Cancer Research

Lung squamous cell carcinoma (LUSC) is an aggressive malignancy characterized by high cellular plasticity and few targeted treatment options. TNIK overexpression is common in LUSC and promotes tumor growth, with TNIK inhibition sensitizing LUSC to radiotherapy, though the underlying mechanisms are not well defined. Through transcriptomic analyses and functional assays, we identified TNIK as a regulator of a MYC-dependent transcriptional network that coordinates epithelial-mesenchymal plasticity and cell proliferation in LUSC. Depletion of TNIK reprogrammed LUSC cells from a hybrid epithelial/mesenchymal state towards an epithelial, senescent-like state characterized by reduced cell migration, invasion, reduced DNA synthesis, and enhanced {beta}-galactosidase activity. Using a small-molecule screen approach, we found that TNIK inhibitors cooperated with agents suppressing the histone methyltransferase and MYC binding partner EZH2, which further suppressed partial epithelial-to-mesenchymal transition (pEMT). Mechanistically, we identified MYC as a key downstream TNIK effector in LUSC cells: MYC depletion phenocopied the effects of TNIK loss on pEMT and senescence, and restoring MYC expression bypassed the effects of TNIK depletion. Collectively, these results implicate TNIK in the mechanisms linking epithelial-mesenchymal plasticity with proliferation and evasion of senescence and provide insights into future strategies for the clinical deployment of TNIK inhibitors in LUSC and other TNIK-dependent malignancies.

ZNF217 is an established oncogenic transcription factor that promotes cancer progression and therapeutic resistance; however, the mechanisms regulating ZNF217 protein abundance remain poorly understood. Here, we identify ubiquitin-specific peptidase 15 (USP15) as a critical regulator of ZNF217 stability and define a reciprocal USP15-ZNF217 signaling loop that sustains malignant phenotypes in ovarian cancer. Stable overexpression of ZNF217 in OVCA420 ovarian cancer cells enhanced proliferation, epithelial-mesenchymal transition, migration, invasion, and extracellular matrix adhesion. Notably, ZNF217 overexpression increased USP15 protein abundance without altering USP15 mRNA levels, whereas ZNF217 depletion reduced USP15 protein levels, suggesting post-transcriptional regulation. Conversely, USP15 depletion markedly reduced ZNF217 protein abundance while increasing ZNF217 mRNA levels, indicating that USP15 regulates ZNF217 predominantly at the post-transcriptional level. Proteasome inhibition restored ZNF217 protein levels following USP15 depletion, further demonstrating that USP15 promotes ZNF217 protein stability. Functionally, USP15 depletion in ZNF217-overexpressing ovarian cancer cells suppressed proliferation and multiple metastatic phenotypes, including migration, invasion, extracellular matrix adhesion, anoikis resistance, and multicellular aggregate formation. In vivo, USP15 depletion significantly reduced tumor progression and metastatic burden and prolonged survival in mice bearing ZNF217-driven ovarian tumors. Furthermore, USP15 depletion enhanced the sensitivity of ZNF217-overexpressing cells to carboplatin, paclitaxel, and doxorubicin. Collectively, these findings identify USP15 as an upstream regulator of ZNF217 protein stability and reveal a positive-feedback loop between USP15 and ZNF217 that reinforces oncogenic signaling. Targeting USP15 may therefore represent an indirect therapeutic strategy for suppressing ZNF217-driven ovarian cancer, particularly given the challenges associated with directly targeting oncogenic transcription factors.

The role of UBE2N in myeloid cell-mediated immune suppression in cancer remains undefined. Here, we examined the function of UBE2N in myeloid cell-mediated tumor progression using a temporally inducible myeloid-specific knockout model (LysMCreERUbe2nfl/fl). Temporally induced deletion of Ube2n in myeloid cells (Ube2nMyeKO) significantly hindered growth of YUMM1.7 melanoma. This was accompanied by reduced myeloid cell burden within the tumor microenvironment. We observed altered abundance of PD-1, PD-L1, and SPP1 in the Ube2nMyeKO tumor microenvironment at the tissue level. In vitro analysis showed that knock-in expression of a catalytically deficient UBE2NC87S mutant in bone marrow-derived macrophages (BMDMs) markedly decreased expression of Spp1. We observed decreased SPP1 secretion in Ube2nMyeKO BMDM-conditioned media (CM). Treatment with Ube2nMyeKO BMDM-CM decreased co-expression of PD-1, TIM-3, and LAG-3 on chronically stimulated T cells. Antibody-mediated neutralization of SPP1 in Ube2nWT BMDM-CM decreased PD-1 expression on CD8+ T cells. Together, these findings suggest a role for myeloid UBE2N in YUMM1.7 progression.

