Session Description:

In recent years cancer patients, who previously had very few treatment options and poor prognosis, have benefitted from new and lasting immuno-oncology therapies. These immuno-oncology treatments are now available to patients with advanced melanoma, and prostate and lung cancer, with other therapeutics and their combinations currently tested in extensive clinical trials. Particular advantage of these therapies is that they may work across a wide-range of cancer types. Therefore, Cancer Immuno Therapies are growing in importance to both healthcare providers and patients, and one that has already seen the success of large collaborative projects and new computational approaches benefiting drug discovery.

Session Description:

With the recent clinical success of checkpoint inhibitors (ipilimumab, pembrolizumab) generating durable responses in the clinic, immune modulation based therapy represents a promising modality for the treatment of cancer. The combination of immunotherapies with targeted therapies, including both small molecule and biotherapeutic, may drive this success further to those patients not responding to or relapsing with single agent immune therapies. Preclinical in vivo models for most immuno-oncology (IO) programs require the use of immunocompetent mice bearing syngeneic tumors. To facilitate model selection for use in preclinical efficacy studies, we characterized a panel of mouse tumor cell lines and syngeneic tumor tissues. Here we report the integrative analysis of gene expression and mutation landscape of these mouse tumor cells grown both in vitro and in vivo. Chromogenic immunohistochemistry (IHC) assays to identify and characterize infiltrating cell populations, including tumor infiltrating lymphocytes, myeloid cells as well as costimulatory and inhibitory markers, were developed and applied to these models.

Transcript expression data in general showed good agreement with the orthogonal IHC data for immune cell subsets. Known IO targets and immune cytolytic activity were up-regulated in the tumor compared to the matched in vitro cell pellet for solid tumors, indicating immune infiltration into the tumor. To further interrogate the immune subsets in the tumor microenvironment of syngeneic tumors, we developed a mouse immune cell-type specific gene signature and applied in silico deconvolution of the immune subsets using the recently published CIBERSORT method. In silico deconvolution and IHC analyses both revealed that these models display characteristics of low T-lymphocyte infiltration but relatively high myeloid cell infiltration. In addition to its high mutational load, the CT26 model also displayed the highest cytolytic activity among all models, in agreement with its known high immunogenicity. In vivo efficacy studies evaluating known IO therapy demonstrated that the in vivo response was correlated to genomic and IHC data. A detailed understanding of syngeneic models at both molecular and cellular level provides information for model selection, correlations with preclinical efficacy and will help translate preclinical findings to patient selection for clinical studies.

Session Description:

Epitopes that arise from a somatic mutation, so called neoepitopes, are now known to play a key role in cancer immunology and immunotherapy. Recent advances in highthroughput sequencing have made it possible to identify all mutations and thereby all potential neoepitope candidates in an individual cancer. It has however become evident that the vast majority of these neoepitope candidates do not induce a T cell response when tested in vivo or in vitro, i.e. they are not immunogenic. Especially in patients with a high mutational load, usually hundreds of potential neoepitopes are detected, highlighting the need to further narrow down this candidate list. Several studies have used different combinations of immunoinformatic tools such as MHC binding predictions to prioritize the initial set of neoepitopes candidates. The tools to use and thresholds to apply for this prioritization has so far been largely based on experience with epitope identification in other settings such as infectious disease and allergy. To establish the appropriate tools and thresholds in the cancer setting, we here curated a set of immunogenic neoepitopes from the published literature and performed detailed analyses to detect what features discriminate immunogenic neoepitopes from a background set of mutated peptides. We experimentally measured the HLA binding affinity of all curated immunogenic neoepitopes. In doing so, we aimed to identify the optimal affinity threshold to effectively identify immunogenic neoepitopes. As a next step, we sought to assess the added value of different immunoinformatics tools, including various HLA binding prediction algorithms, processing prediction, stability prediction, andimmunogenicity prediction, to most effectively detect immunogenic neoepitopes. The obtained results are now going to be used to facilitate the development of more accurate prediction algorithms.

Session Description:

Neoplastic cells reside within a complex tumor microenvironment consisting of tumorinfiltrating leukocytes (TILs) and non-hematopoietic stromal subsets that are necessary for tumor growth and survival. While flow cytometry and immunohistochemistry are commonly used to characterize tissue heterogeneity, the former requires cell dissociation, which can alter representation, while the latter is generally limited to one marker per section. Although single cell RNA sequencing has recently emerged as a powerful technology for defining novel cell subsets, it is currently impractical for large-scale analyses and cannot be applied to formalin fixed specimens. To complement these methods and to facilitate profiling of cellular heterogeneity in fresh, frozen, and fixed tissues, we introduced CIBERSORT, an “in silico flow cytometry” method for enumerating cell subsets of interest from gene expression profiles of intact bulk tumors.

In a pan-cancer analysis of nearly 6,000 human tumor samples, CIBERSORT revealed important new associations between TILs and clinical outcomes. In more recent work, we have extended this technique to accurately estimate cell type-specific gene expression profiles from tumor samples without the need for fluorescence-activated cell sorting.

Together, these methods comprise a versatile framework for digital cytometry with diverse applications in immuno-oncology, including identifying predictive and prognostic cellular biomarkers, and novel therapeutic targets.