Citations

Abstract Purpose: Reducing colorectal cancer incidence and mortality through early detection would improve efficacy if targeted. We developed a colorectal cancer risk prediction model incorporating personal, family, genetic, and environmental risk factors to enhance prevention. Methods: A familial risk profile (FRP) was calculated to summarize individuals' risk based on detailed cancer family history (FH), family structure, probabilities of mutation in major colorectal cancer susceptibility genes, and a polygenic component. We developed risk models, including individuals' FRP or binary colorectal cancer FH, and colorectal cancer risk factors collected at enrollment using population-based colorectal cancer cases (N = 4,445) and controls (N = 3,967) recruited by the Colon Cancer Family Registry Cohort (CCFRC). Model validation used CCFRC follow-up data for population-based (N = 12,052) and clinic-based (N = 5,584) relatives with no cancer history at recruitment to assess model calibration [expected/observed rate ratio (E/O)] and discrimination [area under the receiver-operating-characteristic curve (AUC)]. Results: The E/O [95% confidence interval (CI)] for FRP models for population-based relatives were 1.04 (0.74–1.45) for men and 0.86 (0.64–1.20) for women, and for clinic-based relatives were 1.15 (0.87–1.58) for men and 1.04 (0.76–1.45) for women. The age-adjusted AUCs (95% CI) for FRP models for population-based relatives were 0.69 (0.60–0.78) for men and 0.70 (0.62–0.77) for women, and for clinic-based relatives were 0.77 (0.69–0.84) for men and 0.68 (0.60–0.76) for women. The incremental values of AUC for FRP over FH models for population-based relatives were 0.08 (0.01–0.15) for men and 0.10 (0.04–0.16) for women, and for clinic-based relatives were 0.11 (0.05–0.17) for men and 0.11 (0.06–0.17) for women. Conclusions: Both models calibrated well. The FRP-based model provided better risk stratification and risk discrimination than the FH-based model. Impact: Our findings suggest detailed FH may be useful for targeted risk-based screening and clinical management.

Alcoholic hepatitis (AH) is a severe inflammatory liver disease that develops in some heavy drinkers. The immune system in patients with AH is hyperactive and yet dysfunctional. Here, we investigated whether this immune‐dysregulated state is related to the alcoholic impact on immune checkpoints (ICPs). We used multiplex immunoassays and enzyme‐linked immunosorbent assay to quantify plasma levels of 18 soluble ICPs (sICPs) from 81 patients with AH, 65 heavy drinkers without liver diseases (HDCs), and 39 healthy controls (HCs) at baseline, 33 patients with AH and 32 HDCs at 6‐month follow‐up, and 18 patients with AH and 29 HDCs at 12‐month follow‐up. We demonstrated that baseline levels of 6 sICPs (soluble T‐cell immunoglobulin and mucin domain 3 [sTIM‐3], soluble cluster of differentiation [sCD]27, sCD40, soluble Toll‐like receptor‐2 [sTLR‐2], soluble herpesvirus entry mediator [sHVEM], and soluble lymphotoxin‐like inducible protein that competes with glycoprotein D for herpes virus entry on T cells [sLIGHT]) were up‐regulated, while 11 sICPs (soluble B‐ and T‐lymphocyte attenuator [sBTLA], sCD160, soluble cytotoxic T‐lymphocyte‐associated protein 4 [sCTLA‐4], soluble lymphocyte‐activation gene 3 [sLAG‐3], soluble programmed death 1 [sPD‐1], sPD ligand 1 [sPD‐L1], sCD28, soluble glucocorticoid‐induced tumor necrosis factor receptor‐related protein [sGITR], sGITR ligand [sGITRL], sCD80, and inducible T‐cell costimulator [sICOS]) were down‐regulated in patients with AH compared to HDCs. The up‐regulated sICPs except sLIGHT and down‐regulated sCD80, sCD160, sCTLA‐4, and sLAG‐3 correlated positively or negatively with AH disease severity, bacterial translocation, and inflammatory factors. At follow‐up, abstinent patients with AH still had higher levels of several sICPs compared to HDCs. We also compared expression of 10 membrane‐bound ICPs (mICPs) on peripheral blood mononuclear cells (PBMCs) from patients with AH and HCs by flow cytometry and found that several mICPs were dysregulated on blood cells from patients with AH. The function and regulation of sICPs and mICPs were studied using PBMCs from patients with AH and HCs. Recombinant sHVEM affected tumor necrosis factor (TNF)‐α and interferon‐γ production by T cells from patients with AH and HCs. Conclusion: Both sICPs and mICPs were dysregulated in patients with AH, and alcohol abstinence did not fully reverse these abnormalities. The HVEM axis plays a role in regulating T‐cell function in patients with AH.

