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Importance Although the Martin-Hopkins equation is widely validated and used to estimate low-density lipoprotein cholesterol (LDL-C) for clinical care, a simplified equation could streamline implementation and enable broader adoption. Objective To develop a simplified machine learning–based alternative to the Martin-Hopkins equation using multivariate adaptive regression splines (LDL-C–MH-MARS) and compare its performance with the Friedewald (LDL-C–F), Sampson–National Institutes of Health (LDL-C–S), Modified Sampson (LDL-C–MS), and Martin-Hopkins (LDL-C–MH) equations. Design, Setting, and Participants The Very Large Database of Lipids is a cross-sectional database of clinical lipid measurements from a population-representative convenience sample of adult and pediatric patients. Lipid measurements were performed by Vertical Auto Profile ultracentrifugation between October 1, 2015, and June 30, 2019. The study included patients with complete lipid panel data who were randomly assigned to a training set and a test set. External validation against preparative ultracentrifugation-based LDL-C measurements was performed in reference laboratory datasets from the Mayo Clinic (broad LDL-C concentrations) and the Further Cardiovascular Outcomes Research With Proprotein Convertase Subtilisin/Kexin Type 9 (PCSK9) Inhibition in Subjects with Elevated Risk (FOURIER) trial (observations from patients with low LDL-C concentrations while taking evolocumab). Study data were analyzed from January to October 2025. Exposures LDL-C–F, LDL-C–S, LDL-C–MS, LDL-C–MH, and LDL-C–MH-MARS equations. Main Outcomes and Measures Accuracy of estimated vs ultracentrifugation-measured LDL-C concentration based on bias, root mean square error (RMSE), and concordance by guideline-based categories. Results The study included 4 939 528 patients (mean [SD] age, 56 [16] years; 2 635 486 female [53%]) with complete lipid panel data who were randomly assigned to a training set (n = 3 292 889) and a test set (n = 1 646 639). LDL-C–MH-MARS equation demonstrated a very low median (IQR) bias of −0.1 (−2.1 to 1.8) mg/dL (to convert to millimoles per liter, multiply by 0.0259), comparable with LDL-C–MH. The median (IQR) difference between the MARS and original Martin-Hopkins equations (ie, LDL-C–MH-MARS – LDL-C–MH) was −0.5 (−1.2 to 0) mg/dL, further suggesting comparability between the 2 methods. The RMSE was smallest for LDL-C–MH-MARS (4.7 mg/dL) and LDL-C–MH (4.9 mg/dL), followed by LDL-C–S (5.8 mg/dL), LDL-C-MS (6.0 mg/dL), and LDL-C–F (7.2 mg/dL). The proportion of patients correctly classified according to clinical categories was nearly identical for LDL-C–MH-MARS (89.7%) and LDL-C–MH (89.6%) but lower for LDL-C–S (86.3%), LDL-C–MS (84.7%), and LDL-C–F (83.1%). The LDL-C–MH-MARS, LDL-C–MH, LDL-C–MS, LDL-C–S, and LDL-C–F equations underestimated LDL-C concentration in 16%, 17%, 28%, 39%, and 60%, respectively, when classifying LDL-C less than 70 mg/dL in patients with triglyceride concentrations of 200 to 399 mg/dL (to convert to millimoles per liter, multiply by 0.0113). The patterns of results were similar in external validation datasets, with the LDL-C–MH-MARS and LDL-C–MH equations demonstrating the highest accuracy. Conclusions and Relevance Study results suggest that the new machine learning–based LDL-C equation provided results comparable with those of the original Martin-Hopkins method while simplifying implementation to a single equation.

