Nature Cancer

The role of comprehensive genomic profiling for therapeutic decision-making is established in high-risk pediatric cancers, but its utility in rare and diagnostically challenging tumors is unclear. Here we report 123 non-high-risk patients enrolled in the Australian ZERO Childhood Cancer Program for diagnostic uncertainty, clinician request to address a specific molecular query, or other rare tumors. Comprehensive multi-omic profiling led to a change in diagnosis in 17.9% (22/123) of patients, with overall diagnostic utility in 35% (43/123). Molecular queries were resolved in 97.6% (40/41). Multi-omic results informed conventional management in 20.3% (25/123). Precision-guided therapy was recommended in 67.5% (83/123), and administered in 36.1% (30/83), with an objective response or prolonged (>6 months) stable disease in 88.9% of evaluable cases (16/18). Findings were confirmed in an independent cohort from the Canadian KiCS program (n=41). In conclusion, in rare and diagnostically challenging pediatric tumors, multi-omic profiling improved diagnostic accuracy and informed clinical management, supporting its integration into routine care.

Cancer outcomes vary widely between individual patients, each accumulating an irregular record of treatments, diagnoses, measurements, and complications. Current prognostic models reduce this complexity into a single snapshot, focus on narrow clinical settings, and rarely generalize across hospitals. Here we introduce Chronicle, an explainable transformer that learns from entire patient trajectories, predicts diverse clinical outcomes throughout the disease course while capturing both short- and long-term temporal dependencies. Trained on 53.7 million longitudinal data points from 51,711 patients spanning 67 cancer types, Chronicle operates natively on irregular data without imputation and jointly predicts eight endpoints within a flexible framework adaptable to additional outcomes. Chronicle outperformed cross-sectional models for overall survival prediction (C-index 0.84 vs 0.76-0.79), stratified patients more accurately than established prognostic systems, including TNM stage, and predicted seven adverse event and transfusion endpoints (AUC 0.80-0.92). Applied without retraining to 69,341 patients in Germany, Switzerland, and the United States, Chronicle generalized across healthcare systems and improved further with local fine-tuning. Integrated explainability traced each risk update to patient-specific clinical factors, revealing distinct temporal persistence of prognostic information, with relevance half-lives ranging from weeks for therapies to nearly one year for baseline characteristics. These findings demonstrate that learning from hospital-wide patient trajectories enables interpretable and continuously updated predictions, providing a scalable framework to support individualized treatment decisions.

The dynamic reprogramming of the bone marrow (BM) microenvironment during metastatic progression is poorly defined. Through single-cell profiling of BM samples spanning benign conditions, high-risk bone metastatic potential localized prostate cancer (HMP) and established bone metastatic prostate cancer (PCa), we uncovered a primary tumor-to-BM signaling axis that orchestrates niche formation through neutrophil-mediated remodeling. Tumor-associated neutrophils (TANs) emerge during the HMP stage, persist throughout metastatic progression, correlate with poor patient prognosis, and exhibit direct immunosuppressive activity that contributes to a BM environment permissive to metastatic colonization. Functional and genetic studies showed that tumor-associated systemic IGF1 reprogrammed BM neutrophils through IGF1R-MAPK/ERK-KLF4 signaling, with IL-1{beta}, reactive oxygen species and nitric oxide contributing to TAN-mediated T cell suppression. Tumor-cell Igf1 perturbation, IGF1R blockade and neutrophil-specific Igf1r deletion attenuated TAN-associated BM remodeling, and reduced subsequent bone metastatic progression. In bone-metastasis models, combined IGF1R and PD-1 blockade suppressed bone-metastatic progression. Translationally, our investigator-initiated clinical trial (NCT06866548) of teprotumumab plus tislelizumab in patients with established metastatic castration-resistant prostate cancer (mCRPC) provided initial safety data and exploratory clinical observations. Together, these findings identify IGF1R-dependent neutrophil reprogramming as a functionally important component of the evolving PCa bone metastatic microenvironment and provide a rationale for therapeutic targeting of IGF1R signaling together with immune checkpoint blockade.

