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Patients with B-lymphoid malignancies have been consistently identified as a population at high risk of severe COVID-19. Whether this is exclusively due to cancer-related deficits in humoral and cellular immunity, or whether risk of severe COVID-19 is increased by anticancer therapy, is uncertain. Using data derived from the COVID-19 and Cancer Consortium (CCC19), we show that patients treated for B-lymphoid malignancies have an increased risk of severe COVID-19 compared with control populations of patients with non–B-lymphoid malignancies. Among patients with B-lymphoid malignancies, those who received anticancer therapy within 12 months of COVID-19 diagnosis experienced increased COVID-19 severity compared with patients with non–recently treated B-lymphoid malignancies, after adjustment for cancer status and several other prognostic factors. Our findings suggest that patients recently treated for a B-lymphoid malignancy are at uniquely high risk for severe COVID-19. Significance: Our study suggests that recent therapy for a B-lymphoid malignancy is an independent risk factor for COVID-19 severity. These findings provide rationale to develop mitigation strategies targeted at the uniquely high-risk population of patients with recently treated B-lymphoid malignancies. This article is highlighted in the In This Issue feature, p. 171

Patients with B-lymphoid malignancies have been consistently identified as a population at high risk of severe COVID-19. Whether this is exclusively due to cancer-related deficits in humoral and cellular immunity, or whether risk of severe COVID-19 is increased by anticancer therapy, is uncertain. Using data derived from the COVID-19 and Cancer Consortium (CCC19), we show that patients treated for B-lymphoid malignancies have an increased risk of severe COVID-19 compared with control populations of patients with non-B-lymphoid malignancies. Among patients with B-lymphoid malignancies, those who received anticancer therapy within 12 months of COVID-19 diagnosis experienced increased COVID-19 severity compared with patients with non-recently treated B-lymphoid malignancies, after adjustment for cancer status and several other prognostic factors. Our findings suggest that patients recently treated for a B-lymphoid malignancy are at uniquely high risk for severe COVID-19.SignificanceOur study suggests that recent therapy for a B-lymphoid malignancy is an independent risk factor for COVID-19 severity. These findings provide rationale to develop mitigation strategies targeted at the uniquely high-risk population of patients with recently treated B-lymphoid malignancies. This article is highlighted in the In This Issue feature, p. 171.

Table S1. The improvement in efficiency in RnaSeqSampleSize package. Table S2. Estimated sample size for RNA-Seq experiments in different cancer types by single parameter method. Table S3. Estimated sample size for RNA-Seq experiments in different cancer types by real data distribution based method. For each cancer type, we used the related TCGA dataset to estimate the read count and dispersion distribution. Table S4. Estimated sample size for RNA-Seq experiments in different cancer types by real data distribution based method, only the genes in interested KEGG pathway were considered. Table S5. Estimated sample size for RNA-Seq experiments in different cancer types by real data distribution based method, only the genes in KEGG pathway ID 05200 (Pathways in Cancer) were considered. Figure S1. A screen shot of user interface of RnaSeqSampleSize package. (DOCX 217 kb)

BackgroundHospitalized patients with COVID-19have increased risks of venous (VTE) and arterial thromboembolism (ATE). Active cancer diagnosis and treatment are well-known risk factors; however, a risk assessment model (RAM) for VTE in patients with both cancer and COVID-19 is lacking.ObjectivesTo assess the incidence of and risk factors for thrombosis in hospitalized patients with cancer and COVID-19.MethodsAmong patients with cancer in the COVID-19 and Cancer Consortium registry (CCC19) cohort study, we assessed the incidence of VTE and ATE within 90days of COVID-19-associated hospitalization. A multivariable logistic regression model specifically for VTE was built using a priori determined clinical risk factors. A simplified RAM was derived and internally validated using bootstrap.ResultsFrom March 17, 2020 to November 30, 2020, 2804hospitalized patients were analyzed. The incidence of VTE and ATE was 7.6% and 3.9%, respectively. The incidence of VTE, but not ATE, was higher in patients receiving recent anti-cancer therapy. A simplified RAM for VTE was derived and named CoVID-TE (Cancer subtype high to very-high risk by original Khorana score +1, VTE history +2, ICU admission +2, D-dimer elevation +1, recent systemic anti-cancer Therapy +1, and non-Hispanic Ethnicity +1). The RAM stratified patients into two cohorts (low-risk, 0-2 points, n=1423 vs. high-risk, 3+ points, n=1034) where VTE occurred in 4.1% low-risk and 11.3% high-risk patients (c statistic 0.67, 95% confidence interval 0.63-0.71). The RAM performed similarly well in subgroups of patients not on anticoagulant prior to admission and moderately ill patients not requiring direct ICU admission.ConclusionsHospitalized patients with cancer and COVID-19have elevated thrombotic risks. The CoVID-TE RAM for VTE prediction may help real-time data-driven decisions in this vulnerable population.

