Blood Cancer Discovery最新文献

Significance: We report promising efficacy and good tolerability of Bruton tyrosine kinase (BTK) inhibitor ibrutinib in treatment of newly diagnosed PCNSL. Additionally, we explored the contribution of ctDNA profiling to predictive potential in this prospective study. The consistency of ctDNA clearance from CSF/plasma was associated with more sustained treatment response and survival.

Significance: Patients aged ≥50 years with r/r MCL had superior OS and lower nonrelapse mortality 1 year after receiving brexu-cel compared with alloHCT. However, the long-term PFS and OS are similar for both treatments. Individual risk-benefit evaluation is essential to guide optimal treatment decisions.

In this issue of Blood Cancer Discovery, Pomeroy and Palmer present a new mathematical model, incorporating both inter- and intra-patient heterogeneity, to accurately predict outcomes for first-line combination therapies in diffuse large B-cell lymphoma. Their quantitative framework opens a range of possibilities for optimizing trial design and lymphoma therapy with potential to expedite clinical development. See related article by Pomeroy and Palmer, p. 254.

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.

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.

The role of the proto-oncogene MYC as a driver of lymphoma has been known since the 1980s, but the mechanisms underlying its dysregulation are not completely understood. In this issue of Blood Cancer Discovery, Iyer and colleagues employ a CRISPR interference screen targeted at open chromatin regions to unveil enhancers critical for MYC overexpression in lymphoma, including a novel regulatory element in the MYC locus that controls germinal center reentry and is recurrently amplified in diffuse large B-cell lymphomas (germinal center B-cell diffuse large B-cell lymphoma). See related article by Iyer et al., p. 233.

Significance: Aberrant MYC activity defines the most aggressive GCB-DLBCLs. We characterized a mechanism of MYC transcriptional activation via a native enhancer that is active in MYC-intact GCB-DLBCL, establishing fitness-sustaining cis- and trans-regulatory circuitry in GCB-DLBCL models that lack MYC enhancer-hijacking rearrangement. See related commentary by Mulet-Lazaro and Delwel, p. 149.

Significance: A new model of intratumor and interpatient heterogeneity in response to drug combinations explains and predicts the results of clinical trials of curative-intent treatments for DLBCL. This model can be used to understand and inform optimal design of curative drug combinations and clinical trials. See related commentary by Goldstein et al., p. 153.

Significance: Our investigation of the SOCS1 pathway in AML and T-cell interactions provides insights into potential mechanisms of resistance of AML to allogeneic hematopoietic stem cell transplantation and demonstrates the potential of targeting SOCS1 and its downstream mediators to enhance antileukemic T-cell activity. See related commentary by Fry, p. 157.

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.