Nature Cancer

Follicular lymphoma (FL) is an incurable, prototypical relapse-remitting cancer, implying the existence of therapy-persistent cells that survive frontline treatment and seed disease recurrence1-3. However, these persister cells remain difficult to study directly in patients because immediate post-treatment sampling is ethically and practically challenging. Using a genetically defined mouse model that allows sampling of persistent cells immediately after frontline R-CHOP therapy, we prospectively isolate and functionally define relapse-founding cancer persister cells (CPC). The CPC is an IgM memory-like B-cell with high germinal center re-entry capacity. This state represents a discrete component of a heterogeneous residual pool indicating that residual disease is polytypic and that relapse potential may depend on which cells persist rather than on residual tumour burden alone. By integrating mouse CPC with human FL datasets, we show that an analogous transcriptional programme is detectable at diagnosis and is enriched in patients with inferior clinical outcome across independent cohorts4,5. These findings support the concept that relapse risk is linked to a conserved, genotype-agnostic CPC programme present before therapy. To explore therapeutic vulnerabilities, we developed a scalable in-vitro platform that models the CPC-like state and used it to identify sensitivity to histone deacetylase inhibition. Romidepsin and panobinostat killed CPC-like cells in-vitro, and decreased therapy-persistent cells after R-CHOP treatment in-vivo and in patient-derived organoids. Together, these data define a tractable CPC state in FL, with a validated clinical readout and an immediately testable therapeutic entry point, opening CPC-directed strategies for durable FL control.

Most high-dimensional studies of tumor-immune interactions focus on metastatic models, limiting insight into how immune remodeling in primary tumors shapes metastatic competence. Here, integrating single-cell RNA sequencing, CyTOF, and functional studies across metastatic (4T1) and non-invasive (EMT6) triple-negative breast cancer (TNBC) murine models, we define tumor state-specific immune programs that distinguish metastatic competence. Tumors with metastatic capacity uniquely drive early bone marrow expansion of CXCR2 neutrophils, which infiltrate primary tumors acquiring a CXCL2-producing phenotype that promotes EMT-associated cancer stem cell (CSC) plasticity. This program depends on TGF-{beta}/CEBPD-mediated induction of S100A9. Elevated CXCL2, together with G-CSF, establishes a feed-forward circuit that drives systemic neutrophil mobilization and recruitment to distant organs, where neutrophil-derived S100A8/A9 (calprotectin) promotes MET-driven CSC outgrowth and metastatic colonization. Clinically, gene signatures associated with CXCR2 neutrophils predict poor survival in TNBC patients, whereas monocyte/macrophage (CX3CR1) and T cell activation signatures correlate with improved outcomes. S100A9 ablation disrupts this cascade and enhances immunotherapy responsiveness, defining a TGF-{beta}/S100A9/CXCR2 axis linking immune remodeling, CSC plasticity and metastasis. HighlightsO_LIMetastatic TNBC engages a TGF-{beta}/C/EBP{delta}/S100A9 axis that expands CXCR2 neutrophils C_LIO_LINon-invasive EMT6 tumors retain a CX3CR1 monocyte/macrophage and T-cell landscape C_LIO_LICXCR2+ neutrophils in pre-metastatic niches suppress T cell response while promoting tumor cell proliferation C_LIO_LIS100A9 loss redirects myelopoiesis and potentiates anti-PD-L1 in TNBC models C_LI In BriefAlkan et al. dissect how tumor state programs the myeloid compartment in TNBC. Metastatic 4T1 tumors uniquely engage a TGF-{beta}/C/EBP{delta}/S100A9 axis driving CXCR2 neutrophil expansion and CXCL2/G-CSF-dependent systemic mobilization, coupling immune remodeling to EMT/MET cancer-stem-cell plasticity, while S100A9 loss restores CX3CR1 myeloid identity and unlocks checkpoint-inhibitor responsiveness.

