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AbstractBackgroundDifferences between on‐ and off‐target retention characteristics between [18F]MK6240 and [18F]Flortaucipir (FTP) complicate the harmonization across tracers. Our objective here was to separate the impact of the reference region by evaluating correlations between [18F]MK6240 (MK) and [18F]FTP standard uptake values (SUVs).MethodParticipants (Figure 1, n=90) received an amyloid‐β (Aβ) PET scan ([11C]PIB or [18F]NAV4694) and two tau‐PET scans: [18F]MK (90‐110 minutes post‐injection) and [18F]FTP (80‐100 minutes post‐injection). For all PET scans, the individual frames were realigned, averaged, and co‐registered to a Freesurfer 7.1‐segmented T1‐MPRAGE. SUVs were calculated in the reference region (inferior cerebellar gray), on‐target regions (entorhinal cortex, hippocampus, Braak III, Braak IV, Braak V, Braak VI) and off‐target regions (caudate, pallidum, putamen, thalamus, cerebellar white matter, choroid plexus, meninges, eroded white matter). We examined correlations between [18F]MK and [18F]FTP SUV values as a function of Aβ status, as well as their associations with age.ResultAll correlations (Figure 2) were significant at p<0.001. The interaction with Aβ status was significant for hippocampus, BraakIII‐V and the metatemporal region. In Aβ+ participants, [18F]FTP and [18F]MK6240 have similar SUVs in on‐target regions with a slope close to 1 except for in hippocampus and Braak VI. However, [18F]FTP SUVs, on average, were greater than [18F]MK SUVs in Aβ+ and Aβ‐ subjects in the reference region and all off‐target regions except for meninges, and in Aβ‐ subjects in the on‐target regions. Age correlated with [18F]FTP SUVs in Aβ‐ subjects in all regions (except the reference region, Braak VI and meninges) and [18F]MK SUVs in putamen and pallidum, but this correlation was only significant (p<0.01) when young and old subjects were included.ConclusionDespite [18F]MK’s higher affinity for tau and SUVR dynamic range being twice that of [18F]FTP, the SUVs in on‐target regions are similar. The higher [18F]FTP signal in the reference region appears to be the contributing factor to the lower dynamic range. The higher [18F]FTP signal in on‐target regions for Aβ‐ subjects may reflect the presence of off‐target signals in these subjects. Taking off‐target signals into account should improve correlations between the two tracers, especially when measuring the earliest deposition of tau.

AbstractBackgroundThe association between [18F]Flortaucipir (FTP) and [18F]MK6240, two commonly used tau‐PET tracers in Alzheimer’s disease (AD), varies due to distinct binding properties and off‐target signal regions. Our study aims to elucidate the biological factors influencing this association and evaluate the applicability of a common equation across different on‐target regions.Method113 individuals from the HEAD dataset (11 young, 58 cognitively unimpaired elderly, and 44 cognitively impaired) underwent [18F]MK6240, [18F]FTP and Aβ‐PET scans. Images were processed using the inferior cerebellar grey (CG) as the reference region, with subjects categorized by Aβ status. Stepwise linear fitting identified off‐target (OFF) regions impacting tracer associations within the whole cortex, Braak and temporal‐meta regions of interest (ROIs). OFF regions included were a composite mask (pallidum, putamen, caudate ‐ PPC) to address collinearity in subcortical nuclei, as well as the region with the greatest partial volume effects (PVE) on the ROI (meninges, choroid plexus, and the hotspot medial to entorhinal cortices (EC)). Akaike information criteria (AIC) analyses tested linear models with various off‐target regions.ResultPPC and PVE regions influenced tracer association in on‐target regions. PPC binding is associated with age. PPC and meninges retention, along with Aβ status interaction, demonstrated the best model fit for the whole cortex (Figure 1). Weighting factors derived from the whole cortex model were applied to Braak regions and temporal meta‐ROI. Braak III‐VI and temporal‐meta ROI showed optimal models including OFF regions and Aβ status interaction (Figure 2). Forcing the whole equation gave optimal results. For Braak I (EC), adding the OFF covariates only was the best model. For Braak II (hippocampus), the best model included OFF regions and the interaction with Aβ status (Figure 2). Forcing the whole cortex equation onto Braak I and Braak II yielded suboptimal fits.ConclusionHarmonizing tau‐PET tracers involves considering significant covariates. Integrating meningeal and age‐related off‐target regions enhances [18F]MK6240 and [18F]FTP association in on‐target regions, further improved by Aβ status inclusion. While the whole cortex model may not suit early ROI, it proves effective for Braak III and beyond. Creating an equation for tracer translation necessitates incorporating meningeal and subnuclei off‐target retention.

