Citations

ABSTRACT Disease Overview Plasmablastic lymphoma (PBL) is a rare CD20‐negative aggressive lymphoma with poor prognosis when treated with standard treatment options. Although PBL is associated with HIV infection and other immunosuppressed states, it can also affect immunocompetent individuals. Diagnosis The diagnosis of PBL requires a high clinical suspicion and pathological confirmation. EBV‐encoded RNA (EBER) expression and MYC gene rearrangements are frequently detected in the malignant cells. The differential diagnosis includes EBV+ diffuse large B‐cell lymphoma (DLBCL), extracavitary primary effusion lymphoma, ALK+ DLBCL, and HHV8+ large B‐cell lymphoma, among others. Risk Stratification Age ≥ 60 years, advanced clinical stage, and high International Prognostic Index scores are associated with worse survival. Management Combination chemotherapy regimens, such as infusional EPOCH, are recommended. The addition of bortezomib or daratumumab might improve outcomes. B‐cell maturation antigen‐targeted therapies have shown early efficacy. The participation of patients with PBL in prospective clinical trials is warranted.

Abstract Background: Immune checkpoint inhibitors (ICIs) have improved survival in non-small-cell lung cancer (NSCLC), yet only a subset of patients benefits, and biomarkers like PD-L1 remain limited. Here, we introduce a deep learning-based pathomics framework that utilizes routine H&E-stained slides to predict therapeutic response and survival outcomes in ICI-treated metastatic NSCLC. Methods: The study included 797 ICI-treated NSCLC patients from MD Anderson, with external validation in 280 patients from Mayo Clinic, Gustave Roussy, and the Phase III ICI-naïve LUSC Lung-MAP S1400I trial receiving nivolumab with or without ipilimumab. Path-IO (Pathology-Driven Immunotherapy Optimization) comprised of four major steps: (1) pathologist-verified tissue classifier segmented WSIs into eight compartments—Background, Bronchi, Immune, Lung, Necrosis, Stroma, Tumor, and Vessel—and was validated on TCGA and CPTAC dataset; (2) a survival prediction module generating patient-level risk scores in the MD Anderson cohort and validated across external datasets; (3) integration of Path-IO risk scores with radiomics and clinical features to improve prognostic accuracy; and (4) biological interpretability analyses correlating Path-IO risk immune contexture from multiplex immunofluorescence and transcriptomic signatures from NanoString profiling. Results: Path-IO effectively stratified patients into high and low-risk groups with significant survival differences. In the MD Anderson cohort, it achieved HR = 2.11 (p < 0.001) and HR = 2.51 (p < 0.001) for OS in the discovery and validation sets, and HR = 2.34 (p < 0.001) and HR = 1.87 (p < 0.001) for PFS, respectively. In the multicenter Phase III Lung-MAP S1400I trial, Path-IO achieved HR = 1.78 (p = 0.016) for OS and HR = 2.76 (p = 0.006) for PFS. In external validation, it predicted outcomes in the Gustave Roussy (OS = 1.97, p = 0.003; PFS = 1.51, p = 0.046) and Mayo Clinic (OS = 2.46, p = 0.007; PFS = 2.45, p = 0.027) cohorts. Path-IO outperformed PD-L1 and remained an independent predictor in multivariate analysis (p < 0.001). It enabled data-driven frontline selection between anti-PD-1 monotherapy and chemo-immunotherapy, offering guidance toward more personalized treatment decisions. Integration with radiomics and clinical features further improved predictive accuracy (OS 0.63→0.75; PFS C-index 0.58→0.70), while high Path-IO risk scores correlated with immune-cold phenotypes identified by multiplex immunofluorescence and NanoString transcriptomics. Conclusions: Path-IO demonstrates that deep learning models rooted in histopathologic architecture can generate interpretable and biologically informed survival predictions in NSCLC treated with ICIs. By integrating pathology, radiology, and clinical data, Path-IO provides complementary predictive value beyond established biomarkers such as PD-L1. Citation Format: Rukhmini Bandyopadhyay, Linghzi Hong, Mihaela Aldea, Shenduo Li, Lodovica Zullo, Frank R. Rojas, Maliazurina B. Saad, Maricel C. Marin, Muhammad Waqas, Jiexin Zhang, Eman Showkatian, Claudio A. Arrechedera, Xiaoyu Han, Yuliya Kitsel, Sherif Ismail, Muhammad Aminu, Bo Zhu, Carol C. Wu, Brett W. Carter, Joe Y Chang, Zhongxing Liao, Maria R. Ghigna, Davide Soldato, Hai T. Tran, Xiuning Le, Tina Cascone, Bingnan Zhang, Haniel A. Araujo, Mehmet Altan, Simon Heeke, David Jaffray, Don L. Gibbons, Ara Vaporciyan, J Jack Lee, Neda Kalhor, Cara Haymaker, Ignacio Wistuba, John V. Heymach, Yanyan Lou, Natalie Vokes, Luisa M. Solis Soto, Jianjun Zhang, Jia Wu. Path-IO: A deep learning pathomics framework for personalized immunotherapy selection and outcome prediction in metastatic non-small cell lung cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2026; Part 1 (Regular Abstracts); 2026 Apr 17-22; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2026;86(7 Suppl):Abstract nr 4003.