Supplemental material, sj-pdf-1-aor-10.1177_0003489421995283 for Association of Social Determinants of Health with Time to Diagnosis and Treatment Outcomes in Idiopathic Subglottic Stenosis by Jaclyn Lee, Li-Ching Huang, Lynn D. Berry, Catherine Anderson, Milan R. Amin, Michael S. Benninger, Joel H. Blumin, Jonathan M. Bock, Paul C. Bryson, Paul F. Castellanos, Sheau-Chiann Chen, Matthew S. Clary, Seth M. Cohen, Brianna K. Crawley, Seth H. Dailey, James J. Daniero, Alessandro de. Alarcon, Donald T. Donovan, Eric S. Edell, Dale C. Ekbom, Daniel S. Fink, Ramon A. Franco, C. Gaelyn Garrett, Elizabeth A. Guardiani, Alexander T. Hillel, Henry T. Hoffman, Norman D. Hogikyan, Rebecca J. Howell, Lena K. Hussain, Michael M. Johns, Jan L. Kasperbauer, Sid M. Khosla, Cheryl Kinnard, Robbi A. Kupfer, Alexander J. Langerman, Robert J. Lentz, Robert R. Lorenz, David G. Lott, Anne S. Lowery, Samir S. Makani, Fabien Maldonado, Kyle Mannion, Laura Matrka, Andrew J. McWhorter, Albert L. Merati, Matthew Mori, James L. Netterville, Karla O’Dell, Julina Ongkasuwan, Gregory N. Postma, Lindsay S. Reder, Sarah L. Rohde, Brent E. Richardson, Otis B. Rickman, Clark A. Rosen, Michael J. Rutter, Guri S. Sandhu, Joshua S. Schindler, G. Todd Schneider, Rupali N. Shah, Andrew G. Sikora, Robert J. Sinard, Marshall E. Smith, Libby J. Smith, Ahmed M.S. Soliman, Sigríður Sveinsdóttir, Douglas J. Van Daele, David Veivers, Mepi FRACS, Sunil P. Verma, Paul M. Weinberger, Philip A. Weissbrod, Christopher T. Wootten, Yu Shyr, David O. Francis and Alexander Gelbard in Annals of Otology, Rhinology & Laryngology

