ImmunoTar-integrative prioritization of cell surface targets for cancer immunotherapy

Abstract

Motivation: Cancer remains a leading cause of mortality globally. Recent improvements in survival have been facilitated by the development of targeted and less toxic immunotherapies, such as chimeric antigen receptor (CAR)-T cells and antibody-drug conjugates (ADCs). These therapies, effective in treating both pediatric and adult patients with solid and hematological malignancies, rely on the identification of cancer-specific surface protein targets. While technologies like RNA sequencing and proteomics exist to survey these targets, identifying optimal targets for immunotherapies remains a challenge in the field.

Results: To address this challenge, we developed ImmunoTar, a novel computational tool designed to systematically prioritize candidate immunotherapeutic targets. ImmunoTar integrates user-provided RNA-sequencing or proteomics data with quantitative features from multiple public databases, selected based on predefined criteria, to generate a score representing the gene's suitability as an immunotherapeutic target. We validated ImmunoTar using three distinct cancer datasets, demonstrating its effectiveness in identifying both known and novel targets across various cancer phenotypes. By compiling diverse data into a unified platform, ImmunoTar enables comprehensive evaluation of surface proteins, streamlining target identification and empowering researchers to efficiently allocate resources, thereby accelerating the development of effective cancer immunotherapies.

Availability and implementation: Code and data to run and test ImmunoTar are available at https://github.com/sacanlab/immunotar.

Figure 1.

ImmunoTar feature extraction and surface protein scoring scheme. (A) The pre-defined criteria for an ideal immunotherapeutic target. (B) Summary of features extracted from the user-input expression dataset and the publicly available databases incorporated in ImmunoTar. (C) The steps of analysis within ImmunoTar. The first step involves extracting quantitative features from the user input and handle missing values of the extracted features using the methods listed. After rescaling and handling missing values of the features, the user has the option of applying a curving function to the feature values. After curving the feature values, a weight parameter can be applied and the final score of each gene is calculated as the sum of the weighted-curved-features.

Figure 2.

ImmunoTar evaluation, optimization, and validation datasets. (A) The mean average precision (MAP) score is used to evaluate the performance of ImmunoTar. The MAP score depends on the known-positive and known-negative targets assigned to each phenotype and is calculated based on the ranking of those targets after running ImmunoTar. (B) Optimization methods listed can be used to change the weights and curves of features. The goal of the optimization is to rank the known-positive targets higher in the results thus increasing the overall MAP score of the algorithm. (C) Optimized parameters are generated using a multi-cancer proteomic dataset and applied to other datasets to validate the approach. Validation datasets include multiple myeloma surface proteomics, Ewing sarcoma surface proteomics, and neuroblastoma surface proteomics.

Figure 3.

Optimization of ImmunoTar parameter values using pediatric pan-cancer expression data. (A) The pediatric cancer phenotypes included in the optimization scheme, listing the number of cell lines and the number of known-positive surface protein targets extracted from the drug databases TTD and ADCdb (Supplementary Table S2). (B) Optimization parameters were generated using two approaches, the first being a multi-cancer optimization approach and the second being a phenotype-specific optimization approach. (C) Using the multi-cancer optimization parameters, the MAP score for each individual phenotype increased at varying levels within phenotypes. The phenotype-specific optimization approach increased the MAP score further for each phenotype. (D) While the phenotype-specific parameters performed better on the phenotype itself, they did not perform as well on other phenotypes. Overall, the multi-cancer optimization parameters performed best across all phenotypes with an average MAP score of 14%.

Figure 4.

Validation of ImmunoTar using MM surface-proteomics data. (A) ImmunoTar results from the MM surface proteomics dataset using the multi-cancer optimization parameters. Labeled proteins are ones that were ranked in the top three scoring brackets by Ferguson and colleagues. The dashed line represents the top 5% scoring targets. CCR10, at the top of the curve, was prioritized and validated by Ferguson et al. as a novel target for MM. TXNDC11, at the bottom of the curve, is a prioritized protein that was a false-positive proved by functional validation. (B) The top 5% scoring targets using ImmunoTar, marking targets prioritized in ImmunoTar and the top three scoring brackets and targets that are known-positive targets per TTD and ADCdb. (C) The GSEA enrichment scores comparing three sets of prioritized targets in MM. The ImmunoTar scores of each target were compared to the target scores from the Ferguson et al. scoring scheme. *P-value ≤.05.

Figure 5.

Validation of ImmunoTar using EwS surface proteomics data. (A) ImmunoTar results from the EwS surface proteomics data using the multi-cancer optimization parameter values. The dashed line represents the cut-off for the top 5% of proteins scored by ImmunoTar. The labeled proteins include targets that are known within EwS and highlighted by Mooney and colleagues. CDH11 and ENPP1, at the top of the curve, are novel targets discovered by Mooney and colleagues. (B) Heatmap showing the top 5% scored ImmunoTar targets after implementing a restricted normal-tissue expression filter. (C) The GSEA enrichment scores comparing three sets of prioritized targets in EwS. The ImmunoTar scores, using both the multi-cancer values and the phenotype-specific optimization parameter values, were compared to the original paper scores, separately. *P-value ≤.05. (D) Protein quantification of CADM1 in EwS surface proteomics dataset showing comparable abundance to other EwS-specific known-positive targets.

Figure 6.

ImmunoTar prioritized targets in NBL using surface proteomics data from PDX models and phenotype-specific optimization parameters. (A) Evaluating the top 20 scoring targets per ImmunoTar analysis after applying phenotype-specific parameters. Highlighting known, top-scoring targets, L1CAM, ALK, NCAM1, and CD276. (B) Rank plot from ImmunoTar showing that DLK1 ranks in the top five percentile of scoring targets, comparable to other known targets. (C) Protein quantification of DLK1 in the surface proteomics data showing comparable abundance to other NBL-specific known targets.