ABSTRACT Objective To identify and compare how patient perceptions contribute to Type 1 Delays, defined as delays in deciding to seek care, among individuals with head and neck cancer (HNC) in high‐income countries (HICs) and low‐ and middle‐income countries (LMICs). Methods We conducted a scoping review and systematic search of MEDLINE, Embase, Cochrane Library, and Scopus from inception through November 20, 2023. Eligible studies included patients with HNC in HICs or LMICs who reported patient perceptions contributing to Type 1 Delays. Data extraction was performed in duplicate, and barriers were categorized thematically. Fisher's exact test was used to compare the frequency of reported barriers between HIC and LMIC studies. Results Seventeen studies met inclusion criteria, of which 64.7% ( n = 11) were conducted in HICs and 35.3% ( n = 6) in LMICs, representing 9 countries and 1410 participants. The most frequently cited perceptions contributing to delay were symptom unawareness, misinterpretation, and minimization. Additional barriers included fear of diagnosis, stigma, alternative medicine use, financial challenges, and geographic barriers. Transport/geographic barriers showed a trend toward being more common in LMIC studies ( p = 0.05). Descriptive patterns suggested LMIC patients more often reported structural and sociocultural barriers, whereas HIC patients more commonly cited psychosocial or perceptual barriers. Conclusion Type 1 Delays in HNC are common across both HICs and LMICs, with overlapping and region‐specific contributors. Tailored interventions, such as patient education, stigma reduction, and improved access to diagnostic services, may promote earlier detection and improve global HNC outcomes. Level of Evidence 3.

The NCCN Guidelines for Prostate Cancer include recommendations for staging and risk assessment after a prostate cancer diagnosis and for the care of patients with localized, regional, recurrent, and metastatic disease. These NCCN Guidelines Insights summarize the panel’s discussions on select 2026 updates to the guidelines regarding nonmetastatic disease: the elimination of the very-low-risk group, updates to the Principles of Active Surveillance, and caution in the use of focal therapy in newly diagnosed patients.

Importance Chronic pain is a leading cause of disability, and many affected patients rely on long-term opioid therapy despite its unfavorable adverse effect profile. Spinal cord stimulation is an established therapy for chronic pain that may secondarily reduce opioid consumption by providing sustained analgesia. Objective To quantify changes in opioid use following spinal cord stimulation implantation for the treatment of chronic pain through a systematic review and meta-analysis. Data sources We conducted a comprehensive search of MEDLINE, EMBASE, Cochrane Central Register of Controlled Trials, and Scopus from each database’s inception to July 1, 2024. Study selection We included studies describing patients who underwent spinal cord stimulator implantation for the treatment of chronic pain. A total of 2,784 potential studies were identified and screened by multiple independent reviewers, yielding a final total of 43 studies included in the systematic review. Data extraction and synthesis Data extraction was completed by multiple independent reviewers following Preferred Reporting Items for Systematic Reviews and Meta-Analysis and Meta-analysis Of Observational Studies in Epidemiology guidelines. A random effects model with Hartung-Knapp-Sidik-Jonkman adjustment was used to estimate pooled estimates. Certainty assessment was performed following the Grading of Recommendations Assessment, Development, and Evaluation (GRADE) framework. Main outcome(s) and measure(s) The primary outcomes were change in continuous opioid use (ie, morphine milligram equivalent (MME)) and categorical opioid use (yes/no) from preimplantation to 12 months following spinal cord stimulator implantation. Results Meta-analysis of 12 month continuous MME use comprising 22 study comparisons (1,666 individuals) revealed a weighted mean difference of −18.06 MME (95% CI −24.41 to −11.70; 95% prediction interval −38.29 to 2.17; p<0.001). Meta-analysis of categorical opioid use at 12 months comprising 24 study comparisons (1,527 individuals) revealed an OR of 0.47 (95% CI 0.30 to 0.75; 95% prediction interval 0.10 to 2.11; p=0.003), signifying lower odds of opioid continuation. Conclusions and relevance Spinal cord stimulation is associated with reduced opioid dose consumption and increased odds of opioid cessation at 12 months postimplantation compared with baseline in patients with chronic pain; however, both of these conclusions are supported by evidence of very low GRADE certainty. PROSPERO registration number CRD42024574921.