Pancreatic ductal adenocarcinoma (PDAC) exhibits profound metabolic rewiring and strong resistance to DNA-damaging therapies, yet how metabolic pathways regulate genome maintenance remains poorly understood. The hexosamine biosynthetic pathway (HBP) integrates nutrient availability with protein glycosylation through production of UDP-GlcNAc, but its role in DNA damage response (DDR) regulation is unclear. Here we show that inhibition of the HBP enzyme phosphoglucomutase-3 (PGM3) reduces DNA repair capacity in pancreatic cancer cells. Transcriptomic and functional analyses reveal that the selective PGM3 inhibitor FR054 amplifies gemcitabine-induced replication stress, disrupts ATR-CHK1 and ATM-CHK2 checkpoint signaling, and selectively impairs homologous recombination. Glycoproteomic profiling identifies the AAA+ ATPase RUVBL2 as a key metabolic-DDR node. Gemcitabine increases RUVBL2 O-GlcNAcylation, with Thr81 identified as a modified residue within the Walker A nucleotide-binding motif. Structural modelling predicts that Thr81 O-GlcNAcylation stabilizes the RUVBL1-RUVBL2 complex without compromising ATP-Mg engagement. PGM3 inhibition and Thr81 mutation similarly reduced ATR and ATM abundance and promoted persistent DNA damage, supporting a role for RUVBL2 Thr81 O-GlcNAcylation in sustaining checkpoint signalling and genome stability. Consequently, PGM3 inhibition induces a BRCAness-like state that sensitizes pancreatic cancer cells to PARP inhibition, both in vitro and in vivo, as well as to ionizing radiation. These findings reveal a nutrient-sensitive mechanism linking protein glycosylation to genome maintenance and identify HBP-dependent DNA repair as a potentially actionable vulnerability in pancreatic cancer.

Pancreatic ductal adenocarcinoma (PDAC) is an aggressive malignancy characterized by high metastatic dissemination, therapy resistance, and poor clinical outcome. Inhibitor of differentiation 1 or ID1, is frequently overexpressed in PDAC and is associated with tumour progression and adverse clinical outcome. However, the mechanisms governing its post-transcriptional regulation remain insufficiently characterized. Here, we identify tumour-suppressive miR-615-5p as a regulator of ID1 expression in PDAC. Integrative in-silico target prediction prioritized miR-615-5p based on seed complementarity and thermodynamic stability with the ID1 3' -UTR. Expression analysis of available PDAC clinical datasets revealed reduced miR-615-5p expression associated with increased ID1 expression. Direct association was validated using luciferase reporter assays, where miR-615-5p suppressed 3' -UTR reporter activity of ID1 in a sequence dependent manner, while mutation of the predicted binding site attenuated this effect. Further biotinylated-RIP and AGO2-RIP assays demonstrated the co-enrichment of ID1 transcripts and miR-615-5p with AGO2 associated RISC complexes, while AntimiR mediated inhibition of miR-615-5p perturbs association between miR/ID1 to AGO2, supporting interaction specificity. Functionally, modulation of miR-615-5p altered ID1 expression and impacted PDAC cell migration in vitro. Mechanistic analyses further indicated that the miR-615-5p/ID1 axis influences autophagic flux where miR-615-5p mediated inhibition of autophagy suppresses ID1 dependent cellular migration. Collectively, these findings define a previously uncharacterized miRNA-dependent regulation of ID1 expression and link this axis to autophagy-associated migratory responses in PDAC cells. The study expands the post-transcriptional regulatory landscape of ID1 and provides a possible mechanism where suppression of miR-615-5p leads to ID1 overexpression and subsequent poor clinical outcome in PDAC cells.

