Citations

BACKGROUND We constructed multi-trait polygenic risk scores (PRSs) predicting chronic obstructive pulmonary disease (COPD) and exacerbations, validated their performance in diverse cohorts, and identified PRS-related proteins for potential therapeutic targeting. METHODS PRSmix + , a multi-trait PRS framework, is used to train a composite PRS (PRS multi ) in COPDGene non-Hispanic White participants ( n = 6,647). Associations of PRS multi with COPD status (GOLD 2–4 vs. GOLD 0 or ICD) and exacerbation frequency were tested in COPDGene African American ( n = 2,466), ECLIPSE ( n = 1,858), Mass General Brigham Biobank ( n = 15,152), and All of Us ( n = 118,566). Protein prediction models were applied to GWAS summary statistics from traits contributing to PRS multi and were validated with proteomic data in COPDGene ( n = 5,173) and UK Biobank ( n = 5,012). RESULTS PRSmix + selected 7 traits for PRS multi . In multivariable models, PRS multi was associated with COPD status (meta-analysis random effects [RE] OR 1.58 [95% CI: 1.28–1.94]) and exacerbation frequency (meta-analysis RE β 0.21 [95% CI: 0.11–0.31]), with higher effect sizes observed in smoking-enriched cohorts. PRS multi outperformed traditional single-trait PRS in all tested cohorts. Using protein prediction models, we identified 73 proteins associated with the PRSs that were also validated with measured protein levels in COPDGene and UK Biobank. Of these proteins, 25 were linked to approved or investigational drugs. Notable targets include RAGE/sRAGE, IL1RL1, and SCARF2, all implicated in COPD pathogenesis and exacerbations. CONCLUSIONS Multi-trait PRS improves prediction of COPD and exacerbation risk. Integration with proteomic data identifies druggable protein targets, offering a promising avenue for precision medicine in COPD management. TRIAL REGISTRATION COPDGene: ClinicalTrials.gov NCT00608764; ECLIPSE: ClinicalTrials.gov NCT00292552.

Abstract Glioblastoma (GBM) is an aggressive, high-grade glioma with a near-universally fatal prognosis. Therapeutic failure is often attributed to the highly selective blood brain barrier (BBB), the diffuse infiltrative nature of the tumor, and the marked intratumoral heterogeneity of GBM. Although antibody drug conjugates ADCs have shown promise for high grade gliomas such as GBM, efficacy is limited by ADC size. Aptamers—short, synthetic, single-stranded DNA or RNA molecules—can be ∼6-fold lower in molecular weight than IgG antibodies and have the potential to cross the intact BBB. Unlike other nucleic acid-based therapies, aptamer function arises from three-dimensional shape rather than genetic coding. Here we aim to replace the targeting component of the ADC paradigm with a DNA aptamer, thus creating an aptamer-drug conjugate (ApDC). We employed in vivo SELEX using an orthotopic patient-derived xenograft (PDX) GBM mouse model and a vast (∼100 trillion 80-mer sequences) ApDC library. We report the results from this first in vivo ApDC selection of its kind. We characterize target tissue binding ex vivo , cell association, biodistribution, and pharmacokinetics from this selection. This study exemplifies an unbiased approach to a problem that rational design has yet to overcome, offering a new direction for GBM therapeutic development.

Abstract The adoption of electronic health records (EHRs) has transformed health care, improving efficiency and chart accessibility. However, the widespread reliance on unstructured data entry and the lack of standardized documentation frameworks have resulted in significant data fragmentation across health care systems. The prevalence of unstructured data in EHRs limits their potential for clinical decision support, trial matching, real‐world evidence (RWE) generation, and quality measurement. Data fragmentation in health care triggers a cascade of challenges that ultimately compromise patient care. Clinicians face an excessive documentation burden and struggle to locate critical information buried in unstructured notes. Researchers encounter difficulties in extracting reliable clinical data. EHR vendors grapple with standardizing unstructured information for interoperability, and payers are unable to process unstructured clinical data efficiently to support value‐based care models. These challenges are particularly acute in oncology, where complex clinical elements like cancer staging, disease status, and treatment changes require precise, structured documentation. Emerging artificial intelligence (AI) technologies, such as large language models (LLMs) and ambient listening, offer a path to automate structured data generation while reducing the workload on providers. Here, the authors propose LLM‐based workflows that balance automation with clinician verification, streamlining data entry without compromising accuracy. Realizing these benefits requires coordinated efforts among clinicians, researchers, EHR vendors, payers, and policymakers to align regulatory frameworks with AI‐driven innovations. This article outlines a strategy to enhance structured data capture within EHRs, ultimately improving patient care, research, and health care efficiency.

