Clinical Cancer Research

Purpose: Limited CNS bioavailability and pharmacodynamics are obstacles to effective systemic therapies for glioblastoma. One strategy to overcome these challenges is drug combinations enhancing CNS penetration and/or tumor chemosensitivity. LP-184, a synthetic acylfulvene class alkylator, induces DNA damage and inhibits glioblastoma cell viability in pre-clinical models. LP-184 is a prodrug converted to active metabolites by intracellular prostaglandin reductase 1 (PTGR1) that is over-expressed in >70% of glioblastoma. DNA damage induced by LP-184 is MGMT agnostic and reversed by transcription-dependent NER. Patients: LP-184 was evaluated in a Phase 1a study (NCT05933265) in 63 adult patients with advanced malignancies including 16 patients with recurrent glioblastoma. All patients with glioblastoma received prior standard-of-care therapy and most had received 1 or more additional therapies before enrollment. Results: Patients with glioblastoma experienced more frequent transaminitis, Grade 1-2 nausea and a trend towards more frequent and severe thrombocytopenia compared to the non-glioblastoma cohort. Otherwise, overall toxicity profiles were similar. Clinical pharmacokinetic analysis combined with published pre-clinical intra-tumoral bioavailability data (~20% penetration) predicted that LP-184 at the recommended dose for expansion (RDE) would achieve cytotoxic levels if combined with spironolactone, a BBB permeable ERCC3 degrader and TC-NER inhibitor that sensitizes glioblastoma cells to LP-184 3-6-fold. We show that three daily doses of spironolactone deplete orthotopic glioblastoma PDX ERCC3 protein by ~ 80% and increases tumor LP-184 cytotoxicity 2-fold. Conclusions: LP-184 is well tolerated at the RDE, and we establish a clinically translatable scheme for dosing spironolactone in combination with LP-184 for a future Phase 1b clinical trial.

1.The ABACUS study was a single arm, phase II trial evaluating neoadjuvant atezolizumab in operable urothelial carcinoma (UC). Initial bulk transcriptomic and immunohistochemistry analyses suggested links between immune activation, tissue remodeling, and resistance pathways such as transforming growth factor {beta} (TGF{beta}) that were associated with clinical outcome. To further characterize spatial and phenotypic changes at high resolution, artificial intelligence-assisted digital image analysis of hematoxylin and eosin sections and spatial transcriptomics (10x Genomics Visium) were performed on paired tissue samples. In baseline samples, cells residing in lymphoid aggregates and tertiary lymphoid structures (LAs/TLSs) were more abundant in stable disease than in relapse and exhibited gene expression programs associated with improved survival in UC. Most spatial features reflected shared pharmacodynamic changes between stable disease and relapse; however, carcinoma-endothelial adjacency was reduced significantly following treatment and differed between groups, accompanied by distinct transcriptional programs. Together, these findings indicate that atezolizumab induces localized immune and stromal remodeling within the tumor microenvironment, while non-response despite immune expansion is associated with persistent spatial immune exclusion and carcinoma-endothelial adjacency. Spatial and phenotypic biomarkers identified here may inform rational combination strategies for immune checkpoint inhibitor-refractory urothelial carcinoma.

Background: Immune checkpoint inhibitors (ICIs) have revolutionized cancer therapy but can cause serious immune-related adverse events (irAEs), with pneumonitis (ICI-P) being among the most severe. Early identification of high-risk patients before ICI initiation is critical for closer monitoring, timely intervention, and improved outcomes. Purpose: To develop and validate a deep learning foundation model to predict ICI-P from baseline CT scans in patients with lung cancer. Methods: We designed the Checkpoint-Inhibitor Pneumonitis Hazard EstimatoR (CIPHER), a deep learning foundation model that combines contrastive learning with a transformer-based masked autoencoder to predict ICI-P from baseline CT scans in patients with lung cancer. Using self-supervised learning, CIPHER was pre-trained on 590,284 CT slices from 2,500 non-small cell lung cancer (NSCLC) patients to capture heterogeneous lung parenchymal patterns. After pre-training, the model was fine-tuned on an internal NSCLC cohort for ICI-P risk prediction, using images from 254 patients for model development and 93 patients for internal validation. We compared CIPHER with classical radiomic models and further evaluated it on an external NSCLC cohort of 116 patients. Results: In the internal immunotherapy cohort, CIPHER consistently distinguished patients at elevated risk of ICI-P from those without the event, with AUCs ranging from 0.77 to 0.85. In head-to-head benchmarking, CIPHER achieved an AUC of 0.83, outperforming the radiomic models. In the external validation cohort, CIPHER maintained strong performance (AUC = 0.83; balanced accuracy = 81.7%), exceeding the radiomic models (DeLong p = 0.0318) and demonstrating higher specificity without sacrificing sensitivity. By contrast, the radiomic model showed high sensitivity (85.0%) but markedly lower specificity (45.8%). Confusion matrix analysis confirmed the robust classification performance of CIPHER, correctly identifying 80 of 96 non-ICI-P cases and 16 of 20 ICI-P cases. Conclusions: We developed and externally validated CIPHER for predicting future risk of ICI-P from pre-treatment CT scans. With prospective validation, CIPHER may be incorporated into routine patient management to improve outcomes.