BackgroundData on patients with COVID-19 who have cancer are lacking. Here we characterise the outcomes of a cohort of patients with cancer and COVID-19 and identify potential prognostic factors for mortality and severe illness.MethodsIn this cohort study, we collected de-identified data on patients with active or previous malignancy, aged 18 years and older, with confirmed severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection from the USA, Canada, and Spain from the COVID-19 and Cancer Consortium (CCC19) database for whom baseline data were added between March 17 and April 16, 2020. We collected data on baseline clinical conditions, medications, cancer diagnosis and treatment, and COVID-19 disease course. The primary endpoint was all-cause mortality within 30 days of diagnosis of COVID-19. We assessed the association between the outcome and potential prognostic variables using logistic regression analyses, partially adjusted for age, sex, smoking status, and obesity. This study is registered with ClinicalTrials.gov, NCT04354701, and is ongoing.FindingsOf 1035 records entered into the CCC19 database during the study period, 928 patients met inclusion criteria for our analysis. Median age was 66 years (IQR 57-76), 279 (30%) were aged 75 years or older, and 468 (50%) patients were male. The most prevalent malignancies were breast (191 [21%]) and prostate (152 [16%]). 366 (39%) patients were on active anticancer treatment, and 396 (43%) had active (measurable) cancer. At analysis (May 7, 2020), 121 (13%) patients had died. In logistic regression analysis, independent factors associated with increased 30-day mortality, after partial adjustment, were: increased age (per 10 years; partially adjusted odds ratio 1·84, 95% CI 1·53-2·21), male sex (1·63, 1·07-2·48), smoking status (former smoker vs never smoked: 1·60, 1·03-2·47), number of comorbidities (two vs none: 4·50, 1·33-15·28), Eastern Cooperative Oncology Group performance status of 2 or higher (status of 2 vs 0 or 1: 3·89, 2·11-7·18), active cancer (progressing vs remission: 5·20, 2·77-9·77), and receipt of azithromycin plus hydroxychloroquine (vs treatment with neither: 2·93, 1·79-4·79; confounding by indication cannot be excluded). Compared with residence in the US-Northeast, residence in Canada (0·24, 0·07-0·84) or the US-Midwest (0·50, 0·28-0·90) were associated with decreased 30-day all-cause mortality. Race and ethnicity, obesity status, cancer type, type of anticancer therapy, and recent surgery were not associated with mortality.InterpretationAmong patients with cancer and COVID-19, 30-day all-cause mortality was high and associated with general risk factors and risk factors unique to patients with cancer. Longer follow-up is needed to better understand the effect of COVID-19 on outcomes in patients with cancer, including the ability to continue specific cancer treatments.FundingAmerican Cancer Society, National Institutes of Health, and Hope Foundation for Cancer Research.

Figure S6. Venn diagram of enhancer-gene associations determined by the closest TSS, within 50Â k distance (50Â kb) and 4DGenome (4D) methods. (PPTX 34Â kb)

Figure S5. The length distribution of identified active enhancers in the mouse liver. (PPTX 60Â kb)

Table S1. The improvement in efficiency in RnaSeqSampleSize package. Table S2. Estimated sample size for RNA-Seq experiments in different cancer types by single parameter method. Table S3. Estimated sample size for RNA-Seq experiments in different cancer types by real data distribution based method. For each cancer type, we used the related TCGA dataset to estimate the read count and dispersion distribution. Table S4. Estimated sample size for RNA-Seq experiments in different cancer types by real data distribution based method, only the genes in interested KEGG pathway were considered. Table S5. Estimated sample size for RNA-Seq experiments in different cancer types by real data distribution based method, only the genes in KEGG pathway ID 05200 (Pathways in Cancer) were considered. Figure S1. A screen shot of user interface of RnaSeqSampleSize package. (DOCX 217Â kb)

Figure S8. PRO-seq transcriptional levels around transcription termination sites (TTSs). (PPTX 108Â kb)

Figure S3. The effect of Hdac3 deletion on gene body transcription. (a) Heatmap of log2-transformed fold changes of RNA polymerases ±5 kb from TSSs with 200 bp bin size for all active genes comparing Hdac3 KO to WT mouse livers. Genes were ranked according to changes of gene body read densities. gb up: up-regulated in gene body regions; gb down: down-regulated in gene body regions. (b) Comparative analysis of up-regulated (top) and down-regulated (bottom) genes on P14 and P17 by Gene Set Enrichment Analysis (GSEA). Differentially regulated genes were determined based on gene body densities on P14 and by microarray on P17. (PPTX 194 kb)

Figure S9. Histograms showing histone modification enrichment and GRO-cap transcriptional levels around intergenic bidirectional transcripts vs. unidirectional transcripts. (PPTX 175Â kb)

Figure S10. Illustration of strategies to identify enhancers and enhancer centers. (PPTX 42Â kb)