Tumor-promoting myofibroblastic carcinoma-associated fibroblasts (myCAFs) are induced by activation of transforming growth factor-{beta} signaling. However, the molecular basis of myCAF-specific transcriptional programs regulated by TGF-{beta} signaling remains poorly understood. Using a meta-analysis of single-cell RNA-seq data from 132 human breast tumor and non-tumor tissues, we show that myCAFs activate gene regulatory programs relevant to skeletal and cardiovascular development that are associated with poorer outcomes in breast cancer patients. Of note, distal-less homeobox 5 (DLX5), a master transcription factor for skeletal development, is activated in human breast myCAFs at both epigenetic and transcriptional levels. DLX5 expression is also initiated by TGF-{beta}1 treatment in human mammary fibroblasts. Immunoprecipitation and CUT&RUN assays using DLX5-expressing fibroblasts demonstrate that DLX5 interacts with Smad2/3/4 proteins, enabling their cooperative occupancy at shared genomic binding sites of target genes, thereby promoting canonical TGF-{beta} signaling and the myCAF state. DLX5-primed myCAFs also enhance paracrine TGF-{beta} signaling and neuropilin-2 expression to promote collective tumor invasion. Our findings indicate that DLX5 induces myCAF formation and promotes breast tumor progression in collaboration with canonical TGF-{beta} signaling.

Solid tumors remain refractory to conventional treatments, yet cell surface proteins, by virtue of their extracellular accessibility and critical roles in tumor signaling, represent an attractive class of targets for precision-targeted therapy. Here, we report that TMEM132A is an essential and previously unrecognized pan-cancer target. TMEM132A interacts directly with EGFR and stabilizes its expression, thereby tethering EGFR at the plasma membrane and sustaining constitutive activation of lipid synthesis. Mechanistically, the TMEM132A-EGFR axis promotes lipogenesis by facilitating SREBP nuclear translocation, which in turn upregulates ACLY and ACSS2 expression to drive acetyl-CoA production and downstream lipid biosynthesis, ultimately disrupting lipid droplet homeostasis. To therapeutically target this axis, we developed a nanobody, LFNanoT132A#3, which effectively blocks the TMEM132A-EGFR interaction, abrogates downstream signaling activation, and potently inhibits proliferation across multiple solid tumor types. Notably, LFNanoT132A also exerts robust antitumor activity against H1975 xenografts, a model resistant to first- and second- generation EGFR inhibitors, underscoring its potential to overcome conventional drug resistance. Our findings establish TMEM132A#3 as a critical node in membrane-tethered oncogenic signaling and metabolic rewiring, and position LFNanoT132A#3 as a promising therapeutic candidate for precision cancer therapy.

Bladder carcinoma in situ (CIS) is a multifocal, non-muscle-invasive disease with a high risk of progression to muscle-invasive cancer. Current management strategies are often guided by genomic profiling of single tumor samples, which incompletely capture tumor heterogeneity and may contribute to treatment failure. In particular, the multifocal nature of CIS raises uncertainty regarding the uniformity of genomic, immunologic, and microenvironmental features across anatomically distinct sites within the same patient. To address this, we performed spatial transcriptomic profiling of CIS-containing tissue from four anatomically distinct sites within a single individual. Unsupervised clustering with marker-based annotation, integrated with metabolic inference, identified epithelial tumor populations alongside stromal, immune, and smooth muscle compartments. While key cellular states were conserved, their spatial organization and relative abundance varied by site. Metabolic analysis further revealed region-specific microenvironments shaped by local cellular architecture. These findings indicate that both cellular composition and metabolic activity are spatially structured. Collectively, these results demonstrate that CIS exhibits significant intra-patient heterogeneity not captured by single-site profiling. These findings require validation in larger cohorts but support multi-region sampling could help improve risk stratification, biomarker development, and prediction of response to intravesical therapies, with potential implications for more personalized treatment strategies.

