Citations

Compared to most of nano-sized particles, core–shell-structured nanoflowers have received great attention as bioactive materials because of their high surface area with the flower-like structures. In this study, core–shell-structured Si-based NiO nanoflowers, Si@NiO, were prepared by a modified chemical bath deposition method followed by thermal reduction. The crystal morphology and basic structure of the composites were characterized by powder X-ray diffraction (PXRD), Fourier-transform infrared spectroscopy (FT-IR), scanning electron microscopy (SEM), transmission electron microscopy (TEM), specific surface area (BET) and porosity analysis (BJT), and inductively coupled plasma optical emission spectrometry (ICP-OES). The electrochemical properties of the Si@NiO nanoflowers were examined through the redox reaction of ascorbic acid with the metal ions present on the surface of the core–shell nanoflowers. This reaction favored the formation of reactive oxygen species. The Si@NiO nanoflowers showed excellent anticancer activity and low cytotoxicity toward the human breast cancer cell line (MCF-7) and mouse embryonic fibroblasts (MEFs), respectively, demonstrating that the anticancer activities of the Si@NiO nanoflowers were primarily derived from the oxidative capacity of the metal ions on the surface, rather than from the released metal ions. Thus, this proves that Si-based NiO nanoflowers can act as a promising candidate for therapeutic applications.

Objective: We aimed to discern clinico-demographic predictors of large (≥8) tracheostomy tube size placement, and, secondarily, to assess the effect of large tracheostomy tube size and other parameters on odds of decannulation before hospital discharge. Summary of Background Data: Factors determining choice of tracheostomy tube size are not well-characterized in the current literature, despite evidence linking large tracheostomy tube size with posttracheotomy tracheal stenosis. The effect of tracheostomy tube size on timing of decannulation is also unknown, an important consideration given reported associations between endotracheal tube size and probability of failed extubation. Methods: We collected information pertaining to patients who underwent tracheotomy at 1 of 10 U.S. health care institutions between 2010 and 2019. Tracheostomy tube size was dichotomized (≥8 and <8). Multivariable logistic regression models were fit to identify predictors of (1) large tracheostomy tube size, and (2) decannulation before hospital discharge. Results: The study included 5307 patients, including 2797 (52.7%) in the large tracheostomy cohort. Patient height (odds ratio [OR] = 1.060 per inch; 95% confidence interval [CI] 1.041–1.070) and obesity (1.37; 95% CI 1.1891.579) were associated with greater odds of large tracheostomy tube; otolaryngology performing the tracheotomy was associated with significantly lower odds of large tracheostomy tube (OR = 0.155; 95% CI 0.131–0.184). Large tracheostomy tube size (OR = 1.036; 95% CI 0.885–1.213) did not affect odds of decannulation. Conclusions: Obesity was linked with increased likelihood of large tracheostomy tube size, independent of patient height. Probability of decannulation before hospital discharge is influenced by multiple patient-centric factors, but not by size of tracheostomy tube.

OBJECTIVE The goal of this study was to assess the social determinants that influence access and outcomes for pediatric neurosurgical care for patients with Chiari malformation type I (CM-I) and syringomyelia (SM). METHODS The authors used retro- and prospective components of the Park-Reeves Syringomyelia Research Consortium database to identify pediatric patients with CM-I and SM who received surgical treatment and had at least 1 year of follow-up data. Race, ethnicity, and insurance status were used as comparators for preoperative, treatment, and postoperative characteristics and outcomes. RESULTS A total of 637 patients met inclusion criteria, and race or ethnicity data were available for 603 (94.7%) patients. A total of 463 (76.8%) were non-Hispanic White (NHW) and 140 (23.2%) were non-White. The non-White patients were older at diagnosis (p = 0.002) and were more likely to have an individualized education plan (p < 0.01). More non-White than NHW patients presented with cerebellar and cranial nerve deficits (i.e., gait ataxia [p = 0.028], nystagmus [p = 0.002], dysconjugate gaze [p = 0.03], hearing loss [p = 0.003], gait instability [p = 0.003], tremor [p = 0.021], or dysmetria [p < 0.001]). Non-White patients had higher rates of skull malformation (p = 0.004), platybasia (p = 0.002), and basilar invagination (p = 0.036). Non-White patients were more likely to be treated at low-volume centers than at high-volume centers (38.7% vs 15.2%; p < 0.01). Non-White patients were older at the time of surgery (p = 0.001) and had longer operative times (p < 0.001), higher estimated blood loss (p < 0.001), and a longer hospital stay (p = 0.04). There were no major group differences in terms of treatments performed or complications. The majority of subjects used private insurance (440, 71.5%), whereas 175 (28.5%) were using Medicaid or self-pay. Private insurance was used in 42.2% of non-White patients compared to 79.8% of NHW patients (p < 0.01). There were no major differences in presentation, treatment, or outcome between insurance groups. In multivariate modeling, non-White patients were more likely to present at an older age after controlling for sex and insurance status (p < 0.01). Non-White and male patients had a longer duration of symptoms before reaching diagnosis (p = 0.033 and 0.004, respectively). CONCLUSIONS Socioeconomic and demographic factors appear to influence the presentation and management of patients with CM-I and SM. Race is associated with age and timing of diagnosis as well as operating room time, estimated blood loss, and length of hospital stay. This exploration of socioeconomic and demographic barriers to care will be useful in understanding how to improve access to pediatric neurosurgical care for patients with CM-I and SM.

