Computational principles and practice for decoding immune contexture in the tumor microenvironment

Tumor-infiltrating immune cells (TIICs) have been recognized as crucial components of the tumor microenvironment (TME) and induced both beneficial and adverse consequences for tumorigenesis as well as outcome and therapy (particularly immunotherapy). Computer-aided investigation of immune cell components in the TME has become a promising avenue to better understand the interplay between the immune system and tumors. In this study, we presented an overview of data sources, computational methods and software tools, as well as their application in inferring the composition of tumor-infiltrating immune cells in the TME. In parallel, we explored the future perspectives and challenges that may be faced with more accurate quantitative infiltration of immune cells in the future. Together, our study provides a little guide for scientists in the field of clinical and experimental immunology to look for dedicated resources and more competent tools for accelerating the unraveling of tumor-immune interactions with the implication in precision immunotherapy.

The Influence of Variation among Replicates on Repeatability

This chapter first illustrates how a large number of observations can inform the design of a particular experiment intended to test rigorously the causes of the observed associations. It does so through the discussion and analysis of two case studies drawn from experimental immunology: (i) the efficacy of vaccines for Newcastle disease virus in poultry, and (ii) cancer immunotherapy. It then shows that it is important when repeating experiments to reproduce not only the expected result (i.e., the estimated mean value) but also to reduce the variability in estimates of the mean. Reduction in overall variance can be accomplished both with more precise measurements and by accounting for additional sources of error (covariates or “nuisance” variables). When potentially important covariates are recorded during an experiment, they can be included in the analysis of the data and help to isolate the true “signal” of the experimental treatment from the “noise” of the experimental environment.