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Detection of prognostically distinct subpopulations in non-small cell lung cancer patients treated with epidermal growth factor receptor (EGFR) inhibitors
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| Zeitschriftentitel: | Journal of Clinical Oncology |
|---|---|
| Personen und Körperschaften: | |
| In: | Journal of Clinical Oncology, 25, 2007, 18_suppl, S. 18178-18178 |
| Format: | E-Article |
| Sprache: | Englisch |
| veröffentlicht: |
American Society of Clinical Oncology (ASCO)
|
| Schlagwörter: |
| author_facet |
Stewart, D. J. Stewart, D. J. |
|---|---|
| author |
Stewart, D. J. |
| spellingShingle |
Stewart, D. J. Journal of Clinical Oncology Detection of prognostically distinct subpopulations in non-small cell lung cancer patients treated with epidermal growth factor receptor (EGFR) inhibitors Cancer Research Oncology |
| author_sort |
stewart, d. j. |
| spelling |
Stewart, D. J. 0732-183X 1527-7755 American Society of Clinical Oncology (ASCO) Cancer Research Oncology http://dx.doi.org/10.1200/jco.2007.25.18_suppl.18178 <jats:p> 18178 </jats:p><jats:p> Hypothesis: In patient survival curves that follow 1<jats:sup>st</jats:sup> order kinetics, prognostically distinct subpopulations will be identified by inflection points in plots of log % survival vs time. Prognostically important dichotomous variables (present vs absent, or above vs below a threshold) will give inflection points while important continuous variables will shift curve slope without giving inflection points. Methods: For overall and progression-free survival curves from 35 published clinical trials of EGFR inhibitors, curve height at different times was measured manually and converted to % of height at time 0. Curve nonlinear regression analyses were performed using the Pharsight WinNonlin 5.0.1 program. Results: In assessments done to date, the most common survival curve pattern was conformity to a 2-compartment model, indicating a single inflection point and ≥ 2 prognostically distinct subpopulations. The second most common pattern was conformity to a 1-compartment model, suggesting that some populations are relatively homogenous. Only a minority of curves could be fit to 3-compartment models that would have indicated ≥ 3 distinct subpopulations. In different studies, EGFR inhibitors shifted some patients from poorer to better prognosis compartments compared to placebo, and/or decreased the slope (ie, increased the t1/2) of one or both individual compartments. Similarly, factors that have been found to be associated with prognosis in these studies were associated with formation of prognostically homogeneous groups or with alterations of relative compartment sizes or slopes. Conclusions: It is feasible to perform nonlinear regression analysis of log-linear survival plots to define prognostically distinct subpopulations. Preliminary assessments suggest that EGFR inhibitors and factors known to be important prognostically are associated with alteration of size and survival t1/2 of these subpopulations. Helpful data that could potentially be obtained from such analyses include minimum number, size and t1/2 of prognostically distinct subpopulations, and impact of a therapeutic modality or potential prognostic factor on each subpopulation. </jats:p><jats:p> No significant financial relationships to disclose. </jats:p> Detection of prognostically distinct subpopulations in non-small cell lung cancer patients treated with epidermal growth factor receptor (EGFR) inhibitors Journal of Clinical Oncology |
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| title |
Detection of prognostically distinct subpopulations in non-small cell lung cancer patients treated with epidermal growth factor receptor (EGFR) inhibitors |
| title_unstemmed |
Detection of prognostically distinct subpopulations in non-small cell lung cancer patients treated with epidermal growth factor receptor (EGFR) inhibitors |
| title_full |
Detection of prognostically distinct subpopulations in non-small cell lung cancer patients treated with epidermal growth factor receptor (EGFR) inhibitors |
| title_fullStr |
Detection of prognostically distinct subpopulations in non-small cell lung cancer patients treated with epidermal growth factor receptor (EGFR) inhibitors |
| title_full_unstemmed |
Detection of prognostically distinct subpopulations in non-small cell lung cancer patients treated with epidermal growth factor receptor (EGFR) inhibitors |
| title_short |
Detection of prognostically distinct subpopulations in non-small cell lung cancer patients treated with epidermal growth factor receptor (EGFR) inhibitors |
