Citations

Abstract Oncology Advanced Practice Providers (APPs), including Advanced Practice Registered Nurses, Physician Assistants, and Clinical Pharmacists (CPs), play a vital role in delivering high-quality, patient-centered cancer care across the United States. Despite their widespread presence in oncology practices, APPs remain underutilized in cancer clinical research, representing a missed opportunity to expand trial access and improve patient outcomes. Clinical trials are essential to advancing oncology care, yet participation remains critically low among adult patients. Given Oncology APPs’ and CPs’ central role in cancer care delivery, their meaningful engagement in oncology clinical trial research is imperative as the standard of oncology care. This position article, with contributions from 5 professional societies, presents solutions and resources to barriers limiting APPs’ and CPs’ involvement in cancer clinical research, including gaps in education, role expectations, limited protected time, restrictive policies, insufficient financial support, and underrecognition of contributions. Integrating APPs and CPs more fully into the clinical research enterprise is essential to improving trial access, patient outcomes, closing equity gaps, and accelerating innovation in oncology care.

The NCCN Clinical Practice Guidelines in Oncology (NCCN Guidelines) for Non-Small Cell Lung Cancer (NSCLC) provide recommendations for the treatment of patients with NSCLC, including diagnosis, primary disease management, surveillance, and subsequent treatment. The panel has updated the list of recommended targeted therapies based on recent FDA approvals and clinical data. This selection from the NCCN Guidelines for NSCLC focuses on treatment recommendations for advanced or metastatic NSCLC with actionable biomarkers.

Abstract Breast cancer (BC) is the most common cancer among female adolescents and young adults (AYAs; 15-39 years). While advances in treatment have improved survival, cardiovascular disease (CVD) can result from systemic therapies, and CVD is a leading cause of death in AYA cancer survivors. AYAs with BC face nearly a four-fold increased risk of CVD compared to their peers without cancer. However, data on the effects of systemic treatment on CVD in this population remains limited. We estimated risk of CVD in AYAs diagnosed with invasive BC (2006-2020), who survived ≥2 years, and were diagnosed and treated in the Kaiser Permanente (KP) Northern and Southern California. Patients were categorized by receipt of anthracycline-, alkylating-, HER2-, platinum-, and taxane-based therapies within 2 years of diagnosis. We examined the cumulative incidence of CVD starting 2 years post-diagnosis and used Cox proportional hazards regression to determine factors associated with CVD. Among 3,071 AYAs, 35.1% were non-Hispanic (NH) White, 31.6% were Hispanic, 18.3% were NH Asian, and 8.2% were NH Black. Most received systemic therapy (90.6%), including anthracycline- and alkylator without HER2-targeted therapy (41.9%) and HER2-targeted therapy without anthracycline (20.9%). Fewer AYAs received a taxane and alkylator (13.0%) or anthracycline with alkylator and taxane along with a HER2-targeting agent (6.4%). Mean follow-up after cancer diagnosis was 7.2 years (range: 2.0-17.5). The 10-year cumulative incidence of CVD was highest among AYAs who received anthracycline/alkylator/taxane/HER2-targeting therapy (19.6%), intermediate for those with anthracycline/alkylator without HER2-targeting therapy (13.0%) and those who received HER2-targeted therapy without anthracycline (14.6%), and lowest for those who received taxane/alkylator (7.3%). In the multivariable model adjusted for demographic factors and radiation, compared to taxane/alkylator, anthracycline/alkylator/taxane/HER2-targeting therapy (hazard ratio (HR)=2.63, 95% confidence interval (CI) 1.56-4.43); anthracycline/alkylator without HER2-targeting therapy (HR=1.75, CI 1.13-2.71); and HER2-targeting therapy without anthracycline (HR=2.13, CI 1.11-4.09) were associated with an increased risk of CVD. CVD risk was similar for taxane/alkylator and no systemic therapy. Other factors associated with higher risk of CVD included NH Black race/ethnicity (HR=1.90, CI 1.32-2.73 vs. NH White) and public health insurance (HR=1.67, CI 1.00-2.78 vs private). This study identifies AYA BC survivors at higher risk of CVD based on treatment regimens received, with highest risks found for those receiving anthracycline/alkylator/taxane/HER2 treatment combinations. In addition, AYAs of Black race/ethnicity and those with public health insurance experienced more CVD, underscoring the need for targeted interventions to mitigate these disparities. Citation Format: Theresa H. M. Keegan, Candice A. Sauder, Ann M. Brunson, Renata Abrahao, Anne C. Kirchhoff, Eric Haupt, Mallory Casperson, Ted Wun, Chun R. Chao, Andrew B. Smitherman, Hazel B. Nichols, Jessica Chubak, Erin E. Hahn, Lawrence H. Kushi, Kathryn J. Ruddy. Systemic therapy and cardiovascular disease in adolescent and young adult breast cancer survivors [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 5219.

