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Background Hematologic toxicities, including coagulopathy, endothelial activation, and cytopenias, with CD19-targeted chimeric antigen receptor (CAR) T-cell therapies correlate with cytokine release syndrome (CRS) and neurotoxicity severity, but little is known about the extended toxicity profiles of CAR T-cells targeting alternative antigens. This report characterizes hematologic toxicities seen following CD22 CAR T-cells and their relationship to CRS and neurotoxicity.Methods We retrospectively characterized hematologic toxicities associated with CRS seen on a phase 1 study of anti-CD22 CAR T-cells for children and young adults with relapsed/refractory CD22+ hematologic malignancies. Additional analyses included correlation of hematologic toxicities with neurotoxicity and exploring effects of hemophagocytic lymphohistiocytosis-like toxicities (HLH) on bone marrow recovery and cytopenias. Coagulopathy was defined as evidence of bleeding or abnormal coagulation parameters. Hematologic toxicities were graded by Common Terminology Criteria for Adverse Events V.4.0.Results Across 53 patients receiving CD22 CAR T-cells who experienced CRS, 43 (81.1%) patients achieved complete remission. Eighteen (34.0%) patients experienced coagulopathy, of whom 16 had clinical manifestations of mild bleeding (typically mucosal bleeding) which generally subsided following CRS resolution. Three had manifestations of thrombotic microangiopathy. Patients with coagulopathy had higher peak ferritin, D-dimer, prothrombin time, international normalized ratio (INR), lactate dehydrogenase (LDH), tissue factor, prothrombin fragment F1+2 and soluble vascular cell adhesion molecule-1 (s-VCAM-1). Despite a relatively higher incidence of HLH-like toxicities and endothelial activation, overall neurotoxicity was generally less severe than reported with CD19 CAR T-cells, prompting additional analysis to explore CD22 expression in the central nervous system (CNS). Single-cell analysis revealed that in contrast to CD19 expression, CD22 is not on oligodendrocyte precursor cells or on neurovascular cells but is seen on mature oligodendrocytes. Lastly, among those attaining CR, grade 3–4 neutropenia and thrombocytopenia were seen in 65% of patients at D28.Conclusion With rising incidence of CD19 negative relapse, CD22 CAR T-cells are increasingly important for the treatment of B-cell malignancies. In characterizing hematologic toxicities on CD22 CAR T-cells, we demonstrate that despite endothelial activation, coagulopathy, and cytopenias, neurotoxicity was relatively mild and that CD22 and CD19 expression in the CNS differed, providing one potential hypothesis for divergent neurotoxicity profiles. Systematic characterization of on-target off-tumor toxicities of novel CAR T-cell constructs will be vital as new antigens are targeted.Trial registration number NCT02315612.

Intra-tumor heterogeneity is often associated with resistance to targeted therapy, requiring the design of combinatorial therapies. Here, based on tumor single-cell transcriptomic datasets, the authors develop a computational approach to identify optimal combinatorial treatments targeting membrane receptors for cancer therapy.

Summary: Novel strategies are needed to identify drug targets and treatments for the COVID-19 pandemic. The altered gene expression of virus-infected host cells provides an opportunity to specifically inhibit viral propagation via targeting the synthetic lethal and synthetic dosage lethal (SL/SDL) partners of such altered host genes. Pursuing this disparate antiviral strategy, here we comprehensively analyzed multiple in vitro and in vivo bulk and single-cell RNA-sequencing datasets of SARS-CoV-2 infection to predict clinically relevant candidate antiviral targets that are SL/SDL with altered host genes. The predicted SL/SDL-based targets are highly enriched for infected cell inhibiting genes reported in four SARS-CoV-2 CRISPR-Cas9 genome-wide genetic screens. We further selected a focused subset of 26 genes that we experimentally tested in a targeted siRNA screen using human Caco-2 cells. Notably, as predicted, knocking down these targets reduced viral replication and cell viability only under the infected condition without harming noninfected healthy cells.

The concept of the Myc (c-myc, n-myc, l-myc) oncogene as a canonical, DNA-bound transcription factor has consistently changed over the past few years. Indeed, Myc controls gene expression programs at multiple levels: directly binding chromatin and recruiting transcriptional coregulators; modulating the activity of RNA polymerases (RNAPs); and drawing chromatin topology. Therefore, it is evident that Myc deregulation in cancer is a dramatic event. Glioblastoma multiforme (GBM) is the most lethal, still incurable, brain cancer in adults, and it is characterized in most cases by Myc deregulation. Metabolic rewiring typically occurs in cancer cells, and GBM undergoes profound metabolic changes to supply increased energy demand. In nontransformed cells, Myc tightly controls metabolic pathways to maintain cellular homeostasis. Consistently, in Myc-overexpressing cancer cells, including GBM cells, these highly controlled metabolic routes are affected by enhanced Myc activity and show substantial alterations. On the other hand, deregulated cancer metabolism impacts Myc expression and function, placing Myc at the intersection between metabolic pathway activation and gene expression. In this review paper, we summarize the available information on GBM metabolism with a specific focus on the control of the Myc oncogene that, in turn, rules the activation of metabolic signals, ensuring GBM growth.