Purpose: To test whether histology-derived gene-expression signatures from routine hematoxylin and eosin slides are prognostic for recurrence and predictive of chemotherapy benefit in early breast cancer. Methods: We conducted a multi-cohort study including CALGB 9344 (anthracycline +/- paclitaxel), CALGB 9741 (standard vs dose-dense chemotherapy), a pooled Chicago real-world cohort, and the American Cancer Society (ACS) Cancer Prevention Studies-II and -3. Whole-slide images were processed with a previously described pipeline to generate 61 histology-derived signatures per patient. The primary endpoint was distant recurrence-free interval (DRFI), except in ACS, where breast cancer-specific survival was used. Secondary endpoints include distant recurrence-free survival (DRFS) and overall survival. The most prognostic signature in CALGB 9344, selected by Harrell's C-index, was evaluated in additional cohorts. Signature-treatment interaction was assessed by likelihood-ratio tests. Multivariable Cox models incorporating age, tumor size, nodal status, estrogen/progesterone receptor status, and signature were fit in CALGB 9344 to improve risk stratification. Results: A total of 7,170 patients were included across four cohorts. The top histology-derived signature in CALGB 9344 showed strong prognostic performance for 5-year DRFI (C-index 0.63) and performed well across validation cohorts (C-index 0.60, 0.70, and 0.62 in CALGB 9741, Chicago, and ACS, respectively). The strongest predictive signal for treatment benefit was observed for DRFS. High-risk cases identified by the signature demonstrated greater benefit from taxane in CALGB 9344 (adjusted hazard ratio [aHR] 0.76 for DRFS, 95% CI 0.66-0.88; interaction p=0.028), from dose-dense chemotherapy in CALGB 9741 (aHR 0.69, 95% CI 0.56-0.85; interaction p=0.039), and differential chemotherapy benefit in the Chicago cohort (aHR 0.84, 95% CI 0.59-1.21; interaction p=0.009). Combined clinical-histology models improved risk stratification and identified low-risk groups with a 2%-10% risk of distant recurrence or breast cancer death. Conclusion: Histology-derived signatures from H&E images are broadly prognostic and, unlike clinical factors, may predict chemotherapy benefit.

KIF18A inhibition has emerged as a therapeutic strategy for chromosomally unstable cancers, but clinical development is limited by the absence of a deployable predictive biomarker. Here we identify strong, diffuse p16INK4a expression, a well-established surrogate marker of Rb-pathway inactivation, as a predictive biomarker of response to KIF18A inhibition, and show that Rb-pathway inactivation marks a biologically distinct subset of cancers sensitive to this therapeutic approach. In sensitive models, low Rb activity is associated with robust spindle assembly checkpoint signaling and prolonged mitotic arrest following KIF18A inhibition. Weakening the spindle assembly checkpoint in this context is sufficient to confer resistance. Across three independent pan-cancer sensitivity datasets generated with distinct KIF18A inhibitors, Rb-pathway altered models were significantly more sensitive than histology-matched Rb-intact comparators, with the strongest association observed in cancers harboring direct RB1 loss or inactivating mutation. Guided by this mechanism, we retrospectively analyzed p16INK4a expression by immunohistochemistry (IHC) in pre-treatment tumor biopsies from 79 heavily pre-treated high-grade serous ovarian cancer patients across three dose-escalation or expansion cohorts and treated with two different KIF18A inhibitors (sovilnesib and VLS-1488) sharing a common mechanism of action. p16INK4a-high tumors showed substantially higher objective response rates than their p16INK4a-low counterparts (36.0% versus 2.2%; P = 0.0002) and markedly longer progression-free survival (median 24.3 versus 7.9 weeks; hazard ratio, 0.16; P < 0.0001). These findings establish p16INK4a as a mechanistically-based, clinically implementable biomarker of clinical response to KIF18A inhibition that is poised to support pan-cancer development of KIF18A inhibitors guided by Rb-pathway inactivation.

