Citations

Abstract INTRODUCTION : Despite expanding use of plasma biomarkers for Alzheimer's disease (AD), minimal examination has been undertaken in early‐onset AD (EOAD). Prior analyses assessed criterion and convergent validity of common plasma biomarkers in a well‐characterized sample with sporadic EOAD. METHODS : Plasma amyloid beta (Aβ) 42/40, tau phosphorylated at threonine 231 (p‐tau231), glial fibrillary acidic protein (GFAP), and neurofilament light change (NfL) levels were obtained for 189 EOAD, 52 early‐onset non‐AD (EOnonAD), and 83 cognitively normal (CN) participants. Diagnostic group differences, convergence with imaging biomarkers, and the capacity to predict specific cognitive domains were investigated. RESULTS : After controlling for cognitive status, EOAD participants exhibited more pathologic levels of Aβ42/40, p‐tau231, and GFAP relative to EOnonAD participants. However, NfL displayed the greatest sensitivity to non‐amnestic cognitive impairment across the sample. DISCUSSION : These results establish criterion and convergent validity of plasma biomarkers in sporadic EOAD and highlight complementary roles that AD‐specific and non‐specific plasma markers may play across the course of AD care.

ABSTRACT Purpose To validate a custom smartphone application for at‐home visual acuity (VA) measurement in children. Methods A total of 452 children aged 3–17.5 years participated. Certified examiners measured in‐office test–retest VA (logMAR) using gold‐standard Amblyopia Treatment Study HOTV (3‐to‐6‐year‐olds, younger cohort) or electronic Early Treatment of Diabetic Retinopathy Study (7‐to‐17.5‐year‐olds, older cohort) protocols at 3–4.5 m and app‐based VA at 1.5 m. Caregivers measured at‐home app‐based VA at 1.5 m. Results Comparing at‐home app‐based with gold‐standard VA, in eyes 20/40 or better, 95% (143/151) and 93% (91/98) of the younger and older cohorts were within 2 lines, respectively (mean differences: younger = −0.03, older = −0.04; 95% limits‐of‐agreement half‐width (LOA): younger = ±0.26, older = ±0.22). In eyes 20/50 or worse, 66% (42/64) and 75% (76/101) of the younger and older cohorts were within 2 lines, respectively (mean differences: younger = 0.11, older = 0.13, LOA: younger = ±0.50, older = ±0.51). Comparing in‐office app‐based VA with gold‐standard VA, in eyes 20/40 or better, 98% (160/164) and 94% (99/105) of the younger and older cohorts were within 2 lines, respectively (mean differences: younger = −0.03, older = −0.03; LOA: younger = ±0.22; older = ±0.24). In eyes 20/50 or worse, 85% (60/71) and 91% (101/111) of the younger and older cohorts were within 2 lines, respectively (mean differences: younger = 0.04; older = 0.04; LOA: younger = ±0.39; older = ±0.24). For gold‐standard test–retest, in eyes 20/40 or better, 99% (163/164) and 99% (104/105) of the younger and older cohorts had retest within 2 lines, respectively (mean differences: younger = 0.00; older = 0.01; LOA: younger = ±0.17; older = ±0.11). For 20/50 or worse, 92% (66/72) and 100% (111/111) in the younger and older cohorts were within 2 lines, respectively (mean differences: younger = 0.01; older = 0.02; LOA: younger = ±0.35; older = ±0.15). Conclusions Our app demonstrated good concordance with the gold standard at home and in the office for eyes with VA of 20/40 or better. However, concordance decreased considerably for eyes with VA 20/50 or worse, particularly at home.

