What question did this study set out to answer?

Develop and validate a global graphical method to determine optimal biomarker cut-points and evaluate the robustness of tumor subpopulation divisions.

November 1, 2004Clinical Cancer Research

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Authors

RCRobert L. Camp MDMarisa Dolled‐Filhart DRDavid L. Rimm

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Overview

Validation study demonstrates optimal biomarker cut-point selection in breast cancer cohorts, indicating robust division of tumor subpopulations based on clinical outcome.

Key Points

Develop and validate a global graphical method to determine optimal biomarker cut-points and evaluate the robustness of tumor subpopulation divisions.
Engineered the X-tile plot, a two-dimensional graphical tool projecting every possible subpopulation cut-point against clinical outcomes.
Evaluated marker cut-points using breast cancer patient cohorts and analyzed established prognostic markers including HER2, estrogen receptor, p53, patient age, tumor size, and lymph node status.
X-tile plots revealed substantial tumor subpopulations and demonstrated the statistical robustness of marker-outcome associations.
Generated subpopulation cut-points corresponded accurately with the established biological behavior and known prognostic values of the tested biomarkers.

Cite This Study

Camp et al. (2004) studied this question.

synapsesocial.com/papers/69d7591df182769aa8b8a7ab https://doi.org/10.1158/1078-0432.ccr-04-0713

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November 1, 2004Clinical Cancer Research

X-Tile

View Full Paper

Ask AI

Bookmark

Authors

RCRobert L. Camp MDMarisa Dolled‐Filhart DRDavid L. Rimm

Discussion

Member takes

Overview

Validation study demonstrates optimal biomarker cut-point selection in breast cancer cohorts, indicating robust division of tumor subpopulations based on clinical outcome.

Key Points

Develop and validate a global graphical method to determine optimal biomarker cut-points and evaluate the robustness of tumor subpopulation divisions.
Engineered the X-tile plot, a two-dimensional graphical tool projecting every possible subpopulation cut-point against clinical outcomes.
Evaluated marker cut-points using breast cancer patient cohorts and analyzed established prognostic markers including HER2, estrogen receptor, p53, patient age, tumor size, and lymph node status.
X-tile plots revealed substantial tumor subpopulations and demonstrated the statistical robustness of marker-outcome associations.
Generated subpopulation cut-points corresponded accurately with the established biological behavior and known prognostic values of the tested biomarkers.

Cite This Study

Camp et al. (2004) studied this question.

synapsesocial.com/papers/69d7591df182769aa8b8a7ab https://doi.org/10.1158/1078-0432.ccr-04-0713

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