Mechanistic modeling has long been used as a tool to describe the dynamics of biological systems, especially cancer in response to treatment. Their key advantage lies in interpretability of relationships between input parameters and outcomes of interest. In contrast, machine learning techniques offer strong prediction performance, especially for high dimensional datasets that are common in oncology. Here, we employ a Mechanstic Learning framework that combines the advantages of both approaches by training machine learning models on mechanistic parameters inferred from clinical patient data. The mechanistic model (a Markov chain model) contains sixteen parameters that describe the rate of cell fate transitions that occur in patients with B-cell precursor acute lymphoblastic leukemia. The machine learning (a ridge logistic regression model) is trained on these parameters to predict two clinically-relevant features: BCR::ABL1 fusion gene status (positive or negative) and minimal residual disease status (positive or negative) post-induction chemotherapy. Model training is done in an iterative fashion to assess which (and how many) parameters are critical to maintain high predictive performance. Using machine learning models trained on the clinical flow-cytometry data, we find that the stem-like cell state alone is the most predictive feature for both BCR::ABL1-positive and MRD-positive disease, with combination scores (defined as the average of accuracy, balanced accuracy, and area under the curve) of 0.80 and 0.67, respectively. By comparison, mechanistic learning achieves comparable or improved combination scores for BCR::ABL1-positive and MRD-positive disease, with scores of 0.81 and 0.71, respectively, using only de-differentiation for BCR::ABL1 and primitive-state persistence together with differentiation-directed exit for MRD. Thus, the mechanistic-learning approach not only preserves predictive performance, but also provides a biological hypothesis for why stemness is predictive of these clinically relevant outcomes.

Despite considerable advances, the emergence of treatment resistance to tyrosine kinase inhibitors (TKIs) therapy remains a significant challenge in chronic myeloid leukemia (CML). Here, we report the first clinical case of resistance to combined ponatinib and asciminib therapy in a CML patient who relapsed with B lymphoblastic blast crisis. While at presentation the patient harbored the canonical e13a2 BCR::ABL1 fusion, at relapse his disease harbored the T315I mutation together with a novel e6a3 BCR::ABL1 fusion, arisen by internal deletion in the original translocated allele. Structural modeling and biochemical analyses demonstrated that deletion of exon 2-encoded residues of ABL1 destabilizes the autoinhibited conformation, resulting in a hyperactive kinase with increased propensity for B-cell differentiation. Functional studies revealed that both BCR::ABL1e6a3 and BCR::ABL1e6a3/T315I conferred resistance to ponatinib and asciminib, alone or in combination. BCR::ABL1e6a3 demonstrated enhanced sensitivity to active-state selective inhibitors dasatinib and bosutinib, whereas BCR::ABL1e6a3/T315I remained resistant. Combined drug sensitivity assays showed that axitinib restored inhibitory activity when combined with ponatinib or asciminib. Strikingly, a combination of axitinib and asciminib with low dose ponatinib fully suppressed enzymatic activity of BCR::ABL1e6a3/T315I and cellular proliferation. These data show that treatment with asciminib and ponatinib can select for mutations with notably elevated enzymatic activity, effectively targeted by an axitinib-based triple combination. These data highlight the remarkable mutability of the BCR::ABL1 kinase, including through novel isoforms and provides a strong rationale for the clinical assessment of a triple inhibitor combination as a strategy to overcome resistance to dual ponatinib and asciminib therapy.

Protein arginine methyltransferase 5 (PRMT5) is a synthetic lethal target in methylthioadenosine phosphorylase-deleted (MTAP-null) cancers. Second-generation MTA-cooperative PRMT5 inhibitors preferentially target MTAP-null cells while largely sparing MTAP-wildtype (MTAP-WT) cells, thereby improving tumor selectivity over first-generation PRMT5 inhibitors. Despite encouraging efficacy and safety signals in early clinical studies, the modest objective response rates (ORRs) observed with these inhibitors suggest that intrinsic or acquired resistance may limit their clinical benefit. Here, we investigated mechanisms of acquired resistance to the MTA-cooperative PRMT5 inhibitor BMS-986504/MRTX1719 in MTAP-null non-small cell lung cancer (NSCLC) cells and sought to identify therapeutic vulnerabilities that emerge upon resistance. Using multiple in vitro-derived resistant models, we found that acquired resistance was not fully explained by alterations in PRMT5 activity or reduced MTA levels. Instead, resistance was associated with collateral sensitivity to MEK inhibition and enrichment of MAPK-related transcriptional programs. Together, these findings identify MEK inhibition as an actionable collateral vulnerability in MTAP-null NSCLC cells that acquire resistance to PRMT5 inhibition.

