High-throughput processing of patient biosamples by next-generation sequencing and the comparison of molecular data with patient-level and sample-level clinical data require precise tracking and matching of sample identifiers throughout the biospecimen chain of custody and are critical to enabling robust interpretation of biomarker trial results. In addition to tracing individual steps in the sample and data processing workflows, bioinformatics solutions can be used to confirm that samples originate from the same patient. Here, the use of a bioinformatics workflow to identify matched samples originating from the same individual is showcased. The analysis workflow is suitable for any two or more pairs of NGS datasets to be compared and verified for patient sample origin. A scoring algorithm based on genome-wide comparisons of samples enables the user to determine whether two samples stem from the same individual. Specifically, single-nucleotide polymorphisms (SNPs) within selected linkage disequilibrium blocks are used to identify and compare samples. Threshold combinations for permissive and stringent selection of matched and mismatched samples were identified. The utility of this protocol was demonstrated through its application to the quality control and validation of clinical tumor tissue and blood samples, encompassing multiple omics modalities from over 2,000 patients.

Immune checkpoint inhibitors (ICIs) are a standard treatment across cancers, yet most patients do not respond, and existing biomarkers generalize poorly across tumor types and therapies. Here we present COMPASS, a pan-cancer foundation model that predicts immunotherapy response from bulk tumor transcriptomes using a concept bottleneck transformer. COMPASS encodes gene expression through 44 biologically grounded immune concepts representing immune cell states, tumor-microenvironment interaction and signaling pathways. Trained on 10,184 tumors across 33 cancer types, COMPASS achieves better average performance than 22 methods across 16 clinical cohorts spanning seven cancers and six ICIs, improving accuracy by 8.5% and area under the precision-recall curve by 15.7% on average across cohorts. COMPASS generalizes to cancer types and treatments not represented during fine-tuning and may inform indication selection and patient stratification. In survival analyses, patients classified by COMPASS as responders had longer overall survival (hazard ratio = 4.7, P < 0.0001). Personalized response maps connect gene expression to immune concepts, identifying programs associated with response and resistance; in immune-inflamed non-responders, COMPASS highlights programs including TGFβ signaling, endothelial exclusion, CD4+ T cell dysfunction and B cell deficiency. COMPASS predicts immunotherapy response and provides hypothesis-generating mechanistic insight for trial design and translational studies.

Supplementary Fig. 6. Longitudinal analysis of on-treatment fold changes (log2) in peripheral regulatory T-cell subsets in tobemstomig-treated patients, stratified by cancer type.

Supplementary Fig. 5. Single-nuclei RNA sequencing analyses depicting gene-signature pharmacodynamic effects in NK cells from selected patients. The heatmap colors and significance annotations follow the same scheme as in Fig. 4d. Adj, adjusted; C, cycle; D, day.

Table S5. snRNA-Seq sample overview.

Table S8. Response assessment per RECIST version 1.1.

Abstract Purpose: The immunoglobulin G1–based bispecific antibody tobemstomig (RO7247669) simultaneously targets and blocks programmed cell death protein 1 and lymphocyte activation gene-3 expressed on activated T cells. Patients and Methods: This first-in-human, open-label, phase I clinical trial of tobemstomig included a dose-escalation part in patients with advanced and/or metastatic solid tumors and an expansion part with three tumor-specific cohorts, enrolling checkpoint inhibitor (CPI)–experienced patients with melanoma and non–small cell lung cancer (NSCLC) and CPI-naïve patients with esophageal squamous cell carcinoma (ESCC). Primary and secondary objectives included safety/tolerability, maximum tolerated dose (MTD) and/or recommended dose for expansion (RDE), pharmacokinetics (PK), drug receptor occupancy, and preliminary antitumor activity. Results: Thirty-five (dose-escalation) and sixty-nine patients (expansion) were enrolled. Tobemstomig was well tolerated up to the highest tested dose of 2,100 mg once every 2 weeks. The MTD was not reached, and 2,100 mg once every 2 weeks was established as the RDE. Tobemstomig exhibited linear PK across the studied dose range. Partial responses were achieved by 2 of 4 (600 mg) and 4 of 13 (2,100 mg) patients during dose escalation, 6 of 41 patients with CPI-experienced melanoma [objective response rate (ORR): 15%; 95% confidence interval (CI), 6.6–26.9], and 1 of 8 patients with CPI-naïve ESCC (ORR: 12.5%; 90% CI, 0.6–47.1). Proof of mechanism was demonstrated in patients with CPI-experienced melanoma based on increases in the amounts of CD8+ T cells, expansion of stem-like CD8+ T cells, and the acquisition of cytotoxic effector functions, with limited changes in the regulatory T-cell compartment. Conclusions: Tobemstomig had a tolerable and manageable safety profile across various advanced solid tumor indications. The encouraging antitumor activity associated with pharmacodynamic activity and proof of mechanism in patients with CPI-experienced melanoma indicates the therapeutic potential of tobemstomig and supports further investigation in earlier disease treatment settings.

