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45 Background: dMMR tumors are characterized by rapid primary tumor growth with reduced likelihood for nodal and distant metastases compared to MMR proficient (pMMR) tumors. Neoadjuvant immune checkpoint inhibitors (ICIs) have demonstrated promising efficacy for localized colon cancer with high pathologic complete response rates and low relapse rates. This raises the question if surgical treatment could be avoided with accurate radiographic staging while avoiding overtreatment of early stage tumors. This study assesses the utility of radiographic staging by CT for dMMR tumors compared to pathologic staging. Methods: Patients with stage I-III colon cancer treated with upfront surgical resection were retrospectively reviewed from a single institution from 2012 to 2023 in two cohorts: 1) dMMR colon cancer and 2) pMMR colon cancer limited to similar sample size as matched controls. Clinical stage was determined based on CT scans prior to surgical resection by 2 independent radiologists blinded to pathologic stage, and the results were correlated to pathologic stage. Clinical characteristics were extracted from the electronic medical record. Statistical analysis was performed using SPSS. Results: We identified 80 patients with dMMR colon cancer and 66 with pMMR tumors. CT clinical tumor staging was discordant from pathologic staging for 68% of dMMR tumors and 61% for pMMR tumors. For T4 dMMR tumors, CT had a 74% sensitivity and 44% specificity. T4 tumors were understaged by radiology to less than T4 for 56% of cases. For dMMR nodal staging, CT had 89% sensitivity and 50% specificity (Table 1). For pathologic node positive tumors, 14% were understaged by radiologist CT read to node negative. For pathologic node negative tumors, 50% of tumors were overstaged to node positive by radiologist CT read. There was poor inter-rater reliability for both T stage and N stage for dMMR and pMMR tumors. The weakest agreement between radiologists was for T stage (dMMR kappa = 0.246, mMMR kappa = 0.145). Conclusions: Radiologic clinical staging of dMMR and pMMR colon cancer does not correlate well with pathologic staging. There were high rates of understaging by CT evaluation for patients who may be candidates for neoadjuvant treatment (T4, N+). Furthermore, there was poor inter-rater reliability between radiologists, indicating that CT staging alone may not be sufficient. Additional diagnostic modalities for nodal detection may be necessary to accurately clinically stage patients before neoadjuvant ICIs in patients with locally advanced disease. Comparing utility of pMMR versus dMMR tumors for T4 and node positive disease. pMMR (n = 66) dMMR (n = 80) T4 versus T3 or less Sensitivity (%) 92 74 Specificity (%) 51 47 PPV (%) 80 77 NPV (%) 82 63 Node positive versus node negative Sensitivity (%) 60 50 Specificity (%) 75 89 PPV (%) 81 89 NPV (%) 53 50 Abbreviations: PPV = positive predictive value; NPV = negative predictive value.

Importance There is a lack of long-term efficacy and safety data on hereditary transthyretin amyloidosis with polyneuropathy (hATTR-PN) and on RNA interference (RNAi) therapeutics in general. This study presents the longest-term data to date on patisiran for hATTR-PN. Objective To present the long-term efficacy and safety of patisiran in adults with hATTR-PN. Design, Setting, and Participants This global open-label extension (OLE) of the APOLLO randomized clinical trial and phase 2 OLE study enrolled patients from 43 hospitals or clinical centers across 19 countries between July 2015 and August 2017, with follow-up until November 2022. Of 212 eligible patients with hATTR who completed the phase 3 APOLLO or phase 2 OLE parent studies, 211 enrolled in and 138 completed the global OLE. Intervention Patisiran, 0.3 mg/kg, intravenously once every 3 weeks for up to 5 years. Main Outcomes and Measures Outcomes evaluated at year 5 of the global OLE included disability (polyneuropathy disability [PND] score); polyneuropathy severity (Neuropathy Impairment Score [NIS]), nutritional status (modified body mass index [mBMI]), quality of life (Norfolk Quality of Life–Diabetic Neuropathy [Norfolk QOL-DN]), and Rasch-Built Overall Disability Scale (R-ODS), with no statistical hierarchy. Safety, survival probability, and mortality were also assessed. Results At the global OLE baseline, the mean (SD) age was 61.3 (12.3) years, and 156 patients (73.9%) were male. In 138 patients completing the study, PND scores remained stable or improved in 89 patients (65.0%), NISs showed a mean (SD) change of 10.9 (14.7), and mean (SD) mBMI (calculated as weight in kilograms divided by height in meters squared times serum albumin in grams per liter) increased by 46.4 (120.7) over 5 years from baseline. Norfolk QOL-DN and R-ODS scores showed mean (SD) changes of 4.1 (16.7) and –3.7 (6.2), respectively. Adverse events (AEs) leading to study withdrawal occurred in 47 patients (22.3%). Infusion-related reactions were the most common treatment-related AE (n = 34 [16.1%]). Overall, 41 patients (19.4%) died during the study. Patisiran treatment in the parent study and low familial amyloid polyneuropathy score at parent study baseline were associated with significantly improved survival. Conclusions and Relevance In the longest study of an RNAi therapeutic for any disease, patisiran treatment resulted in modest changes for patients with hATTR-PN with an acceptable safety profile. These results highlight the importance of initiating early treatment for hATTR and the potential of RNAi therapeutics in medicine. Trial Registration ClinicalTrials.gov Identifier: NCT02510261