Metastatic castration-resistant prostate cancer (mCRPC) remains lethal as adaptive resistance to standard-of-care therapy develops, often driven by AR splice variants alongside transcriptional and translational reprogramming. To identify strategies capable of overcoming these mechanisms, we performed an unbiased high-throughput screen of 2,480 mechanistically annotated compounds across advanced prostate cancer models. Exportin-1 (XPO1)-mediated nuclear export emerged as a critical dependency, and matrix-based combination screening uncovered robust synergy between inhibitors of XPO1 and the translation initiation factor EIF4A1. Dual inhibition induced coordinated disruption of oncogenic protein networks, including AR/AR-V7, triggering apoptosis and suppressing cell-cycle and metabolic programs. These effects extended to genetically diverse patient-derived organoids and in vivo xenografts at low doses, approximately 8-fold (Eltanexor) and 12-fold (Zotatifin) below established human single-agent regimens. Together, these findings reveal concurrent control of nuclear export and protein translation as a therapeutic vulnerability in mCRPC, providing a strong rationale for clinical evaluation of XPO1-EIF4A1 co-inhibition to overcome AR-driven resistance. STATEMENT OF SIGNIFICANCEUnbiased combinatorial screening reveals co-inhibition of nuclear export and translation initiation as a vulnerability in metastatic castration-resistant prostate cancer. Dual targeting of XPO1 and EIF4A1 drives synergistic collapse of oncogenic protein networks, including AR/AR-V7 signaling, to overcome key resistance mechanisms and induce potent antitumor responses across heterogeneous models. Notably, these effects are achieved at substantially reduced doses using clinically tractable agents, defining a mechanistically grounded therapeutic strategy poised for rapid clinical translation.

Genomics-guided precision oncology has improved survival in cancer entities with actionable mutations but cannot capture oncogenic signaling that manifests at the protein level. Here, we report a prospective, real-world pan-cancer study profiling proteomes and phosphoproteomes of 1,998 tumor samples from adults and children with rare or advanced cancers enrolled in the German precision oncology programs DKFZ/NCT/DKTK MASTER, CATCH and INFORM and their molecular tumor boards (MTBs). We developed tumor proteome activity status (TOPAS) scores for 46 clinically relevant kinases, an immune activity score capturing antigen presentation and T-cell activation and identified therapeutically targetable cell-surface proteins for 94% of patients. These readouts enhance MTB recommendations by exposing actionable non-genomic kinase activity, refining interpretation of oncogenic genome alterations, and highlighting cell-surface treatment options. Three proof-of-concept analyses indicate clinical utility including kinase activity-stratified pazopanib response in sarcoma, immune activity score-tracked checkpoint-inhibitor outcomes pan-cancer, and a phosphoproteomic biomarker distinguishing EGFR-inhibitor response in chordoma.

Activating HER2 mutations are significantly enriched in both primary and metastatic invasive lobular breast cancer (ILC), with large public datasets of primary breast tumors linking them to a worse prognosis in ILC. Despite their oncogenic role, no FDA-approved therapies currently target HER2-mutant breast cancers. While the HER2-directed antibody-drug conjugate (ADC) trastuzumab deruxtecan (T-DXd) has shown efficacy in HER2-mutant non-small cell lung cancer, its activity in HER2-mutant ILC remains unknown. Using the Caris real-world database, one of the largest cohorts with survival data in advanced breast cancers, we confirmed that HER2 mutations are more prevalent in advanced ILC than in invasive breast cancer of no special type (NST) tumors, are associated with worse survival in both histologies, yet predict improved response to T-DXd across subtypes, highlighting the need for mutation-directed, histology-informed therapies. Using endogenous HER2-mutant ILC cell lines (UACC3133-S310F, BCK4-L755S) and CRISPR-engineered isogenic ILC models with clinically relevant HER2 mutations (S310F, V777L), we found these mutations drive HER2/HER3 hyperactivation and downstream signaling, conferring increased sensitivity to HER2 tyrosine kinase inhibitors (TKIs) and T-DXd. Mechanistically, HER2 mutants showed enhanced receptor ubiquitination, internalization, and lysosomal degradation upon T-DXd treatment, explaining the observed drug sensitivity. While combining T-DXd with neratinib or the HSP90 inhibitor ganetespib yielded synergistic effects in long-term growth assays, accompanied by increased HER2 ubiquitination, the concurrent hyperactivation of HER3 in HER2-mutant cells suggested that co-targeting HER3 could provide an effective alternative strategy. Accordingly, HER2-mutant ILC exhibited enhanced sensitivity to the HER3-directed ADC patritumab deruxtecan (P-DXd) or LJM716, a HER3-targeting antibody. We further uncovered a previously unrecognized mechanism of P-DXd beyond HER3 ligand blockade and payload delivery: P-DXd promotes HER2/HER3 association, increases HER2 ubiquitination, and enhances T-DXd internalization, resulting in potent synergy with T-DXd. Mechanistically, we identified HER3 extracellular domains I and II as essential for P-DXd binding and for mediating P-DXd-induced HER2/HER3 association, establishing a structural basis for this activity. In vivo, both T-DXd and P-DXd suppressed UACC3133 and BCK4 xenograft growth, with combination therapy trending toward greater efficacy and prevented regrowth of tumors. Extending these findings beyond HER2-mutant ILC, combination treatment with T-DXd and P-DXd demonstrated synergistic activity across multiple breast cancer models, including (i) HER2-amplified NST patient-derived organoids (PDOs) harboring hotspot HER2 mutations, (ii) HER2-wild-type NST PDOs with clinically intrinsic or acquired T-DXd resistance, and (iii) isogenic HER2-mutant ILC PDOs with experimentally induced resistance after prolonged T-DXd exposure. Collectively, these findings support HER2 as an actionable target in HER2-mutant ILC and position T-DXd-based regimens, particularly in combination with HER3 inhibition, as a promising therapeutic strategy for this underserved patient population.

