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The NCCN Clinical Practice Guidelines in Oncology (NCCN Guidelines) for Small Cell Lung Cancer provide recommendations for diagnostic workup, staging, and treatment. These NCCN Guidelines Insights highlight recent updates, with a particular focus on changes to systemic therapies and radiation treatment in the NCCN Guidelines for Small Cell Lung Cancer.

Abstract Ferritin, consisting of ferritin heavy chain (FTH1) and light chain (FTL) subunits, is an essential intracellular iron storage protein fundamental for cellular function. However, the source and the biological role of extracellular ferritin (ex-ferritin) are less understood. Recent studies have linked elevated serum ex-ferritin with adverse outcomes in individuals with acute respiratory distress syndrome (ARDS). In this study, we demonstrate that both FTH1 and FTL are significantly enriched in the serum, blood monocytes, and alveolar macrophages (AMs) of individuals with ARDS, a phenomenon we successfully replicate in a murine hyperoxia-induced acute lung injury (HALI) model. We show that FTH1 is consistently upregulated in macrophages during lung injury development, and mice with a targeted deletion of FTH1 in myeloid (LysMcre) or resident lung macrophage (Cd11ccre) populations exhibit attenuated HALI. This reduced injury is linked to macrophage resistance to ferroptotic cell death, ferritinophagy, altered airway inflammatory responses, and lower lung extracellular iron and higher levels of FTL-ex-ferritin. Transplantation of FTL-ex-ferritin-enriched bronchoalveolar lavage fluid to wild-type mice protected against HALI. The ratio of FTL-ex-ferritin to FTH1 in the serum of individuals with ARDS who died was higher than that of those that survived, suggesting that the balance between FTH1 and FTL may play a role in injury modulation. Our findings highlight macrophage ferritin as a key regulator of macrophage survival and the response of the lung to injury, presenting a potentially targetable pathway for ARDS treatment.

Abstract Background Tau‐PET tracers have been used to monitor the progression of Alzheimer's disease (AD). However, different tracers present distinct patterns of binding throughout the brain, challenging the harmonization of their findings. Leveraging the HEAD Study, the largest head‐to‐head study of tau‐PET tracers, we recently developed the Uniτ scale, which cross‐sectionally harmonizes Flortaucipir and MK6240 onto a universal tau‐PET measurement. Here, we provide a preliminary evaluation of the Uniτ scale's longitudinal performance in HEAD and two independent cohorts. Methods We assessed 422 individuals across the AD spectrum with longitudinal tau‐PET from three cohorts: HEAD [13 cognitively unimpaired (CU), 9 cognitively impaired (CI) individuals, scanned head‐to‐head with Flortaucipir and MK6240], ADNI [74 CU, 208 CI, tracer: Flortaucipir], and TRIAD [72 CU, 46 CI, tracer: MK6240]. Standardized uptake ratios (SUVRs) were harmonized to Uniτ using the Uniτ Ecosystem ( unitau.app ). Braak I‐II and the Meta‐Temporal regions were used as regions of interest. Annual tau‐PET uptake change was calculated, and the effect size was defined as the mean annual change divided by its standard deviation. Results In HEAD, annual change in tau‐PET uptake in Braak I‐II across CU and CI was only detectable in MK6240 (Figure 1A). However, both tracers detected significant annual changes in the Meta‐Temporal ROI for CI (Figure 1B). In both ADNI (Flortaucipir) and TRIAD (MK6240), changes in tau‐PET uptake in Braak I‐II regions were not significant (Figures 2‐3A). However, across all cohorts, the Meta‐Temporal ROI consistently showed detectable annual tau‐PET increases—most pronounced among CI—leading to larger effect sizes than in Braak I‐II (Figures 1‐3B). The Uniτ harmonization did not fundamentally alter the pattern of the findings. However, in ADNI (Flortaucipir), Uniτ yielded a slightly higher effect size than SUVR in CU for both Braak I‐II and Meta‐Temporal regions. Conclusion In this large longitudinal sample, our findings confirm that Flortaucipir and MK6240 can detect tau PET changes over time likely associated with the progression of tau tangle pathology. While MK6240 appears to show greater progression in CU, both tracers progress similarly in CI. Our data also suggested that Uniτ harmonized tau PET measurements maintain the longitudinal characteristics of each tau PET tracer for use in clinical trials.

