Bioinformatics and in vitro validation synergize powerfully from initial screening through subtype refinement to iterative target confirmation, enabling a precision medicine approach for cervical cancer-specific immunotherapies.
Abstract
Aims: Cervical cancer is the second most common cancer globally in women of reproductive age. Current immunotherapies offer only moderate improvement in overall survival for metastatic or refractory disease, highlighting the need for more effective, patient-directed immunotherapies.
Methods: A unified pipelinereprocessed all The Cancer Genome Atlas (TCGA) and genotype tissue expression (GTEx) samples from raw reads using identical alignment and quantification parameters to screen a curated set of 259 high-confidence surface protein genes. This analysis provided the foundation for selecting differentially overexpressed cell surface receptors as potential antibody-drug conjugate (ADCs) targets . Recombinant single chain variable fragment (scFv)-O6-alkylguanine-DNA alkyltransferase (SNAP) fusion proteins were expressed, purified and characterised using immunoblotting. The dose-dependent and selective cytotoxicity of an auristatin F-conjugated (scFv)-SNAP fusion protein was verified in vitro.
Results: Bioinformatic screening revealed 30 potential targets overexpressed in cervical carcinoma (Cervical Squamous Cell Carcinoma and Endocervical Adenocarcinoma (CESC)) vs. healthy tissues. Tissue-of-origin analysis (endocervix vs. ectocervix) further refined priorities. These results, together with supporting evidence from the literature, justified drug production.. The (scFv)-SNAP fusion proteins exhibited strong surface binding across cervical cancer cell lines and, when conjugated to auristatin F, showed dose-dependent cytotoxicity at nanomolar concentrations, confirming the differences observed in transcriptomic analyses of endo- vs. ectocervix profiling.
Conclusion: Bioinformatics and in vitro validation synergize powerfully from initial screening through subtype refinement to iterative target confirmation, enabling a precision medicine approach for cervical cancer-specific immunotherapies. The (scFv)-SNAP-auristatin F (ADC) conjugates reproduced this refinement, showing highly promising potential.
Small cell lung cancer (SCLC) remains a highly lethal disease with limited targetable surface antigens beyond delta-like ligand 3 (DLL3). We sought to systematically identify and validate tumor-selective cell-surface targets in relapsed SCLC. To this end, we developed an integrated proteogenomic pipeline combining single-cell RNA sequencing, combinatorial optimization, mass spectrometry–based proteomics, and immunohistochemistry to systematically map the SCLC surfaceome of 49 tumors across 25 patients with relapsed SCLC. Using this approach we found claudin-3 (CLDN3) to be a consistently expressed and tumor-selective antigen, with broader coverage than DLL3, which is the current clinical benchmark. CLDN3 was highly expressed across various treatment states, while maintaining low expression in most nonmalignant tissues. Functional validation using a novel CLDN3-specific monoclonal antibody (ABN501) demonstrated NK cell–mediated cytotoxicity in vitro and tumor regression in vivo, as well as a favorable safety profile. These findings support clinical development of CLDN3-directed therapies and demonstrate the utility of integrated proteogenomic approaches for antigen discovery in solid tumors.
Brett Schroeder, Jaehyun Choi, Alejandro A Schaffer et al.· bioRxiv· 0 citations
Head and neck squamous cell carcinoma (HNSCC) comprises biologically diverse tumors, and durable responses to immune-checkpoint blockade are achieved by only a subset of patients. There remains a need for markers that connect clinical outcome with malignant-cell phenotypes and tissue-level immune organization.
We integrated The Cancer Genome Atlas HNSCC cohort (TCGA-HNSC), five Gene Expression Omnibus (GEO) validation cohorts, single-cell RNA sequencing, Visium spatial transcriptomics, cellular indexing of transcriptomes and epitopes by sequencing (CITE-seq)-informed protein-potential inference, pharmacogenomic screening, genetic-risk analysis and experimental validation. A reconstructed 296-pipeline survival modelling framework was used to prioritize prognostic hub genes across validation-cohort-specific analyses.
