Cancer Cell

GD2-CAR T cell therapy has demonstrated clinical benefit in patients with H3K27M+ diffuse midline glioma (DMG), but the durability of response has been limited in many patients1,2. To identify mechanisms of therapeutic resistance, we conducted longitudinal single-cell RNA and TCR sequencing of cerebrospinal fluid (CSF) lymphocytes from DMG patients receiving intravenous followed by sequential intracerebral GD2-CAR therapy, with lymphodepleting chemotherapy administered once prior to the start of CAR T cell therapy (NCT04196413). CSF GD2-CAR T cells manifested limited persistence and clonal expansion, while non-engineered CSF lymphocytes underwent significant clonal expansion and repertoire stabilization, ultimately dominating the CSF immune compartment. Concurrently, peripheral blood CD4+ and CD8+ T cells manifested anti-CAR immune reactivity targeting epitopes enriched within murine-derived and engineered junctional regions of the CAR construct. This was associated with appearance of circulating Human Anti-CAR Antibodies (HACAs) that bound cells expressing the GD2-CAR, as well as clonal expansion of CSF B cells which produced HACA which impeded the cytotoxic activity of GD2-CAR T cells. In several cases, appearance of circulating HACA temporally correlated with disease progression and across the patient population, and levels of circulating HACA inversely correlated with circulating CAR T cell persistence. These findings reveal robust induction of systemic and CNS adaptive T cell and B cell responses to GD2-CAR T cells following intravenous then sequential intracerebroventricular GD2-CAR therapy and provide strong evidence that anti-CAR immunity is a significant contributor to therapeutic resistance in this setting.

Linking glioblastoma (GBM) evolution to clinical progression is challenged by multiple factors, including tumor location for repeated sample collection, and short patient survival. In a single individual, we collected and analysed samples from 11 operations distributed across 31 months of multi-relapsing and multifocal GBM, including terminal leptomeningeal progression. All samples shared genomic ancestry of the retinoblastoma protein 1 (RB1) and neurofibromin 1 (NF1) mutations while advanced progression and extracranial metastases featured mutations of tuberous sclerosis complex 2 (TSC2), PBRM1, CD22 and Fanconi anemia supplementation group I (FANCI), correlated with clinical resistance to immunotherapies and DNA-damaging agents. Single-cell analytics revealed distinct yet reversible shifts in response to the precision medicine arsenal. GBM parenchymal dissemination and extracranial progression were associated with strengthening of neuron-like cell phenotypes. Our multidimensional study describes GBM evolution over a rarely reported time scale, and provides a valuable resource linking genetic, molecular, cellular and clinical progressions.

Immune checkpoint therapies have transformed clinical practice; however, reliable biomarkers to predict response remain limited. Tumor mutational burden (TMB) has emerged as an important biomarker because it is thought to reflect neoantigen load, yet its predictive utility has been inconsistent. This limitation may partly arise because TMB primarily captures tumor-intrinsic immunogenicity, which is heterogeneous and does not fully reflect the state of antitumor immunity. To identify transcriptomic surrogates that capture both high mutational burden and antitumor immune activation, we investigated whether mRNA expression of mutagenic APOBEC3 family members could serve as surrogates for high TMB and T cell-rich tumors. Using a pan-cancer computational framework, we evaluated the association of four APOBEC3 genes with mutational burden, neoantigen load, immune infiltration, and immune checkpoint blockade response. Among APOBEC3A, APOBEC3B, APOBEC3G, and APOBEC3H, APOBEC3G emerged as the strongest and most consistent marker of a TMBhighCD8high and NeoantigenhighCD8high tumor phenotypes. Single-cell analyses further demonstrated that APOBEC3G is enriched in both malignant cells and T cells compared with other APOBEC3 family members, with APOBEC3G-positive CD8+ T cells exhibiting elevated activation markers including GZMB and IFNG. Importantly, retrospective analyses of 50 immune checkpoint blockade cohorts showed that APOBEC3G had the most consistent association among APOBEC3 family members with treatment response and clinical outcomes. Together, these findings identify APOBEC3G as a candidate transcriptomic marker of a TMB-associated, T cell-inflamed tumor state linked to immune-checkpoint blockade benefit, warranting further prospective validation.

