Cancer Research

Pancreatic ductal adenocarcinoma (PDAC) exhibits a desmoplastic stroma with context-dependent tumor-restraining and tumor-promoting functions, highlighting the need to selectively reprogram stromal states. Although intratumoral coagulation activity is frequently observed, its role in driving these states remains unclear. Here, we identify extravascular coagulation as a tumor-amplified regulatory module that stabilizes pro-fibrotic stromal states via tumor-intrinsic protease-activated receptor-1 (PAR1) signaling. To establish clinical relevance and enable mechanistic interrogation, we combined human tumor bioinformatics with a cross-scale experimental workflow integrating microphysiological tumor-stroma (MPTS) models and in vivo systems to define and test this regulatory axis. Analysis of The Cancer Genome Atlas (TCGA) revealed heterogeneous F2R (PAR1) expression across tumors, with elevated expression associated with fibrotic transcriptional programs and reduced survival. Consistently, thrombin induced coordinated pro-fibrotic programs in tumor cells and cancer-associated fibroblasts (CAFs), which were recapitulated in microphysiological models where tumor-intrinsic PAR1 was required for amplification of extracellular matrix deposition and CAF activation. Mechanistically, PAR1 signaling amplified tumor-stroma communication, in part through induction of TGF-{beta}1-dependent pathways, establishing a reinforcing feedback loop that stabilizes fibrotic remodeling. Pharmacologic inhibition of PAR1 suppressed pro-fibrotic CAF states, reprogrammed stromal states and attenuated tumor progression across microphysiological and in vivo models. These findings establish extravascular coagulation as a systems-level regulator of stromal state architecture in PDAC and define a cross-scale framework for targeting tumor-stroma regulatory circuits.

Aberrant epigenetic reprogramming together with dysregulated mTOR signaling are hallmarks of cancer, where altered chromatin methylation and nutrient-sensing pathways cooperate to drive tumor progression. S-adenosylmethionine (SAM), the universal methyl donor, is essential for these processes, yet how tumors sustain elevated SAM availability to support oncogenic transmethylation reactions remains poorly defined. Here, using prostate cancer (PCa) as a model system, we identify nicotinamide N-methyltransferase (NNMT) as a critical metabolic-epigenetic regulator and tumor suppressor. Using a prostate-specific Nnmt knockout mouse model, we demonstrate that NNMT loss accelerates PCa progression, particularly in the context of Pten deletion, resulting in infiltrating carcinoma and reduced survival. Mechanistically, NNMT functions as a "SAM-sink," and its loss increases intracellular SAM abundance, thereby activating mTORC1 signaling through SAMTOR-dependent sensing and broadly enhancing chromatin methylation. In human PCa, recurrent genomic deletions of NNMT occur in up to 7% of cases, and NNMT protein expression is largely absent in primary tumors and metastases. NNMT-deficient PCa cells exhibit elevated SAM:SAH ratios, increased histone methylation, and heightened mTORC1 activity, enabling sustained tumor growth even under dietary methionine-restriction (MR). Notably, combined MR and pharmacologic mTORC1 inhibition synergistically suppresses the growth of NNMT-deficient tumors, revealing a previously unrecognized therapeutic vulnerability. Collectively, these findings establish NNMT as a key tumor suppressor that constrains SAM-driven epigenetic and signaling programs in PCa and suggest a rational, diet-based therapeutic strategy for advanced cancers with NNMT loss.

