Citations

Abstract Background Ultrasound (US)‐guided hydrodissection is a minimally invasive treatment for cubital tunnel syndrome (CuTS) with promising early results. However, data on patient‐perceived outcomes, durability of relief, and impact on surgical need remain limited. Objective To assess the effectiveness of US‐guided cubital tunnel hydrodissection in improving CuTS symptoms via patient‐centered outcomes. Design Retrospective chart review with cross‐sectional telephone follow‐up. Setting Quaternary medical center multidisciplinary outpatient hand clinic. Participants Seventeen patients (19 arms) with clinical CuTS who received US‐guided hydrodissection from one physician. Interventions Patients were contacted by telephone for cross‐sectional outcome assessment. Main Outcome Measures Patient‐reported improvements in weakness, paresthesias, and/or pain, percent pain relief, symptom improvement duration, adverse events, and Patient Global Impression of Change (PGIC) scores. Results At mean 7 ± 5 month follow‐up, improvement in weakness, paresthesia, and pain was reported in 12 (63%) extremities (95% confidence interval [CI] 41%–85%; p < .001) . Pain relief ≥50% occurred in 12 (63%) and ≥75% in 8 (42%) extremities, with mean duration 4 months. PGIC scores showed 14 (74%; 95% CI 54%–94%; p < .001) at least “minimally improved” and 8 (42%) (95% CI 20%–64%; p < .001) “much improved.” No adverse events occurred. Two patients without improvement underwent surgical decompression with anterior transposition at 1 and 3 months after hydrodissection, achieving 90%–100% pain relief and “much or very much improved” PGIC scores. Of 19 extremities, 7 (37%) were McGowan Grade I and 12 (63%) Grade II; none were Grade III. Grade II extremities showed significantly greater overall symptom relief ( p = .004) and pain relief ≥50% ( p = .004). PGIC scores did not differ significantly between grades (both p > .062). Conclusion US‐guided cubital tunnel hydrodissection was associated with meaningful symptom improvement and no adverse events. Limitations include small sample size, no control group, single operator, and potentially time‐limited relief. The procedure appears safe and a potential nonoperative treatment option.

Abstract Endometriosis affects an estimated 10% of reproductive-age women and girls worldwide, approximately 190 million individuals, yet its molecular pathogenesis remains incompletely understood and current treatments rely largely on hormonal suppression, surgery, and symptom management. We developed a multi-stage single-cell transcriptomic pipeline to identify lesion-associated cellular niches that may coordinate immune tolerance, failed lesion clearance, fibrotic remodeling, and chronic pelvic pain, with the goal of prioritizing non-hormonal therapeutic hypotheses. Using the public Tan et al. atlas, GSE179640, comprising more than 122,000 cells from 14 individuals across control endometrium, eutopic endometrium, peritoneal lesions, adjacent peritoneal tissue, and ovarian lesions, the workflow combines Harmony integration, GenoRefine dimensionality reduction, unsupervised clustering, tissue-type stratification, and donor-aware pseudobulk differential expression. A mechanism-anchored scoring framework ranks GenoRefine clusters across T-cell suppression, macrophage tolerance, neuroimmune pain, CGRP/RAMP1 macrophage reprogramming, efferocytosis context, perivascular angiogenic remodeling, fibrosis, and lesion-type enrichment. Candidate immune-pain hubs are evaluated using curated ligand-receptor and mediator-axis analyses and exported for CellPhoneDB/LIANA validation, enabling complex-aware communication scoring across cytotoxic T-cell and neuroimmune-responsive compartments. The prostaglandin pathway is modeled as PTGS2/PTGES-driven PGE2 production coupled to PTGER2/PTGER4 receptor response, avoiding an incorrect direct ligand-receptor representation. Drug mapping proceeds by consolidating hub markers, pseudobulk-supported genes, ligand-receptor partners, and mediator-axis targets into disease-axis target sets; annotating them through DGIdb-informed drug-gene interactions and druggability classes; and formatting lesion up/down signatures for CLUE/LINCS perturbational reversal. Drug prioritization generates separate shortlists for pain/neuroimmune signaling, immune tolerance, fibrosis/angiogenesis, CGRP/RAMP1 macrophage biology, and efferocytosis-context targets. Each candidate is annotated by