Functional ex vivo assays using live tumor tissues have demonstrated strong predictive accuracy for response to immune checkpoint inhibitors (ICIs) but are not scalable, requiring manual processing of large resections collected at academic centers. Here, an ex vivo live tumor fragment (LTF) platform was developed using standard-of-care biopsies from 228 patients with suspected malignancy collected across prospective, multicenter observational trials and biobanks. Hierarchical clustering of ICI-mediated changes in cytokine production identified two groups: responders and nonresponders. A binary classifier (elive index) using 8 cytokines achieved an AUC of 0.99 for cluster prediction. elive index correctly predicted clinical benefit in 93% (26/28) of patients (P = 3.2x10-5) and accurately identified 83% (10/12) of objective responders. Critically, elive responders were identified among biomarker-negative patients, highlighting the platform as a scalable approach that complements existing companion diagnostics and expands the population of patients identified to benefit from ICI therapy.

Circulating tumor DNA (ctDNA) from blood plasma has emerged as a promising biomarker for noninvasive profiling of tumor mutational landscapes and disease monitoring across cancers. In this study, we developed a targeted next-generation sequencing approach to explore the role of ctDNA for comprehensive tumor genotyping, early response prediction, and characterization of clonal heterogeneity in patients with advanced and rare cancers treated within molecular tumor boards. We applied our technology to 157 plasma specimens from 57 patients at distinct disease milestones and detected tumor variants in 96% of baseline samples, with 65% of them harboring actionable aberrations. Longitudinal monitoring of baseline mutations in on-treatment plasma revealed that ctDNA dynamics were significantly associated with clinical outcomes and enabled early prediction of disease progression. Finally, we observed substantial clonal heterogeneity over time, identifying emerging mutations in all analyzed plasma samples obtained at progression, including potentially targetable variants for subsequent personalized therapies.

Advancements in immunogenomics and immuno-oncology have enabled the development of personalized cancer vaccines (PCVs) that target cancer cell-specific somatic variants. A subset of these variants produce neoantigens that, when presented on tumor cells by MHC molecules, have the potential to elicit a robust and specific immune response. To date, there are over one hundred interventional studies listed on clinicaltrials.gov that explore the use of PCVs. We have supported a number of these trials through the creation of bioinformatic pipelines, tools, and procedures for the identification of patient-specific neoantigen candidates. While many of these steps have been automated, the final selection of neoantigen candidates often relies on expert manual review, creating a bottleneck that limits scalability and full automation of PCV workflows. Addressing this challenge, we introduce NEAT (Neoantigen Evaluation & Automated Triage), a machine learning-based approach that enables automated neoantigen candidate prioritization and supports the transition toward more scalable and reproducible PCV design. We implemented a prediction model trained and tested on existing vaccine design results from 33 patients and 1,943 peptides, across 3 clinical trials, including 439 peptides prioritized for PCV inclusion. This model uses features such as tumor variant allele frequency, RNA expression, driver gene status, binding/presentation scores, and transcript support level to automatically predict whether a peptide will be accepted, rejected, or require further human review before inclusion in a vaccine. The model achieved a sensitivity of 0.847 and specificity of 0.924, with an area under the curve of 0.955. The model predictions have been incorporated in pVACtools version 7. By integrating this model into the vaccine development pipeline, we foresee a significant reduction in the time required to transition from patient sample collection to vaccine manufacturing, thereby enhancing the efficiency and scalability of PCV production.

Protein immunotherapies can elicit potent tumor rejection, but reversible target engagement, incomplete tumor retention, and systemic leakage often erode spatial control. Here, we develop covalently anchored tumor immunotherapeutic proteins (CATIPs), a modular platform that uses proximity-enabled covalent chemistry to immobilize immune cues on tumor-cell surfaces after intratumoral administration. CATIPs combine tumor-targeting nanobodies with payloads for T cell engagement, co-stimulation, and cytokine support. In human PBMC-reconstituted NSG mice, CATIPs completely eradicated treated EGFR-positive tumors, outperforming matched non-covalent proteins while limiting redistribution, systemic T cell activation, cytokine release, xGVHD-associated morbidity, and on-target, off tumor toxicity. In immunocompetent melanoma models, CATIPs remodeled the tumor microenvironment, expanded antigen-specific CD8+ T cells, induced antigen-restricted abscopal control, and generated durable protection against local and metastatic rechallenge. CATIP-engineered tumor cells further functioned as whole-cell vaccines. Thus, covalent tumor anchoring converts local protein delivery into tumor-surface immune programming, enabling systemic, tumor-specific, durable antitumor immunity while limiting systemic immunopathology.