Radiosensitizing nanoparticles represent a promising approach for enhancing the efficacy of proton radiotherapy; however, their performance is constrained by restricted penetration into tumor tissue, resulting in preferential perivascular accumulation. Here, we develop a spatially distributed mathematical model of a growing tumor undergoing proton therapy with intravenously administered radiosensitizing nanoparticles to investigate treatment optimization strategies. Using physiologically plausible parameter ranges informed by our own experimental measurements and published data, we demonstrate that co-administration of targeted nanoparticles with antibodies binding to the same tumor receptors can overcome transport-induced localization and promote a more uniform intratumoral redistribution of nanoparticles before irradiation. Population-level simulations across heterogeneous parameter sets suggest that moderate antibody doses consistently prolong tumor regrowth time, whereas higher antibody doses produce a pronounced and robust increase in tumor cure probability under a single high-dose irradiation regimen representative of preclinical settings. A key conceptual result of our analysis is the asymmetric risk associated with antibody co-administration. In contrast to antibody--drug conjugates, for which excessive dosing of unconjugated antibodies may severely compromise therapeutic efficacy, co-administration of antibodies with nanoparticle-based radiosensitizers constitutes a "safe-by-design" strategy with respect to tumor cell kill in the modeled single high-dose irradiation setting: although excessive antibody doses may yield suboptimal outcomes, they cannot reduce tumor cell kill below that achieved with targeted nanoparticles administered without antibodies. These findings identify antibody-mediated spatial redistribution of radiosensitizing nanoparticles as a favorable strategy that is expected to provide robust therapeutic benefit despite substantial variability in tumor characteristics.

High-grade serous ovarian carcinoma (HGSC) responds poorly to immune checkpoint blockade, partly due to a macrophage-dominated immunosuppressive microenvironment. We integrated single-cell spatial proteomics and spatial transcriptomics across 50 HGSC tumors and applied SPACEstat to resolve higher-order immune communities and their transcriptional programs. We identified six immune community types, with macrophage-dominated Myelonets representing the predominant spatial pattern of immune organisation. In chemotherapy-exposed tumors, Myelonets showed coordinated lipid metabolism-immunosuppression and inflammation-MHC-II macrophage transcriptional programs, with SPP1, C1Q, VEGF, MMPs, and CCL18 linked to immunosuppressive states and fibroblasts emerging as key mediators of macrophage communication. Chemotherapy contracted large Myelonets while increasing CD8+ T-cell organization into Lymphonets. Persistent macrophage dominance within Myelonets was associated with adverse outcomes among patients who achieved a complete response to treatment. Together, we identify Myelonets as clinically relevant, multicellular immunoregulatory niches sustained by spatiotemporally coordinated macrophage programs and stromal crosstalk.

KRAS is one of the most frequently mutated oncoproteins in cancer. Its ability to induce malignant transformation relies on metabolic reprogramming that causes cells to become dependent on aerobic glycolysis as a primary source of energy and for generating biological building blocks. Thus far, the signaling mechanism used by oncogenic KRAS to promote these changes in cancer cell metabolism has not been fully elucidated. However, through studies in pancreatic ductal adenocarcinoma (PDAC) cell lines and patient-derived organoids, we now demonstrate how oncogenic KRAS triggers an increase in glycolytic activity and identify Survivin as a newly discovered and critical KRAS-signaling partner essential for promoting these metabolic changes. We show that oncogenic KRAS potently upregulates the expression of Survivin in PDAC cells and patient-derived organoids undergoing increased glycolysis, whereas depleting Survivin expression inhibits their glycolytic activity and growth. Through a combination of cellular, biochemical, and imaging approaches, we further show that Survivin promotes the formation of unique microtubule-based structures that resemble invadosome rosettes, allowing for the recruitment of the glycolytic enzymes triose phosphate isomerase (TPI) and glyceraldehyde-3-phosphate dehydrogenase (GAPDH) to these super-structures which drives the increases in glycolysis. These findings demonstrate that by directing the assembly of a microtubule-based complex of metabolic enzymes, Survivin serves as a vital link in a KRAS signaling pathway responsible for promoting the metabolic changes necessary for the accelerated growth of PDAC cells, and thus potentially highlight new therapeutic strategies for treating KRAS-dependent cancers.

