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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.

The addition of elotuzumab (a monoclonal antibody against SLAMF7) to lenalidomide plus dexamethasone produced a significant increase in progression-free survival as compared with lenalidomide plus dexamethasone alone. Multiple myeloma, a malignant disease of monoclonal plasma cells, has a median overall survival of approximately 5 years. 1 Despite improvements in treatment outcomes with proteasome inhibitors and immunomodulatory drugs, most patients continue to have a relapse, and new treatment approaches are needed. Combination therapy may be key to overcoming drug resistance and improving long-term treatment outcomes. Lenalidomide, an immunomodulatory drug, in combination with dexamethasone is a standard regimen in patients with relapsed or refractory disease. 2 , 3 Three-drug combinations are emerging for patients with previously treated multiple myeloma 3 but may be limited by toxic effects. Agents with new mechanisms of action . . .

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.

The outcome of acute myeloid leukemia patients aged 70 years or older is poor. Defining the best treatment option remains controversial especially when choosing between intensive chemotherapy and hypomethylating agents. We set up a multicentric European database collecting data of 3 700 newly diagnosed acute myeloid leukemia patients ≥70 years. The primary objective was to compare overall survival in patients selected for intensive chemotherapy (n = 1199) or hypomethylating agents (n = 1073). With a median follow-up of 49.5 months, the median overall survival was 10.9 (95% CI: 9.7–11.6) and 9.2 months (95% CI: 8.3–10.2) with chemotherapy and hypomethylating agents, respectively. Complete remission or complete remission with incomplete hematologic recovery was 56.1% and 19.7% with chemotherapy and hypomethylating agents, respectively (P < 0.0001). Treatment effect on overall survival was time-dependent. The Royston and Parmar model showed that patients treated with hypomethylating agents had a significantly lower risk of death before 1.5 months of follow-up; no significant difference between 1.5 and 4.0 months, whereas patients treated with intensive chemotherapy had a significantly better overall survival from four months after start of therapy. This study shows that intensive chemotherapy remains a valuable option associated with a better long-term survival in older AML patients.

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.

Resistant disease is still a main obstacle in acute myeloid leukemia (AML) treatment. Therefore, individual genetic variations affecting therapy response are gaining increasing importance. Both SNPs and ABC transporter genes could already be associated with drug resistance. Here, we report allelic variants of MRP1 (ABCC1) SNPs rs129081, rs212090, and rs212091 with significant influences on survival in AML patients. DNA was extracted from bone marrow samples (n = 160) at diagnosis. Genotyping 48 SNPs within seven different ABC transporter genes using real-time PCR revealed rs129081 GG variant with a significant higher OS (p = 0.035) and DFS (p = 0.01). Comparing TT and AA rs212090 variants showed significant influences on DFS (p = 0.021). SNP rs212091 GG expression was associated with worse OS (p = 0.006) and a significant difference in DFS between alleles GG and AA (p = 0.018). The multivariable models confirmed a significant influence on OS for rs212091 (AA HR = 0.296, 95% CI 0.113–0.774, p = 0.013 and GG p = 0.044). Rs129081 variant CG, TT of rs212090, AA, and AG of rs212091 demonstrated significant impact on DFS (p = 0.024, p = 0.029, p = 0.017, and p = 0.042, respectively). This analysis demonstrates a significant influence of MRP1 SNPs on survival in AML. As they were not associated to prognostic characteristics, we suggest these SNPs to be independent prognostic markers for AML.

Multiple myeloma is a malignant disease characterised by proliferation of clonal plasma cells in the bone marrow and typically accompanied by the secretion of monoclonal immunoglobulins that are detectable in the serum or urine. Increased understanding of the microenvironmental interactions between malignant plasma cells and the bone marrow niche, and their role in disease progression and acquisition of therapy resistance, has helped the development of novel therapeutic drugs for use in combination with cytostatic therapy. Together with autologous stem cell transplantation and advances in supportive care, the use of novel drugs such as proteasome inhibitors and immunomodulatory drugs has increased response rates and survival substantially in the past several years. Present clinical research focuses on the balance between treatment efficacy and quality of life, the optimum sequencing of treatment options, the question of long-term remission and potential cure by multimodal treatment, the pre-emptive treatment of high-risk smouldering myeloma, and the role of maintenance. Upcoming results of ongoing clinical trials, together with a pipeline of promising new treatments, raise the hope for continuous improvements in the prognosis of patients with myeloma in the future.