Abstract Introduction: Multiple gene expression profile assays have successfully prognosticated the overall survival (OS) outcomes of patients with Newly Diagnosed Multiple Myeloma (NDMM) and are now available for use in clinical practice. The genes included in these profiles encompass several aspects of cancer biology broadly such as cell-cycle biology, energy metabolism, migration, adhesion, immune evasion etc. Since altered energy metabolism is one of the hallmarks of all cancer cells, our goal was to take a deep-dive into specific genes associated with intracellular central carbon energy metabolism (CCEM) pathways such as oxidative phosphorylation (OxPhos) as well as glycolysis and lactate fermentation (Glyco-Lac) in MM cells to determine their impact on the pathogenesis and prognosis of MM patients. Methods: Gene expression level data via RNA sequencing from the COMMPASS dataset (made available by the Multiple Myeloma Research Foundation (MMRF)) was utilized. The expression levels of all genes associated with the OxPhos (N = 118) and Glyco-Lac (N = 34) pathways were evaluated in CD138+ cells derived from patients with NDMM. Each gene was dichotomized into low and high expression groups -based on their expression level above and below the median fragments per kilobase of transcript per million (FPKM), respectively. The prognostic significance of dichotomizing each of the 152 genes in this NDMM cohort was evaluated by levels of their hazard ratios (HRs) for OS determined by the log-rank method. Genes from the OxPhos and Glyco-Lac pathways were selected based on their significant negative impact on OS, wide representation of their biologic activity throughout the pathway and high independence from each other. This resulted in the selection of 12 distinct genes of interest. A scoring system consisting of assigning a point for every gene where their FPKM was above the median yielded a minimum of 0 and a maximum of 12 for the set of genes in the OxPhos and Glyco-Lac pathways to create an energy metabolism score (EMS). Patients were then categorized into "low" (0-8) or "high" (9-12) EMS groups. In addition, patients were designated as having high-risk cytogenetics if they had any of the following: t(4;14), t(14;16), t(14;20), del17p, and Amp 1q. Survival analysis was performed using the Kaplan-Meier survival analysis and compared via the log-rank method. Finally, the distribution of the EMS score among patients within a spectrum of plasma cell disorders such as, monoclonal gammopathy of undetermined significance (MGUS), smoldering multiple myeloma and MM as well as normal plasma cells was assessed using a previously published transcriptome dataset (GSE6477). Results: A total of 766 patients were included in this study. The selected genes included in this scoring system and their biological relevance in the OxPhos and Glyco-Lac pathways are depicted in Figure A. The distribution of low and high EMS groups across ISS stage and cytogenetic risk are depicted in Figure B and C respectively. The median OS (mOS) and PFS for the high (N = 248) vs. low (N = 518) EMS groups was 55 vs. 94 months (HR: 2.41; (95%CI: 1.85-3.14); P &lt;0.0001) (Figure D) and 24 vs. 42 months (HR: 1.74; (95%CI: 1.42-2.13); P &lt;0.0001). Patients with ISS 2 stage disease categorized as having a high or low EMS score had a similar mOS to patients with ISS 3 and ISS 1 stage disease respectively (Figure E). Similarly, the mOS was similar among patients with standard risk and high-risk cytogenetics when categorized by low and high EMS (Figure F). Finally, the relative expression of most of the 12 select CCEM genes (Figure G) and the probability of a high EMS score increased among patients along the spectrum of