Cancer Cell

Breast cancer is a highly heterogeneous disease shaped by dynamic interactions between malignant cells, immune infiltrates, stromal compartments, and the extracellular matrix. Among the molecular regulators of these interactions, cysteine cathepsins and legumain have emerged as important proteases involved in tissue remodeling, immune regulation, and tumor progression, yet their distribution and functional status across human breast cancer ecosystems remain insufficiently defined. Here, we performed an integrated protease-centric analysis of breast cancer specimens from 66 patients using high-dimensional single-cell mass cytometry of matched peripheral blood and tumor samples, imaging mass cytometry of intact tissues, and activity-based TOF probes for in situ detection of active proteases. Systemic immune profiling identified two patient clusters associated primarily with neoadjuvant therapy and tumor grade, accompanied by distinct cytokine and circulating protease patterns. In tumors, single-cell analysis revealed pronounced interpatient heterogeneity in tissue architecture and immune infiltration, while protease profiling uncovered reproducible cell type-associated modules, including cathepsin B/L-cystatin C and legumain-cystatin E/M axes. Cathepsins B and L were prominent in tumor-infiltrating immune cells and variably expressed in epithelial cells, whereas cathepsin D showed broader tumor distribution and cathepsin S remained more restricted. In epithelial cells, HER2 expression did not consistently coincide with high cathepsin B or L abundance, enabling identification of a limited subgroup of patients with combined HER2-high/protease-high states relevant to protease-cleavable antibody-drug conjugates. Spatial imaging further localized cathepsins B and D to tumor-stroma interfaces and macrophage-rich niches, and activity-based IMC confirmed the presence of catalytically active cathepsin B in human breast tumor tissue. Together, these findings define cysteine cathepsins as spatially and cellularly organized components of breast tumor ecosystems and provide a framework for protease-informed patient stratification and biomarker-protease pairing in targeted therapy.

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.

Barretts esophagus (BE) is the precursor lesion of esophageal adenocarcinoma (EAC). It affects approximately 5% of adults in the United States and significantly increases the risk of developing EAC. However, current surveillance strategies cannot reliably distinguish patients who will progress from those who will remain stable. Direct studies of progressor BE are extremely limited due to availability of tissue with known progression outcomes, and have largely been restricted to genomic profiling approaches. The premalignant cellular landscape of progressor BE remains poorly understood. Here, we used complementary spatial transcriptomic and proteomic imaging to profile 34 non-dysplastic BE patients under endoscopic surveillance, including those who subsequently progressed to dysplasia or EAC, termed "Progressors" and those who remained stable, termed "Non-progressors". Transcriptomics based Xenium analysis captured 974,604 cells across 70 whole-biopsy regions, while protein based imaging mass cytometry profiled 372,242 cells across 119 selected regions. FUME-TCRseq further quantified T cell clonotypes from matched tissues scrolls. Cellular composition was generally similar between Progressors and Non-progressors. However, Progressors showed increased intestinal Barretts columnar cells, B cells and gastric progenitor-like cells, together with enhanced immune-epithelial interactions, whereas Non-progressors retained coordinated stromal organization. Spatial interaction features strongly outperformed cell composition and density for progression prediction. Combined spatial interaction model achieved an area under the curve (AUC) of 0.97, compared with 0.62 and 0.68 for comparison and density alone. Complementary imaging mass cytometry further resolved the underlying immune programs, identifying cytotoxic and antigen presenting myeloid features enriched in progressors, and CD56 associated memory T cell interactions enriched in non progressors. Together, these findings support a model that BE progression is driven by progressive remodeling of epithelial-immune-stromal architecture rather than emergence of distinct dysplasia-like cell subsets. Increased T cell clonal diversity and recruitment of cytotoxic and antigen-presenting immune niches may also reflect an evolving response to genomic alteration prior to dysplasia. These results establish spatial tissue architecture, rather than specific cell types, captures progression associated microenvironmental states in BE and provides a framework for spatially informed patient stratification and early cancer risk assessment.

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.