Figure S1. Examples of enhancers identified by NRSA, dREG and groHMM in K562 GRO-seq data. (PPTX 117Â kb)

Pediatric oncology patients are at an increased risk of invasive bacterial infection due to immunosuppression. The risk of such infection in the absence of severe neutropenia (absolute neutrophil count ≥ 500/μL) is not well established and a validated prediction model for blood stream infection (BSI) risk offers clinical usefulness.A 6-site retrospective external validation was conducted using a previously published risk prediction model for BSI in febrile pediatric oncology patients without severe neutropenia: the Esbenshade/Vanderbilt (EsVan) model. A reduced model (EsVan2) excluding 2 less clinically reliable variables also was created using the initial EsVan model derivative cohort, and was validated using all 5 external validation cohorts. One data set was used only in sensitivity analyses due to missing some variables.From the 5 primary data sets, there were a total of 1197 febrile episodes and 76 episodes of bacteremia. The overall C statistic for predicting bacteremia was 0.695, with a calibration slope of 0.50 for the original model and a calibration slope of 1.0 when recalibration was applied to the model. The model performed better in predicting high-risk bacteremia (gram-negative or Staphylococcus aureus infection) versus BSI alone, with a C statistic of 0.801 and a calibration slope of 0.65. The EsVan2 model outperformed the EsVan model across data sets with a C statistic of 0.733 for predicting BSI and a C statistic of 0.841 for high-risk BSI.The results of this external validation demonstrated that the EsVan and EsVan2 models are able to predict BSI across multiple performance sites and, once validated and implemented prospectively, could assist in decision making in clinical practice. Cancer 2017;123:3781-3790. © 2017 American Cancer Society.

Chemotherapies Utilized in Sequential Boost Cohort. Table S2. Acute toxicity in sequential and integrated boost cohorts among patients who received 5 or more cycles of concurrent chemotherapy. (DOCX 12 kb)

Opinion dynamics, aiming to understand the evolution of collective behavior through various interaction mechanisms of opinions, represents one of the most challenges in natural and social science. To elucidate this issue clearly, binary opinion model becomes a useful framework, where agents can take an independent opinion. Inspired by the realistic observations, here we propose two basic interaction mechanisms of binary opinion model: one is the so-called BSO model in which players benefit from holding the same opinion; the other is called BDO model in which players benefit from taking different opinions. In terms of these two basic models, the synthetical effect of opinion preference and equivocators on the evolution of binary opinion is studied under the framework of evolutionary game theory (EGT), where the replicator equation (RE) is employed to mimick the evolution of opinions. By means of numerous simulations, we show the theoretical equilibrium states of binary opinion dynamics, and mathematically analyze the stability of each equilibrium state as well., Comment: 15 pages, 6 figures

Background Identification of expression alternations between early and late stage cancers is helpful for understanding cancer development and progression. Much research has been done focusing on stage-dependent gene expression profiles. In contrast, relatively fewer studies on isoform expression profiles have been performed due to the difficulty of quantification and noisy splicing. Here we conducted both gene- and isoform-level analysis on RNA-seq data of 234 stage I and 81 stage IV kidney renal clear cell carcinoma patients, aiming to uncover the stage-dependent expression signatures and investigate the advantage of isoform expression profiling for identifying advanced stage cancers and predicting clinical outcome. Results Both gene and isoform expression signatures are useful for distinguishing cancer stages. They provide common and unique information associated with cancer progression and metastasis. Combining gene and isoform signatures even improves the classification performance and reveals additional important biological processes, such as angiogenesis and TGF−beta signaling pathway. Moreover, expression abundance of a number of genes and isoforms is predictive of the risk of cancer death in an independent dataset, such as gene and isoform expression of ITPKA, the expression of a functional important isoform of UPS19. Conclusion Isoform expression profiling provides unique and important information which cannot be detected by gene expression profiles. Combining gene and isoform expression signatures helps to identify advanced stage cancers, predict clinical outcome, and present a comprehensive view of cancer development and progression.