Neoantigen immunogenicity prediction is fundamental to personalized cancer vaccines, tumor-infiltrating lymphocyte (TIL) therapy, and TCR-T cell engineering. Existing computational predictors rely primarily on in-vitro correlates of peptide presentation or models trained against assay-based reactivity, and they are typically validated within a single therapeutic setting. We reasoned that the most direct evidence of neoantigen immunogenicity is longitudinal in-vivo elimination: under immune checkpoint blockade (ICB), subclones bearing recognized neoantigens are selectively depleted over time. Here, we present the Neoantigen Elimination Model (NEMo), a two-compartment (CD8 and CD4) machine learning classifier trained on the in-vivo editing (IVE) of neoantigens across serially sequenced, ICB-treated tumors. By using mechanistically inspired NeoPrecis features designed to capture determinants of immunogenicity beyond MHC binding affinity, NEMo recovered assay-confirmed immunogenic neoantigens across four independent, unseen clinical settings -- pre-existing immunogenicity screening, personalized cancer vaccines, TIL therapy, and a radiotherapy +/- ICB ctDNA cohort -- and stratified progression-free survival more strongly than ELISPOT-confirmed reactivity. The editing signal further revealed an immune-evasion architecture in which oncogenic drivers and neoantigens restricted to lost or silenced HLA alleles are systematically spared from editing.

Many cancers evade immunity by converting neutrophils into polymorphonuclear myeloid-derived suppressor cells (PMN-MDSCs), but the chromatin mechanisms that maintain this pathological state remain unclear. Here we show that tumor-induced suppressive neutrophils depend on mSWI/SNF chromatin remodeling. Two orally bioavailable mSWI/SNF ATPase antagonists, a degrader and a catalytic inhibitor, produced immune-dependent tumor control across syngeneic models, including tumors resistant to PD-1 blockade, and enhanced checkpoint therapy without overt toxicity. Treatment rapidly depleted intratumoral PMN-MDSCs, restored functional CD8+ T cells, and blocked tumor explant-induced expansion and suppressive activity of mouse and human PMN-MDSCs. mSWI/SNF antagonism collapsed chromatin accessibility and PU.1/C/EBP{beta} occupancy at regulatory elements that sustain PMN-MDSC identity. Thus, suppressive neutrophils harbor a druggable chromatin vulnerability that can be exploited to restore cancer immunity.

Treatment toxicity constrains the use of effective cancer therapies, but its clinical and genetic determinants remain poorly defined. We developed a large language model-based approach to produce MSK-Tox, a pan-cancer resource capturing the incidence, temporality, and grade of toxicity to anti-cancer therapy across more than 50,000 patients. Analysis of six representative adverse events - pneumonitis, adrenal insufficiency, liver toxicity, colitis, hyperthyroidism and hypothyroidism - revealed distinct toxicity landscapes shaped by cancer type, treatment regimen, and clinical context. Pretreatment clinical features enabled individualized prediction of toxicity risk across adverse events, supporting risk assessment before therapy initiation. Beyond clinical predictors, we identified two modes of germline susceptibility to treatment toxicity: an organ-intrinsic mode, in which germline variation confers risk across systemic therapies, exemplified by a regulatory variant near FOXE1 associated with hypothyroidism; and an immune-mediated mode, confined to immune checkpoint inhibitor-treated patients, in which HLA-DRB1*15 was a major determinant of adrenal insufficiency. Notably, the same allele predisposes to multiple sclerosis in individuals without cancer, indicating that immune checkpoint inhibition unmasks a latent autoimmune predisposition. These findings provide an empirical basis for a new precision safety paradigm for predicting who will be harmed by a therapy on the same principles that guide prediction of therapeutic benefit.

