Vega, Francisco

Summary Even in the era of highly active combination antiretroviral therapy (cART), patients with HIV have a disproportionate risk of developing aggressive lymphomas that are frequently Epstein‐Barr virus (EBV)‐related. Here, we investigate HIV‐associated diffuse large B‐cell lymphoma (HIV‐DLBCL) and compare EBV‐positive and EBV‐negative cases. HIV‐DLBCL were identified from two academic medical centres and characterised by immunohistochemistry, EBV status, fluorescence in situ hybridisation, cell of origin determination by gene expression profiling, and targeted deep sequencing using a custom mutation panel of 334 genes. We also applied the Lymphgen tool to determine the genetic subtype of each case. Thirty HIV‐DLBCL were identified, with a median patient age of 46 years and male predominance (5:1). Thirteen cases (48%) were EBV‐positive and 14 (52%) EBV‐negative. Nine of the 16 tested cases (56%) had MYC rearrangement, three (19%) had BCL6 (two of which were double hit MYC / BCL6 ) and none had BCL2 rearrangements. Using the Lymphgen tool, half of the cases (15) were classified as other. All HIV‐DLBCL showed mutational abnormalities, the most frequent being TP53 (37%), MYC (30%), STAT3 (27%), HIST1H1E (23%), EP300 (20%), TET2 (20%), SOCS1 (17%) and SGK1 (17%). EBV‐negative cases were mostly of germinal centre B‐cell (GCB) origin (62%), showed more frequent mutations per case (a median of 13·5/case) and significant enrichment of TP53 (57% vs. 15%; P = 0·046), SGK1 (36% vs. 0%; P = 0·04), EP300 (43% vs. 0%; P = 0·02) and histone‐modifying gene (e.g. HIST1H1E , HIST1H1D , 79% vs. 31%; P = 0·02) mutations. EBV‐positive cases were mostly of non‐GCB origin (70%), with fewer mutations per case (median 8/case; P = 0·007), and these tumours were enriched for STAT3 mutations ( P = 0·10). EBV‐positive cases had a higher frequency of MYC mutations but the difference was not significant (36% vs. 15%; P = 0·38). EBV‐association was more frequent in HIV‐DLBCLs, arising in patients with lower CD4 counts at diagnosis (median 46·5 vs. 101, P = 0·018). In the era of cART, approximately half of HIV‐DLBCL are EBV‐related. HIV‐DLBCL are enriched for MYC rearrangements, MYC mutations and generally lack BCL2 rearrangements, regardless of EBV status. Among HIV‐DLBCL, tumours that are EBV‐negative and EBV‐positive appear to have important differences, the latter arising in context of lower CD4 count, showing frequent non‐GCB origin, lower mutation burden and recurrent STAT3 mutations.

AbstractBackgroundBlood involvement by mycosis fungoides (MF)/Sézary syndrome (SS) influences prognosis and therapeutic decisions. MF/SS blood stage is currently determined by absolute CD4 + CD26− or CD4 + CD7‐cell counts, which quantification method may overestimate MF/SS by including CD26− or CD7− normal CD4+ T‐cells, or underestimate disease burden when MF/SS cells show incomplete loss of CD26 and/or CD7. Recently, through the standardization effort led by the International Clinical Cytometry Society (ICCS), recommendation was made to quantify MF/SS by enumerating immunophenotypically aberrant CD4+ T‐cells, rather than CD26− or CD7− in isolation.MethodsWe compared these two quantitation methods in 309 MF/SS patients who had blood samples analyzed by flow cytometry immunophenotyping (FCI) over a 1‐year period.ResultsUsing the European Organization of Research and Treatment of Cancer (EORTC)/International Society for Cutaneous Lymphomas (ISCL) criteria, 221 (71.5%) patients had a blood stage corresponding to B0, 57 (18.4%) to B1, and 31 (10%) to B2. By FCI analysis, a total of 62 patients (20.0%) were found positive for MF/SS. Among EORTC B0 patients, 11/221 (5%) were positive by FCI (false negatives), and among EORTC Stage B1 patients, 35/57 (61%) were negative by FCI (false positives). Regarding patients positive for MF/SS cells by FCI, there was an overall excellent correlation (r = .999, p < .001) between the EORTC/ISCL method and FCI method; however, four (6.5%) patients would have an altered B stage between B0 and B1.ConclusionThe MF/SS cell quantification method using immunophenotypic aberrancies, as recommended by the ICCS, allows to distinguish MF/SS cells from background benign T‐cells and enables for more accurate staging, especially among patients currently being considered to have B0 and B1 stage diseases.

