Citations

Abstract Background IDH-mutant astrocytomas are classified as WHO grade 4 in the presence of conventional high-grade histologic features and/or homozygous CDKN2A/B deletion in the 5th edition of the WHO Classification of Central Nervous System Tumour guidelines. However, work over the past decade has indicated a number of other molecular alterations that warrant consideration as potential prognostic markers. Methods We used univariate Kaplan-Meier and multivariate Cox proportional hazards regression analysis to evaluate the prognostic effects of homozygous CDKN2A/B deletion, CDK4 amplification, CCND2 amplification, PDGFRA amplification/mutation, PIK3R1 mutation, PIK3CA mutation, MYCN amplification, EGFR amplification/mutation, TERT promoter mutation, and grade 4 histologic features in two independent cohorts of WHO grade 2-4 IDH-mutant astrocytoma (n = 840 and n = 367). Results The presence of CDK4 amplification, CCND2 amplification, PDGFRA alteration, PIK3R1 mutation, MYCN amplification, and EGFR alteration were each associated with reduced overall survival compared to WHO grade 2/3 astrocytomas without these molecular features. 17.7% (148/837) of otherwise grade 2/3 astrocytomas had one or more of these molecular criteria, with resulting intermediate clinical outcome in terms of overall survival (median survival of 67.3-82.0 months) compared to grade 2/3 astrocytomas without these molecular features (median survival of 135.0-140.7 months) and grade 4 astrocytomas (median survival of 35.3-45.0 months). Conclusions The presence of CDK4, CCND2, PDGFRA, PIK3R1, MYCN, and EGFR alterations result in an intermediate patient survival in IDH-mutant astrocytoma. Adding these molecular alterations should be considered in future diagnostic classification systems to improve stratification of high-risk patients.

Abstract Glioblastoma (GBM) remains the most common and lethal adult malignant primary brain cancer with few treatment options. A significant issue hindering GBM therapeutic development is intratumor heterogeneity and plasticity. GBM tumors contain neoplastic cells within a fluid spectrum of diverse transcriptional states. Identifying effective therapeutics requires a platform that predicts the differential sensitivity and resistance of these states to various treatments. Here, we develop scFOCAL (Single-Cell Framework for -Omics Connectivity and Analysis via L1000), to quantify the cellular drug sensitivity and resistance landscape. Using single-cell RNA sequencing of newly diagnosed and recurrent GBM tumors, we identify compounds from the LINCS L1000 database with transcriptional response signatures selectively discordant with distinct GBM cell states, and leverage this capability to predict combination synergy. We validate the significance of these findings in vitro, ex vivo, and in vivo, and use the Olig2 inhibitor CT-179 as a reference drug to identify additional small molecules that would maximize the cell-drug discordance across the GBM transcriptional landscape. Our analysis leads to the combination of Olig2 inhibition with treatment with Depatux-M, of with which we demonstrate synergy in vivo. Our studies suggest that scFOCAL identifies cell states that are sensitive and resistant to targeted therapies in GBM using a measure of cell and drug connectivity, which can be applied to identify new synergistic combinations. Citation Format: Robert K. Suter, Anna M. Jermakowicz, Rithvik Veeramachaneni, Matthew D'Antuono, Longwei Zhang, Rishika Chowdary, Simon Kaeppeli, Madison Sharp, Pravallika Palwai, Vasileios Stathias, Grace Baker, Luz Ruiz, Winston Walters, Maria Cepero, Danielle Burgenske, Edward B. Reilly, Anatol Oleksijew, Mark G. Anderson, Sion Ll. Williams, Michael E. Ivan, Ricardo J. Komotar, Macarena I. De La Fuente, Gregory Stein, Alexandre Wojcinski, Santosh Kesari, Jann N. Sarkaria, Stephan C. Schürer, Nagi G. Ayad. Drug and single-cell gene expression integration identifies heterogeneity-aware synergistic combinations for glioblastoma [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Brain Cancer; 2026 Mar 23-25; Philadelphia, PA. Philadelphia (PA): AACR; Cancer Res 2026;86(6_Suppl):Abstract nr B032.

Abstract Purpose: The Pancreatic Cancer Detection Consortium (PCDC) performed a blinded Early Detection Research Network–defined phase II biomarker bakeoff study of blood-based biomarker panels. The aims were to evaluate panel performance, to compare the panels’ performance with that of cancer antigen 19-9 (CA19-9) alone, and to evaluate the performance of new combinations of the individual biomarkers. Experimental Design: Ten biomarkers representing eight biomarker panels and CA19-9 were evaluated using plasma, serum, and germline DNA from 140 stage I to IV pancreatic ductal adenocarcinoma (PDAC) cases and 140 controls from three tertiary care institutions, with controls frequency matched to cases on age and sex. LASSO regression was employed to explore new biomarker combinations. The primary metric was area under the receiver operating characteristic curve (AUC). Results: The study population was 51% female, with median age 67.3 (minimum: 45, maximum: 90) years. Biomarker panel AUCs ranged from 89.9 to 96.3; the AUC for serum CA19-9 alone was 91.7 [95% confidence interval (CI), 87.8–95.6]. Two panels had significantly higher AUCs than serum CA19-9 alone, the CA19-9/FUT2/3 panel (AUC = 96.3, P = 0.002), and the tissue factor pathway inhibitor/tenascin C (TFPI/TNC-FNIII-C) panel (AUC = 95, P = 0.01). Exploratory models to recombine biomarkers retained all but two biomarkers [optimism-corrected AUC = 96.4 (94–98.9)]. Conclusions: The CA19-9/FUT2/3 panel was the best performing panel in this biomarker bakeoff. Its evaluation in larger studies is warranted. Biomarker bakeoffs are an effective strategy for comparing the performance of promising biomarkers for pancreatic cancer early detection, and the PCDC is well poised to conduct such studies. Recommendations for performing such studies are provided.