Tumor-associated macrophages (TAM) exert essential functions during the immune response to cancer. However, investigations of TAM within a native human tumor microenvironment (TME) have been impeded by a lack of appropriate model systems. Here, patient-derived organoids (PDO) from air-liquid interface (ALI)-grown tumor fragments, containing a human TME that encompassed stroma and immune subsets, robustly preserved TAM that were maintained by endogenous CSF-1 and appropriately responded to polarization signals. Antibody blockade of the CD47 regulatory checkpoint in organoids stimulated phagocytosis and remodeled TAM cytokine secretion profiles that were confirmed in anti-CD47 phase I trial patients. Amongst PDO histologies screened, anti-CD47 tumor killing was notable in clear cell renal cell carcinoma (ccRCC) which was associated with increased TAM infiltration. PDO contained diverse previously described TAM subsets; however, anti-CD47 reprogrammed organoid TAM toward an immunosuppressive SPP1+ phenotype, highlighting a negative feedback mechanism. Our findings uncover a resistance circuit engaged by macrophage checkpoint blockade and position ALI PDO as a robust translational platform for dissecting human macrophage biology and informing precision immunotherapy.

T cell receptors (TCRs) and TCR-mimicking antibodies recognize peptide antigens in the context of specific Human Leucocyte Antigen (HLA-I) allotypes, and the extreme polymorphism of the HLA locus limits the breadth of immunotherapy development. Key barriers include divergent molecular surfaces on HLA proteins and differences in the peptide structure. As a result, existing modalities cannot confer therapeutic coverage across patients of divergent genetic backgrounds. Here, we develop an approach which combines a peptide conformational prediction tool, PepPred, with a cross-HLA binding protein engineering system, TRACeR-I1, to outline a generalized framework for developing binders (xTRACeRs) with compatibility across HLA allotypes while maintaining high levels of specificity towards the peptide antigen. We use our system to develop and validate xTRACeRs against clinically relevant, established peptide antigens presented across common alleles within five HLA-A/B/C supertypes2. Cryo-EM structures of xTRACeR-pHLA complexes for an oncofetal antigen from PRAME and a neuroblastoma-specific peptide from PHOX2B reveal effective mechanisms to navigate polymorphic HLA surface residues, and extensive interactions with the peptide. We implemented these two xTRACeRs as Chimeric Antigen Receptor (CAR) T cells and demonstrated their potent killing efficacy and specificity. Overcoming restriction across HLA supertypes lifts a key barrier in HLA-targeted immunotherapy by expanding patient coverage.

Immune checkpoint B7-H3 is an emerging target for immunotherapy. DS-7300a is an advanced B7-H3-targeting antibody-drug conjugate (ADC) warheaded with the topoisomerase I inhibitor DXd. DS-7300a has demonstrated clinical activity, but molecular biomarkers to predict its therapeutic response remain elusive. TP53 is one of the most mutated tumor suppressor genes across cancers, and effective therapies are urgently needed for TP53-deficient cancers. Using prostate cancer (PCa) as a model system, we reported that DS-7300as anti-tumor efficacy is highly dependent on functional p53 in cancer cells, and TP53 defects confer resistance to DS-7300a. Mechanistically, we found that DS-7300a and its payload, DXd, induce DNA damage and activate the ATM/ATR/CHK signaling cascade, thereby stabilizing p53 and inducing a pro-apoptotic and senescence-associated transcriptome. In contrast, TP53-deficient cells fail to detect DXd-induced DNA damage, maintain a high proliferation rate, and exhibit low levels of apoptosis and senescence, thereby conferring resistance to DS-7300a. Ferroptosis is an iron-dependent form of regulated cell death triggered by lipid peroxidation, which is mechanistically and morphologically distinct from apoptosis. Interestingly, DS-7300a treatment elevates lipid peroxidation in TP53-deficient cancer cells and upregulates glutathione peroxidase 4 (GPX4), an antioxidant enzyme that mitigates lipid peroxidation. Using isogeneic xenograft models and a newly developed humanized B7-H3 PCa model, we demonstrated that inducing ferroptosis by pharmacological inhibition of GPX4 enhances DS-7300as efficacy in TP53-deficient tumors. Our studies demonstrate that TP53 status dictates anti-tumor responses to DS-7300a, and ferroptosis induction represents a promising therapeutic approach to overcome resistance to DS-7300a in malignancies harboring TP53 defects.

