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DNA repair biomarkers predict response to neoadjuvant chemoradiotherapy in esophageal cancer.

PURPOSE: The addition of neoadjuvant chemoradiotherapy prior to surgical resection for esophageal cancer has improved clinical outcomes in some trials. Pathologic complete response (pCR) following neoadjuvant therapy is associated with better clinical outcome in these patients, but only 22% to 40% of patients achieve pCR. Because both chemotherapy and radiotherapy act by inducing DNA damage, we analyzed proteins selected from multiple DNA repair pathways, using quantitative immunohistochemistry coupled with a digital pathology platform, as possible biomarkers of treatment response and clinical outcome.

METHODS AND MATERIALS: We identified 79 patients diagnosed with esophageal cancer between October 1994 and September 2002, with biopsy tissue available, who underwent neoadjuvant chemoradiotherapy prior to surgery at the Massachusetts General Hospital and used their archived, formalin-fixed, paraffin-embedded biopsy samples to create tissue microarrays (TMA). TMA sections were stained using antibodies against proteins in various DNA repair pathways including XPF, FANCD2, PAR, MLH1, PARP1, and phosphorylated MAPKAP kinase 2 (pMK2). Stained TMA slides were evaluated using machine-based image analysis, and scoring incorporated both the intensity and the quantity of positive tumor nuclei. Biomarker scores and clinical data were assessed for correlations with clinical outcome.

RESULTS: Higher scores for MLH1 (p = 0.018) and lower scores for FANCD2 (p = 0.037) were associated with pathologic response to neoadjuvant chemoradiation on multivariable analysis. Staining of MLH1, PARP1, XPF, and PAR was associated with recurrence-free survival, and staining of PARP1 and FANCD2 was associated with overall survival on multivariable analysis.

CONCLUSIONS: DNA repair proteins analyzed by immunohistochemistry may be useful as predictive markers for response to neoadjuvant chemoradiotherapy in patients with esophageal cancer. These results are hypothesis generating and need confirmation in an independent data set.

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