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Lymphoma is the most common hematologic malignancy comprising a diverse group of neoplasms arising from multiple blood cell lineages. Any structure of the thorax may be involved at any stage of disease. Imaging has a central role in the initial staging, response assessment, and surveillance of lymphoma, and updated standardized assessment criteria are available to assist with imaging interpretation and reporting. Radiologists should be aware of the modern approaches to lymphoma treatment, the role of imaging in posttherapeutic surveillance, and manifestations of therapy-related complications.

Metastatic neuroendocrine tumour (NET) to brain has been reported in 1.5–5% of patients with NETs. Differentiation between intracranial NET metastasis and meningiomas can cause a diagnostic dilemma. We present a symptomatic case of a 66-year-old male with a history of left-sided skull base mass. The diagnosis of a meningioma was made based on the MRI findings and clinical presentation. The patient received radiation and the mass remained stable on serial MRI images at follow-up visits. Five years after his initial presentation, the patient’s mass showed further growth. He also complained of worsening of his recent diagnosis of irritable bowel syndrome and fluctuations in his blood pressure. Surgical resection was performed, and histopathological features were consistent with moderately differentiated neuroendocrine tumour. Further evaluation with 68 Gallium-DOTATATE positron emission-computed tomography (Ga-68 PET/CT) showed metastatic disease involving the bones, lymph nodes, and liver without convincing evidence of the location of primary malignancy within the bowel loops or the pancreas. The patient was started on combination of capecitabine and temozolomide with partial response and significant improvement of his symptoms. This case highlights the clinical and radiological behaviour of intracranial NET that can mimic the diagnosis of meningioma.

Abstract Introduction: Multiple myeloma (MM) is a complex hematological malignancy with the heterogenous immune bone marrow (BM) environment contributing to tumor growth, drug resistance, and immune escape. T-Cells play a critical role in the clearance of malignant plasma cells from the tumor environment. However, T-Cells in multiple myeloma demonstrate impaired cytotoxicity, proliferation, and cytokine production due to the activation of immune inhibitory receptors from ligands produced by the myeloma cells. In this study, we investigate the behavior of T-Cells in MM patients by using single-cell RNA-Seq (scRNA-Seq) to compare the transcriptomic profiles of BM T-Cells of patients with rapid progressing (FP; PFS < 18mo) and non-progressing (NP; PFS > 4yrs) disease. Methods: Newly diagnosed MM patients (n=18) from the Multiple Myeloma Research Foundation (MMRF) CoMMpass study (NCT01454297) were identified as either rapid progressors or non-progressors based on their progression free survival since diagnosis. To capture transcriptomic data, scRNA-Seq was performed on 48 aliquots of frozen CD138-negative BM cells at three medical centers/universities (Beth Israel Deaconess Medical Center, Boston, Washington University in St. Louis, and Mount Sinai School of Medicine, NYC). Samples were collected at diagnosis prior to treatment. Surface marker expression for 29 proteins was captured for at least one sample per patient using CITE-Seq. After integration and batch correction, clustering was performed to identify cells of T or NK lineage. Uniform Manifold Approximation and Projection (UMAP) and differential expression were used to identify T-Lymphoid subtypes, and differences in NP and FP samples. Results: In this study, single cell transcriptomic profiles were identified for ~102,207 cells from 48 samples of 18 MM patients. 