML and prominent nucleoli in wild-type NPM1 AML enabling the DL model to provide accurate class predictions.

Precision oncology is grounded in the increasing understanding of genetic and molecular mechanisms that underly malignant disease and offer different treatment pathways for the individual patient. The growing complexity of medical data has led to the implementation of machine learning techniques that are vastly applied for risk assessment and outcome prediction using either supervised or unsupervised learning. Still largely overlooked is reinforcement learning (RL) that addresses sequential tasks by exploring the underlying dynamics of an environment and shaping it by taking actions in order to maximize cumulative rewards over time, thereby achieving optimal long-term outcomes. Recent breakthroughs in RL demonstrated remarkable results in gameplay and autonomous driving, often achieving human-like or even superhuman performance. While this type of machine learning holds the potential to become a helpful decision support tool, it comes with a set of distinctive challenges that need to be addressed to ensure applicability, validity and safety. In this review, we highlight recent advances of RL focusing on studies in oncology and point out current challenges and pitfalls that need to be accounted for in future studies in order to successfully develop RL-based decision support systems for precision oncology.

Background Acute promyelocytic leukemia (APL) is considered a hematologic emergency due to high risk of bleeding and fatal hemorrhages being a major cause of death. Despite lower death rates reported from clinical trials, patient registry data suggest an early death rate of 20%, especially for elderly and frail patients. Therefore, reliable diagnosis is required as treatment with differentiation-inducing agents leads to cure in the majority of patients. However, diagnosis commonly relies on cytomorphology and genetic confirmation of the pathognomonic t(15;17). Yet, the latter is more time consuming and in some regions unavailable. Methods In recent years, deep learning (DL) has been evaluated for medical image recognition showing outstanding capabilities in analyzing large amounts of image data and provides reliable classification results. We developed a multi-stage DL platform that automatically reads images of bone marrow smears, accurately segments cells, and subsequently predicts APL using image data only. We retrospectively identified 51 APL patients from previous multicenter trials and compared them to 1048 non-APL acute myeloid leukemia (AML) patients and 236 healthy bone marrow donor samples, respectively. Results Our DL platform segments bone marrow cells with a mean average precision and a mean average recall of both 0.97. Further, it achieves high accuracy in detecting APL by distinguishing between APL and non-APL AML as well as APL and healthy donors with an area under the receiver operating characteristic of 0.8575 and 0.9585, respectively, using visual image data only. Conclusions Our study underlines not only the feasibility of DL to detect distinct morphologies that accompany a cytogenetic aberration like t(15;17) in APL, but also shows the capability of DL to abstract information from a small medical data set, i. e. 51 APL patients, and infer correct predictions. This demonstrates the suitability of DL to assist in the diagnosis of rare cancer entities. As our DL platform predicts APL from bone marrow smear images alone, this may be used to diagnose APL in regions were molecular or cytogenetic subtyping is not routinely available and raise attention to suspected cases of APL for expert evaluation.

Introduction: Azacitidine (AZA) either single-agent or with donor lymphocytes infusions (DLI) has been used as a salvage treatment for acute myeloid leukemia (AML) and myelodysplastic syndromes (MDS) relapsing after allogeneic hematopoietic stem cell transplantation (HSCT). To date, the majority of data come from patients relapsed after HSCT from full-matched donors. Methods: We report a multicenter, collaborative, retrospective analysis of 71 patients with hematologic (n = 40, 56%) and molecular relapse (n = 31, 44%) of myeloid neoplasms after HSCT from alternative donors (mismatched unrelated, n = 39, 55%; haploidentical, n = 29, 41%) consecutively treated at three European centers with AZA ± DLI. Results: Median time from HSCT to relapse was 9 months. Additional DLI were given to 33 patients (46%). After a median of four cycles, overall response rate (ORR) was 49% and complete response (CR) rate was 38%. CR lasted for a median of 17 months (range 5–89 months). Median follow-up in the entire cohort was 11 months (range 1–115 months). Event-free survival (EFS) and overall survival (OS) at 1 year were 26% and 53%, respectively. Treatment of molecular relapse granted higher CR rate (65% versus 15%; p = 0.0001), 1-year EFS (43% versus 13%; p = 0.006), and 1-year OS (79% versus 34%; p < 0.001) compared to hematologic relapses. Addition of DLI resulted in significantly higher responses and longer 1-year EFS and OS (Mantel–Byar test, p = 0.004 and p = 0.002, respectively). When applied to our cohort, the APSS-R score confirmed its ability to stratify patients into distinct prognostic groups with significantly different response rates (p = 0.0005) and survival (p < 0.0001). Treatment was well tolerated, with the incidence of late acute and chronic graft-versus-host disease of 27% and 18%, respectively. Conclusion: AZA ± DLI proved feasible and effective in AML and MDS relapsing after HSCT from alternative donors. Despite modest efficacy among hematologic relapses, pre-emptive treatment with AZA ± DLI fared better in molecular relapse. Additional DLI contributed to improving efficacy and ensuring longer survival.