plasma cell disorders (Normal plasma cell -&gt; MGUS -&gt; SMM -&gt; MM) (Figure H). Conclusions: The expression levels of specific genes associated with the activity of intracellular CCEM pathways such as glycolysis, lactate fermentation and oxidative phosphorylation in plasma cells provides evidence for the impact of cellular energy metabolism on the pathogenesis and survival outcomes of MM. This provides an opportunity for developing future studies aimed at understanding the energy metabolism pathways of plasma cell malignancies in greater detail so as to exploit them for diagnostic and therapeutic purposes in MM. Figure 1 Figure 1. Disclosures Kumar: Novartis: Research Funding; Amgen: Consultancy, Research Funding; Beigene: Consultancy; Carsgen: Research Funding; Merck: Research Funding; Antengene: Consultancy, Honoraria; Janssen: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Astra-Zeneca: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Oncopeptides: Consultancy; Roche-Genentech: Consultancy, Research Funding; KITE: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Takeda: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Tenebio: Research Funding; Bluebird Bio: Consultancy; Celgene: Membership on an entity's Board of Directors or advisory committees, Research Funding; BMS: Consultancy, Research Funding; Abbvie: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Adaptive: Membership on an entity's Board of Directors or advisory committees, Research Funding; Sanofi: Research Funding. Dispenzieri: Alnylam: Research Funding; Sorrento Therapeutics: Consultancy; Oncopeptides: Consultancy; Pfizer: Research Funding; Takeda: Research Funding; Janssen: Consultancy, Research Funding. Kapoor: AbbVie: Research Funding; Takeda: Research Funding; Karyopharm: Consultancy; Cellectar: Consultancy; BeiGene: Consultancy; Pharmacyclics: Consultancy; Sanofi: Consultancy; Amgen: Research Funding; Ichnos Sciences: Research Funding; Regeneron Pharmaceuticals: Research Funding; Glaxo SmithKline: Research Funding; Karyopharm: Research Funding; Sanofi: Research Funding. Dingli: Apellis: Consultancy; Sanofi: Consultancy; Janssen: Consultancy; GSK: Consultancy; Novartis: Research Funding; Alexion: Consultancy. Lin: Gamida Cell: Consultancy; Takeda: Research Funding; Kite, a Gilead Company: Consultancy, Research Funding; Juno: Consultancy; Novartis: Consultancy; Legend: Consultancy; Janssen: Consultancy, Research Funding; Celgene: Consultancy, Research Funding; Bluebird Bio: Consultancy, Research Funding; Sorrento: Consultancy; Merck: Research Funding; Vineti: Consultancy. Gertz: Akcea Therapeutics, Ambry Genetics, Amgen Inc, Celgene Corporation, Janssen Biotech Inc, Karyopharm Therapeutics, Pfizer Inc (to Institution), Sanofi Genzyme: Honoraria; Aurora Biopharma: Other: Stock option; AbbVie Inc, Celgene Corporation: Other: Data Safetly & Monitoring; Ionis Pharmaceuticals: Other: Advisory Board; Akcea Therapeutics, Alnylam Pharmaceuticals Inc, Prothena: Consultancy. Fonseca: Aduro: Consultancy; Kite: Consultancy; Juno: Consultancy; Merck: Consultancy; Takeda: Consultancy; Caris Life Sciences: Membership on an entity's Board of Directors or advisory committees; Patent: Prognosticaton of myeloma via FISH: Patents & Royalties; AbbVie: Consultancy; Scientific Advisory Board: Adaptive Biotechnologies: Membership on an entity's Board of Directors or advisory committees; OncoTracker: Consultancy, Membership on an entity's Board of Directors or advisory committees; Mayo Clinic in Arizona: Current Employment; Amgen: Consultancy; BMS: Consultancy; Celgene: Consultancy; Bayer: Consultancy; Janssen: Consultancy; Novartis: Consultancy; Pharmacyclics: Consultancy; Sanofi: Consultancy; GSK: Consultancy.

