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AbstractBackgroundSolanezumab is a monoclonal antibody targeting soluble forms of β‐amyloid protein important in the pathogenesis of Alzheimer’s disease (AD). Three previous 18‐month double‐blind placebo‐controlled trials of low‐dose solanezumab in late‐onset sporadic AD found inconsistent benefits on cognitive and functional assessments. Dominantly‐inherited mutation‐associated AD subjects both before and after onset of symptoms form an ideal population to study potential benefits of solanezumab therapy.MethodMutation‐carrying asymptomatic (CDR 0, N=41) or mildly symptomatic (CDR 0.5 – 1, N=28) patients were treated for a minimum of 4 years and up to 7 years in a double‐blind 3 to 1 active versus placebo randomized clinical trial that measured disease progression by clinical, neuropsychological and biomarker evaluations. The trial was initiated with a dose of 400 mg every 4 weeks and escalated to 1600 mg when low dose trials in sporadic AD did not meet their primary endpoints. The primary cognitive outcome measure was DIAN‐MCE, composed of Delayed Recall Score of the International Shopping List Test, the Delayed Recall score of the Logical Memory IIa subtest from the Wechsler Memory Scale‐Revised, the Digit Symbol Substitution Test total score from the WAIS‐R and the MMSE total score. Secondary outcomes included a battery of other cognitive and functional measures. The study was powered to detect delay of cognitive disease progression in the DIAN‐MCE. Biomarkers include imaging modalities (volumetric MRI, FDG, amyloid and Tau PET). CSF markers included β‐amyloid, Tau and PhosphoTau species. NfL was measured in both CSF and plasma. The study used a pre‐specified Bayesian multivariate disease progression model, which included dynamic borrowing of control subjects from the DIAN Observational study.ResultThe topline efficacy, safety and biomarker results will be reported.ConclusionThis study provides the first test of targeting soluble abeta forms in DIAD. It addresses the efficacy of early initiation of higher doses of solanezumab targeting soluble forms of amyloid as a disease modifying therapy. While these results are specific to DIAD, they have the potential to inform the application of anti‐amyloid therapy in sporadic AD.

Abstract Glioblastoma (GBM) is the most common and aggressive primary brain tumor in adults. Currently, treatment is focused on achieving maximal safe resection, followed by chemo- and radiotherapy. Although surgery remains the most important therapeutic component, complete surgical resection is virtually impossible, as GBM cells readily infiltrate the adjacent brain parenchyma. One fundamental mechanism for GBM cell migration is the ability of these cells to use cell volume regulation as a driving force of cell infiltration and potentially interfering with apoptosis and cell cycle progression. In this work, we targeted the master regulators of cell volume, the kinases STE20/SPS1-Related Proline-Alanine-Rich Protein Kinase (SPAK) and Oxidative stress-responsive kinase-1) (OSR1) by using a novel proprietary small molecule inhibitor. In order to study the effects of inhibiting SPAK and OSR1, we analyzed cell proliferation and migration. First, we determined the IC50 using six different patient-derived GBM cell lines; we found IC50 values ranging from 0.2–2μM. Next, cell proliferation was determined by cell cycle analysis through Edu labeling. We found a significant decrease in S phase and cell cycle arrest in G2M (p= 0.001, n=6). In addition, cell migration was determined using boyden chamber assay. We found a dose-dependent reduction in cell migration (p=0.001, n=3). These results correlated with the phenomenon observed in an orthotopic murine model of GBM, in which the inhibitor showed a decrease in tumor growth (p=0.0026, n=4) and greater survival rates in vivo (p=0.0046, n=8). When used in combination with radiation therapy, this small molecule inhibitor was capable of radio-sensitizing GBM cells decreasing clonogenicity (p=0.01, n=3) were observed in vitro. In summary, by targeting the SPAK/OSR1 kinases with a small molecule inhibitor, GBM cells become less aggressive, mainly by interfering with cell migration and proliferation and becoming more sensitive to radiation.

