Integrative analysis of CRISPR screening data uncovers new opportunities for optimizing cancer immunotherapy

Background In recent years, the application of functional genetic immuno-oncology screens has showcased the striking ability to identify potential regulators engaged in tumor-immune interactions. Although these screens have yielded substantial data, few studies have attempted to systematically aggregate and analyze them. Methods In this study, a comprehensive data collection of tumor immunity-associated functional screens was performed. Large-scale genomic data sets were exploited to conduct integrative analyses. Results We identified 105 regulator genes that could mediate resistance or sensitivity to immune cell-induced tumor elimination. Further analysis identified MON2 as a novel immune-oncology target with considerable therapeutic potential. In addition, based on the 105 genes, a signature named CTIS (CRISPR screening-based tumor-intrinsic immune score) for predicting response to immune checkpoint blockade (ICB) and several immunomodulatory agents with the potential to augment the efficacy of ICB were also determined. Conclusion Overall, our findings provide insights into immune oncology and open up novel opportunities for improving the efficacy of current immunotherapy agents.

Artificial Neural Networks Predicted the Overall Survival and Molecular Subtypes of Diffuse Large B-Cell Lymphoma Using a Pancancer Immune Oncology Panel

Diffuse large B-cell lymphoma (DLBCL) is one of the most frequent subtypes of non-Hodgkin lymphomas. We used artificial neural networks (multilayer perceptron and radial basis function), machine learning, and conventional bioinformatics to predict the overall survival and molecular subtypes of DLBCL. The series included 106 cases and 730 genes of a pancancer immune oncology panel (nCounter) as predictors. The multilayer perceptron predicted the outcome with high accuracy, with an area under the curve (AUC) of 0.98, and ranked all the genes according to their importance. In a multivariate analysis, ARG1, TNFSF12, REL, and NRP1 correlated with favorable survival (hazard risks: 0.3–0.5), and IFNA8, CASP1, and CTSG, with poor survival (hazard risks = 1.0–2.1). Gene set enrichment analysis (GSEA) showed enrichment toward poor prognosis. These high-risk genes were also associated with the gene expression of M2-like tumor-associated macrophages (CD163), and MYD88 expression. The prognostic relevance of this set of 7 genes was also confirmed within the IPI and MYC translocation strata, the EBER-negative cases, the DLBCL not-otherwise specified (NOS) (High-grade B-cell lymphoma with MYC and BCL2 and/or BCL6 rearrangements excluded), and an independent series of 414 cases of DLBCL in Europe and North America (GSE10846). The perceptron analysis also predicted molecular subtypes (based on the Lymph2Cx assay) with high accuracy (AUC = 1). STAT6, TREM2, and REL were associated with the germinal center B-cell (GCB) subtype, and CD37, GNLY, CD46, and IL17B were associated with the activated B-cell (ABC)/unspecified subtype. The GSEA had a sinusoidal-like plot with association to both molecular subtypes, and immunohistochemistry analysis confirmed the correlation of MAPK3 with the GCB subtype in another series of 96 cases (notably, MAPK3 also correlated with LMO2, but not with M2-like tumor-associated macrophage markers CD163, CSF1R, TNFAIP8, CASP8, PD-L1, PTX3, and IL-10). Finally, survival and molecular subtypes were successfully modeled using other machine learning techniques including logistic regression, discriminant analysis, SVM, CHAID, C5, C&R trees, KNN algorithm, and Bayesian network. In conclusion, prognoses and molecular subtypes were predicted with high accuracy using neural networks, and relevant genes were highlighted.

Evaluation of single domain antibodies as nuclear tracers for imaging of the immune checkpoint receptor human lymphocyte activation gene-3 in cancer

Recent advancements in the field of immune-oncology have led to a significant increase in life expectancy of patients with diverse forms of cancer, such as hematologic malignancies, melanoma and lung cancer. Unfortunately, these encouraging results are not observed in the majority of patients, who remain unresponsive and/or encounter adverse events. Currently, researchers are collecting more insight into the cellular and molecular mechanisms that underlie these variable responses. As an example, the human lymphocyte activation gene-3 (huLAG-3), an inhibitory immune checkpoint receptor, is increasingly studied as a therapeutic target in immune-oncology. Noninvasive molecular imaging of the immune checkpoint programmed death protein-1 (PD-1) or its ligand PD-L1 has shown its value as a strategy to guide and monitor PD-1/PD-L1-targeted immune checkpoint therapy. Yet, radiotracers that allow dynamic, whole body imaging of huLAG-3 expression are not yet described. We here developed single-domain antibodies (sdAbs) that bind huLAG-3 and showed that these sdAbs can image huLAG-3 in tumors, therefore representing promising tools for further development into clinically applicable radiotracers.