Glioblastomas are incurable and lethal brain cancers. Immunotherapies offer new and promising treatment options for glioblastoma patients, but the highly immunosuppressive nature of these cancers presents a challenging clinical obstacle. Glioblastoma immune evasion is driven by cell-cell interactions in the tumor microenvironment and recent studies have identified astrocytes as important contributors to immune silencing [1, 2]. Cell plasticity is a key feature of reactive astrocytes that drives heterogeneous, pro- or anti-inflammatory states [3], but the molecular regulators of astrocyte-immune interactions remain incompletely understood. Here, we investigate whether cell plasticity of glioblastoma-associated astrocytes promotes or opposes tumor progression and show that loss of astrocyte plasticity results in T-cell recruitment and immune activation. We evaluate how astrocytic cell plasticity contributes to immune functions in the glioblastoma microenvironment using single cell sequencing from preclinical models, in vivo genetic perturbations and in vitro mouse and human experimental systems. We show that astrocytes surrounding glioblastoma express the stem cell-associated transcription factor, ZEB1, and that conditional-inducible astrocytic deletion of Zeb1 remarkably reduces glioblastoma growth and extends survival. Increased recruitment and activation of T cells in astrocytic Zeb1-deficient mouse models is linked to increased expression of the immunoattractant cytokine CXCL14, and viral delivery of CXCL14 in experimental glioblastoma models increases survival. Our data support that CXCL14 is a candidate therapeutic target for reprogramming the tumor microenvironment that can restrict and reduce glioblastoma growth and progression.

The specific contribution of tissue-resident macrophages (TRMs) to pancreatic ductal adenocarcinoma (PDAC) progression remains unclear. Here, we found that a high abundance of TRM-derived tumor-associated macrophages (TRM-TAMs) is an independent indicator of poor prognosis in patients with PDAC. To elucidate the underlying mechanism, we established an advanced organoid platform (iMac-FPCO), which incorporates macrophages derived from human induced pluripotent stem cells to reflect the differentiation process of TRMs. Single-cell transcriptomic analysis revealed this model recapitulates the transcriptional identity of TRM-TAMs in patient tissue. We demonstrated that TRM-TAMs drive cancer cell proliferation, while maintaining chemoresistance, and identified TRM-derived insulin-like growth factor 1 (IGF1) as the critical mediator. This result provides a rationale for why previous trials targeting IGF1 receptor (IGF1R) failed to improve survival in unselected patient populations. We hypothesize that stratifying patients by TRM-TAM abundance could help identify a responsive subgroup, thereby reviving IGF1R-targeted therapy as a viable treatment for PDAC.

Glioblastoma (GBM) is a devastating primary brain tumor with remarkable inter- and intra-tumoral heterogeneity. GBM cells assume a spectrum of neurodevelopmental-like phenotypes and co-opt normal neurophysiological processes, which include synaptic integration with their neuronal microenvironment. This is mediated by neuron-tumor synapses (NTS) that predominantly involve glutamatergic receptors, which drive calcium elevations that promote tumor proliferation and invasion. The exact relationship between synaptic signaling and tumor cell fate specification, however, remains largely unexplored. Here, we develop and leverage a synapse-optimized human organoid tumor transplantation (so-HOTT) model of GBM to decipher how glutamatergic signaling impacts GBM lineage trajectories. so-HOTT preserves patient tumor heterogeneity, features excitatory NTS, and enables clonal lineage tracing of tumor cells after NTS perturbations. Genetic and pharmacological inhibition of AMPA and kainate receptors in so-HOTT shifts tumor cell composition from neuronal fates toward progenitor-proximal astrocytic/mesenchymal states. This occurs through the attenuation of calcium signaling and reduced plasticity of malignant radial glia (RG)-like progenitors, a previously unrecognized target of NTS. Through the integration of inputs from the neuronal microenvironment into glutamatergic signaling, progenitor populations modulate their transcriptional programs and cell fate, ultimately shaping GBM tumor heterogeneity. Targeting synaptic input may thus constrain the heterogeneity that fuels GBM adaptation and therapeutic escape.