Abstract Background We showed that astrocyte reactivity, as measured by plasma GFAP levels, influences Aβ mediated tau PET pathology in cognitively unimpaired (CU) Aβ‐positive individuals. However, the link between GFAP and tau PET in individuals without detectable Aβ pathology remains elusive. The aim of the current study is to investigate the association between plasma GFAP and tau PET in CU Aβ PET‐negative individuals. Method We studied 147 CU Aβ PET‐negative participants from the HEAD cohort with plasma GFAP and p ‐tau217, as well as tau PET Flortaucipir and MK6240 data. Aβ positivity was determined by Aβ PET visual reading and Centiloid 12. Voxel‐wise linear regression models adjusted for age and sex tested the association of plasma GFAP and p ‐tau217 with tau PET. Further, the associations of plasma GFAP with peak tau PET SUVR values extracted from voxel‐wise association were fitted with a linear regression model adjusted for age and sex. Result Voxel‐wise analysis showed that plasma GFAP levels, but not plasma p ‐tau217 levels, were associated with tau PET in the medial temporal lobe (e.g., amygdala, entorhinal cortex, hippocampus) predominantly for Flortaucipir tau PET [Figure 1A, B, C, D]. The association between plasma p ‐tau217 and tau PET was weak in Aβ‐negative individuals. These results were similar when Aβ positivity was defined based on Centiloid 12. Furthermore, plasma GFAP and peak tau PET SUVR of Flortaucipir showed stronger association than that of MK6240 [Flortaucipir: β=0.4017, p <0.0001; MK6240: β=0.194, p = 0.0424; Figure 2A, B]. Conclusion We found an association between GFAP levels and tau PET uptake in individuals not expected to exhibit high levels of tau tangle‐related tracer uptake. Further analysis will be designed to elucidate the underpinning of this association, which could represent low levels of tau pathology, astrogliosis, or other factors.

Abstract Background Quantifying tau aggregates in the human brain can be achieved using Positron Emission Tomography (PET) techniques, which can potentially be affected by binding competition due to medication use. Patients with dementia often have high rates of comorbidities and polypharmacy. Therefore, this study aims to investigate the potential influence of multiple medications on the uptake of the tau tracers MK6240 (MK) and Flortaucipir (FTP). Method Five classes of medications were evaluated: Anti‐Hypertensives, Statins, Anti‐Diabetics, Psychoactive drugs, and NSAIDs (Table 1). We included 292 individuals [170 cognitively unimpaired (CU) Aβ‐negative and 122 cognitively impaired (CI) Aβ‐positive] from the HEAD study (Table 2). We compared MK and FTP SUVR in the Medial Temporal Lobe (MTL) and Neotemporal Cortex (NTC) in individuals on and off medications. The linear regressions that tested associations were corrected for confounding factors, including age, sex, education, and MoCA score. Correction for multiple comparisons was applied using the Bonferroni method (adjusted p ‐value at 0.00125). Result Among CI Aβ‐positive individuals, Anti‐Diabetics were associated with lower SUVR in the NTC for both FTP and MK. However, these associations did not remain significant after correction for multiple comparisons. (Table 3). Conclusion Our findings indicate that there are no significant associations between the use of the medications studied and MK or FTP uptake when accounting for covariates and applying multiple comparison corrections.

Abstract Background Many risk factors can contribute to the occurrence of Alzheimer`s Disease (AD). However, little is known about the impact of dementia risk factors to the uptake of tau‐PET tracers. Therefore, in this work we aim to investigate the influence of dementia risk factors and comorbidities on 18 F‐Flortaucipir (FTP) and 18 F‐MK6240 (MK) tau‐PET tracers’ uptake. Additionally, we will assess how these factors impact the association of tau‐PET and cognition. Method We accessed 436 individuals across the aging and AD spectrum (251 amyloid negative and 185 amyloid positive) from the HEAD study, with available Aβ‐PET, FTP, MK, and clinical assessments. Linear regression models corrected for age, sex, clinical diagnosis, and study site tested the association of factors with tau‐PET tracers in the medial temporal lobe (MTL). A tau‐PET × risk factor term was added to test the influence of risk factors to the association of tau with cognition. Result In amyloid‐β negative individuals, high BMI were positively associated with the uptake of both FTP and MK, whereas hearing loss were positively associated only with MK in the MTL (Figure 1A). In amyloid‐β positive individuals, high body mass index (BMI), hearing loss and sleep disorders were negatively associated with the uptake of both tau‐PET tracers in the MTL. On the other hand, hypertension showed negative association only with MK uptake (Figure 1B). Using Mini‐Mental State Examination (MMSE) scores as outcome, we observed that amyloid‐β negative individuals with high BMI showed worse cognitive performance as a function of both MK and FTP in the MTL, whereas individuals with vision impairment and hearing loss showed worse cognitive performance as a function of MK only (Figure 2A). Amyloid‐β positive individuals with hypercholesterolemia and hypertension presented worse cognitive performance as a function of both MK and FTP in the MTL (Figure 2B). Conclusion In this preliminary analysis, sleep disorders, hypertension, and high BMI were independently associated with tau‐PET tracer uptake, with the effects varying according to amyloid‐β pathology. These prevalent factors in the elderly also changed the association between tau‐PET and cognition, underscoring the need for further studies to better understand their role in modulating this relationship.