Effective therapies for high-risk medulloblastoma (MB), particularly MYC-driven Group 3 (G3) MB, remain elusive due to limited druggable mutations, poor blood-brain barrier (BBB) penetration, and rapid resistance. We developed SINBA (Synergy Inference by Data-driven Network-Based Bayesian Analysis), a systems biology framework that computationally prioritizes synergistic, BBB-permeable drug combinations by identifying hidden drivers sustaining oncogenic programs. Integrating MB-specific networks, transcriptomic data, and drug-gene interactions, SINBA nominated 32 candidates, of which 19 were experimentally validated as synergistic. Through iterative prioritization and experimental refinement, the MEK inhibitor mirdametinib and p38 inhibitor regorafenib emerged as the top brain-penetrant pair, suppressing G3 MB progression and extending survival in xenograft and immunocompetent models, with efficacy enhanced by low-dose radiation. Single-cell analysis revealed selective targeting of developmental origins and immune reprogramming. These findings establish SINBA as a computationally assisted discovery framework for clinically actionable combinations in high-risk MB.

Across human malignancies, the ability of cancer cells to switch states (plasticity) drives progression, metastasis, and therapy resistance 1-3. Here we investigated the transcriptional and epigenetic basis of plasticity in small-cell lung cancer (SCLC), an aggressive, treatment-refractory cancer 4, 5. SCLC tumours display plasticity along a neuroendocrine (NE) to nonNE axis; however, the trajectories of lineage transition and the gene regulatory network underlying it, remain poorly defined 6. Using single-cell multi-omics, we resolved distinct transcriptional SCLC cell states, including a previously unrecognised Intermediate state comprised of low-identity immunogenic SCLC cells that serve as an obligatory route in NE to nonNE lineage transition. Using single-cell gene expression and chromatin accessibility data, we next developed a Boolean Bayes model for transcription factor networks (BoBa-T) to predict regulators of SCLC cell states. We validated RORB as a novel gatekeeper of the NE state that represses the nonNE identity in SCLC. RORB inactivation drives NE cells into the Intermediate state, upregulates immunogenic programs, and inhibits tumour growth in immunocompetent hosts. Our work identifies a plastic, immunogenic waypoint for SCLC lineage switching and reveals strategies to target it therapeutically.

PARP inhibitors (PARPi) drive efficacy in BRCA-mutant breast cancer (BC) via DNA damage-induced synthetic lethality and immune activation, supporting their combination with immune checkpoint blockade. In the TALAVE study, patients with advanced BRCA-mutant or wild-type (WT) HER2-negative BC received talazoparib followed by talazoparib plus avelumab. Only BRCA-mutant BC responded clinically. Serial multi-omic profiling revealed BRCA-dependent tumor-immune remodeling, including sustained H2AX-pTBK1 signaling with increased CD8+ T cells, tumor cell depletion, and enrichment of CD163+ macrophages. In contrast, BRCA-WT tumors remained compact and immunosuppressed with reduced T cells after therapy. PD-1+ T cells localized to CD4+-rich neighborhoods and correlated with longer progression-free survival in BRCA-mutant tumors. However, PD-L1+ cells were rapidly depleted or confined to immune-excluded regions, spatially segregating them from PD-1+ T cells, impairing effective checkpoint blockade. These findings suggest limited benefit of PD-1/PD-L1 blockade in augmenting PARPi activity and highlight the need for alternative strategies to sustain PARPi-induced immunity.

Circulating cell-free DNA (cfDNA) offers a minimally invasive lens into temporal tumor evolution. However, the accurate quantification of clonal composition from cfDNA remains challenging, particularly in low tumor fraction (TF) settings. Existing liquid biopsy deconvolution frameworks are frequently constrained by their reliance on bulk tissue references, simplified copy-number assumptions, and incomplete representations of clonal architecture, which collectively compromise sensitivity and bias evolutionary inferences. To address these limitations, we developed cfClone, a Bayesian framework that integrates single-cell whole-genome sequencing (scWGS) derived clonal structures with cfDNA whole-genome sequencing data to enable high-resolution, tissue-informed clonal tracking. Notably, while cfClone inherently leverages genomic instability, we demonstrate that cfClone achieves accurate TF estimates and circulating tumor DNA (ctDNA) detection even in malignancies with limited copy-number variant (CNV) burden. We validate cfClone in low and high CNV burden cases using simulated data derived from real patient data, establishing sensitive detection thresholds across a range of aneuploidy levels. By jointly modeling local copy-number alterations and allele-specific signals via Bayesian model selection and Markov chain Monte Carlo (MCMC) sampling, the algorithm yields uncertainty-aware estimates of clonal prevalence and TF. Applied to longitudinal clinical cohorts, cfClone reconstructs real-time evolutionary trajectories and uncovers clonal selection driving therapeutic resistance, including the de novo detection of emergent clonal populations. Github link: https://github.com/Roth-Lab/cfclone