Importance Accurate prediction of patient mortality after kidney transplant is an unmet need. Objective To develop and validate an integrative prediction model that predicts short- and long-term patient mortality for kidney recipients. Design, Setting, and Participants This international cohort study included patients who underwent transplants between 2004 and 2023 from 14 academic medical centers from Europe and the US. The derivation cohort consisted of 1566 consecutive adult kidney recipients in a deeply phenotyped cohort prospectively recruited in 1 French center between 2004 and 2014. External validation cohorts consisted of 10 951 kidney recipients from 7 centers in France, 3 centers in Europe, and 3 centers in North America who underwent kidney transplants between 2006 and 2023. Data were analyzed from January 2023 to June 2025. Main Outcome and Measures All-cause mortality was assessed, and 121 candidate prognostic factors, comprising demographics and clinical, biological, imaging, and immunological parameters, were collected. Results Among 12 517 kidney transplant recipients, including 1566 in the derivation cohort (mean [SD] age, 50.05 [14.31] years; 942 male [60.15%]) and 10 951 in validation cohorts (mean [SD] age, 53.32 [13.97] years; 6766 male [61.78%]), 2486 patients (19.9%) died after a median (IQR) follow-up of 5.08 (2.97-7.00) years. Fourteen prognostic factors (including clinical, biological, and imaging risk factors) were independently associated with patient death (eg, patient age: hazard ratio per 1-year increase in age, 1.07 [95% CI, 1.06-1.08]; P < .001) and were combined into a risk prediction model (mBox). The model exhibited accurate calibration and discrimination in the derivation cohort, with C statistics of 0.82 (95% CI, 0.77-0.87) and 0.80 (95% CI, 0.78-0.82) at 1 and 10 years, respectively, after the transplant. Abbreviated models were developed and validated to ensure model generalizability. Performance of abbreviated models was confirmed in external validation cohorts from France (C statistic, 0.76 [95% CI, 0.73-0.78]), Europe (C statistic, 0.74 [95% CI, 0.72-0.76]), the US (C statistic, 0.74 [95% CI, 0.70-0.78]), the Greater Paris University Hospital database (C statistic, 0.79 [95% CI, 0.77-0.81]), and the University of California at San Francisco database (C statistic, 0.70 [95% CI, 0.65-0.74]). The model was also validated in a series of subpopulations (eg, women vs men: C statistic, 0.81 [95% CI, 0.77-0.84] vs 0.79 [95% CI, 0.77-0.82]) and clinical scenarios (eg, before COVID-19 era: C statistic, 0.79 [95% CI, 0.77-0.81]) with good and stable performance. Conclusions and Relevance In this study, an accurate prediction model for mortality among kidney recipients, computable at the time of transplant, was developed and externally validated. Results suggest that this model may help stratify patient risk of death, allowing for improved medical decisions.

Abstract In this systematic review, we aimed to assess the sensitivity of serum immunofixation (SIFE), urine immunofixation (UIFE), and serum free light chain (sFLC) in detecting plasma cell dyscrasia associated with light chain (AL) amyloidosis. We searched the literature using PubMed, Embase, and CENTRAL databases since inception through March 2024. We included studies reporting the diagnostic accuracy of immunofixation and/or sFLC in patients with AL amyloidosis. We used the GRADE (Grading of Recommendations, Assessment, Development, and Evaluation) approach to assess the certainty of the evidence and QUADAS-2 (Quality Assessment of Diagnostic Accuracy Studies-2) to assess the risk of bias. After deduplication, 29 237 records were screened, and 44 studies (n = 18 071) were included. For the meta-analysis, 28 studies reported sensitivity for SIFE (n = 3390), 24 studies for UIFE (n = 2830), 19 studies for SIFE and UIFE (n = 2819), 21 studies for sFLC ratio (n = 3678), 6 studies for SIFE and sFLC ratio (n = 372), 3 studies for UIFE and sFLC ratio (n = 242), and 10 studies for all 3 tests (n = 1430). Pooled sensitivities were as follows: SIFE, 0.68 (95% confidence interval [CI], 0.62-0.73); UIFE, 0.77 (95% CI, 0.72-0.81); SIFE and UIFE, 0.89 (95% CI, 0.84-0.93); and sFLC ratio, 0.82 (95% CI, 0.78-0.86). When SIFE, UIFE, and sFLC ratio were combined, the sensitivity reached 0.99 (95% CI, 0.96-1.00). The high sensitivity of combining SIFE, UIFE, and sFLC ratio supports their use as a reliable initial testing strategy to raise suspicion for AL amyloidosis and guide decisions regarding future diagnostic modalities.