AbstractBackgroundThe association between [18F]Flortaucipir (FTP) and [18F]MK6240, two commonly used tau‐PET tracers in Alzheimer’s disease (AD), varies due to distinct binding properties and off‐target signal regions. Our study aims to elucidate the biological factors influencing this association and evaluate the applicability of a common equation across different on‐target regions.Method113 individuals from the HEAD dataset (11 young, 58 cognitively unimpaired elderly, and 44 cognitively impaired) underwent [18F]MK6240, [18F]FTP and Aß‐PET scans. Images were processed using the inferior cerebellar grey (CG) as the reference region, with subjects categorized by Aß status. Stepwise linear fitting identified off‐target (OFF) regions impacting tracer associations within the whole cortex, Braak and temporal‐meta regions of interest (ROIs). OFF regions included were a composite mask (pallidum, putamen, caudate ‐ PPC) to address collinearity in subcortical nuclei, as well as the region with the greatest partial volume effects (PVE) on the ROI (meninges, choroid plexus, and the hotspot medial to entorhinal cortices (EC)). Akaike information criteria (AIC) analyses tested linear models with various off‐target regions.ResultPPC and PVE regions influenced tracer association in on‐target regions. PPC binding is associated with age. PPC and meninges retention, along with Aß status interaction, demonstrated the best model fit for the whole cortex (Figure 1). Weighting factors derived from the whole cortex model were applied to Braak regions and temporal meta‐ROI. Braak III‐VI and temporal‐meta ROI showed optimal models including OFF regions and Aß status interaction (Figure 2). Forcing the whole equation gave optimal results. For Braak I (EC), adding the OFF covariates only was the best model. For Braak II (hippocampus), the best model included OFF regions and the interaction with Aß status (Figure 2). Forcing the whole cortex equation onto Braak I and Braak II yielded suboptimal fits.ConclusionHarmonizing tau‐PET tracers involves considering significant covariates. Integrating meningeal and age‐related off‐target regions enhances [18F]MK6240 and [18F]FTP association in on‐target regions, further improved by Aß status inclusion. While the whole cortex model may not suit early ROI, it proves effective for Braak III and beyond. Creating an equation for tracer translation necessitates incorporating meningeal and subnuclei off‐target retention.

AbstractBackgroundTau‐PET tracers have been used to diagnose and stage Alzheimer’s disease. However, different tau tracers present distinct patterns of binding throughout the brain, challenging the harmonization of their results. We hypothesize that the choice of a reference region can impact the harmonization of the tau‐PET standardized uptake value ratio (SUVR). In this context, we aimed to explore how different cerebellar reference regions impact the association between [18F]Flortaucipir and [18F]MK‐6240 SUVR values.MethodWe studied 185 individuals across the aging and AD spectrum with head‐to‐head Flortaucipir and MK‐6240 tau PET (HEAD Study). SUVRs were processed to a common 8mm FWHM using 15 different reference regions defined in the spatially unbiased atlas template of the cerebellum (SUIT) (Diedrichsen, 2009). Regression models investigated the association between Flortaucipir and MK‐6240 using multiple combinations of reference regions. R2 statistic was used to estimate goodness‐of‐fitting.Result225 combinations of associations Flortaucipir and MK‐6240 SUVR were tested, where the SUVRs are not necessarily quantified using the same reference region. Figure 1 presents the top 25 strongest and 25 weakest associations, ordered based on the coefficient of determination (R²). Flortaucipir and MK‐6240 exhibited the most robust associations when the inferior cerebellar gray matter or Crus I were employed for SUVR determination (R2 = 0.89, Figure 1‐2). Conversely, combinations incorporating the fastigial region consistently yielded weaker associations between the two tracers (R2<0.70, Figure 1‐2).ConclusionInterestingly, we showed that the inferior cerebellum, when used for both tracers, serves as a robust reference region for determining the most similar SUVRs for Flortaucipir and MK‐6240. Notably, these two regions have been commonly utilized in previous studies involving these tau tracers. Our results imply that the inferior cerebellum could be the optimal reference region for research aimed at harmonizing tau PET tracers using statistical scales.

