Immune modulation is considered a hallmark of
cancer initiation and progression. The recent development of
immunotherapies has ushered in a new era of
cancer treatment. These
therapeutics have led to revolutionary breakthroughs; however, the efficacy of
immunotherapy has been modest and is often restricted to a subset of patients. Hence, identification of which
cancer patients will benefit from
immunotherapy is essential. Multiplex immunofluorescence (mIF) microscopy allows for the assessment and visualization of the
tumor immune microenvironment (TIME). The data output following image and machine learning analyses for cell segmenting and phenotyping consists of the following information for each
tumor sample: the number of positive cells for each marker and phenotype(s) of interest, number of total cells, percent of positive cells for each marker, and spatial locations for all measured cells. There are many challenges in the analysis of mIF data, including many tissue samples with zero positive cells or "zero-inflated" data, repeated measurements from multiple TMA cores or tissue slides per subject, and spatial analyses to determine the level of clustering and co-localization between the cell types in the TIME. In this review paper, we will discuss the challenges in the statistical analysis of mIF data and opportunities for further research.