Genomic instability is a major driver of tumor evolution, promoting diversification and adaptation while simultaneously increasing the accumulation of deleterious alterations. How tumor populations balance these opposing effects remains poorly understood. Here, we introduce a computational framework that explicitly represents diploid genomes, functional gene classes, point mutations, and chromosome-segregation errors in spatially constrained and well-mixed tumor populations. We identify a viability boundary separating sustained tumor expansion from instability-induced population collapse. Within the viable regime, mutation and selection generate a stable distribution of genomic-instability classes that is accurately captured by an analytical replicator--mutator description. Near the viability boundary, tumor dynamics exhibit prolonged extinction transients and strong sensitivity to stochastic fluctuations, with important differences between solid and liquid architectures. Chromosomal alterations further modify growth by creating transient benefits through increased gene dosage and genetic redundancy, while ultimately increasing genomic fragility. Finally, simulated interventions show that eliminating low-instability subpopulations or increasing the global mutational burden can displace tumors beyond their viability boundary and trigger irreversible collapse. These results identify genome instability as both an evolutionary advantage and an intrinsic vulnerability, providing a quantitative framework for developing therapies that exploit the limits of tumor evolution.

Keloids are benign fibroproliferative disorders majorly characterized by excessive extracellular matrix deposition, with recurrence rates exceeding 80% following conventional therapy. Although epigenetic dysregulation has been implicated in keloid pathogenesis, whether genome-wide DNA methylation actively drives pathological cellular reprogramming, and whether this state is therapeutically reversible, remains unclear. We performed genome-wide DNA methylation profiling on keloid tissues, matched primary keloid fibroblasts, and normal controls. Our analysis revealed a shared DNA hypermethylation pattern between keloid tissues and fibroblasts, which was validated by three independent public cohorts. By integrating DNA methylome and transcriptome, we demonstrated that DNA methylation-regulated genes were enriched in osteochondrogenesis-related pathways, such as cartilage and bone development pathways. Furthermore, pharmacologic inhibition of DNA hypermethylation by DNA demethylating agent decitabine reduced the expression of osteochondrogenic markers and inhibited collagen deposition and keloid growth in primary keloid fibroblasts and patient-derived xenograft (PDX) model, offering a potential therapeutic strategy of keloid.

MYCN amplification drives replication stress in high-risk neuroblastoma, yet how MYCN-amplified tumour cells tolerate this stress to sustain proliferation remains poorly understood. Here we show that FBXL12, an SCF ubiquitin ligase substrate receptor that targets the Fanconi anaemia protein FANCD2 for degradation at replication forks, as well as the broader Fanconi anaemia and replication stress transcriptional program are elevated in high-risk and MYCN-amplified neuroblastoma. High FBXL12 expression independently predicts poor survival across neuroblastoma patient cohorts. FBXL12 loss stabilizes FANCD2 on chromatin, elevates ATR-dependent replication stress signalling and DNA damage during S phase, and impairs proliferation of MYCN-amplified neuroblastoma cells in vitro and in vivo. Mechanistically, MYCN directly engages the FBXL12-FANCD2 complex and antagonises FBXL12-mediated degradation of FANCD2 at replication forks, revealing that the oncogenic driver of replication stress also actively preserves the chromatin-bound FANCD2 pool required to tolerate it. Beyond S phase, FBXL12 loss disrupts FANCD2-dependent mitotic DNA synthesis and transmits unresolved replication intermediates into daughter cells. FBXL12-deficient cells consequently show transcriptional activation of MYC target gene, ATR, and mTOR signalling programs, and this pathway-concordant state confers differential sensitivity to ATR, and mTOR-targeting compounds, nominating candidate therapeutic strategies for this disease subset. Together, these findings define a MYCN-FBXL12-FANCD2 axis as a clinically relevant vulnerability in high-risk neuroblastoma.

In large data sets discovery is often limited to pre-conceived hypotheses and data fishing. Here we tested whether systematic exploration of AI generated hypotheses could uncover clinically meaningful signals in extensively studied data. We deployed AutoDiscovery, a newly launched large language model (LLM) framework designed to search for hypotheses based on surprisal and systematically interrogate complex datasets, on The Cancer Genome Atlas breast cancer cohort. The system did not identify clinically meaningful novel findings without human input. However, a seeded warm-start run with minimal text input from an oncologist revealed multiple interesting and surprising hypotheses. Among these was that a robust immune signature was present across all subtypes of invasive lobular carcinoma (ILC) that exceeded invasive ductal carcinoma (IDC). This observation was independently validated in independent cohorts and confirmed by high-sensitivity multi-immunofluorescence tumor tissue analyses. These results suggest immunotherapy approaches should be tested in ILC including early stage ER+HER2- ILC; these patients are currently excluded from large neoadjuvant immunotherapy trials. They further demonstrate that surprisal-based hypothesis generation frameworks can extract previously unappreciated patterns from deeply interrogated cancer datasets and imply that disease domain experts working with LLMs can derive more meaningful insights from complex data than either could achieve alone.

