Journal of Cancer · 2013
Modeling efficacy of bevacizumab treatment for metastatic colon cancer.
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Islam Rezwan, Chyou Po-Huang, Burmester James K
Аннотация
PURPOSE: Bevacizumab, an FDA-approved adjuvant treatment for metastatic colon cancer, has extended survival for many patients. However, factors predicting response to treatment remain undefined.
PATIENTS AND METHODS: Relevant clinical and environmental data were abstracted from medical records of 149 evaluable patients treated with bevacizumab for metastatic colon cancer at a multi-specialty clinic. Tumor response was calculated from radiologic reports using Response Evaluation Criteria in Solid Tumors (RECIST) criteria and verified by oncologist review. Patients with at least one occurrence of complete or partial response or stable disease were classified as responders; those exhibiting progressive disease were classified as non-responders.
RESULTS: Univariate analysis demonstrated that blood in stool (P<0.05), unexplained weight loss (P<0.05), primary colon cancer site (P<0.05), chemotherapy treatment of primary tumor site (P<0.05), and adenocarcinoma versus adenoma subtype (P<0.05) was associated with tumor responsiveness. Factors remaining statistically significant following multivariate modeling included adenocarcinoma as tumor cell type versus other adenocarcinoma subtypes (OR=6.35, 95% CI: 1.08-37.18), chemotherapy treatment applied to primary tumor (OR= 0.07, 95% CI: 0.0-0.76,), tumor localization to cecal/ascending colon (OR=0.061, 95% CI: 0.006-0.588,), and unexplained weight loss (OR=0.1, 95% CI: 0.02-0.56,). Chemotherapy treatment of primary tumor, unexplained weight loss, and cecal/ascending localization of the tumor were associated with poorer outcomes. Adenocarcinoma as cell type compared to other adenocarcinoma subtypes was associated with better response to bevacizumab treatment.
CONCLUSION: RESULTS suggest that response to bevacizumab therapy may be predicted by modeling clinical factors including symptomology on presentation, tumor location and type, and initial response to chemotherapy.
PATIENTS AND METHODS: Relevant clinical and environmental data were abstracted from medical records of 149 evaluable patients treated with bevacizumab for metastatic colon cancer at a multi-specialty clinic. Tumor response was calculated from radiologic reports using Response Evaluation Criteria in Solid Tumors (RECIST) criteria and verified by oncologist review. Patients with at least one occurrence of complete or partial response or stable disease were classified as responders; those exhibiting progressive disease were classified as non-responders.
RESULTS: Univariate analysis demonstrated that blood in stool (P<0.05), unexplained weight loss (P<0.05), primary colon cancer site (P<0.05), chemotherapy treatment of primary tumor site (P<0.05), and adenocarcinoma versus adenoma subtype (P<0.05) was associated with tumor responsiveness. Factors remaining statistically significant following multivariate modeling included adenocarcinoma as tumor cell type versus other adenocarcinoma subtypes (OR=6.35, 95% CI: 1.08-37.18), chemotherapy treatment applied to primary tumor (OR= 0.07, 95% CI: 0.0-0.76,), tumor localization to cecal/ascending colon (OR=0.061, 95% CI: 0.006-0.588,), and unexplained weight loss (OR=0.1, 95% CI: 0.02-0.56,). Chemotherapy treatment of primary tumor, unexplained weight loss, and cecal/ascending localization of the tumor were associated with poorer outcomes. Adenocarcinoma as cell type compared to other adenocarcinoma subtypes was associated with better response to bevacizumab treatment.
CONCLUSION: RESULTS suggest that response to bevacizumab therapy may be predicted by modeling clinical factors including symptomology on presentation, tumor location and type, and initial response to chemotherapy.