Abstract Background: To address the current and long-term unmet health needs of the growing population of patients diagnosed with non-Hodgkin lymphoma (NHL) and NHL survivors, we established the Lymphoma Epidemiology of Outcomes (LEO) cohort study (NCT02736357; https://leocohort.org/) with recruitment at eight centers (Cornell University, Emory University, Mayo Clinic, MD Anderson, University of Iowa, University of Miami, University of Rochester, and Washington University). Here we report on the first two years of enrollment. Methods: All patients ≥18 years with NHL (exclusive of chronic lymphocytic leukemia) newly diagnosed within 183 days of enrollment are eligible. LEO reviews all pathology diagnoses; annotates and harmonizes all cases with clinical, epidemiologic, pathology and treatment data; builds a NHL tumor bank that includes an H&E slide, a formalin-fixed, paraffin-embedded tissue sample and extracted tumor DNA and RNA; collects peripheral blood and banks DNA, serum, plasma and buffy coat in a central biorepository; and prospectively follows patients regularly to ascertain disease progression/relapse, retreatment, transformation, second cancers, survival, updated exposures, and patient-reported outcomes. We examined the demographics, clinical characteristics, and NHL subtypes for patients enrolled in the first two years (2015-2017) in LEO Cohort and compared these characteristics to population-based registry data from the United States Surveillance, Epidemiology, and End Results (SEER) Program for 2011-2015. We also examined baseline body mass index (BMI), comorbidities and quality of life (QOL) measured with the Functional Assessment of Cancer Therapy - General (FACT-G) scores (0-100 scale) overall and by NHL subtype that were collected in LEO but not available in SEER or any other large, prospective US NHL cohort. Results: From 2015-2017, LEO enrolled 3244 NHL patients across 47 states (Figure) with a median age at diagnosis of 62 years (range, 18-94 years) and 55.9% male. Based on self-identified race/ethnicity, 85% of the participants were White, 7.4% were Black/African American, 2.6% were Asian, and 3.5% other or more than one race; 9.9% were Hispanic (regardless of race). The most common subtypes were diffuse large B-cell (DLBCL, 33.9%), follicular (FL, 22.2%), mantle cell (MCL, 9.3%), marginal zone (MZL, 8.3%), T-cell (TCL, 10.9%), and all other NHL subtypes (other NHL, 15.3%). These distributions were all within 5% of SEER data, except that 6.8% of participant in LEO were in the age 80+ years group compared to 18.5% in SEER (Table). The overall prevalence of self-reported comorbidities at enrollment was 13.5% for heart disease (range, 12.1% for FL to 16.6% for MCL), 8.8% for diabetes (range, 7.1% for FL to 9.8% for DLBCL), 4.6% for osteoporosis (range, 3.0% for MCL to 6.3% for MZL), and 5.9% for autoimmune disease (range, 2.0% for MCL to 12.6% for MZL). The median BMI was 27.9 kg/m2 (range, 26.7 kg/m2 for TCL to 28.3 kg/m2 for MZL). A total of 2151 participants completed the FACT-G with a median score of 78.7 (range, 75.9 for TCL to 83.3 for MZL). Complete IPI data were available on 2296 participants: 40.4% IPI 0-1, 32.7% IPI 2, 18.3% IPI 3, 8.7% IPI 4-5. Overall, 73.7% of participants were initially treated with systemic therapy (range, 40.0% for MZL and 91.8% for DLBCL); 14.5% were initially managed with observation (range, 1.4% DLBCL and 31.8% for FL); and 2.0% were initially manage with radiotherapy (range, 0.2% for MCL and 3.8% for DLBCL). Outcomes are being prospectively collected. Conclusions: LEO is a large and diverse cohort of newly diagnosed NHL patients and the first two years of enrollment reflects the broader United States