Abstract
Background – Immunotherapy has transformed cancer care, but its effectiveness is often constrained by tumor heterogeneity and immune escape. The discovery of novel, robust immunotherapy targets remains a major challenge, limiting the expansion of patient benefit. Innovative, scalable strategies are urgently needed to identify and validate new targets that can drive the next generation of immunotherapies.

Methods – To address this need, we developed TargetR, a comprehensive computational framework integrating public and real-world multi-omics datasets to accelerate immunotherapy target discovery and validation across solid and hematological indications. Our approach unifies pharmacologic, transcriptomic, genomic,1 proteomic,2 surfacesome3 and perturbation datasets to automatically generate detailed,4 5 gene-centric reports evaluating therapeutic target potential. Publicly available resources are systematically mined, and findings are validated using the Tempus AI database—one of the world’s largest real-world oncology datasets, comprising over three hundred thousand curated mulit-modal patient samples across diverse cancer types and treatments. The Tempus database is a growing multimodal resource, integrating genomic, transcriptomic, pathological, proteomic, imaging and clinical data obtained from clinical sequencing workstreams and EHR integrations.

Results – The framework employs advanced analytics to assess drug target potential, including target prevalence across variant types, normal tissue expression, surface targetability score, protein abundance distributions, gene expression profiles, protein-gene correlations, tumor versus normal comparisons at both transcript and protein levels, mutational load and variant analysis, CNV distribution and its correlation with expression, and gene dependency and functional impact from perturbation studies. Regulatory and drug target network analyses further contextualize genes of interest across pan-cancer cohorts. Findings are then expanded on using real-world data from the Tempus database, which provides a much larger cohort and extensive clinical features and outcomes. Results are delivered via an automated, user-friendly HTML report featuring interactive visualizations and actionable insights, enabling rapid interrogation and target ranking across large-scale datasets. This integrative platform empowers researchers and clinicians to efficiently validate targets and generate new hypotheses, distinguishing our framework as a powerful, scalable solution for immunotherapy research.

Conclusions – Our computational framework addresses key challenges in immunotherapy target discovery by uniting multi-omics analysis, large-scale validation, and automated reporting. This user-friendly platform accelerates the identification and prioritization of targets for targeted and immunotherapies, including but not limited to bispecific T-cell engager therapy, antibody-drug conjugate therapy, immune checkpoint inhibitors and CAR T-cell therapies, supporting the development of next-generation treatments and ultimately improving patient outcomes.

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Ethics Approval – This study was conducted on de-identified health information subject to an IRB exempt determination (Advarra Pro00072742) and did not involve human subjects research.

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