Abstract Introduction: The gut microbiome is a potentially modifiable factor in treatment related outcomes in allogeneic hematopoietic cell transplant (HCT). Prior studies have linked pre- or mid-treatment gut microbiome diversity with risk for treatment related morbidity and mortality. However, these studies have been limited by the inclusion of one or only a few institutions and the lack of longitudinal sampling with high quality metadata. These limitations complicate the interpretation of microbiome alterations over the course of HCT. Methods: To overcome these, we devised and implemented a large-scale biospecimen collection protocol in conjunction with BMT CTN 1703, a randomized, multicenter, Phase III trial of tacrolimus/methotrexate vs. post-transplant cyclophosphamide/tacrolimus/mycophenolate mofetil in reduced intensity conditioning (RIC) allogeneic HCT (NCT03959241). Patients enrolled on 1703 were optionally co-enrolled in the companion immune and microbiome profiling study, 1801 (MI-IMMUNE). This involved blood, urine and stool sampling before conditioning (PCON), then weekly starting on day 0 through day 77 (through day 84 for blood), and on days 98, 180, 270, 365 and 730. For all enrolled participants where the donor consented, residual donor cells were saved from the empty hematopoietic stem cell product bag for later analysis. Additionally, the protocol included one-time blood, urine and stool sample collection from consented matched related donors (MRDs) prior to stem cell collection. Starting with protocol version 4.0 on February 1, 2021, participation in 1801 stool collection was required for the first six out of eighteen sample collection timepoints. Participation in later stool and urine timepoint collections remained optional. Here we review the feasibility of creating a multi-institutional biobank. Additionally, we assess the success of our strategies by calculating sample collection compliance and standard deviation in compliance across centers for each timepoint. Results: On June 18, 2021, BMT CTN 1703/1801 closed to accrual with 431 patients enrolled on 1703; 323 patients from 36 centers were co-enrolled on 1801. 304 (94%) provided study samples, making this the largest prospective microbiome and immune profiling study in HCT patients to date. As of July 6, 2021, 3,683 blood, 2,668 urine, and 2,098 stool samples had been collected. Across the first 6 timepoints for all participating centers, blood, urine and stool sample collection averaged 93%, 82%, and 74% compliance, respectively. Of the 99 (30%) patients enrolled on 1801 with a MRD, 34 (34%) donors consented to sample collection. Sample collection compliance was lower for MRDs than for patients on the study with 76%, 74%, and 62% of expected blood, urine and stool samples collected, respectively, from this group. For stool collection exclusively, a median of 5 samples were collected per patient across the first 6 timepoints (median of 6 across all timepoints) with 93 (31%) of patients completing a full sample set through Day 28. 139 (46%) patients provided at least one sample after day 28; these represented 37% of the total samples collected to date. The PCON sample, which provides an important measure of pre-treatment gut microbiome diversity, had the third highest compliance with 74% of patients providing a sample. Surprisingly, Day 28 had the lowest compliance (66%) and highest standard deviation (37%) possibly because this timepoint often falls around the time of hospital discharge. Between PCON and day 28, the standard deviation between sites in the average collection compliance (24%) and number of samples collected per patient (1.1) was small indicating the successful adoption of stool collection across institutions. Table 1 summarizes sample collection statistics. Conclusion: Overall this study has resulted in a large, novel biobank of blood, urine and stool samples from patients undergoing RIC allogeneic HCT at 36 centers across the US. This will serve as a valuable resource for investigating the role of the gut microbiome in long term health outcomes following HCT. Although the results of 1801 are forthcoming given ongoing sample collection, the size and composition of the biobank to date clearly demonstrate the feasibility of implementing multi-institutional stool collection. This study represents a critical step towards the large-scale adoption of microbiome sampling as a diagnostic