Radiation-induced DNA double-strand breaks (DSBs) drive cellular mortality, mutagenesis, and severe evolutionary bottlenecks. While classical phenomenological models, such as the Linear-Quadratic (LQ) framework, reliably predict macroscopic population survival, they obscure the intrinsic single-cell stochasticity that governs critical rare events like tumor recurrence or the emergence of radioresistant persisters. To bridge this divide, we develop a mathematically exact stochastic differential equation (SDE) framework that models continuous DSB induction and repair as a Feller square-root process. By deriving exact closed-form expressions for the foci moments, we establish a highly efficient Maximum Likelihood Estimation (MLE) pipeline that circumvents computationally exhaustive Monte Carlo simulations, allowing the direct extraction of deterministic repair velocities and intrinsic molecular noise from empirical single-cell $\gamma$-H2AX data. Integrating this kinetic model with a cumulative damage hazard via the Feynman-Kac formalism, our framework seamlessly recovers the classic macroscopic LQ survival topology from microscopic first principles. Furthermore, systematic sensitivity analysis uncovers a fundamental evolutionary duality: while initial physical damage operates additively, ultimate cellular fate is driven by a nonlinear survival response governed by the trade-off between the damage hazard rate and intrinsic molecular noise strength. Crucially, we demonstrate that this molecular noise inherently enhances population survival. Governed by Jensen's inequality, stochastic variance acts as a non-genetic bet-hedging mechanism that buffers the population by favoring cells with transiently low damage loads. Ultimately, this exact stochastic framework bridges microscopic biophysics and macroscopic demographics, offering deep mechanistic insights into the evolutionary roots of radioresistance.

A subset of untreated primary prostate cancer (PCa) contain substantial focal T-cell infiltrates, but whether these reflect antitumor responses that could potentially be enhanced by immune checkpoint blockade (ICB) remains unclear. We used immunohistochemistry, immunofluorescence, whole-slide spatial analysis, bulk RNA sequencing, and immune-cell deconvolution to characterize immune infiltrates in untreated primary PCa. Absolute CD8 T-cell density generally increased with total CD3 T-cell density, but the CD8/CD3 ratio decreased as overall T-cell density increased, indicating a preferential increase in CD4 T cells. Highly infiltrated tumors also had lower GZMB abundance relative to CD8 T-cell abundance. Multiplex analysis showed trends toward greater TIM3 and LAG3 expression among PD1CD8 T cells and increased regulatory T-cell features in highly infiltrated tumors. TIGIT cell density and the TIGIT/CD3 ratio increased with T-cell infiltration, whereas PD1/CD3 was not associated with overall CD3 T-cell density. Both TIGIT/CD3 and PD1/CD3 ratios were enriched within lymphoid aggregates compared with matched tumor and benign regions, consistent with these structures being checkpoint-rich immune niches. Transcriptomic analyses supported a shift in relative immune composition toward CD4 T cells and selective increases in immune checkpoints. Together these findings suggest that effective immune responses in a subset of primary PCa with increased T-cell infiltration are being repressed by several mechanisms and may respond to therapies targeting specific immunosuppressive mechanisms.

Liver metastases (LM) remain a major cause of death from different cancer types, in particular malignancies of the gastrointestinal tract. Liver metastases predict a poor response to immunotherapy due, among others, to the immunotolerant microenvironment (ME) of the liver and loss of local and systemic cytotoxic T cells. Thus, strategies that can reprogram the immune ME of the liver and restore cytotoxic T cell reactivity are being sought. We previously reported that estrogen signaling blockade impedes the growth of LM by reducing MDSC accumulation and monocyte/macrophage polarization. The aim of this study was to elucidate the underlying mechanism(s) and assess whether estrogen signaling in the myeloid lineage was driving the immunotolerant ME of LM. To this end, we generated mice with conditional myeloid cell-specific deletions of estrogen receptors (ER) or ER{beta} and analyzed in these mice the effect of ER loss on the liver immune ME and the outgrowth of LM. In mice with ER, but not with ER{beta} deletion, we observed a marked reduction in the growth of murine colon carcinoma MC-38 liver metastases as compared to their respective controls. Flow cytometry and immunohistochemistry revealed a decrease in macrophages that were polarized to the pro-tumorigenic M2-like phenotype and a concomitant increase in activated CD8+ T and NK cells relative to controls. Bulk RNAseq analysis performed on hepatic immune cells infiltrating the liver revealed changes in the expression of key cytokines/chemokines mediating immune cell recruitment, activation and polarization, including Ccl5 (upregulated) and Csf1 (downregulated). Taken together, the data suggest that ER signaling in myeloid-derived cells programs the immune landscape and contributes to an immunosuppressive and metastases-growth permissive ME in the liver.