Homologous recombination deficiency (HRD) is an actionable vulnerability found in a substantial fraction of human cancers, yet current HRD-directed therapies are limited by toxicity, incomplete responses, and acquired resistance. Many DNA-damaging agents were developed before DNA repair biomarkers were available, suggesting that abandoned agents may harbor previously unrecognized genotype-selective activity. Here, through a focused screen of DNA-damaging agents in isogenic homologous recombination-proficient and -deficient models, we identify CB1954, a decades-old nitrobenzamide aziridine prodrug, as highly selective for BRCA2-deficient tumor cells. CB1954 forms DNA interstrand crosslinks independent of HR status, but selectively induces DNA-damage signaling, apoptosis, and loss of clonogenic survival in HR-deficient cells. Targeted DDR CRISPR screening and isogenic validation define a distinct repair dependency for the Fanconi anemia and homologous recombination pathways, with limited dependence on mismatch repair or nucleotide excision repair. Genetic and pharmacologic perturbation of NQO2, the bioactivating enzyme for CB1954, reveals a bifurcated mechanism in which NQO2-dependent activation selectively contributes to HRD cytotoxicity, while aziridine-dependent lesions likely account for residual activity in HR-proficient cells. CB1954 exhibits favorable preclinical pharmacokinetic properties and genotype-dependent antitumor activity in BRCA2-deficient xenografts. These findings reposition CB1954 as a historically overlooked HRD-selective agent and demonstrate that biomarker-guided profiling of DNA-damaging agents can uncover new opportunities for precision oncology.

Drug-response measurements across pre-clinical pharmacogenomic studies remain poorly correlated, which limits biomarker discovery, precision oncology, and predictive modelling. The drivers of this inconsistency have been debated but not yet resolved. By integrating 15 pharmacogenomic studies encompassing 760 small-molecule compounds, 1,111 cell models, and 9.8 million dose-response measurements, we demonstrate that dose-response metric is the strongest driver of inconsistency, followed by experimental factors, such as treatment duration, plate format, and viability readout; in contrast, cell line molecular features contribute only minimally to reproducibility. Among drug classes, hormone therapies and PARP inhibitors show the highest concordance, whereas antimetabolites, topoisomerase inhibitors, and mitotic inhibitors exhibit substantial response variability across studies. To improve consistency, we developed a Drug Response Score (DRS), a proximity-weighted measure that emphasize pharmacologically informative concentrations near IC50, and we demonstrate in systematic benchmarking how DRS markedly improved cross-dataset concordance. Applications to patient-derived neuroblastoma organoids and leukemia patients primary cells demonstrate that DRS improves replicate-level consistency in patients drug-response profiles. To improve reproducible pharmacogenomic studies, we make openly available an integrated Drug Response Resource (iDRR, https://aittokallio.group/iDRR/), a standardized 15-dataset portal that supports robust biomarker discovery and cross-study benchmarking.