AbstractBackgroundDifferences between on‐ and off‐target retention characteristics between [18F]MK6240 and [18F]Flortaucipir (FTP) complicate the harmonization across tracers. Our objective here was to separate the impact of the reference region by evaluating correlations between [18F]MK6240 (MK) and [18F]FTP standard uptake values (SUVs).MethodParticipants (Figure 1, n=90) received an amyloid‐β (Aβ) PET scan ([11C]PIB or [18F]NAV4694) and two tau‐PET scans: [18F]MK (90‐110 minutes post‐injection) and [18F]FTP (80‐100 minutes post‐injection). For all PET scans, the individual frames were realigned, averaged, and co‐registered to a Freesurfer 7.1‐segmented T1‐MPRAGE. SUVs were calculated in the reference region (inferior cerebellar gray), on‐target regions (entorhinal cortex, hippocampus, Braak III, Braak IV, Braak V, Braak VI) and off‐target regions (caudate, pallidum, putamen, thalamus, cerebellar white matter, choroid plexus, meninges, eroded white matter). We examined correlations between [18F]MK and [18F]FTP SUV values as a function of Aβ status, as well as their associations with age.ResultAll correlations (Figure 2) were significant at p<0.001. The interaction with Aβ status was significant for hippocampus, BraakIII‐V and the metatemporal region. In Aβ+ participants, [18F]FTP and [18F]MK6240 have similar SUVs in on‐target regions with a slope close to 1 except for in hippocampus and Braak VI. However, [18F]FTP SUVs, on average, were greater than [18F]MK SUVs in Aβ+ and Aβ‐ subjects in the reference region and all off‐target regions except for meninges, and in Aβ‐ subjects in the on‐target regions. Age correlated with [18F]FTP SUVs in Aβ‐ subjects in all regions (except the reference region, Braak VI and meninges) and [18F]MK SUVs in putamen and pallidum, but this correlation was only significant (p<0.01) when young and old subjects were included.ConclusionDespite [18F]MK’s higher affinity for tau and SUVR dynamic range being twice that of [18F]FTP, the SUVs in on‐target regions are similar. The higher [18F]FTP signal in the reference region appears to be the contributing factor to the lower dynamic range. The higher [18F]FTP signal in on‐target regions for Aβ‐ subjects may reflect the presence of off‐target signals in these subjects. Taking off‐target signals into account should improve correlations between the two tracers, especially when measuring the earliest deposition of tau.