Intracellular iron homeostasis is increasingly recognized as a regulator of cancer cell behavior, but how iron distribution influences extracellular matrix (ECM) organization and invasion remains poorly understood. Here, we show that loss of divalent metal transporter 1 (DMT1/SLC11A2) disrupts intracellular iron homeostasis and promotes cancer cell invasion through an iron-ER-ECM axis. In MDA-MB-231 cells, DMT1 knockout (KO) reduced total iron content but increased the labile iron pool (LIP) in both 2D and 3D culture models, indicating altered intracellular iron distribution. Across transcriptomic and phenotypic readouts, DMT1-dependent effects were more evident in 3D than in 2D models, with DMT1 KO inducing endoplasmic reticulum (ER) stress and impaired collagen/ECM organization. Functionally, the DMT1-loss phenotype was marked by reduced 2D motility, whereas in 3D spheroid models DMT1 KO cells displayed enhanced invasive outgrowth in both Matrigel and collagen matrices. Iron chelation further modulated this phenotype in a DMT1-dependent manner. Pharmacologic induction of ER stress phenocopied the loose spheroid architecture and invasive behavior, supporting ER stress as a mechanistic link between altered iron handling and ECM destabilization. Together, these findings identify intracellular iron distribution, rather than total iron abundance alone, as a determinant of ECM integrity and context-dependent cancer cell invasion. Significance StatementOur study identifies an iron-ER-ECM axis through which intracellular iron homeostasis regulates cancer cell invasion. Total cellular iron content alone is insufficient to predict invasive behavior without considering how iron is distributed within the cell. By preserving intracellular iron homeostasis and ER function, DMT1 supports collagen synthesis and maintains ECM integrity. In contrast, DMT1 loss disrupts these processes, promoting formation of loosely aggregated spheroids and enhanced invasion in 3D tumor models despite reduced total iron levels. These findings challenge the assumption that lowering bulk iron uniformly suppresses invasive phenotypes and instead highlight intracellular iron trafficking as a potential therapeutic target for limiting cancer cell invasion.

Peritoneal carcinomatosis is a major cause of death in gastric cancer, yet effective therapies remain limited. Tumor-derived soluble factors are increasingly recognized as key regulators of the peritumoral microenvironment. Here, we nominate osteopontin (SPP1) as a tumor-derived mediator that orchestrates macrophage-driven immunoregulation in gastric peritoneal carcinomatosis. Using integrated analyses of human clinical datasets and murine models, we demonstrate that tumor-secreted SPP1 promotes macrophage recruitment and induces tolerogenic IL-10 production. Clinically, SPP1 correlated with inferior overall survival and progression-free survival in gastric cancer. In syngeneic murine models of gastric peritoneal carcinomatosis, intracavitary pharmacologic inhibition of SPP1 restricted peritoneal dissemination, impaired macrophage infiltration and suppressed IL-10 production. Consistent with these findings, macrophage depletion phenocopied antitumor effects of SPP1 inhibition, resulting in decreased metastatic burden. Collectively, these findings define a mechanism of tumor-macrophage crosstalk that promotes peritoneal dissemination and provide a rationale for therapeutic targeting of SPP1 in gastric peritoneal carcinomatosis.

Neuroendocrine prostate cancer (NEPC) persists in a profoundly hypoxic microenvironment, yet the mechanisms enabling tumor adaptation to this metabolically challenging niche remain undefined. Here, we identify the lipid kinase PIKfyve as overexpressed in NEPC, functioning as a central node in a stress-lipid kinase axis that drives adaptation to persistent endoplasmic reticulum (ER) stress. Mechanistically, NEPC requires PIKfyve-mediated lysosomal degradation and lipid recycling to maintain metabolic homeostasis under hypoxia. PIKfyve inhibition disrupts lysosomal function, leading to ER stress accumulation and activation of a compensatory, sterol regulatory element-binding protein (SREBP)-dependent de novo lipogenesis program essential for NEPC survival. This stress-lipid axis creates a synthetic vulnerability between PIKfyve and fatty acid synthase (FASN), where dual inhibition synergistically amplifies ER stress, triggers the terminal unfolded protein response, and induces tumor cell death. These findings reveal a metabolic adaptation in NEPC and provide preclinical evidence that co-targeting PIKfyve and FASN can overcome hypoxia-associated stress adaptation.

Pancreatic ductal adenocarcinoma (PDAC) is characterized by a dense, desmoplastic microenvironment that drives disease progression, yet conventional models fail to capture this complex tumor-stroma coevolution. Here, we utilize the chick chorioallantoic membrane (CAM) platform to investigate tumor-stroma interactions using murine PDAC cell lines and patient-derived organoids (PDOs). Integrating single-cell RNA sequencing and spatial transcriptomics, we show that the CAM microenvironment supports the emergence of complex tumor ecosystems while preserving patient-specific characteristics. Within five days, in ovo tumors faithfully recapitulated the structural and molecular features of parental tumors. Histological analysis revealed the rapid recruitment and spatial organization of heterogeneous host cancer-associated fibroblast (CAF) populations, showcasing distinct myofibroblastic and inflammatory stromal states. Crucially, the model preserved intrinsic tumor heterogeneity and permitted functional interrogation of subtype-specific extracellular matrix remodeling and metastatic dissemination. Together, our findings demonstrate that the CAM provides a highly permissive niche for tumor-stroma coevolution. As a rapid, scalable, and biologically relevant platform, this in ovo model offers a powerful approach for studying stromal composition, metastatic progression, and patient-specific tumor biology in pancreatic cancer.