IDH-mutant gliomas stratified by glioma CpG island methylator phenotype (G-CIMP) into High (GCH) and Low (GCL) exhibit markedly divergent clinical outcomes, yet cellular and regulatory determinants of this distinction remain incompletely defined. Integrating single-nucleus RNA-and ATAC-sequencing across 18 tumor specimens from 10 patients, we resolved six malignant cellular states whose differential enrichment across G-CIMP strata delineates the GCL epigenomic transition. GCL tumors were enriched for independently prognostic Mesenchymal and Mitotic Proliferative states, driven by convergent E2F, MYC, MEF2, and NFI-family regulatory networks confirmed across chromatin, histone, and transcriptomic modalities. Pseudotime trajectory inference revealed a multifurcating developmental model from an Astrocytic-like origin, with GCL tumors gaining preferential access to proliferative and mesenchymal endpoints. A reorganized immune microenvironment and candidate therapeutic axes including CDK4/6-E2F, MYC/BET, KIF11, and NOTCH, potentially combinable with vorasidenib as an epigenomic backbone provide a translationally actionable framework for intercepting GCL progression in IDH-mutant glioma.

Tertiary lymphoid structures (TLS) predict benefit from immune checkpoint inhibitors (CPIs), yet mature, germinal-center-rich TLS are infrequent in solid tumors by histological review. Here, using 38-plex MIBI spatial proteomics across 165 lymphoid structures from 14 NSCLC resections, we establish a continuum of TLS maturity using high dimensional compositional, spatial, and molecular features. We demonstrate that histologically-defined lymphoid aggregates (LA) comprise a heterogeneous class of structures, which span this continuum of maturity. We identify a subset of lymphoid aggregates that harbor follicular dendritic cell networks, T follicular helper cells, and activated B cell states characteristic of mature TLS, yet are not readily distinguished from other LA structures in our histological review. We developed a novel digital pathology classifier to identify mature LAs in CPI trials, and demonstrate in a retrospective analysis of Atezolizumab in advanced NSCLC that the inclusion of mature LAs greatly expands the biomarker-eligible population while maintaining strong predicted benefit. Together, these data redefine the biological spectrum of tumor-associated lymphoid aggregates and provide a framework for implementing maturity-informed TLS biomarker strategies.

Conventional short-read sequencing cannot determine whether co-occurring variants within a cancer gene reside on the same allele (cis) or on opposing alleles (trans), a distinction with direct biological and therapeutic consequences. Trans configurations confirm biallelic tumor suppressor inactivation and inform therapy selection, while cis configurations generate compound oncogenic alleles with enhanced activity. We analyzed 768 patients with prostate, breast, or ovarian cancers in the PROBLEM cohort, using mutational signatures to nominate cryptic genomic instability cases where the causative biallelic event was not apparent from short-read sequencing. Long-read nanopore sequencing resolved 32 of 46 cryptic cases (69.6%), leveraging its unique advantages in direct methylation detection, long insertion resolution, and complex structural variant characterization, confirming trans biallelic inactivation in all resolved tumor suppressor cases. Systematic analysis of 4,496 MiOncoSeq samples identified 17,519 multi-hit gene pairs, of which 78.7% exceeded the 500 bp short-read phasing limit. Long-read phasing further revealed recurrent compound cis oncogenic alleles in NOTCH1, PIK3CA, PDGFRB, and KIT with functionally synergistic activity. Haplotype phasing resolves a systematically overlooked gap in cancer variant interpretation and warrants broader integration into precision oncology workflows. Statement of SignificanceShort-read sequencing cannot resolve whether co-occurring variants within a cancer gene are cis or trans, a distinction critical for clinical interpretation. Long-read nanopore sequencing addresses this gap through direct haplotype phasing, methylation detection, and complex structural variant resolution, confirming biallelic tumor suppressor inactivation and revealing compound cis oncogenic alleles with enhanced activity.

