ABSTRACT The wealth of morphological, histological, and molecular data from human cancers available to pathologists means that pathology is poised to become a truly quantitative systems

science. By measuring morphological parameters such as tumor stage and grade, and by measuring molecular biomarkers such as hormone receptor status, pathologists have sometimes accurately

predicted what will happen to a patient's tumor. While 'omic' technologies have seemingly improved prognostication and prediction, some molecular 'signatures' are

not useful in clinical practice because of the failure to independently validate these approaches. Many associations between gene 'signatures' and clinical response are correlative

mathematical models and systems biology, and how to make the resultant model sufficiently robust to be of practical use. We discuss the difficulties in using mathematics to model cancer, and

review some approaches that may be used to allow systems biology to be successfully applied in the clinic. KEY POINTS * Morphological assessment by histopathology can be a good surrogate

for underlying molecular biology, which may be used in treatment decision-making * Drug mechanisms of action and signaling pathways that may not be understood fully may be predicted by

mathematical models * Cancer is dynamic, complex, and heterogeneous, and therefore requires integration of molecular biology and pathology data in new predictive frameworks that embrace the

dynamic nature of the disease * Developments in mathematics and quantitative pathology provide the foundation for this new predictive framework to aid in the diagnosis, prognostication, and

prediction of response in patients with cancer * Mathematical models can be used to predict drug responses under a given set of conditions or to predict what combinations of drug could

result in a given cell fate * Systems biology has been successful in predicting molecular responses in well-described cancer pathways (for example, mitogen activated protein kinase pathway)

or in physiology, but has not yet been applied to the cancer clinic Access through your institution Buy or subscribe This is a preview of subscription content, access via your institution

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about institutional subscriptions * Read our FAQs * Contact customer support SIMILAR CONTENT BEING VIEWED BY OTHERS UNRAVELING NON-GENETIC HETEROGENEITY IN CANCER WITH DYNAMICAL MODELS AND

COMPUTATIONAL TOOLS Article 24 April 2023 SINGLE-CELL TRANSCRIPTOMICS IN CANCER: COMPUTATIONAL CHALLENGES AND OPPORTUNITIES Article Open access 15 September 2020 SYSTEMS BIOLOGY IN

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outcome. _Cancer Res._ 66, 5487–5494 (2006). Article  CAS  Google Scholar  Download references ACKNOWLEDGEMENTS This work is supported by Breast Cancer Campaign and Breakthrough Breast

Cancer. AUTHOR INFORMATION AUTHORS AND AFFILIATIONS * Division of Pathology and Breakthrough Research Unit, Institute of Genetics and Molecular Medicine, University of Edinburgh, Edinburgh,

UK Dana Faratian & David J. Harrison * SIMBIOS, University of Abertay, Dundee, UK Robert G. Clyde & John W. Crawford Authors * Dana Faratian View author publications You can also

search for this author inPubMed Google Scholar * Robert G. Clyde View author publications You can also search for this author inPubMed Google Scholar * John W. Crawford View author

publications You can also search for this author inPubMed Google Scholar * David J. Harrison View author publications You can also search for this author inPubMed Google Scholar

CORRESPONDING AUTHOR Correspondence to Dana Faratian. ETHICS DECLARATIONS COMPETING INTERESTS The authors declare no competing financial interests. RIGHTS AND PERMISSIONS Reprints and

permissions ABOUT THIS ARTICLE CITE THIS ARTICLE Faratian, D., Clyde, R., Crawford, J. _et al._ Systems pathology—taking molecular pathology into a new dimension. _Nat Rev Clin Oncol_ 6,

455–464 (2009). https://doi.org/10.1038/nrclinonc.2009.102 Download citation * Published: 07 July 2009 * Issue Date: August 2009 * DOI: https://doi.org/10.1038/nrclinonc.2009.102 SHARE THIS

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