Background/Objectives: Individuals with type 2 diabetes (T2D) achieve less total body weight loss (TBWL) with obesity medications compared to those without T2D. The individualized metabolic surgery (IMS) score, originally developed to predict T2D remission after bariatric surgery, inversely correlates with TBWL response to semaglutide. IMS reflects T2D severity, incorporating HbA1c and T2D duration and medication use. This study aims to evaluate TBWL with tirzepatide across IMS severity categories and identify predictors of response in a real-world cohort. Methods: This retrospective analysis included 717 adults with T2D using tirzepatide for overweight or obesity within the Mayo Clinic Health System. Patients were stratified by IMS severity (mild, moderate, severe) and quartiles. Primary endpoint: TBWL% at 15 months. Secondary endpoints: categorical thresholds (≥5%, ≥10%, ≥15%, ≥20%) and predictors of TBWL%. Linear mixed-effects models and regression models were employed. Results: At 15 months, TBWL was greater in mild versus severe IMS groups (14.8% vs. 11.0%, p = 0.015), with similar trends across quartiles. The proportion achieving ≥ 20% TBWL was nearly two-fold higher in mild versus severe IMS (27% vs. 14%, p = 0.03). Female sex independently predicted greater TBWL, whereas insulin use, higher T2D medication burden (particularly weight-promoting agents), and HbA1c > 7% were associated with less TBWL. Conclusions: Tirzepatide produced clinically meaningful TBWL across all IMS categories, although TBWL declined with increasing IMS severity. Glycemic control and T2D medication use emerged as strong predictors of TBWL. The IMS score may serve as a practical tool to anticipate weight-loss trajectories, guide personalized treatment decisions, and inform patient counseling.

Abstract Bone marrow is the primary immune organ responsible for stem cell maintenance and hematopoiesis. However, the contribution of the bone marrow niche for the generation of adaptive immune responses is less well-understood. We therefore assessed the capacity of virus antigen-specific CD8 T cells to be generated and expanded in bone marrow following acute neurotropic virus infection. Intracranial infection with the neurotropic Theiler’s Murine Encephalomyelitis Virus (TMEV) results in an acute infection in the C57BL/6 mouse which is cleared within 4 weeks post infection due to the generation of an antigen-specific CD8 T cell response against the immunodominant epitope VP2 121-130 . We determined that 1-10% of CD8 T cells present in the femoral, and sternal bone marrow were virus antigen-specific 5-7 days after intracranial TMEV infection. We determined that antigen-specific CD8 T cells were generated in the bone marrow with similar kinetics to that of conventional responses in the secondary lymphoid organs. Importantly, continuous treatment with FTY720, which sequesters T cells outside of the blood, did not eliminate antigen-specific CD8 T cells in the bone marrow compartment indicating generation within the bone marrow niche. Similarly, injection of Poly I:C admixed with fluorescently labeled ovalbumin into the brain generated a similar antigen-specific CD8 T cell response in the bone marrow indicating these responses occur following various brain insults. This model also identified the likely antigen presenting cell (APC) responsible for scavenging antigens from the brain and trafficking into the bone marrow as a migratory myeloid-derived APC. Moreover, following clearance of TMEV infection, CD8 T cells in the bone marrow established durable memory. Antigen-specific memory CD8 T cells in the bone marrow reactivated and expanded upon cognate antigen reencounter. Antigen-specific reactivation of memory CD8 T cells within the bone marrow caused a concurrent increase in Lineage-, Sca-1+, c-Kit+ (LSK) and CD11b+ MHCII+ myeloid cells. This increase in the LSKs and myeloid cells in the bone marrow niche was abrogated with CD8 T cell depletion. We conclude that brain viral infections induce in situ effector and memory T cell responses within the bone marrow compartment. Memory recall CD8 T cell responses induce niche dysregulation by expanding LSK cells and lead to an influx of MHCII+ myeloid cells. Our data pave the way for crucial studies of bone marrow resident antigen-specific CD8 T cells in health and diseases.