Background Corticosteroid injections (CSIs) are widely employed in facet and sacroiliac joint pain. Similar to CSIs at other sites (peripheral nerve blocks, joints, epidural), these injections are associated with potential adverse events. These multisociety consensus recommendations aim to develop evidence-based statements and recommendations on the safe use of CSIs in facet joint and sacroiliac joint injections. Methods Development of the consensus recommendations was approved by the American Society of Regional Anesthesia and Pain Medicine Board of Directors and several other societies that agreed to participate. The scope of statements and recommendations was agreed on to include safety of the injection technique (landmark-guided, ultrasound, or radiology-aided injections); effect of the addition of the corticosteroid on effectiveness (vs local anesthetic or saline); and adverse events related to the injection. Experts were assigned topics to perform a comprehensive literature review and draft statements and recommendations, which were refined and voted for consensus (>75% agreement) using a modified Delphi process. A modified version of the US Preventive Services Task Force grading of evidence and strength of recommendation was followed. Results All statements and recommendations were approved by all participants after four rounds of discussion. The Practice Guidelines Committees and Boards of Directors of the participating societies also approved all statements and recommendations. Injection of corticosteroid into the facet joint in patients with joint inflammation may relieve pain and improves function. Intra-articular, extra-articular (periarticular), and combined administration are effective for sacroiliac joint injections. No dose-response studies exist, but CSIs containing 10 mg of methylprednisolone or triamcinolone per facet joint and 40 mg per sacroiliac joint (SIJ) (or their respective pharmacologic equivalents) are reasonable. Conclusions In this practice recommendation, we provide statements and recommendations on facet and sacroiliac joint CSIs, the optimal doses, and intervals and criteria for repeating the CSIs in patients with facet joint and sacroiliac joint pain.