Definitive radiotherapy (RT) for prostate cancer (PC) with dose intensification and/or focal boosting has excellent oncologic outcomes, but many patients experience adverse events. Dose escalation to the whole prostate improves outcomes at the expense of increased late adverse events. Intraprostatic recurrence after definitive RT typically occurs at the site of the primary tumor, suggesting that dose to the site of the dominant lesion is an important predictor of future failure. The efficacy and safety of tumor-focused RT compared to that of standard RT for definitive treatment of localized PC has not been assessed. RadTARGET (RAdiation Dose TAiloRing Guided by Enhanced Targeting) is a phase II randomized trial that aims to demonstrate superior safety of image-guided, tumor-focused RT compared to standard RT for acute genitourinary (GU) or gastrointestinal (GI) in the setting of definitive RT for intermediate- and high-risk PC. The study intervention is image-guided, tumor-focused RT with dose intensification of cancer visible on imaging and dose de-intensification to remaining prostate. Patients will be randomized to two arms: those who receive standard RT dose and those that receive tumor-focused RT. The study population will be patients with intermediate- or high-risk PC planning to undergo definitive RT with or without systemic therapy. The primary endpoint to compare between randomized arms is acute GU or GI grade [&ge;]2 adverse events. Participant and study duration are 5 years and 8 years, respectively. RadTARGET will compare the efficacy and safety of tumor-focused RT to that of standard RT for definitive treatment of localized PC. We hypothesize that the tumor-focused approach will substantially reduce adverse events after prostate RT while retaining high efficacy. If this hypothesis is confirmed, we will conclude that a phase III randomized control trial is warranted to formally establish oncologic non-inferiority compared to the current standard of whole-gland dose escalation.

High-grade serous ovarian carcinoma (HGSC) is an aggressive malignancy for which bulk transcriptomic subtypes are used to stratify tumors, interpret biology, and guide biomarker development. The four TCGA-derived subtypes, mesenchymal (C1.MES), immunoreactive (C2.IMM), proliferative (C5.PRO), and differentiated (C4.DIF), are consistently observed across cohorts. However, despite their prominence, these subtypes have not translated into therapeutic utility, and their biological basis remains unresolved. Here, we show that HGSC transcriptomic subtypes are largely determined by tumor cellular composition rather than intrinsic malignant transcriptional programs. By integrating controlled single-cell-derived pseudobulk simulations with deconvolution-based analysis of 1,834 primary HGSC tumors across RNA-seq and microarray cohorts, we demonstrate that subtype probabilities align along a composition-driven axis of stromal and immune variation. Cellular composition alone predicted subtype labels with high accuracy (ROC-AUC = 0.81-0.95) and explained a substantial fraction of subtype-associated transcriptomic variation, with the mesenchymal (C1.MES) subtype representing the most robust and reproducible example of composition-driven signal. Although a secondary, composition-independent expression signal is detectable, it does not define the dominant structure of subtype classification. These findings redefine HGSC transcriptomic subtypes as features of the tumor ecosystem rather than discrete malignant states. This reinterpretation has immediate implications for studies that use subtype labels to infer tumor-intrinsic biology and provides a generalizable framework for separating composition-driven and intrinsic signals in bulk tumor data. Significance StatementHGSC transcriptomic subtypes lack consistent clinical utility and remain biologically ambiguous. We show subtype assignments are largely driven by tumor cellular composition, and less so by distinct intrinsic tumor states.

Glioblastoma (GBM) is the most prevalent primary brain cancer, with poor prognosis and limited therapeutic options available. The genetic and cellular heterogeneity characteristic of GBM contributes to poor response rates. Activating mutations of the epidermal growth factor receptor (EGFR) gene are among the most frequent alterations in GBM, occurring in roughly half of cases. Despite the prevalence of EGFR mutations, EGFR inhibition has shown limited success in GBM. The transcription factor C/EBP{beta} is a master regulator of the mesenchymal transformation in GBM, an aggressive state characterized by increased invasiveness and resistance to chemotherapy. Lucicebtide is a C/EBP{beta} antagonist peptide with demonstrated single agent activity in patients with recurrent GBM that is currently being evaluated in a clinical trial in combination with radiation and temozolomide in patients with newly-diagnosed GBM (NCT04478279), with emerging data supporting clinical activity in that setting. Here we show that in the TCGA-GBM dataset, patients with EGFR mutations display significant enrichment of a high C/EBP{beta} activity signature. Functionally, genetic inactivation of EGFR by CRISPR results in synthetic lethality in the presence of lucicebtide in GBM cell lines, and synergistic in vitro cytotoxicity and suppression of C/EBP{beta} target gene expression was observed in combination experiments with lucicebtide and EGFR inhibitors. Finally, enhanced anti-tumor activity was demonstrated in vivo in the combination setting, as combined subpharmacologic dose levels of lucicebtide and the EGFR inhibitor osimertinib potently suppressed GBM xenograft growth. These data identify EGFR and C/EBP{beta} dependencies in GBM and support lucicebtide combination with EGFR inhibitors as a potential therapeutic option for a sizable fraction of GBM patients.

