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The oncogenome of the domestic cat

Although cancer is a common cause of death in domestic cats, little is known about the range of cancer genes in cat tumors, and how this range might compare with the oncogenome in people.

Now, researchers in Science have sequenced cancer genes in 493 samples from 13 different types of feline cancer and matched healthy control tissue, gaining a clearer picture of the cat oncogenome and comparing the genes to known cancer-causing mutations in humans.

Cancer is a common cause of morbidity and mortality in domestic cats. Because the mutational landscape of domestic cat tumors remains uncharacterized, we performed targeted sequencing of 493 feline tumor–normal tissue pairs from 13 tumor types, focusing on the feline orthologs of ~1000 human cancer genes. TP53 was the most frequently mutated gene, and the most recurrent copy number alterations were loss of PTEN or FAS or gain of MYC. By identifying 31 driver genes, mutational signatures, viral sequences, and tumor-predisposing germline variants, our study provides insight into the domestic cat oncogenome. We demonstrate key similarities with the human oncogenome, confirming the cat as a valuable model for comparative studies, and identify potentially actionable mutations, aligning with a “One Medicine” approach.

Is artificial general intelligence already here? A new case that today’s LLMs meet key tests

Will artificial intelligence ever be able to reason, learn, and solve problems at levels comparable to humans? Experts at the University of California San Diego believe the answer is yes—and that such artificial general intelligence has already arrived. This debate is tackled by four faculty members spanning humanities, social sciences, and data science in a recently published Comment invited by Nature.

Computer scientist Alan Turing first posed this question in his landmark 1950 paper, though he didn’t use the term artificial general intelligence (AGI). His “imitation game,” now known as the Turing Test, asked whether a machine could pass as human in text-based conversation with humans. Seventy-five years later, that future is here.

Over the past year, Associate Professor of Philosophy Eddy Keming Chen, Professor of Artificial Intelligence, Data Science and Computer Science Mikhail Belkin, Associate Professor of Linguistics and Computer Science Leon Bergen, and Professor of Data Science, Philosophy and Policy David Danks engaged in extensive dialogue on this question. These discussions happened as another set of researchers at UC San Diego found in March 2025 that the large language model GPT-4.5 was judged to be human 73% of the time in a Turing test—much more often than actual humans.

‘Molecular glue’ from this San Diego startup makes cancer self-destruct: Clinical trial begins

This local biotech says it has found a way to trick cancer cells into destroying themselves with its molecular glue. Now it’s putting that claim to the test. After attracting global attention from researchers and billions of dollars from Big Pharma, Neomorph announced that it has begun its first clinical trial. The molecular glue aims to treat a form of kidney cancer.

The first trial will dose one patient with the molecular glue, NEO-811, to treat clear cell renal cell carcinoma, the most common kind of kidney cancer.

In San Diego County, roughly 500 people are diagnosed with kidney cancer each year—that’s more than one new case every day, according to the California Cancer Registry. Among residents 65 and older, the rate jumps to more than four times higher than the overall population: 2,000 San Diegan’s over 65 are diagnosed with a form of kidney cancer each year.

Lipid nanoparticle GM-CSF replacement for autoimmune pulmonary alveolar proteinosis

One of the most-viewed PNAS articles in the last week is “Lipid nanoparticle GM-CSF replacement for autoimmune pulmonary alveolar proteinosis.” Explore the article here: https://ow.ly/QMWH50Yl87H

For more trending articles, visit https://ow.ly/pmKX50Yl87I.

Granulocyte–macrophage colony-stimulating factor (GM-CSF) deficiency drives autoimmune pulmonary alveolar proteinosis (aPAP), a disease characterized by impaired macrophage-mediated clearance of pulmonary surfactants. Clinical data suggest that inhaled recombinant GM-CSF reduces symptoms in aPAP patients, providing a rationale for mRNA-based GM-CSF replacement therapies. However, these require effective mRNA delivery after nebulization. Here, we report the iterative in vivo design of a lipid nanoparticle, named nebulized lung delivery 2 (NLD2), that efficiently delivers mRNA after nebulization. NLD2 carrying GM-CSF mRNA transfected alveolar macrophages in vivo, leading to interleukin-10 pathway activation and subsequent surfactant lipoprotein clearance. In a preclinical disease model of aPAP, GM-CSF mRNA delivery reduced surfactant protein thickness more than recombinant GM-CSF. These data support continued exploration of nebulized lipid nanoparticle therapies for aPAP.

Monocyte-derived IL-10 drives sex differences in pain duration

A new Science Immunology study suggests that IL-10-producing monocytes help resolve pain faster in male mice and humans when compared with females, which could inform therapeutics for sex-specific pain conditions.

Peripheral monocytes and IL-10 are associated with shorter pain duration in males in both mice and humans.

Clinically informed AI outperforms foundation models in spinal cord disease prediction

Cervical spondylotic myelopathy (CSM) refers to spinal cord compression from arthritis in the neck and is the leading cause of spinal cord dysfunction in older adults. CSM is a chronic, progressive condition that can cause neck pain, muscle weakness, difficulty walking and other debilitating symptoms. While the diagnosis is sometimes clear, often the diagnosis can take years because symptoms aren’t recognized until the later stages, and by then, treatment options are limited.

A multidisciplinary team of surgeon-scientists, computer scientists and researchers at WashU developed an artificial intelligence (AI)-based approach that could help clinicians screen for and diagnose CSM up to 30 months earlier, opening new opportunities for earlier treatment. The findings are published in npj Digital Medicine.

Salim Yakdan, MD, a postdoctoral research fellow in the Taylor Family Department of Neurosurgery at WashU Medicine, and Ben Warner, a doctoral student in computer science and engineering at the McKelvey School of Engineering, co-first authors on the research, used seven different AI models to analyze large datasets containing electronic health record data of more than 2 million people with and without CSM. The models examined patterns of health-care interactions, such as tests and diagnoses, recorded in electronic health records to spot patients whose medical histories resemble those already diagnosed with CSM, helping to flag individuals who may be at higher risk.

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