| title_sort |
detection of prognostically distinct subpopulations in non-small cell lung cancer patients treated with epidermal growth factor receptor (egfr) inhibitors |
| topic |
Cancer Research Oncology |
| url |
http://dx.doi.org/10.1200/jco.2007.25.18_suppl.18178 |
| publishDate |
2007 |
| physical |
18178-18178 |
| description |
<jats:p> 18178 </jats:p><jats:p> Hypothesis: In patient survival curves that follow 1<jats:sup>st</jats:sup> order kinetics, prognostically distinct subpopulations will be identified by inflection points in plots of log % survival vs time. Prognostically important dichotomous variables (present vs absent, or above vs below a threshold) will give inflection points while important continuous variables will shift curve slope without giving inflection points. Methods: For overall and progression-free survival curves from 35 published clinical trials of EGFR inhibitors, curve height at different times was measured manually and converted to % of height at time 0. Curve nonlinear regression analyses were performed using the Pharsight WinNonlin 5.0.1 program. Results: In assessments done to date, the most common survival curve pattern was conformity to a 2-compartment model, indicating a single inflection point and ≥ 2 prognostically distinct subpopulations. The second most common pattern was conformity to a 1-compartment model, suggesting that some populations are relatively homogenous. Only a minority of curves could be fit to 3-compartment models that would have indicated ≥ 3 distinct subpopulations. In different studies, EGFR inhibitors shifted some patients from poorer to better prognosis compartments compared to placebo, and/or decreased the slope (ie, increased the t1/2) of one or both individual compartments. Similarly, factors that have been found to be associated with prognosis in these studies were associated with formation of prognostically homogeneous groups or with alterations of relative compartment sizes or slopes. Conclusions: It is feasible to perform nonlinear regression analysis of log-linear survival plots to define prognostically distinct subpopulations. Preliminary assessments suggest that EGFR inhibitors and factors known to be important prognostically are associated with alteration of size and survival t1/2 of these subpopulations. Helpful data that could potentially be obtained from such analyses include minimum number, size and t1/2 of prognostically distinct subpopulations, and impact of a therapeutic modality or potential prognostic factor on each subpopulation. </jats:p><jats:p> No significant financial relationships to disclose. </jats:p> |
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| description | <jats:p> 18178 </jats:p><jats:p> Hypothesis: In patient survival curves that follow 1<jats:sup>st</jats:sup> order kinetics, prognostically distinct subpopulations will be identified by inflection points in plots of log % survival vs time. Prognostically important dichotomous variables (present vs absent, or above vs below a threshold) will give inflection points while important continuous variables will shift curve slope without giving inflection points. Methods: For overall and progression-free survival curves from 35 published clinical trials of EGFR inhibitors, curve height at different times was measured manually and converted to % of height at time 0. Curve nonlinear regression analyses were performed using the Pharsight WinNonlin 5.0.1 program. Results: In assessments done to date, the most common survival curve pattern was conformity to a 2-compartment model, indicating a single inflection point and ≥ 2 prognostically distinct subpopulations. The second most common pattern was conformity to a 1-compartment model, suggesting that some populations are relatively homogenous. Only a minority of curves could be fit to 3-compartment models that would have indicated ≥ 3 distinct subpopulations. In different studies, EGFR inhibitors shifted some patients from poorer to better prognosis compartments compared to placebo, and/or decreased the slope (ie, increased the t1/2) of one or both individual compartments. Similarly, factors that have been found to be associated with prognosis in these studies were associated with formation of prognostically homogeneous groups or with alterations of relative compartment sizes or slopes. Conclusions: It is feasible to perform nonlinear regression analysis of log-linear survival plots to define prognostically distinct subpopulations. Preliminary assessments suggest that EGFR inhibitors and factors known to be important prognostically are associated with alteration of size and survival t1/2 of these subpopulations. Helpful data that could potentially be obtained from such analyses include minimum number, size and t1/2 of prognostically distinct subpopulations, and impact of a therapeutic modality or potential prognostic factor on each subpopulation. </jats:p><jats:p> No significant financial relationships to disclose. </jats:p> |