PURPOSE Patients with lower-risk (LR) myelodysplastic syndromes (MDS) who are RBC transfusion dependent and have experienced relapse after or are refractory to erythropoiesis-stimulating agent (ESA) have limited treatment options. High telomerase activity and human telomerase reverse-transcription expression in clonal hematopoietic cells have been reported in patients with MDS. Imetelstat, a first-in-class competitive inhibitor of telomerase enzymatic activity, targets cells with active telomerase. We report efficacy, safety, and biomarker data for patients with LR MDS who are RBC transfusion dependent and who were relapsed/refractory to ESAs. PATIENTS AND METHODS In this two-part phase II/III study (MDS3001), the primary end point was 8-week RBC transfusion independence (TI) rate, with key secondary end points of 24-week RBC TI rate, TI duration, and hematologic improvement-erythroid. RESULTS Data from the phase II part of the study are reported. Of 57 patients enrolled and treated (overall population), 38 were non-del(5q) and hypomethylating agent and lenalidomide naïve (subset population). The 8- and 24-week RBC TI rates in the overall population were 37% and 23%, respectively, with a median TI duration of 65 weeks. In the subset population, 8- and 24-week RBC TI rates were 42% and 29%, respectively, with a median TI duration of 86 weeks. Eight-week TI rate was observed across all subgroups evaluated. Cytogenetic and mutational data revealed a reduction of the malignant clones, suggesting disease modification activity. The most common adverse events were cytopenias, typically reversible within 4 weeks. CONCLUSION Imetelstat treatment results in a meaningful, durable TI rate across a broad range of heavily transfused patients with LR MDS who are ineligible for or relapsed/refractory to ESAs. Biomarker analyses indicated effects on the mutant malignant clone.

(1) Background: Arthrofibrosis is a common cause of patient debility and dissatisfaction after total knee arthroplasty (TKA). The diversity of molecular pathways involved in arthrofibrosis disease progression suggest that effective treatments for arthrofibrosis may require a multimodal approach to counter the complex cellular mechanisms that direct disease pathogenesis. In this study, we leveraged RNA-seq data to define genes that are suppressed in arthrofibrosis patients and identified adiponectin (ADIPOQ) as a potential candidate. We hypothesized that signaling pathways activated by ADIPOQ and the cognate receptors ADIPOR1 and ADIPOR2 may prevent fibrosis-related events that contribute to arthrofibrosis. (2) Methods: Therefore, ADIPOR1 and ADIPOR2 were analyzed in a TGFβ1 inducible cell model for human myofibroblastogenesis by both loss- and gain-of-function experiments. (3) Results: Treatment with AdipoRon, which is a small molecule agonist of ADIPOR1 and ADIPOR2, decreased expression of collagens (COL1A1, COL3A1, and COL6A1) and the myofibroblast marker smooth muscle α-actin (ACTA2) at both mRNA and protein levels in basal and TGFβ1-induced cells. (4) Conclusions: Thus, ADIPOR1 and ADIPOR2 represent potential drug targets that may attenuate the pathogenesis of arthrofibrosis by suppressing TGFβ-dependent induction of myofibroblasts. These findings also suggest that AdipoRon therapy may reduce the development of arthrofibrosis by mediating anti-fibrotic effects in joint capsular tissues.