Pancreatic ductal adenocarcinoma (PDAC) remains one of the deadliest solid tumors and continues to face low treatment-trial participation, fragmented evidence workflows, and labor-intensive ab- straction of unstructured clinical text. Existing oncology-focused language models show promise, but many depend on private institutional corpora, limiting reproducibility and practical reuse across centers. We present Onca, an open 9B dense model designed for four PDAC-relevant tasks: trial eligibility screening, case-specific clinical reasoning, structured pathology report extraction, and molecular variant evidence reasoning. Onca is fine-tuned from Qwopus3.5-9B-v3 with a single Un- sloth BF16 LoRA adapter on 37,364 training rows drawn from openly available sources. The evalu- ation spans 11 panels and compares Onca against Woollie-7B, CancerLLM-7B, OpenBioLLM-8B, and the unmodified Qwopus base. Onca achieves the strongest overall results on Trial Screening (81.6 F1), Clinical Reasoning (14.1 composite), Pathology Extraction (30.5 field exact-match), Pub- MedQA Cancer (68.3 macro-F1), and PubMedQA (66.5 macro-F1). The strongest gains appear in tasks closest to routine oncology workflow, especially trial review and pathology structuring. These findings suggest that clinically targeted pancreatic-cancer language models can be built from open data with competitive performance while remaining practical to train on a single workstation-scale GPU setup.

Objective: To demonstrate the proof of principle that machine learning (ML) can be used to quantify Gleason Pattern (GP) 4 on digitized biopsy slides using multiple measurement approaches, allowing direct comparison of their prognostic performance. Methods: We assembled a convenience sample of 726 patients with grade group 2-4 prostate cancer on systematic biopsy who underwent radical prostatectomy between 2014 and 2023. Digitized biopsy slides were analyzed using a machine-learning algorithm (PAIGE-AI) to quantify GP4 using multiple measurement approaches, particularly with respect to how gaps between cancer foci (interfocal stroma) were handled. GP4 extent was quantified using linear measurements or a pixel-based area metric. Discrimination of each GP4 quantification approach, along with Grade Group (GG), was assessed for adverse radical prostatectomy pathology and biochemical recurrence. Results: We identified 15 different quantification approaches and observed differences between their discrimination. The highest discrimination was in the pixel-counting method (AUC 0.648). GP4 quantification outperformed GG for predicting adverse pathology (AUC 0.627 vs 0.608). Amount of GP3 was non-predictive once GP4 was known. These findings were consistent for BCR. Conclusions: We were able to measure slides using 15 distinct measurement approaches and replicated prior findings using ML to quantify GP4. Our findings support the use of ML as a research tool to compare different GP4 quantification approaches. We intend to use our method on larger cohorts to determine with which measurement approach best predicts oncologic outcome.

BackgroundGangliogliomas (GGs) are low-grade glioneuronal tumors that frequently present with drug-resistant epilepsy. Although their indolent course contrasts with their high epileptogenic potential, the oncogenic mechanisms sustaining neuronal precursor-like populations within the tumor microenvironment remain poorly defined. MethodsWe performed spatial transcriptomic profiling on eight histologically confirmed GGs and matched healthy cortex to map the cellular and molecular architecture of the tumor microenvironment. Integrated analysis with weighted gene correlation network analysis (WGCNA) defined recurrent oncogenic programs and spatially resolved tumor-stroma interactions. ResultsEight conserved gene modules emerged, encompassing physiological cortical, reactive glial, and oncopathological programs. The latter captured extracellular matrix (ECM) remodeling, vascular-immune signaling, and persistence of immature, proliferative neuronal-like states. Spatial modeling revealed that these oncopathological programs form structured niches at the tumor-brain interface, where radial glia-derived neuronal-like tumor cells coexist with immune and stromal elements engaged in ECM turnover and cytokine signaling. ConclusionsGanglioglioma represents a hybrid glioneuronal neoplasm in which developmental neuronal programs are co-opted by tumor-associated stromal and immune cues. This convergence establishes a permissive oncogenic niche that sustains precursor-like tumor cells and provides a mechanistic basis for both the tumors benign growth and its intrinsic epileptogenicity. Key PointsO_LISpatial transcriptomics identifies reproducible transcriptional programs that define the ganglioglioma microenvironment. C_LIO_LITumor-associated regions show transcriptional programs consistent with immature neuronal states together with ECM remodelling and immune activity. C_LIO_LISingle-cell reference data indicate that immature neuronal programs in ganglioglioma resemble radial glia-derived developmental states. C_LI Importance of the StudyGanglioglioma is a low-grade glioneuronal tumor that combines benign growth with pronounced epileptogenicity, yet the molecular basis of this dual behavior remains poorly understood. Through spatial transcriptomics integrated with single-cell analysis, we reveal that ganglioglioma architecture is defined by two interacting transcriptional axes: a residual glioneuronal network and a tumoral niche enriched for extracellular-matrix, vascular, and immune programs. Within these niches, immature neuronal-like tumor cells persist in a developmentally arrested state maintained by ECM-immune signaling. This spatially organized interplay between physiological and pathological programs explains both the low oncologic aggressiveness and high excitability of these lesions. Our findings provide molecular signatures that may refine diagnostic classification within the LEAT spectrum, delineate epileptogenic zones, and identify candidate pathways for therapeutic modulation of the ganglioglioma microenvironment.