Abstract Background: Incorporation of Polygenic Risk Scores (PRS) can refine traditional breast cancer (BC) risk assessment models to provide precise estimates of BC risk. However, the impact of such an integrated model on BC risk estimates and clinical decision-making related to BC screening and preventive strategies in germline pathogenic variant (PV) carriers is not fully understood. Methods: The GENRE-2 is a prospective single-arm multisite clinical trial (NCT04474834) incorporating 313-PRS into standard BC risk assessment models to determine the impact of PRS on risk estimates and clinical decisions on prevention and screening. All women received pre- and post-PRS BC risk estimates and completed surveys on their screening and cancer prevention decisions before and after integrating PRS estimates. Herein, we report on the impact of integrating PRS into CanRisk-based BC risk estimates among women enrolled in GENRE-2 due to PVs in ATM, BRCA1, BRCA2, CHEK2, or PALB2. Women were categorized into average risk (<20%), moderate risk (20-40%), or high risk (>40%) for BC based on clinically relevant lifetime risk thresholds for supplemental MRI screening and risk-reducing bilateral mastectomies. Changes in categories of lifetime BC risk estimates were then evaluated before and after integration of PRS. Impact of PRS on patient decisions to undertake screening or preventive action (medication or prophylactic mastectomy) was evaluated based on pre- and post-PRS surveys. Results: Between 2020 and 2025, 933 women were enrolled, 633 received PRS results and completed a survey to date. Of those who received PRS results, 214 (33.8%) were PV carriers (ATM: 24, BRCA1: 54, BRCA2: 73, CHEK2: 54 and PALB2: 9). Among PV carriers, the CanRisk-based median 10-year and lifetime pre-PRS risk estimates were 6.0% and 23.9% for ATM, 22.5% and 77.5% for BRCA1, 17.9% and 77.6% for BRCA2, 7.1% and 26.6% for CHEK2, and 16.6% and 42.4% for PALB2, respectively. A total of 7 (29.2%) ATM PV carriers had a shift in lifetime BC risk categories after integration of PRS, with 1 (4.2%) shifting from average to moderate-risk, 3 (12.5%) shifting from moderate to average-risk category, and 3 (12.5%) shifting from moderate to high-risk category. Among CHEK2 PV carriers, 9 (16.7%) had a shift in lifetime risk categories, with 4 (7.4%) shifting from moderate to average-risk, 2 (3.7%) shifting from moderate to high-risk, and 3 (5.6%) shifting from high to moderate-risk. None of the BRCA1 and 4.1% of BRCA2 PV carriers had a shift in lifetime risk categories after PRS integration. Only 1 (11.1%) PALB2 PV carrier had a change in lifetime risk category after integration of PRS, although this was limited by a small sample. A significantly higher proportion of PV carriers with a high lifetime risk intended to take preventative action compared to the PV carriers with average lifetime risk (41.4% vs. 20.0%, p=0.007). Conclusions: Integration of PRS into BC lifetime risk estimates led to a clinically meaningful change in risk categories in 29.2% of ATM PV carriers and 16.7% of CHEK2 PV carriers, but in <5% of BRCA1 or BRCA2 PV carriers. These findings suggest that PRS may be helpful for BC risk stratification in moderate-risk genes such as ATM and CHEK2 but may have limited clinical utility in high-risk genes such as BRCA1 or BRCA2. Citation Format: S. Yadav, C. Klassen, D. Stan, J. Fraker, S. Sahni, S. A. Khan, L. Khatri, C. Vachon, D. Schaid, J. P. Sinnwell, E. Carlson, L. Hasadsri, F. J. Couch, S. Pruthi. Impact of Polygenic Risk Scores on Breast Cancer Risk Assessment and Clinical Decision Making in Carriers of Germline Pathogenic Variants in ATM, BRCA1, BRCA2, CHEK2, and PALB2: Results from the Prospective Multisite GENRE-2 Clinical Trial [abstract]. In: Proceedings of the San Antonio Breast Cancer Symposium 2025; 2025 Dec 9-12; San Antonio, TX. Philadelphia (PA): AACR; Clin Cancer Res 2026;32(4 Suppl):Abstract nr PD4-04.

Abstract Background: Artificial intelligence (AI) algorithms based on deep learning approaches show promise in improving breast cancer (BC) detection on mammography and may also improve prediction of future BC risk compared with clinical risk prediction models. Historical clinical risk prediction models underperform among women with BBD; however, AI models for BC detection on mammography have not been tested among women at elevated BC risk due to benign breast disease (BBD). We evaluated if an AI cancer detection algorithm can aid BC risk prediction among women with BBD. Methods: We examined women without a prior BC who had an initial BBD biopsy at Mayo Clinic, Rochester, between 2002 and 2013. Incident BC occurring after BBD was identified using the Mayo Tumor Registry and supplemented by follow-up questionnaires. Only women with a full field digital mammogram at least 2 years prior to BC diagnosis were eligible. A breast pathologist evaluated BBD according to increasing severity, as non-proliferative (NP), proliferative disease without atypia (PDWA) or atypical hyperplasia (AH). AI malignancy scores derived from the Transpara detection algorithm (1-10) and volumetric percent density (VPD) from Volpara (per one standard deviation, SD) were assessed from mammograms close to BBD diagnosis. Cox proportional hazards regression models were applied to estimate hazard ratios (HRs) with 95% Confidence Intervals (CIs), adjusted for age and BMI. C-statistics were estimated to assess the contribution of each factor to BC risk prediction. Likelihood ratio tests (LRTs) and bootstrapping methods were used to compare model performance with the addition of AI malignancy score. Results: The BBD cohort with mammography consisted of 3,125 women followed for a median 12.9 years, with 250 incident BC. Of these, 221 BC occurred at least two years after the mammogram and were used in analyses. As expected, increased BC risk was associated with BBD severity [for AH HR=3.18 (95%CI: 2.04, 4.95) and for PDWA HR=1.59 (95%CI: 1.17, 2.14) compared to NP] and with higher VPD [HR=1.24 per SD (95% CI: 1.05, 1.45)] (Table ). The AI-malignancy score alone was also associated with BC risk [HR=1.16 per 1 unit score (95%CI: 1.09,1.23)] and showed similar discriminatory accuracy [C-statistic=0.626 (95%CI: 0.586, 0.665)] as the model with BBD severity and VPD combined [C-statistic=0.627, (95%CI: 0.585, 0.669)] (Table ). In models with BBD severity and VPD, the AI-malignancy score was an independent risk factor for BC [HR=1.14 (95%CI: 1.07, 1.21); PLRT<0.001] but only achieved a marginally significant improvement in discriminatory accuracy [C-statistic=0.651 (0.609, 0.693), ΔC-statistic=0.024 (95% CI: 0.000, 0.047)]. Conclusion: In this preliminary study, an AI BC detection algorithm achieved similar performance in predicting future BC risk compared to established risk factors. Citation Format: C. M. Vachon, S. Winham, M. Jensen, D. Hursh, L. Pacheco-Spann, A. Norman, J. Fischer, S. Schrup, L. Seymour, D. Gehling, S. Nyante, M. Troester, N. Karssemeijer, R. Vierkant, D. Radisky, C. Scott, S. I. Maimone, A. Degnim, M. Sherman. An AI algorithm for breast cancer detection improves future BC risk prediction among women with benign breast disease [abstract]. In: Proceedings of the San Antonio Breast Cancer Symposium 2025; 2025 Dec 9-12; San Antonio, TX. Philadelphia (PA): AACR; Clin Cancer Res 2026;32(4 Suppl):Abstract nr PD4-02.

