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Nulsen, J.

Publications and source records attributed to Nulsen, J..

4 recordsLinked to original sources

Single cell functional immunogenomics of the fallopian tube reveals a precursor immune surveillance network for ovarian cancer prevention

The fallopian tube (FT) is increasingly recognized as the site of origin for high-grade serous ovarian cancer (HGSOC), yet its immune landscape and potential role in tumor surveillance remain poorly understood. Here, we employ single-cell RNA sequencing (scRNA-seq) and paired single-cell T-cell receptor sequencing (scTCR-seq) to profile tissue-resident memory-like T cells (TRMLs) from matched non- cancerous FT, metastatic omental tumors, and peripheral blood in HGSOC patients. Surprisingly, we identify a substantial clonal and functional overlap between TRMLs from non-cancerous FT and tumor- infiltrating TRMLs, with 18.4% of tumor TRML clonotypes being shared with FT TRMLs, significantly exceeding overlap with circulating T cells. Shared TCR clonotypes are preferentially enriched in exhausted CD8+ T cells (CD8-Tex) and proliferative exhausted CD8+ subsets, suggesting prior antigenic exposure. Notably, we identify a previously uncharacterized CD8+ SIK3-high subset enriched in tumors, with gene expression signatures implicating epigenetic plasticity and metabolic adaptation. Functionally, non-cancerous FT-derived TRMLs recognize autologous tumor antigens, as evidenced by robust IFN-{gamma} responses in tumor organoid co-culture assays and CD137 upregulation. Whole-genome and transcriptomic analyses reveal shared somatic mutations between the FT and tumor, supporting the FT as the tumors evolutionary origin. Predicted tumor neoantigens elicit TRML responses in the non-cancerous FT, and single-cell TCR profiling confirms that neoantigen-reactive T cells in FT share clonotypes with tumor-infiltrating TRMLs, further reinforcing their role in early immune surveillance. Importantly, FT-derived TRMLs exhibit lower exhaustion signatures than their tumor counterparts, suggesting their potential as a preferable source for adoptive T cell therapies. Our findings uncover a precursor immune surveillance network in the FT and provide a rationale for leveraging FT-resident T cells in cancer immunotherapy and prevention.

immunology↗

Comparative assessment of genes driving cancer and somatic evolution.

Genetic alterations of somatic cells can drive nonmalignant clone formation and promote cancer initiation. However, the link between these processes remains unclear hampering our understanding of tissue homeostasis and cancer development. Here we collect a literature-based repertoire of 3355 well-known or predicted drivers of cancer and noncancer somatic evolution in 122 cancer types and 12 noncancer tissues. Mapping the alterations of these genes in 7953 pancancer samples reveals that, despite the large size, the known compendium of drivers is still incomplete and biased towards frequently occurring coding mutations. High overlap exists between drivers of cancer and noncancer somatic evolution, although significant differences emerge in their recurrence. We confirm and expand the unique properties of drivers and identify a core of evolutionarily conserved and essential genes whose germline variation is strongly counter-selected. Somatic alteration in even one of these genes is sufficient to drive clonal expansion but not malignant transformation. Our study offers a comprehensive overview of our current understanding of the genetic events initiating clone expansion and cancer revealing significant gaps and biases that still need to be addressed. The compendium of cancer and noncancer somatic drivers, their literature support and properties are accessible at http://www.network-cancer-genes.org/.

cancer biology↗

Pan-cancer detection of driver genes at the single-patient resolution

BackgroundIdentifying the complete repertoire of genes that drive cancer in individual patients is crucial for precision oncology. Most established methods identify driver genes that are recurrently altered across patient cohorts. However, mapping these genes back to patients leaves a sizeable fraction with few or no drivers, hindering our understanding of cancer mechanisms and limiting the choice of therapeutic interventions. ResultsWe present sysSVM2, a machine learning software that integrates cancer genetic alterations with gene systems-level properties to predict drivers in individual patients. Using simulated pan-cancer data, we optimise sysSVM2 for application to any cancer type. We benchmark its performance on real cancer data and validate its applicability to a rare cancer type with few known driver genes. We show that drivers predicted by sysSVM2 have a low false-positive rate, are stable and disrupt well-known cancer-related pathways. ConclusionssysSVM2 can be used to identify driver alterations in patients lacking sufficient canonical drivers or belonging to rare cancer types for which assembling a large enough cohort is challenging, furthering the goals of precision oncology. As resources for the community, we provide the code to implement sysSVM2 and the pre-trained models in all TCGA cancer types (https://github.com/ciccalab/sysSVM2).

cancer biology↗

The Network of Cancer Genes (NCG): a comprehensive catalogue of known and candidate cancer genes from cancer sequencing screens.

The Network of Cancer Genes (NCG) is a manually curated repository of 2,372 genes whose somatic modifications have a known or predicted cancer driver role. These genes were collected from 275 publications, including two sources of known cancer genes and 273 cancer sequencing screens of 119 cancer types in 31 primary sites from 34,905 cancer donors. This represents a more than 1.5-fold increase in content as compared to the previous version. NCG also annotates properties of cancer genes, such as duplicability, evolutionary origin, RNA and protein expression, miRNA and protein interactions, protein function and essentiality. NCG is accessible at http://ncg.kcl.ac.uk/.

cancer biology↗