The molecular tumor board (MTB) is central to precision oncology, providing personalized treatment recommendations based on molecular profiles of patient tumors. Genomics is instrumental for MTBs but often fails to identify clinically actionable targets, a gap that phosphoproteomics can fill. We present the tumor proteome activity status (TOPAS) platform, an end-to-end analysis pipeline that converts terabytes of phosphoproteomic data into patient-specific reports for MTB discussions, focusing on clinically relevant signaling linked to oncogenic mechanisms and therapeutic targets. Designed to scale with growing cohorts, the platform integrates data from 1,998 tumor samples to support patient- and cohort-level hypothesis generation. A web portal handles quality control, calculates TOPAS scores, identifies tumor antigens and immune checkpoints, and offers interactive analyses of differential protein abundance and outlier detection. The TOPAS platform is open source, addresses a critical unmet need and facilitates broader adoption of phosphoproteomics in precision oncology in the future.

Melanoma is an aggressive malignancy that rapidly adapts to therapy. While chemotherapy resistance has traditionally been attributed to tumour-intrinsic mechanisms, growing evidence implicates the tumour microenvironment in shaping drug tolerance. However, few in vitro models capture the stromal complexity needed to study this interaction. We developed two multicellular melanoma spheroid models of increasing stromal complexity: a baseline model of melanoma, endothelial, and macrophage cells (BEM), and a fibroblast-containing counterpart (BEMF), and compared their transcriptional response to doxorubicin. Fibroblast inclusion increased the doxorubicin concentration required to achieve comparable growth inhibition. While untreated BEMF spheroids exhibited only modest baseline transcriptional differences, they showed a profoundly reshaped transcriptional response after doxorubicin exposure, displaying broader and higher-magnitude changes. These responses were characterized by suppression of proliferative and cell-cycle programmes, together with activation of inflammatory, immune-associated, metabolic, and stress-adaptive pathways. Higher-resolution pathway analyses further revealed coordinated attenuation of mitotic progression, checkpoint regulation, homologous recombination repair, and Rho GTPase signalling, consistent with a shift toward stress-adaptive and phenotypically plastic states, rather than classical resistance mechanisms. Transcriptome-derived transcription factor activity inference supported this regulatory rewiring. Integration with curated resistance-associated genes and external transcriptomic datasets demonstrated strong conservation of core transcriptional features across heterogeneous experimental systems, including consistent suppression of proliferation-associated genes and induction of inflammatory signalling programmes. Together, these findings indicate that fibroblasts redirect chemotherapy responses toward a stress-adaptive, persister-like phenotype and establish fibroblast-containing 3D melanoma spheroids as a physiologically relevant platform for studying tumour microenvironment-mediated chemotherapy tolerance and stromal-tumour interactions.

Cancers with similar histology often exhibit divergent clinical behavior, reflecting molecular heterogeneity not captured by current classification systems. Although driver mutations are central to tumorigenesis, their broader systems-level consequences have not been systematically leveraged. We integrated genomic and transcriptomic data across cancers to define driver mutation signatures (DMS), coordinated transcriptional programs associated with cancer driver mutations. From 121 candidate drivers, we derived 90 robust signatures and quantified their activity in individual tumors using mutation signature scores (MSS). DMS analysis revealed a hierarchical organization of tumors into molecular subgroups that transcended tissue boundaries while preserving driver-associated features. Continuous MSS profiles further defined high-resolution molecular fingerprints for individual tumors. DMS provides a quantitative framework for tumor classification and patient stratification and links driver-associated programs to potential therapeutic vulnerabilities. Together, these findings establish a pan-cancer molecular taxonomy that bridges genotype and phenotype and may inform precision oncology.