Abstract Background: Despite its widespread adoption, there is a lack of data as to whether breast MRI for local staging and surgical planning of newly diagnosed breast cancer patients improves oncologic outcomes. The Alliance A011104 phase III trial was designed to test the hypothesis that MRI detection of mammographically occult disease helps to tailor surgical intervention thereby reducing local recurrence and improving local regional control. Here we report on the primary outcome of 5-year local regional recurrence. Methods: Patients with newly diagnosed clinical stage I-II, ER/PR- and HER2- negative (triple- negative) or ER/PR-negative and HER2-positive breast cancer, deemed eligible for breast- conserving surgery (BCS) based on clinical exam, mammography +/- ultrasound were randomized to undergo staging breast MRI or no further local staging. Patients with multifocal tumors that could be encompassed in a single operative resection were eligible. Patients who were carriers of gBRCA 1/2, or who had bilateral breast cancer, or a prior history of breast cancer were excluded. Initial eligibility was limited to patients not scheduled to receive neoadjuvant chemotherapy; however, a subsequent amendment allowed for enrollment after completion of neoadjuvant chemotherapy. Categorical variables were compared between arms with a chi-square or Fisher’s exact test. LRR, distant recurrence and overall survival (OS) were estimated with Kaplan-Meier point estimates and 95% confidence intervals (CIs). Hazard ratios (HRs) and 95% CIs were obtained from Cox proportional hazards models. Results: Of 319 patients enrolled from 2/2014-12/2019,161 were randomized to undergo breast MRI and 158 to no breast MRI evaluation. The mean age at enrollment was 58.9 years (range 29-85). Most patients (72.1%) had T1 tumors, 93.4% were cN0 and 19.7% were HER2 positive. Systemic chemotherapy was utilized in 85% with 17.6% receiving treatment neoadjuvantly; receipt of systemic therapy was unknown for 15 patients (4.7%). Two hundred and ninety-eight patients (93.4%) underwent surgical intervention; 91.9% underwent BCS as initial surgical procedure with no significant differences noted between groups (92.7% in the no MRI group and 91.9% in the MRI group [p=0.62]). Of patients who underwent breast surgery, 293 underwent axillary evaluation with the majority (270 patients, 92%) undergoing sentinel lymph node biopsy as the only axillary intervention. Among patients who received neoadjuvant chemotherapy, the overall pathologic complete response rate was 39.3%, 29.0%in the MRI arm vs. 52.0% no MRI arm (p=0.10). Receipt of adjuvant radiation was comparable between arms, 85.4% and 85.0% receiving radiation in the no MRI and MRI arms, respectively. Among 298 patients evaluable for the primary endpoint, with median follow-up of 61.1 months (0.2, 68.1), there were no differences in rates of LRR; 5-year local regional control was 93.2% (89.0-97.6%) in the MRI group and 95.7% (92.3-99.1%) in the no MRI group (HR: 1.1, 95% CI: 0.3, 3.9). For the entire cohort, 5-year distant recurrence-free rate and OS were 94.3% (91.7, 97.1) and 92.2% (89.1, 95.4), respectively, with no differences between groups. Conclusions: With multimodality therapy, rates of 5-year LRR in patients with early stage triple- negative and HER2-positive breast cancer are very low. Breast MRI for local staging and surgical planning in this setting does not result in improved local regional control. Support: U10CA180821, U10CA180882; UG1CA189823; U10CA180868 (NRG Oncology); ClinicalTrials.gov Identifier: NCT01805076; https://acknowledgments.alliancefound.org Citation Format: I. Bedrosian, K. Ballman, L. M. McCall, C. E. Comstock, G. W. Unzeitig, T. Yen, A. Weiss, B. G. Haffty, J. M. Bensenhaver, S. M. DeSnyder, J. J. Liu, A. H. Partridge, L. A. Carey, K. K. Hunt.Effect of Preoperative Breast MRI Staging on Local Regional Recurrence (LRR) in Early Stage Breast cancer: Alliance A011104/ACRIN 6694 [abstract]. In: Proceedings of the San Antonio Breast Cancer Symposium 2025; 2025 Dec 9-12; San Antonio, TX. Philadelphia (PA): AACR; Clin Cancer Res 2026;32(4 Suppl):Abstract nr GS2-07.