Background: With the incorporation of positron emission tomography (PET) imaging as part of the standard staging evaluation of follicular lymphoma (FL), it is generally recommended to obtain the diagnostic biopsy from a lesion with the highest standardized uptake value (SUV) to rule out de novo histologic transformation (HT). In some cases such an approach might be impractical, while in other cases a biopsy from a diseased area with a high SUV may still demonstrate an indolent histology. To date, there is no data to guide treatment choices in patients (pts) with FL with high SUVmax but with no documented evidence of HT. Specifically, it remains unclear whether anthracycline-containing regimens, such as R-CHOP, provide a better outcome than R-Bendamustine (BR). Furthermore, it is unknown whether rituximab (R) maintenance is beneficial in this setting. Therefore, we aimed to compare the efficacy of R-CHOP vs BR in newly diagnosed FL pts with high SUVmax at baseline PET and to clarify the role of maintenance. Patients and Methods: We retrospectively identified 261 consecutive pts with newly diagnosed biopsy-proven FL1-3A and a SUVmax≥13 on baseline PET, who were treated with frontline R-CHOP (n=183) or BR (n=78) at 7 US cancer centers based on the physician's choice. Progression-free survival (PFS) was calculated from starting treatment until progression, relapse or death. Cut-offs of SUVmax ≥13≤18 and SUVmax>18 were used for sub-analysis (Haematologica;2020;105:1907-1913). Results: Median age was 59 years. The baseline characteristics of the two groups differed significantly for a younger age (58 vs 62 years, p=0.009), a higher rate of B-symptoms (26% vs 10%, p=0.005) and baseline SUVmax>18 (49% vs 36%, p=0.04) in the R-CHOP group. A diagnostic biopsy was obtained from the site at the highest SUV in 129 (71%) and 43 (55%) pts, respectively (p=0.01). These suggest a potential selection of R-CHOP in pts with adverse features. At the end of therapy (EoT), R-CHOP treated pts achieved higher PET-CT complete response rates (CR 82% vs 69%, p=0.02). This superiority held in pts with diagnostic biopsy at the highest SUV site (CR 83% vs 67%, p=0.03), but not in the others (CR 80% vs 71%, p=0.37). Progression occurred at EoT in 11 and 14 pts receiving R-CHOP and BR (6% vs 18%, p=0.02), with a higher rate of HT in the BR group (13% vs 4%, p=0.006). After a median follow-up of 76 months (range, 4-208 months), there was no significant difference in PFS between R-CHOP and BR treated pts (hazard ratio [HR] 1.22, p=0.34). PFS was strongly associated with FLIPI 2 (HR 2.32, p=0.01) and 3-5 (HR 2.69, p=0.003) in the univariate as well as in the multivariate analysis (FLIPI 2 HR 2.19, p=0.02; FLIPI 3-5 HR 2.51, p=0.01). There was a trend toward overall survival (OS) benefit in the R-CHOP group (Fig.1A). In the univariate analysis for OS, BR regimen (HR 2.12, p=0.03), age >60 (HR 2.54, p=0.006), FLIPI 3-5 (HR 5.13, p=0.03) and biopsy at the highest SUV site (HR 0.44, p=0.01) were prognostic, however none of those remained significant in the multivariate analysis (BR regimen HR 1.84, p=0.09; age >60 HR 1.92, p=0.08; FLIPI 3-5 HR 3.11, p=0.14; biopsy at the highest SUV site HR 0.60, p=0.13; SUVmax>18 HR 1.87, p=0.055). Among pts with EoT response after R-CHOP or BR (n=170 and n=64, respectively), one third in each group (36% vs 41%, respectively) received R-maintenance for a median of 8 administrations (range, 2-12). A significant PFS advantage (landmark analysis) was observed with R-maintenance (Fig.1B; HR 0.53, p=0.02); however, this did not translate in survival benefit (HR 0.67, p=0.49). In a multivariable model, R-maintenance (HR 0.53, p=0.02), FLIPI 2 (HR 2.47, p=0.03) and 3-5 (HR 2.79, p=0.01) remained independent prognosticators of PFS, while no parameters significantly affected OS. The 2-year HT rate (10% vs 17%) and cumulative incidence did not statistically differ between R-CHOP and BR groups (Fig.1C, p=0.159). Early progression (POD24) occurred in 43 and 22 pts who received R-CHOP and BR, respectively (24% vs 28%) and resulted in a significantly higher risk of death (HR 4.12, p=0.006). Conclusion: In newly diagnosed FL pts with SUVmax≥13 at baseline PET, R-CHOP was more effective in achieving CR and reducing the risk of early HT compared with BR, and it was associated with a trend toward survival advantage. Rituximab maintenance significantly prolonged PFS. A prospective evaluation with a larger population will be needed to confirm our findings. Disclosures Joffe: Epizyme: Membership on an entity's Board of Directors or advisory committees; AstraZeneca: Membership on an entity's Board of Directors or advisory committees. Ruella:Abclon, BMS, NanoString: Consultancy; Abclon: Consultancy, Research Funding; UPenn/Novartis: Patents & Royalties. Svoboda:Adaptive: Consultancy; Astra-Zeneca: Consultancy, Research Funding; BMS: Consultancy, Research Funding; Imbrium: Consultancy; Incyte: Research Funding; Pharmacyclics: Consultancy, Research Funding; Merck: Research Funding; Seattle Genetics: Consultancy, Research Funding; TG: Research Funding; Genmab: Consultancy; Atara: Consultancy. Witzig:Immune Design: Research Funding; Spectrum: Consultancy; Karyopharm Therapeutics: Research Funding; Acerta: Research Funding; Celgene: Consultancy, Research Funding; AbbVie: Consultancy; MorphSys: Consultancy; Incyte: Consultancy. Smith:TG Therapeutics: Consultancy, Research Funding; Janssen: Consultancy; BMS: Consultancy; Celgene: Consultancy, Research Funding; Pharmacyclics: Research Funding; Karyopharm: Consultancy, Research Funding; FortySeven: Research Funding; Acerta: Research Funding; Genentech/Roche: Consultancy, Other: Support of parent study and funding of editorial support, Research Funding. Armand:Affimed: Consultancy, Research Funding; Otsuka: Research Funding; Bristol-Myers Squibb: Consultancy, Honoraria, Research Funding; IGM: Research Funding; Infinity: Consultancy; Genentech: Research Funding; Sigma Tau: Research Funding; Celgene: Consultancy; Adaptive: Consultancy, Research Funding; Tensha: Research Funding; Roche: Research Funding; Merck & Co., Inc.: Consultancy, Honoraria, Research Funding; Pfizer: Consultancy; ADC Therapeutics: Consultancy. Nastoupil:Janssen: Honoraria, Research Funding; LAM Therapeutics: Research Funding; Gilead/KITE: Honoraria; Gamida Cell: Honoraria; TG Therapeutics: Honoraria, Research Funding; Merck: Research Funding; Novartis: Honoraria, Research Funding; Genentech, Inc.: Honoraria, Research Funding; Karus Therapeutics: Research Funding; Bayer: Honoraria; Celgene: Honoraria, Research Funding; Pfizer: Honoraria, Research Funding. Ansell:Takeda: Research Funding; Regeneron: Research Funding; Bristol Myers Squibb: Research Funding; Seattle Genetics: Research Funding; Trillium: Research Funding; Affimed: Research Funding; AI Therapeutics: Research Funding; ADC Therapeutics: Research Funding. Zelenetz:Novartis: Consultancy; Adaptive Biotechnology: Consultancy; Genentech/Roche: Consultancy; Gilead: Consultancy; Gilead: Research Funding; Celgene: Consultancy; Roche: Research Funding; Amgen: Consultancy; BeiGene: Membership on an entity's Board of Directors or advisory committees; Janssen: Consultancy; Sandoz: Research Funding; Celgene: Research Funding; MorphoSys: Research Funding; MEI Pharma: Research Funding. Younes:BioPath, Xynomic, Epizyme, and F. Hoffmann-La Roche: Consultancy; Janssen, Curis, Merck, Bristol-Myers Squibb, Syndax Pharmaceuticals, F. Hoffmann-La Roche, Curis (Inst), Johnson & Johnson (Inst), Novartis (Inst): Research Funding; Janssen, AbbVie, Merck, Curis, Epizyme, F. Hoffmann-La Roche, Takeda, Bristol-Myers Squibb, Bayer HealthCare Pharmaceuticals, Celgene, Incyte, Janssen Pharmaceuticals, Merck, Sanofi, Seattle Genetics, Takeda Millennium: Honoraria; AstraZeneca: Current Employment; MSKCC: Ended employment in the past 24 months.

