IMMUNOTAR - Integrative prioritization of cell surface targets for cancer immunotherapy.

Cancer remains a leading cause of mortality globally. Recent improvements in survival have been facilitated by the development of less toxic immunotherapies; however, identifying targets for immunotherapies remains a challenge in the field. To address this challenge, we developed IMMUNOTAR, a computational tool that systematically prioritizes and identifies candidate immunotherapeutic targets. IMMUNOTAR integrates user-provided RNA-sequencing or proteomics data with quantitative features extracted from publicly available databases based on predefined optimal immunotherapeutic target criteria and quantitatively prioritizes potential surface protein targets. We demonstrate the utility and flexibility of IMMUNOTAR using three distinct datasets, validating its effectiveness in identifying both known and new potential immunotherapeutic targets within the analyzed cancer phenotypes. Overall, IMMUNOTAR enables the compilation of data from multiple sources into a unified platform, allowing users to simultaneously evaluate surface proteins across diverse criteria. By streamlining target identification, IMMUNOTAR empowers researchers to efficiently allocate resources and accelerate immunotherapy development.

Surface and Global Proteome Analyses Identify ENPP1 and Other Surface Proteins as Actionable Immunotherapeutic Targets in Ewing Sarcoma.

PURPOSE: Ewing sarcoma is the second most common bone sarcoma in children, with 1 case per 1.5 million in the United States. Although the survival rate of patients diagnosed with localized disease is approximately 70%, this decreases to approximately 30% for patients with metastatic disease and only approximately 10% for treatment-refractory disease, which have not changed for decades. Therefore, new therapeutic strategies are urgently needed for metastatic and refractory Ewing sarcoma. EXPERIMENTAL DESIGN: This study analyzed 19 unique Ewing sarcoma patient- or cell line-derived xenografts (from 14 primary and 5 metastatic specimens) using proteomics to identify surface proteins for potential immunotherapeutic targeting. Plasma membranes were enriched using density gradient ultracentrifugation and compared with a reference standard of 12 immortalized non-Ewing sarcoma cell lines prepared in a similar manner. In parallel, global proteome analysis was carried out on each model to complement the surfaceome data. All models were analyzed by Tandem Mass Tags-based mass spectrometry to quantify identified proteins. RESULTS: The surfaceome and global proteome analyses identified 1,131 and 1,030 annotated surface proteins, respectively. Among surface proteins identified, both approaches identified known Ewing sarcoma-associated proteins, including IL1RAP, CD99, STEAP1, and ADGRG2, and many new cell surface targets, including ENPP1 and CDH11. Robust staining of ENPP1 was demonstrated in Ewing sarcoma tumors compared with other childhood sarcomas and normal tissues. CONCLUSIONS: Our comprehensive proteomic characterization of the Ewing sarcoma surfaceome provides a rich resource of surface-expressed proteins in Ewing sarcoma. This dataset provides the preclinical justification for exploration of targets such as ENPP1 for potential immunotherapeutic application in Ewing sarcoma. See related commentary by Bailey, p. 934.

A proteogenomic surfaceome study identifies DLK1 as an immunotherapeutic target in neuroblastoma.

Cancer immunotherapies have produced remarkable results in B-cell malignancies; however, optimal cell surface targets for many solid cancers remain elusive. Here, we present an integrative proteomic, transcriptomic, and epigenomic analysis of tumor specimens along with normal tissues to identify biologically relevant cell surface proteins that can serve as immunotherapeutic targets for neuroblastoma, an often-fatal childhood cancer of the developing nervous system. We apply this approach to human-derived cell lines (N=9) and cell/patient-derived xenograft (N=12) models of neuroblastoma. Plasma membrane-enriched mass spectrometry identified 1,461 cell surface proteins in cell lines and 1,401 in xenograft models, respectively. Additional proteogenomic analyses revealed 60 high-confidence candidate immunotherapeutic targets and we prioritized Delta-like canonical notch ligand 1 (DLK1) for further study. High expression of DLK1 directly correlated with the presence of a super-enhancer spanning the DLK1 locus. Robust cell surface expression of DLK1 was validated by immunofluorescence, flow cytometry, and immunohistochemistry. Short hairpin RNA mediated silencing of DLK1 in neuroblastoma cells resulted in increased cellular differentiation. ADCT-701, a DLK1-targeting antibody-drug conjugate (ADC), showed potent and specific cytotoxicity in DLK1-expressing neuroblastoma xenograft models. Moreover, DLK1 is highly expressed in several adult cancer types, including adrenocortical carcinoma (ACC), pheochromocytoma/paraganglioma (PCPG), hepatoblastoma, and small cell lung cancer (SCLC), suggesting potential clinical benefit beyond neuroblastoma. Taken together, our study demonstrates the utility of comprehensive cancer surfaceome characterization and credentials DLK1 as an immunotherapeutic target. Highlights: Plasma membrane enriched proteomics defines surfaceome of neuroblastomaMulti-omic data integration prioritizes DLK1 as a candidate immunotherapeutic target in neuroblastoma and other cancersDLK1 expression is driven by a super-enhancer DLK1 silencing in neuroblastoma cells results in cellular differentiation ADCT-701, a DLK1-targeting antibody-drug conjugate, shows potent and specific cytotoxicity in DLK1-expressing neuroblastoma preclinical models.

