Blood Cancer Discovery最新文献

Acute myeloid leukemia with complex karyotype (ckAML) is characterized by high genomic complexity, including frequent TP53 mutations and chromothripsis. Genomic rearrangements that reposition active enhancers near proto-oncogenes, leading to their aberrant expression, have not been systematically investigated in AML. To facilitate the discovery of such "enhancer hijacking" events, we developed pyjacker, a computational tool, and applied it to 39 ckAML samples. Pyjacker identified several enhancer hijacking events in AML patient samples, including aberrant expression of motor neuron and pancreas homeobox 1 (MNX1), which can result from del(7)(q22q36) and is associated with hijacking of a CDK6 enhancer. MNX1 activation occurs in 1.4% of AML patients and shows significant co-occurrence with BCOR mutations. Through a xenograft mouse model, we demonstrated that MNX1 is required for leukemia cell fitness. Pyjacker is an easy-to-use, accurate, and broadly applicable tool for identifying consequences of genomic events driving tumorigenesis, especially when germline genomic data is missing.

Significance: We discuss the mechanisms of AML immune evasion including loss or downregulation of MHC class I and II, reduced TRAIL receptor expression, inhibitory metabolite production, inhibitory ligand expression, impaired proinflammatory cytokine production, and AML niche alterations.

Artificial intelligence could enhance chimeric antigen receptor T-cell therapy outcomes through optimization of all steps, from target identification, vector design, and manufacturing to personalized data-driven clinical decisions. In this report, we highlight steps toward unlocking this potential, including the need for standardized, comprehensive data repositories as a way for addressing barriers to artificial intelligence learning, such as data heterogeneity and patient privacy.

Drugs targeting metabolism have been effectively used in patients with T-cell acute lymphoblastic leukemia (T-ALL) for decades; still, the full therapeutic potential of targeting metabolism has not been completely exploited yet. Here, we highlight the critical need for metabolic biomarkers to advance precision medicine in T-ALL, explore the identification of novel metabolic vulnerabilities, and discuss the potential of targeted therapies and dietary interventions to optimize treatment outcomes.

High expression of MYC and its target genes identify germinal center B-cell diffuse large B-cell lymphomas (GCB-DLBCL) associated with poor outcomes. We used CRISPR-interference profiling of human lymphoma cell lines to define essential enhancers in the MYC locus and non-immunoglobulin rearrangement partner loci, including a recurrent rearrangement between MYC and the BCL6 locus control region. GCB-DLBCL cell lines without MYC rearrangement are dependent on an evolutionarily-conserved enhancer we name "germinal center MYC enhancer 1" (GME-1), which is activated by the transcription factor complex of OCT2, OCA-B, and MEF2B, shows an active chromatin state in normal human and mouse germinal center B cells, and demonstrates selective acetylation and MYC promoter topological interactions in MYC-intact GCB-DLBCL biopsies. Whole-genome sequencing identified tandem copy gains of the GME-1 enhancer as a rare but recurrent event in DLBCL. Our findings shed new light on mechanisms that dysregulate MYC, a key driver of B cell malignancy.

Significance: This review underscores our shared responsibility to champion multidimensional strategies rooted in basic and translational science, community involvement, and societal responsiveness for a meaningful impact. Unifying themes include the need to enhance collaborative infrastructure to engage laboratory researchers, epidemiologists, data scientists, clinicians, patients, community leaders, and policymakers; patient-level support services; outreach, education, and navigation for patients at the community level; recruitment and retention of underrepresented groups in the healthcare and research workforce; and funding for these efforts.

Significance: In recent years, remarkable technological advances have illuminated aspects of the pathogenesis of myeloid malignancies-yet outcomes for patients with these devastating diseases have not significantly improved. We posit that a synthesized view of the three dimensions through which hematopoietic cells transit during their healthy and diseased life-clonal evolution, stem cell hierarchy, and ontogeny-promises high yields in new insights into disease pathogenesis and new therapeutic avenues.

Significance: This study reveals differences in IR patterns after CAR-T therapy in patients with large B-cell lymphoma, with early NK cell recovery emerging as a key predictor of survival. These findings provide potential future avenues of research for improving patient outcomes and tailoring post-therapy management strategies to mitigate relapse risk.

Significance: As survival of HCT recipients continues to improve, late treatment effects gain importance. We demonstrate that pediatric HCT recipients show increased risk of CH compared with age-matched controls. Prospective studies are crucial to understand the clinical implications of posttransplant CH in this young population.

Models of tumor drug response have illuminated important concepts in oncology, but there remains a need for theory that combines intra-tumor and inter-patient heterogeneity to explain patient outcomes, especially for curative treatments. We present a mathematical model of multi-drug therapy that describes both cell-to-cell and patient-to-patient heterogeneity as distributions of drug sensitivity, and apply it to simulate curative combination therapies for Diffuse Large B-Cell Lymphoma (DLBCL). Simulated trials reproduced Progression-Free Survival and changes in circulating tumor DNA observed under standard therapy, and accurately predicted success or failure of nine randomized trials of first-line combinations based on drugs' efficacies in relapsed/refractory DLBCL. Finally, we used the model to explore how drug synergies, biomarkers, and subtype-specific endpoints could improve the chance of success of targeted combination therapies. This study offers a quantitative model of curative drug combinations and suggests that predictive simulations could aid the design of new regimens with curative intent.