Background The importance of the immune system in modulating the trajectory of lymphoma outcomes has been increasingly recognized. We recently showed that CD4+ cells are associated with clinical outcomes in a prospective cohort of almost 500 patients with follicular lymphoma (FL). Specifically, we showed that the absence of CD4+ cells inside follicles was independently associated with increased risk of early clinical failure. These data suggest that the composition, as well as the spatial distribution of immune cells within the tumor microenvironment (TME), play an important role in FL. To further define the architecture of the TME in FL we analyzed a FL tumor section using the Co-Detection by Indexing (CODEX) multiplex immunofluorescence system. Methods An 8-micron section from a formalin-fixed paraffin-embedded block containing a lymph node specimen from a patient with FL was stained with a cocktail of 15 CODEX antibodies. Five regions of interest (ROIs) were imaged using a 20X air objective. Images underwent single-cell segmentation using a Unet neural network, trained on manually segmented cells (Fig 1A). Cell type assignment was done after scaling marker expression and clustering using Phenograph. Each ROI was manually masked to indicate areas inside follicles (IF) and outside follicles (OF). Relative and absolute frequencies of cell types were calculated for each region. Cellular contacts were measured as number and types of cell-cell contacts within two cellular diameters. To identify proximity communities, we clustered cells based on number and type of neighboring masks using Phenograph. The number of cell types and cellular communities were calculated inside and outside follicles after adjustment for total IF and OF areas. The significance of cell contact was measured using a random permutation test. Results We identified 13 unique cell subsets (11 immune, 1 endothelial, 1 unclassified) in the TME of our FL section (Fig. 1A). The unique phenotype of each subset was confirmed using a dimensionality reduction tool (t-SNE). The global composition of the TME varied minimally across ROIs and consisted primarily of B cells, T cells, and macrophages subsets - in decreasing order of frequency. Higher spatial heterogeneity across ROIs was observed in the frequency of T cell subsets in comparison to B cells subsets. Inspecting the spatial distribution of T cell subsets (Fig. 1B), we observed that cytotoxic T cells were primarily located in OF areas, whereas CD4+ T cells were found in both IF and OF areas. Notably, the majority of CD4+ T cells inside the follicles expressed CD45RO (memory phenotype), while most of the CD4+ T cells outside the follicles did not. Statistical analysis of the spatial distribution of CD4+ memory T cell subsets confirmed a significant increase in their frequency inside follicles compared to outside (20.4% vs 11.2%, p < 0.001; Fig. 1D). Cell-cell contact analysis (Fig 1C) showed increased homotypic contact for all cell types. We also found a higher frequency of heterotypic contact between Ki-67+CD4+ memory T cells and Ki-67+ B cells. Pairwise analysis showed these findings were statistically significant, indicating these cells are organized in niches rather than randomly distributed across image. Analysis of cellular communities (Fig. 1C) identified 13 niches, named according to the most frequent type of cell-cell contact. All CD4+ memory T cell subsets were found to belong to the same neighborhood (CD4 Memory community). Analysis of the spatial distribution of this community confirmed that these niches were more frequently located inside follicles rather than outside (26.3±4% vs 0.004%, p < 0.001, Fig. 1D). Conclusions Analysis of the TME using CODEX provides insights on the complex composition and unique architecture of this FL case. Cells were organized in a pattern characterized by (1) high degree of homotypic contact and (2) increased heterotypic interaction between activated B cells and activated CD4+ memory T cells. Spatial analysis of both individual cell subsets and cellular neighborhoods demonstrate a statistically significant increase in CD4+ memory T cells inside malignant follicles. This emerging knowledge about the specific immune-architecture of FL adds mechanistic details to our initial observation around the prognostic value of the TME