Novel meriolin derivatives potently inhibit cell cycle progression and transcription in leukemia and lymphoma cells via inhibition of cyclin-dependent kinases (CDKs).

A key feature of cancer is the disruption of cell cycle regulation, which is characterized by the selective and abnormal activation of cyclin-dependent kinases (CDKs). Consequently, targeting CDKs via meriolins represents an attractive therapeutic approach for cancer therapy. Meriolins represent a semisynthetic compound class derived from meridianins and variolins with a known CDK inhibitory potential. Here, we analyzed the two novel derivatives meriolin 16 and meriolin 36 in comparison to other potent CDK inhibitors and could show that they displayed a high cytotoxic potential in different lymphoma and leukemia cell lines as well as in primary patient-derived lymphoma and leukemia cells. In a kinome screen, we showed that meriolin 16 and 36 prevalently inhibited most of the CDKs (such as CDK1, 2, 3, 5, 7, 8, 9, 12, 13, 16, 17, 18, 19, 20). In drug-to-target modeling studies, we predicted a common binding mode of meriolin 16 and 36 to the ATP-pocket of CDK2 and an additional flipped binding for meriolin 36. We could show that cell cycle progression and proliferation were blocked by abolishing phosphorylation of retinoblastoma protein (a major target of CDK2) at Ser612 and Thr82. Moreover, meriolin 16 prevented the CDK9-mediated phosphorylation of RNA polymerase II at Ser2 which is crucial for transcription initiation. This renders both meriolin derivatives as valuable anticancer drugs as they target three different Achilles' heels of the tumor: (1) inhibition of cell cycle progression and proliferation, (2) prevention of transcription, and (3) induction of cell death.

Novel meriolin derivatives activate the mitochondrial apoptosis pathway in the presence of antiapoptotic Bcl-2.

Meriolin derivatives represent a new class of kinase inhibitors with a pronounced cytotoxic potential. Here, we investigated a newly synthesized meriolin derivative (termed meriolin 16) that displayed a strong apoptotic potential in Jurkat leukemia and Ramos lymphoma cells. Meriolin 16 induced apoptosis in rapid kinetics (within 2-3 h) and more potently (IC50: 50 nM) than the previously described derivatives meriolin 31 and 36 [1]. Exposure of Ramos cells to meriolin 16, 31, or 36 for 5 min was sufficient to trigger severe and irreversible cytotoxicity. Apoptosis induction by all three meriolin derivatives was independent of death receptor signaling but required caspase-9 and Apaf-1 as central mediators of the mitochondrial death pathway. Meriolin-induced mitochondrial toxicity was demonstrated by disruption of the mitochondrial membrane potential (ΔΨm), mitochondrial release of proapoptotic Smac, processing of the dynamin-like GTPase OPA1, and subsequent fragmentation of mitochondria. Remarkably, all meriolin derivatives were able to activate the mitochondrial death pathway in Jurkat cells, even in the presence of the antiapoptotic Bcl-2 protein. In addition, meriolins were capable of inducing cell death in imatinib-resistant K562 and KCL22 chronic myeloid leukemia cells as well as in cisplatin-resistant J82 urothelial carcinoma and 2102EP germ cell tumor cells. Given the frequent inactivation of the mitochondrial apoptosis pathway by tumor cells, such as through overexpression of antiapoptotic Bcl-2, meriolin derivatives emerge as promising therapeutic agents for overcoming treatment resistance.

Communicator-Driven Data Preprocessing Improves Deep Transfer Learning of Histopathological Prediction of Pancreatic Ductal Adenocarcinoma.

Pancreatic cancer is a fatal malignancy with poor prognosis and limited treatment options. Early detection in primary and secondary locations is critical, but fraught with challenges. While digital pathology can assist with the classification of histopathological images, the training of such networks always relies on a ground truth, which is frequently compromised as tissue sections contain several types of tissue entities. Here we show that pancreatic cancer can be detected on hematoxylin and eosin (H&E) sections by convolutional neural networks using deep transfer learning. To improve the ground truth, we describe a preprocessing data clean-up process using two communicators that were generated through existing and new datasets. Specifically, the communicators moved image tiles containing adipose tissue and background to a new data class. Hence, the original dataset exhibited improved labeling and, consequently, a higher ground truth accuracy. Deep transfer learning of a ResNet18 network resulted in a five-class accuracy of about 94% on test data images. The network was validated with independent tissue sections composed of healthy pancreatic tissue, pancreatic ductal adenocarcinoma, and pancreatic cancer lymph node metastases. The screening of different models and hyperparameter fine tuning were performed to optimize the performance with the independent tissue sections. Taken together, we introduce a step of data preprocessing via communicators as a means of improving the ground truth during deep transfer learning and hyperparameter tuning to identify pancreatic ductal adenocarcinoma primary tumors and metastases in histological tissue sections.