40,328 T (CD3+) and NK (CD3-, NKG7+) cells were isolated, and subclustered for further analysis (Fig 1A). Using differentially expressed markers for each cluster, the T-Lymphoid subset was refined into seven subtypes, consisting of various CD4+ T-Cells, CD8+ T-Cells, and NK cells (Fig 1B). The CD8+ cells were divided into three distinct phenotypes, namely a GZMK-, GZMB- CD8+ T-Cell cluster, a GZMK+ CD8+ Exhausted T-Cell cluster enriched in TIGIT and multiple chemokines (CCL3, CCL4, XCL2), and a GZMB+ NkT cluster enriched in cytolytic markers (PRF1, GNLY, NKG7) (Fig 1C). Differential expression between NP and FP samples in this CD8+ subset showed enrichment of the NkT cytotoxic markers in NP samples, while FP samples were enriched in the CD8+ Exhausted chemokine markers (Fig 1D). Furthermore, the proportion of CD8+ Exhausted T-Cells was enriched in FP samples (p.val < 0.05) (Fig 1E). Exhaustion markers were measured through both RNA and surface marker levels. In RNA, TIGIT was uniquely associated with the FP-enriched CD8+ Exhausted T-Cell cluster, and CD160 was uniquely expressed in FP samples (Fig 1F). CITE-Seq surface marker expression confirms enrichment of both TIGIT and PD1 in the CD8+ Exhausted T-Cell cluster, and along with more exhaustion in FP samples (p.val < 0.01). Conclusion: In this study, we have identified significant differences in T-Cell activity in patients with non-progressing and rapid-progressing multiple myeloma. T-Cells in rapid progressing patients appear to be in a suppressed state, with low cytolytic activity and enriched exhaustion markers. This GZMK+ T-Cell population shows strong similarities with an aging-associated subtype of effector memory T-Cells found to be enriched in older populations (Mogilenko et al, Immunity 54, 2021). These findings will be further validated in an expanded study, consisting both of a larger number of samples, and multiple samples at different timepoints from the same patient. Figure 1 Figure 1. Disclosures Jayasinghe: MMRF: Consultancy; WUGEN: Consultancy. Vij: BMS: Research Funding; Takeda: Honoraria, Research Funding; Sanofi: Honoraria, Research Funding; BMS: Honoraria; GSK: Honoraria; Oncopeptides: Honoraria; Karyopharm: Honoraria; CareDx: Honoraria; Legend: Honoraria; Biegene: Honoraria; Adaptive: Honoraria; Harpoon: Honoraria. Kumar: Carsgen: Research Funding; KITE: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Beigene: Consultancy; Bluebird Bio: Consultancy; Janssen: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Tenebio: Research Funding; Oncopeptides: Consultancy; Antengene: Consultancy, Honoraria; Roche-Genentech: Consultancy, Research Funding; Merck: Research Funding; Astra-Zeneca: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Celgene: 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; Novartis: Research Funding; Amgen: Consultancy, Research Funding; Abbvie: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; BMS: Consultancy, Research Funding; Adaptive: Membership on an entity's Board of Directors or advisory committees, Research Funding; Sanofi: Research Funding. Avigan: Celgene: Membership on an entity's Board of Directors or advisory committees, Research Funding; Pharmacyclics: Research Funding; Kite Pharma: Consultancy, Research Funding; Juno: Membership on an entity's Board of Directors or advisory committees; Partner Tx: Membership on an entity's Board of Directors or advisory committees; Karyopharm: Membership on an entity's Board of Directors or advisory committees; Bristol-Myers Squibb: Membership on an entity's Board of Directors or advisory committees; Aviv MedTech Ltd: Membership on an entity's Board of Directors or advisory committees; Takeda: Membership on an entity's Board of Directors or advisory committees; Legend Biotech: Membership on an entity's Board of Directors or advisory committees; Chugai: Membership on an entity's Board of Directors or advisory committees; Janssen: Consultancy; Parexcel: Consultancy; Takeda: Consultancy; Sanofi: Consultancy.