Oral squamous cell carcinoma (OSCC) is an aggressive head and neck malignancy characterized by high morbidity, therapeutic resistance and intratumoral heterogeneity driven by plastic cell states. Given that metabolic inputs can shape cell identities via epigenetic mechanisms, we investigated how metabolism of a non-essential amino acid, serine, affects histone modifications with key roles in cell plasticity: H3K27me3, which represses differentiation genes, and H3K4me3 which activates stemness and epithelial-to-mesenchymal transition (EMT) genes. Using a panel of human OSCC patient-derived cell lines and an orthotopic murine isograft model, we show that OSCC cells depend on exogenous serine for proliferation. Dietary serine deprivation induced de novo serine synthesis with a concomitant increase in a-ketoglutarate (aKG), a cofactor for KDM6B and KDM5A/B demethylases of H3K27me3 and H3K4me3, respectively. RNA-seq-derived serine deprivation gene signatures revealed activation of keratinization program and suppression of EMT and proliferation genes and tracked with good OSCC patient outcomes in TCGA. Furthermore, CUT & RUN profiling showed site-specific losses of H3K27me3 at differentiation genes and reduction of H3K4me3 at stemness, EMT and cell cycle genes. However, inhibition of aKG with 2-hydroxyglutarate was not sufficient to rescue cell proliferation. Instead, genome-wide analysis revealed widespread H3K27me3-H3K4me3 bivalency, with extensive transcriptional repression of proliferation and oncogenic programs. Functionally, serine deprivation impaired orthotopic tumor growth and improved the immune landscape in syngeneic mice. Our studies identify a metabolic serine-aKG-KDM-H3K27me3/H3K4me3 bivalency axis that globally reprograms OSCC chromatin as a potential therapeutic strategy to impede tumor plasticity and evolution to advanced disease.

Comprehensive genomic profiling (CGP) supports precision medicine in cancer care, but accurate assessment of missense variant pathogenicity, especially for variants without established consensus, remains challenging. Various computational tools have been developed for variant functional prediction, but most current tools rely solely on variant-level features and do not capture the clinical context of individual patients. To address this limitation, we developed MARiO (Missense Alteration Risk for Oncogenicity), a machine-learning model that integrates variant-level features and patient-level clinical and genomic contexts to effectively predict the pathogenicity of missense variants in cancer. We collected a total of 10,642 missense variants from 1271 patients, and evaluated candidate features for their association with variant pathogenicity, identifying informative features including in silico functional predictions, population allele frequency, variant allele frequency, and tumor mutational burden. Using these selected features, MARiO was developed with extreme gradient boosting. The model integrates multiple in silico prediction tools and patient-specific genomic contexts while accommodating missing values frequently observed in real-world CGP datasets. MARiO outperformed existing tools, achieving an area under the receiver operating characteristic curve of 0.942. The model demonstrated strong generalizability across multiple external datasets and showed consistency with real-world molecular treatment proposals. MARiO offers a robust and clinically relevant approach for missense variant pathogenicity assessment by integrating variant- and patient-level features and serves as a valuable tool to support clinical decision-making.

Pancreatic ductal adenocarcinoma (PDAC) is one of the most challenging cancers to treat due to the dismal survival rate, poor post-treatment outcome and profound resistance to a wide range of therapies. With mutant KRAS being a key driver, small molecule inhibitors targeting KRAS or pan-RAS (KRASi) have demonstrated exciting preclinical and early clinical anti-tumor efficacy, and the pan-RAS(ON) inhibitor daraxonrasib (RMC-6236) recently achieved Phase 3 clinically meaningful improvements in patient survival compared to chemotherapy. But resistance to RAS/KRAS inhibitor inevitably develops, which limits and compromises the treatment outcome. In this study, we investigated the combination of stereotactic body radiation therapy (SBRT) and KRAS inhibition (MRTX1133 and daraxonrasib) in the treatment of preclinical PDAC models. We found that this combination strategy synergistically suppresses PDAC cell growth in vitro and enhances tumor control while minimizing local recurrence in orthotopically implanted KPC (LSL-KrasG12D/+;Trp53R172H/+;Pdx1-Cre) murine PDAC tumors in vivo. As radiation therapy (RT) induces ferroptosis in multiple cancer types and mutant KRAS promotes various anti-ferroptotic mechanisms, we tested the role of ferroptosis in promoting tumor-control efficacy. Intriguingly, the addition of a ferroptosis inhibitor, liproxstatin-1, to the combination therapy significantly abrogated the in vivo synergism between SBRT and KRAS inhibition, suggesting that treatment-induced ferroptosis at least partially drives the synergistic efficacy of this combination strategy. Our study indicates that this SBRT-KRASi combination has the potential to overcome treatment resistance and improve outcomes in PDAC patients. These data directly support the design of a planned multi-center Phase 2 clinical trial with this combination strategy in locally advanced PDAC.