desired intervention direction, evidence level, lesion-type enrichment, cell-type specificity, pseudobulk support, communication support, and translation caution. CGRP/RAMP1 is prioritized as a clinically tractable neuroimmune axis because CGRP-pathway agents are approved in migraine and preclinical endometriosis models implicate nociceptor-to-macrophage CGRP signaling in pain and lesion growth; however, endometriosis-specific efficacy and reproductive safety require validation. Neuronal markers are interpreted as lesion-context signals pending spatial validation. This framework provides a reproducible drug-discovery strategy for identifying immune-pain hubs and prioritizing druggable non-hormonal axes in endometriosis. Citation Format: Ariana F. Rahman. Decoding the endometriosis immune–pain axis: A single-cell pipeline for neuroimmune hub discovery and drug prioritization [abstract]. In: Proceedings of AACR Drug Discovery and Development (AACR D3) Conference; 2026 Jul 21-24; Boston, MA. Philadelphia (PA): AACR; Clin Cancer Res 2026;32(14_Suppl):Abstract nr A044.

Abstract Background: Pancreatic ductal adenocarcinoma (PDAC) is the most lethal common cancer (5-year survival ∼13%), emerging through years-to-decades progression involving chronic fibro-inflammatory injury and precursor transformation processes including ADM and PanIN. KRAS-driven acinar-to-ductal metaplasia (ADM) is among the earliest experimentally tractable and potentially interceptable events; KRAS (mutated in &gt;90% of PDAC) has remained difficult to therapeutically target, particularly for prevention; emerging KRAS inhibitors are developed for treatment of established malignancy rather than early interception. We aimed to prioritize KRAS-axis interception candidates via AI-guided network analysis of TCGA-PAAD restricted to a curated KRAS-driven ADM/PanIN-initiation mechanism module. Methods: The 38-gene module (n=183 TCGA-PAAD samples; 37 genes available, PRSS2 absent) spans KRAS-MAPK, RTKs, TGF-β/SMAD, ECM, inflammation, transcription, tumor suppressors, and ductal/acinar genes from canonical pancreatic-cancer-progression biology, linking established-tumor transcriptomes to early-interception biology. A stability-selected Spearman-correlation network served as the network backbone (PCMCI+/NOTEARS extensions ongoing), paired with patient-level bootstrap filtering (B=200 resamples; ≥70% retention). A composite Interception Score I(v) = α·CB + β·Σ|w| + γ·U + δ·R integrating betweenness centrality, summed edge-weight influence, druggability (literature-curated drug-target prior), and bootstrap robustness ranked candidate interception nodes (unoptimized weights; tuning ongoing). Results: Spearman correlation yielded a 37-node, 248-edge network baseline; bootstrap resampling (B=200) evaluated 856 distinct candidate edges, of which 194 (22.7%) achieved ≥70% retention, defining the stable subgraph used for scoring. The Interception Score placed 7 KRAS-axis genes among the top-10 candidates: ERBB2 (0.953), KRAS (0.743), ERBB3 (0.742), MAPK1 (0.708), EGFR (0.675), MAP2K2 (0.605), and BRAF (0.589). Inflammation (IL6, 0.755), TGF-β/SMAD2 (0.726), and ECM/MMP2 (0.638) also ranked highly, recapitulating PDAC initiation mechanisms (stromal-epithelial crosstalk; inflammatory-fibrotic priming). ERBB2 (HER2), a clinically druggable RTK with approved inhibitors in other malignancies, emerged as the top-ranked candidate, supporting evaluation of HER2-axis signaling for interception in molecularly selected PDAC subsets. These findings align with canonical PDAC biology, supporting biological plausibility prior to formal weight optimization and external validation. Conclusions: AI-guided network analysis of TCGA-PAAD prioritizes biologically coherent KRAS-axis and stromal-inflammatory candidates with clinically available HER2-, EGFR-, MEK-, BRAF-, and KRAS-pathway inhibitors, supporting hypothesis generation for future pancreatic cancer interception studies. Stage-resolved analysis, causal-inference extensions, and sex-/age-stratified validation are ongoing. Citation Format: Jianfu Li, Yingyun Yang, Michael Wallace, Cui Tao, Yan Bi. AI-Driven Network-Based Discovery on TCGA-PAAD Identifies KRAS-Axis Drug-Repurposing Targets for Pancreatic Cancer Interception [abstract]. In: Proceedings of AACR Drug Discovery and Development (AACR D3) Conference; 2026 Jul 21-24; Boston, MA. Philadelphia (PA): AACR; Clin Cancer Res 2026;32(14_Suppl):Abstract nr A029.