Background: Glioblastoma (GBM) is the most aggressive primary brain tumor in adults. Radioresistance, partly mediated by glioma stem-like cells, represents a major clinical challenge which could be overcome by the identification of the modulators of radioresistance. Existing CRISPR screens in human GBM models have largely used two-dimensional cultures with short-term viability readouts, failing to capture the long-term clonogenic behaviour underlying tumour recurrence after radiotherapy. Method: We developed ClonoScreen3D-CRISPRi, combining CRISPRi-mediated gene knockdown with three-dimensional clonogenic survival assays. Two GBM cell lines (G7 and GBML20), differing in MGMT promoter methylation status, were engineered to express the KRAB-dCas9 editor. Nine candidate radiosensitivity modifiers, selected through transcriptomic analysis, pharmacological studies, and literature review, were examined in both lines. Target validation was performed using full radiation dose-response assays and a pharmacological inhibitor. Results: The majority of candidate genes significantly altered survival fraction following irradiation in both cell lines. Knockdown of NFKB2, RELB, and CDK9 produced the most potent radiosensitization, with sensitizer enhancement ratios of 1.39-1.70 in validation studies, exceeding those of established radiosensitizers including PARP and ATM inhibitors. Notably, knockdown of these genes induced no significant cytotoxicity in the absence of radiation. Pharmacological validation using an IKK inhibitor confirmed these findings, implicating non-canonical NF-{kappa}{beta} signalling and CDK9-dependent transcriptional elongation as critical adaptive mechanisms in GBM radioresistance. Conclusions: ClonoScreen3D-CRISPRi is a scalable, physiologically relevant platform for identifying genetic modifiers of radioresistance. The non-canonical NF-{kappa}{beta} pathway and CDK9 represent promising radiosensitizing targets, and larger screens could enable systematic prioritisation of candidates for clinical translation.

Metabolic dysfunction is increasingly recognized as a risk factor for poor outcomes in breast cancer, but whether incretin-based therapies confer survival benefit beyond weight loss remains unresolved. Using a federated electronic health record platform spanning nearly 29 million patients, we evaluated breast cancer survival after semaglutide and tirzepatide initiation in routine care. In 1:1 propensity-matched pooled-comparator analyses, semaglutide was associated with improved overall survival versus metformin, sodium-glucose cotransporter 2 (SGLT2) inhibitor, and dipeptidyl peptidase 4 (DPP4) inhibitor users, with 54 deaths among 2,433 semaglutide users (2.2%) versus 395 deaths among 2,433 comparators (16.2%) over 24 months (log-rank P < 0.001). Tirzepatide showed a favorable survival association relative to pooled anti-diabetic comparators that did not meet statistical significance (P = 0.24), with 3 deaths among 220 users (1.4%) versus 64 deaths among 220 comparators (29.1%). In a head-to-head propensity-score-matched comparison, overall survival did not differ significantly between semaglutide and tirzepatide treated patients with pre-existing breast cancer (2,117 per arm; P = 0.12). In semaglutide-treated patients alive and observable at the 1-year landmark, higher maximum dose achieved was significantly associated with lower post-landmark mortality (P = 0.034), with an event rate of approximately 1.0% in the high-dose group (>=1.7 mg) versus approximately 4.5% in the low-dose group (0.25-1.0 mg). Despite a linear dose weight loss relationship for semaglutide, however, weight loss strata did not separate survival outcomes (global P = 0.22). In tirzepatide-treated patients alive and observable at the same landmark, neither maximum dose achieved nor weight loss strata separated post-landmark survival (P = 0.98 and P = 0.50, respectively). Structured EHR and AI-based clinical note analyses further showed significantly lower frequency of documented metastatic disease in semaglutide-treated patients relative to pooled anti-diabetic comparators, including any metastasis (7.0% versus 15.0%, rate ratio 0.5, P < 0.001), bone metastasis (1.0% versus 5.2%, rate ratio 0.2, P < 0.001), and liver, lung, or brain metastases (all P < 0.001). LLM-derived cause-of-death extraction further showed a 60% lower relative proportion of cancer-associated deaths in semaglutide-treated patients (19% of ascertainable deaths) than in matched pooled anti-diabetic comparators (47% of ascertainable deaths), with comparator deaths more often attributed to cancer progression involving metastatic breast cancer, leptomeningeal carcinomatosis, and cancer-driven organ failure. Overall, this study demonstrates that semaglutide use in patients with pre-existing breast cancer is associated with a dose correlated but weight loss independent improvement in overall survival. These findings motivate prospective trials of GLP-1 receptor agonists in breast cancer across various stages and treatment settings.