BackgroundTumor cells are increasingly understood as physically connected collectives whose intercellular communication is gated by gap junctions and modulated by microen-vironmental ion fluxes. While spatial transcriptomics provides the geometric substrate for building transcriptomic proxies of bioelectric organization, no robust pipeline currently translates spot-level connectivity features into independent clinical prognostic markers. MethodsWe analyzed 12 oral squamous cell carcinoma (OSCC) Visium sections (GSE208253). A K-nearest-neighbor (K=6) spatial graph was built on full-resolution coordinates and edge-weighted by a conductance-like transcriptomic proxy in which gap-junction proxy expression was scaled by an exponential acid-gating penalty. Geometric edge artefacts were controlled with concave-hull edge distance and partial rank correlation under permutation testing. A 25-gene BCI-Signature was extracted by intra-sample top/bottom conductance differential expression and cross-sample consensus voting ([≥] 6/12). The signature was spatially back-projected, directionally decomposed from prior biology, and then projected to TCGA-HNSC (n = 519) and GSE65858 (n = 270) for survival analysis. Cohort-level effects were combined by inverse-variance fixed-effect meta-analysis. ResultsDiagnostic controls falsified the initial isolation-driven hypothesis: across all 12 sections, the partial rank correlation between the isolation index and depolarization-footprint expression was negative after edge-distance adjustment. Feature ablation identified the conductance sum as the best transcriptomic proxy of physical network state, and section-level sensitivity analyses preserved the positive conductance-stress direction after long-edge removal and graph-parameter perturbation. Spatial back-projection showed that aggressive and differentiation programs are positively correlated within every section (median{rho} = 0.43) and co-enrich in high-conductance regions. This predicted bulk-level signal cancellation: the unweighted 25-gene mean was non-prognostic in TCGA-HNSC (HR=1.17, p=0.35), whereas the locked directional composite BCI_net was independently associated with worse OS (HR=1.38, 95% CI 1.06-1.79, p=0.015 after adjustment for age, stage, HPV status and gender). The effect persisted after separate adjustment for composition, EMT and proliferation proxies, but attenuated in a saturated all-proxy benchmark model. The biologically matched HPV-negative oral-cavity subset of GSE65858 (n = 77) preserved the direction with a larger effect size (HR=2.45, 95% CI 0.96-6.27, p=0.062). Inverse-variance fixed-effect pooling of the two cohorts yielded a significant pooled effect (HR=1.48, 95% CI 1.07-2.05, p=0.019). ConclusionsSpatial graph features can be transferred to bulk transcriptomic cohorts only after the structural and aggressive programs that co-localize within the same physical network are explicitly deconvolved. The equal-weight directional metric BCI_net is a biology-driven candidate prognostic readout that remains preliminary pending broader independent validation.

In pediatric brain tumors medulloblastoma (MB) and diffuse midline glioma (DMG), tumor-associated myeloid cells (TAMs) support malignant progression by secreting paracrine growth factors and suppressing local immune function. We studied the potential for reversing this cancer-supportive phenotype by stimulating TAM pathogen receptors using ResiPOx, a brain-permeant, polyoxazoline nanoparticle formulation of the TLR7/8 agonist resiquimod. ResiPOx showed blood-brain barrier penetration and anti-tumor efficacy, extending progression-free survival (PFS) in mice with MB and DMG. Integrated cellular and molecular analysis including scRNA-seq showed that ResiPOx expanded TAM populations and reprogrammed TAMs toward anti-tumoral states, blocking paracrine IGF1 signaling and inducing local cytokine signaling and phagocytosis of tumor cells. In rhesus macaques, systemic ResiPOx was well tolerated and induced brain transcriptional patterns that resembled ResiPOx responses in DMG and MB mouse models, indicating effects in non-human primates that highlight translational potential. Our data show that ResiPOx reshapes the brain tumor microenvironment to inhibit tumor growth. As a systemically administered, brain penetrant immunomodulator, ResiPOx is able to reach multifocal and unresectable brain tumors, including MB and DMG.

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.