Clinical poly (ADP)-ribose polymerase (PARP) inhibitors (PARPi) are limited by toxicities associated with inhibition of multiple PARP family proteins and acquired resistance. As PARP1-specific inhibitors, like saruparib (AZD5305), move toward standard-of-care status for BRCA and HR-deficient cancers replacing less specific PARPi, defining mechanisms of intrinsic and acquired resistance is essential for developing effective treatment strategies. Here, we established 5 saruparib-resistant (SR) cell lines from BRCA1-deficient MDA-MB-436 triple negative breast cancer (TNBC) cells using a selection strategy of high-level dosing consistent with clinical exposure, yielding models that are >1,000-fold resistant to saruparib. Whole genome sequencing identified PARP1 catalytic domain mutations in all SR cell lines, and in vitro reconstitution of these PARP1 mutants confirmed them as drivers of saruparib resistance, in contrast to HR restoration as observed in the case of less-selective PARPi. PARP1 mutations also induce altered saruparib-dependent PARP1 trapping and PARylation inhibition. While these mutations render cells highly resistant to saruparib, differential sensitivity to other PARPi was observed and SR cell lines retain, and in some cases, increase, sensitivity to alternative clinical PARPi and DNA damage response (DDR)-targeted therapeutics. Our findings demonstrate that high-intensity selection pressure favors target-site mutation over pathway restoration as a primary escape mechanism from PARP1-selective inhibition. This study provides a first-in-class characterization of saruparib resistance and maps a clear therapeutic path forward. By identifying these specific PARP1 mutations and their collateral DDR vulnerabilities, we provide the molecular framework necessary to monitor and treat patients who progress on next-generation PARP1-selective inhibitors.

Rectal cancers are often treated with neoadjuvant chemoradiotherapy (CRT), yet 85% of patients do not achieve a pathological complete response. To identify the molecular determinants of CRT response, we profiled the single-cell signalling, DNA-damage, cell-cycle, apoptotic, and cell-fate responses of 2,769 patient-derived organoid cultures treated with CRT, cancer-associated fibroblasts (CAFs), and signal-rewiring agents. We find that CRT response is determined by stem cell-fate. CRT triggers comparable DNA-damage in isogenic proliferative (proCSC) and revival (revCSC) colonic stem cells, but proCSC retain damage and die whereas revCSC resolve damage and persist. Both CRT and CAFs drive proCSC to a common treatment-resistant revCSC fate and high revCSC predicts worse survival in patients. Pharmacologically constraining stem-cell plasticity increases CRT sensitivity, and Spatial Perturbation of ARrayed Tumour Assembloids (SPARTA) confirms YAP/TEAD inhibition improves chemotherapy responses in human stromal-tumour models. These results suggest that cancer cell-fate, not genotoxic damage itself, ultimately governs response to standard-of-care chemoradiotherapy. HIGHLIGHTSO_LIRectal cancer stem cell-fate determines chemoradiotherapy-induced apoptosis C_LIO_LIproCSCs retain DNA-damage and die, whereas revCSCs repair damage and persist C_LIO_LICAFs and chemoradiotherapy converge on a common chemo-radioresistant revCSC state C_LIO_LISPARTA reveals TEAD inhibition blocks DNA-repair persisters in stromal assembloids C_LI

Transcriptional addiction to the androgen receptor (AR) underlies metastatic castration-resistant prostate cancer (mCRPC), where AR maintains oncogenic enhancer programs through dynamic, domain-specific interactions with the lysine acetyltransferases p300/CBP and associated cofactors. Here, we describe a mechanistically distinct therapeutic modality, Domain-ALTeration Chimeras (DALTACs), designed to rewire endogenous protein complexes by enforcing non-native domain-domain interactions rather than degrading or inhibiting individual components. Our first-in-class molecule, AR-p300/CBP DALTAC-1, induces a synthetic proximity between the AR ligand-binding domain and the p300/CBP bromodomain, thereby misconfiguring the native AR-p300/CBP interface and locking the complex into a non-productive, transcriptionally inert state. DALTAC-1 triggers a profound "super-inhibitory" effect, suppressing AR-driven transcription and proliferation more potently than combined AR and p300/CBP inhibition. Mechanistically, DALTAC-1 reprograms the substrate specificity of p300/CBP, extinguishing the enhancer-associated histone mark H2B N-terminal acetylation (H2BNTac) while inducing neomorphic acetylation of AR and SRC2/3, culminating in collapse of the AR neo-enhanceosome. Chromatin profiling revealed widespread redistribution of AR and p300 toward canonical palindromic AREs, coupled with attenuation of ERG/BRD4 recruitment and a near complete loss of histone H2BNTac acetylation and RNA polymerase II loading at oncogenic AR/ERG neo-enhancers. Strikingly, DALTAC-1 exhibits exquisite lineage selectivity, displaying potent activity in AR-positive prostate cancer cells and patient-derived organoids while sparing AR-negative or non-prostate lineages. In multiple in vivo models, including castration-resistant and patient-derived xenograft tumors, DALTAC-1 induces deep and durable tumor regressions with favorable tolerability. Together, these findings establish DALTACs as a broadly applicable strategy to rewire disease-defining protein complexes by altering their domain topology, expanding the conceptual and therapeutic landscape of induced proximity agents. The precision and lineage-selective action of DALTAC-1 highlight its strong translational potential for treating AR-driven prostate cancer.