Neoadjuvant chemoradiotherapy (CRT) is standard for locally advanced rectal cancer (LARC), yet many patients retain residual disease. To resolve CRT-associated remodeling of the tumor microenvironment, we generated a multimodal spatial atlas from serial sections of paired pretreatment and post-treatment specimens from 24 patients using Xenium single-cell spatial transcriptomics and PhenoCycler multiplex proteomics, profiling 2.8 million cells; matched Visium HD datasets were generated on adjacent serial sections. Resistance was most strongly associated with fibroblast and myeloid programs adjacent to residual tumor. We identify a periostin (POSTN)-expressing CAF subset selectively enriched around residual tumor cells in non-responders, displaying a myofibroblastic phenotype and activating extracellular matrix remodeling, noncanonical WNT signaling, and immunosuppressive pathways. Tumor cells neighboring POSTN+ CAFs show consistent epithelial-mesenchymal transition signatures. Together, this atlas enables interrogation of CRT-induced spatial remodeling and nominates POSTN+ CAFs as key mediators and targets of CRT resistance, with direct relevance to CRT-based combination strategies.

Importance: While gut dysbiosis is known to impair response to immune checkpoint inhibitors (ICIs), the relative clinical impact of antibiotic timing (pre- vs. post-ICI initiation) remains unclear. Objective: To evaluate whether antibiotic timing differentially influences overall survival (OS) in a large, multi-institutional pan-cancer cohort. Design, Setting, and Participants: This retrospective cohort study utilized deidentified electronic health record data from six academic medical centers within the University of California Health system. We included 21,108 adults with any malignancy who received PD-1, PD-L1, or CTLA-4 inhibitors between January 2014 and December 2024. Exposures: Antibiotic exposure windows were categorized as pre-only (-60 to -1 days), post-only (+1 to +60 days), both windows, or none. Main Outcomes and Measures: The primary outcome was overall survival (OS) calculated from the first ICI dose. Multivariable Cox proportional hazards models adjusted for demographics, tumor type, line of therapy, and baseline health indicators (albumin, NLR, and recent hospitalization). Results: Among 21,108 patients, 17.3% had pre-only exposure, 13.3% had post-only exposure, and 60.6% had no exposure. In the multivariable model, post-only exposure (HR, 1.27; 95% CI, 1.20-1.35) and combined pre- and post- exposure (HR, 1.31; 95% CI, 1.23-1.40) were significantly associated with higher mortality. Pre-only exposure was not significantly associated with OS (HR, 1.04; 95% CI, 0.99-1.10). Subgroup analyses by tumor type showed consistent trends across major malignancies, including head and neck (Post HR, 1.46) and renal cell carcinoma (Post HR, 1.26). Conclusions and Relevance: In contrast to some smaller studies, this large-scale analysis indicates that antibiotic exposure after ICI initiation carries a greater risk than exposure prior to treatment. These findings highlight the need for rigorous antibiotic stewardship strategies specifically during the early phases of immunotherapy treatment.