High grade serous ovarian cancer patients initially respond to platinum-based chemotherapy, but usually relapse within two years and ultimately develop therapy resistance. Management of response and effective clinical decisions are currently based on unspecific biomarkers and limited imaging techniques, illustrating the clear clinical need for reliable predictors of response. In this work, we evaluated the performance of patient-derived organoids generated from ascitic fluid and functionally tested in parallel to the patients clinical course, in the prediction of treatment response, and guiding clinical decision-making in a patient-specific manner. Ascites derived organoids reliably recapitulated the histological and molecular features of a paradigmatic HGSOC patient with an apparent dissociated response, and demonstrated chemoresistance months before laparoscopy confirmed persistent inoperable disease with poor pathological response. Drug screening identified alternative therapeutic options, while multi-omics provided additional insights into the tumor-specific biological features, to assist in the personalized clinical management in ovarian cancer.

Faithful proliferation requires coordinated DNA replication with centrosome maturation and spindle-pole integrity. SLD5, encoded by GINS4, is a core component of the GINS replication complex and is frequently elevated in tumors, but whether it links replication-associated cancer states to centrosome control has remained unclear. Here, we show that GINS4/SLD5 is recurrently upregulated across human cancers at transcript and protein levels and marks tumor programs enriched for DNA replication, chromosome segregation, and mitotic control. In cancer cells, Sld5 depletion dispersed PCM1, AZI1, and CEP290-positive centriolar satellites without eliminating these satellite proteins, reduced dynein heavy chain expression, and destabilized dynein-dynactin localization at spindle poles. Direct depletion of dynein heavy chain, co-depletion analyses, and pharmacological inhibition of dynein motor activity with ciliobrevin D phenocopied Sld5 loss, causing satellite dispersion, defective recruitment of PLK1, Aurora A, CEP192, and CEP215 to centrosomes, and multipolar spindle formation. These defects occurred without detectable DNA damage or checkpoint activation, indicating a non-canonical Sld5 function beyond its role in the replisome. Cancer dependency and kinase network analyses further nominate SLD5-associated mitotic and checkpoint pathways as therapeutic targets. Our findings identify SLD5/GINS4 as a regulator of dynein-dependent centrosome maturation and a candidate vulnerability in replication-driven cancers, with potential value for biomarker-guided therapeutic stratification. Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=136 SRC="FIGDIR/small/723511v1_ufig1.gif" ALT="Figure 1"> View larger version (53K): org.highwire.dtl.DTLVardef@e845d8org.highwire.dtl.DTLVardef@141719aorg.highwire.dtl.DTLVardef@1895e1corg.highwire.dtl.DTLVardef@181aa16_HPS_FORMAT_FIGEXP M_FIG C_FIG

Cancer cachexia is a wasting syndrome that remodels the anatomy of the patient. How this remodeling unfolds across tissues, whether it defines distinct disease states, and how these states relate to underlying biology remain unknown. We used longitudinal computed tomography imaging from 4,516 patients to quantify evolution of muscle, adipose, and organs during cachexia. Across two independent institutional cohorts, unsupervised analysis identified three reproducible anatomical subtypes of cachexia, including an inflammatory Type A marked by progressive hepatosplenic enlargement and inferior survival, a Type B dominated by visceral organ atrophy, and a mild Type C. These anatomical subtypes were associated with distinct serological signatures and reflected in molecular phenotypes in tumors and non-cancerous liver tissue, establishing cachexia as discrete anatomical disease states that link whole-body remodeling to systemic and tissue-level biology. This anatomy-first framework for cachexia classification provides a foundation for future patient stratification and development of subtype-specific anti-cachexia therapies.

Patient-reported outcomes (PROs) capture the patient voice and have been associated with improved clinical outcomes in oncology, but their prognostic and predictive value remains underutilized due to challenges in interpreting these highly variable and noisy PRO data. Here, we developed a quantitative modeling framework integrating nonlinear mixed-effects (NLME) and item response theory (IRT) to characterize symptom-level PRO trajectories and transform them into clinically actionable predictors. Using longitudinal PRO data from 589 patients with metastatic cancers in the PRO-TECT trial, we modeled 332,920 symptom responses to estimate patient-specific PRO trajectory parameters while accounting for variability and noise. IRT-NLME modeling captured heterogeneous symptom-level PRO dynamics and is more informative than modeling with composite PRO scores. PRO trajectory parameters were strongly associated with overall survival, acute care utilization, and treatment modifications. Machine learning models leveraging these parameters achieved robust prediction of survival (AUC-ROC 0.80) and retained prognostic performance using the first 30 - 180 days of PRO observations, with AUCs of 0.69-0.78. Similar predictive performance was observed for hospitalization (AUC 0.75), emergency department visit (AUC 0.65), treatment discontinuation (AUC 0.71), and dose reduction (AUC 0.67). These findings demonstrate that longitudinal PRO trajectories can serve as early, patient-centered biomarkers of clinical risk. By converting complex symptom data into interpretable and predictive metrics, this quantitative framework provides a practical pathway to integrate the patient voice into clinical decision-making and advance precision oncology. ClinicalTrials.gov registration: NCT03249090