Desmoid fibromatosis (DF) is a rare mesenchymal neoplasm with an unpredictable clinical course, where spontaneous regression or progression occurs in a significant subset of patients through largely undefined mechanisms. The use of active surveillance (AS) offers the opportunity to investigate whether tumor- or host-driven systemic and local immune features may explain these divergent outcomes, improving patient management. A prospective observational study enrolled 55 patients with primary sporadic DF managed with AS. Clinical evolution was categorized as progression, regression, or stable disease according to RECIST 1.1. Immunomonitoring with multicolor flow cytometry identified distinct systemic T-helper polarization states stratifying clinical trajectories: regressors showed a Th2-skewed profile, while progressors displayed activated T-helper cells and Th1/Th9/Th17 subsets. Higher baseline Th2 levels associated with regression and longer progression-free survival. Plasma proteomic and whole-blood transcriptomic analyses confirmed coordinated IL-4/IL-13-linked pro-resolving programs in regressors and inflammatory, early T-cell activation signatures in progressors. Tumor transcriptomics revealed adaptive, antigen-presentation and restrained immune programs in regressing lesions versus innate inflammatory, interferon and TGF-{beta}-driven fibrotic pathways in progressing tumors. These findings identify systemic T-helper polarization as a biomarker of DF behavior and highlight coordinated systemic-tumoral immune programs underlying clinical outcomes, supporting more precise clinical management.

A substantial proportion of cancer patients fail to benefit from their prescribed combination regimens, yet identifying superior alternatives from the vast pharmacological space prior to treatment failure remains an unsolved clinical challenge. Existing computational approaches either rely on multi-omics profiles unavailable in standard oncological practice or reduce drug efficacy to scalar metrics that discard the dose-dependent resolution essential for therapeutic optimization. Here, we present XACT, a hierarchical deep learning framework that reconstructs full dose-dependent drug responses for both monotherapy and drug combinations using only clinically accessible transcriptomic profiles. By leveraging an asymmetric X-Linear Attention mechanism that models second-order interactions between molecular drug substructures and intracellular signaling pathway activities, XACT captures concentration-dependent pharmacodynamics with state-of-the-art accuracy and generalizability to unseen transcriptomic landscapes. When applied to the TCGA pan-cancer cohort, XACT-derived resistance scores were significantly associated with clinical treatment outcomes and stratified overall survival as the strongest independent prognostic factor after multivariate adjustment for tumor stage and cancer type. Systematic virtual screening revealed therapeutic vulnerabilities and nominated alternative regimens for treatment-refractory sarcoma and pancreatic adenocarcinoma. These results establish XACT as a scalable, interpretable, and clinically translatable framework that advances precision oncology from computational prediction toward data-driven therapeutic prescription.

BackgroundInvasive lobular breast cancer (ILC) is the most commonly diagnosed special histological subtype of breast cancer (BC). Metastatic ILC (mILC) is less sensitive to FDG-PET imaging and often metastasizes to unusual sites --peritoneum, gastrointestinal (GI) tract, ovaries, urinary tract, and orbit--which may go unrecognized after a long disease-free interval. Some metastatic sites cause nonspecific symptoms, like abdominal/epigastric pain, with numerous published case reports of mILC misdiagnosed as gastric cancer. These atypical BC metastatic sites may lead to late and/or misdiagnosis, thereby delaying effective treatments. ObjectiveWe developed a patient survey to investigate the patient-reported prevalence of delayed diagnosis or misdiagnosis of mILC and their potential impact upon treatment outcomes. MethodsA 45-question survey was developed and piloted with breast cancer researchers, clinical oncologists, and patient advocates. This IRB-approved survey was then distributed to patients with ILC. Analyses including data QC and visualization were conducted in R using descriptive statistics. Incomplete or inconsistent responses were excluded, and summary statistics were stratified by four common mILC sites to highlight subgroup differences. Results525 patient surveys were completed, with 450 patients diagnosed with ILC, and of those 321 diagnosed with mILC. For those with mILC, 33.3% (n=107) were diagnosed with de novo mILC at initial presentation. Of the patients diagnosed with mILC, 32.1% (n=103) presented with other medical conditions at diagnosis. Misdiagnosis was reported by 26.2% (n=84) of patients with mILC, and of these cases, 31% (n=26) had [&ge;]2 misdiagnoses. The top 5 misdiagnoses were bone-related condition (24.7%), benign breast condition (23.4%), another type of BC (7.8%), diagnostic delay (7.8%), and menopause related (5.2%). 44.5% of patients waited [&ge;]1 year for an accurate diagnosis. 49 patients were treated for their misdiagnosis, and 6 received incorrect cancer treatments. The most frequently reported contributors to delayed or misdiagnosis were inconclusive imaging, providers lack of ILC knowledge, and initial misdiagnosis. Of the 321 patients with mILC, 138 (42.9%) reported symptoms before diagnosis; the most common were back pain (16.5%), fatigue/malaise (14.9%), GI symptoms (11.8%), bloating (8.4%), and weight loss (8.1%). Although 40% of patients reported having a mammogram at the time of their initial misdiagnosis, ILC was detected in only 20.5% (24/116) of these cases, and mammography detected only 5 (25%) of the 20 de novo mILC cases. Patients reported additional diagnostic testing within 1-3 months of their initial mammogram, includingbiopsy, ultrasound (US), and MRI. 47.9% of patients were in active BC surveillance after curative intent therapy at the time of their mILC diagnosis; however, no statistical difference was seen in time to diagnosis versus those patients not under surveillance. ConclusionOur survey results underscore the urgent need to improve diagnostic strategies for mILC. Addressing delays and diagnostic errors in mILC is critical to optimizing treatment strategies and improving patient outcomes.