AbstractBackground: Retinoblastoma (RB) is the most frequent pediatric retinal tumor. In the present study, to elucidate chemoresistance mechanisms and identify potential biomarkers in RB, we utilized RNA sequencing (RNAseq) technological platforms to reveal transcriptome profiles and identify any differentially expressed genes (DEGs) between an etoposide drug-resistant subline (Y79/EDR) and parental Y79 cells. Methods: To test whether Y79/EDR cells showed resistance to antineoplastic agents for RB, we treated the cells with etoposide, carboplatin and vincristine and analyzed them with a Cell Counting Kit-8 (CCK-8). Y79/EDR and parental Y79 cells were used for RNAseq and bioinformatics analysis to enable a genome-wide review of DEGs between the two lines using the DESeq R package (1.10.1). Then, DEG enrichment in Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways was analyzed with KOBAS software. Next, real-time quantitative reverse transcription polymerase chain reaction (real time QRT-PCR) and cytotoxicity assays were performed to experimentally and functionally validate the identified candidate biomarkers. Results: Y79/EDR cells showed resistance to etoposide, carboplatin and vincristine at different concentrations. In total, 524 transcripts were differentially expressed in Y79/EDR cells based on analysis of fragments per kilobase of transcript per million fragments mapped (FPKM); among these, 57 genes were downregulated and 467 genes were upregulated in Y79/EDR cells compared to parental Y79 cells. We selected candidate DEGs, including ARHGAP9 , HIST1H4H , RELN , DDIT4 , HK2 , STC1 and PFKFB4, for mRNA expression validation with real time QRT-PCR assays and found that the expression levels determined by real time QRT-PCR were consistent with the RNAseq data. Further studies involving downregulation of ARHGAP9 with a specific siRNA showed that ARHGAP9 altered the cellular sensitivity of Y79 cells to etoposide and carboplatin.Conclusion: Our initial findings provided a genomic view of the transcription profiles of etoposide-induced acquired resistance in RB. Follow-up studies indicated that ARHGAP9 might be a chemoresistance biomarker in RB, providing insight into potential therapeutic targets for overcoming acquired chemoresistance in RB. These findings can aid in understanding and overcoming chemoresistance during treatment of RB in the clinic.

Mycoplasma hominis is a commensal in the genital tract of women and has been associated with urogenital and extragenital infections. However, central nervous system infections with this organism in adults are very rare. Here we describe the recovery of M. hominis from a brain abscess associated with a postpartum infection. Seroconversion to the isolated strain was detected by both a metabolic inhibition test and an immunoblotting assay. This case demonstrates the pathogenic potential of M. hominis and the need for rapid recognition of the organism so that appropriate chemotherapeutic intervention can occur.

ABSTRACT A branched-DNA (bDNA) signal amplification method was used to detect the mecA gene directly from blood culture broth growing staphylococci. BACTEC blood culture bottles with positive growth indices and containing staphylococcus-like organisms as shown by Gram stain were tested for the presence of the mecA gene. Comparison of test results was done among 225 patients (one blood culture from each patient). Compared with PCR, the sensitivity and specificity of the bDNA method are 100 and 99%, respectively. The bDNA test is carried out in a 96-well format and requires approximately 6 h to perform. Our preliminary results suggest that direct detection of the mecA gene by bDNA signal amplification is (i) sensitive enough to detect mecA directly from blood culture bottles without the requirement for subculture and (ii) as sensitive and specific as the PCR-based method.