Abstract Multiple Myeloma (MM) is the second most common hematologic malignancy, marked by uncontrolled clonal expansion of plasma cells. MM genome is complex and heterogeneous, with a high frequency of structural variants (SVs) and copy-number abnormalities (CNAs). Single-cell sequencing technologies offer advantages over traditional bulk methods in cancer genomics research for evaluating cellular heterogeneity and investigating evolution of cellular subpopulations within the tumor. Thus, there is an impetus to translate the growing understanding of the genomic landscape of MM into dissecting the tumor heterogeneity using scRNA-seq. So far, there are no reported studies systematically comparing tumor and immune populations differences between MM NON-progressors (NPs) and RAPID-progressor (RPs) and investigating how clonal plasma cells contribute to progression in a large cohort. In addition, the bone marrow microenvironment plays an important role in the evolution of premalignant MM and MM progression. A previous study of single-cell transcriptomics analysis of Monoclonal gammopathy of undetermined significance (MGUS) and MM tumor microenvironment (TME) revealed that natural killer (NK) cell abundance is frequently increased in the early stages and associated with altered chemokine receptor expression. This study shed the light on the role of immune cells on disease progression from asymptomatic MGUS to symptomatic MM. However to date, how immune cells influence disease progression within symptomatic MM is still unclear. Here, we subjected 344 CD138-negative Bone Marrow Mononuclear cells (BMMC) samples to scRNA-seq. To control for the first line therapy, 272 patients were treated with RVD and Autologous stem cell transplant (ASCT) and were stratified into 3 groups based on progression: 200 NON-progressors (NPs) (PFS> 5 years), 7 Intermediate Progressors (IPs, PFS 2-4 years) and 38 RAPID-progressors (RPs) (PFS<=18 months). In addition, there are 72 subjects treated with RVD but without ASCT, including 34 NPs, 2 IPs and 36 RPs. From MMRF CoMMpass study (NCT01454297), we also have whole exome sequencing (WES) and bulk RNA-seq from CD138-positive fraction of BMMC samples. Our preliminary analysis of scRNA-seq of 6 samples treated by the same first-line therapies (RVD but without ASCT) revealed that plasma cells (PCs) from two patients (MMRF2550 and MMRF2562) with chromosome 13q deletion clustered together, whereas PCs from a patient (MMRF2187) with t(8;14) Myc translocation formed a distinct cluster. Interestingly, PCs from a patient (MMRF2271) with both chromosome 13q deletion and t(8;14) formed another independent cluster. These observations highlight the important role of genetic drivers in transcriptome profiles of plasma cells. In addition, we further investigated how progression features correlate with the immune microenvironment and identified differentially expressed genes between NK cells from RPs versus those from NPs, which could potentially serve as MM progression markers. Interestingly, several cytotoxicity genes are significantly upregulated in FPs, such as GZMB and KLRB1 (log Fold Change=1.44, and 8.41 respectively). Overall, our preliminary results provide a small glimpse of the interconnected nature of driver genetic alterations and progression features in MM tumor and TME. This study will provide a sufficiently broad, deep, and diverse vast dataset for accurately characterizing MM at single-cell resolution to help interrogate how genetic alterations and disease progression interplay MM tumor and TME. We hope this study could identify novel candidate targets for therapeutic approaches, and ultimately stratify patients by risk of progression for early intervention in the clinic. Disclosures Kumar: Roche-Genentech: Consultancy, Research Funding; Merck: Research Funding; Takeda: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; KITE: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Amgen: Consultancy, Research Funding; Novartis: Research Funding; Abbvie: 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; Beigene: Consultancy; Carsgen: Research Funding; Bluebird Bio: Consultancy; Astra-Zeneca: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Celgene: Membership on an entity's Board of Directors or advisory committees, Research Funding; Oncopeptides: Consultancy; Tenebio: Research Funding; BMS: Consultancy, Research Funding; Antengene: Consultancy, Honoraria; Adaptive: Membership on an entity's Board of Directors or advisory committees, Research Funding; Sanofi: Research Funding. Oh: Abbvie: Membership on an entity's Board of Directors or advisory committees; Blueprint Medicines: Membership on an entity's Board of Directors or advisory committees; Celgene Bristol Myers Squibb: Membership on an entity's Board of Directors or advisory committees; Constellation: Membership on an entity's Board of Directors or advisory committees; CTI Biopharma: Membership on an entity's Board of Directors or advisory committees; Disc Medicine: Membership on an entity's Board of Directors or advisory committees; Geron: Membership on an entity's Board of Directors or advisory committees; Incyte: Membership on an entity's Board of Directors or advisory committees; Kartos Therapeutics: Membership on an entity's Board of Directors or advisory committees; PharamaEssentia: Membership on an entity's Board of Directors or advisory committees; Sierra Oncology: Membership on an entity's Board of Directors or advisory committees. Vij: BMS: Research Funding; Takeda: Honoraria, Research Funding; Sanofi: Honoraria, Research Funding; BMS: Honoraria; GSK: Honoraria; Oncopeptides: Honoraria; Karyopharm: Honoraria; CareDx: Honoraria; Legend: Honoraria; Biegene: Honoraria; Adaptive: Honoraria; Harpoon: Honoraria. Avigan: Celgene: Membership on an entity's Board of Directors or advisory committees, Research Funding; Pharmacyclics: Research Funding; Kite Pharma: Consultancy, Research Funding; Juno: Membership on an entity's Board of Directors or advisory committees; Partner Tx: Membership on an entity's Board of Directors or advisory committees; Karyopharm: Membership on an entity's Board of Directors or advisory committees; Bristol-Myers Squibb: Membership on an entity's Board of Directors or advisory committees; Aviv MedTech Ltd: Membership on an entity's Board of Directors or advisory committees; Takeda: Membership on an entity's Board of Directors or advisory committees; Legend Biotech: Membership on an entity's Board of Directors or advisory committees; Chugai: Membership on an entity's Board of Directors or advisory committees; Janssen: Consultancy; Parexcel: Consultancy; Takeda: Consultancy; Sanofi: Consultancy.