Objective: The identification of reliable biomarkers in trauma patients (pts) remains a clinical challenge. Nucleosomes, which are DNA wrapped around histone protein cores, have been used as reliable markers for quantification in sepsis, acute respiratory distress syndrome, and lymphoma. We hypothesized that: 1) levels of circulating nucleosomes quantified after traumatic injury, specifically histone H3.1 and its citrullinated R8 posttranslation modification (H3R8 Cit), would be increased after trauma and 2) that levels of circulating nucleosomes would be further, increased in pts who went on to develop venous thromboembolism (VTE), as these nucleosomes may be clot-enhancing mediators. Methods: Trauma pts presenting to a Level I trauma center were evaluated for inclusion in a prospective case-cohort study, and citrated blood samples were collected within 12 hours of injury. Pts were followed for up to 90 days and VTE occurrence was confirmed via autopsy or imaging. Pts who developed incident, symptomatic VTE, and those who did not develop VTE were selected at a 1:3 ratio. The effect of trauma was assessed by comparison with healthy volunteer samples. Circulating nucleosomes were quantified using Nu.Q H3.1 and Nu.Q H3R8 Cit assays. Data are presented as median [interquartile range] or n (%), with Wilcoxon Rank-Sum or chi-squared test performed between trauma pts who developed symptomatic VTE versus those who did not, with a P value < 0.05 as statistically significant. Results: A total of 639 trauma pts were analyzed [51 years (33, 65), 71.0% male, 94.1% blunt, injury severity scores 17 (9, 27)]. Both H3.1 [359.7 (58.4, 1769.3) vs. 21.5 (15.3, 26.6) ng/mL, P < 0.001] and H3R8 Cit levels [209.4 (45.4, 665.5) vs. 22.3 (16.2, 28.9) ng/mL, P < 0.001] were greater in trauma pts than in 10 healthy volunteers. The 160 pts with VTE were then compared with 479 without VTE. VTE pts had a median time to VTE of 8.5 days, with 91 developing symptomatic deep venous thrombosis, 48 with pulmonary embolism, and 21 with both deep venous thrombosis and pulmonary embolism. No significant differences were found in age, sex, or mechanism between pts with or without VTE. However, VTE pts had higher injury severity scores [24 (14, 34), 14 (9, 24), P < 0.001] and body mass index [29.1 (24.3, 34.2), 27.6 (24.2, 32.0) kg/m 2 , P = 0.037]. A greater percentage of VTE pts underwent surgery requiring general anesthesia (53.1%, 34.0%, P < 0.001) and received blood transfusions within 24 hours of injury (51.9%, 24.0%, P < 0.001). H3.1 levels were significantly greater in trauma pts who developed VTE compared with those who did not [828.4 (112.0, 3186.5), vs. 276.7 (50.3, 1237.0) ng/mL, P < 0.001], as were H3R8 Cit levels [350.4 (77.4, 729.5), vs. 189.2 (40.8, 632.6) ng/mL, P = 0.005]. No differences in levels of H3.1 and H3R8 Cit were found between early (diagnosed day 0–7 after injury) and late (diagnosed > day 7) VTE. Conclusions: Levels of the H3.1 and H3R8 Cit nucleosomes are elevated early after traumatic injury, especially in those who developed VTE. These findings underscore the importance of understanding the pathophysiology of nucleosomes in inducing VTE and their role as biomarkers

ABSTRACT We report a case of fulminant Clostridium perfringens septicemia complicated by massive intravascular hemolysis in a 19‐year‐old male with acute myelogenous leukemia. The patient presented with severe anemia and laboratory evidence of profound hemolysis. Despite the high mortality associated with this condition, the patient was successfully treated with broad‐spectrum antibiotics, including clindamycin, and three consecutive sessions of therapeutic plasma exchange (TPE). TPE was employed to remove circulating α‐toxin and cell‐free hemoglobin, thereby mitigating ongoing hemolysis and organ injury. This case highlights the potential role of TPE, in conjunction with appropriate antimicrobial therapy, as an intervention in C. perfringens septicemia with hemolysis.

