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Back to the drawing board—the need for more realistic model systems for immunotherapy
Author(s) -
Alexander Peter
Publication year - 1977
Publication title -
cancer
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 3.052
H-Index - 304
eISSN - 1097-0142
pISSN - 0008-543X
DOI - 10.1002/1097-0142(197707)40:1+<467::aid-cncr2820400709>3.0.co;2-6
Subject(s) - immunogenicity , immunotherapy , medicine , immunology , transplantation , boosting (machine learning) , antigen , immune system , disease , pathology , computer science , machine learning
In experimental animals the growth of tumors which display strong immunogenicity can be slowed by immunological maneuvers that increase the magnitude of the host response to the specific tumor antigens. Such immunogeneic tumors do not, in general, cause distant metastases and may, therefore, not be relevant to the treatment of disseminated disease in man. This may explain why the current experience with immunotherapy based on such animal models has, with very few exceptions, been disappointing. Animal tumors which are not immunogenic by standard transplantation tests frequently disseminate and it seems likely that clinically useful immunotherapy has to be based on procedures which are effective against such tumors. The lack of immunogenicity detectable by transplantation may be due to the absence of tumor‐specific transplantation antigens (TSTAs), in which case if there is to be any immunotherapy it will have to be directed at boosting some innate type of host resistance. Alternatively, the lack of immunogenicity may be attributable to the intervention of “escape mechanisms” which pervert the immunologically specific host response to TSTAs. In this case, the immunological maneuvre should be directed at overcoming the escape problem and not at boosting the magnitude of the specific host reaction to the TSTAs. Cancer 40:467–470, 1977.