Abstract Background: The limited efficacy of oncolytic virotherapy (OV) in gastrointestinal malignancies has been attributed to an immunologically inert tumor microenvironment. While cisplatin is widely used as a cytotoxic agent, its immunomodulatory effects remain incompletely defined. We hypothesized that cisplatin may reprogram the tumor immune landscape to enable more effective OV-mediated anti-tumor responses. Methods: We evaluated the effects of cisplatin in combination with two distinct oncolytic platforms, vesicular stomatitis virus (VSV) and adenovirus (AdV), across both in vitro and in vivo colorectal cancer models. In vivo efficacy was assessed using MC38 subcutaneous and peritoneal metastatic models, with analysis of tumor growth, survival, and toxicity. Immune profiling was performed using single-cell RNA sequencing (scRNA-seq), flow cytometry, and bulk RNA sequencing to interrogate changes in CD8+ T cell states and functional programs. Guided by these preclinical findings, a clinical trial combining XELOX, PD-1 blockade, and OV therapy was initiated (ChiCTR2500107607). Results: Cisplatin markedly enhanced OV-mediated anti-tumor efficacy across models, resulting in sustained tumor control and improved survival in both subcutaneous and peritoneal disease settings. This effect was conserved across viral platforms, indicating a virus-agnostic interaction. Mechanistically, cisplatin induced a robust interferon (IFN)–dominant transcriptional program, characterized by upregulation of interferon-stimulated genes, including IRF7, IRF9, and ISG15. Single-cell analysis revealed that combination therapy reshaped the CD8+ T cell compartment, promoting the expansion of a population with transcriptional and functional features of central memory T cells (Tcm), alongside reduced exhaustion signatures. These changes were accompanied by enhanced cytokine signaling pathways associated with T cell maintenance and function, including IL-2, IL-7, and IL-15. In vitro, the combination of cisplatin and OV increased tumor cell death and interferon-γ production, supporting a model in which cytotoxic and immunostimulatory effects are mechanistically linked. A phase I clinical trial has been initiated. In gastric cancer, patients with retroperitoneal metastases are being treated with OV in combination with PD-1 blockade (ongoing). In colorectal cancer, the strategy is being evaluated in the neoadjuvant setting with an rectal preservation approach. Conclusions: Our findings identify a previously unrecognized role for cisplatin in priming an interferon-driven immune program that promotes Tcm differentiation and function, thereby enabling durable responses to oncolytic virotherapy. This work provides a mechanistic rationale for leveraging chemotherapy-induced immune reprogramming to enhance viro-immunotherapy and supports the clinical translation of this strategy in gastrointestinal malignancies. Citation Format: Joseph Brandon. Parker, Zhongyi Dong, Tao Liu, Qisen Guo, Haoyu Zhang, Bo Ni, Long Bai, Yiwen Chen, Jiasheng Zong, Xiang Xia, Jiaojiao Zheng, Hoang Tran Pham, Aya Elalfy, Demarcus Ingram, Ahmed Abdel-Hakeem, Muhammad Ali, Shannon Hartzell, Zizhen Zhang, Vivienne Yang. Cisplatin primes an interferon-driven CD8+ central memory T cell program to enable oncolytic virotherapy in gastrointestinal cancer malignancies [abstract]. In: Proceedings of AACR Drug Discovery and Development (AACR D3) Conference; 2026 Jul 21-24; Boston, MA. Philadelphia (PA): AACR; Clin Cancer Res 2026;32(14_Suppl):Abstract nr B024.

