Genomic approaches to accelerate cancer interception

Beane, Jennifer; Campbell, Joshua D.; Lel, Julian; Vick, Jessica; Spira, Avrum

doi:10.1016/s1470-2045(17)30373-x

Cited by 50 publications

(37 citation statements)

References 98 publications

(73 reference statements)

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“…Moreover, it demonstrated a better resolution not only with respect to the separation between healthy and CRC samples, but it is also able to discriminate samples with adenoma, being a promising tool for CRC prevention by detecting early stages in which intervention is easier and more effective. And finally, the model has a biological interpretation that provides important clues to better understand the mechanistic implications of the gut microbiota in CRC as well as in the previous stages of adenoma, which can have an interesting potential in preventive medicine and, specifically, in cancer interception [63].…”

Section: Discussionmentioning

confidence: 99%

Interpretable machine learning analysis of functional metagenomic profiles improves colorectal cancer prediction and reveals basic molecular mechanisms.

Casimiro-Soriguer

Loucera

Peña‐Chilet

et al. 2020

Preprint

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Background Gut microbiome is gaining interest because its links with several diseases, including colorectal cancer (CRC). Results Here we performed a meta-analysis of 851 fecal metagenomic samples from five publicly available studies. We used an interpretable machine learning approach based on functional profiles, instead of the conventional taxonomic profiles, to produce a highly accurate predictor of CRC with better precision than those of previous proposals. Moreover, this approach is also able to discriminate samples with adenoma, which makes this approach very promising for CRC prevention by detecting early stages in which intervention is easier and more effective. In addition, interpretable machine learning methods allows extracting features relevant for the classification, which reveals basic molecular mechanisms accounting for the changes underwent by the microbiome functional landscape in the transition from healthy gut to adenoma and CRC conditions. Conclusion Functional profiles provide superior accuracy in predicting CCR and adenoma conditions than taxonomic profiles and additionally, in a context of explainable machine learning, provide useful hints on the molecular mechanisms operating in the microbiota behind these conditions.

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Section: Discussionmentioning

confidence: 99%

Interpretable machine learning analysis of functional metagenomic profiles improves colorectal cancer prediction and reveals basic molecular mechanisms.

Casimiro-Soriguer

Loucera

Peña‐Chilet

et al. 2020

Preprint

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“…The complexity, diversity and pathways of precancer development and transformation causes great intra-and intertumor heterogeneity and individual variation. Emerging molecular and cellular studies exploiting advanced bigdata approaches are beginning map this complexity (33). Recent studies of colorectal and myeloproliferative neoplasia highlighted below have identified premalignant genomic subgroups, critical to refining preventive screening, risk stratification, and target identification (34).…”

Section: State Of Cancer Prevention Sciencementioning

confidence: 99%

AACR White Paper: Shaping the Future of Cancer Prevention – A Roadmap for Advancing Science and Public Health

Lippman

Abate‐Shen

Maresso

et al. 2018

Cancer Prevention Research

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The recent pace, extent, and impact of paradigmchanging cancer prevention science has been remarkable. The American Association for Cancer Research (AACR) convened a 3-day summit, aligned with five research priorities: (i) Precancer Atlas (PCA). (ii) Cancer interception. (iii) Obesity-cancer linkage, a global epidemic of chronic low-grade inflammation. (iv) Implementation science. (v) Cancer disparities. Aligned with these priorities, AACR co-led the Lancet Commission to formally endorse and accelerate the NCI Cancer Moonshot program, facilitating new global collaborative efforts in cancer control. The expanding scope of creative impact is perhaps most startling-from NCI-funded built environments to AACR Team Science Awarded studies of Asian cancer genomes informing global primary prevention policies; cell-free epigenetic marks identifying incipient neoplastic site; practice-changing genomic subclasses in myeloproliferative neoplasia (including germline variant tightly linked to JAK2 V617F haplotype); universal germline genetic testing for pancreatic cancer; and repurposing drugs targeting immune-and stem-cell signals (e.g., IL-1b, PD-1, RANK-L) to cancer interception. Microbiota-driven IL-17 can induce stemness and transformation in pancreatic precursors (identifying another repurposing opportunity). Notable progress also includes hosting an obesity special conference (connecting epidemiologic and molecular perspectives to inform cancer research and prevention strategies), co-leading concerted national implementation efforts in HPV vaccination, and charting the future elimination of cancer disparities by integrating new science tools, discoveries and perspectives into community-engaged research, including targeted counter attacks on e-cigarette ad exploitation of children, Hispanics and Blacks. Following this summit, two unprecedented funding initiatives were catalyzed to drive cancer prevention research: the NCI Cancer Moonshot (e.g., PCA and disparities); and the AACR-Stand Up To Cancer bold "Cancer Interception" initiative.

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“…It is, therefore, evident that, in clinical cancer management, tumor classifications cannot be uniformly applied across the world today. However, in the era of precision medicine and cancer genomics [5], the application of these internationally accepted classifications is considered obligatory as the starting point for cancer management. Clinical guidelines rating strength of evidence indicate which gene tests are recommended in defined clinical settings, and how management decisions are impacted by gene test results.…”

Section: Introductionmentioning

confidence: 99%

Cancer Classification at the Crossroads

Carbone

2020

Cancers

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Internationally accepted classifications of malignant tumors, developed by the World Health Organization (WHO) and the Union for International Cancer Control (UICC), are based on the histotype, site of origin, morphologic grade, and spread of cancer throughout the body. The WHO classifications are the foundation of cancer diagnosis and the starting point for cancer management. Starting in 2000, the WHO classifications began to include biologic and molecular–genetic features. These developments are having a strong impact on cancer diagnosis and treatment, and this impact is amplifying, given the advances in cancer genomics. Molecular–genetic profiling can be used to refine existing classifications of tumors and, for a small but increasing number of cancers, even determine the treatment irrespective of histotype. Here I discuss how cancer classifications may change in the era of cancer genomics.

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Genomic approaches to accelerate cancer interception

Cited by 50 publications

References 98 publications

Interpretable machine learning analysis of functional metagenomic profiles improves colorectal cancer prediction and reveals basic molecular mechanisms.

Interpretable machine learning analysis of functional metagenomic profiles improves colorectal cancer prediction and reveals basic molecular mechanisms.

AACR White Paper: Shaping the Future of Cancer Prevention – A Roadmap for Advancing Science and Public Health

Cancer Classification at the Crossroads

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