Clinical-grade RAS inhibitors raise an unresolved question as to whether KRAS-alleles impose constraints on adaptive resistance that can be exploited therapeutically. Using daraxonrasib (RMC-6236), a multi-selective RAS(ON) inhibitor, we compared resistance mechanisms between KRASG12D and KRASG12R, alleles with fundamentally different RAS network dynamics. Daraxonrasib inhibited KRASMUT primarily through steric occlusion of effector binding, while engaging RASWT only modestly ([~]20%). KRASG12R is marked by its inability to transactivate RASWT, and it was observed that daraxonrasib resistant KRASG12R PDAC cells utilize EGFR/RASWT-GTP signaling as the dominant adaptive route. In contrast, KRASG12D resistance arose through retained KRASG12D-GTP signaling, with a decrease of cyclophilin A (CypA) protein, the binding partner required for daraxonrasib activity. The shift from KRASG12R dependence to the EGFR/RASWT conferred sensitivity to trametinib. We confirmed this clinically: a KRASG12R PDAC patient who progressed after 10 months on daraxonrasib showed intratumoral EGFR/RASWT activation, and rapid 3D-bioprinted patient-derived toroid modeling predicted sensitivity to trametinib-based combination therapy. Given the aggressive disease trajectory and lack of response to the two immediately preceding lines of therapy, sixth-line trametinib-based combination therapy achieved approximately 5 months of disease control. This patient ultimately achieved 40 months of overall survival, far exceeding the 8-12 month median for metastatic PDAC. Collectively, these data establish a framework in which allele-specific RAS network topology dictates the adaptive resistance landscape, enabling rational selection of targeted therapies with meaningful clinical benefit in metastatic PDAC. STATEMENT OF SIGNIFICANCEDaraxonrasib resistance mechanisms have allele-specific routes: CypA becomes downregulated in KRASG12D and reliance on EGFR/RASWT in KRASG12R. Rapid patient-derived toroids identified sixth-line targeted therapy strategies with an overall survival of 40 months.

Oxaliplatin is a common component of various chemotherapeutic regimens for the treatment of gastrointestinal cancers. However, the majority of patients exhibit resistance to oxaliplatin-based therapy. Here, we integrated knockout and transcription-activation CRISPR screens in patient-derived gastric cancer organoids (GC PDOs) to comprehensively profile major genetic and transcriptomic changes observed over the course of resistance acquisition. Our screens identified UBP1, a transcription factor frequently lost in GC, as a critical determinant of oxaliplatin resistance development. Leveraging a large GC organoid biobank from a co-clinical trial and primary tumor omics data, we reveal that downregulation of specifically MYC-driven ribosome biogenesis drives oxaliplatin resistance, highlighting the drugs role as a ribosome biogenesis stressor. Mechanistically, UBP1 loss reduced expression of its direct target, MAX, a MYC cofactor, leading to downregulation of ribosome biogenesis and protection against nucleolar stress. Crucially, we discover that such downregulation is inevitably followed by a compensatory reliance on translation initiation, making it a therapeutic vulnerability in oxaliplatin-resistant tumors. Consequently, the resistance could be overcome by a synergistic action of the translation initiation repressor 4EGI, and the effect was also maintained in PDO that acquired resistance in vivo under clinically relevant conditions. Our data uncover a common marker of oxaliplatin resistance and identify a novel therapeutic strategy to reverse resistance to one of the most frequently used anticancer drugs.

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.