Abstract Cholangiocarcinoma (CCA) is an aggressive biliary malignancy with poor prognosis and limited treatment options, underscoring the need for targeted RNA-based therapeutic strategies. Though DNA- aptamers could be one of the promising targeted ligands, conventional enrichment-driven selection often fails to reliably identify high-affinity binders due to PCR bias, stochastic drift, and sequencing errors. In this study, we combined cell-SELEX with a data-driven artificial intelligence framework to identify DNA aptamers targeting CCA cells. Eleven rounds of selection were performed on a library exceeding 6×1014 sequences, with iterative negative selection against normal hepatocytes and cholangiocytes to enhance tumor specificity. Given the limitations of enrichment-based ranking in prioritizing functional binders, we developed and evaluated four deep learning architectures—MLP, CNN, BiLSTM, and a hybrid CNN–BiLSTM—using aptamer pools derived from the final SELEX round. These models were built by integrating one-hot sequential features and DNA structural descriptors of the dataset along with their enrichment patterns across SELEX rounds. Among them, the hybrid CNN–BiLSTM model demonstrated superior performance by capturing both local sequence motifs and long-range dependencies. Model performance was evaluated using AUC-ROC and complementary classification metrics on an independent test set. The hybrid CNN–BiLSTM model achieved the best overall performance with an AUC-ROC of 0.97 and a precision of 68%, indicating strong discriminative power under class imbalance (strong: non-binder=1:68). The model exhibited high specificity (99.5%; 29,855/30,005 true negatives) and moderate sensitivity (71.7%; 312/435 true positives), reflecting its ability to reliably filter non-binding sequences while retaining true functional binders. Model-prioritized candidates were experimentally validated using qPCR-based aptamer–cell binding assays. Notably, two low-abundance/high-prediction aptamers (Read counts: 4) exhibited 2–3-fold higher binding than the reference aptamer (Positive Control) derived from traditional SELEX enrichment. A high-read/high-prediction candidate showed ∼2-fold greater binding than the positive control, highlighting the model’s ability to recover overlooked functional binders. To assess translational relevance, selected aptamers were used to functionalize the lactosome-based nanovesicles for siRNA delivery targeting PD-L1, resulting in measurable gene silencing in HuCCT1 cells after 48 hours. Collectively, this integrative framework enables accurate identification and prioritization of high-affinity, functionally relevant aptamers beyond enrichment alone. This approach enhances SELEX efficiency, supports rational ligand design, and provides a scalable strategy for developing aptamer-guided RNA therapeutics for any target. Citation Format: Sourav Pal, Julia Driscoll, Brandon Wilbanks, Ayano Kabashima, Rory Smoot, Tushar Patel. Integrating cell-SELEX and deep learning method for discovery of high-affinity aptamers targeting cholangiocarcinoma [abstract]. In: Proceedings of AACR Drug Discovery and Development (AACR D3) Conference; 2026 Jul 21-24; Boston, MA. Philadelphia (PA): AACR; Clin Cancer Res 2026;32(14_Suppl):Abstract nr A033.