Computational tools for phylogenetic inference of early tumor evolution in real time are currently lacking. We present LACHESIS, a standardized R package and Shiny app that times early and most recent common ancestors of individual tumors from whole-genome sequencing data. LACHESIS automates mutational signature-aware molecular clock modeling for trajectory reconstruction and evolution-based risk stratification. We validate its utility for childhood and adult malignancies, providing a broadly applicable pan-cancer workflow.

Pancreatic cancer (PC) is largely refractory to immune checkpoint blockade (ICB), although homologous recombination-deficient (HRD) tumors may derive benefit. In the POLAR trial of maintenance pembrolizumab plus olaparib after platinum-based chemotherapy for metastatic PC, responses remained heterogeneous. To define determinants of productive antitumor immunity, we integrated longitudinal blood TCR sequencing with tumor single-cell and spatial profiling. Durable benefit was associated with rare tumor-infiltrating, peripherally expanding (TIE) CD8+ T cell clonotypes, a subset of which were functionally neoantigen-reactive. TIE patients showed markedly prolonged survival beyond established genomic and immune biomarkers. Conversely, resistance was associated with spatial T cell exclusion, myCAF-rich stromal remodeling, basal-like/KRAS-associated malignant-cell programs, and expansion of CTLA4 regulatory T cells linked to local immunosuppressive remodeling. These findings define a clonotype-resolved framework for immune monitoring and identify complementary stromal, tumor-intrinsic, and regulatory immune barriers that may guide rational combination immunotherapy in PC.

The Oncotype DX (ODX) assay guides adjuvant chemotherapy in ER+/HER2- breast cancer but overlooks drivers from the tumour stroma, leaving some low-risk and chemotherapy-spared patients vulnerable to rapid relapse. Here, we show that perivascular mural {beta}3-integrin protein expression predicts early (3-year) relapse in ODX low-risk patients (HR = 15.80, P = 0.010), from which we derived a translatable 6-gene stromal signature, termed Stroma6. High Stroma6 expression predicted early recurrence in the ODX low-risk discovery cohort, with zero early relapses in the low-expression group. External validation in the METABRIC dataset confirmed that Stroma6 independently predicts rapid recurrence in ODX low-risk patients (HR = 11.68, P = 0.002). Furthermore, Stroma6 utility extends to clinical chemoresistance, independently predicting early events in a pan-subtype cohort with residual disease following neoadjuvant chemotherapy (HR = 2.29, P = 0.003). Ultimately, Stroma6 is a robust prognostic signature identifying patients at high risk of early recurrence across diverse clinical contexts.