Background. Study of myeloid neoplasms (MN) has been rapidly transformed by genome characterization. Gene mutations have been reported to influence disease phenotype and risk of disease progression, and the evaluation of mutational status can provide valuable insights to improve the decision-making process. In this international study, we evaluated the clinical impact of mutational screening in patients with CMML, which is characterized by significant clinical and genomic heterogeneity, including a substantial proportion of patients with poor outcomes and unmet needs. Currently, only limited molecular information has been incorporated into CMML classification and prognostication. Methods. We retrospectively collected 3182 CMML patients. We used MOSAIC, an AI-based framework for multimodal analysis in rare cancers (PMID: 38875514) to develop innovative, molecular-based tools for classification and prognostication. An independent validation of the results on 516 prospectively collected patients was performed. Results. Bayesian Networs and Hierarchical Dirichlet Processes were used to identify genomic associations and define a CMML molecular classification. We identified 11 clusters with distinct clinical and genomic features, including splicing machinery, signal transduction and tyrosine kinase pathways aberrations, and high-risk molecular signatures (i.e., AML-like and TP53 mutations). Up to 15% of patients showed clear genomic overlap with other MN. Importantly, extensive multi-color flow cytometry on T lymphocytes, NK and myeloid cells (BD FACSymphony A5 Cell Analyzer) revealed specific immunologic and inflammation signatures associated with molecular subgroups. We then integrated molecular and clinical information to build an international CMML Prognostic Scoring System (iCPSS). We assessed the performance of different statistical and AI-based methods; L1-penalized Cox Model resulted as the best performing method. Selected features included hematological parameters (WBC, Hb, PLT and marrow blasts), cytogenetic abnormalities, and mutations in 10 genes (ASXL1, DNMT3A, EZH2, NRAS, RUNX1, SETBP1, STAG2, TET2, TP53, U2AF1). By Bayesian thresholds optimization, we identified 5 risk classes (i.e., very-low, low, intermediate, high and very-high) with median overall survival (OS) ranging from 99 to 9 months (P&lt;.001), and median leukemia-free survival (LFS) ranging from 210 to 18 months (P&lt;.001). iCPSS provided better patient discrimination across all clinical endpoints compared to currently available prognostic tools, with a Concordance Index (CI) for OS of 0.75 versus 0.62-0.64, respectively. Notably, compared to available scoring systems, up to 40% of patients were reassigned to higher or lower risk classes by the iCPSS. External validation on an independent prospective cohort confirmed that the performance of iCPSS is superior to existing prognostic models (CI for OS 0.71 vs 0.54-0.61, respectively) To demonstrate the clinical utility of iCPSS, we focused on 753 patients who underwent allogeneic stem cell transplantation (HSCT). In multivariable analysis, iCPSS stratified the probability of OS post-HSCT (P&lt;.001) and identified groups of patients with different probabilities of disease relapse, ranging from 9% to 62% (P&lt;.001). We employed a clinical- and genomic-based decision support system (PMID: 38723212) to determine the optimal timing of HSCT. Preliminary analyses indicated that higher-risk patients according to iCPSS (i.e., intermediate, high, and very high) benefit from an immediate HSCT strategy. Lower-risk patients (i.e., very low and low) had life expectancy maximized with delayed HSCT. Modeling decision analysis using iCPSS compared to conventional scores, resulted in a change in transplantation policy for a significant proportion of patients (up to 25%). Conclusion. Molecular information significantly improves the classification of CMML patients, providing a basis for refining diagnostic boundaries with other MN and for a more rational inclusion of patients in clinical trials. The iCPSS demonstrated superior performance over currently available scoring systems and improved HSCT decision making process.