PURPOSE:Bromodomain and extra-terminal domain (BET) inhibitors (BETi) have demonstrated epigenetic modulation capabilities, specifically in transcriptional repression of oncogenic pathways. Preclinical assays suggest that BETi potentially attenuates the PD1/PD-L1 immune checkpoint axis, supporting its combination with immunomodulatory agents. PATIENTS AND METHODS:A Phase 1b clinical trial was conducted to elucidate the pharmacokinetic and pharmacodynamic profiles of the BET inhibitor RO6870810 as monotherapy and in combination with the PD-L1 antagonist atezolizumab in patients with advanced ovarian carcinomas and triple-negative breast cancer (TNBC). Endpoints included maximum tolerated dosages, adverse event profiling, pharmacokinetic evaluations, and antitumor activity. Pharmacodynamic and immunomodulatory effects were assessed in tumor tissue (by immunohistochemistry and RNA-seq) and in peripheral blood (by flow cytometry and cytokine analysis). RESULTS:The study was terminated prematurely due to a pronounced incidence of immune-related adverse effects in patients receiving combination of RO6870810 and atezolizumab. Antitumor activity was limited to 2 patients (5.6%) showing partial response. Although target engagement was confirmed by established BETi pharmacodynamic markers in both blood and tumor samples, BETi failed to markedly decrease tumor PD-L1 expression and had a suppressive effect on antitumor immunity. Immune effector activation in tumor tissue was solely observed with the atezolizumab combination, aligning with this checkpoint inhibitor's recognized biological effects. CONCLUSIONS:The combination of BET inhibitor RO6870810 with the checkpoint inhibitor atezolizumab presents an unfavorable risk-benefit profile for ovarian cancer and TNBC (triple-negative breast cancer) patients due to the increased risk of augmented or exaggerated immune reactions, without evidence for synergistic antitumor effects. TRIAL REGISTRATION:ClinicalTrials.gov ID NCT03292172; Registration Date: 2017-09-25.

Hematologic adverse events are common dose-limiting toxicities in drug development. Classical animal models for preclinical safety assessment of immunotherapies are often limited due to insufficient cross-reactivity with non-human homologous proteins, immune system differences, and ethical considerations. Therefore, we evaluate a human bone marrow (BM) microphysiological system (MPS) for its ability to predict expected hematopoietic liabilities of immunotherapeutics. The BM-MPS consists of a closed microfluidic circuit containing a ceramic scaffold covered with human mesenchymal stromal cells and populated with human BM-derived CD34+ cells in chemically defined growth factor-enriched media. The model supports on-chip differentiation of erythroid, myeloid and NK cells from CD34+ cells over 31 days. The hematopoietic lineage balance and output is responsive to pro-inflammatory factors and cytokines. Treatment with a transferrin receptor-targeting IgG1 antibody results in inhibition of on-chip erythropoiesis. The immunocompetence of the chip is established by the addition of peripheral blood T cells in a fully autologous setup. Treatment with T cell bispecific antibodies induces T cell activation and target cell killing consistent with expected on-target off-tumor toxicities. In conclusion, this study provides a proof-of-concept that this BM-MPS is applicable for in vitro hematopoietic safety profiling of immunotherapeutics.