Abstract Despite growing interest in AI for pancreatic cyst management, no prior study has systematically investigated how lesion characteristics influence model behavior or provided a comprehensive explainability analysis in IPMN malignancy risk stratification. We present the first multi-center study integrating explainable AI with uncertainty quantification to evaluate how cyst type, size, and location affect deep learning performance in IPMN malignancy prediction. Our retrospective study analyzed 170 IPMNs from seven centers using a radiomics-deep learning fusion model. Cases were stratified by dysplasia grade, IPMN type, size, and location; with model interpretability assessed using SHAP, LIME, Grad-CAM, and uncertainty quantification. Overall accuracy was 67.1%, with model uncertainty lower for correct versus incorrect predictions (0.72 vs 0.78, p < 0.001). On subgroup analysis: Low-Risk lesion accuracy exceeded High-Risk (81.3% vs. 42.9%, p < 0.001), BD accuracy was inversely correlated with cyst size while MD remained stable, and body and whole-pancreas cysts stratified more accurately than tail cysts (77.8%, 76.6% vs. 47.8%). AI models face significant, previously uncharacterized performance drops when evaluating complex high-risk IPMNs. Our study shows that integrating uncertainty quantification can successfully flag unreliable predictions, enabling a selective-prediction framework where clinicians can confidently rely on AI for low-risk, low-uncertainty lesions while deferring high-uncertainty cases to expert review.

Abstract Vertebral metastatic disease results from many types of cancer and can have a devastating impact on patient mobility, psychological health, quality of life, and ultimately overall patient survival. However, the development of radiotherapy and surgical techniques has rapidly surged in conjunction with ongoing advances in basic science and translational studies. In this review, we discuss the paradigm shift in our understanding of the epidemiology and treatment algorithms for spinal oncology, ranging from preoperative optimization strategies, radiation and surgical techniques, the utilization of molecular markers and targeted therapeutics in medical oncology, and prognostication tools that underscore a new multidisciplinary approach to spinal oncology care.

Introduction/Objective: As fewer physicians enter primary care, Advanced Practice Providers (APPs) play an increasingly important role in meeting the demand for patient care. Factors contributing to work status changes among primary care APPs are not well studied. We aimed to evaluate differences in primary care APP work status across electronic health record (EHR) workload measures. Methods: We conducted a retrospective cohort study within a Midwestern healthcare system from 2020 to 2022, evaluating the relationship between primary care APP work status defined as retention at current full-time equivalent (FTE), reduction of FTE within primary care, or relocation outside of primary care and 3 time-based EHR workload metrics. Descriptive statistics, univariate, and multivariable analyses were reported. Results: There were no differences between work status (retained, reduced, or relocated) across the EHR workload measurements of in-basket time per appointment, system time per appointment and note time per appointment ( P > .05) on univariate analysis. With multivariable analysis, there was no difference in likelihood of combined relocation/reduction adjusting for EHR workload variables ( P > .05). Conclusion: The EHR workload was not associated with work status changes for primary care APPs in our study. Further exploration is needed to identify factors that may contribute to reduction in primary care APP workforce capacity.

BACKGROUND Bertolotti syndrome is an underrecognized cause of chronic low back pain. Back pain generated from Bertolotti syndrome is believed to result from altered weight dissipation mechanics and/or instability of the pseudoarticulation, while local nerve root compression may result in radiculopathy. Here, the authors describe the findings of a patient with treatment-refractory low back pain found to have a cystic lesion arising from a Bertolotti joint causing L5 nerve root compression. To their knowledge, this has not been previously described in the literature. OBSERVATIONS Despite early recognition of Bertolotti syndrome, the patient’s back pain remained resistant to many years of both conservative and surgical management. The novel finding of a cyst arising from the pseudoarticulation complicated the management. The patient is scheduled to undergo further surgery this year. LESSONS This case highlights a pseudocyst associated with a Bertolotti joint. The cyst introduced additional clinical uncertainty to what is already an uncommon and difficult to treat condition. Resection of the Bertolotti joint failed to improve the patient’s symptoms. Postoperatively, a complex hemorrhagic pseudocyst was seen on imaging, possibly due to the pseudoarticulation with persistent motion. Ongoing efforts to characterize Bertolotti syndrome and its associated imaging findings are necessary to better optimize management strategies in these patients. https://thejns.org/doi/10.3171/CASE25948