tool. Figure 1 Figure 1. Disclosures Chhabra: GSK: Honoraria. Clark: Kadmon: Consultancy. Horowitz: Mesoblast: Research Funding; Shire: Research Funding; Vertex: Research Funding; Stemcyte: Research Funding; Vor Biopharma: Research Funding; Janssen: Research Funding; Miltenyi Biotech: Research Funding; Kiadis: Research Funding; Sobi: Research Funding; Kite/Gilead: Research Funding; Pfizer, Inc: Research Funding; Jazz Pharmaceuticals: Research Funding; Magenta: Consultancy, Research Funding; Medac: Research Funding; Novartis: Research Funding; GlaxoSmithKline: Research Funding; Daiicho Sankyo: Research Funding; Xenikos: Research Funding; Omeros: Research Funding; Orca Biosystems: Research Funding; Pharmacyclics: Research Funding; Regeneron: Research Funding; Tscan: Research Funding; Takeda: Research Funding; CSL Behring: Research Funding; Genentech: Research Funding; Gamida Cell: Research Funding; Chimerix: Research Funding; Bristol-Myers Squibb: Research Funding; bluebird bio: Research Funding; Astellas: Research Funding; Amgen: Research Funding; Allovir: Consultancy; Actinium: Research Funding; Sanofi: Research Funding; Seattle Genetics: Research Funding. Jenq: Microbiome DX: Consultancy; Merck: Consultancy; Prolacta: Consultancy, Membership on an entity's Board of Directors or advisory committees; Kaleido: Consultancy, Current holder of stock options in a privately-held company, Membership on an entity's Board of Directors or advisory committees, Research Funding; Seres: Consultancy, Current holder of stock options in a privately-held company, Membership on an entity's Board of Directors or advisory committees, Patents & Royalties, Research Funding; LisCure: Consultancy, Membership on an entity's Board of Directors or advisory committees; MaaT Pharma: Consultancy, Membership on an entity's Board of Directors or advisory committees; Karius: Consultancy. Levine: Equillium Bio: Membership on an entity's Board of Directors or advisory committees; Incyte: Consultancy, Research Funding; Kamada: Research Funding; Biogen: Research Funding; Omeros: Membership on an entity's Board of Directors or advisory committees; Symbio: Membership on an entity's Board of Directors or advisory committees; Jazz Pharmaceuticals: Membership on an entity's Board of Directors or advisory committees; Talaris Therapeutics: Membership on an entity's Board of Directors or advisory committees; Viracor: Patents & Royalties: GVHD biomarker patent with royalties from Viracor; Mesoblast: Consultancy, Research Funding; X4 Pharmaceuticals: Membership on an entity's Board of Directors or advisory committees. Murthy: CRISPR Therapeutics: Research Funding. Riches: ATARA Biotherapeutics: Other: Payment; BioIntelect: Membership on an entity's Board of Directors or advisory committees; Jazz Pharmaceuticals: Other: Payment. Sung: Merck: Research Funding; Novartis: Research Funding; Enterome: Research Funding; Seres: Research Funding; AVROBIO: Consultancy; Abbott Nutrition: Honoraria; Clasado: Other: Research Product; DSM: Other: Research Product. Al Malki: Neximmune: Consultancy; Jazz Pharmaceuticals, Inc.: Consultancy; Rigel Pharma: Consultancy; Hansa Biopharma: Consultancy; CareDx: Consultancy. Rezvani: Kaleido: Other: One-time scientific advisory board; Nohla Therapeutics: Other: One-time scientific advisory board; Pharmacyclics-Abbvie: Research Funding; US Department of Justice: Consultancy. Bolaños-Meade: Incyte Corp: Consultancy. Holtan: Incyte: Consultancy, Research Funding; Generon: Consultancy. Saber: Govt. COI: Other. Hamadani: Sanofi, Genzyme, AstraZeneca, BeiGene: Speakers Bureau; Janssen, Incyte, ADC Therapeutics, Omeros, Morphosys, Kite: Consultancy; Takeda, Spectrum Pharmaceuticals and Astellas Pharma: Research Funding. Kean: Bluebird Bio: Research Funding; Bristol Myers Squibb: Patents & Royalties: From clinical trial data, Research Funding; Vertex: Consultancy; Novartis: Consultancy; Gilead: Research Funding; Regeneron: Research Funding; EMD Serono: Consultancy. Perales: Cidara: Honoraria; Servier: Honoraria; Incyte: Honoraria, Other; Equilium: Honoraria; Takeda: Honoraria; Novartis: Honoraria, Other; Nektar Therapeutics: Honoraria, Other; NexImmune: Honoraria; MorphoSys: Honoraria; Omeros: Honoraria; Karyopharm: Honoraria; Sellas Life Sciences: Honoraria; Merck: Honoraria; Miltenyi Biotec: Honoraria, Other; Medigene: Honoraria; Bristol-Myers Squibb: Honoraria; Kite/Gilead: Honoraria, Other; Celgene: Honoraria.

