Near future of tumor immunology: Anticipating resistance mechanisms to immunotherapies, a big challenge for clinical trials

Carregando...
Imagem de Miniatura
Citações na Scopus
7
Tipo de produção
article
Data de publicação
2017
Título da Revista
ISSN da Revista
Título do Volume
Editora
TAYLOR & FRANCIS INC
Citação
HUMAN VACCINES & IMMUNOTHERAPEUTICS, v.13, n.5, p.1109-1111, 2017
Projetos de Pesquisa
Unidades Organizacionais
Fascículo
Resumo
The success of immunotherapies brings hope for the future of cancer treatment. Even so, we are faced with a new challenge, that of understanding which patients will respond initially and, possibly, develop resistance. The examination of the immune profile, especially approaches related to the immunoscore, may foretell which tumors will have a positive initial response. Ideally, the mutation load would also be analyzed, helping to reveal tumor associated antigens that are predictive of an effective cytolytic attack. However, the response may be hindered by changes induced in the tumor and its microenvironment during treatment, perhaps stemming from the therapy itself. To monitor such alterations, we suggest that minimally invasive approaches should be explored, such as the analysis of circulating tumor DNA. When testing new drugs, the data collected from each patient would initially represent an N of 1 clinical trial that could then be deposited in large databases and mined retrospectively for trends and correlations between genetic alterations and response to therapy. We expect that the investment in personalized approaches that couple molecular analysis during clinical trials will yield critical data that, in the future, may be used to predict the outcome of novel immunotherapies.
Palavras-chave
cancer, checkpoint blockade, circulating tumor DNA, immunoscore, personalized medicine
Referências
  1. Asghar W, 2016, SCI REP-UK, V6, DOI 10.1038/srep21163
  2. Bendell J, 2016, J CLIN ONCOL, V34, P1764
  3. Catenacci DVT, 2015, MOL ONCOL, V9, P967, DOI 10.1016/j.molonc.2014.09.011
  4. Chang-Hao Tsao S, 2015, SCI REP, V5, P11198, DOI 10.1038/SREP11198
  5. Galon J, 2006, SCIENCE, V313, P1960, DOI 10.1126/science.1129139
  6. Guinney J, 2015, NAT MED, V21, P1350, DOI 10.1038/nm.3967
  7. Holzel M, 2016, TRENDS IMMUNOL, V37, P364, DOI 10.1016/j.it.2016.03.009
  8. Juergens Rosalyn A, 2016, Biomark Cancer, V8, P1, DOI 10.4137/BIC.S31805
  9. Kirilovsky A, 2016, INT IMMUNOL, V28, P373, DOI 10.1093/intimm/dxw021
  10. Le DT, 2015, NEW ENGL J MED, V372, P2509, DOI 10.1056/NEJM0A1500596
  11. Pitt JM, 2016, IMMUNITY, V44, P1255, DOI 10.1016/j.immuni.2016.06.001
  12. Catani JPP, 2016, TRANSL ONCOL, V9, P565, DOI 10.1016/j.tranon.2016.09.011
  13. Shankaran V, 2001, NATURE, V410, P1107, DOI 10.1038/35074122
  14. Snyder A, 2014, NEW ENGL J MED, V371, P2189, DOI 10.1056/NEJMoa1406498
  15. Tan ACL, 2015, J IMMUNOTHER CANCER, V3, DOI 10.1186/s40425-015-0093-x
  16. VANDERBRUGGEN P, 1991, SCIENCE, V254, P1643, DOI 10.1126/science.1840703
  17. Xi LQ, 2016, CLIN CANCER RES, V22, P5480, DOI 10.1158/1078-0432.CCR-16-0613
  18. Zaretsky JM, 2016, NEW ENGL J MED, V375, P819, DOI 10.1056/NEJMoa1604958