Patients with stage III melanoma are at high risk of relapse. The NADINA trial evaluating neoadjuvant nivolumab plus ipilimumab and the SWOG-1801 trial evaluating neoadjuvant pembrolizumab have demonstrated superior clinical outcomes with neoadjuvant versus adjuvant checkpoint inhibition. Morpheus-Melanoma was a phase 1b/2, randomized umbrella trial evaluating tobemstomig (anti-PD-1/anti-LAG-3 bispecific antibody; n = 40), tobemstomig plus tiragolumab (anti-TIGIT monoclonal antibody; n = 20) and atezolizumab (PD-L1-targeting monoclonal antibody) plus tiragolumab (n = 20) versus nivolumab (anti-PD-1 monoclonal antibody) plus ipilimumab (anti-CTLA-4 monoclonal antibody; n = 22) in stage III melanoma. The primary endpoint was pathological response by independent pathological review. Additional endpoints included safety and exploratory biomarkers. Here tobemstomig showed a similar pathological response rate (pRR) versus nivolumab plus ipilimumab (80.0% (32/40) versus 77.3% (17/22)); major pathological responses were less frequent with tobemstomig versus nivolumab plus ipilimumab treatment (62.5% (25/40) versus 72.7% (16/22)). Tobemstomig plus tiragolumab and atezolizumab plus tiragolumab showed a lower pRR versus nivolumab plus ipilimumab (60.0% (12/20) and 45.0% (9/20) versus 77.3% (17/22), respectively). Tobemstomig demonstrated improved safety versus nivolumab plus ipilimumab, with 2.5% (1/40) and 22.7% (5/22) of patients experiencing grade 3 or higher treatment-related adverse events (TRAEs), respectively, and 0% (0/40) and 13.6% (3/22) of patients discontinuing treatment due to TRAEs, respectively. Grade 3 or higher TRAEs were reported by 15% (3/20) of patients in the tobemstomig plus tiragolumab arm and by no patients in the atezolizumab plus tiragolumab arm. Baseline CD8+ and CD3+ tumor-infiltrating T cell density, IFNγ pathway and effector T cell gene expression, tumor mutational burden and pre-surgery circulating tumor DNA correlated with pathological response across treatments. In conclusion, in the Morpheus-Melanoma study, tobemstomig demonstrated a similar pathological response and improved safety profile versus nivolumab plus ipilimumab in patients with resectable stage III melanoma. ClinicalTrials.gov identifier: NCT05116202 .

Immune checkpoint inhibitors have become standard care across many cancers, but most patients do not respond. Predicting response remains challenging due to complex tumor-immune interactions and the poor generalizability of current biomarkers and models. Predictors such as tumor mutational burden, PD-L1 expression, and transcriptomic signatures often fail across cancer types, therapies, and clinical settings. There is a clear need for a robust, interpretable model that captures shared immune response principles and adapts to diverse clinical contexts. We present Compass, a foundation model for predicting immunotherapy response from pan-cancer transcriptomic data using a concept bottleneck architecture. Compass encodes tumor gene expression through 44 biologically grounded immune concepts representing immune cell states, tumor-microenvironment interactions, and signaling pathways. Trained on 10,184 tumors across 33 cancer types, Compass outperforms 22 baseline methods in 16 independent clinical cohorts spanning seven cancers and six immune checkpoint inhibitors, increasing precision by 8.5%, Matthews correlation coefficient by 12.3%, and area under the precision-recall curve by 15.7%, with minimal or no additional training. The model generalizes to unseen cancer types and treatments, supporting indication selection and patient stratification in early-phase clinical trials. Survival analysis shows that Compass-stratified responders have significantly longer overall survival (hazard ratio = 4.7, p < 0.0001). Personalized response maps link gene expression to immune concepts, revealing distinct mechanisms of response and resistance. For example, among immune-inflamed non-responders, Compass identifies distinct resistance programs involving TGF- β signaling, endothelial exclusion, CD4+ T cell dysfunction, and B cell deficiency. By combining mechanistic interpretability with transfer learning, Compass provides mechanistic insights into treatment response variability, supports clinical decision-making, and informs trial design.