SIRPG was repeatedly ranked among the top ten selected genes in all five validation cohorts. At single-cell resolution, SIRPG-high tumor cells showed stronger malignant-cell features, immune-inhibitory and metabolic programs, Scissor-positive risk association, CLCA2/P53-related perturbation signals and inferred SIRPG-CD47/signal regulatory protein (SIRP) communication. Spatial analyses placed this axis within an immune-checkpoint-coupled niche, supported by Maxspin/multiview intercellular spatial modelling (MISTy) spatial coupling, communication analysis by optimal transport (COMMOT)-inferred CD47-SIRPG communication and scProTrans-inferred CD47/SIRPG protein-potential overlap. Functionally, SIRPG knockdown reduced HNSCC cell viability and increased apoptosis, whereas re-expression of short hairpin RNA (shRNA)-resistant SIRPG restored the CLCA2-BAX/BCL2 protein response.
Together, these findings identify SIRPG as an immune-related prognostic hub and context-dependent tumor-cell regulator associated with apoptosis, immune communication and spatial microenvironmental organization in HNSCC.
Jiaqi Tang, Yulun He, Yuqi Wang et al.· Frontiers in Immunology· 0 citations
BACKGROUND
Burkitt lymphoma (BL) is a highly aggressive malignancy with limited effective treatments due to toxicity/resistance. Identifying and prioritizing candidate biomarkers and potential therapeutic targets from complex transcriptomic data, ahead of functional validation, remains a major unmet need.
METHODS
We integrated differential gene expression analysis across BL vs. control cohorts (Gene Expression Omnibus [GEO] datasets GSE43677/GSE12453) with Random Forest machine learning to prioritize candidates. Validated top genes via immunohistochemistry in an independent cohort (n = 10 BL, n = 10 reactive lymphoid hyperplasia [RLH] controls), followed by immune cell infiltration analysis.
RESULTS
This approach identified four candidate genes; only Chromatin Assembly Factor 1 Subunit A (CHAF1A) showed profound protein-level overexpression in BL tumors vs. RLH. In a single-dataset CIBERSORT analysis, CHAF1A expression showed exploratory correlational associations with several immune-cell fractions, most strongly a positive association with M0 macrophages; these in silico associations are hypothesis-generating and do not by themselves establish a mechanistic role in the tumor immune microenvironment.
CONCLUSION
CHAF1A is identified as a candidate biomarker associated with BL that, in exploratory single-dataset analysis, also correlates with immune-cell infiltration; these findings nominate it as a potential therapeutic target that warrants functional validation in future studies.
CAR T-cell therapy has transformed treatment of blood cancers but faces persistent barriers in solid tumors. A major limitation is the absence of single-cell approaches that integrate gene expression with protein and post-translational modification (PTM) data to capture dynamic CAR signaling. Conventional scRNA-seq provides only transcriptional snapshots. We applied InTraSeq™, a multimodal single-cell platform enabling simultaneous quantification of mRNA, surface, cytoplasmic, and nuclear proteins–including PTMs–to comprehensively profile CAR activity.
Jurkat and anti-CD20 CAR-Jurkat cells were analyzed under four conditions: monocultures or co-cultures with CD20+ Raji cells. Each was assessed at 2 and 24 hours (eight total samples). Samples were labeled with unique Histone H3 InTraSeq™ barcodes and pooled for processing with Genomics 3’ kits. An anti-Whitlow/218 linker antibody InTraSeq™ conjugate distinguished CAR-Jurkat cells. Combined transcriptomic and proteomic data identified activation- and signaling-related changes.
All samples were accurately demultiplexed using Histone H3 barcodes. The anti-Whitlow/218 probe confirmed CAR expression and enabled detection of engineered Jurkats. In CAR-Jurkat co-cultures, unique RNA clusters and protein signaling states emerged at both timepoints, highlighting CAR activation. Apoptosis scoring revealed elevated apoptotic protein signals specifically in Raji cells after 24-hour co-culture with CAR-Jurkats, validating targeted cytotoxic function.
InTraSeq™ provides high-resolution, multi-omic insight into CAR-T activation, integration of RNA and protein data, and effector-target interactions. This approach delivers a unified view of CAR signaling and function, offering a valuable framework for refining CAR-T therapy design and optimization.