Pancreatic ductal adenocarcinoma (PDAC) exhibits extensive molecular and microenvironmental heterogeneity, yet how tumor lineage states interact with spatial immune organization in advanced disease remains poorly understood. Here, we performed multiplexed spatial proteomic profiling using tissue cyclic immunofluorescence (t-CyCIF) in 27 patients with advanced PDAC and integrated these analyses with spatial transcriptomic profiling of representative tumors. Tumors were classified into Classical, Hybrid, Basal, and Null epithelial states based on GATA6 and CK5 expression, revealing distinct immune architectures associated with clinical outcome. Classical and Hybrid tumors displayed immune-inflamed microenvironments enriched for lymphocytes, whereas Basal and Null tumors exhibited immune-excluded, macrophage-dominated landscapes characterized by increased M2 macrophages. Spatial transcriptomic analysis further revealed that Hybrid tumors were not homogeneous intermediate states but instead contained spatially segregated Hybrid_Classical and Hybrid_Basal regions with distinct transcriptional programs, immune niches, and cell-cell communication networks. Hybrid_Basal regions were associated with increased M2 macrophage enrichment and preferential activation of macrophage-derived SPP1-CD44 signaling, implicating localized immune-epithelial interactions in epithelial plasticity and lineage-state transitions. To quantify spatial immune organization, we developed a spatial immune score that captures the relative positioning of CD8 cytotoxic T cells with respect to CD4 helper T cells and CD163 M2 macrophages. Higher scores were associated with worse survival and provided stronger prognostic information than conventional immune cell abundance metrics. Integration of the spatial immune score with GATA6 expression achieved superior prognostic discrimination (AUC = 0.822) compared with either feature alone. Together, these findings demonstrate that tumor lineage state and spatial immune organization represent complementary dimensions of PDAC biology and highlight spatial tumor-immune interactions as determinants of clinical outcome in advanced pancreatic cancer. SummaryPancreatic ductal adenocarcinoma (PDAC) exhibits marked molecular and microenvironmental heterogeneity, yet how tumor lineage states interact with the spatial immune microenvironment in advanced disease remains poorly understood. Here, the authors apply multiplexed spatial proteomics and spatial transcriptomics to advanced PDAC and show that epithelial lineage states defined by GATA6 and CK5 are associated with distinct immune architectures and macrophage-enriched signaling niches. Hybrid tumors contain spatially segregated epithelial states with differential immune engagement and SPP1-CD44 signaling. The authors further identify a spatial immune score based on the relative positioning of CD8 T cells, CD4 T cells, and M2 macrophages that predicts patient survival. Integration of spatial immune organization with tumor lineage information improves prognostic stratification, highlighting the clinical relevance of spatial tumor-immune interactions in advanced PDAC.

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.

Scaffold integrity is essential for the activity of proteins that function through protein-protein interactions rather than catalytic output. RAF1 exemplifies this duality: although it is a bona fide kinase and a core component of the MAPK cascade, its tumor-promoting role is largely kinase-independent, relying instead on scaffold-mediated suppression of apoptosis. Genetic Raf1 ablation in KRAS-driven lung adenocarcinoma mouse models induces tumor regression without systemic toxicity, making it an attractive candidate for targeted protein degradation. Chemogenetic systems like the dTAG platform are widely used for preclinical target validation. Here, we generated a dTAG-RAF1 mouse model and showed that pharmacological degradation is efficient and systemically well tolerated, but fails to reproduce the tumor regression observed upon genetic Raf1 ablation. Mechanistically, the N-terminal FKBP12F36V tag (dTAG) perturbs the RAF1 interactome, including scaffold associations with apoptotic regulators, thereby blunting the phenotypic consequences of its degradation. These results establish scaffold integrity as a determinant of chemogenetic system fidelity and argue that degradation tools must be validated at the functional level, not only for target elimination, before assessing their therapeutic relevance.