Abstract Inflammatory bowel disease (IBD) is a chronic immune-mediated disorder of the gastrointestinal tract whose genetic basis is only partly resolved because most risk variants identified by genome-wide association studies (GWAS) lie in non-coding regions, limiting direct gene assignment and biological interpretation 1,2 . Here we analysed whole-exome and whole-genome sequencing data from 86,213 IBD cases and 478,363 controls of European ancestry. We identified 68 IBD genes directly implicated by conditionally independent protein-coding associations across the allele frequency spectrum. Many newly implicated IBD genes are supported by orthogonal genomic or pleiotropic evidence, pointing to disease-related pathways and nominating targets with therapeutic relevance. We further identified allelic series and non-additive effects at key loci such as NOD2 and TYK2. These results show that large-scale sequencing can resolve disease genes and pathways that remain ambiguous from non-coding association alone, providing a more direct route from human genetics to biological insight and therapeutic hypotheses.

Abstract Background Fibroblast growth factor receptors inhibitors (FGFRi) are approved treatments for various malignancies. FGFRi-associated retinopathy is a class effect of FGFRi, including erdafitinib, pemigatinib, infigratinib, and rogaratinib. Patients and Methods We summarized FGFRi-associated retinopathy with erdafitinib across 5 clinical studies in patients with metastatic urothelial cancer (mUC pooled studies) and one study in patients with advanced solid tumors (RAGNAR). We also present changes in visual acuity, retinal pigment epithelial elevation, and subretinal fluid in RAGNAR. Results One hundred and three (21.5%) of 479 patients in the mUC pooled studies and 43/314 (13.7%) patients in RAGNAR experienced FGFRi-associated retinopathy. Most FGFRi-associated retinopathy were Grade 1/2 events. Grade 3 FGFRi-associated retinopathy were infrequent (mUC pooled studies, 11/479 [2.3%]; RAGNAR, 3/314 [1.0%]). No Grade 4 events occurred. Most FGFRi-associated retinopathy events (mUC pooled studies, 81/103 [78.6%]; RAGNAR, 30/43 [70.0%]) occurred within 90 days of treatment initiation and were managed with erdafitinib dose interruption (mUC pooled studies, 41/479 [8.6%]; RAGNAR, 23/314 [7.3%]) or reduction (mUC pooled studies, 58/479 [12.1%]; RAGNAR, 21/314 [6.7%]); few required treatment discontinuation (mUC pooled studies, 14/479 [2.9%]; RAGNAR, 3/314 [1.0%]). By the data cutoff, 63.1% (65/103) patients in the mUC pooled studies and 65.1% (28/43) patients in RAGNAR had their FGFRi-associated retinopathy events resolve. Most unresolved FGFRi-associated retinopathy events were persistent Grade 1 events. In RAGNAR, 92.0% of patients had their visual acuity return to baseline and 78.5% of patients had their retinal pigment epithelial elevation resolve. Conclusions FGFRi-associated retinopathy with erdafitinib can be managed with dose reductions, interruptions, or discontinuations. Proactive collaboration between oncologists and ophthalmologists is required to ensure that erdafitinib delivers clinical benefit to patients while managing their potential adverse symptoms of FGFRi-associated retinopathy.