Abstract INTRODUCTION The locus coeruleus (LC) degenerates early in Alzheimer's disease (AD). However, the extent of rostrocaudal degeneration across clinicopathologic heterogeneity remains underexplored in AD. METHODS Using digital pathology, we quantified LC neuronal density and area at three neuroanatomic levels in a large AD series. RESULTS Analysis of neuropathologic AD subtypes revealed greater middle LC vulnerability in hippocampal sparing AD compared to typical and limbic predominant AD. Regression analyses identified distinct predictor variables associated with the degeneration of rostral and middle LC. Age at onset predicted 24% of the variability in rostral LC neuronal density, whereas Braak stage, brain weight, Lewy body disease, and age at onset accounted for 15% of the variability in middle LC. Analyses of clinical presentations revealed lower rostral LC neuronal density in non‐amnestic compared to amnestic AD cases. DISCUSSION These insights demonstrate greater LC degeneration in atypical clinicopathologic forms of AD, including hippocampal sparing, young‐onset, and non‐amnestic presentations.

Abstract Objective To test the effectiveness of a surgical web‐based decision aid (DA) in improving knowledge. Summary of Background Data DAs support decision making by providing information about the options. Methods A stepped wedge trial was conducted in 10 National Cancer Institute Community Oncology Research Program clinics (Alliance for Clinical Trials in Oncology). Clinics were randomized to time of transition from usual care (UC) to delivery of a web‐based DA. Patients with stage 0 through 3 breast cancer being considered for surgery were enrolled. Knowledge (primary outcome) was measured using the Breast Cancer Surgery Decision Quality Instrument and patients were asked, “How informed do you feel?” Intervention effects were tested with linear mixed‐effects models, accounting for surgeon and clinic‐level clustering, time, and enrollment after COVID. Additional models controlled for demographics. Results A total of 44% of DA arm patients reviewed the DA and 58% in UC arm reported reviewing “any information.” Being in the DA arm versus UC was not associated with knowledge. However, “review of information” was associated with higher knowledge. In addition, non‐White race and lower education were associated with lower knowledge. The DA arm was associated with higher perceptions of feeling informed (parameter estimate 1.36; 95% CI, 0.18–2.55; p = .02); this persisted even when controlling for review of information or demographics. Conclusion Improved knowledge was not demonstrate with a web‐based DA versus UC. Interestingly, the DA was associated with a higher likelihood of feeling informed. Future research will explore the discrepancy between patients feeling informed but having low knowledge, especially for disadvantaged patients. Trial Registration ClinicalTrials.gov Identifier: NCT0376600