Tumor cell senescence is often induced by cancer therapeutics. Senescent cells secrete many proteins that may affect both malignant and non-malignant components of the tumor. The goal of this study was to determine how therapy-induced senescence (TIS) affects the immune microenvironment in melanoma. We used inhibitors of cell cycle kinases AURKA and CDK4/6 (AURKAi, CDK4/6i) to induce senescence in melanoma tumors. RNA sequencing analysis of AURKAi-treated syngeneic mouse tumors demonstrated that response to AURKA inhibition was strongly associated with induction of an immune transcriptome (p = 3.5E-29). Immunofluorescent staining and flow cytometric analysis of digested tumors showed correlation between the numbers of tumor-infiltrating leukocytes (TILs) and AURKAi response (Spearman r = -0.87, p Citation Format: Anna E. Vilgelm, C Andrew Johnson, Nripesh Prasad, Jinming Yang, Sheau-Chiann Chen, Gregory D. Ayers, Jeffrey A. Sosman, Jeffrey A. Ecsedy, Shyr Yu, Shawn E. Levy, Ann Richmond. The link between therapy-induced senescence and anti-tumor immune microenvironment in melanoma. [abstract]. In: Proceedings of the 107th Annual Meeting of the American Association for Cancer Research; 2016 Apr 16-20; New Orleans, LA. Philadelphia (PA): AACR; Cancer Res 2016;76(14 Suppl):Abstract nr 5126.

Figure S7. Gene body transcriptional changes between biological replicates after normalization. gb: gene body regions: gbd: read density in gene body regions; WT1: wildtype liver replicate 1; WT2; wildtype liver replicate 2; KO1: Hdac3-deleted liver replicate 1; KO2: Hdac3-deleted liver replicate 2. (PPTX 47Â kb)

Figure S2. Transcriptional levels of common and unique enhancers identified in K562 GRO/PRO-seq data. (PPTX 88Â kb)

Figure S2. Transcriptional levels of common and unique enhancers identified in K562 GRO/PRO-seq data. (PPTX 88Â kb)

Figure S4. The relationship of transcriptional changes between promoter proximal levels (x-axis) and gene body levels (y-axis) in mice-liver Hdac3 knockout (green), triptolide (purple) and flavopiridol (blue) treatment. (PPTX 711Â kb)

Figure S7. Gene body transcriptional changes between biological replicates after normalization. gb: gene body regions: gbd: read density in gene body regions; WT1: wildtype liver replicate 1; WT2; wildtype liver replicate 2; KO1: Hdac3-deleted liver replicate 1; KO2: Hdac3-deleted liver replicate 2. (PPTX 47Â kb)

Inhibition of proliferation in estrogen receptor-positive (ER+) breast cancers after short-term antiestrogen therapy correlates with long-term patient outcome. We profiled 155 ER+/human epidermal growth factor receptor 2-negative (HER2-) early breast cancers from 143 patients treated with the aromatase inhibitor letrozole for 10 to 21 days before surgery. Twenty-one percent of tumors remained highly proliferative, suggesting that these tumors harbor alterations associated with intrinsic endocrine therapy resistance. Whole-exome sequencing revealed a correlation between 8p11-12 and 11q13 gene amplifications, including FGFR1 and CCND1, respectively, and high Ki67. We corroborated these findings in a separate cohort of serial pretreatment, postneoadjuvant chemotherapy, and recurrent ER+ tumors. Combined inhibition of FGFR1 and CDK4/6 reversed antiestrogen resistance in ER+FGFR1/CCND1 coamplified CAMA1 breast cancer cells. RNA sequencing of letrozole-treated tumors revealed the existence of intrachromosomal ESR1 fusion transcripts and increased expression of gene signatures indicative of enhanced E2F-mediated transcription and cell cycle processes in cancers with high Ki67. These data suggest that short-term preoperative estrogen deprivation followed by genomic profiling can be used to identify druggable alterations that may cause intrinsic endocrine therapy resistance.