Pancreatic ductal adenocarcinoma (PDAC) is driven by oncogenic KRAS in roughly 90% of cases, and KRAS-pathway inhibition has finally become clinically active. Durable benefit, however, will require identifying the adaptive and baseline vulnerabilities that shape response to KRAS inhibition. Two resistance mechanisms have been proposed separately in the literature - receptor-tyrosine-kinase bypass of KRAS, and dependence on the adhesion kinase FAK - but whether they are one target class or two, and which should partner a KRAS inhibitor, is unresolved. We integrate public perturbation, dependency, and survival data to nominate them as mechanistically separable candidate combination partners. Two findings define the separation. First, KRAS loss increases ERBB2/3 receptor expression. This appeared in both an inducible genetic KRAS-extinction model and, independently, in five PDAC lines treated with pharmacological KRAS-G12C/D inhibitors, while MAPK output collapsed as expected. The signal was clearest for ERBB2 and in the genetic model; in the small pharmacological cohort the effect was modest and its confidence intervals crossed zero, so we treat ERBB2/3 up-regulation as a candidate adaptive response - ERBB2-dominant and ERBB3-compatible - not a proven resistance mechanism. Second, focal adhesion kinase (FAK/PTK2) is the top-ranked standing druggable dependency within the KRAS/Src/RTK/adhesion network we examined (essential in 58% of pancreatic lines), yet it is not induced by KRAS shutdown. FAK dependency is present at baseline and, in DepMap, is statistically independent of a line's KRAS dependency (Spearman rho = +0.05, n.s.) - a genuinely standing vulnerability rather than a KRAS-rebound effect. The candidate adaptive response and the standing dependency are not positively co-regulated across the perturbed lines (pooled Spearman rho = -0.43, but n = 8 and n.s., so this cannot by itself establish independence); we therefore treat them as separable on mechanistic grounds - each nominated by different data and engaged by a different drug - rather than as statistically demonstrated independent programs. A Src-centered signaling-landscape analysis associates patient prognosis with the coordinated invasion-and-RTK program these nodes organize, rather than with any single transcript; this program remains prognostic after adjustment for a conventional EMT/stromal signature, which does not (Src-neighborhood per-standard-deviation OS hazard ratio 1.9, p = 3 x 10^-5; EMT signature null on adjustment). Together, these results motivate a concrete, testable hypothesis: that FAK inhibition (a standing dependency) and ERBB inhibition (a candidate induced adaptive response) are separable candidate partners for a KRAS inhibitor, best evaluated as distinct arms of a biomarker-stratified platform. They also clarify why single-agent Src inhibition - a non-oncogene dependency tested as monotherapy, without a KRAS backbone, in advanced rather than micro-metastatic disease - was not positioned to surface either mechanism. No protein-level, phospho-signaling, or combination-response validation is performed here; all findings are computational nominations that require experimental validation before any clinical inference.

Spatial proteomic imaging technologies enable the simultaneous assessment of immune cell abundance and spatial organization within the tumor microenvironment. Spatial clustering is commonly summarized using measures such as Ripleys K or nearest-neighbor G-functions at a fixed radius. However, these approaches depend on scale selection and may obscure biologically relevant patterns occurring across spatial ranges. We propose a functional data analysis (FDA) framework to model spatial clustering trajectories derived across a continuum of radii. Functional principal component analysis (FPCA) was used to summarize dominant modes of spatial variation, and resulting scores were incorporated into Cox proportional hazards models as both main effects and interaction with immune cell abundance. The approach was applied to multiplex immunofluorescence data from five ovarian cancer studies, comprising 773 high-grade ovarian serous tumors. Analyses focused on CD3+ and CD8+ T cell populations within the tumor compartment of the tissue, adjusting for age at diagnosis and cancer stage, with study-specific estimates combined using random-effects meta-analysis. Higher abundance of both T cells and CD8+ T cells was consistently associated with improved overall survival. Beyond abundance, spatial features captured by the leading functional principal component were independently associated with survival, particularly for CD8+ T cells. Interaction models further showed that the prognostic effect of immune infiltration depended on spatial clustering, with tumors characterized by high abundance and low spatial clustering exhibiting the most favorable outcomes. These findings indicate that spatial organization provides complementary prognostic information beyond abundance alone and suggests that more diffuse immune infiltration may reflect more effective anti-tumor activity in ovarian cancer. Overall, FDA offers a flexible and interpretable framework for modeling spatial clustering across scales and identifying prognostic spatial features not captured by fixed-radius or distance analyses.