Abstract Introduction and Purpose: Immune checkpoint signaling represents a major barrier to effective antitumor immunity by suppressing cytotoxic T-cell activity. Cytokine-inducible SH2-containing protein (CISH) is a genetically validated intracellular immune checkpoint that constrains cytotoxic T-cell and natural killer cell signaling. Emerging evidence supports a critical, T cell-intrinsic role for CISH in lymphoma. Despite strong biological validation, CISH targeting has thus far been limited to genetic manipulation in adoptive cell therapies. We sought to establish CISH as a drug-tractable target by discovering a first-in-class, cell-permeable small-molecule CISH inhibitor/degrader suitable for scalable immunotherapy development. Methods: CISH biology was characterized using datasets of patients with B-cell lymphoma and isogenic cellular models to define target-dependence benchmarks. Structure-guided small-molecule discovery was enabled through homology modeling and AlphaFold-based predictions of CISH structure. A focused library of covalent CISH binders was synthesized using systematic variation of SH2-engaging motifs and electrophilic warheads. Compounds were evaluated using biophysical binding assays, mass spectrometry–based covalent engagement, cellular degradation, and signaling readouts. Results: Cellular indexing of transcriptomes and epitopes by sequencing (CITE-seq) analysis identified CISH as one of the most highly expressed genes in intratumoral T cells associated with a poor prognosis in patients with non-Hodgkin lymphoma, supporting disease relevance and target enrichment. Genetic ablation or knockdown of CISH in a T cell model (KARPAS299) induced reproducible, target-dependent phenotypes, including enhanced S-phase entry and augmented STAT5 phosphorylation following IL-2 stimulation, establishing rigorous cellular benchmarks for pharmacologic modulation. We designed and synthesized over 50 candidate CISH binders incorporating covalent SH2-domain engagement. Multiple compounds showed direct binding to CISH by thermal shift analysis and selective covalent modification at Cys144 by mass spectrometry. Introduction of a masked phosphate motif significantly improved intracellular exposure, confirmed using a BODIPY-labeled analog. Lead compounds demonstrated target engagement by cellular thermal shift assay and induced rapid, concentration-dependent degradation of endogenous CISH within 4 hours in cells, while maintaining high selectivity over related SOCS family members, demonstrating both target engagement and specificity. Conclusions: This work establishes CISH as a chemically tractable intracellular immune checkpoint and reports the discovery of the first selective, cell-permeable small-molecule CISH degrader. These data provide a strong foundation for further structure activity relationship optimization and position CISH degradation as a promising drug discovery strategy to modulate T cell function and enhance antitumor immunity. Citation Format: Hao Xie, Xinyi Tang, Sujeewa Ranatunga, Stephen M. Ansell. Discovery of a small-molecule degrader targeting the intracellular immune checkpoint CISH [abstract]. In: Proceedings of AACR Drug Discovery and Development (AACR D3) Conference; 2026 Jul 21-24; Boston, MA. Philadelphia (PA): AACR; Clin Cancer Res 2026;32(14_Suppl):Abstract nr B049.

The accurate diagnosis of disorders of gut-brain interaction (DGBI), such as functional dyspepsia and irritable bowel syndrome, is crucial for improving patient symptoms, outcomes and ultimately, quality of life. However, busy clinicians often face challenges in diagnosing and managing these conditions, particularly when patients present with non-specific gastrointestinal symptoms together with psychosocial distress and/or social issues. DGBI are among the most common reasons for gastroenterology referral, with a global prevalence of approximately 40% in adults based on large-scale epidemiologic studies. While diagnostic algorithms aid in positively identifying DGBI, they often overlook key factors influencing treatment decisions. The Rome V Multidimensional Clinical Profile (MDCP) offers a comprehensive, patient-centered framework that extends beyond diagnosis alone. It integrates five core domains: (1) the primary diagnosis, (2) clinical modifiers, (3) impact on daily activities and quality of life, (4) psychosocial modifiers, and (5) physiological features and biomarkers. By considering comorbidities, illness severity, psychosocial contributors, and potential biomarkers, the MDCP enables clinicians to develop a holistic understanding of the patient's condition. This multidimensional approach facilitates tailored, evidence-based treatment plans that address the biopsychosocial aspects of DGBI, ultimately enhancing symptom management and patient well-being. Furthermore, it highlights gaps in knowledge and stimulates targeted research into these highly prevalent disorders. This case-oriented educational review will evaluate three DGBIs – functional dyspepsia, irritable bowel syndrome, and chronic abdominal pain – using the MDCP approach.