Abstract Introduction: Depth of response is associated with long-term outcomes in multiple myeloma (MM); however, the effect of response kinetics on outcomes in NDMM is variable and less clear. Although some studies have shown that achieving a ≥very good partial response (VGPR) at 4 months (mos) from diagnosis is associated with increased overall survival (Garderet Leukemia 2018), other reports have shown worse outcomes in pts with early vs late responses (Yan Blood Adv 2019). The double-blind, randomized TOURMALINE-MM2 (NCT01850524) trial (Facon Blood 2021) showed a clinical meaningful progression-free survival (PFS) benefit with IRd vs pbo-Rd (median 35.3 vs 21.8 mos; hazard ratio, 0.830; 95% confidence interval, 0.676-1.018; P=0.073; median follow-up, 53.3 and 55.8 mos, respectively) in NDMM pts. Safety data were consistent with the established toxicity profile of IRd. We evaluated PFS and duration of response (DOR) by depth of best confirmed response and time to best response in TOURMALINE-MM2. Methods: Pts were randomized to receive oral ixazomib 4 mg (n=351) or placebo (n=354) on days 1, 8, and 15, plus oral lenalidomide 25 mg (10 mg if creatinine clearance ≤60 mL/min) on days 1-21 and oral dexamethasone 40 mg (20 mg in pts aged &gt;75 years) on days 1, 8, 15, and 22 in 28-day cycles. After 18 cycles, treatment was continued without dexamethasone and reduced doses of ixazomib (3 mg) and lenalidomide (10 mg) until progressive disease (PD)/toxicity. Response assessments were performed every cycle until PD, or every 4 weeks in pts who discontinued treatment prior to PD. PFS and DOR were analyzed post-hoc in subgroups defined by depth of response and in subgroups defined by time to best confirmed response; 'early' and 'late' responses were defined by time to best confirmed response of 0-4 and &gt;4 mos, respectively (additional analyses were performed by time to best confirmed response of 0-6 and &gt;6 mos). Pts in either subgroup could have recorded an initial response prior to their best response. PFS and DOR were evaluated from randomization to progression and time of initial response to progression, respectively. To address potential guarantee-time bias in the PFS analysis, and to eliminate potential bias due to transient responses in DOR analysis, sensitivity analyses were conducted in pts with PFS / DOR of ≥6 mos. Results: Among the 705 pts in the intention-to-treat (ITT) population, 20% (26% IRd vs 14% placebo-Rd) had a best confirmed response of complete response (CR) or stringent CR, 35% (37 vs 34%) had VGPR, 26% (19 vs 32%) had PR, 10% (9 vs 10%) had stable disease (SD), 3% (1 vs 4%) had PD. 7% (8 vs 6%) of pts were not evaluable. In a pooled analysis of both arms, achieving a deeper response was associated with longer PFS (Figure, A) and DOR (median not reached [NR], 42.8, and 15.0 mos for pts with ≥CR, VGPR, and PR, respectively). In 570 pts with ≥PR (288 IRd; 282 pbo-Rd), 152 (53%) and 136 (47%) in the IRd arm and 143 (51%) and 139 (49%) pts in the pbo-Rd arm were defined as early (0-4 mos) and late (&gt;4 mos) responders, respectively. For early vs late responders, 46% vs 40% were aged ≥75 years, 21% vs 12% had International Staging System stage III MM, and 44% vs 33% had expanded high-risk cytogenetic abnormalities. Median PFS was prolonged among late vs early responders with IRd (65.7 vs 21.2 mos) and pbo-Rd (62.6 vs 18.2 mos), as was median DOR (IRd, NR vs 22.6 mos; pbo-Rd, 64.1 vs 17.2 mos). The PFS sensitivity analysis among pts with PFS of ≥6 mos confirmed the association of late response with improved outcomes; among late vs early responders achieving ≥PR, median PFS was 65.7 vs 23.9 mos with IRd and 62.6 vs 18.4 mos with pbo-Rd (Figure, B), and among late vs early responders achieving ≥VGPR, median PFS was 65.7 vs 35.3 mos with IRd and 64.4 vs 21.7 with pbo-Rd. Conclusions: Achieving a deeper response was associated with prolonged PFS and DOR in NDMM patients in TOURMALINE-MM2. PFS benefit on the ITT analysis was driven by the higher rates of deep responses (≥VGPR) with IRd vs pbo-Rd. PFS and DOR were also longer in pts achieving a late vs early best confirmed response of ≥PR or ≥VGPR. Consistent with results from a similar analysis in relapsed/refractory MM in the TOURMALINE-MM1 trial (Garderet Leukemia 2018), our findings support the continuation of therapy with the aim of achieving a deeper response over time. Additional sensitivity analyses will be presented. Figure 1 Figure 1. Disclosures Richardson: Sanofi: Consultancy; AstraZeneca: Consultancy; Celgene/BMS: Consultancy, Research Funding; GlaxoSmithKline: Consultancy; Regeneron: Consultancy; Oncopeptides: Consultancy, Research Funding; Protocol Intelligence: Consultancy; Secura Bio: Consultancy; Janssen: Consultancy; Takeda: Consultancy, Research Funding; AbbVie: Consultancy; Karyopharm: Consultancy, Research Funding; Jazz Pharmaceuticals: Consultancy, Research Funding. Venner: Takeda: Honoraria; BMS: Honoraria; Janssen: Honoraria; Pfizer: Honoraria; Sanofi: Honoraria; GSK: Honoraria. Bahlis: GlaxoSmithKline: Consultancy, Honoraria; Janssen: Consultancy, Honoraria; Takeda: Consultancy, Honoraria; Karyopharm: Consultancy, Honoraria; BMS/Celgene: Consultancy, Honoraria; Amgen: Consultancy, Honoraria; Abbvie: Consultancy, Honoraria; Genentech: Consultancy; Sanofi: Consultancy, Honoraria; Pfizer: Consultancy, Honoraria. White: Amgen: Consultancy, Honoraria; Antengene: Consultancy, Honoraria; BMS: Consultancy, Honoraria; Forus: Consultancy, Honoraria; Sanofi: Consultancy, Honoraria; GSK: Consultancy, Honoraria; Janssen: Consultancy, Honoraria; Takeda: Consultancy, Honoraria; Karyopharm: Consultancy, Honoraria. Karlin: Celgene-BMS: Honoraria, Other: member of advisory board; Sanofi: Honoraria; oncopeptide: Honoraria; Janssen: Honoraria, Other: member of advisory board, travel support; Abbvie: Honoraria; GSK: Honoraria, Other: member of advisory board; Amgen: Honoraria, Other: travel support and advisory board ; Takeda: Honoraria, Other: member of advisory board. Rigaudeau: Takeda: Membership on an entity's Board of Directors or advisory committees. Suzuki: Bristol-Myers Squibb: Honoraria, Research Funding; Celgene: Consultancy, Honoraria, Research Funding; Amgen: Consultancy, Honoraria, Research Funding; Takeda: Consultancy, Honoraria; ONO: Honoraria; Novartis: Honoraria; Sanofi: Honoraria; Abie: Honoraria; Janssen: Consultancy, Honoraria. Shibayama: Mundi Pharma: Honoraria; Otsuka: Honoraria; Pfizer: Honoraria; Bristol-Myers Squibb: Honoraria; Sanofi: Honoraria; Nippon Shinyaku: Honoraria; Fujimoto: Honoraria; Daiichi Sankyo: Honoraria; AstraZeneca: Honoraria, Membership on an entity's Board of Directors or advisory committees; Essentia Pharma Japan: Research Funding; Chugai: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Janssen: Honoraria, Research Funding; Novartis: Honoraria, Research Funding; Eisai: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Avvie: Honoraria, Research Funding; Takeda: Honoraria, Research Funding; Ono: Honoraria, Research Funding; Celgene: Research Funding. Zhang: Takeda: Current Employment. Kumar: Takeda: Current Employment, Current holder of stock options in a privately-held company. Twumasi-Ankrah: Takeda: Current Employment. Labotka: Takeda: Current Employment. Rifkin: Takeda: Membership on an entity's Board of Directors or advisory committees; Sanofi: Membership on an entity's Board of Directors or advisory committees; Fresenius-Kabi: Membership on an entity's Board of Directors or advisory committees; Coherus: Membership on an entity's Board of Directors or advisory committees; McKesson: Current Employment, Current equity holder in publicly-traded company; Karyopharm: Membership on an entity's Board of Directors or advisory committees; Bristol Myers Squibb (Celgene): Membership on an entity's Board of Directors or advisory committees; Amgen: Membership on an entity's Board of Directors or advisory committees. Lonial: AMGEN: Consultancy, Honoraria; TG Therapeutics: Membership on an entity's Board of Directors or advisory committees; BMS/Celgene: Consultancy, Honoraria, Research Funding; GlaxoSmithKline: Consultancy, Honoraria, Research Funding; Abbvie: Consultancy, Honoraria; Janssen: Consultancy, Honoraria, Research Funding; Takeda: Consultancy, Honoraria, Research Funding; Merck: Honoraria. Kumar: Antengene: Consultancy, Honoraria; Carsgen: Research Funding; Astra-Zeneca: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Oncopeptides: Consultancy; Celgene: Membership on an entity's Board of Directors or advisory committees, Research Funding; Bluebird Bio: Consultancy; Beigene: Consultancy; Tenebio: Research Funding; Roche-Genentech: Consultancy, Research Funding; Novartis: Research Funding; KITE: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Janssen: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Merck: Research Funding; Takeda: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Amgen: Consultancy, Research Funding; BMS: Consultancy, Research Funding; Abbvie: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Adaptive: Membership on an entity's Board of Directors or advisory committees, Research Funding; Sanofi: Research Funding. Moreau: Amgen: Honoraria; Oncopeptides: Honoraria; Janssen: Honoraria; Celgene BMS: Honoraria; Sanofi: Honoraria; Abbvie: Honoraria. OffLabel Disclosure: Use of the oral proteasome inhibitor ixazomib in newly diagnosed multiple myeloma patients who are ineligible for transplant.

