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Mas Martin, G.

Publications and source records attributed to Mas Martin, G..

2 recordsLinked to original sources

Co-targeting menin and LSD1 dismantles oncogenic programs and restores differentiation in MLL-rearranged AML

Acute myeloid leukemia (AML) harboring MLL (MLL1, KMT2A) rearrangement (MLL-r) remains a lethal subtype with limited durable responses to single-agent menin inhibition. To define rational combination strategies, we performed a high-throughput screen of >900 epigenetic modulators in combination with menin inhibition in MLL-r AML models. This uncovered consistent synergy between menin and lysine-specific demethylase 1 (LSD1) inhibition, including with the clinical agent iadademstat. Mechanistically, LSD1 was found to interact with LEDGF/p75 (PSIP1), a chromatin-anchoring cofactor of the menin-MLL complex at H3K36me3 marked euchromatin. Chromatin profiling revealed extensive co-occupancy of LSD1 and menin-MLL components at leukemogenic loci in MLL-r AML cells. Dual inhibition of menin and LSD1 dismantled this chromatin complex, evicted H3K36me3 from LEDGF-bound sites, and reprogrammed transcription toward myeloid differentiation. Combined menin and LSD1 blockade repressed canonical MLL targets, including HOXA9, MYC, FLT3, PBX3, and CDK6, while restoring H3K36me3 and H3K4me3 and activating differentiation-associated genes. In vivo, the combination produced potent antileukemic effects in both MOLM-13 and MLL-r patient-derived xenografts, markedly reducing leukemic burden and extending survival without overt toxicity. These findings identify LSD1 as a critical cofactor of the menin-MLL-LEDGF axis and establish concurrent menin and LSD1 inhibition as a mechanistically informed combinatorial therapeutic approach in MLL-r AML.

cancer biology↗

Development and Implementation of Novel Chatbot-based Genomic Research Consent

ObjectiveTo conduct a retrospective analysis comparing traditional human-based consenting to an automated chat-based consenting process. Materials and MethodsWe developed a new chat-based consent using our IRB-approved consent forms. We leveraged a previously developed platform (Gia(R), or "Genetic Information Assistant") to deliver the chat content to candidate participants. The content included information about the study, educational information, and a quiz to assess understanding. We analyzed 144 families referred to our study during a 6-month time period. A total of 37 families completed consent using the traditional process, while 35 families completed consent using Gia. ResultsEngagement rates were similar between both consenting methods. The median length of the consent conversation was shorter for Gia users compared to traditional (44 vs. 76 minutes). Additionally, the total time from referral to consent completion was faster with Gia (5 vs. 16 days). Within Gia, understanding was assessed with a 10-question quiz that most participants (96%) passed. Feedback about the chat consent indicated that 86% of participants had a positive experience. DiscussionUsing Gia resulted in time savings for both the participant and study staff. The chatbot enables studies to reach more potential candidates. We identified five key features related to human-centered design for developing a consent chat. ConclusionThis analysis suggests that it is feasible to use an automated chatbot to scale obtaining informed consent for a genomics research study. We further identify a number of advantages when using a chatbot.

genetics↗