Longitudinal circulating tumor DNA (ctDNA) measurements offer a noninvasive means to monitor treatment response, but clinical trial data present substantial methodological challenges due to high-dimensional short longitudinal ctDNA sequences and limited sample sizes. We introduce SHERLOC, a deep learning framework specifically designed for survival analysis using longitudinal on-treatment ctDNA data, which integrates shared temporal representations of gene-level variant allele frequencies, feature-specific temporal trajectories of panel-level ctDNA biomarkers, and survival-aware genomic representations pre-trained on a large pan-cancer tissue-biopsy dataset (MSK-CHORD), within an interpretable Cox proportional hazards framework. Benchmarked against diverse statistical, ensemble, and deep learning approaches in a non-small-cell lung cancer cohort from the phase III IMpower150 trial, SHERLOC consistently achieved superior survival discrimination and calibration, while remaining interpretable and robust to reductions in the number of available longitudinal liquid biopsy time points per patient. The resulting ctDNA-based risk score provided prognostic information both independent of and complementary to standard radiographic response assessments, and enabled patient stratification within homogeneous RECIST response groups--highlighting its potential as an early, non-invasive decision-support tool to guide treatment adaptation and patient management.

The epidemiological surge of early-onset colorectal cancer (EOCRC) is characterized by accelerated biological kinetics and disproportionately high rates of systemic relapse following curative-intent surgery. Because standard anatomical staging (TNM) lacks the resolution to accurately capture the intrinsic regenerative capacity of microscopic residual disease, we investigated the transcriptomic architecture of post-surgical failure in a strictly defined, curative-intent clinical pan-cohort. Unbiased transcriptomic profiling of the localized (M0) discovery sub-cohort identified IGF2 as the most significantly upregulated correlate of metachronous relapse. High-resolution isoform analysis revealed that this transcriptional output is predominantly driven by the embryonic (P4) and placental (P5) promoters. Systematic allele-specific expression (ASE) analysis supported widespread biallelic IGF2 expression consistent with relaxation of imprinting-domain control. This signal was not restricted to relapsing tumors, suggesting a recurrence-independent oncofetal baseline across the EOCRC spectrum. Because this foundational epigenetic unlocking is functionally insufficient on its own to execute systemic metastasis, we distilled the additional transcriptional plasticity required for dissemination into an internally derived and bootstrap-stabilized 5-gene recurrence-risk module Multivariable analysis across the combined pan-cohort supported an independent association between the high-risk module and systemic relapse (p < 0.001), capturing prognostic dimensions completely unresolved by classical pathological covariates and baseline staging. Ultimately, our findings reframe EOCRC aggressiveness as the product of a dual-hit architecture. This framework resolves the clinical paradox of widespread IGF2 LOI co-existing with heterogeneous outcomes, offering a biologically grounded basis for molecular risk stratification beyond anatomical boundaries.

Combination targeted therapy with BRAF/MEK inhibitors and immune therapy show promising therapeutic outcomes in melanoma; however, the development of drug resistance still represents a formidable challenge. Remaining unexplored is the possibility that BRAF/MEK inhibitors themselves inadvertently compromise the tumor immune microenvironment, limiting the efficacy of immunotherapy when it is used in combination with targeted inhibitors. Herein, we profiled the landscape of the BRAF regulatome identifying a novel transcription factor, TFAP2A, newly linking BRAF/MEK drug resistance to antitumor immunity. Specifically, we found that BRAF/MEK inhibitors significantly upregulate TFAP2A. Further, genetic disruption of TFAP2A overcomes BRAF/MEK-inhibitor resistance, promotes stromal enrichment, and enhances intratumoral infiltration of macrophages in an immune-compromised mouse model. In a syngeneic mouse model, TFAP2a knockout not only suppresses tumor growth but also induces potent anti-tumor tertiary lymphoid structures (TLSs). Single cell transcriptomics revealed that the absence of TFAP2A shapes the antitumor microenvironment with an influx of M1-like macrophages, CD8+ T cells and mature dendritic cells. By identifying TFAP2A as a shared driver of both targeted therapy resistance and immunosuppression, our work offers a one-stone-two-bird strategy to overcome drug resistance and elicit antitumor immunity.

T-cell receptor (TCR) repertoires encode the organization of adaptive immunity and its reshaping by cancer and therapy, but disentangling treatment-associated structure from V(D)J recombination constraints remains challenging. We present CRAFT (Cancer Repertoire Anomaly Finding Transformer), a conditional sequence-to-sequence transformer that learns a nucleotide-level generative grammar of productive TCR-beta CDR3 sequences from healthy-donor repertoires, conditioned on germline V(D)J assignments. A dual-head decoder mirrors the independence of V-D and D-J recombination, and curriculum training yields embeddings that serve as a reference coordinate system for quantifying structured deviations in cancer-associated repertoires. In proof-of-concept analyses of a checkpoint blockade cohort (n=18) and a two-patient single-cell study of oncolytic immunotherapy, CRAFT-derived geometric metrics capture response-associated immune remodeling, including longitudinal shifts in repertoire organization. In antigen-labeled benchmarks, CRAFT yields coherent organization across specificity classes while highlighting settings where CDR3-beta alone provides partial signal.