The annualised incidence of primary immune thrombocytopenia is approximately 3·3 cases per 100 000 people among adults. 1 Despite several treatment options, including approved thrombopoietin receptor agonists and commonly used antibody-reducing approaches—such as B-cell depletion with the anti-CD20 monoclonal antibody rituximab, BTK inhibitors, SYK inhibitors, corticosteroids, and splenectomy—a subset of patients develop refractory immune thrombocytopenia. 2 For these individuals, who are estimated to comprise less than 10% of the total immune thrombocytopenia population, conventional therapies do not achieve durable platelet recovery, resulting in life-threatening bleeding episodes and substantially impaired quality of life. In April, 2024, after a case review by an independent interdisciplinary committee, the hospital board, and the hospital's legal department, the patient gave written informed consent to receive autologous CD19 CAR T-cell therapy as an individualised treatment under German law, which allows the use of unlicensed therapies ( appendix p 1). Within the reconstituting B cell compartment, we observed predominantly CD21 + CD27 – naive cells, with low to absent (switched or non-switched) CD21 + CD27 + memory B cells and CD38 + CD20 – plasmablasts ( figure D), which is also consistent with previous data in patients with autoimmune diseases receiving CAR T cells. 3,9 To avoid withdrawal effects after stopping thrombopoietin receptor agonist, romiplostim treatment, which had been interrupted since day –9, was resumed on day 16 after infusion. Pathogenic autoantibodies against glycoprotein IIb/IIIa were no longer detectable after day 42 ( appendix p 8). [...]the patient's chronic fatigue had resolved 3 months after treatment, and he was planning to return to work in December, 2024.

Background Artificial intelligence (AI) is influencing our society on many levels and has broad implications for the future practice of hematology and oncology. However, for many medical professionals and researchers, it often remains unclear what AI can and cannot do, and what are promising areas for a sensible application of AI in hematology and oncology. Finally, the limits and perils of using AI in oncology are not obvious to many healthcare professionals. Methods In this article, we provide an expert-based consensus statement by the joint Working Group on “Artificial Intelligence in Hematology and Oncology” by the German Society of Hematology and Oncology (DGHO), the German Association for Medical Informatics, Biometry and Epidemiology (GMDS), and the Special Interest Group Digital Health of the German Informatics Society (GI). We provide a conceptual framework for AI in hematology and oncology. Results First, we propose a technological definition, which we deliberately set in a narrow frame to mainly include the technical developments of the last ten years. Second, we present a taxonomy of clinically relevant AI systems, structured according to the type of clinical data they are used to analyze. Third, we show an overview of potential applications, including clinical, research, and educational environments with a focus on hematology and oncology. Conclusion Thus, this article provides a point of reference for hematologists and oncologists, and at the same time sets forth a framework for the further development and clinical deployment of AI in hematology and oncology in the future.

The fusion genes CBFB/MYH11 and RUNX1/RUNX1T1 block differentiation through disruption of the core binding factor (CBF) complex and are found in 10–15% of adult de novo acute myeloid leukemia (AML) cases. This AML subtype is associated with a favorable prognosis; however, nearly half of CBF-rearranged patients cannot be cured with chemotherapy. This divergent outcome might be due to additional mutations, whose spectrum and prognostic relevance remains hardly defined. Here, we identify nonsilent mutations, which may collaborate with CBF-rearrangements during leukemogenesis by targeted sequencing of 129 genes in 292 adult CBF leukemia patients, and thus provide a comprehensive overview of the mutational spectrum (‘mutatome’) in CBF leukemia. Thereby, we detected fundamental differences between CBFB/MYH11- and RUNX1/RUNX1T1-rearranged patients with ASXL2, JAK2, JAK3, RAD21, TET2, and ZBTB7A being strongly correlated with the latter subgroup. We found prognostic relevance of mutations in genes previously known to be AML-associated such as KIT, SMC1A, and DHX15 and identified novel, recurrent mutations in NFE2 (3%), MN1 (4%), HERC1 (3%), and ZFHX4 (5%). Furthermore, age >60 years, nonprimary AML and loss of the Y-chromosomes are important predictors of survival. These findings are important for refinement of treatment stratification and development of targeted therapy approaches in CBF leukemia.