NHL population. Enrollment is ongoing. This unique resource will enable examination of the interactions among a broad array of clinical and molecular factors and their impact on multiple outcomes, to improve prognostication, identify new approaches to improve outcomes and survivorship, and facilitate clinical trials using this infrastructure. Disclosures Flowers: Acerta: Research Funding; Pharmacyclics/ Janssen: Consultancy; OptumRx: Consultancy; Janssen Pharmaceutical: Research Funding; V Foundation: Research Funding; Burroughs Wellcome Fund: Research Funding; Spectrum: Consultancy; Eastern Cooperative Oncology Group: Research Funding; Abbvie: Consultancy, Research Funding; Bayer: Consultancy; Millennium/Takeda: Research Funding; Abbvie: Research Funding; Celgene: Research Funding; Gilead: Consultancy; BeiGene: Research Funding; Karyopharm: Consultancy; Denovo Biopharma: Consultancy; Genentech/Roche: Consultancy; Gilead: Research Funding; Genentech/Roche: Research Funding; National Cancer Institute: Research Funding; TG Therapeutics: Research Funding; Pharmacyclics: Research Funding. Nastoupil:TG Therappeutics: Research Funding; Merck: Honoraria, Research Funding; Celgene: Honoraria, Research Funding; Janssen: Research Funding; Spectrum: Honoraria; Gilead: Honoraria; Genentech: Honoraria, Research Funding; Karus: Research Funding; Novartis: Honoraria; Juno: Honoraria. Kahl:Acerta: Consultancy; Genentech: Consultancy; Seattle Genetics: Consultancy; Juno: Consultancy; CTI: Consultancy; ADC Therapeutics: Consultancy; Gilead: Consultancy; Celgene: Consultancy; AstraZeneca: Consultancy; Abbvie: Consultancy. Casulo:Celgene: Research Funding; Gilead: Honoraria. Friedberg:Bayer: Honoraria. Lossos:Affimed: Research Funding. Martin:Seattle Genetics: Consultancy; Kite: Consultancy; Janssen: Consultancy; Gilead: Consultancy; Bayer: Consultancy; AstraZeneca: Consultancy. Leonard:Bayer: Consultancy; Celgene: Consultancy; Biotest: Consultancy; United Therapeutics: Consultancy; BMS: Consultancy; MEI Pharma: Consultancy; Juno: Consultancy; Sutro: Consultancy; Karyopharm: Consultancy; ADC Therapeutics: Consultancy; Pfizer: Consultancy; AstraZeneca: Consultancy; Gilead: Consultancy; Genentech/Roche: Consultancy; Novartis: Consultancy. Bernal-Mizrachi:Kodikaz Therapeutic Solutions: Consultancy, Equity Ownership; Takeda Pharmaceutical Company: Research Funding. Maurer:Morphosys: Research Funding; Nanostring: Research Funding; Celgene: Research Funding. Cerhan:Nanostring: Research Funding; Jannsen: Other: Scientific Advisory Board; Celgene: Research Funding.

Abstract The t(14;19)(q32;q13) involving the IGH@ and BCL3 loci is an infrequent cytogenetic abnormality detected in B-cell malignancies. We describe the clinicopathologic, cytogenetic, and molecular genetic characteristics of 14 cases of chronic lymphocytic leukemia/small lymphocytic lymphoma (CLL/SLL) with t(14;19)(q32;q13). All patients (10 men and 4 women) had lymphocytosis; 10 had lymphadenopathy. Blood and bone marrow lymphocytes were predominantly small, but cytologically and immunophenotypically atypical. In all cases, t(14;19) was found in the neoplastic stem line; it was the sole abnormality in 4. Ten cases showed additional cytogenetic abnormalities, including trisomy 12 in 9 and complex karyotypes in 7. Fluorescence in situ hybridization demonstrated IGH@/BCL3 fusion gene in all cases. In all cases, the IGHV genes were unmutated, but only 7 expressed ZAP70. Seven cases preferentially used IGHV4-39. Our results indicate that t(14;19)(q32;q13) identifies a subset of CLL/SLL with distinctive clinicopathologic and genetic features. Furthermore, t(14;19) may represent an early, possibly primary, genetic event.