Imatinib blocks tyrosine phosphorylation of Smad4 and restores TGF-ß growth-suppressive signaling in BCR-ABL1-positive leukemia.

Loss of TGF-ß-mediated growth suppression is a major contributor to the development of cancers, best exemplified by loss-of-function mutations in genes encoding components of the TGF-ß signaling pathway in colorectal and pancreatic cancers. Alternatively, gain-of-function oncogene mutations can also disrupt antiproliferative TGF-ß signaling. However, the molecular mechanisms underlying oncogene-induced modulation of TGF-ß signaling have not been extensively investigated. Here, we show that the oncogenic BCR-ABL1 of chronic myelogenous leukemia (CML) and the cellular ABL1 tyrosine kinases phosphorylate and inactivate Smad4 to block antiproliferative TGF-ß signaling. Mechanistically, phosphorylation of Smad4 at Tyr195, Tyr301, and Tyr322 in the linker region interferes with its binding to the transcription co-activator p300/CBP, thereby blocking the ability of Smad4 to activate the expression of cyclin-dependent kinase (CDK) inhibitors and induce cell cycle arrest. In contrast, the inhibition of BCR-ABL1 kinase with Imatinib prevented Smad4 tyrosine phosphorylation and re-sensitized CML cells to TGF-ß-induced antiproliferative and pro-apoptotic responses. Furthermore, expression of phosphorylation-site-mutated Y195F/Y301F/Y322F mutant of Smad4 in Smad4-null CML cells enhanced antiproliferative responses to TGF-ß, whereas the phosphorylation-mimicking Y195E/Y301E/Y322E mutant interfered with TGF-ß signaling and enhanced the in vivo growth of CML cells. These findings demonstrate the direct role of BCR-ABL1 tyrosine kinase in suppressing TGF-ß signaling in CML and explain how Imatinib-targeted therapy restored beneficial TGF-ß anti-growth responses.

AMLnet, A deep-learning pipeline for the differential diagnosis of acute myeloid leukemia from bone marrow smears.

Acute myeloid leukemia (AML) is a deadly hematological malignancy. Cellular morphology detection of bone marrow smears based on the French-American-British (FAB) classification system remains an essential criterion in the diagnosis of hematological malignancies. However, the diagnosis and discrimination of distinct FAB subtypes of AML obtained from bone marrow smear images are tedious and time-consuming. In addition, there is considerable variation within and among pathologists, particularly in rural areas, where pathologists may not have relevant expertise. Here, we established a comprehensive database encompassing 8245 bone marrow smear images from 651 patients based on a retrospective dual-center study between 2010 and 2021 for the purpose of training and testing. Furthermore, we developed AMLnet, a deep-learning pipeline based on bone marrow smear images, that can discriminate not only between AML patients and healthy individuals but also accurately identify various AML subtypes. AMLnet achieved an AUC of 0.885 at the image level and 0.921 at the patient level in distinguishing nine AML subtypes on the test dataset. Furthermore, AMLnet outperformed junior human experts and was comparable to senior experts on the test dataset at the patient level. Finally, we provided an interactive demo website to visualize the saliency maps and the results of AMLnet for aiding pathologists' diagnosis. Collectively, AMLnet has the potential to serve as a fast prescreening and decision support tool for cytomorphological pathologists, especially in areas where pathologists are overburdened by medical demands as well as in rural areas where medical resources are scarce.