ABSTRACT Background Fosaprepitant is non‐inferior to aprepitant in adults receiving highly emetogenic chemotherapy (HEC). We evaluated whether fosaprepitant is non‐inferior to aprepitant in children receiving HEC. Methods In this open‐label, non‐inferiority trial, children aged 5–18 years, receiving their first cycle of HEC, were randomized 1:1 to the fosaprepitant or aprepitant group. The fosaprepitant group received a single intravenous dose of 4 mg/kg (max 150 mg). The aprepitant group received oral aprepitant capsules (weight 15 to <30 kg: 80 mg Days 1–3; weight ≥30 kg, or age ≥12 years: 125 mg Day 1, 80 mg Days 2 and 3). Both groups received ondansetron and dexamethasone. The primary outcome was the complete response (CR) rate of vomiting during the acute phase (AP) with a non‐inferiority margin of 15%. Secondary outcomes included CR rates in the delayed (DP) and overall phases (OP), grade of vomiting, incidence and severity of nausea, and adverse effects. Results A total of 279 children were included in the modified intention‐to‐treat analysis (fosaprepitant group: 140; aprepitant group: 139). In AP, the difference of CR rate between fosaprepitant [76 (54.3%)] and aprepitant groups [84 (60.4%)] was −6.1% [90% CI: −15.7% to +3.6%], with the lower limit exceeding −15%, failing to establish non‐inferiority. The CR rate with fosaprepitant versus aprepitant was 62.1% ( n = 87) versus 65.5% ( n = 91) in DP (difference: –3.4%; 90% CI: –12.7% to 6.1%) and 42.9% ( n = 60) versus 49.6% ( n = 69) in the OP (difference: –6.7%; 90% CI: –16.5% to 3.1%). Grade of vomiting, incidence and severity of nausea, and adverse effects were similar between the two groups. Conclusions Non‐inferiority of fosaprepitant compared to aprepitant was not demonstrated in children undergoing their first cycle of HEC. Trial Registration Clinical Trials Registry, India (CTRI/2019/05/019082).

Abstract Background Inflammatory bowel disease (IBD), including ulcerative colitis (UC) and Crohn’s disease (CD), is characterized by immune dysregulation1,2. Blood-based biomarkers in diagnosing patients with IBD are lacking. We tested the hypothesis that circulating immune cell types and immune repertoire could distinguish patients with IBD from controls. Methods We performed deep immunophenotyping with scRNA-seq with T-cell receptor (TCR) and B-cell receptor (BCR) sequencing on peripheral blood mononuclear cells (PBMCs) from patients with IBD and healthy controls (87 UC, 127 CD, 35 controls). We applied machine learning to quantify cell abundance (miloR3), construct diagnostic classifiers (immuneML4), and infer cell-cell ligand-receptor interactions (MultiNicheNet5) between patients with IBD and controls. Results A scRNA-seq atlas of over 1.1 million PBMCs consisting of 53 distinct cell types was generated (Fig1A). In classifying UC vs controls, support vector machine model had a balanced accuracy of 0.90, consisting of increased IgA and IgG plasma B cells, switched memory B cells, TRegs, and decreased CD16 monocytes, cDC1, non-switched B cells, and MAIT (Fig.1B). In classifying CD vs controls, K-nearest neighbors model had a balanced accuracy of 0.87, consisting of increased IgA plasma B cells, non-switched B cells, CD4 Th22, TReg cytotoxic, CD4 Th1/Th17, and pDC and decreased CD16 monocytes, gdT, CD4 Th17, CD4 Th2, and MAIT cells.Fig.1C). Inflammation was associated with increased IgG plasma B in patients with both UC and CD. Using 4-mer amino acid motif of the BCR complementary-determining region 3 (CDR3) logistic regression model, immuneML had a balanced accuracy of 0.82 (0.75-0.92) in classifying CD vs controls (Fig.1D with models showing top 25 BCR predictive features). Using 4-mer amino acid motif of the BCR CDR3 logistic regression model, immuneML had a balanced accuracy of 0.90 (0.70-1.00) in classifying UC vs controls (Fig.1E with models showing top 25 BCR predictive features). Using 3-mer amino acid motif of the TCR CDR3 logistic regression model, immuneML had a balanced accuracy of 0.76 (0.72-081) in classifying CD vs UC. (Fig.1F with models showing top 25 TCR predictive features). In UC vs controls, MultiNicheNet