BackgroundGlioblastoma (GBM) invasion is clinically decisive but difficult to model systematically. Existing patient-derived xenograft (PDX) resources rarely couple reproducible in vivo invasion phenotypes with matched multi-omic profiles at scale, limiting mechanistic insight and phenotype-informed therapeutic hypotheses. MethodsWe established the HGCC Phenobank, comprising 65 patient-derived GBM stem-like cultures with matched multi-omic profiling and orthotopic engraftment in 449 mice. Blinded histopathology quantified ten invasion traits per case. These phenotypes were integrated with RNA sequencing, DNA methylation, and mass-spectrometry-based proteomics. Multi-Omic Factor Analysis (MOFA) identified latent molecular programs. Phenotype-specific RNA signatures were matched to LINCS drug-perturbation profiles and validated in 3D gliomasphere and ex vivo brain-slice assays. ResultsTwo dominant, reproducible invasion modes emerged across models: diffuse parenchymal infiltration and perivascular/condensed growth. Proneural cultures formed more aggressive tumors in immunodeficient mice, and mouse survival showed a modest correlation with patient survival in matched cases (Pearson r = 0.1832, p = 0.045). MOFA identified 15 latent factors; Factor 1, enriched for ASCL1/OLIG1/OLIG2 programs and associated with TP53/DCHS2/WNK2 alterations, was linked to increased tumor formation, diffuse invasion, and shorter mouse survival, and stratified GBM patients in TCGA and in our matched patient cohort. Drug-signature matching separated mechanisms targeting diffuse versus perivascular invasion. Experimental validation confirmed phenotype-selective sensitivities, and inhibitors PIK-75 and buparlisib suppressed invasion dynamics across representative models in 3D and brain-slice assays. ConclusionsThe HGCC Phenobank provides the first openly available PDX resource that systematically links GBM invasion phenotypes to multi-omic programs and therapeutic predictions. This framework enables reproducible model selection, mechanistic dissection of invasion modes, and phenotype-guided therapeutic discovery. Key PointsO_LIDiffuse and perivascular invasion define orthogonal GBM axes C_LIO_LIASCL1/OLIG factor links initiation, diffuse growth, and survival C_LIO_LIPhenotype-matched drugs validated; PIK-75 and buparlisib curb invasion dynamics C_LI Importance of the StudyGlioblastoma invasion varies substantially between patients, yet existing patient-derived xeno-graft resources rarely combine reproducible in vivo phenotyping with matched multi-omic profiling at scale. The HGCC Phenobank addresses this gap with standardized, blinded scoring of ten invasion traits across 449 orthotopic xenografts from 65 molecularly characterized GBM stem-like cultures, integrated with transcriptomic, methylomic, and proteomic data. We identify two dominant, reproducible invasion modes and a cross-modal neurodevelopmental program, the ASCL1/OLIG1/2-associated Factor 1, that links tumor initiation, diffuse growth, and survival in mice, and stratifies GBM patients in TCGA and in our matched patient cohort. In a spatially resolved xenograft section, Factor 1 signal localizes to the invasive tumor periphery. By matching phenotype-specific RNA signatures to drug-induced transcriptional responses, we show that invasion phenotypes nominate selective vulnerabilities, exemplified by PIK-75. This openly shared resource enables reproducible model selection, mechanistic dissection of invasion programs, and phenotype-guided therapeutic discovery.

Colorectal cancer (CRC) remains a major cause of cancer mortality, with limited options for poor-prognosis subtypes such as CMS4. Antigen-targeted therapies show promise but tend to fail due to inadequate target selection and insufficient patient stratification. Effective prioritization requires large harmonized data capturing CRC heterogeneity - a resource that is currently lacking. To address this need, we built a harmonized multi-omic CRC knowledge base and applied a scalable discovery pipeline to identify antigen targets specifically associated with CMS4 biology and with strong translational potential. We constructed a harmonized CRC atlas by integrating 79 transcriptomics datasets (5,033 tumors, 161 normal samples) using proprietary AI-powered data scouting, integration, and curation technologies. Consensus Molecular Subtypes (CMS) were inferred to capture CMS4-specific expression patterns and this atlas was then combined with 3 bulk RNA-seq reference datasets, 2 single-cell atlases, and 8 protein annotation databases to form a unified multi-omic CRC knowledge base of unmatched scale. From this integrated system, we identified genes differentially expressed in CMS4 patients encoding druggable cell-surface proteins, which we then prioritized using a weighted efficacy- and safety-based scoring model. We identified 236 CMS4-enriched candidates, including 124 not detectable at the CRC-wide level, demonstrating the added resolution gained through subtype stratification. Recovery of known investigational CRC (LGR5, MET, TACSTD2) and CMS4-associated targets of clinical emerging interest (PDGFRB, ALK5/TGFBR1, FAP) support the biological and methodological validity of our approach. Benchmarking against thresholds from FDA-approved pan-cancer targets and terminated trials identified 32 candidates with comparable or superior therapeutic profiles. Among these, 11 were enriched for CMS4-defining pathways, including epithelial-mesenchymal transition, angiogenesis, and stromal invasion, and 5 showed strong profile similarity to established CRC and CMS4 benchmarks. After extensive data exploration, particularly promising candidates were shortlisted for further validation. This work shows that CMS4-focused molecular stratification, when combined with an unprecedentedly large harmonized multi-omic knowledge base, yields a refined set of antigen candidates with enhanced specificity, safety, and biological relevance. The prioritized targets illustrate the power of subtype-resolved discovery to uncover clinically actionable insights. Our pipelines modular design can extend to other tumor contexts, offering a robust foundation for accelerating targeted therapy development.

