Journal
MODERN PATHOLOGY
Volume 30, Issue 4, Pages 577-586Publisher
NATURE PUBLISHING GROUP
DOI: 10.1038/modpathol.2016.221
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Funding
- Alberta Innovates Health Solutions CRIO
- Alberta Cancer Foundation
- Biotheranostics
- Definiens
- Diacarta
- Alberta Innovates [201201239] Funding Source: researchfish
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Several of the cancer immunotherapies under investigation or in clinical use target the programmed death-ligand 1/programmed death-1 (PD-L1/PD-1) signaling axis. PD-L1 expression in tumor samples has been used as a predictive marker for response to these therapeutics, and may also have independent prognostic utility when assessed along with immune cell markers. Our objectives were to assess the expression of PD-L1 in tumor specimens from a uniformly treated patient cohort with locally advanced cervical cancer, and to determine its prognostic significance along with the density of tumor-infiltrating T cells. We identified 120 patients with locally advanced cervical cancer treated with radical chemoradiotherapy, and built tissue microarrays from their formalin-fixed, paraffin-embedded pre-treatment biopsies. We used conventional brightfield and fluorescence immunohistochemistry to detect PD-L1, and quantified protein expression using both manual pathologist scoring and automated software analysis. We also evaluated the effect of PD-L1 expression in tumors, along with the presence and density of intra-tumoral CD8(+) T cells, on patient survival outcomes. Approximately 96% of the tumor samples expressed PD-L1, as determined using quantitative software analysis. Neither expression of PD-L1 nor density of CD8(+) T cells was associated with progression-free or overall survival. However, there was a trend towards worse progression-free survival in patients whose tumors expressed PD-L1 but lacked CD8(+) T cells (hazard ratio = 0.43 (0.18-1.01), P=0.053). Nevertheless, the high percentage of cervical cancer tumor samples expressing PD-L1 suggests that anti-PD-L1 or anti-PD-1 therapies are potential treatment options for this patient population.
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