Monitoring neutrophil gene expression is a powerful tool for understanding disease mechanisms, developing diagnostics, enhancing therapies, and optimizing clinical trials. Neutrophils are sensitive to the processing, storage, and transportation steps that are involved in clinical sample analysis. This study evaluates the capabilities of technologies from 10× Genomics, PARSE Biosciences, and HIVE (Honeycomb Biotechnologies) to generate single-cell RNA sequencing (scRNA-seq) data from human blood-derived neutrophils. Our comparative analysis shows that all methods produced high-quality data, importantly capturing the transcriptomes of neutrophils. Here, we establish a reliable scRNA-seq workflow for neutrophils in clinical trials: we offer guidelines on sample collection to preserve RNA quality and demonstrate how each method performs in capturing sensitive cell populations in clinical practice.

ABSTRACT:Effective T-cell responses not only require the engagement of T-cell receptors (TCRs; "signal 1"), but also the availability of costimulatory signals ("signal 2"). T-cell bispecific antibodies (TCBs) deliver a robust signal 1 by engaging the TCR signaling component CD3ε, while simultaneously binding to tumor antigens. The CD20-TCB glofitamab redirects T cells to CD20-expressing malignant B cells. Although glofitamab exhibits strong single-agent efficacy, adding costimulatory signaling may enhance the depth and durability of T-cell-mediated tumor cell killing. We developed a bispecific CD19-targeted CD28 agonist (CD19-CD28), RG6333, to enhance the efficacy of glofitamab and similar TCBs by delivering signal 2 to tumor-infiltrating T cells. CD19-CD28 distinguishes itself from the superagonistic antibody TGN1412, because its activity requires the simultaneous presence of a TCR signal and CD19 target binding. This is achieved through its engineered format incorporating a mutated Fc region with abolished FcγR and C1q binding, CD28 monovalency, and a moderate CD28 binding affinity. In combination with glofitamab, CD19-CD28 strongly increased T-cell effector functions in ex vivo assays using peripheral blood mononuclear cells and spleen samples derived from patients with lymphoma and enhanced glofitamab-mediated regression of aggressive lymphomas in humanized mice. Notably, the triple combination of glofitamab with CD19-CD28 with the costimulatory 4-1BB agonist, CD19-4-1BBL, offered substantially improved long-term tumor control over glofitamab monotherapy and respective duplet combinations. Our findings highlight CD19-CD28 as a safe and highly efficacious off-the-shelf combination partner for glofitamab, similar TCBs, and other costimulatory agonists. CD19-CD28 is currently in a phase 1 clinical trial in combination with glofitamab. This trial was registered at www.clinicaltrials.gov as #NCT05219513.

Understanding protein function and developing molecular therapies require deciphering the cell types in which proteins act as well as the interactions between proteins. However, modeling protein interactions across biological contexts remains challenging for existing algorithms. Here, we introduce Pinnacle, a geometric deep learning approach that generates context-aware protein representations. Leveraging a multi-organ single-cell atlas, Pinnaclelearns on contextualized protein interaction networks to produce 394,760 protein representations from 156 cell type contexts across 24 tissues. Pinnacle’s embedding space reflects cellular and tissue organization, enabling zero-shot retrieval of the tissue hierarchy. Pretrained protein representations can be adapted for downstream tasks: enhancing 3D structure-based representations for resolving immuno-oncological protein interactions, and investigating drugs’ effects across cell types. Pinnacleoutperforms state-of-the-art models in nominating therapeutic targets for rheumatoid arthritis and inflammatory bowel diseases, and pinpoints cell type contexts with higher predictive capability than context-free models. Pinnacle’s ability to adjust its outputs based on the context in which it operates paves way for large-scale context-specific predictions in biology.

Cell lines used in this study.

Supplementary Video Legends and Supplementary Figures 1-10