Acute myeloid leukemia with mutated NPM1 mimics acute promyelocytic leukemia presentation.

OBJECTIVES: To investigate clinicopathological and molecular features of NPM1-mutated acute myeloid leukemia that presented with infrequent acute promyelocytic leukemia (APL)-like phenotype and clinical presentation. METHODS: Cases with both de novo or secondary Acute Myeloid Leukemia (AML) were retrieved. Data from flow cytometry immunophenotyping, cytogenetics, molecular studies, and clinical presentation were analyzed. RESULTS: Cases presented with abnormal coagulation parameters and low platelets count; four of them showed a DIC index compatible with overt DIC. Two cases showed Auer rods. In all cases, immunophenotypes mimicked APL: blasts expressed CD33, CD13, and cytoplasmic MPO but did not express CD34, HLA-DR, or CD11b. Notably, CD4 expression was observed in all cases. Neither t(15;17) nor PML/RARα gene rearrangement was detected. NPM1 gene mutation was identified in all cases. In four cases, TET2 or IDH2 co-mutations were identified. CONCLUSIONS: Our findings provide additional evidence of association between NPM1-mutated AML with TET2 or IDH2 co-mutations and the APL-like immunophenotype. This AML subset was found to exist in both de novo and secondary AML. High WBC count and blasts with low to moderate side scatter and significant expression of CD4 are observed features that could assist in the differential diagnosis with APL. The occurrence of significant elevated D-dimer levels, or even overt DIC observed at diagnosis in these cases could be relevant for this AML subtype.

Automated Diagnosis of Lymphoma with Digital Pathology Images Using Deep Learning.

Recent studies have shown promising results in using Deep Learning to detect malignancy in whole slide imaging, however, they were limited to just predicting a positive or negative finding for a specific neoplasm. We attempted to use Deep Learning with a convolutional neural network (CNN) algorithm to build a lymphoma diagnostic model for four diagnostic categories: (1) benign lymph node, (2) diffuse large B-cell lymphoma, (3) Burkitt lymphoma, and (4) small lymphocytic lymphoma. Our software was written in Python language. We obtained digital whole-slide images of Hematoxylin and Eosin stained slides of 128 cases including 32 cases for each diagnostic category. Four sets of 5 representative images, 40x40 pixels in dimension, were taken for each case. A total of 2,560 images were obtained from which 1,856 were used for training, 464 for validation, and 240 for testing. For each test set of 5 images, the predicted diagnosis was combined from the prediction of five images. The test results showed excellent diagnostic accuracy at 95% for image-by-image prediction and at 100% for set-by-set prediction. This preliminary study provided a proof of concept for incorporating automated lymphoma diagnostic screen into future pathology work-flow to augment the pathologists' productivity.

MLLT11/AF1q boosts oncogenic STAT3 activity through Src-PDGFR tyrosine kinase signaling.

Constitutive STAT3 activation by tyrosine phosphorylation of mutated or amplified tyrosine kinases (pYSTAT3) is critical for cancer initiation, progression, invasion, and motility of carcinoma cells. We showed that AF1q is associated with STAT3 signaling in breast cancer cells. In xenograft models, enhanced AF1q expression activated STAT3 and promoted tumor growth and metastasis in immunodeficient NSG mice. The cytokine secretory phenotype of MDA-MB-231LN breast cancer cells with altered AF1q expression revealed changes in expression of platelet-derived growth factor subunit B (PDGF-B). AF1q-induced PDGF-B stimulated motility, migration, and invasion of MDA-MB-231LN cells, and AF1q up-regulated platelet-derived growth factor receptor (PDGFR) signaling. Further, AF1q-induced PDGFR signaling enhanced STAT3 activity through Src kinase activation, which could be blocked by the Src kinase inhibitor PP1. Moreover, AF1q up-regulated tyrosine kinase signaling through PDGFR signaling, which was blockable by imatinib. In conclusion, we demonstrated that enhanced AF1q expression contributes to persistent and oncogenic pYSTAT3 levels in invasive carcinoma cells by activating Src kinase through activation of the PDGF-B/PDGFR cascade. Therefore, AF1q plays an essential role as a cofactor in PDGF-B-driven STAT3 signaling.

Novel targeted therapies for resistant ALK-rearranged non-small-cell lung cancer: ceritinib and beyond.

Lung cancer is the leading cause of cancer-related mortality in both sexes, accounting for over one quarter of cancer deaths. Non-small-cell lung cancer (NSCLC) comprises 85%-90% of lung cancer diagnoses and despite advances in multimodality therapies, 5-year survival rates remain dismal with a median survival for patients with metastatic disease of 1 year. The positive outcomes of targeted therapies against the kinase domain of epidermal growth factor receptor in NSCLC triggered consistent efforts to identify the so-called driver mutations as other potential targets. Anaplastic large-cell kinase (ALK) gene rearrangements were identified and targeted resulting in promising response rates in early studies. Unfortunately, most of the patients treated with crizotinib, the first-generation ALK inhibitor, progressed within 9 months. Ceritinib is a second-generation ALK inhibitor that has demonstrated activity in crizotinib-resistant patients, becoming a promising treatment option in this population. Furthermore, additional novel ALK inhibitors and agents targeting alternative pathways have been recruited to rechallenge this evasive disease post-crizotinib resistance.

Outcome of acute myeloid leukemia and high-risk myelodysplastic syndrome according to health insurance status.

BACKGROUND: Age, cytogenetic status, and molecular features are the most important prognostic factors in acute myeloid leukemia (AML). This study aimed to analyze the outcomes of patients with AML or high-risk myelodysplastic syndrome (MDS) according to insurance status. PATIENTS AND METHODS: A retrospective chart review was performed, covering all patients with AML and high-risk MDS evaluated and treated at Akron General Medical Center between 2002 and 2012. A Cox regression model was analyzed to account for survival over time, adjusted for insurance type, while controlling for patient age at diagnosis and patient risk of mortality. RESULTS: A total of 130 adult patients (age ≥ 18 years) were identified. Insurance information was available for 97 patients enrolled in the study; 3 were excluded because of self-pay status. Cox regression analysis with insurance type as the predictor found that overall survival declines over time and that the rate of decline may be influenced by insurance type (χ(2)(2) = 6.4; P = .044). The likelihood of survival in patients with Medicaid or Medicare without supplemental insurance was .552 (95% CI, .338-.903; P = .018) times the likelihood in patients who had Medicare with supplemental insurance. To explain the difference, variables of age, gender, and risk of mortality were added to the model. Age and risk of mortality were found to be significant predictors of survival. The addition of insurance type to the model did not significantly contribute (χ(2)(3) = 3.83; P = .147). CONCLUSION: No significant difference in overall survival was observed when patients with AML or high-risk MDS were analyzed according to their health insurance status. The overall survival was low in this study compared with the national average. Early referral to a specialized center or possible clinical trial enrollment may be a good alternative to improve outcome.