Although platinum salts or PARP inhibitors are effective in delivering anti-tumor responses in people with BRCA1 or BRCA2 mutated cancers, drug resistance is common and is often caused by secondary BRCA1/2 reversion mutations that restore function. By collating and analyzing 848 BRCA1/2 reversion mutations in 384 cancer patients with drug resistance, we confirm that pathogenic BRCA1/2 mutation type influences the acquisition of reversions, and that large BRCA1/2 deletions are an underappreciated form of reversion. Integrating reversion data with systematic CRISPR-Cas9 screens that delete BRCA1/2 exons, we also show that both proteins contain privileged domains whose structure is essential for drug resistance, including the PALB2 interacting domains of both BRCA1 and BRCA2. Reversions in PALB2 also conserve both BRCA1 and BRCA2 binding domains. Surprisingly, exon 11 of BRCA2, which encodes BRC repeats 1-8, is not essential for resistance. Using this patient and functional information, we estimate the likelihood of pathogenic BRCA2 mutations to revert. We show that risk of reversion correlates with both the presence of clinical reversions and the response to treatment, suggesting that the propensity to revert could be a useful clinical parameter.

Macrophages are innate immune cells of embryonic or adult origin with tissue specific roles in homeostasis, disease surveillance, and wound repair that can be co-opted to promote tumor growth and spread1-11. An understanding of the specific roles of macrophage subsets in lung tumor initiation and progression could promote new therapeutic approaches for this deadly disease. Here, we show that KRASG12D mutations in lung epithelium drive proliferation of resident, embryonically-derived alveolar macrophages, which then promote tumor cell proliferation and protection from ferroptosis, leading to tumor progression. Using genetically engineered mouse models of mutant KRASG12D non-small cell lung cancer12,13, we found that alveolar macrophages accumulate by proliferation in response to tumor cell-secreted IL-34, recapitulating events observed in late embryonic lung development. Tumor alveolar macrophages in turn drive IGF-1-dependent tumor cell proliferation. Neutralization or deletion of IL-34 suppresses IGF-1 expression, reduces macrophage and tumor cell proliferation and inhibits tumor progression. High IL34 and IGF1 correlate with poor survival in KRASG12D/V lung adenocarcinomas and in other solid tumors, indicating that bi-directional proliferative signaling between resident macrophages and tumor cells can drive human lung tumor progression. These studies identify resident macrophage-tumor cell interactions as key interception points for lung cancer therapy.

MotivationThe tumor microenvironment (TME) dictates cancer progression and therapeutic response, yet translating TME subtypes into robust clinical biomarkers remains a significant challenge. Existing classification models typically rely on static gene signatures and cohort-dependent normalization, making them ill-suited for application to the small, unbalanced datasets common in early-phase clinical trials. To better guide drug development, methods are required that offer the flexibility to target specific biological contexts and bridge the gap between the discovery of tumor archetypes and their robust translation to individual patient samples. ResultsWe developed TumorArchetypeR, a modular R package that unifies unsupervised subtype discovery with the generation of rank-based, single-sample classifiers. By leveraging a systematic parameter grid search, the framework identifies stable, data-driven subtypes rather than relying on arbitrary defaults. Crucially, to ensure clinical translatability, the package includes a module to train a robust classifier using binary gene-pair rules, enabling prediction without cohort-level preprocessing. Applying TumorArchetypeR to colorectal cancer, we resolved the heterogeneity of fibrotic tumors, distinguishing an immunosuppressive "Immune-enriched/Fibrotic" state from an immune-excluded "Fibrotic/Myeloid" phenotype. Furthermore, we identified a distinct "Th/B-cell enriched" archetype associated with superior survival, a group largely obscured by existing pan-cancer models. With our rank-based classifier demonstrating robust performance on previously unseen samples, these findings highlight TumorArchetypeR as a scalable, end-to-end solution for refining patient stratification and optimizing precision oncology strategies. The TumorArchetypeR package and documentation are openly available on GitHub at https://github.com/lutgem/TumorArchetypeR.