Justin Jee

Systemic iron sequestration restricts the intratumoral CD8+ T cell landscape in pancreas cancer

This study shows that systemic iron sequestration via hepcidin reduces CD8+ T cells in pancreas cancer, linking iron dysregulation to tumoral immunity.

SMARCA4, STK11, and KEAP1 co-inactivation associates with poor prognosis and upregulation of the TGF-β pathway in lung adenocarcinoma

The paper identifies SMARCA4/STK11/KEAP1 triple mutant lung adenocarcinoma as a poor prognosis subset and links it to TGF-β pathway activation.

Integrated Clinicogenomic Risk Modeling for Metachronous Second Primary Cancers

The paper develops and validates a clinicogenomic risk model to predict second primary cancers in cancer survivors using genetic, clinical, and lifestyle factors.

Generalist large language models complement tailor-made predictors for tumor genomics interpretation

This paper evaluates generalist large language models (LLMs) in tumor genomic interpretation tasks, finding they can complement or outperform task-specific models in some cases.

Integrated histopathologic modeling of detailed tumor subtypes and actionable biomarkers

The paper develops Mosaic, a machine learning system that classifies tumors into detailed subtypes and identifies molecular phenotypes using histopathologic and genomic data from a large patient cohort.

Multimodal histopathologic models stratify hormone receptor-positive early breast cancer

This paper develops a multimodal transformer model using H&E images and text reports to predict recurrence scores in hormone receptor-positive breast cancer, achieving high accuracy and generalizability.

Clinico-genomic features predict distinct metastatic phenotypes in cutaneous melanoma

This study identifies distinct metastatic phenotypes in cutaneous melanoma using clinico-genomic data and machine learning, linking specific mutations and clinical factors to organ-specific metastasis.

Machine learning predictions improve identification of real-world cancer driver mutations

The paper uses machine learning to identify cancer driver mutations, finding that methods incorporating protein structure or functional genomic data outperform those using only evolutionary data, and validates their predictions using real-world patient cohorts.