Background: Circulating tumor DNA (ctDNA) is an emerging biomarker of treatment response and recurrence risk, while residual cancer burden (RCB) after neoadjuvant treatment (NAT) is a well-established risk factor for distant recurrence. Here, we examined the association between high ctDNA concentration at diagnosis and risk of distant recurrence after neoadjuvant treatment (NAT), in the context of RCB. Methods: The study included 712 patients with high-risk breast cancer in the neoadjuvant I-SPY2 trial. Tumor-informed ctDNA test results at diagnosis were used to stratify patients into ctDNA-negative and ctDNA-positive groups. For this analysis, the ctDNA-positive group was divided into tertiles (low, intermediate, high) based on ctDNA concentration reported as mean tumor molecules per mL [MTM/mL] of plasma. Correlations between MTM/mL at diagnosis and ctDNA dynamics during NAT, residual cancer burden (RCB), and distant recurrence-free survival (DRFS) were examined across all subtypes. Results: In all subtypes, high ctDNA concentration at diagnosis was associated with worse DRFS, whereas low ctDNA concentration or ctDNA-negative status was associated with improved DRFS, even with high tumor burden after NAT (RCB-II/RCB-III). We also found that patients with high ctDNA concentration, regardless of subtype, were less likely to experience early ctDNA clearance; however, those who did had a significantly higher likelihood of achieving a favorable response (RCB-0/RCB-I) than those with late or no ctDNA clearance. Furthermore, across all subtypes, patients with early ctDNA clearance, including those with substantial residual cancer (RCB-II/RCB-III) after NAT, had improved DRFS, irrespective of the ctDNA concentration at diagnosis. Conclusions: Across all subtypes, pathologic response and ctDNA clearance reduce the risk of distant recurrence associated with high ctDNA concentration at diagnosis. ctDNA concentration at diagnosis and ctDNA clearance dynamics during NAT may facilitate the prediction of treatment response and further stratify the risk of metastatic recurrence in non-responders.

A central challenge in oncology is delivering potent drugs to tumors without harming healthy tissues. We therefore asked whether tumor cells themselves could be genetically programmed to produce therapeutic molecules directly within the tumor. We present LENTRA (LENtiviral Tumor-Reprogramming Activator), an antigen-targeted lentiviral platform that enables selective transduction and genetic modification of tumor cells in vivo. Following intravenous administration, LENTRA achieves tumor restricted expression of immune checkpoint inhibitors, cytokines, or therapeutic enzymes in multiple murine solid tumor models. Local production of anti CTLA 4 or IL 12 triggered potent antitumor responses while avoiding systemic toxicities. Tumor restricted secretion of sialidase enhanced dendritic cell and T cell infiltration, and reprogrammed tumor associated macrophages toward an immunostimulatory phenotype, resulting in tumor regression and durable immune memory, including in tumors with heterogeneous antigen expression. In patient derived tumor explants, LENTRA mediated efficient transduction, immune remodeling, and reduced tumor viability. By engineering tumors to produce therapeutic proteins, LENTRA converts resistant tumors into autonomous therapeutic sources and active immune hubs, achieving local TME reprogramming and systemic antitumor immunity with an improved safety profile.

Precision oncology seeks to match each tumor with the most effective anti-cancer therapy. Advances in pharmacogenomics and machine learning enabled drug response prediction models with strong performance in cancer cell lines. Nonetheless, patient-centric evaluation of drug prioritization and systematic assessment of model generalization in patient-derived systems across cancer types remain largely absent. Here we introduce a translational framework combining patient-centric benchmarking with a pan-cancer pharmacogenomic atlas of patient-derived organoids, together with NELLY, a deep learning model integrating transcriptomic and chemical information to predict drug response and prioritize therapies. NELLY outperformed existing methods for patient-specific drug prioritization across cancer cell lines and patient-derived organoids, including under out-of-distribution evaluation. Its dynamic weighting mechanism provided patient-specific gene attributions, offering a route to connect predicted drug response to molecular programs associated with drug resistance. Our results support NELLY as a promising framework for translationally relevant and interpretable drug response prediction in precision oncology.

Brain tumor recurrence remains the leading cause of mortality in neuro-oncology, and there is a lack of preclinical models replicating the clinical cycle of surgical resection and relapse. To bridge this gap, we developed a novel microneurosurgical survival platform in mice using the NICO Myriad system. We orthotopically implanted pediatric medulloblastoma cells into the mouse cerebral cortex or cerebellum, followed by longitudinal microneurosurgical resection. Bioluminescence imaging and gross fluorescence verified successful resection, local and distal recurrence and metastasis. Comparative bulk RNA sequencing revealed extensive stage-specific transcriptomic divergence alongside conserved core gene sets (2,702 genes in the cerebral cortex and 3,240 genes in the cerebellum) across primary, locally recurrent, and distally recurrent stages. Pathway analysis shows activation of cellular growth, second messenger signaling, and cellular stress adaptation pathways. Targeted qPCR validation demonstrated that post-surgical relapse is driven by a distinct molecular program: recurrent tumors downregulate primary developmental drivers (PTCH1, MYCBP2), canonical suppressors (FOS, PTEN), and chromatin regulators (HDAC2), while selectively upregulating post-transcriptional machinery (RBM8A), endosomal trafficking regulators (RAB5C), acetyltransferases (NAA15), and the m6A RNA demethylase ALKBH5. These findings reveal that medulloblastoma shifts from a primary oncogenic state toward post-transcriptional and transcriptomic survival mechanisms following surgery. Identifying persistent candidates within this conserved core framework provides a roadmap for next-generation precision immunotherapies.