Primary enlargement of the cerebrospinal fluid (CSF)–filled brain ventricles, known as congenital cerebral ventriculomegaly (CCV), is a hallmark of congenital hydrocephalus. CCV is also enigmatically but frequently associated with autism and other neurodevelopmental disorders. To gain insight into the developmental genetic regulation of the human CSF-ventricular system, we conducted an integrated, multiomic study of about 2700 trio-based exomes from patients with primary CCV. We found that about 25% of cases were associated with rare, damaging de novo variants in mutation-intolerant genes, many of which are linked to other dominant Mendelian disorders. Thirty-five exome-wide significant CCV genes and dozens of other high-confidence CCV genes converged on pathways involved in ATP-dependent Brahma-related gene 1/Brahma-associated factor chromatin remodeling, histone H3 lysine 4 methylation, and phosphoinositide 3-kinase signaling. Knockout of selected CCV genes in mouse models supported that de novo variants in CCV genes caused ventriculomegaly by impairing both CSF dynamics and cortical cytoarchitecture through dysregulation of neuroprogenitor cell growth and maturation in the ventricular and subventricular zones. These findings indicated that genetic and epigenetic programs coordinate the “hand-in-glove” development of the CSF-ventricular system with that of the cerebral cortex and establish a genetic connection between CCV and neurodevelopmental disorders, potentially explaining why some patients with hydrocephalus continue to exhibit CCV and neurodevelopmental disorders despite CSF shunting. We suggest that combined brain imaging and whole-exome sequencing could enable early detection of, and intervention for, autism and other neurodevelopmental disorders.

Background/Objectives: To develop and evaluate a deep learning-based quality control system for Lumbar Spinal Digital Radiographs (LSDR), designed to automate and improve their evaluation and reduce reliance on manual reviews. Methods: This retrospective study utilized a deep learning workflow comprising image segmentation, feature extraction, and a classification model. The dataset, including anteroposterior (AP) and lateral (LAT) X-ray images, was expanded through data augmentation techniques. Four U-Net-based models were assessed: standard U-Net, Swin-UNet, Attention U-Net, and Attention U-Net with the weight map, with the latter selected for its superior performance. Extracted features, such as brightness, contrast, and anatomical positioning, were used in an XGBoost classifier, which was evaluated using mean intersection over union (mIoU), accuracy, sensitivity, specificity, and AUC. Results: The Attention U-Net with weighted attention outperformed the other models, achieving high mIoU scores in both AP and LAT views. The XGBoost classifier achieved the best performance in classifying images as “qualified” or “unqualified,” with an AUC of approximately 0.9, high accuracy, and balanced sensitivity and specificity. This approach effectively addressed class imbalances and improved model accuracy compared to traditional machine learning models such as MLP and SVM. Conclusions: The developed automated quality control system demonstrated potential for enhancing image quality, enhancing diagnostic reliability, and optimizing clinical workflow efficiency.

Acute lymphoblastic leukemia (ALL) is the most common pediatric cancer, arising from both B and T cell lineages (B-ALL and T-ALL). Current therapy exploits ALL cells’ low expression of asparagine synthetase (ASNS) by using L-asparaginase, a bacterial enzyme that depletes circulating asparagine. However, resistance can emerge through induction of ASNS, mediated in part by the amino acid stress sensor GCN2. In this study, we addressed the efficacy of L-asparaginase in combination with genetic or pharmacological inhibition of GCN2 and the ASNS inhibitor ASX-173. Using a Kras G12D -driven mouse model of T-ALL, we found that GCN2 is dispensable for leukemogenesis. However, genetic inactivation or pharmacologic inhibition of GCN2 sensitized ALL cells to asparagine depletion, correlating with impaired ASNS induction. While GCN2 targeting enhanced sensitivity to asparagine depletion, a subset of Gcn2 –/– T-ALL cells retained high ASNS expression and remained resistant to L-asparaginase. Likewise, some human T-ALL cells with elevated ASNS levels were refractory to GCN2 inhibition even under asparagine-depleted conditions. When combined with L-asparaginase, ASX-173 effectively eliminated ASNS hi leukemic cells in vitro and in vivo. These findings suggest that direct targeting of ASNS provides therapeutic benefit in leukemias that express high levels of ASNS and are resistant to GCN2 inhibition under asparagine-depleted conditions.

ABSTRACT Biological aging greatly impacts the body’s ability to handle glucose, and represents a major risk factor the development and progression of type 2 diabetes (T2D). Nonetheless, despite advances in cellular senescence research and the development of new senolytic therapies, the heterogeneity of cellular senescence in the human endocrine pancreas, as well as its roles in normal aging, remains to be elucidated at the single-cell level. Here, we performed single-cell–resolved spatial proteomics and transcriptomics on intact pancreas from 26 donors (ages 20–80) and multiplexed single-cell RNA sequencing and functional assays on dispersed islets from 14 donors (ages 34–69). We identify two discrete SnC subpopulations distinguished by relative expression of CDKN1A and CDKN2A. CDKN1A ⁺ senescent cells (SnCs) exhibit loss of β-cell identity, impaired insulin secretion, and a proinflammatory SASP associated with increased islet immune infiltration. In contrast, CDKN2A ⁺ SnCs retain transcriptional identity and functional competence, with lower inflammatory signaling. Together, these findings identify heterogeneous and functionally divergent senotypes in the human pancreas, distinguishing an adaptive ( CDKN2A ⁺) from a maladaptive ( CDKN1A ⁺) senescence program, thus providing a mechanism-guided framework for senescence-targeted therapies in T2D.