Advancing childhood cancer research through young investigator and advocate collaboration.

Cancer advocates and researchers share the same goal of driving science forward to create new therapies to cure more patients. The power of combining cancer researchers and advocates has become of increased importance due to their complementary expertise. Therefore, advocacy is a critical component of grant structures and has become embedded into the Stand Up 2 Cancer (SU2C) applications. To date, the optimal way to combine these skillsets and experiences to benefit the cancer community is currently unknown. The Saint Baldrick's Foundation (SBF)-SU2C now called St. Baldrick's Empowering Pediatric Immunotherapies for Childhood Cancer (EPICC) Team is comprised of a collaborative network across nine institutions in the United States and Canada. Since SU2C encourages incorporating advocacy into the team structure, we have assembled a diverse team of advocates and scientists by nominating a young investigator (YI) and advocate from each site. In order to further bridge this interaction beyond virtual monthly and yearly in person meetings, we have developed a questionnaire and conducted interviews. The questionnaire is focused on understanding each member's experience at the intersection between science/advocacy, comparing to previous experiences, providing advice on incorporating advocacy into team science and discussing how we can build on our work. Through creating a YI and advocate infrastructure, we have cultivated a supportive environment for meaningful conversation that impacts the entire research team. We see this as a model for team science by combining expertise to drive innovation forward and positively impact pediatric cancer patients, and perhaps those with adult malignancies. Significance: Questionnaire results show both advocates and YI's see this structure to be valuable and beneficial. YI's communicated their research to a non-scientific audience and learned advocate's experience. This was their first advocacy experience for most YIs. Advocates learned more about the research being conducted to provide hope. They can also aid with fundraising, publicity and lobbying. This collaboration improves science communication, designing patient-friendly clinical trials and sharing experience across institutions.

High-Quality Genome Assemblies Reveal Long Non-coding RNAs Expressed in Ant Brains.

Ants are an emerging model system for neuroepigenetics, as embryos with virtually identical genomes develop into different adult castes that display diverse physiology, morphology, and behavior. Although a number of ant genomes have been sequenced to date, their draft quality is an obstacle to sophisticated analyses of epigenetic gene regulation. We reassembled de novo high-quality genomes for two ant species, Camponotus floridanus and Harpegnathos saltator. Using long reads enabled us to span large repetitive regions and improve genome contiguity, leading to comprehensive and accurate protein-coding annotations that facilitated the identification of a Gp-9-like gene as differentially expressed in Harpegnathos castes. The new assemblies also enabled us to annotate long non-coding RNAs in ants, revealing caste-, brain-, and developmental-stage-specific long non-coding RNAs (lncRNAs) in Harpegnathos. These upgraded genomes, along with the new gene annotations, will aid future efforts to identify epigenetic mechanisms of phenotypic and behavioral plasticity in ants.

Graphical Interpretation and Analysis of Proteins and their Ontologies (GiaPronto): A One-Click Graph Visualization Software for Proteomics Data Sets.

Here we present a free interactive web tool to process and visualize proteomics data sets with a single click. GiaPronto can process all proteomics quantification methods, i.e. label-free, SILAC and isobaric labeling, and analyze post-translational modifications (PTMs). The software performs normalization and statistics, assists determination of regulated proteins, biomarkers and Gene Ontology (GO) enrichment, and provides high resolution images and tables for further data analysis. We foresee that GiaPronto will become the most rapid and simple tool for assessing data quality and determining most relevant features of proteomic data sets. GiaPronto is available at giapronto.diskinlab.org.

Subtelomeric CTCF and cohesin binding site organization using improved subtelomere assemblies and a novel annotation pipeline.

Mapping genome-wide data to human subtelomeres has been problematic due to the incomplete assembly and challenges of low-copy repetitive DNA elements. Here, we provide updated human subtelomere sequence assemblies that were extended by filling telomere-adjacent gaps using clone-based resources. A bioinformatic pipeline incorporating multiread mapping for annotation of the updated assemblies using short-read data sets was developed and implemented. Annotation of subtelomeric sequence features as well as mapping of CTCF and cohesin binding sites using ChIP-seq data sets from multiple human cell types confirmed that CTCF and cohesin bind within 3 kb of the start of terminal repeat tracts at many, but not all, subtelomeres. CTCF and cohesin co-occupancy were also enriched near internal telomere-like sequence (ITS) islands and the nonterminal boundaries of subtelomere repeat elements (SREs) in transformed lymphoblastoid cell lines (LCLs) and human embryonic stem cell (ES) lines, but were not significantly enriched in the primary fibroblast IMR90 cell line. Subtelomeric CTCF and cohesin sites predicted by ChIP-seq using our bioinformatics pipeline (but not predicted when only uniquely mapping reads were considered) were consistently validated by ChIP-qPCR. The colocalized CTCF and cohesin sites in SRE regions are candidates for mediating long-range chromatin interactions in the transcript-rich SRE region. A public browser for the integrated display of short-read sequence-based annotations relative to key subtelomere features such as the start of each terminal repeat tract, SRE identity and organization, and subtelomeric gene models was established.