STRUCTURED ABSTRACTO_ST_ABSImportanceC_ST_ABSSexual dysfunction can occur after midurethral sling (MUS) and transvaginal prolapse surgery. It remains unclear whether these procedures impact the clitoris, despite its role in sexual function and proximity to the MUS and vagina. ObjectivesTo compare postoperative sexual function and clitoral features by MUS and vaginal surgery approach after transvaginal prolapse repair with/without concomitant MUS. DesignCross-sectional ancillary study of magnetic resonance imaging (MRI) and sexual function data from the Defining Mechanisms of Anterior Vaginal Wall Descent study. SettingEight clinical sites in the US Pelvic Floor Disorders Network. Participants: 88 women with uterovaginal prolapse who underwent vaginal mesh hysteropexy or vaginal hysterectomy with uterosacral ligament suspension with/without MUS between 2013-2015. Data were analyzed between September 2021-June 2023. ExposuresBetween June 2014-May 2018, participants underwent pelvic MRI 30-42 months after surgery, or earlier if reoperation was desired. Sexual activity and function at baseline and 24-48-month follow-up were evaluated using the Pelvic Organ Prolapse/Incontinence Sexual Questionnaire, IUGA-Revised (PISQ-IR). Clitoral features were obtained from postoperative MRI-based 3-dimensional models. Main Outcomes and MeasuresPISQ-IR scores and clitoral features (size, position). ResultsEighty-two women (median [range] age, 65 [47-79] years) were analyzed: 45 MUS (22 hysteropexy, 23 hysterectomy) and 37 No-MUS (19 hysteropexy, 18 hysterectomy). Postoperatively, 25 MUS, 12 No-MUS, 20 hysteropexy, and 17 hysterectomy patients were sexually active (SA). Overall, within the MUS and vaginal surgery groups, sexual function remained unchanged or improved (most PISQ-IR change from baseline scores were [&ge;]0) among SA and NSA women. Among SA women after surgery, the MUS group (vs No-MUS) had a poorer PISQ-IR arousal/orgasm (SA-AO) score (median, 3.5 vs 4.3; P=.02). The hysteropexy group (vs hysterectomy) had less improvement in PISQ-IR SA-AO score (median, 0.0 vs 0.3; P=.01). Women with MUS (vs without) had a smaller clitoral glans thickness (median, 9.0 mm vs 10.0 mm; P=.008) and clitoral body volume (median, 2783.5 mm3 vs 3587.4 mm3; P=.01). Conclusions and RelevanceSA women with MUS (vs without) or hysteropexy (vs hysterectomy) experienced poorer postoperative sexual function. MUS was linked to a smaller clitoris. Future studies should explore surgery-induced changes in clitoral anatomy and sexual function. KEY POINTSO_ST_ABSQuestionC_ST_ABSHow do sexual function and clitoral anatomy differ by midurethral sling placement and vaginal surgery approach? FindingsThis cross-sectional study compared patient-reported sexual function outcomes and 30-42-month postoperative magnetic resonance imaging-based 3-dimensional clitoral models of 82 women after vaginal prolapse surgery with or without concomitant midurethral sling. Midurethral sling (vs no sling) and vaginal mesh hysteropexy (vs vaginal hysterectomy) were associated with poorer postoperative sexual function outcomes. Additionally, midurethral sling was associated with a smaller clitoral glans and body. MeaningMidurethral sling and vaginal mesh hysteropexy were associated with, and may adversely alter, postoperative sexual function and/or clitoral anatomy. VISUAL ABSTRACT/PROMOTIONAL IMAGE O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=113 SRC="FIGDIR/small/26351291v1_ufig1.gif" ALT="Figure 1"> View larger version (33K): org.highwire.dtl.DTLVardef@904497org.highwire.dtl.DTLVardef@187514aorg.highwire.dtl.DTLVardef@e9e799org.highwire.dtl.DTLVardef@640f1a_HPS_FORMAT_FIGEXP M_FIG C_FIG