Patients with refractory or relapsed acute myeloid leukemia (R/R AML) have a poor prognosis, with a high unmet medical need. Idasanutlin is a small-molecule inhibitor of MDM2, a negative regulator of tumor suppressor p53. By preventing the p53–MDM2 interaction, idasanutlin allows for p53 activation, particularly in patients with wild-type (WT) status. MIRROS (NCT02545283) is a randomized Phase III trial evaluating idasanutlin + cytarabine versus placebo + cytarabine in R/R AML. The primary end point is overall survival in the -WT population. Secondary end points include complete remission rate (cycle 1), overall remission rate (cycle 1) and event-free survival in the -WT population. MIRROS has an innovative design that integrates a stringent interim analysis for futility; continuation criteria were met in mid-2017 and accrual is ongoing. NCT02545283

Gemtuzumab ozogamicin (GO), a humanized anti-CD33 monoclonal antibody conjugated to the cytotoxic antibiotic agent calicheamicin, is approved for the treatment of newly-diagnosed CD33 + AML in adults and children ≥ 1 month old, and relapsed or refractory CD33 + AML in adults and children ≥ 2 years old. GO treatment has been associated with an increased risk of hepatotoxicity and hepatic veno-occlusive disease/sinusoidal obstruction syndrome (VOD/SOS), especially following hematopoietic stem cell transplantation. Other non-specific serious adverse events (SAEs) associated with GO treatment are myelosuppression, bleeding/thrombocytopenia, infusion-related reaction, and tumor lysis syndrome. This report summarizes an expert panel of physicians’ recommendations for the evaluation and management of SAEs following GO, emphasizing the prevention and management of VOD/SOS.

Background Given the geographical sparsity of Rare Diseases (RDs), assembling a cohort is often a challenging task. Common data models (CDM) can harmonize disparate sources of data that can be the basis of decision support systems and artificial intelligence-based studies, leading to new insights in the field. This work is sought to support the design of large-scale multi-center studies for rare diseases. Methods In an interdisciplinary group, we derived a list of elements of RDs in three medical domains (endocrinology, gastroenterology, and pneumonology) according to specialist knowledge and clinical guidelines in an iterative process. We then defined a RDs data structure that matched all our data elements and built Extract, Transform, Load (ETL) processes to transfer the structure to a joint CDM. To ensure interoperability of our developed CDM and its subsequent usage for further RDs domains, we ultimately mapped it to Observational Medical Outcomes Partnership (OMOP) CDM. We then included a fourth domain, hematology, as a proof-of-concept and mapped an acute myeloid leukemia (AML) dataset to the developed CDM. Results We have developed an OMOP-based rare diseases common data model (RD-CDM) using data elements from the three domains (endocrinology, gastroenterology, and pneumonology) and tested the CDM using data from the hematology domain. The total study cohort included 61,697 patients. After aligning our modules with those of Medical Informatics Initiative (MII) Core Dataset (CDS) modules, we leveraged its ETL process. This facilitated the seamless transfer of demographic information, diagnoses, procedures, laboratory results, and medication modules from our RD-CDM to the OMOP. For the phenotypes and genotypes, we developed a second ETL process. We finally derived lessons learned for customizing our RD-CDM for different RDs. Discussion This work can serve as a blueprint for other domains as its modularized structure could be extended towards novel data types. An interdisciplinary group of stakeholders that are actively supporting the project's progress is necessary to reach a comprehensive CDM. Conclusion The customized data structure related to our RD-CDM can be used to perform multi-center studies to test data-driven hypotheses on a larger scale and take advantage of the analytical tools offered by the OHDSI community.