Neoadjuvant immunotherapy has transformed cancer treatment, yet the spatial molecular architecture governing response and resistance across distinct immune checkpoint blockade (ICB) regimens remains poorly defined. We assembled the largest neoadjuvant ICB (NICB) spatial multi-omics cohort to date, profiling over 112 million cells at single-cell resolution across three melanoma ICB regimens using MERFISH spatial transcriptomics, multiplexed immunofluorescence, and scRNA-sequencing. These analyses revealed the full multicellular spatial architecture of the NICB tumor microenvironment, including mature TLS with germinal centers, TCF7+ stem-like T cells, myeloid cells organized into spatially distinct cellular neighborhoods with unique intercellular signaling circuits, and CCL19/CCL21-expressing fibroblasts as a previously unrecognized stromal scaffold sustaining these immune hubs. We developed three purpose-built computational tools that together enabled comprehensive quantification of this microenvironment for the first time: SCIRA for whole-slide single-cell receptor-ligand quantification, GCSCAN for molecularly grounded TLS and germinal center structural delineation, and PathNet-TLS for automated TLS detection on H&E images. Applying these tools across the cohort, we defined the immune and stromal composition and cellular neighborhood organization distinguishing responders from non-responders. We also quantified cell-cell interactions and regimen-specific immune architectures, including a markedly stronger mature TLS/germinal center response with IPI-NIVO than NIVO-RELA. Importantly, GCSCAN-quantified TLS and germinal center density each stratified disease-free survival, with responders that lack germinal centers having an elevated risk of relapse. Open-top light-sheet imaging and CODA-based 3D reconstruction further uncovered interconnected germinal center-TLS tunnels invisible to standard 2D histopathology. These findings establish a discovery-to-tool paradigm linking single-cell tumor microenvironment interrogation to clinically deployable computational pathology for biomarker-driven NICB assessment across cancer types.

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.

Discovering effective drug combinations requires testing many dose combinations across a diverse panel of tumor models. This approach is limited in rare cancers by a scarcity of cell lines and representative high-throughput models that would make exhaustive screening feasible and predictive. Patient-derived xenograft (PDX) models are genomically representative but too low-throughput for this purpose. Culturing PDX cells ex vivo in three-dimensional (3D) matrices offers a genomically representative and clinically relevant platform for preclinical drug testing, capturing the microenvironmental cues that shape in vivo drug response while requiring only limited tissue per assay. Toward this end, we designed and validated an experimental-computational framework, "ex vivo assessment of combination therapies" (EXACT), to enable drug combination discovery in rare tumors. Using PDX models of malignant peripheral nerve sheath tumors (MPNST), we built a platform to culture PDX cells ex vivo over multiple days, monitoring drug sensitivity and measuring transcriptomic responses to treatment. Computational analysis of these transcriptomic responses then identifies which compensatory pathway creates a unique vulnerability to a second drug. EXACT thus offers a biologically informed, scalable approach for prioritizing drug combinations in rare tumors, nominating drugs alongside biological rationale. Using this methodology, we identified a MEK inhibitor plus HDAC inhibitor combination with enhanced activity in vitro and in vivo, forming the basis of an active clinical trial. This platform could be adapted for real-time use with primary patient specimens, enabling personalized therapeutic discovery. SignificanceEXACT integrates PDX-derived 3D drug screening with biologically informed computational analysis to identify and explain effective combinations, providing a scalable strategy for therapeutic discovery in rare cancers such as MPNST.