Citations

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 .

Abstract Background Sleep disorders are increasingly recognized as important risk factors for cardiovascular disease (CVD), yet their contribution to cardiovascular mortality remains insufficiently examined at the population level. This study aimed to evaluate long-term trends in CVD mortality where sleep disorders were identified as contributing causes of death. Methods We analyzed U.S. national mortality data from 1999 to 2020 among individuals aged 15 to 84 years. Cases were selected where CVD (ICD-10: I11.0, I11.9, I20-I25, I48, I49.0, I49.9, I50.0, I50.1, I50.9) was the underlying cause and sleep disorders (ICD-10: G47.0-G47.3) were listed as contributing causes. Joinpoint regression was employed to assess temporal changes in age-adjusted mortality rates (AARs), with standard errors provided in the input data and model selection performed using Weighted BIC. APCs were estimated using the Empirical Quantile Method with 5001 resamples. Results A total of 118,440 deaths were included across 22 annual time points. Joinpoint regression identified two significant inflection points (2005 and 2018). From 1999 to 2005, AARs increased sharply (APC = 14.47%; 95% CI: 11.56-19.54; p = 0.0016), followed by a slower rise from 2005 to 2018 (APC = 6.62%; 95% CI: 4.23-10.83; p &lt; 0.000001), and a renewed acceleration from 2018 to 2020 (APC = 11.54%; 95% CI: 7.01-14.12; p &lt; 0.000001). Males had more than twice the AAR compared to females (3.00 vs. 1.31 per 100,000). Black individuals had the highest burden by race (AAR = 2.55), and geographic variation ranged widely from 0.70 in New York to 3.38 in Wyoming. Conclusions Over two decades, cardiovascular mortality with contributory sleep disorders has risen significantly, with widening demographic and geographic disparities. These findings reinforce that sleep disorders are not merely comorbidities but pivotal, modifiable drivers of cardiovascular morbidity and mortality. Untreated sleep conditions such as insomnia, obstructive sleep apnea, and circadian disruptions exacerbate autonomic dysfunction, inflammation, hypertension, and metabolic dysregulation. Addressing sleep health must become a core component of cardiovascular risk reduction strategies, both in clinical practice and public health policy. Failure to integrate sleep disorder screening, diagnosis, and management into CVD care pathways risks perpetuating a preventable and growing burden of disease. This abstract is funded by: None

Abstract Solitary plasmacytoma of bone (SPB) is a localized plasma cell neoplasm associated with a substantial risk of progression to multiple myeloma (MM), yet the role of adjuvant systemic therapy following definitive radiation therapy (RT) remains undefined. We conducted a randomized phase III trial (Alliance A061402) to evaluate whether the addition of systemic therapy with ixazomib, lenalidomide, and dexamethasone (IXA-LEN-DEX) to zoledronic acid (ZA) improves outcomes compared with ZA alone in patients with SPB and minimal (< 10%) bone marrow plasma cell involvement following RT. Patients were randomized to receive six 28-day cycles of IXA-LEN-DEX-ZA or ZA alone. The primary endpoint was time to progression (TTP) to MM. The study was closed early due to poor accrual after enrolling 11 patients (IXA-LEN-DEX-ZA, n = 5; ZA, n = 6). All patients completed RT and planned treatment without dose reductions. The combination regimen was well tolerated, with limited grade ≥ 3 toxicities, including hypophosphatemia and lymphopenia. At follow-up extending beyond 4 years, the 1- and 4-year progression rates to MM in the IXA-LEN-DEX-ZA arm were 0% and 33%, respectively, compared with 37.5% and 58.3% at 1 and 2 years in the ZA-alone arm. Although underpowered, these findings demonstrate the feasibility and tolerability of an all-oral adjuvant systemic regimen in SPB and provide prospective data supporting further investigation of systemic strategies to delay progression to MM.

