1. T cell subpopulations in lymph nodes may not be predictive of patient outcome in colorectal cancer
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John L. McCall, Vicky Phillips, Michael A. Black, Roslyn A. Kemp, Han-Seung Yoon, Ahmad Anjomshoaa, and Anthony E. Reeve
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CD4-Positive T-Lymphocytes ,Male ,Oncology ,Cancer Research ,medicine.medical_specialty ,Colorectal cancer ,T cell ,Priming (immunology) ,T-Lymphocytes, Regulatory ,lcsh:RC254-282 ,Immunoenzyme Techniques ,Immune system ,T-Lymphocyte Subsets ,Internal medicine ,medicine ,Humans ,Aged ,Neoplasm Staging ,business.industry ,Research ,Interleukin-2 Receptor alpha Subunit ,FOXP3 ,Forkhead Transcription Factors ,Prognosis ,medicine.disease ,lcsh:Neoplasms. Tumors. Oncology. Including cancer and carcinogens ,medicine.anatomical_structure ,Immunology ,Immunohistochemistry ,Female ,Lymph Nodes ,Lymph ,Neoplasm Recurrence, Local ,Colorectal Neoplasms ,business ,CD8 - Abstract
Background The immune response has been proposed to be an important factor in determining patient outcome in colorectal cancer (CRC). Previous studies have concentrated on characterizing T cell populations in the primary tumour where T cells with regulatory effect (Foxp3+ Tregs) have been identified as both enhancing and diminishing anti-tumour immune responses. No previous studies have characterized the T cell response in the regional lymph nodes in CRC. Methods Immunohistochemistry was used to analyse CD4, CD8 or Foxp3+ T cell populations in the regional lymph nodes of patients with stage II CRC (n = 31), with (n = 13) or without (n = 18) cancer recurrence after 5 years of follow up, to determine if the priming environment for anti-tumour immunity was associated with clinical outcome. Results The proportions of CD4, CD8 or Foxp3+ cells in the lymph nodes varied widely between and within patients, and there was no association between T cell populations and cancer recurrence or other clinicopathological characteristics. Conclusions These data indicate that frequency of these T cell subsets in lymph nodes may not be a useful tool for predicting patient outcome.
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