Publications by authors named "L V Alexandrov"

Hepatocellular carcinoma (HCC) originates from differentiated hepatocytes undergoing compensatory proliferation in livers damaged by viruses or metabolic-dysfunction-associated steatohepatitis (MASH). While increasing HCC risk, MASH triggers p53-dependent hepatocyte senescence, which we found to parallel hypernutrition-induced DNA breaks. How this tumour-suppressive response is bypassed to license oncogenic mutagenesis and enable HCC evolution was previously unclear.

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Acral melanoma, which is not ultraviolet (UV)-associated, is the most common type of melanoma in several low- and middle-income countries including Mexico. Latin American samples are significantly underrepresented in global cancer genomics studies, which directly affects patients in these regions as it is known that cancer risk and incidence may be influenced by ancestry and environmental exposures. To address this, here we characterise the genome and transcriptome of 128 acral melanoma tumours from 96 Mexican patients, a population notable because of its genetic admixture.

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Article Synopsis
  • High-grade endometrial cancers (EAC) are aggressive and often have mutations in the RAS/MAPK pathways, making them difficult to treat.
  • Researchers studied the effectiveness of the drug avutometinib, in combination with FAK inhibitors defactinib or VS-4718, on various EAC cell lines and mouse models.
  • The results showed that the combination therapies significantly inhibited tumor growth and affected molecular pathways, suggesting potential for clinical trials in treating high-grade EAC patients.
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The identification and classification of carcinogens is critical in cancer epidemiology, necessitating updated methodologies to manage the burgeoning biomedical literature. Current systems, like those run by the International Agency for Research on Cancer (IARC) and the National Toxicology Program (NTP), face challenges due to manual vetting and disparities in carcinogen classification spurred by the volume of emerging data. To address these issues, we introduced the Carcinogen Detection via Transformers (CarD-T) framework, a text analytics approach that combines transformer-based machine learning with probabilistic statistical analysis to efficiently nominate carcinogens from scientific texts.

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