Introduction: Ibrutinib, a Bruton tyrosine kinase inhibitor (BTKi), changed the treatment landscape by demonstrating improved overall survival (OS) compared with chemotherapy/chemoimmunotherapy across multiple phase 3 trials in patients (pts) with CLL, including those with high-risk genomic features. Recently, data were reported for pts treated with ibrutinib in the phase 3 RESONATE-2 trial with up to 10 years of follow-up, representing the longest follow-up of any BTKi used in first-line treatment of CLL. With the most robust long-term follow-up data among BTKis, ibrutinib therapy trials are uniquely positioned to assess the long-term OS benefit of first-line BTKi treatment in pts with CLL across pt subgroups. We previously demonstrated that first-line ibrutinib is associated with OS rates that are similar to those in the age-matched general population, with a median follow-up of 5.9 years since initial diagnosis (Ghia P et al. HemaSphere 2024), but it remains unclear whether this holds true for pts with high-risk genomic features. This updated analysis evaluated long-term OS outcomes with a median follow-up of 8 years since randomization and 10 years since initial diagnosis in a pooled population of pts with previously untreated CLL, including those with high-risk features, who received first-line ibrutinib treatment in 2 phase 3 trials with comparison of survival estimates to the US age-matched general population. Methods: Data were pooled for pts who received first-line treatment with single-agent ibrutinib or ibrutinib + rituximab in the RESONATE-2 (NCT01722487) and ECOG-ACRIN E1912 (NCT02048813) trials, respectively. OS probabilities from the time of randomization and from the time of initial diagnosis for ibrutinib-treated pts were compared with an age-matched general population using 2021 life tables for the total US population published by the Centers for Disease Control and Prevention using the Kaplan-Meier method. Subgroup analyses were performed for high-risk pts, defined as those with del(11q), del(17p), mutated TP53, and/or unmutated IGHV (uIGHV). Results: A total of490 pts were pooled across the 2 studies: 352 pts (71.8%) were treated with ibrutinib + rituximab, and 135 pts (27.6%) were treated with single-agent ibrutinib; 3 pts did not receive study treatment. The median age at time of randomization was 61 years; 36.5% (179/490) were aged ≥65 years, 21.8% had del(11q), 7.8% had del(17p) and/or mutated TP53, and 54.7% had uIGHV. The median time from initial CLL diagnosis to randomization was 20.9 months (range, 0.0-341.8); the median follow-up was 123.5 months (10.3 years) from initial diagnosis and 99.2 months (8.3 years) from the time of randomization. From the time of randomization, estimated 9-year OS rates were 81.2% (95% CI, 76.8-84.9) in all ibrutinib-treated pts versus 82.0% (95% CI, 78.3-85.2) in the age-matched population (hazard ratio [HR] 1.17; 95% CI, 0.86-1.58). In high-risk pts, estimated 9-year OS rates were 79.5% (95% CI, 73.5-84.3) for ibrutinib-treated pts versus 83.2% (95% CI, 78.5-86.9) in the age-matched general population (HR 1.24; 95% CI, 0.84-1.84). In pts aged ≥65 years, estimated 9-year OS rates were 68.7% (95% CI, 60.5-75.6) for ibrutinib-treated pts and 68.7% (95% CI, 61.4-75.0) in the age-matched general population (HR 1.12; 95% CI, 0.77-1.63). In pts aged &lt;65 years, estimated 9-year OS rates were 89.3% (95% CI, 84.6-92.7) for ibrutinib-treated pts and 90.0% (95% CI, 86.1-92.9) in the age-matched general population (HR 1.15; 95% CI, 0.68-1.94). From the time of initial diagnosis, estimated 15-year OS rates were 78.4% (95% CI, 72.8-83.0) in all ibrutinib-treated pts versus 72.0% (95% CI, 67.8-75.7) in the age-matched general population (HR 0.83; 95% CI, 0.62-1.11). Study treatment had been discontinued in 59.8% (293/490) of pooled ibrutinib-treated pts; the most frequent reasons for discontinuation were adverse events (43.0%; 126/293) and progressive disease (20.5%; 60/293). Study treatment was ongoing in 31.8% of pooled pts (156/490). Conclusions: With the longest follow-up time for any commercially available targeted therapy, this pooled analysis demonstrates that, regardless of evaluation from randomization or initial diagnosis, and irrespective of age or high-risk features, first-line treatment with ibrutinib provides long-term OS benefit with survival estimates that appear similar to those of a US age-matched cohort.