Prognostic models in oncology are developed one cancer at a time, from that cancer's own labelled outcomes, and fail where prognostic information is scarcest. Rare cancers account for roughly a fifth of diagnoses and most paediatric malignancies, yet seldom supply enough events for a reliable time-to-event model. We therefore asked whether a representation learned without outcome labels can supply what those cohorts cannot. A Transformer encoder was pretrained by masked field-value modelling on 9425135 tumour records from the SEER 17 registries, diagnosed in 2000 to 2023. Only diagnosis-time fields passing a fail-closed coding-verification gate were admitted, and each record was emitted as an era-specific and a harmonised view, keeping two decades of recoding auditable. The encoder was then frozen and read by a linear Cox head for overall survival. Nine rare cancers were removed from the pretraining corpus entirely, each requiring an independent pretraining run. On a sealed test partition, all nine exceeded an architecture-identical random frozen encoder in Harrell concordance by +0.0034 to +0.0368, every lower confidence limit above zero. At 256 labelled patients, all 67 cancers favoured the pretrained representation over budget-matched Cox regression, median difference +0.0283. The advantage was bounded: given the entire training set, Cox regression was favoured in seven of nine rare cancers. The encoder did not outperform a field-frequency baseline on its own objective, so upstream reconstruction did not predict downstream transfer. Outcome-agnostic registry pretraining carries prognostic signal into cancers it has never seen, and is most useful where labels are fewest, without establishing clinical utility.

Next-generation sequencing (NGS) for the detection of somatic variants has become the method of choice in a variety of molecular oncology fields and in the clinic. Its use ranges from sequencing entire tumor genomes and transcriptomes to targeted clinical diagnostic gene panels. The NYU Langone Genome PACT (Profiling of Actionable Cancer Targets, LG-PACT) assay is a qualitative in vitro diagnostic test that uses targeted next generation sequencing (NGS) of formalin-fixed paraffin-embedded (FFPE) tumor tissue matched with normal specimens from patients to detect gene alterations in a targeted panel covering 606 genes and the TERT promoter. Indications for testing are cancer (solid tumors and hematological malignancies) where a mutational profile from multiple genes would be informative for disease stratification, prognosis, or treatment options including targeted therapies and eligibility for clinical trials. The test is intended to provide information on somatic mutations including point mutations, small insertions/deletions (indels), and copy number aberrations for diagnostic and treatment decisions. LG-PACT is a United States Food and Drug Administration (FDA) cleared diagnostic test (510K: K202304). The clinical interpretation of sequencing data of molecular tumor markers from NGS encompasses automated variant calling tools with human interpretation. This final mostly manual review of data step is intensive, involving highly trained scientists, encompassing literature review, interpretation and clinical tier classification by pathologists, who then provide a complete molecular diagnostic report to the treating oncologists. We provide analysis of 1339 clinical genomic profiles from 31 different cancers and their subtypes, comprising of central nervous system (CNS) 792 (59%) cases (incl. meningioma, glioma and glioblastoma), with 267 (20%) cases predominantly of lung, pancreatic and colorectal and 280 of others (21%). Here, we present the technical challenges of validating an NGS oncological diagnostic targeted assay for clinical grade accuracy and sensitivity for patient care. We show how copy number alterations provide a more comprehensive description of the tumors genomic profile. We then outline the utility of targeted panel sequencing based on certified pathologist selection of reportable variants for our current patient cohort. Where analysis of variant detection has led to 49.4% (661/1339) of our clinical tumor samples containing mutations in known therapy targeted genes, 35.6% (477/1339) with mutation detected in other genes, and 15% (201/1339) cases being negative.