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Technological Innovations in Immunology (TECH)
Majd M. Ariss, Amrik Singh, S. Beausoleil et al.· Journal of Immunology· 0 citations
Background LGR5 marks colorectal cancer stem cells and is associated with poor outcome, but its expression on normal intestinal stem cells has constrained direct therapeutic targeting, and the molecular landscape of LGR5-high tumors remains incompletely defined. A transcriptional signature is not itself a set of drug targets: its constituent genes differ in whether and how they can be engaged pharmacologically, a distinction rarely applied systematically to a tumor-defined gene set. Methods We stratified 396 colorectal tumors from The Cancer Genome Atlas by LGR5 expression and compared transcriptional, somatic mutation, and copy number profiles between LGR5-high and LGR5-low groups using non-parametric testing with combined significance and effect-size thresholds. Genome-wide CRISPR knockout data were interrogated to test genetic dependency. Each signature gene was then triaged by pharmacological tractability rather than essentiality, along three axes: surface accessibility, from surfaceome annotation and membrane topology; cavity ligandability, from pocket detection on predicted structures using three independent algorithms; and condensate propensity, from saturation concentration prediction and coarse-grained molecular dynamics simulation. Results LGR5-high tumors displayed a coordinated program spanning Wnt signaling, stemness, and matrix remodeling, arising on an APC-mutant background with co-occurring IGF2 amplification. No constituent gene scored as a selective dependency. The three axes partitioned the signature with minimal overlap and nominated three candidates engaged by orthogonal modalities: ENPP3, a single-pass ectoenzyme presenting an accessible ectodomain and carrying clinical antibody-drug conjugate precedent; PLCB4, combining a well-defined catalytic pocket with additional predicted ligandable sites; and NKD1, accessible by neither route but undergoing RNA-stabilized homotypic phase separation, unlike SATB1 and MEX3A. Simulations further indicated that NKD1 partitions into DVL2-containing condensates and reduces DVL2-Wnt contacts, suggesting a biophysical basis for its negative-feedback role. Conclusions LGR5 expression defines a colorectal cancer subset that is pharmacologically tractable despite the absence of genetic dependency. Triaging by modality rather than essentiality converts descriptive tumor signatures into stratified, experimentally testable therapeutic hypotheses, including condensate-directed modulation of NKD1 as a route to targets inaccessible by antibody- or pocket-based approaches.
Lucía Paniagua-Herranz, A. Feito, Cristian Privat et al.· bioRxiv· 0 citations
BACKGROUND
Accurately assessing chemosensitivity to drugs such as cisplatin is crucial for optimizing outcomes of high-grade serous ovarian carcinoma (HGSOC) patients. Developing prediction models and identifying biomarkers can assist differentiate chemotherapy-sensitive from chemotherapy-resistant subtypes.
METHODS
We integrated 908 transcriptome samples from 12 HGSOC datasets of GEO and TCGA across four detection platforms. Differentially ranked genes (DRGs) were identified by applying intra-sample rank transformation and performing T-test. To refine the selection of informative DRGs, a recurrent logistic regression (RLR) model was employed, followed by model optimization using the Pairwise Analysis of Gene Expression (PAGE) algorithm to establish clinically applicable gene pair signature.
RESULTS
Kinesin Family Member 20 A (KIF20A) and Chromogranin B (CHGB) were paired to establish a robust gene pair signature, KIF20A::CHGB Pair (KCP), which exhibited remarkable discriminant accuracy (Accuracy = 0.9397; AUROC = 0.9668; AUPRC = 0.9799) in distinguishing HGSOC from controls in eight independent validation cohorts. Further survival and functional analyses revealed that altered rank relationships between KIF20A and CHGB were significantly associated with prognosis and platinum resistance in HGSOC. Following exposure to standard chemotherapy regimens, we conducted RT-qPCR, CCK-8(Cell Counting Kit-8) viability assays, EdU proliferation assays, flow cytometry, and SynergyFinder, which collectively assessed cellular proliferation and cell-cycle responses, demonstrating that both genes contribute comparably to the malignant phenotype.
CONCLUSIONS
Elevated KIF20A or CHGB expression drives chemoresistance and correlates with poor prognosis in HGSOC. Silencing either gene significantly enhances the growth‑inhibitory effects of cisplatin and other standard chemotherapeutic agents. Our findings establish KCP as a robust diagnostic and prognostic biomarker and highlight KIF20A and CHGB as synergistic targets for overcoming platinum resistance in ovarian cancer.
Li Cheng, Xiao Wang, Jie Zhang et al.· Journal of Ovarian Research· 0 citations