Abstract The sentinel lymph node (SLN) is often the first site of melanoma spread and a critical immune checkpoint that determines local tumor dissemination to systemic metastasis. Although lymph nodes are central to antitumor immunity, the mechanisms that render them permissive to metastasis remain poorly understood. This study used spatial imaging to characterize immune and stromal phenotypic alteration in tumor-negative SLNs (SLN-) and tumor-positive SLNs (SLN+) from patients with cutaneous melanoma, compared to non-melanoma-associated normal lymph nodes (NLN). We performed multiplex immunofluorescence (MxIF) analysis of formalin-fixed, paraffin-embedded lymph node tissues from patients with cutaneous melanoma and control subjects, including two subjects per group (NLN, SLN-, and SLN+). Furthermore, quantitative single-cell segmentation and classification using QuPath generated median fluorescence intensities for immune, stromal, and tumor markers across LN, SLN- and SLN+ regions of interest. In addition to the initial analysis, we employed spatial neighborhood analysis to characterize markers expression profiles surrounding target immune population. This approach quantified local microenvironment based on marker normalized intensity-based co-expression pattern and stromal remodeling signatures. We grouped these markers based on the functional panels representing T-cell activation and exhaustion, myeloid polarization, tumor interaction, stromal remodeling, and apoptosis pathways providing insights into how prometastatic niche evolve within SLN. Normalized data, via centered log ratio (CLR) transformation and Gaussian Mixture Model (GMM) gating was analyzed using principal component analysis, UMAP/tSNE embedding, and hierarchical clustering in in python. Progressive immune remodeling was observed from normal NLN to SLN+, including expansion of regulatory T cells, enrichment of exhausted T cells as well as myeloid-derived suppressor cells (MDSC), and transition toward an M2-like macrophage phenotype. The spatial data from SLN- samples showed an early sign of immune suppression, with reduced cytotoxic capacity. These results show that SLN gets features of peripheral immune tolerance for melanoma early, prior to pathological dissection of metastatic spread supporting the existence of a pre-metastatic niche that facilitates lymphatic colonization of tumor cells. This work provides insights into early immune escape and may inform strategies for immunotherapy and early intervention in melanoma. Citation Format: Shankar Suman, Wendy K. Nevala, Noah A. Stueven, Raymond M. Moore, Chathu L. Atherton, Jeffrey E. Johnson, Ray Guo, James W. Jakub, Svetomir N. Markovic. Spatial remodeling of immune and stromal microenvironments in sentinel lymph nodes during melanoma progression [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 6193.

Abstract Background: Epidemiologic and other studies have identified environmental, occupational, and genetic risk factors for lymphoid and myeloid malignancies, but most studies have been conducted in Western populations. Investigations in populations with differing exposure patterns, distributions of disease subtypes, and genetic architecture are needed to fully understand hematopoietic tumor etiology. Methods: We conducted a large, multicenter, hospital-based case-control study of lymphoid and myeloid neoplasms in East Asia (AsiaLymph), including 5,671 lymphoid cases, 1,879 myeloid cases, and 3,858 controls. Participants completed a computer-assisted personal interview and provided biospecimens. Cases underwent central pathology review and were coded into the WHO Classification. Herein, we describe the study methods in detail and report association results for education, body mass index, and family history. Results: Greater BMI at age 20 but not at age 40 was associated with odds of total lymphoid neoplasm (per 5 kg/m2 increase: OR [95% CI]: 1.18 [1.09-1.27]), total myeloid neoplasm (OR [95% CI]: 1.17 [1.05-1.29]), and several subtypes. Greater educational attainment was associated with increased odds of total lymphoid neoplasm (for college vs. less than primary education: OR [95% CI]: 1.41 [1.21-1.65]) but not myeloid neoplasm (OR [95% CI]: 1.17 [0.95-1.45]; p-heterogeneity=0.02). There were also positive associations between family history of hematologic cancer in first degree relatives and odds of lymphoid neoplasm (OR [95% CI]: 1.53 [1.15-2.03]) and myeloid neoplasm (OR [95% CI]: 1.65 [1.11-2.44]) and specific subtypes. None of the evaluated risk factors were associated with NK/T-cell lymphoma, which supports a distinct etiology for this subtype. Conclusion: The AsiaLymph Study is one of the largest molecular epidemiology studies of both lymphoid and myeloid neoplasms with standardized World Health Organization classification of histopathologic subtypes and will serve as a valuable resource for etiologic investigations into risk factors for these malignancies. Citation Format: Qing Lan, Lauren M. Hurwitz, John K. Chan, Tai Hing Lam, Kexin Chen, Yok Lam Kwong, Xu Caigang, Brian CH Chiu, Raymond Liang, Ip Dennis, Wei Hu, Bryan Bassig, Mark Purdue, Jun Xu, Sarah Locke, Sophia S. Wang, James R. Cerhan, Sonja Berndt, Jonathan N. Hofmann, Jianxin Shi, Kai Yu, Shahinaz Gadalla, Lisa J. McReynolds, Rena Jones, Hongji Dai, Zhangyan Lyu, Lugui Qiu, Wei Liu, Huilai Zhang, Xianhuo Wang, Lindsay M. Morton, Stephen Chanock, Martha Linet, Melissa C. Friesen, Roel Vermeulen, Nathaniel Rothman. A multi-center hospital-based case-control study of lymphoid and myeloid neoplasms (AsiaLymph): Study design and initial findings for education, body mass index, and family history [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 5051.