demonstrated increased signaling in ADAM10-NOTCH2 between TRegs and naïve B cells and IL15-IL2RA between CD14 monocytes and CD4 Th17 cells. In CD vs controls, there was increased signaling in IL15-IL2RA between CD14 monocytes and CD4 Th17 cells and ADAM10-TSPAN5 between CD14 monocytes and TRegs (Fig.1G). Conclusion Applying machine learning models to scRNA-seq and immune repertoire datasets enable blood-based deep immunophenotyping with diagnostic potential and reveals novel insights into cell signaling in IBD. References: 1.Kong L, Pokatayev V, Lefkovith A, Carter GT, Creasey EA, Krishna C, Subramanian S, Kochar B, Ashenberg O, Lau H, Ananthakrishnan AN, Graham DB, Deguine J, Xavier RJ. The landscape of immune dysregulation in Crohn’s disease revealed through single-cell transcriptomic profiling in the ileum and colon. Immunity. 2023 Feb 14;56(2):444-458.e5. doi: 10.1016/j.immuni.2023.01.002. Epub 2023 Jan 30. Erratum in: Immunity. 2023 Dec 12;56(12):2855. doi: 10.1016/j.immuni.2023.10.017. PMID: 36720220; PMCID: PMC9957882. 2.Smillie CS, Biton M, Ordovas-Montanes J, Sullivan KM, Burgin G, Graham DB, Herbst RH, Rogel N, Slyper M, Waldman J, Sud M, Andrews E, Velonias G, Haber AL, Jagadeesh K, Vickovic S, Yao J, Stevens C, Dionne D, Nguyen LT, Villani AC, Hofree M, Creasey EA, Huang H, Rozenblatt-Rosen O, Garber JJ, Khalili H, Desch AN, Daly MJ, Ananthakrishnan AN, Shalek AK, Xavier RJ, Regev A. Intra- and Inter-cellular Rewiring of the Human Colon during Ulcerative Colitis. Cell. 2019 Jul 25;178(3):714-730.e22. doi: 10.1016/j.cell.2019.06.029. PMID: 31348891; PMCID: PMC6662628. 3.Dann E, Henderson NC, Teichmann SA, Morgan MD, Marioni JC. Differential abundance testing on single-cell data using k-nearest neighbor graphs. Nat Biotechnol. 2022 Feb;40(2):245-253. doi: 10.1038/s41587-021-01033-z. Epub 2021 Sep 30. PMID: 34594043; PMCID: PMC7617075. 4.Pavlović M, Scheffer L, Motwani K, Kanduri C, Kompova R, Vazov N, Waagan K, Bernal FLM, Costa AA, Corrie B, Akbar R, Al Hajj GS, Balaban G, Brusko TM, Chernigovskaya M, Christley S, Cowell LG, Frank R, Grytten I, Gundersen S, Haff IH, Hovig E, Hsieh PH, Klambauer G, Kuijjer ML, Lund-Andersen C, Martini A, Minotto T, Pensar J, Rand K, Riccardi E, Robert PA, Rocha A, Slabodkin A, Snapkov I, Sollid LM, Titov D, Weber CR, Widrich M, Yaari G, Greiff V, Sandve GK. The immuneML ecosystem for machine learning analysis of adaptive immune receptor repertoires. Nat Mach Intell. 2021 Nov;3(11):936-944. doi: 10.1038/s42256-021-00413-z. Epub 2021 Nov 16. PMID: 37396030; PMCID: PMC10312379. 5.Browaeys, R., Gilis, J., Sang-Aram, C., De Bleser, P., Hoste, L., Tavernier, S., Lambrechts, D., Seurinck, R. and Saeys, Y., 2023. MultiNicheNet: a flexible framework for differential cell-cell communication analysis from multi-sample multi-condition single-cell transcriptomics data. BioRxiv, pp.2023-06. Conflict of interest: Dr. Gubatan, John Mark: No conflict of interest Jiayu, Ye: No conflict of interest Christin, Lund-Andersen: No conflict of interest Boye, Theresa: No conflict of interest Hoang, Jacqueline: No conflict of interest Sojwal, Raoul: No conflict of interest Fardeen, Touran: No conflict of interest Tran, Tracy: No conflict of interest Sokzini, Peacha: No conflict of interest Koh, Ally: No conflict of interest Gibson, Marielle: No conflict of interest Sinha, Sidhartha: No conflict of interest Rogalla, Stephan: No conflict of interest Rosen, Michael: No conflict of interest Nielsen, Ole Haagen: No conflict of interest Sandve, Geir Kjetil: No conflict of interest