in this disease. These data support future studies using modulation of the TME as a therapeutic target in FL. Figure 1 Disclosures Galkin: BostonGene: Current Employment, Patents & Royalties. Svekolkin:BostonGene: Current Employment, Current equity holder in private company, Patents & Royalties. Postovalova:BostonGene: Current Employment, Current equity holder in private company. Bagaev:BostonGene: Current Employment, Current equity holder in private company, Patents & Royalties. Ovcharov:BostonGene: Current Employment, Current equity holder in private company, Patents & Royalties. Varlamova:BostonGene: Current Employment, Current equity holder in private company, Patents & Royalties. Novak:Celgene/BMS: Research Funding. Witzig:AbbVie: Consultancy; MorphSys: Consultancy; Incyte: Consultancy; Acerta: Research Funding; Karyopharm Therapeutics: Research Funding; Immune Design: Research Funding; Spectrum: Consultancy; Celgene: Consultancy, Research Funding. Nowakowski:Nanostrings: Research Funding; Seattle Genetics: Consultancy; Curis: Consultancy; Ryvu: Consultancy, Membership on an entity's Board of Directors or advisory committees, Other; Kymera: Consultancy; Denovo: Consultancy; Kite: Consultancy; Celgene/BMS: Consultancy, Research Funding; Roche: Consultancy, Research Funding; MorphoSys: Consultancy, Research Funding. Cerhan:BMS/Celgene: Research Funding; NanoString: Research Funding. Ansell:Trillium: Research Funding; Takeda: Research Funding; Regeneron: Research Funding; Affimed: Research Funding; Seattle Genetics: Research Funding; Bristol Myers Squibb: Research Funding; AI Therapeutics: Research Funding; ADC Therapeutics: Research Funding.

Abstract Background The current standard of care for the management of patients with newly diagnosed glioblastoma (GBM) includes maximal safe resection followed by radiotherapy (RT) with concurrent and adjuvant temozolomide (TMZ). While it is well established that TMZ has better efficacy in patients with MGMT promoter methylation, it remains an area of debate whether TMZ should be omitted when treating GBM patients with unmethylated MGMT. Methods We conducted a systematic review and meta-analysis to provide separate estimates of median overall survival (OS) and progression-free survival (PFS) for patients with methylated and unmethylated GBM treated with RT with or without TMZ. We searched multiple databases from inception to January 13, 2020. Results The median OS for patients with unmethylated GBM treated with RT/TMZ pooled from 5 phase III studies (N = 655) was 14.11 months (95% confidence interval [CI], 13.18–15.04) with a median PFS of 4.99 months (95% CI, 4.25–5.72). In contrast, the median OS for patients with methylated GBM pooled from 6 studies (N = 753) was 24.59 months (95% CI, 22.19–26.99) with a median PFS pooled from 7 studies (N = 805) of 9.51 months (95% CI, 7.41–11.61). There is a paucity of prospective data pertaining to OS/PFS in unmethylated patients treated with RT only and therefore a direct comparison was not possible. Conclusions This meta-analysis provides estimates of survival for patients with MGMT methylated or unmethylated GBM treated with RT/TMZ. Further research is needed to delineate whether TMZ should be withheld for patients with unmethylated GBM outside of the setting of clinical trials.

Summary This paper provides guidance for researchers with some mathematical background on the conduct of time‐to‐event analysis in observational studies based on intensity (hazard) models. Discussions of basic concepts like time axis, event definition and censoring are given. Hazard models are introduced, with special emphasis on the Cox proportional hazards regression model. We provide check lists that may be useful both when fitting the model and assessing its goodness of fit and when interpreting the results. Special attention is paid to how to avoid problems with immortal time bias by introducing time‐dependent covariates. We discuss prediction based on hazard models and difficulties when attempting to draw proper causal conclusions from such models. Finally, we present a series of examples where the methods and check lists are exemplified. Computational details and implementation using the freely available R software are documented in Supplementary Material. The paper was prepared as part of the STRATOS initiative.