Spatial transcriptomics can reveal localized tumor-immune relationships, but thousands of spots from one tissue section do not provide thousands of biological replicates. We evaluated the distinction between within-section association and patient-level reproducibility using public breast cancer datasets. In a 10x Genomics Visium discovery section containing 3,798 spots, hypoxia-related transcription was inversely associated with cytotoxic gene activity in neighboring spots (Spearman{rho} = -0.202). High-hypoxia spots also had lower neighborhood cytotoxic scores than low-hypoxia spots (rank-biserial effect = -0.286). We then tested the directional association in an independent HER2-positive cohort comprising 36 sections, 13,619 spots, and eight patients. Only 19 of 36 sections and five of eight patients showed negative associations. The median patient-level correlation was -0.043 and did not differ from zero in a one-sided exact Wilcoxon test (P = 0.473). Sensitivity analyses using alternative cytotoxic and hypoxia signatures, neighborhood sizes, and Kendall correlation did not support a consistent inverse patient-level effect. Thus, a strong single-section association did not consistently reproduce across patients. These results caution against interpreting spot-level spatial associations from one section as patient-level biological effects.

Computational modeling provides a powerful framework for in silico exploration of anti-cancer therapeutic targets and tumor response mechanisms. Oncogenic signaling pathways play a central role in tumor behavior and represent promising targets for personalized combination therapies. However, these pathways are complex, and although logic-based models are well suited for representing signaling dynamics, they are often constrained by model-specific data requirements, limited scalability, and time-consuming manual curation. Here, we introduce Functional Integration of Contextualized Omics for Unraveling regulatory dynamicS (FICUS), a framework that integrates omics-driven network contextualization with dynamic Boolean and logic-ODE modeling. FICUS enables automated, data-driven protein network inference and patient stratification, allowing shared signaling mechanisms to be identified across patient subgroups while preserving patient-specific dynamic responses. We applied FICUS to the SU2C-MARK lung cancer cohort and the The Cancer Genome Atlas kidney cancer cohort, demonstrating its utility for post-hoc analyses and downstream interrogation of dynamic tumor models. Overall, our results highlight the flexibility of FICUS in capturing heterogeneous signaling mechanisms across patient subgroups, addressing a key challenge in precision oncology.

Cytotoxic T lymphocytes (CTLs) play a central role in antitumor immunity; however, metabolic reprogramming within the tumor microenvironment often compromises their effector function, making metabolic targeting crucial for the improvement of T cell function. Folate-dependent purine synthesis, a core pathway sustaining the nucleotide pool, is highly activated in tumors, yet its role in regulating tumor immune sensitivity remains unclear. Here, by establishing a co-culture system of melanoma cells and human T Cell Receptor (TCR)-engineered T cells, we systematically evaluated the effects of folate-dependent purine synthesis inhibitors on tumor cell response to CD8+ T cell cytotoxicity. We found that inhibition of key enzymes such as methylenetetrahydrofolate dehydrogenase 2 (MTHFD2) and glycinamide ribonucleotide transformylase (GART) markedly enhanced tumor cell sensitivity to T cell killing, an effect also observed with exogenous nucleoside supplementation. Mechanistically, inhibition of folate-dependent purine synthesis suppresses glycolysis by downregulating critical glycolytic enzymes, thereby reducing lactate production. Reduction in lactate further weakens lactylation and stability of the immune checkpoint protein PD-L1. In parallel, impaired purine synthesis disrupts uridine metabolism, blocks ribose salvage, and distally influences glycolysis. Collectively, our study identified the folate-dependent purine synthesis-glycolysis axis as key regulator of tumor immune response and highlights metabolic targeting as a promising strategy to improve cancer immunotherapy.