Abstract TP53 mutations are frequent in IDH-mutant astrocytomas but unusual in oligodendroglioma and the clinical significance of TP53 mutations in oligodendroglioma are not well characterized. We reviewed genetically defined oligodendroglioma (i.e., IDH-mutant, whole-arm 1p/19q-codeleted diffuse glioma) cases that were molecularly profiled (2017-2020) at our institution and identified 7 cases with TP53 mutation (9%; n=76). Molecular testing was performed using targeted neuro-oncology NGS panel (50-gene mutation and/or 187-gene mutation/rearrangement) and OncoScan™ microarray. Four (of 7) patients were female. Median age at diagnosis was 43 years (range, 23-63). Most common presenting symptom was seizures (3 of 7). All tumors were supratentorial. Histologically, 3 tumors were WHO grade II and 4 were WHO grade III. Two (of 3) patients with a WHO grade II tumor underwent biopsy and radiotherapy at diagnosis followed by temozolomide at recurrence (progression at 67 and 157 months after diagnosis; overall survival of 124 and 201 months). Three (of 4) patients with a WHO grade III tumor were diagnosed within the last two years and are currently progression-free after standard therapy. Molecularly, in addition to TP53 mutation(s), all cases had an IDH1 and TERT promoter mutation as well as other gene mutation(s) including FUBP1 (n=5), SETD2 (n=4), PIK3R1 (n=4), PIK3CA (n=3), NF1 (n=3) and CIC (n=3). In 3 (of 7) cases, the mutational profile with high mutation count enriched for C &gt;T/G &gt;A transitions was highly suggestive of a hypermutation phenotype (2 cases were recurrent tumors treated with temozolomide; a recurrent and a treatment-naïve tumor had mismatch repair gene mutation). Five (of 7) cases, including the 3 hypermutant cases, lacked functional TP53 (1 case with 2 mutations, 2 cases with 1 mutation plus loss of other copy, 2 cases with 1 mutation plus copy neutral loss-of-heterozygosity). TP53 mutations are uncommon in oligodendroglioma and appear enriched in hypermutant tumors.

AbstractAimSurgical treatment of splenic flexure cancer (SFC) still presents some debated issues, including the role of laparoscopic surgery. The literature is based on small single‐centre series, while randomized controlled studies comparing open and laparoscopic treatment for colon cancer exclude SFC. This study aimed to determine the role of laparoscopic surgery in the treatment of SFC, comparing short‐ and long‐term outcomes with open surgery.MethodThis was an international multicentre retrospective cohort study that analysed patients from 10 tertiary referral centres. From a cohort of 641 cases, 484 patients with Stage I–III SFC submitted to elective surgery with curative intent were selected. After 1:1 propensity score matching, 130 patients in the laparoscopic group (LapGroup) were compared with 130 patients in the open surgery group (OpenGroup).ResultsAfter propensity score matching, the two groups were comparable for demographic and clinical parameters. OpenGroup presented a higher incidence of overall (P = 0.02) and surgery‐related complications (P = 0.05) but a similar rate of severe complications (P = 0.75). Length of stay was notably shorter in the LapGroup (P = 0.001). Overall (P = 0.793) as well as cancer‐specific survival (P = 0.63) did not differ between the two groups.ConclusionsElective laparoscopic surgery for Stage I–III SFC is feasible and associated with improved short‐term postoperative outcomes compared to open surgery. Moreover, laparoscopic surgery appears to provide excellent long‐term cancer outcomes.

The Internet of Things (IoT) has penetrated many aspects of everyday human life. The use of IoT in healthcare has been expanding over the past few years. In this review, we highlighted the current applications of IoT in the medical literature, along with the challenges and opportunities. IoT use mainly involves sensors and wearables, with potential applications in improving the quality of life, personal health monitoring, and diagnosis of diseases. Our literature review highlights that the current main application studied in the literature is physical activity tracking. In addition, we discuss the current technologies that would help IoT-enabled devices achieve safe, quick, and meaningful data transfer. These technologies include machine learning/artificial intelligence, 5G, and blockchain. Data on current IoT-enabled devices are still limited, and future research should address these devices’ effect on patients’ outcomes and the methods by which their integration in healthcare will avoid increasing costs.