AbstractBackgroundThe residual approach has found wide application in researching cognitive resilience, a phenomenon conceptually understood as cognitive performance being better‐than‐typical for an individual, despite apparent AD pathology. The standard residual approach extracts information about an individual’s resilience from the residuals of a linear model predicting cognition. This approach is subject to several limiting assumptions. Here, we explore the implications of these assumptions and propose an alternative predictive modeling approach which circumvents them.MethodsOur alternative approach relies on training a machine learning model on a sample of participants who follow a “typical” clinical progression (Fig. 1A). Residuals are then extracted for a heterogeneous dataset that potentially includes resilient participants by comparing observed cognitive outcomes to those predicted by the “typical” model (Fig. 1B). Additionally, our estimate can be refined by effectively estimating and removing model error from the extracted residual by leveraging a second predictive model (Fig. 1C). Using simulated data, we explore the assumptions made by the standard approach and the consequences of their violation. We then contrast this against our approach.ResultsOur simulations demonstrated that the standard residual approach only extracted accurate resilience estimates if the predictors used in the model explained cognition sufficiently well, while being unrelated to resilience itself. That is, as the predictors explain more of the resilience, the residuals became increasingly determined by model error (Fig. 2). We found that residuals achieved with our approach are more precise and provide individualized estimates that are unaffected by the aforementioned trade‐off (Fig. 3).ConclusionsOur approach holds several advantages over the standard approach: The definition of resilience can be freely adjusted and enforced via different definitions of “typical”, it allows for incorporating complex, non‐linear models without removing relevant information from the residual, the error component of the residual can be estimated through cross‐validation, and all predictors that potentially explain cognition can be included into the model without losing resilience information from the residual. Our approach can be used to measure any phenomenon that can be operationalized as a deviation from a typical outcome and, thus, is also applicable for researching resistance to pathology.

AbstractBackgroundSystemic inflammation plays a pivotal role in many chronic diseases including Alzheimer’s disease (AD). Assessing the composition of immune pathways in neurodegenerative diseases can contribute to precision medicine. Using publicly available transcriptomic data, we sought to elucidate transcriptional networks pertinent to inflammatory pathways across brain regions and peripheral blood in AD/mild cognitive impairment (MCI) and peripheral blood in Parkinson’s disease (PD).MethodFor the AD/MCI vs. control dataset, we analyzed bulk‐RNAseq collected from 6 brain regions of donors from ROSMAP, Mayo Clinic, and Mount Sinai School of Medicine (MSSM) brain banks available from the AMP‐AD consortium. Ante‐mortem, blood RNAseq expression data was retrieved from the AMP‐AD Emory Vascular cohort and Mayo Clinic Study of Aging (MCSA). We also collected blood‐derived microarray expression data from the Alzheimer’s Disease Neuroimaging Initiative (ADNI). For the PD vs Control dataset, blood‐derived bulk‐RNAseq from the PDBP and PPMI cohorts were available through the AMP‐PD consortium. Following quality control, normalization, and residual generation to account for biological and technical variables, co‐expression network modules and their enriched pathways were identified using WGCNA within each dataset. Module/trait correlation tests for aging and diagnosis (cases [AD/MCI or PD] vs control) phenotypes were evaluated. Gene Ontology enrichment analyses were executed to identify enriched pathways and brain or blood cell types within the modules. Modules were tested for preservation across cohorts.ResultWe identified conserved immune signatures across brain regions and cohorts. Modules involved in immune response were preserved across all cohorts. Blood consensus modules involved in immune response were preserved in the brain and vice versa. Some immune modules were associated with AD/MCI, PD, and/or aging. Brain immune modules are significantly associated with aging and/or AD. Significant correlations (q<0.05) with PD diagnosis were present. In the MCSA and Emory vascular cohorts, there were no significant (q<0.05) associations between modules and diagnosis, while in ADNI there were nominal (p<0.05) associations.ConclusionPreserved transcriptional immune networks were identified across blood and brain and across two neurodegenerative diseases. Expanding gene co‐expression network analyses to other diseases and integrating additional omics measures and phenotypes can further strengthen these findings to unravel the immune signatures across complex diseases.