Endometrial cancer (EC) exhibits one of the most striking racial disparities in oncology with black women disproportionately affected by aggressive high-grade subtypes that have poorer outcomes. While social and environmental factors undoubtedly contribute, the molecular underpinnings of these disparities remain critically understudied. To bridge this knowledge gap, we performed matched tumor-normal whole-genome sequencing and tumor transcriptome sequencing on 71 predominantly high-grade EC patient samples from an ancestrally diverse cohort of women recruited at a large hospital system in the New York metropolitan area. Our analysis characterized the germline and somatic mutation landscape, identifying ancestry-associated molecular differences. Notably, focal amplification of the EVI1 transcription factor (encoded at the MECOM locus) was significantly more frequent in African ancestry patients and associated with poorer clinical outcomes in an external validation cohort. Additionally transcriptome analysis revealed decreased CD8+ T cell infiltration with increasing African ancestry, suggesting tumor immune microenvironment differences with potential therapeutic implications. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=163 SRC="FIGDIR/small/721962v1_ufig1.gif" ALT="Figure 1"> View larger version (63K): org.highwire.dtl.DTLVardef@12125b9org.highwire.dtl.DTLVardef@133c787org.highwire.dtl.DTLVardef@707af0org.highwire.dtl.DTLVardef@97615c_HPS_FORMAT_FIGEXP M_FIG C_FIG HighlightsO_LIThis study represents the most ancestrally diverse whole-genome sequencing characterization of high-grade endometrial cancer, with 62% of patients of African ancestry. C_LIO_LIMECOM focal amplification preferentially targets the oncogenic short isoform (EVI1) and is more frequent in patients of African ancestry. C_LIO_LIAfrican ancestry is associated with reduced CD8+ T cell infiltration and differential activation of immune and metabolic pathways in copy-number high endometrial tumors. C_LI

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.

Head and neck squamous cell carcinoma (HNSC) is a prevalent malignancy associated with poor prognosis despite recent therapeutic advances. We hypothesized that a comprehensive understanding of the spatial heterogeneity and organization of the tumor microenvironment (TME) can substantially improve risk stratification and prediction of treatment response in HNSC. As spatial transcriptomics (ST) remains labor-intensive and costly, we developed HEiST (H&E-Inferred Spatial Transcriptomics), a deep learning framework that predicts spatially resolved gene expression profiles directly from routine hematoxylin and eosin (H&E)-stained histology slides. After rigorous validation across two independent external ST cohorts, we applied HEiST to infer spatial transcriptomes across 1,500 HNSC patient tumors spanning two publicly available datasets and two newly generated cohorts, one treated with concurrent chemoradiotherapy (CCRT) and one with immunotherapy. This large-scale analysis uncovered reproducible spatial clusters characterizing the HNSC TME, defining two distinct prognostic Spatiotypes, Immune-Exhausted and Immune-Activated, with significantly distinct survival outcomes. Critically, spatial cluster composition accurately predicts HPV status and yields treatment response predictors for both CCRT/radiotherapy and immunotherapy that outperform costly gene-expression and direct image-based approaches. Notably, the ST cluster-based predictor of immunotherapy response markedly surpasses the performance of commonly used FDA-approved biomarkers, including CPS, TPS, and their combination. To the best of our knowledge, this represents the first virtual spatial profiling effort and the most comprehensive large-scale spatial TME analysis in HNSC to date. HEiST thus introduces a scalable, low-cost, and spatially grounded biomarker discovery for precision oncology in HNSC.