566 Background: Endocrine therapy (ET) resistance (ETR) remains a primary challenge in ER+ breast cancer. Analyzing pretreatment tumor transcriptomes across trials with early response endpoints can reveal shared and specific ETR signatures. This study utilizes baseline RNA data from the Phase III ALTERNATE trial (Alliance A011106, NCT01953588; Anastrozole [A], Fulvestrant [F], or AF) and the ACOSOG Z1031B trial (NCT00824941) to identify predictors of early Ki67 response in postmenopausal ER+/HER2– patients. Methods: ETR was defined as week-4 on-treatment Ki67 >10%. Baseline gene expression from ALTERNATE was analyzed to identify differentially expressed (DE) genes (Wilcoxon test, P<0.05) and Hallmark pathways associated with ETR, both across and within individual treatment arms. Feature selection was performed using mixOmics. A Pan-Endocrine Therapy Signature (PETS) was developed by uniting DE genes identified across all three ALTERNATE arms and Z1031B. All statistical analyses were conducted in R (P<0.05). Results: Overall ETR rate in the ALTERNATE RNA-seq cohort (n=733) was 26%. ETR was associated with high Risk of Recurrence (ROR), Oncotype RS, and Mammaprint scores (calculated from RNA-seq data in research setting). In luminal tumors (n=649), ETR was linked to chr 3q13.33, 8q24.13, and 20q13.12 cytoband upregulation and 17q21, 18q23, 3p21.1, and 10q24.32 cytoband downregulation. ETR tumors showed T-cell, E2F target, and interferon-γ enrichment; sensitive tumors favored early estrogen response and muscle differentiation. At individual gene level, high MYBL2 , PIF1 , TROAP and with low HJURP predicted ETR across all samples (AUC>0.70). A deep learning model using all protein-coding genes achieved AUC 0.82 (training) and 0.79 (test) in predicting ETR. Cross-trial integration identified ETR-associated PETS, enriched for genomic instability. PETS performed comparably to established signatures and strongly correlated with MYBL2 signature (r=0.93). Top ETR predictors were MYBL2 , AURKB , and EME1 for Arm A and IL4I1 , TNFAIP6 , and ANLN for Arm AF. AF-resistant tumors were enriched for systemic lupus and RIG-I–like receptor signaling; sensitive tumors favored PI3K–AKT, EGFR TKI resistance, AMPK, and insulin signaling. Conclusions: Baseline transcriptomics identify shared and therapy-specific ETR markers. The 15-gene PETS defines a convergent resistance signature, performing similar to established signatures in predicting ETR and correlating with MYBL2. Enrichment of cell-cycle and immune pathways in resistant tumors may suggest patient stratification approach for alternative or combinatorial strategies to overcome early ETR in ER+ breast cancer. Acknowledgement: https://acknowledgments.alliancefound.org. Support: U10CA180821, U10CA180882, U24CA1. Clinical trial information: NCT01953588 .