Objectives: Multicenter data on the characteristics and outcomes of children hospitalized with coronavirus disease 2019 are limited. Our objective was to describe the characteristics, ICU admissions, and outcomes among children hospitalized with coronavirus disease 2019 using Society of Critical Care Medicine Discovery Viral Infection and Respiratory Illness Universal Study: Coronavirus Disease 2019 registry. Design: Retrospective study. Setting: Society of Critical Care Medicine Viral Infection and Respiratory Illness Universal Study (Coronavirus Disease 2019) registry. Patients: Children (< 18 yr) hospitalized with coronavirus disease 2019 at participating hospitals from February 2020 to January 2021. Interventions: None. Measurements and Main Results: The primary outcome was ICU admission. Secondary outcomes included hospital and ICU duration of stay and ICU, hospital, and 28-day mortality. A total of 874 children with coronavirus disease 2019 were reported to Viral Infection and Respiratory Illness Universal Study registry from 51 participating centers, majority in the United States. Median age was 8 years (interquartile range, 1.25–14 yr) with a male:female ratio of 1:2. A majority were non-Hispanic (492/874; 62.9%). Median body mass index (n = 817) was 19.4 kg/m2 (16–25.8 kg/m2), with 110 (13.4%) overweight and 300 (36.6%) obese. A majority (67%) presented with fever, and 43.2% had comorbidities. A total of 238 of 838 (28.2%) met the Centers for Disease Control and Prevention criteria for multisystem inflammatory syndrome in children, and 404 of 874 (46.2%) were admitted to the ICU. In multivariate logistic regression, age, fever, multisystem inflammatory syndrome in children, and pre-existing seizure disorder were independently associated with a greater odds of ICU admission. Hospital mortality was 16 of 874 (1.8%). Median (interquartile range) duration of ICU (n = 379) and hospital (n = 857) stay were 3.9 days (2–7.7 d) and 4 days (1.9–7.5 d), respectively. For patients with 28-day data, survival was 679 of 787, 86.3% with 13.4% lost to follow-up, and 0.3% deceased. Conclusions: In this observational, multicenter registry of children with coronavirus disease 2019, ICU admission was common. Older age, fever, multisystem inflammatory syndrome in children, and seizure disorder were independently associated with ICU admission, and mortality was lower among children than mortality reported in adults.

Abstract BackgroundAlterations in RNA splicing are associated with different malignancies including leukemia, lymphoma, and solid tumors. The RNA splicing modulators such as FD-895 and pladienolide B have been investigated in different malignancies to target/modulate spliceosome for therapeutic purpose. MethodsThe different cell lines were screened using RNA splicing modulator to test in vitro cytotoxicity as well as ability to modulate RNA splicing capability via induction of intron retention (using RT-PCR and qPCR) was assessed. The Cignal Finder Reporter Array was used to screen the HeLa cell line to know the pathway affected the most by the splice modulators. Further the candidates associated with the pathways were validated at protein levels using western blot assay and gene-gene interaction study were carried out using GeneMANIA.ResultsWe show that FD-895 and pladienolide B have more apoptotic activities than conventional chemotherapy in different solid tumors. We found FD-895 or pladienolide B modulate Wnt signaling pathways and mRNA splicing. We showed that FD-895 or pladienolide B significantly down regulates not only Wnt signaling pathway-associated transcripts (GSK3β and LRP5), but also both transcript and proteins including LEF1, CCND1, LRP6, and pLRP6. ConclusionThese results indicate FD-895 and pladienolide B inhibit Wnt signaling by impeding with phosphorylation of LRP6 and modulates mRNA splicing through induction of intron retention in cervical cancer cells.