ABSTRACT The identification of methicillin-resistant staphylococcus isolates in the clinical laboratory has typically been performed by using methods that detect phenotypic expression of resistance determinants. However, these methods may be difficult to interpret and some isolates do not express resistance until selective pressure is administered. Assays that detect genetic determinants are not subject to these limitations and have been effective in distinguishing isolates that are capable of expressing the resistance phenotype. In this study, a novel branched-DNA (bDNA) hybridization assay was used to test for the mecA gene in 416 clinical staphylococcal isolates. The results were compared with those obtained by a PCR-based assay and oxacillin disk diffusion. For 155 Staphylococcus aureus and 261 coagulase-negative Staphylococcus isolates, the bDNA assay and PCR results were 100% concordant. Among the S. aureus isolates, 20 were MecA + and 135 were MecA − . For the coagulase-negative staphylococci, 150 were MecA + and 111 were MecA − . The results from the genotypic detection methods were compared with those obtained by oxacillin disk diffusion. No discrepancies were detected among the S. aureus isolates; however, 10 coagulase-negative isolates were MecA + but oxacillin sensitive and 1 isolate was MecA − but oxacillin resistant. Oxacillin resistance was induced in 6 of the 10 MecA + isolates previously classified as oxacillin sensitive. These results suggest that the bDNA method described here is a sensitive and efficient method for detection of methicillin resistance in staphylococci and that genetic detection methods may be useful for detection of potential methicillin resistance in the clinical laboratory.

Abstract Background Although 20 pancreatic cancer susceptibility loci have been identified through genome-wide association studies in individuals of European ancestry, much of its heritability remains unexplained and the genes responsible largely unknown. Methods To discover novel pancreatic cancer risk loci and possible causal genes, we performed a pancreatic cancer transcriptome-wide association study in Europeans using three approaches: FUSION, MetaXcan, and Summary-MulTiXcan. We integrated genome-wide association studies summary statistics from 9040 pancreatic cancer cases and 12 496 controls, with gene expression prediction models built using transcriptome data from histologically normal pancreatic tissue samples (NCI Laboratory of Translational Genomics [n = 95] and Genotype-Tissue Expression v7 [n = 174] datasets) and data from 48 different tissues (Genotype-Tissue Expression v7, n = 74–421 samples). Results We identified 25 genes whose genetically predicted expression was statistically significantly associated with pancreatic cancer risk (false discovery rate < .05), including 14 candidate genes at 11 novel loci (1p36.12: CELA3B; 9q31.1: SMC2, SMC2-AS1; 10q23.31: RP11-80H5.9; 12q13.13: SMUG1; 14q32.33: BTBD6; 15q23: HEXA; 15q26.1: RCCD1; 17q12: PNMT, CDK12, PGAP3; 17q22: SUPT4H1; 18q11.22: RP11-888D10.3; and 19p13.11: PGPEP1) and 11 at six known risk loci (5p15.33: TERT, CLPTM1L, ZDHHC11B; 7p14.1: INHBA; 9q34.2: ABO; 13q12.2: PDX1; 13q22.1: KLF5; and 16q23.1: WDR59, CFDP1, BCAR1, TMEM170A). The association for 12 of these genes (CELA3B, SMC2, and PNMT at novel risk loci and TERT, CLPTM1L, INHBA, ABO, PDX1, KLF5, WDR59, CFDP1, and BCAR1 at known loci) remained statistically significant after Bonferroni correction. Conclusions By integrating gene expression and genotype data, we identified novel pancreatic cancer risk loci and candidate functional genes that warrant further investigation.

Abstract There is a need for new approaches and endpoints in oncology drug development, particularly with the advent of immunotherapies and the multiple drug combinations under investigation. Tumor dynamics modeling, a key component to oncology “model-informed drug development,” has shown a growing number of applications and a broader adoption by drug developers and regulatory agencies in the past years to support drug development and approval in a variety of ways. Tumor dynamics modeling is also being investigated in personalized cancer therapy approaches. These models and applications are reviewed and discussed, as well as the limitations and issues open for further investigations. A close collaboration between stakeholders like clinical investigators, statisticians, and pharmacometricians is warranted to advance clinical cancer therapeutics.