Abstract Background Revaccination is required for patients who receive a hematopoietic cell transplant (HCT) to reduce vaccine preventable illness. Revaccination requires an individualized plan due to differences in immunosuppressive therapy, immune reconstitution, age, and other challenges such as graft versus host disease. A structured approach is required to ensure prompt initiation of re-vaccinations and completion of the re-vaccination schedule. This study sought to evaluate the re-vaccination process at our center as a baseline for quality improvement. Methods We conducted a retrospective chart review of pediatric patients who received an autologous or allogeneic HCT between January 2019 and March 2024 to assess time to starting re-vaccination after post-HCT medical clearance and percentage of patients up to date within each series of vaccinations per internal protocol. Results A total of 64 patients were evaluated, of whom 23 were female and 35 underwent allogeneic stem cell transplant. For analysis, 13 were excluded due to death prior to clearance for vaccination. Among those included, 42 patients were cleared for restarting vaccines, and 37 (88%) of those have received at least one vaccine, while only 25 (59%) have completed the first series of recommended vaccines. The average time from transplant to administration of the first vaccine was 431 days for autologous HCT and 459 days for allogeneic HCT. The average time from medical clearance to the first vaccine was 66 days. The mean interval between the first and second vaccine doses was 97 days. Conclusion The majority of eligible patients initiated re-vaccination after transplant, but rates for the receipt of full recommended initial series remained suboptimal, with notable delays with notable delays between transplant and the day of clearance as well as clearance and the first vaccine administered. These findings highlight significant gaps in post-transplant immunization practices and underscore the need for targeted interventions to optimize protection for this vulnerable population. Disclosures All Authors: No reported disclosures

Abstract Background Ten years after the original publication, the Centiloid framework is now broadly used to harmonize amyloid‐PET quantification, facilitate data sharing and comparison across cohorts, and even assist visual interpretation in clinical settings. We evaluated the global implementation of Centiloids by comparing their distribution and corresponding positivity thresholds across cohorts. Methods We gathered data from publicly available cohorts and reached out to investigators across the world to collect cross‐sectional Centiloids, demographic and clinical information, and visual read data. Gaussian mixture models (GMM, k=2) were fitted to Centiloid values for each cohort and cutoffs were calculated as mean + 2SD of the lower Gaussian. When visual reads were available, we determined Centiloid cutoffs that maximized correspondence with visual reads (Cohen's kappa). Data was combined across cohorts using random effects meta‐analyses. Results As of January 2025, we included 37 cohorts ( n = 41,678 participants) with heterogeneous pipelines, radiotracers, and clinical and demographic characteristics (Table‐1). The low Gaussian peaks ranged from ‐9 to 10CL; the meta‐analysis identified a common peak at 1CL. The second peaks were more heterogeneous (range=38‐102CL; meta‐analysis outcome=64CL). Across cohorts, the proportion of cognitively unimpaired versus impaired participants impacted the position of both peaks, with better separation in cohorts enriched in impaired individuals (Figure 1C). The meta‐analysis indicated a GMM‐based cutoff of 19CL (95%CI: 16‐21CL, Figure 1B); subgroup analyses showed no evidence of significant effect between single versus multicenter settings (17 versus 20CL, p = 0.30), MRI‐based or PET‐only processing (18 versus 19CL, p = 0.88), and no evidence of difference across radiotracers (Flutemetamol: 16CL; PIB: 17CL, Flutafuranol: 18CL, Florbetaben: 19CL, Florbetapir: 20CL, p = 0.78). In a subset of 29,496 participants with visual reads available, binary visual reads corresponded well to Centiloids (common kappa=0.86, Figure 2A). The visual read‐based cutoff of 24CL (95%CI: 21‐27CL, Figure 2B) maximized correspondence between visual read and quantification and was slightly higher than the GMM‐based cutoff. All meta‐analysis models showed high non‐random heterogeneity (I 2 >80%) across studies, suggesting non‐random differences in peaks and cutoffs. Conclusions Meta‐analysis‐based cutoffs align well with thresholds from the existing literature. High heterogeneity among studies underscores the need to investigate contributing factors, raising concerns about applying common cutoffs.