Abstract Background The Alzheimer's Association workgroup criteria (AA‐2024) suggest four biological stages based on PET imaging. However, the application of this framework may vary depending on the tau PET tracer used, and the distribution of clinical stages by biological stage may differ. Here, we aim to compare the diagnostic performance of the biological staging of Alzheimer Disease using two different tau PET tracers. Method We analyzed 446 individuals and further stratified 167 Aβ‐positive participants from the HEAD study (Longitudinal multicenter head‐to‐head harmonization of tau PET tracers) that underwent both MK‐6240 and Flortaucipir (FTP) tau PET. Four clinical stages (normal, transitional, MCI, and dementia) were defined based on the AA‐2024 criteria. Within stage‐2 we assessed subgroups: subjective cognitive decline (SCD), subtle objective cognitive decline (SOCD), and mild behavioral impairment (MBI). Logistic and linear models corrected by age and sex tested association of groups with amyloid and tau pathology. The biological staging was performed using tracer‐specific thresholds for regional SUVR and testing two regions of interest schemes: (i) proposed by AA‐2024 and (ii) Braak stages (Figure 1). Cohen's weighted‐kappa (K) statistic measured clinical‐biological agreement. Positive predictive value (PPV) of an individual being clinically impaired (MCI or dementia) was calculated for each biological stage. Result Clinical stages showed a progressively increased prevalence of Aβ‐positivity and higher entorhinal tau PET pathology from cognitively normal (CN) to SOCD, MCI, and dementia in both tracers. MBI and SCD transitional groups showed no increased Aβ or tau pathology compared to CN (Figure 2). For this reason, only SOCD was considered part of stage 2 for following analyses. All tested biological staging strategies showed moderate agreement (0.34‐0.59) with clinical stages using either tracers. The distribution of biological across clinical stages were similar for FTP and MK, with MK‐6240 showing lower PPV values in earlier stages than FTP (Table 1). Conclusion Our results support that AA‐2024 biological and clinical stages show similar clinical‐biological concordance using either Flortaucipir or MK‐6240, although this can vary based on methods tested in the study. The MBI and SCD clinical subgroups showed no difference of pathological burden to clinical stage 1, while SOCD transitional stage showed higher burden compared to stage 1.

Abstract Background HEAD is a longitudinal, multi‐site, non‐randomized study aiming to harmonize different tau PET tracers onto a common scale for enrichment and monitoring of anti‐tau clinical trials. To achieve this, the Uniτ scale is being developed using data from participants scanned head‐to‐head with Flortaucipir and MK6240, with a subset also scanned with PI2620 and RO948. However, access to the Uniτ scale has been limited to the study community and collaborators. To address this limitation, we have developed the Uniτ Ecosystem – a set of tools for tau PET harmonization designed to support research and clinical trials ( www.unitau.app ). Method The Uniτ Ecosystem was developed for multiple operating systems (Web/macOS/Windows/Ubuntu) (Figure 1) and currently include four tools. The Uniτ harmonization is an online ROI harmonization module that allows the user to harmonize SUVRs from spreadsheets. The Uniτ packages (R, Python and MATLAB) facilitates the transformation of SUVR values to the Uniτ scale using the most used programming languages in research settings. The Uniτ calculator app allows rapid and convenient harmonization. The Uniτ desktop app is a 3D harmonization module for voxel‐wise harmonization. The Uniτ parameters were calculated using a training set of 200 participants with head‐to‐head MK6240 and Flortaucipir, and 90 individuals with additional PI2620 and RO948. Researchers can use their own datasets and convert tau PET data to the Uniτ scale. Result The online ROI harmonization module allows users to harmonize tau PET SUVRs uploaded from spreadsheets and offers interactive visualizations such as scatter plots and histograms for tau positivity thresholds (Figure 2). The calculator app enables users to harmonize single SUVR values effortlessly. The desktop application for 3D tau PET harmonization leverages the functionalities of the previous modules to harmonize entire 3D images, eliminating the need for predefined ROIs. Additionally, this module provides visualization tools for the parametric Uniτ images in 2D and 3D (Figure 3). Uniτ tools never store any data on any external servers. Conclusion The Uniτ Ecosystem represents a significant advance in tau PET harmonization, providing a user‐friendly platform to harmonize and visualize tau PET data for research and clinical trials.