Multiple myeloma (MM) is a neoplasm thought to arise from a damaged germinal center B-cell that progresses to a plasma cell clone arising in bone marrow. MM comprises 20% of all hematologic cancer deaths. Persons of African ancestry (AA) have a 1.5 to 2-fold higher risk compared to individuals of European ancestry (EA). Genetically driven differences in hematopoiesis may lead to variation in the levels white blood cell (WBC) subsets which could, in turn, be associated with MM etiology. There are differences in genetic determinants of WBC traits between EA and AA populations, with possible implications for the racial disparity in risk. We tested the above hypothesis using Mendelian randomization (MR), an approach that leverages genetic determinants of specific traits (i.e. WBC counts) to estimate their effects on the risk of an outcome; and a transcriptome-wide association study (TWAS), which utilizes genetic predictors of gene expression to identify susceptibility genes. These analytic approaches were applied to data from the African American Multiple Myeloma Study (AAMMS) consisting of 1813 cases and 8871 AA cancer-free controls to examine how differences in heritable WBC gene expression profiles influence MM risk. Genetic determinants of variation in WBC subsets in AA were obtained from the literature and supplemented with new genome-wide association findings in AA subjects from the UK Biobank cohort (n=6108). Odds ratios (OR) for MM per 1 standard deviation (SD) increase in each WBC phenotype were estimated using independent (linkage disequilibrium (LD) r2<0.10) variants with P<10-6 as genetic instruments. Analyses based on variants associated with WBC traits in AA populations did not identify any statistically-significant associations between MM risk and WBC overall (p=0.81, using 15 SNPs) or subsets (lymphocytes, monocytes, eosinophils, neutrophils and basophils, p>0.05 for each). However, when we applied genetic determinants of WBC identified in 330,000 cancer-free EA UK Biobank participants (P<10-8, replication P<0.05, LD r2<0.05), a statistically significant inverse relationship emerged between increasing lymphocyte counts and MM risk (OR=0.80, 95% CI: 0.66-0.97, p=0.02, using 385 SNPs), as well as increasing basophil counts (OR=0.63, 95% CI: 0.41-0.96, p=0.03, using 140 SNPs). Next, we examined the association between WBC gene expression profiles and MM risk in AAMMS data. We applied published and validated ancestry-specific models developed using the PrediXcan approach, which leverage germline genetic and transcriptomic data from the Multi-Ethnic Study of Atherosclerosis (MESA) (Mogil et al. PMID: 30096133). The primary TWAS used gene expression models trained in AA subjects (n=233), with sensitivity analyses using models developed in AA and Hispanic subjects (n=585). The TWAS significance threshold was based on the number of genes with significant germline prediction models (p<0.05 and R2 ≥0.05) in AA, corresponding to P < 0.05/2700 = 1.85×10-5. The expression of two genes was significantly associated with MM risk: KANK1at 9p24.3 (P = 1.01×10-5) and DNAJC27at 2p23.3 (P = 1.56×10-5). KANK1is a candidate tumor suppressor gene for renal cell carcinoma and has recently been associated with MM risk in AA [Du, Blood 2017 130:3058]. Here we provide additional evidence for its role in MM etiology via gene expression-mediated mechanisms. DNAJC27 (previously known as RBJ) is a novel MM risk gene linked to constitutive activation of ERK in solid tumors. We also identified two suggestively associated genes: PRR14 (P = 1.34×10-4; combined AA-Hispanic sample: P = 1.56×10-6), which has been linked to MM risk in EA populations, and PARP16 (P = 9.46×10-5). To our knowledge this is the first study to comprehensively examine variation in WBC traits and gene expression profiles with respect to MM risk in AA. Our TWAS analysis leveraged data from the largest collection of genetic and gene expression data in AA, enabling ancestry-matched inference and identification of two novel risk genes. Although the limited availability of genetic instruments for WBC limited the power of MR analysis, findings using variants identified in European populations may offer some insight into trans-ethnic etiologic pathways and contribute to risk stratification strategies using genetic and blood cell count biomarkers. Future studies, particularly with MGUS-free controls, are needed to validate these results. Disclosures Song: Millennium Pharmaceuticals Inc: Employment. Rand:Ancestry.com: Employment. Ailawadhi:Cellectar: Research Funding; Janssen: Consultancy, Research Funding; Celgene: Consultancy; Pharmacyclics: Research Funding; Amgen: Consultancy, Research Funding; Takeda: Consultancy. Nooka:Takeda: Honoraria, Other: advisory board participation; Janssen: Honoraria, Other: advisory board participation; GSK: Honoraria, Other: advisory board participation; Spectrum pharmaceuticals: Honoraria, Other: advisory board participation; Adaptive technologies: Honoraria, Other: advisory board participation; Amgen: Honoraria, Other: advisory board participation; Celgene: Honoraria, Other: advisory board participation; BMS: Honoraria, Other: advisory board participation. Singhal:Bureau of Millennium/Takeda, Celgene, Janssen, Celgene, Bristol-Myers Squibb and Bluebird: Speakers Bureau. van Rhee:Takeda: Consultancy; Sanofi Genzyme: Consultancy; Castleman Disease Collaborative Network: Consultancy; EUSA: Consultancy; Adicet Bio: Consultancy; Kite Pharma: Consultancy; Karyopharm Therapeutics: Consultancy. Mehta:Millennium/Takeda, Celgene; stock in Celgene, Bristol-Myers Squibb and Bluebird: Speakers Bureau. Wolf:Takeda: Consultancy; Celgene: Consultancy; Novartis: Consultancy; Janssen: Consultancy; Amgen: Consultancy. Martin:Roche and Juno: Consultancy; Amgen, Sanofi, Seattle Genetics: Research Funding. Fiala:Incyte: Research Funding. Terebelo:Jannsen: Speakers Bureau; Celgene: Honoraria; Newland Medical Asociates: Employment. Anderson:Bristol-Myers Squibb: Other: Scientific Founder; Oncopep: Other: Scientific Founder; Amgen: Consultancy, Speakers Bureau; Janssen: Consultancy, Speakers Bureau; Takeda: Consultancy, Speakers Bureau; Celgene: Consultancy, Speakers Bureau; Sanofi-Aventis: Other: Advisory Board. Vij:Bristol-Myers Squibb: Honoraria, Research Funding; Celgene: Honoraria, Research Funding; Genentech: Honoraria; Janssen: Honoraria; Karyopharm: Honoraria; Sanofi: Honoraria; Takeda: Honoraria, Research Funding. Bernal-Mizrachi:TAKEDA: Research Funding; Kodikas Therapeutic Solutions, Inc: Equity Ownership; Winship Cancer Institute: Employment, Patents & Royalties. Morgan:Amgen, Roche, Abbvie, Takeda, Celgene, Janssen: Honoraria, Membership on an entity's Board of Directors or advisory committees; Celgene: Other: research grant, Research Funding. Zonder:Celgene Corporation: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Takeda: Membership on an entity's Board of Directors or advisory committees; Amgen: Consultancy, Membership on an entity's Board of Directors or advisory committees; Intellia: Consultancy, Membership on an entity's Board of Directors or advisory committees; Caelum: Consultancy, Membership on an entity's Board of Directors or advisory committees; Alnylam: Consultancy, Membership on an entity's Board of Directors or advisory committees; BMS: Consultancy, Honoraria, 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; Oncopeptides: Consultancy, Membership on an entity's Board of Directors or advisory committees. Huff:Member of Safety Monitoring Board for Johnson and Johnson: Membership on an entity's Board of Directors or advisory committees; Karyopharm, Sanofi, MiDiagnostics: Consultancy. Lonial:Karyopharm: Consultancy; Takeda: Consultancy, Research Funding; Amgen: Consultancy; BMS: Consultancy; Janssen: Consultancy, Research Funding; GSK: Consultancy; Celgene Corporation: Consultancy, Research Funding; Genentech: Consultancy. Orlowski:Poseida Therapeutics, Inc.: Research Funding.

Background: Relapsed/refractory (R/R) large B cell lymphoma (LBCL) is associated with poor outcomes to standard salvage therapy (Crump M, et al. Blood. 2017). In SCHOLAR-1, a large multicenter, patient-level, retrospective study, patients with R/R diffuse LBCL had a 26% objective response rate (ORR) to the next line of therapy, a 7% complete response (CR) rate, and a median overall survival of 6.3 months (Crump M, et al. Blood 2017). Axicabtagene ciloleucel (axi-cel) is an autologous anti-CD19 chimeric antigen receptor (CAR) T cell therapy approved for patients with R/R LBCL with ≥ 2 prior systemic therapies. With a median follow-up of 27.1 months in ZUMA-1, the ORR with axi-cel was 83% (58% CR rate) in patients with refractory LBCL (Locke FL, et al. Lancet Oncol. 2019). Activation of the costimulatory receptor 4-1BB (CD137) on CAR T cells may enhance axi-cel antitumor activity by enhancing T cell proliferation, function, and survival. Utomilumab (uto), an investigational monoclonal antibody agonist of the 4-1BB pathway, enhanced T cell function and survival in preclinical studies (Fisher TS, et al. Cancer Immunol Immunother. 