Immune checkpoint inhibitors (ICIs) have transformed cancer therapy, but they can also induce immune-related adverse events (irAEs). Checkpoint inhibitor-induced liver injury (ChILI) is among the most frequent irAEs, yet its pathophysiology remains poorly understood. Here, we assembled a cohort of liver biopsies from cancer patients with ChILI and used a multi-modal analysis integrating single-cell spatial proteomics, bulk T cell receptor (TCR) sequencing and single-cell spatial transcriptomics to construct the first single-cell spatial atlas of ChILI. Integrating bulk and spatial TCR analyses revealed expanded T cell clones with a cytotoxic CD8+ phenotype that were shared between the liver and tumour. Intercellular communication analyses further indicated close interactions between the shared T cell clones and macrophages involving CCL5-CCR1 signalling. Our work provides in situ evidence of tumour-associated T cell contributions to ChILI. Furthermore, it establishes a framework for gaining mechanistic insights into ChILI and identifying therapeutic targets.

Hypermutational processes, including those driven by the APOBEC3 family of cytidine deaminases, generate abundant neoantigens yet give rise to tumors that evade immune recognition. Here, using multi-omics analyses followed by functional validation, we identified a tumor-intrinsic immune-cloaking mechanism in neoantigen-rich epithelial cancers, characterized by coordinated suppression of antigen presentation, immune-recruiting cytokines and immune-checkpoint programs. In bladder cancer, genome-wide copy-number analysis identified recurrent 1q23.3 amplification as a genomic feature of a neoantigen-high/CD8-low tumor state. Within this locus, NECTIN4 emerged as the dominant candidate effector, outperforming extrachromosomal DNA status as a predictor of immune-neoantigen discordance. Similar associations were observed across breast and lung cancers. Functional studies demonstrated that NECTIN4 was sufficient to establish a T-cell-poor tumor microenvironment and confer resistance to PD-1 blockade in immunocompetent mice. Mechanistically, NECTIN4 engaged a DDR1-SHP2 axis that suppressed STAT1 phosphorylation, silencing tumor-cell immune-engagement programs. NECTIN4 blockade restored STAT1 activity and reduced tumor growth, indicating that the cloaked state is pharmacologically reversible. Mutational signature, breakpoint motif, timing and clonality analyses, together with APOBEC3B expression and germline genetic evidence, linked APOBEC3-mediated mutagenesis to recurrent 1q23.3 amplification encompassing NECTIN4. These findings reveal how neoantigen-generating mutational processes can be coupled to structural genome evolution to enable tumor-intrinsic immune cloaking through a therapeutically targetable NECTIN4-DDR1-SHP2 axis.

Colorectal cancer (CRC) displays inter-patient heterogeneity in molecular tumor features and immune cell composition, which influence therapy response. Humanized immune system (HIS) mouse models offer a promising in vivo model to study human tumor-immune interactions, yet their ability to recapitulate the CRC tumor immune microenvironment at single-cell resolution remains incompletely defined. Here, we performed single-cell RNA sequencing of systemic and tumor-infiltrating human immune cells in HIS mice bearing human CRC tumors and benchmarked these data against reference datasets of healthy human spleens and primary CRC tumors. Major immune lineages and transcriptional programs characteristic of the human systemic immune compartment were identified, and HIS mouse tumors developed complex, human-like immune infiltrates. Tumor-infiltrating immune cells comprised diverse T cell, myeloid, natural killer, and B cell populations, including exhausted T cell states marked by expression of PDCD1, TIGIT, HAVCR2, LAG3, and CTLA4. We further demonstrate CRC consensus molecular subtype-associated spatial differences in immune infiltration. Collectively, our findings support the use of HIS mice as a relevant model for studying CRC immune landscapes and preclinical evaluation of immunomodulatory therapies.