Abstract Objective: Determine if automated struts can successfully power a balanced cable transport (BCT) during tibial bone transport and thus benefit from the advantages of automation. Design: Retrospective observational case series. Setting: Level 1 trauma center. Patients/Participants: A retrospective analysis of 7 tibial BCTs using automated struts was performed with an average defect size of 102 mm. Outcomes: Union rate, complications, external fixation index, bone healing index, and mechanical alignment. Results: The average bone healing index was found to be 21.9 days/cm in bifocal BCT and 13.7 days/cm in trifocal BCT. Automated struts successfully powered 7 BCTs but were not without complications. Four patients had strut malfunctions necessitating unscheduled return to clinic. Other complications not secondary to the struts included pin tract infections (n = 4) and unplanned return to the operating room (n = 2, docking site nonunion and revision ankle arthrodesis). Conclusions: Automated struts powering BCT is feasible but not without complications. This treatment strategy combines the advantages of automation with the advantages of BCT. Incorporating automation into these complex constructs will allow for ease of use for patient and caregiver during the long process of bone transport, theoretically decrease noncompliance, which otherwise can cause severe complications, and increased segmentation, which may improve quality of regenerate bone formation. Level of Evidence: Level 4.

Background: Heart failure with preserved ejection fraction (HFpEF) accounts for over half of all heart failure (HF) cases and remains diagnostically challenging due to nonspecific symptoms and a lack of accessible noninvasive screening tools, leading to widespread underdiagnosis and delayed treatment. The electrocardiogram (ECG) is a low-cost, widely available tool that reflects myocardial electrical remodeling. We previously developed and externally validated an ECG-based artificial intelligence (ECG-AI) model capable of classifying ECGs into four categories: reduced ejection fraction (rEF), mid-range ejection fraction (mEF), HFpEF, and controls. In this study, we evaluate the ability of this ECG-AI model to predict future HFpEF diagnosis using real-world data from a large integrated health system. Methods: We applied the validated ECG-AI model to an independent cohort of 7713 patients from Wake Forest Baptist Health (WFBH), using one ECG per patient recorded before the clinical diagnosis of HFpEF. Model discrimination was evaluated across multiple prediction windows from six months up to ten years before diagnosis using the area under the receiver operating characteristic curve (AUC), with comparisons performed using DeLong’s test. Performance was also compared head-to-head with the H2FPEF score, a validated clinical score for HFpEF, in the subset of patients with complete data for score calculation. ECG-AI outputs were stratified into quartiles and evaluated using Kaplan–Meier survival analysis and multivariable Cox proportional hazards regression adjusted for demographics and major comorbidities. Results: Of the 7713 patients, 283 (3.7%) were subsequently diagnosed with HFpEF. The ECG-AI model achieved an AUC of 0.79 (95% CI, 0.72–0.86) using ECGs recorded within six months before diagnosis. Discrimination remained stable across prediction windows extending up to ten years before diagnosis (AUC range, 0.78–0.80; all DeLong p > 0.01 vs. the six-month window). Compared with the H2FPEF score, ECG-AI demonstrated superior discrimination, improving the AUC by 0.06–0.07 across the evaluated prediction windows. Patients in the highest ECG-AI risk quartile had an unadjusted hazard ratio (HR) of 11.18 (95% CI, 7.27–17.21; C-index, 0.75) and an adjusted HR of 6.60 (95% CI, 4.24–10.28; C-index, 0.82). Adding clinical covariates to ECG-AI improved the C-index by 0.07 and 0.09 for the five- and ten-year prediction windows, respectively. Conclusions: An independently validated ECG-AI model predicted incident HFpEF up to ten years before clinical diagnosis and outperformed the H2FPEF score using ECG alone, indicating that ECG-derived signatures precede clinical recognition of the syndrome. ECG-AI shows promise as a prognostic, risk-stratification tool to prioritize further evaluation; prospective validation and single-lead assessment are needed before screening or wearable deployment.