Transl Lung Cancer Res. 2025 Jul 31;14(7):2369-2373. doi: 10.21037/tlcr-2025-523. Epub 2025 Jul 17.
NO ABSTRACT
PMID:40799451 | PMC:PMC12337028 | DOI:10.21037/tlcr-2025-523
Nature, Published online: 13 August 2025; doi:10.1038/s41586-025-09398-w
A study presents ALPACA, a computational method for inferring clone- and allele-specific copy numbers of individual clones from multi-sample bulk DNA-sequencing data, and demonstrates its use to study metastasis trajectories.
Artificial intelligence models that can discover drugs and write code still fail at puzzles a lay person can master in minutes. This phenomenon sits at the heart of the challenge of artificial general intelligence (AGI). Can today’s AI revolution produce models that rival or surpass human intelligence across all domains? If so, what underlying enablers—whether hardware, software, or the orchestration of both—would be needed to power them?
Dario Amodei, co-founder of Anthropic, predicts some form of “powerful AI” could come as early as 2026, with properties that include Nobel Prize-level domain intelligence; the ability to switch between interfaces like text, audio, and the physical world; and the autonomy to reason toward goals, rather than responding to questions and prompts as they do now. Sam Altman, chief executive of OpenAI, believes AGI-like properties are already “coming into view,” unlocking a societal transformation on par with electricity and the internet. He credits progress to continuous gains in training, data, and compute, along with falling costs, and a socioeconomic value that is
“super-exponential.”
Optimism is not confined to founders. Aggregate forecasts give at least a 50% chance of AI systems achieving several AGI milestones by 2028. The chance of unaided machines outperforming humans in every possible task is estimated at 10% by 2027, and 50% by 2047, according to one expert survey. Time horizons shorten with each breakthrough, from 50 years at the time of GPT-3’s launch to five years by the end of 2024. “Large language and reasoning models are transforming nearly every industry,” says Ian Bratt, vice president of machine learning technology and fellow at Arm.
This content was produced by Insights, the custom content arm of MIT Technology Review. It was not written by MIT Technology Review’s editorial staff.
This content was researched, designed, and written entirely by human writers, editors, analysts, and illustrators. This includes the writing of surveys and collection of data for surveys. AI tools that may have been used were limited to secondary production processes that passed thorough human review.
Cell Cycle. 2025 Aug 13:1-22. doi: 10.1080/15384101.2025.2539645. Online ahead of print.
ABSTRACT
Epithelial carcinoma cells require penetration of the basement membrane (BM) to metastasize. The BM is a thin layer of extracellular matrix beneath epithelial and endothelial tissues. It acts as a structural barrier, preventing cancer cells from invading and undergoing endocytosis and exocytosis. Thus, understanding the relationship between the BM and tumor immunity can lead to new strategies for halting cancer progression and metastasis. Gene expression data of 33 cancers were obtained from the Cancer Genome Atlas database. The study analyzed the correlation between BM regulatory genes, copy number variations, immune-related genes, and tumor immune dysfunction rejection (TIDE). Immunohistochemical methods were used to analyze the expression of regulatory genes. And the BM score was calculated using single-sample gene set enrichment analysis. Single-cell transcriptional sequencing determined the activation status of the BM in the tumor microenvironment. The expression of BM-related genes (BMGs) exhibited significant heterogeneity across different cancer types. Most genes were up-regulated in tumor tissues. Major single nucleotide polymorphisms of BMGs included missense mutations, while major copy number variations were heterozygous deletion and heterozygous amplification. Additionally, the expressions of immune checkpoint molecules CD276, NRP1, and C10orf54 showed positive correlations with BMS. Numerous tumors displayed a significant positive correlation between BMS and TIDE scores. We demonstrate that BM regulatory genes undergo alterations specific to different cancer types, which are associated with the expression of immune checkpoints and immune dysfunction. This indicates that BM remodeling plays an active role in modulating immune resistance, rather than being a passive structural alteration.
PMID:40799172 | DOI:10.1080/15384101.2025.2539645
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