Charting the transcriptomic landscape of primary and metastatic cancers in relation to their origin and target normal tissues

Sanghvi, Neel; Calvo-Alcañiz, Camilo; Rajagopal, Padma S.; Scalera, Stefano; Canu, Valeria; Sinha, Sanju; Schischlik, Fiorella; Wang, Kun; Madan, Sanna; Shulman, Eldad; Papanicolau-Sengos, Antonios; Blandino, Giovanni; Ruppin, Eytan; Nair, Nishanth Ulhas

doi:10.1101/2023.10.30.564810

Cited by 3 publications

(2 citation statements)

References 46 publications

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“…Site-specific archetype adaptation may present a novel therapeutic vulnerability in metastatic cancers. Consistent with our findings, prior studies indicate that metastatic tumors express genes associated with their target tissues [26,50]. Thus, the dual role of archetypes as markers for such tissue-specific gene expression patterns and predictors of tumor drug sensitivities suggests that site-specific archetype enrichment patterns may highlight vulnerabilities in metastatic tumors to treatments associated with the archetypes of their destination tissues.…”

Section: Normal Tissue Signature Enrichment Influences Pan-cancer Dru...supporting

confidence: 89%

Functional transcriptional signatures for tumor-type-agnostic phenotype prediction

Weistuch

Murgas

Zhu

et al. 2023

Preprint

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Gene expression predicts tumor characteristics such as resistance to anticancer therapy. However, generalizing these predictors to multiple cancer types and data sets to motivate new therapeutic strategies has proven difficult. Here, we present a nonnegative matrix factorization (NMF) approach that decomposes gene expression into a universal set of archetype fingerprints. By restricting our analysis to five well-defined biological pathways, we show that trade-offs between normal tissues constrain oncogenic heterogeneity. Thus, the resulting six archetypes unify gene expression variation across 54 tissue types, 1504 cancer cell lines, and 1770 patient samples. The archetype mixtures correlate with cancer cell line sensitivity to several common anticancer therapies, even among cancers of the same type. They also explain subtype-specific breast cancer characteristics and define poor prognostic subgroups in breast, colorectal, and pancreatic cancers. Overall, the approach offers an evolvable resource for understanding commonalities across cancers, which could eventually lead to more robust therapeutic strategies.

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Section: Normal Tissue Signature Enrichment Influences Pan-cancer Dru...supporting

confidence: 89%

Functional transcriptional signatures for tumor-type-agnostic phenotype prediction

Weistuch

Murgas

Zhu

et al. 2023

Preprint

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“…Modeling the molecular development of BC in canine mammary tumors revealed that lipid metabolic processes were significantly enriched in BC progression expression patterns and in mammary carcinomas [ 71 ]. Additionally, recent evidence found enhanced expression of bile acid metabolism in a subset of breast and colon liver metastasis [ 72 ].…”

Section: Discussionmentioning

confidence: 99%

Discovering predisposing genes for hereditary breast cancer using deep learning

Passi,

Lieberman,

Zahdeh

et al. 2024

Briefings in Bioinformatics

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Breast cancer (BC) is the most common malignancy affecting Western women today. It is estimated that as many as 10% of BC cases can be attributed to germline variants. However, the genetic basis of the majority of familial BC cases has yet to be identified. Discovering predisposing genes contributing to familial BC is challenging due to their presumed rarity, low penetrance, and complex biological mechanisms. Here, we focused on an analysis of rare missense variants in a cohort of 12 families of Middle Eastern origins characterized by a high incidence of BC cases. We devised a novel, high-throughput, variant analysis pipeline adapted for family studies, which aims to analyze variants at the protein level by employing state-of-the-art machine learning models and three-dimensional protein structural analysis. Using our pipeline, we analyzed 1218 rare missense variants that are shared between affected family members and classified 80 genes as candidate pathogenic. Among these genes, we found significant functional enrichment in peroxisomal and mitochondrial biological pathways which segregated across seven families in the study and covered diverse ethnic groups. We present multiple evidence that peroxisomal and mitochondrial pathways play an important, yet underappreciated, role in both germline BC predisposition and BC survival.

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Normal tissue transcriptional signatures for tumor-type-agnostic phenotype prediction

Weistuch,

Murgas,

Zhu

et al. 2024

Sci Rep

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Charting the transcriptomic landscape of primary and metastatic cancers in relation to their origin and target normal tissues

Cited by 3 publications

References 46 publications

Functional transcriptional signatures for tumor-type-agnostic phenotype prediction

Functional transcriptional signatures for tumor-type-agnostic phenotype prediction

Discovering predisposing genes for hereditary breast cancer using deep learning

Normal tissue transcriptional signatures for tumor-type-agnostic phenotype prediction

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