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This article describes the Cell Maps for Artificial Intelligence (CM4AI) project and its goals, methods, standards, current datasets, software tools , status, and future directions. CM4AI is the Functional Genomics Data Generation Project in the U.S. National Institute of Health's (NIH) Bridge2AI program. Its overarching mission is to produce ethical, AI-ready datasets of cell architecture, inferred from multimodal data collected for human cell lines, to enable transformative biomedical AI research.
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Dose-limiting toxicity poses a major limitation to the clinical utility of targeted cancer therapies, often arising from target engagement in nonmalignant tissues. This obstacle can be minimized by targeting cancer dependencies driven by proteins with tissue-restricted and/or tumor-restricted expression. In line with another recent report, we show here that, in acute myeloid leukemia (AML), suppression of the myeloid-restricted PIK3CG/p110γ-PIK3R5/p101 axis inhibits protein kinase B/Akt signaling and compromises AML cell fitness. Furthermore, silencing the genes encoding PIK3CG/p110γ or PIK3R5/p101 sensitizes AML cells to established AML therapies. Importantly, we find that existing small-molecule inhibitors against PIK3CG are insufficient to achieve a sustained long-term antileukemic effect. To address this concern, we developed a proteolysis-targeting chimera (PROTAC) heterobifunctional molecule that specifically degrades PIK3CG and potently suppresses AML progression alone and in combination with venetoclax in human AML cell lines, primary samples from patients with AML and syngeneic mouse models.