AbstractObjectiveWe describe neurologic phenotype, clinical associations, and outcomes in autoimmune brainstem encephalitis.MethodsMedical records of neural‐IgG positive autoimmune brainstem encephalitis patients diagnosed at Mayo Clinic (January 1, 2006–December 31, 2022) were reviewed.ResultsNinety‐eight patients (57 male) were included. Median age of symptom onset was 51 years (range, 8 months‐85 years). Frequent presenting features were ≥1: diplopia (80%), ataxia (78%), dysarthria (68%), vestibulocochlear symptoms (67%), dysphagia (61%), nausea/vomiting (42%), and facial weakness (32%). Altered mental status (11%) was uncommon. Neural antibodies detected were as follows: KLHL‐11 (26 patients), GAD65 (high titer, 12), ANNA‐1 (anti‐Hu, 8), ANNA‐2 (anti‐Ri, 8), Ma2 (7), IgLON‐5 (6), AQP4 (6), MOG (4), glycine receptor (4), GQ1B (4), PCA‐1 (anti‐Yo, 4), DPPX (2), neurochondrin (2), neurofilament (2), NMDA‐R (2), AGNA‐1 (SOX‐1, 1), ANNA‐3 (DACH1, 1), amphiphysin (1), CRMP‐5 (1), ITPR‐1 (1), PCA‐Tr (DNER, 1), and PDE10A (1). Cancer was identified in 55 patients: germ cell (23 patients; 3 extra‐testicular), ductal breast adenocarcinoma (8), small cell carcinoma (6, lung 4), adenocarcinomas (6), neuroendocrine carcinoma (3), hematologic (2), squamous cell (2), and other (7). Median modified Ranking score (mRS) at last follow‐up was 3 (range, 0–6). Factors associated with poor outcome included abnormal brain MRI, bulbar symptoms, and elevated CSF IgG index. Kaplan–Meier analysis revealed faster progression to wheelchair in patients who were immunotherapy refractory and with elevated CSF IgG index. Diagnostic criteria for autoimmune brainstem encephalitis (definite and probable) are proposed.InterpretationAutoimmune brainstem encephalitis is a distinct clinical subphenotype of autoimmune encephalitis. Abnormal brain MRI, bulbar symptoms, and elevated CSF‐IgG index associate with poor outcome.

Background The Minnesota Retaining Employment and Talent After Injury and Illness Network (MN RETAIN) is a randomized controlled study designed to evaluate early interventions promoting workforce retention among workers in Minnesota. The study compares a cohort receiving increased support for stay at work and return to work (SAW/RTW) services from healthcare organizations and resources through workforce development to a control cohort provided solely a list of SAW/RTW resources within the state they can access on their own. Objective This manuscript aims to describe the design of the MN RETAIN Phase 2 study and experience to date including enrollment. Target enrollment for Phase 2 of MN RETAIN is 3200 workers with approximately half receiving SAW/RTW services from a Return-to-Work Case Manager (RWTCM) who is embedded within the healthcare team. MN RETAIN is structured to be accessible to all workers within the state, with a focus on equity and reaching historically underserved populations. Methods Phase 2 of the RETAIN model expanded the focus from the southeast region of MN to state-wide coverage. Enrollment and services were designed to acquire a large enough cohort to reach adequate statistical power to detect differences in disability costs and rates of SAW/RTW in participating study subjects. Impact analysis of RETAIN will be performed by the federal evaluator at a future date. Results Characteristics of enrolled participants to date are summarized as well as considerations for sustainability of the program upon study completion. Conclusions To date, the study has reached diverse workers from throughout the state of MN to provide assistance with SAW/RTW.

Abstract The newly proposed revised criteria for diagnosis and staging of Alzheimer's disease (AD) by the Alzheimer's Association (AA) Workgroup represent a significant milestone in the field. These criteria offer objective measures for diagnosing and staging biological AD, bridging the gap between research and clinical care. Although implementation feasibility may vary across regions and settings, improving the availability and accuracy of biomarkers, especially plasma biomarkers, is expected to enhance the applicability of these criteria in clinical practice. The Fall 2023 Alzheimer's Association Research Roundtable (AARR) meeting served as a forum for gathering industry perspectives and feedback on these revised criteria, ensuring that the new criteria inform research, clinical trial design, and clinical care. In this article, we outline a summary of the newly proposed “Revised Criteria for Diagnosis and Staging of AD: AA Workgroup” and provide highlights from the AARR meeting in fall 2023. Highlights The Alzheimer's Association Research Roundtable (AARR) convened leaders from industry, academia, and government, to review the Revised Criteria for Diagnosis and Staging of AD: AA Workgroup , and gather industry perspectives and feedback on these revised criteria before its publication. The newly proposed revised criteria for diagnosis and staging of Alzheimer's disease (AD) by the AA's Workgroup represent a significant milestone, offering objective measures for the biological and staging of AD and bridging the gap between research and clinical care. Improving the availability and accuracy of biomarkers, especially blood‐based biomarkers (BBMs) is expected to improve clinical research and enhance the applicability of these criteria in clinical practice.