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 Renal tumors are highly heterogeneous, ranging from benign lesions and indolent renal cell carcinoma (RCC) to highly aggressive phenotypes. Accurately determining tumor aggressiveness before treatment remains a major clinical challenge. Conventional MR imaging lacks sensitivity to microarchitectural differences underlying tumor biology, and biopsy is limited by sampling error and tumor heterogeneity. Radiomics offers a noninvasive approach to extract high dimensional features that may capture biologically relevant patterns. In this IRB-approved retrospective study, we evaluated MR-based radiomics combined with machine learning (ML) to differentiate indolent from aggressive tumors. Pre-operative clinically acquired MR images obtained from eighty-two RCC patients (aggressive, n = 56; indolent, n = 26) who underwent nephrectomy were included for the analysis. Regions of interest (ROIs) of the entire tumor were manually drawn using 3D Slicer on the morphological T2-weighted images and four phases (pre-contrast [PRE], corticomedullary [CM], nephrographic [NG], and delayed [DEL]) from the contrast-enhanced MR images, normalized, and used for radiomic feature extraction (PyRadiomics, v3.1.0). Independently, tumors were grouped into aggressive and indolent phenotypes based on the established histological classification of Mayo Clinic. Statistical analysis used two-sided t-test. Features selected based on mutual information were used for ML classification (Random Under-Sampling Boosting (RUSBoost)) and performance assessed using accuracy, sensitivity, precision, specificity, F1-score, and area under the curve (AUC). Of 107 radiomics features extracted, the following numbers showed statistically significant differences between aggressive and indolent tumors (p &lt; 0.05): CM phase (n = 19), NG phase (n = 24), DEL phase (n = 22), and T2-weighted images (n = 29). Several features consistently differed among contrast-enhanced images and included first-order (length, surface area, volume), GLCM (correlation, IDM, IMC1, IMC2), GLDM (dependence entropy, dependence non-uniformity, dependence variance), GLRLM (gray-level non-uniformity, run entropy, run-length non-uniformity), GLSZM (high gray-level emphasis, zone entropy), and NGTDM (coarseness). Using the 15 features with highest mutual information across 50 patients (aggressive, n = 31; indolent, n = 19), a five-fold cross-validation RUSBoost classifier achieved accuracy 0.81, sensitivity 0.84, precision 0.87, specificity 0.77, F1-score 0.85, and AUC 0.80 for identifying the aggressive phenotype. MRI radiomics features show potential to differentiate aggressive from indolent renal tumors. ML-based classification using these features demonstrates promising accuracy. Further optimization and validation in larger cohorts, may help guide individualized care, including consideration of active surveillance for indolent renal tumors. Citation Format: Huan Lu, Salim Rukhsar, Xueqing Yin, Garima Suman, Ashish Khandelwal, Ananth J. Madhuranthakam, Durga Udayakumar. MRI radiomic features to differentiate aggressive and indolent renal tumors [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 2142.

Abstract Introduction. This study aimed to characterize the population pharmacokinetics (PopPK) of nab-paclitaxel following the administration of the nano-immunoconjugate nab-paclitaxel-bevacizumab to patients with unresectable stage IV melanoma or gynecological cancers (NCT02020707). The core objective was to characterize the population mean drug concentration-time profile, understand the variability in drug concentrations and identify factors that explain differences in paclitaxel plasma clearance. Also, we sought to explore the impact of the nano-immunoconjugate formulation on paclitaxel clearance. Methods. Using Pumas AI - an advanced pharmacokinetic software program, integrating Machine Learning methods - a PopPK analysis was conducted using concentration-time data collected from 29 subjects, (681 concentration measurements). Patients received 75 - 175 mg/m2 (total dose, 160 - 360 mg). Data were fitted to a 2-compartment model based biphasic decline observed in plasma concentration following intravenous administration. A critical requirement for the model was the inclusion of saturable elimination to accurately describe the drug’s disposition. A comprehensive modeling workflow was implemented, starting with base models (mixed vs. non-linear clearance), followed by the sequential addition of covariates (Age, Sex, BSA, Tumor, Cycle). The Parsimony Principle was applied to select the most appropriate model. Results. Population estimates for paclitaxel clearance and volume of distribution were 41.7 L/hr and 312 L, respectively. The analysis found paclitaxel clearance higher when compared with published data for nab-paclitaxel alone (21.1 or 25 L/h/m2; Sparreboom et al, CCR 11:4136, 2005 and Ando et al, CCP 69:457, 2012). A covariate model incorporating BSA and Gender is the best. For every 2 m2 increase in BSA, linear clearance (CL) increases by 6.9%. Females have 16.6% lower CL than males, which successfully characterized systematic differences in PK between subgroups of subjects. Conclusion. The administration of nab-paclitaxel via the nano-immunoconjugate formulation led to increased clearance for paclitaxel consistent with known clearance mechanisms for therapeutic antibodies. The differences in drug clearance among patients were primarily attributable to covariates BSA and gender. These findings are crucial for identifying safe and effective dosing regimens and informing future clinical simulations. Citation Format: Mohd Beshr Chama, Matthew Stephen Block, Vera J. Suman, Joel M. Reid. Population pharmacokinetics of nab-paclitaxel following administration of a nano-immunoconjugate with bevacizumab [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 301.