MYC oncoprotein deregulation is a common catastrophic event in human cancer and limiting its activity restrains tumor development and maintenance, as clearly shown via Omomyc, an MYC-interfering 90 amino acid mini-protein. MYC is a multifunctional transcription factor that regulates many aspects of transcription by RNA polymerase II (RNAPII), such as transcription activation, pause release, and elongation. MYC directly associates with Protein Arginine Methyltransferase 5 (PRMT5), a protein that methylates a variety of targets, including RNAPII at the arginine residue R1810 (R1810me2s), crucial for proper transcription termination and splicing of transcripts. Therefore, we asked whether MYC controls termination as well, by affecting R1810me2S. We show that MYC overexpression strongly increases R1810me2s, while Omomyc, an MYC shRNA, or a PRMT5 inhibitor and siRNA counteract this phenomenon. Omomyc also impairs Serine 2 phosphorylation in the RNAPII carboxyterminal domain, a modification that sustains transcription elongation. ChIP-seq experiments show that Omomyc replaces MYC and reshapes RNAPII distribution, increasing occupancy at promoter and termination sites. It is unclear how this may affect gene expression. Transcriptomic analysis shows that transcripts pivotal to key signaling pathways are both up- or down-regulated by Omomyc, whereas genes directly controlled by MYC and belonging to a specific signature are strongly down-regulated. Overall, our data point to an MYC/PRMT5/RNAPII axis that controls termination via RNAPII symmetrical dimethylation and contributes to rewiring the expression of genes altered by MYC overexpression in cancer cells. It remains to be clarified which role this may have in tumor development.

Chronic Lymphocytic Leukemia (CLL) represents the most common leukemia in the western world and remains incurable. Leukemic cells organize and interact in the lymphoid tissues, however what actually occurs in these sites has not been fully elucidated yet. Studying primary CLL cells in vitro is very challenging due to their short survival in culture and also to the fact that traditional two-dimensional in vitro models lack cellular and spatial complexity present in vivo. Based on these considerations, we exploited for the first time three-dimensional (3D) bioprinting to advance in vitro models for CLL. This technology allowed us to print CLL cells (both primary cells and cell lines) mixed with the appropriate, deeply characterized, hydrogel to generate a scaffold containing the cells, thus avoiding the direct cell seeding onto a precast 3D scaffold and paving the way to more complex models. Using this system, we were able to efficiently 3D bioprint leukemic cells and improve their viability in vitro that could be maintained up to 28 days. We monitored over time CLL cells viability, phenotype and gene expression, thus establishing a reproducible long-term 3D culture model for leukemia. Through RNA sequencing (RNAseq) analysis, we observed a consistent difference in gene expression profile between 2D and 3D samples, indicating a different behavior of the cells in the two different culture settings. In particular, we identified pathways upregulated in 3D, at both day 7 and 14, associated with immunoglobulins production, pro-inflammatory molecules expression, activation of cytokines/chemokines and cell-cell adhesion pathways, paralleled by a decreased production of proteins involved in DNA replication and cell division, suggesting a strong adaptation of the cells in the 3D culture. Thanks to this innovative approach, we developed a new tool that may help to better mimic the physiological 3D in vivo settings of leukemic cells as well as of immune cells in broader terms. This will allow for a more reliable study of the molecular and cellular interactions occurring in normal and neoplastic conditions in vivo, and could also be exploited for clinical purposes to test individual responses to different drugs.

Fanconi anemia is a complex disease affecting multiple DNA repair proteins that resolve DNA crosslinks which can block vital processes. Here the authors use parallel genome-wide screens that identify the BLM helicase complex as a suppressor of Fanconi anemia phenotypes.

A test of the performance of two probabilistic classifiers (random forests and multinomial logit models) in automatically defining cancer cases has been carried out on 5608 subjects, registered by the Venetian Tumour Registry (RTV) during the years 1987-1996 and manually checked for possible second cancers that occurred during the 1997-1999 period. An eightfold cross-validation was performed to estimate the classification error; 63 predictive variables were entered into the model fitting. The random forest allows to automatically classify 45% of subjects with a classification error lower than 5%, while the corresponding error is 31% for the multilogit model. The performance of the former classifier is appealing, indicating a potential drop of manually checked cases from 1750 to 960 per incidence year with a moderate error rate. This result suggests to refine the approach and extend it to other categories of manually treated cases.