2012) and had favorable single-agent safety in patients (Segal NH, et al. Clin Cancer Res. 2018). Possible mechanisms of resistance to axi-cel are thought to be suboptimal CAR T cell expansion an exclusionary tumor microenvironment and CD19 target antigen loss (Neelapu SS, et al. Blood 2017, Rossi JM, et al J Immunother Cancer. 2018). Combination strategies that increase proliferation, expansion, and persistence of CAR T cells or prevent activation-induced cell death of CAR T cells may improve clinical outcomes observed with axi-cel. ZUMA-11 is a Phase 1/2 study investigating the efficacy and safety of axi-cel + uto in patients with refractory LBCL. Methods: The primary objectives of this study are to determine the safety, recommended Phase 2 dosing and timing (Phase 1), and efficacy (Phase 2) of axi-cel + uto in adult patients with refractory LBCL. Patients with progressive or stable disease as the best response to second-line chemotherapy or relapse ≤ 12 months after autologous stem cell transplantation, a prior anti-CD20 antibody and anthracycline-containing regimen, and Eastern Cooperative Oncology Group performance status 0-1 are eligible. Patients with histologically proven primary mediastinal B cell lymphoma, history of Richter's transformation or chronic lymphocytic lymphoma, prior CAR T cell therapy, or central nervous system involvement of lymphoma are ineligible. In Phase 1, ≈24 patients in ≤ 3 cohorts will receive a single dose of axi-cel and escalating doses of uto (10, 30, or 100 mg) using a 3 + 3 design in up to 4 of 6 cohorts. The recommended uto dose will be based on dose-limiting toxicities and other factors. Patients will be leukapheresed and may receive optional, nonchemotherapy bridging therapy per investigator decision. After conditioning chemotherapy, patients will receive a single infusion of axi-cel (target dose, 2 × 106 CAR T cells/kg) on Day 0 followed by uto on Day 1 and every 4 weeks for 6 months or until progressive disease. Patients will be treated one at a time during Phase 1, and patients treated with axi-cel will be staggered by ≥ 2 weeks. Day 21 uto administration will be explored if toxicity is unacceptable with Day 1 administration. The primary endpoints are incidence of dose-limiting toxicities in Phase 1 and CR rate in Phase 2. Secondary endpoints include ORR, duration of response, progression-free survival, overall survival, safety, and levels of CAR T cells and cytokines in blood. This study uses a single-arm design to estimate the true CR rate; with a sample size of 27 patients, of which ≤ 3 patients will have been treated in the Phase 1 portion, the maximum half-width of the 95% confidence interval about response will be ≥ 21%. ZUMA-11 is open and accruing patients. Disclosures Reshef: Kite, a Gilead Company: Consultancy, Honoraria, Research Funding; Celgene: Research Funding; Incyte: Consultancy, Research Funding; Shire: Research Funding; BMS: Consultancy; Atara: Consultancy, Research Funding; Magenta: Consultancy; Pfizer: Consultancy; Pharmacyclics: Consultancy, Research Funding. Miklos:Pharmacyclics: Consultancy, Patents & Royalties, Research Funding; Precision Bioscience: Consultancy; Adaptive Biotechnologies: Consultancy, Research Funding; Miltenyi: Consultancy, Research Funding; Becton Dickinson: Consultancy; Janssen: Consultancy; AlloGene: Consultancy; Novartis: Consultancy; Kite, A Gilead Company: Consultancy, Research Funding; Celgene-Juno: Consultancy. Timmerman:Spectrum Pharmaceuticals: Research Funding; Kite, A Gilead Company: Consultancy, Honoraria, Other: travel support, Research Funding; ImmunGene: Research Funding; Merck: Research Funding; Bristol-Myers Squibb: Consultancy, Honoraria, Other: travel support, Research Funding. Jacobson:Novartis: Consultancy, Honoraria, Other: travel support; Bayer: Consultancy, Other: travel support; Precision Biosciences: Consultancy, Other: travel support; Humanigen: Consultancy, Other: travel support; Celgene: Consultancy, Other: travel support; Pfizer: Research Funding; Kite, a Gilead Company: Consultancy, Honoraria, Other: travel support. Bennani:Kite, A Gilead Company: Consultancy, Research Funding. Rossi:Kite, A Gilead Company: Employment. Sherman:Kite, A Gilead Company: Employment. Sun:Kite, A Gilead Company: Employment. Palluconi:Kite, A Gilead Company: Employment. Kim:Kite, A Gilead Company: Employment. Jain:Kite/Gilead: Consultancy.