Somatic copy-number amplification is pervasive in cancer, and the genes it carries are candidate drug targets - but only those whose amplification is transmitted to accessible surface protein can be reached by an antibody-drug conjugate (ADC). We build an integrated map of copy-number-to-protein transmission across six tumour types and ask, for every amplified gene, whether its dosage reaches the surface. Copy number transmits to mRNA (median per-gene r = 0.21) but is attenuated at the protein level in 85% of genes, and the mRNA ranking is largely preserved to protein (rho = 0.70); the ranking is set principally at the chromatin/transcription step - among directly measured regulatory inputs, promoter DNA methylation and tumour chromatin accessibility each explain about an order of magnitude more of the transmission variance than gene structure, and do so complementarily. Critically, transmissibility is a stable, gene-intrinsic property: it is predictable from gene properties alone, with no proteomic input, at a leave-gene-out rank correlation of 0.52 (R2 = 0.29); it is not positional (holding out whole chromosome arms changes accuracy by 0.001); and it transfers across lineages (Kendall W = 0.97 across leave-one-lineage-out refits). This licenses a predictor that nominates surface targets in cancer types that lack a tissue-referenced proteome, combining direct protein measurement where it is available with prediction where it is not. Requiring co-elevation on a recurrent amplicon with measured transmissibility and an accessible extracellular ectodomain nominates 22 surface antigens on 18 distinct recurrent amplicons across four cancer types (renal, endometrial and both lung subtypes) - for example ITGB8+TSPAN13+TTYH3 on lung 7p, NCSTN+HSD17B7+MPZL1 on 1q (recurrent in several types), the transferrin receptor TFRC on squamous 3q, and FZD1 on clear-cell renal 7q; 21 of the 22 are non-driver passengers and 10 are confirmed on the experimental Cell Surface Protein Atlas. In single malignant cells, against a null that controls for per-cell sequencing depth, the co-detected constructs sit at a modest 1.05-1.45x above independence (p < 0.001, donor-block bootstrap intervals clear of 1.0), and at binding-relevant thresholds the normal-tissue co-expression collapses - so an avidity AND-gate that binds stably only where the antigens co-occur would spare normal cells that carry only one. Observed transmissibility itself transfers strongly between the two lung subtypes ({rho} = 0.88) and remains positive across distant lineages, consistent with the shared cell-of-origin regulation the map implies. Single-cell co-detection is demonstrated wherever a malignant single-cell atlas exists (both lung subtypes and glioblastoma - the latter entirely from prediction, using no GBM surface-abundance measurement); the remaining cohorts are nominated on the same genetic and topological evidence. The result is a pan-cancer, confidence-tiered catalogue of multi-antigen ADC co-target sets with a concrete plan to test them.

Mucosal cannabidiol formulations are known regulators of the glioblastoma microenvironment, yet the underlying origin point triggering this stroma-remodeling efficacy remains entirely unknown. Here, by mapping innate cell trafficking pathways, we define a novel baseline neuro-immune-microbiome axis in orthotopic glioblastoma, characterized by diverse microbial communities, likely seeded via blood-brain barrier disruption, paired with dense infiltration of host mast cells and mature, crystalloid-containing eosinophils. Localized intranasal administration of a synthetic cannabidiol formulation achieved striking therapeutic efficacy, driving dramatic tumor regression. Mechanistically, high-throughput 16S rRNA sequencing and quantitative flow cytometry revealed this progression was subverted by taming the tumor ecosystem; cannabidiol restricted chaotic microbial diversity, selectively filtering the landscape toward Delftia and depleting Archaea, while simultaneously suppressing hyper-inflammatory host mast cell and eosinophil populations. This study builds upon established innate trafficking frameworks to present the first therapeutically targetable stromal-microbial axis in neuro-oncology.

Small cell lung cancer (SCLC) is a highly aggressive malignancy with limited therapeutic advances. Unlike many other cancers, its immune landscape, particularly immune competence and T cell recognition, remains poorly characterized. Here, we generate a single-cell transcriptome atlas of the SCLC immune microenvironment with paired T cell receptor (TCR) sequencing. By linking T cell states with clonality and a multilayered functional screening, we identify 6 tumor-reactive TCRs that recognize and eradicate autologous SCLC cell lines. We delineate a novel SCLC-reactive CD8+ T cell signature (SCLC_TR), enabling the identification of 47 further SCLC-reactive TCRs. The SCLC_TR signature performs extremely well in pancreatic ductal adenocarcinoma (PDAC), another immune-cold tumor indication, and, most strikingly, patients with elevated SCLC_TR signature scores exhibited significantly improved survival, underlining its prognostic potential. Comparative cell-cell interaction analyses implicate several immunosuppressive mechanisms, with myeloid cells and CD4+ regulatory T cells possibly acting as counterbalances to effector T cell activity in SCLC. In summary, our study challenges the prevailing notion of SCLC as an immune-cold tumor type by providing direct evidence of tumor-reactive T cell responses and introduces the SCLC_TR signature as a tool to identify tumor-specific T cells and their microenvironmental restraints and escape mechanisms, ultimately shaping next-generation immunotherapeutic strategies.