Abstract Background: In metastatic cancer, Circulating Tumor cells (CTCs) are established prognostic indictors of patients (pts) less likely to respond to new lines of systemic therapy, with poor clinical outcomes, such as shorter progression free survival (PFS) and overall survival (OS). However, CTCs are typically found in specific malignancies (breast, prostate & colon), often in <20% of pts with metastatic disease, and pts without CTCs may also rapidly progress. Recently, an inflammatory pro-tumorigenic macrophage emanating from tumor stroma (i.e. Cancer associated macrophage-like cell [CAML]) was found in >90% of metastatic cancer pts, and whose phagocytic engorgement appears to correlate with poor outcomes, independent of CTCs. As CTCs and CAMLs are isolated in conjunction from a single blood sample, and both are prognostic for outcomes, we evaluated their utilization prior to induction of new systemic therapy in 6 types of metastatic cancer to model pt risk stratification based on 2 year outcomes. Methods: A prospective 2 year blind multi-institutional study was undertaken to model CTCs and CAMLs in prognosticating outcomes prior to induction of a new line of systemic therapy (n=233) in metastatic: Breast (n=60), Prostate (n=40), Pancreas (n=25), Colon (n=28), Renal Cell Carcinoma (RCC) (n=39), and Lung (n=40). Blood was filtered by CellSieveTM filters with subtypes of CTCs & hyper-enlarged CAMLs (≥100µm) enumerated. A machine learning algorithm was trained on this initial data set to develop predictive models which could stratify pt populations by risk for likelihood of PFS & OS over 2 years, including known clinical variables. Results: CTCs were absent in 80% (n=185/233) of pts, and their absence was prognostic for better PFS (HR=1.6, p=0.046), but not OS (HR=1.3, p=0.2045). In parallel, enlarged CAMLs (≥100µm) were found in 28% (n=51/185) of pts without CTCs, and were also prognostic for worse PFS (HR=2.0, p=0.0082) and OS (HR=1.9, p=0.0412). Specifically, CTCs were found in 55% (n=33) breast, 20% (n=8) prostate, 12% (n=3) pancreas, 11% (n=3) colon, 0% (n=0) RCC, and 0% (n=0) lung pts. Enlarged CAMLs were found in 45% (n=27) breast, 20% (n=8) prostate, 44% (n=11) pancreas, 44% (n=17) colon, 18% (n=7) RCC and 20% (n=8) lung pts. Overall, models indicated that ≥1 CTC (n=47) had mPFS=3.9 & mOS=14.9, while pts with 0 CTCs and ≥100µm CAMLs (n=51) had a mPFS=6.9 & mOS=13.8, and pts with 0 CTCs and <100µm CAMLs (n=134) had a mPFS=10.7 & mOS>24 months. Conclusions: These initial models confirm that both CTCs and enlarged CAMLs are prognostic indicators of worse PFS & OS. The simultaneous quantification both CTCs and CAMLs allows for more accurate pan-cancer risk stratification in an array of cancer pt populations. Additional clinical variables incorporated into the models may allow better risk subtyping and possibly forecast optimal treatment regimens. Citation Format: Daniel L. Adams, Steven H. Lin, Massimo Cristofanilli, Carolina Reduzzi, Saranya Chumsri, Martin J. Edelman, Susan Tsai, Raymond C. Bergan, Mohammed Aldakkak, Thai H. Ho, Cha-Mei Tang. Combining circulating tumor cells and cancer associated macrophage-like cells enhances risk stratification models in pan-cancer metastatic disease [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 1068.

ABSTRACT Predicting delayed graft function (DGF) relies on donor, recipient, and perioperative factors. Despite the growing recognition that DGF duration strongly influences patient outcomes, no models currently address or predict its impact—highlighting an important gap in current research and clinical practice. This study aimed to develop an ensemble‐based machine learning model using perioperative data to predict DGF occurrence and duration. The gradient‐boosted decision trees model was trained and validated on 2725 patients, with k‐fold cross‐validation in an external cohort of 284 patients. Model performance was evaluated based on accuracy, ROC‐AUC, and other metrics using R and Python libraries. The binary DGF prediction model achieved a ROC‐AUC of 0.77, while the DGF duration classification model had an accuracy of 79.2%. DGF duration AUC values by time interval were 0.76 for 0–1 weeks, 0.81 for >1–2 weeks, 0.80 for >2–3 weeks, and 0.87 for >3 weeks; the overall macro‐averaged AUC was 0.81. The mean Brier score for multi‐class predictions was 0.14. External validation showed a 78% accuracy for DGF duration prediction. Acute kidney injury (AKI) and donor donation after circulatory death (DCD) status were key DGF predictors. The use of gradient‐boosted decision trees (GBDT) improves the prediction of both the likelihood and duration of DGF, addressing a current gap in kidney transplant patient care. By facilitating personalized transplant care, this model supports more effective perioperative planning and timely interventions, which may contribute to better patient outcomes.