Background and Objective The Alberta Stroke Program Early CT Score (ASPECTS) is a validated grading system to assess ischemic changes on CT in acute ischemic stroke. Magnetic resonance imaging with diffusion weighted imaging (DWI) sequence is commonly used to identify the final ischemic changes. We examined the difference between the relationship of NIHSS at admission and ASPECT score calculated using CT scan versus MRI DWI sequence. Methods We conducted a retrospective analysis of prospectively collected data from 99 cases of acute ischemic stroke treated with IV rt-PA by time criteria, admitted to Mayo Clinic from March, 2002 through June, 2011. CT head at 24 hours and MRI DWI sequence were used to assign ASPECT score. We dichotomized ASPECTS (categorized as 0 to 7 versus 8 to 10) and favorable patient outcome at 3 month (modified Rankin score less than equal to 2 and more than 2). Univariate analysis including t-test, Chi-square, and Fisher Exact test was used when appropriate. Results Mean age was 70±14 years. Mean admission NIHSS score was 8±4. DWI ASPECTS (p<0.001) and CT ASPECTS (p=0.127) were inversely associated with admission NIHSS. Higher (8-10) CT ASPECTS (p=0.001) or DWI ASPECTS (p=0.002) were associated with good outcome (mRS ≤2) at 3 months. Sensitivity, specificity, positive predictive value and negative predictive value for good outcome identified by CT ASPECTS versus DWI ASPECTS were 81% vs 52%, 54% vs 54%, 83% vs 59% and 50% vs 47% respectively. Conclusion CT and MRI DWI are comparably useful to calculate the ASPECTS for estimation of functional outcome, but CT scan at 24 hours may be more sensitive for the prediction of good recovery.

INTRODUCTION After aneurysm treatment, patients with subarachnoid hemorrhage (SAH) are typically kept in the intensive care unit (ICU) to be monitored for signs of delayed cerebral ischemia (DCI). Identifying patients at very low risk of developing this complication who could be safely transferred out of the ICU early could have a very positive financial impact. METHOD We analyzed 307 consecutive patients admitted to the intensive care unit from October 2001 to June 2011. Demographic, clinical and neuroimaging data were recorded. The relationship between these variables and absent DCI was analyzed using univariate and multivariate logistic regression models. RESULTS DCI did not develop in 169 patients (57.9%). Among factors at admission, age ≥ 68 years (p=0.0003; OR 3.16, 95% CI 1.67-6.39), WFNS I-III at presentation (p= 0.0003;OR 2.73, 95% CI 1.57-4.79), WFNS I-III at worst (p= 0.0003; OR 2.39, 95% CI 1.48-3.87), WFNS I-III post resuscitation (p= 0.0006; OR 2.85 95% 1.56-5.32), modified Fisher grade 1-2 (p= 0.0021; OR 2.43, 95% CI 1.37-4.47), absence of intracranial hematoma (p= 0.0042; OR 2.26, 95% CI 1.29-4.01), absence of intraventricular hematoma (p=0.045; OR 1.61, 95% CI 1.01-2.58), absence of cerebrospinal fluid diversion (p= 0.0002; OR 2.54, 95% CI 1.56-4.20) and aneurysm in the posterior circulation (p=0.0253; OR 1.74, 95% CI 1.07-2.87) were associated with absence of DCI. On multivariate analysis, a model including age ≥ 68 years (p= 0.0003; OR 3.23, 95% CI 1.68-6.62), WFNS at presentation I-III (p= 0.0092; OR 2.18, 95% CI 1.21-3.99) and modified Fisher grade 1-2 (p=0.02; OR 2.04, 95% CI 1.11-3.89) was independently uniformly predictive of the absence of DCI (specificity and positive predictive value of 100%; p<0.0001). CONCLUSION We propose a new model that can reliably identify a group of patients with aneurysmal SAH who have very low risk of developing DCI. These patients could be candidates for early transfer to the general ward after treatment of the ruptured aneurysm.