11502 Background: To report results from the open-label alveolar soft part sarcoma (ASPS) cohort of the phase III APROMISS trial (NCT03016819) evaluating Catequentinib hydrochloride (AL3818), an oral multitargeted receptor tyrosine kinase inhibitor, in adults with advanced or metastatic ASPS. Methods: Adult patients with pathologic and molecularly confirmed advanced ASPS, either treatment-naive or previously treated (excluding cediranib), were enrolled in the open-label, non-randomized ASPS cohort of the phase III APROMISS trial conducted across the United States, Asia, and Europe. Catequentinib hydrochloride was administered orally at 12 mg once daily in 21-day cycles (14 days on/7 days off) until disease progression, toxicity, or death. The primary endpoint was objective response rate (ORR) assessed per RECIST by Blinded Independent Central Review (BICR) (predefined threshold: 31%). Secondary endpoints included duration of response (DOR) with a predefined target of =/>6 months, progression free survival (PFS) and overall survival (OS). Results: From July 2017 to August 2022, a total of 56 patients entered the trial; 56 patients were evaluable for the primary endpoint. Median (m-) age was 29.5 years (range: 18 – 66), with 24 female and 32 male. Best overall response included 1/56 complete response (1.8%), 14/56 partial responses (25.0%), 36/56 stable disease (64.3%), and 3/56 (5.4%) progressive disease. RECIST ORR by BICR was 26.8% (95% CI, 15.8–40.3); m-DOR was 22.57 months (95%CI: 10.38-NE). The study data was collected close to 8 years for all patients. mPFS was 18.33 months (95% CI, 14.19–23.13), with 6- and 12-month progression-free rates of 88.77% (95% CI, 76.68–94.79) and 65.54% (95% CI, 50.42–77.06), respectively. mOS was 59.27 months (95% CI, 44.09–NE), with 6- and 12-month overall survival rates of 94.64% (95% CI, 84.30–98.24) and 90.81% (95% CI, 79.30–96.08), respectively. Overall, 27/56 (48.2%) patients experienced grade ≥3 treatment-related adverse events, most commonly diarrhea (50.0%), hypertension (44.6%), fatigue (37.5%), nausea (35.7%), and palmar-plantar erythrodysesthesia syndrome (30.4%); one patient (1.8%) had a grade 5 event (respiratory failure). Conclusions: Catequentinib hydrochloride demonstrated meaningful antitumor activity and durable responses in adults with advanced ASPS. Despite the ORR of 26.8% slightly below the predefined 31% threshold, disease control was prolonged, with a m-DOR exceeding 22 months. Favorable PFS and OS outcomes, together with a manageable safety profile, support its clinical benefit in this ultra-rare sarcoma. Clinical trial information: NCT03016819 .

11064 Background: Local and state paid sick leave (PSL) mandates have been associated with increased use of preventive services, including cancer screening, and reduced emergency care utilization. Little research has examined PSL policies and cancer after diagnosis. This study evaluates associations of PSL policies and timely treatment initiation among women with early-stage breast cancer. Methods: We identified patients aged 18-64 years newly diagnosed with stages 0-II breast cancer from the National Cancer Database in 2004-2023 and linked then to area-level policy implementation, measured as the month and year the state, county, or city required PSL. Primary outcomes were receipt of definitive surgery or neoadjuvant therapy within 30, 60, and 90 days of diagnosis. We compared changes in treatment initiation between patients residing in areas with and without PSL policies during and after the month and year of diagnosis. We used a staggered difference-in-differences design to account for differential timing of implementation, adjusting for patient demographics, clinical, and area-level characteristics. Random errors were clustered at the state level. Supplemental analyses stratified by county-level unemployment rates and individual insurance coverage. Results: The study included 1,891,411 patients, of whom 249,313 (13.2%) resided in an area with PSL policies at diagnosis. Patients in areas with PSL policies were more likely to have Medicaid, reside in higher-income and metropolitan areas. PSL policy implementation was significantly associated with a 3.4 percentage point (PPT) increase in the probability of treatment ≤30 days (95% CI: 1.1-5.7), 3.0 PPT increase ≤60 days (95% CI: 1.6-4.3), 1.3 PPT increase ≤90 days (95% CI: 0.5-2.1) (Table). Improvements in timely treatment were concentrated in low-unemployment areas; no significant improvements were observed in high-unemployment areas. The magnitude of associations between PSL policies and treatment initiation was stronger among patients without private insurance than with private insurance. Conclusions: Local and state PSL mandates were associated with earlier treatment initiation among working-age patients with early-stage breast cancer, especially in areas with low female unemployment rate. Findings suggest that PSL policies play an important role in timely cancer treatment for employed women who may not have PSL through their jobs. Association of paid sick leave policies and breast cancer treatment initiation. Time from diagnosis to surgery/neoadjuvant therapy (days) DID PPT Lower 95% CI Upper 95% CI P-value Overall ≤30 3.4 1.1 5.7 0.004 ≤60 3.0 1.6 4.3 0.000 ≤90 1.3 0.5 2.1 0.001 Low female unemployment ≤30 2.2 -0.7 5.0 0.136 ≤60 3.8 1.8 5.8 0.000 ≤90 1.2 0.3 2.2 0.012 High female unemployment ≤30 1.8 -0.6 4.2 0.145 ≤60 -1.2 -4.5 2.0 0.459 ≤90 -1.4 -3.7 0.9 0.232