ABSTRACT BACKGROUND Increasing evidence suggests that accurate prediction of Alzheimer’s disease (AD) symptom onset requires more than amyloid- and tau-centric biomarkers such as cerebrospinal fluid (CSF) Aβ42/40, total tau and p-tau181 and plasma p-tau217. Autosomal dominant AD (ADAD), caused by pathogenic PSEN1, PSEN2 and APP mutations with predictable age at symptom onset, presents a unique opportunity to characterize the chronological changes in proteins beyond amyloid and tau and clarify them as early biomarkers of disease onset or as biomarkers related to disease staging and progression monitoring. METHODS We measured 972 CSF samples corresponding to 484 participants of the Dominantly Inherited Alzheimer Disease Network (DIAN) using the NULISASeq 120 CNS Disease Panel. We first benchmarked the technology against gold-standard measurements followed by the identification of proteins that were differentially abundant in relation to mutation status and symptomatology. Next, we determined the chronological emergence of protein changes in relation to the estimated years to onset (EYO). Finally, we assessed whether specific protein measures improved the prediction of EYO in the ADAD. FINDINGS NULISA measurements were comparable to those previously published. We demonstrated that known early alterations in CSF amyloid and tau were followed by inflammatory and neurodegenerative responses suggesting that clinical manifestation of AD happens before the inflammatory processes is fully developed. Finally, we found a multi-protein composite approach for predicting EYO that outperformed single biomarker values. INTERPRETATION Our results suggest that the main CSF proteomic landscape changes in ADAD are due to the presence of a pathogenic mutation and occur prior to symptom onset. Improved performance of multi-protein composite to predict EYO compared to single biomarker values highlights the added value of multiplex proteomic signatures for biomarker panel development. FUNDING National Institute on Aging, Alzheimer’s Association, German Center for Neurodegenerative Diseases, Raul Carrea Institute for Neurological Research, Japan Agency for Medical Research and Development, Ministry of Health & Welfare and Ministry of Science and ICT, Republic of Korea, Spanish Institute of Health Carlos III.

Abstract Allogeneic hematopoietic cell transplantation (allo-HCT) is a potentially curative treatment for many hematological malignancies, but graft-versus-host disease (GVHD) is a common complication. Low gut microbiome diversity is associated with higher GVHD risk and shorter survival in multiple studies. Recently, the BMT CTN 1703 clinical trial demonstrated superiority of a GVHD-prophylaxis regimen including post-transplant cyclophosphamide (PTCy) compared to the standard prophylaxis (tacrolimus and methotrexate, Tac/MTX) in terms of GVHD-free, relapse-free survival at one year among reduced intensity conditioning allo-HCT recipients. However, the effect of PTCy on the gut microbiome and its association with clinical outcome have not been described. Here, we report on a companion randomized clinical controlled trial (BMT CTN 1801), which collected 2575 longitudinal stool samples from 304 study participants. Samples were obtained up to weekly up to day 84 post allo-HCT and at less frequent intervals thereafter, up to 2 years. Microbiome diversity and absolute microbial load were lower in the PTCy group compared to the Tac/MTX group on days 14–28 post-HCT. However, diversity at the timepoint closest to neutrophil engraftment was not significantly associated with non-relapse mortality after one year or other clinical outcomes, contrary to expectations from previous studies. Microbial domination events, when a single species exceeds 30% relative abundance, were comparable across treatment arms and reflected both pathogen blooms as well as less severe disruptions of the microbial community. Clostridium scindens and secondary bile acid metabolism pathways were less prevalent in the PTCy arm than in the Tac/MTX arm post-HCT, yet presence of secondary bile acid metabolism pathways was associated with a lower risk of chronic GVHD. Given that PTCy was associated with a greater disruption of the microbiome as measured by diversity, absolute microbial abundance, and bile acid metabolism capability, but better clinical outcomes overall, these data suggest that the importance of the microbiome in modulating the host immune systems after allo-HCT is specific to different types of GVHD prophylaxis.