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Using high-throughput transcriptomic data for prognosis: a critical overview and perspectives.

Eytan Domany1

  • 1Department of Physics of Complex Systems, Weizmann Institute of Science, Rehovot, Israel eytan.domany@weizmann.ac.i.

Cancer Research
|September 4, 2014
PubMed
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Accurate cancer prognosis remains challenging despite advances in omic methods. This review explores limitations in current prognostic predictors and proposes novel approaches integrating clinical data and pathway analysis for improved patient stratification.

Area of Science:

  • Oncology
  • Bioinformatics
  • Machine Learning in Medicine

Background:

  • Personalized cancer treatment relies on accurate prognosis and prediction of therapy response.
  • High-throughput omic methods have yet to significantly impact clinical practice for cancer prognosis.
  • Existing prognostic gene lists show poor concordance and unclear biological relevance, with often overoptimistic reported success rates.

Purpose of the Study:

  • To critically review the derivation of prognostic classifiers using machine learning.
  • To identify reasons for the limitations and shortcomings of current prognostic predictors.
  • To present promising new approaches for improving cancer prognosis and patient stratification.

Main Methods:

  • Critical examination of machine learning methodologies used in deriving prognostic classifiers.

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  • Review of existing literature on omic-based prognostic predictors for cancer.
  • Proposal of two novel approaches: combining molecular omics with clinical predictors, and inferring pathway deregulation scores from expression data.

    • Main Results:

    • Identified significant limitations in current omic-based prognostic classifiers, including lack of gene overlap and unclear biological meaning.
    • Highlighted overoptimistic reporting of success rates for existing prognostic signatures.
    • Proposed two promising avenues for improved prognosis: integration of clinical and molecular data, and pathway-centric analysis.

    Conclusions:

    • Current omic-based prognostic predictors have not revolutionized clinical cancer treatment due to methodological issues.
    • Future improvements in cancer prognosis may arise from integrating prior knowledge, such as combining clinical and molecular predictors.
    • Pathway deregulation scores derived from expression data offer a 'phenomenological' approach with potential for significant impact on cancer studies and patient stratification.