Abstract Background Sex, education and race/ethnicity are all associated with risk of Alzheimer's disease dementia. Here, we assess the effects of self‐reported sex, educational attainment and race/ethnicity on amyloid‐positivity, and tau‐PET‐positivity in 12,048 (7,394 cognitively unimpaired [CU], 2,177 MCI, and 2,477 dementia) individuals from 42 cohorts worldwide. Method Logistic generalized estimating equations were used to estimate frequency of amyloid‐positivity (using cohort‐specific thresholds for amyloid‐PET [84%] or CSF) and tau‐PET‐positivity (cohort‐specific thresholds of 2SD above mean temporal uptake in amyloid‐negative controls). We assessed: i) sex and APOEε4 ( N = 10,098) associations, to complement earlier findings of a higher frequency of tau‐positivity in females, ii) effects of lower/higher education ( N = 10,970; cohort‐specific median‐split), and iii) effects of race/ethnicity (non‐Hispanic White [hereafter: White], N = 4880; Asian, N = 116; Black or African‐American [hereafter: Black], N = 353; Hispanic, N = 356, only from Northern‐American cohorts). Outcomes were frequency of amyloid‐positivity in CU individuals only, and tau‐PET‐positivity in both amyloid‐positive (AB+) CU and cognitively impaired (CI, i.e. MCI and dementia) individuals. Interaction effects on the relationship between age and amyloid/tau‐positivity were assessed and only retained in the models when significant. Result Female sex was associated with an APOEε4 ‐independent increased frequency of amyloid‐positivity (β=0.51[0.22], p = 0.02) in CU and increase of tau‐positivity in both AB+CU (β=0.27[0.08]) and AB+CI (β=0.37[0.08], both p <0.01). Remarkably, tau‐positivity frequencies of female APOEε4 non‐carriers were equivalent to male APOEε4 carriers in AB+CI (Figure 1). No significant sex* APOE interactions were observed. In CU, higher education was associated with lower amyloid‐positivity frequency (β=‐0.12[0.05], p = 0.02). In contrast, among AB+CU, there was an age*education interaction effect that indicated more pronounced age effects on tau‐positivity in individuals with higher education (age*education:β interaction =0.03[0.01], p <0.01). There were no education effects in AB+CI (Figure 2). In CU, an age*race/ethnicity interaction effect was observed across all non‐White groups compared to White (Hispanic:β interaction =‐0.05[0.01], p <0.01; Black:‐0.04[0.01], p <0.01; Asian:‐0.02[0.01], p = 0.04). This suggests that the impact of age on amyloid‐positivity was less pronounced in non‐White groups. Furthermore, in AB+CI, Hispanic ethnicity was related to higher tau‐positivity frequency than White (β=0.51[0.22], p = 0.02; Figure 3). Conclusion In this multi‐center initiative comprised of clinical and community‐based cohorts, we observed that self‐reported sex, educational attainment and race/ethnicity were related to positivity‐frequencies of Alzheimer's disease pathology.