789 Generation of a Bicycle NK-TICA™, a novel NK cell engaging molecule to enhance targeted tumor cytotoxicity

BackgroundNatural killer (NK) cells are immune cells that can detect and eliminate tumor cells and bridge innate to adaptive immune responses. Tumor specific activation of NK cells is thus an area of active investigation in immune oncology, but to date has relied on complex biologic modalities (e.g., antibodies, fusion proteins, or cell therapies), each of which has inherent disadvantages in this application. Thus, alternative approaches are warranted. Bicycle® are small (ca. 1.5 kDa), chemically synthetic, structurally constrained peptides discovered via phage display and optimized using structure-driven design and medicinal chemistry approaches. We have now applied this technology to identify Bicycles that bind specifically to the key activating receptors, NKp46 and CD16a. When chemically coupled to tumor antigen binding Bicycles this results in highly potent, antigen-dependent receptor activation and NK cell activation. We term this new class of fully synthetic molecules Bicycle® natural killer- tumor-targeted immune cell agonists (NK-TICAs™) and we will describe their discovery and evaluation in this presentation.MethodsUsing our unique phage display screening platform, we have identified high affinity, selective binders to NKp46 and CD16a. By conjugating the Bicycle® NK cell-engaging binders to a model tumor antigen EphA2-binding Bicycle®, we have developed a bifunctional Bicycle NK-TICA™ molecule. In in vitro functional assays, we evaluated the ability of the Bicycle NK-TICAs™ to induce NK cell activation as well as cell-mediated cytotoxicity and cytokine production in NK-tumor co-culture assays.ResultsWe have developed a novel modular compound with high affinity and selectivity to NK cell receptors with specific tumor targeting capability. We demonstrate potent, selective binding of our Bicycles to receptor-expressing cells and the capability of the bifunctional molecule to induce NK cell function. With Bicycle's novel NK-TICA™ compound, we demonstrate engagement of NK cells, specific activation and function of NK cells, and enhanced EphA2-expressing tumor cytotoxicity, in a dose dependent manner.ConclusionsBicycle NK-TICAs™ are novel therapeutic agents capable of enhancing the landscape of immune oncology. We hypothesize that utilization of Bicycle NK-TICA™ as a multifunctional immune cell engager will promote modulation of NK cells, and infiltration and anti-tumor activity of NK cells in solid tumors. The data presented here provide initial proof of concept for application of the Bicycle technology to drive NK cell-mediated tumor immunity.

Mistic: an open-source multiplexed image t-SNE viewer

Understanding the complex ecology of a tumor tissue and the spatio-temporal relationships between its cellular and microenvironment components is becoming a key component of translational research, especially in immune-oncology. The generation and analysis of multiplexed images from patient samples is of paramount importance to facilitate this understanding. In this work, we present Mistic, an open-source multiplexed image t-SNE viewer that enables the simultaneous viewing of multiple 2D images rendered using multiple layout options to provide an overall visual preview of the entire dataset. In particular, the positions of the images can be taken from t-SNE or UMAP coordinates. This grouped view of all the images further aids an exploratory understanding of the specific expression pattern of a given biomarker or collection of biomarkers across all images, helps to identify images expressing a particular phenotype or to select images for subsequent downstream analysis. Currently there is no freely available tool to generate such image t-SNEs. Mistic is open-source and can be downloaded at: https://github.com/MathOnco/Mistic.

Unsatisfied Reporting Quality of Clinical Trials Evaluating Immune Checkpoint Inhibitor Therapy in Cancer

BackgroundMore and more immune-oncology trials have been conducted for treating various cancers, yet it is unclear what the reporting quality of immune-oncology trials is,and characteristics associated with higher reporting quality.ObjectiveThis study aims to evaluate the reporting quality of immune-oncology trials.MethodsThe PubMed and Cochrane library were searched to identify all English publications of clinical trials assessing immunotherapy for cancer. Reporting quality of immune-oncology trials was evaluated by a quality score with 11 points derived from the Trial Reporting in Immuno-Oncology (TRIO) statement, which contained two parts: an efficacy score of 6 points and toxicity score of 5 point. Linear regression was used to identify characteristics associated with higher scores.ResultsOf the 10,169 studies screened, 298 immune-oncology trial reports were enrolled. The mean quality score, efficacy score, and toxicity score were 6.46, 3.61, and 2.85, respectively. The most common well-reported items were response evaluation criteria (96.0%) and toxicity grade (98.7%), followed by Kaplan-Meier survival analyses (80.5%). Treatment details beyond progression (12.8%) and toxicity onset time and duration (7.7%) were poorly reported. Multivariate regression revealed that higher impact factor (IF) (IF >20 vs. IF <5, p < 0.001), specific tumor type (p = 0.018 for lung, p = 0.021 for urinary system, vs. pan cancer), and a certain kind of immune checkpoint blocking agent (p < 0.001 for anti-PD-1 or multiagents, vs. anti-CTLA-4) were independent predictors of higher-quality score. Similar independent predictive characteristics were revealed for high-efficacy score. Only IF >20 had a significant high-toxicity score (p < 0.001).ConclusionImmune-oncology trial reports presented an unsatisfied quality score, especially in the reporting of treatment details beyond progression and toxicity onset time and duration. High IF journals have better reporting quality. Future improvement of trial reporting was warranted to the benefit-risk assessment of immunotherapy.