Expression of cDNA libraries from human melanoma, renal cancer, astrocytoma, and Hodgkin disease in Escherichia coli and screening for clones reactive with hightiter IgG antibodies in autologous patient serum lead to the discovery of at least four antigens with a restricted expression pattern in each tumor. Besides antigens known to elicit T-cell responses, such as MAGE-1 and tyrosinase, numerous additional antigens that were overexpressed or specifically expressed in tumors of the same type were identified. Sequence analyses suggest that many of these molecules, besides being the target of a specific immune response, might be of relevance for tumor growth. Antibodies to a given antigen were usually confined to patients with the same tumor type. The unexpected frequency of human tumor antigens, which can be readily defined at the molecular level by the serological analysis of autologous tumor cDNA expression cloning, indicates that human neoplasms elicit multiple specific immune responses in the autologous host and provides diagnostic and therapeutic approaches to human cancer.

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As discussed in Section 6.4 and at the beginning of Section 6.5, the F-test from the ANOVA table allows us to test the null hypothesis "The population means of all of the groups/treatments are equal." The alternate hypothesis is simply that "At least two are not equal." Often this isn't what we want to know! Say we are comparing 20 possible treatments for a disease. The ANOVA F-test (sometimes called the omnibus test), could only tell us that at least one of the treatments worked differently than the others. We might, however, want to be able to rank the 20 from best to worst, and say which of these differences are significant. We might want to compare all the treatments produced by one company to those of another, or maybe all the treatments based on one idea to those based on another.An obvious suggestion in each of these cases would be to simply do a large number of t-tests. To rank the 20 from best to worst, we could simply do a separate t-test for each possible comparison (there are 190 of them). To compare the two companies or two ideas, we could simply group all of the observations from the related methods together and use t-tests to see if they differ. One difficulty with this is that the α-level (probability of a Type I error) may no longer be what we want it to be. Sidak's FormulaStepping back from the ANOVA setting for a minute, say we wish to conduct one-sample t-tests on twenty completely independent populations. If we set α=0.05 for the first test, that means that: 0.05 = α = P[reject H 0 for test one | H 0 is true for test one]We could write the same for the other nineteen populations as well. If we are concerned about all twenty populations though, we might be more interested in the probability that we reject a true null hypothesis at all. That is, Using the rules of probability, and the fact that we assumed the tests were independent for this example, we can calculate what α T would be if we used α=0.05 for the comparison-wise rate.

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BackgroundImmune checkpoint inhibiting antibodies were introduced into routine clinical practice for cancer patients. Checkpoint blockade has led to durable remissions in some patients, but may also induce immune-related adverse events (irAEs). Lung cancer patients show an increased risk for complications, when infected with influenza viruses. Therefore, vaccination is recommended. However, the efficacy and safety of influenza vaccination during checkpoint blockade and its influence on irAEs is unclear. Similarly, the influence of vaccinations on T cell-mediated immune reactions in patients during PD-1 blockade remains poorly defined.MethodsWe vaccinated 23 lung cancer patients and 11 age-matched healthy controls using a trivalent inactivated influenza vaccine to investigate vaccine-induced immunity and safety during checkpoint blockade.ResultsWe did not observe significant differences between patients and healthy controls in vaccine-induced antibody titers against all three viral antigens. Influenza vaccination resulted in protective titers in more than 60% of patients/participants. In cancer patients, the post-vaccine frequency of irAEs was 52.2% with a median time to occurrence of 3.2 months after vaccination. Six of 23 patients (26.1%) showed severe grade 3/4 irAEs. This frequency of irAEs might be higher than the rate previously published in the literature and the rate observed in a non-study population at our institution (all grades 25.5%, grade 3/4 9.8%).ConclusionsAlthough this is a non-randomized trial with a limited number of patients, the increased rate of immunological toxicity is concerning. This finding should be studied in a larger patient population.