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| spelling | Stewart, D. J. 0732-183X 1527-7755 American Society of Clinical Oncology (ASCO) Cancer Research Oncology http://dx.doi.org/10.1200/jco.2007.25.18_suppl.18178 <jats:p> 18178 </jats:p><jats:p> Hypothesis: In patient survival curves that follow 1<jats:sup>st</jats:sup> order kinetics, prognostically distinct subpopulations will be identified by inflection points in plots of log % survival vs time. Prognostically important dichotomous variables (present vs absent, or above vs below a threshold) will give inflection points while important continuous variables will shift curve slope without giving inflection points. Methods: For overall and progression-free survival curves from 35 published clinical trials of EGFR inhibitors, curve height at different times was measured manually and converted to % of height at time 0. Curve nonlinear regression analyses were performed using the Pharsight WinNonlin 5.0.1 program. Results: In assessments done to date, the most common survival curve pattern was conformity to a 2-compartment model, indicating a single inflection point and ≥ 2 prognostically distinct subpopulations. The second most common pattern was conformity to a 1-compartment model, suggesting that some populations are relatively homogenous. Only a minority of curves could be fit to 3-compartment models that would have indicated ≥ 3 distinct subpopulations. In different studies, EGFR inhibitors shifted some patients from poorer to better prognosis compartments compared to placebo, and/or decreased the slope (ie, increased the t1/2) of one or both individual compartments. Similarly, factors that have been found to be associated with prognosis in these studies were associated with formation of prognostically homogeneous groups or with alterations of relative compartment sizes or slopes. Conclusions: It is feasible to perform nonlinear regression analysis of log-linear survival plots to define prognostically distinct subpopulations. Preliminary assessments suggest that EGFR inhibitors and factors known to be important prognostically are associated with alteration of size and survival t1/2 of these subpopulations. Helpful data that could potentially be obtained from such analyses include minimum number, size and t1/2 of prognostically distinct subpopulations, and impact of a therapeutic modality or potential prognostic factor on each subpopulation. </jats:p><jats:p> No significant financial relationships to disclose. </jats:p> Detection of prognostically distinct subpopulations in non-small cell lung cancer patients treated with epidermal growth factor receptor (EGFR) inhibitors Journal of Clinical Oncology |
| spellingShingle | Stewart, D. J., Journal of Clinical Oncology, Detection of prognostically distinct subpopulations in non-small cell lung cancer patients treated with epidermal growth factor receptor (EGFR) inhibitors, Cancer Research, Oncology |
| title | Detection of prognostically distinct subpopulations in non-small cell lung cancer patients treated with epidermal growth factor receptor (EGFR) inhibitors |
| title_full | Detection of prognostically distinct subpopulations in non-small cell lung cancer patients treated with epidermal growth factor receptor (EGFR) inhibitors |
| title_fullStr | Detection of prognostically distinct subpopulations in non-small cell lung cancer patients treated with epidermal growth factor receptor (EGFR) inhibitors |
| title_full_unstemmed | Detection of prognostically distinct subpopulations in non-small cell lung cancer patients treated with epidermal growth factor receptor (EGFR) inhibitors |
| title_short | Detection of prognostically distinct subpopulations in non-small cell lung cancer patients treated with epidermal growth factor receptor (EGFR) inhibitors |
| title_sort | detection of prognostically distinct subpopulations in non-small cell lung cancer patients treated with epidermal growth factor receptor (egfr) inhibitors |
| title_unstemmed | Detection of prognostically distinct subpopulations in non-small cell lung cancer patients treated with epidermal growth factor receptor (EGFR) inhibitors |
| topic | Cancer Research, Oncology |
| url | http://dx.doi.org/10.1200/jco.2007.25.18_suppl.18178 |