Importance The clinical effects of risankizumab (a monoclonal antibody that selectively targets the p19 subunit of IL-23) for the treatment of ulcerative colitis are unknown. Objective To evaluate the efficacy and safety of risankizumab when administered as an induction and a maintenance therapy for patients with ulcerative colitis. Design, Setting, and Participants Two phase 3 randomized clinical trials were conducted. The induction trial was conducted at 261 clinical centers (in 41 countries) and enrolled 977 patients from November 5, 2020, to August 4, 2022 (final follow-up on May 16, 2023). The maintenance trial was conducted at 238 clinical centers (in 37 countries) and enrolled 754 patients from August 28, 2018, to March 30, 2022 (final follow-up on April 11, 2023). Eligible patients had moderately to severely active ulcerative colitis; a history of intolerance or inadequate response to 1 or more conventional therapies, advanced therapies, or both types of therapies; and no prior exposure to risankizumab. Interventions For the induction trial, patients were randomized 2:1 to receive 1200 mg of risankizumab or placebo administered intravenously at weeks 0, 4, and 8. For the maintenance trial, patients with a clinical response (determined using the adapted Mayo score) after intravenous treatment with risankizumab were randomized 1:1:1 to receive subcutaneous treatment with 180 mg or 360 mg of risankizumab or placebo (no longer receiving risankizumab) every 8 weeks for 52 weeks. Main Outcomes and Measures The primary outcome was clinical remission (stool frequency score ≤1 and not greater than baseline, rectal bleeding score of 0, and endoscopic subscore ≤1 without friability) at week 12 for the induction trial and at week 52 for the maintenance trial. Results Among the 975 patients analyzed in the induction trial (aged 42.1 [SD, 13.8] years; 586/973 [60.1%] were male; and 677 [69.6%] were White), the clinical remission rates at week 12 were 132/650 (20.3%) for 1200 mg of risankizumab and 20/325 (6.2%) for placebo (adjusted between-group difference, 14.0% [95% CI, 10.0%-18.0%], P &amp;lt; .001). Among the 548 patients analyzed in the maintenance trial (aged 40.9 [SD, 14.0] years; 313 [57.1%] were male; and 407 [74.3%] were White), the clinical remission rates at week 52 were 72/179 (40.2%) for 180 mg of risankizumab, 70/186 (37.6%) for 360 mg of risankizumab, and 46/183 (25.1%) for placebo (adjusted between-group difference for 180 mg of risankizumab vs placebo, 16.3% [97.5% CI, 6.1%-26.6%], P &amp;lt; .001; adjusted between-group difference for 360 mg of risankizumab vs placebo, 14.2% [97.5% CI, 4.0%-24.5%], P = .002). No new safety risks were detected in the treatment groups. Conclusion and Relevance Compared with placebo, risankizumab improved clinical remission rates in an induction trial and in a maintenance trial for patients with moderately to severely active ulcerative colitis. Further study is needed to identify benefits beyond the 52-week follow-up. Trial Registration ClinicalTrials.gov Identifiers: NCT03398148 and NCT03398135

Abstract To investigate the cellular and molecular mechanisms associated with targeting CD30-expressing Hodgkin lymphoma (HL) and immune checkpoint modulation induced by combination therapies of CTLA4 and PD1, we leveraged Phase 1/2 multicenter open-label trial NCT01896999 that enrolled patients with refractory or relapsed HL (R/R HL). Using peripheral blood, we assessed soluble proteins, cell composition, T-cell clonality, and tumor antigen-specific antibodies in 54 patients enrolled in the phase 1 component of the trial. NCT01896999 reported high (&gt;75%) overall objective response rates with brentuximab vedotin (BV) in combination with ipilimumab (I) and/or nivolumab (N) in patients with R/R HL. We observed a durable increase in soluble PD1 and plasmacytoid dendritic cells as well as decreases in plasma CCL17, ANGPT2, MMP12, IL13, and CXCL13 in N-containing regimens (BV + N and BV + I + N) compared with BV + I (P &lt; 0.05). Nonresponders and patients with short progression-free survival showed elevated CXCL9, CXCL13, CD5, CCL17, adenosine–deaminase, and MUC16 at baseline or after one treatment cycle and a higher prevalence of NY-ESO-1-specific autoantibodies (P &lt; 0.05). The results suggest a circulating tumor-immune-derived signature of BV ± I ± N treatment resistance that may be useful for patient stratification in combination checkpoint therapy. Significance: Identification of multi-omic immune markers from peripheral blood may help elucidate resistance mechanisms to checkpoint inhibitor and antibody–drug conjugate combinations with potential implications for treatment decisions in relapsed HL.