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KILT: A RANDOMIZED NON-COMPARATIVE PHASE II LYSA STUDY OF LACUTAMAB WITH GEMOX VERSUS GEMOX IN RELAPSED/REFRACTORY PERIPHERAL T-CELL LYMPHOMA B Background: b Peripheral T-cell Lymphoma (PTCL) is a heterogeneous group of mature T-cell lymphomas (TCL) with adverse outcomes. KIR3DL2 is a killer immunoglobulin-like receptor that is expressed across different subtypes of TCL, including approximately 40% of PTCL. [Extracted from the article]

Gonorrhea is a common sexually transmitted infection, which can present as the ‘arthritis-dermatitis syndrome’. Patients with systemic lupus erythematosus often develop disseminated neisserial infections, because of inherited and acquired complement deficiencies. Neisserial infection, and particularly gonococcemia, can mimic a lupus flare. We report one case of gonococcemia presenting as acral papulo-vesiculous lesions of the digits in a young woman with lupus. [ABSTRACT FROM PUBLISHER]

Background: Peripheral T-cell lymphomas are aggressive non-Hodgkin lymphomas with few treatment options for relapsed or refractory disease. Valemetostat tosylate (valemetostat) is a potent, novel, dual inhibitor of EZH2 and EZH1. We investigated the clinical activity and safety of valemetostat in patients with relapsed or refractory peripheral T-cell lymphoma, and its safety in patients with relapsed or refractory adult T-cell leukaemia/lymphoma., Methods: VALENTINE-PTCL01 was a multicentre, open-label, single-arm, phase 2 trial performed at 47 hospitals in 12 countries across Asia, Europe, North America, and Oceania. Patients with either peripheral T-cell lymphoma or adult T-cell leukaemia/lymphoma, aged 18 years or older with an Eastern Cooperative Oncology Group performance status of 0-2 received oral valemetostat at 200 mg per day in continuous 28-day cycles until disease progression or unacceptable toxicity. The primary endpoint for patients with peripheral T-cell lymphoma was the CT-based objective response rate by blinded independent central review (BICR) using 2014 Lugano response criteria. Patients who received valemetostat and had a confirmed eligible peripheral T-cell lymphoma subtype on central review were included in the efficacy analysis. The primary endpoint for patients with adult T-cell leukaemia/lymphoma was the safety and tolerability of valemetostat. Safety in both cohorts was assessed in all patients who received at least one dose of valemetostat. The trial is registered with ClinicalTrials.gov, NCT04703192, and EudraCT, 2020-004954-31, and is closed to enrolment., Findings: Between June 16, 2021, and Aug 10, 2022, 133 patients with relapsed or refractory peripheral T-cell lymphoma (median age 69·0 years [IQR 58·0-74·0]; 91 [68%] were male, and 42 [32%] were female) and 22 patients with adult T-cell leukaemia/lymphoma (66·5 years [54·0-73·0]; 15 [68%] were male, and seven [32%] were female) were enrolled. The median follow-up time was 12·3 months (95% CI 11·8-13·8). 52 (44%; 95% CI 35-53) of 119 efficacy-evaluable patients with relapsed or refractory peripheral T-cell lymphoma had an objective response. The most common grade 3-4 adverse events were thrombocytopenia (31 [23%] of 133 patients in the peripheral T-cell lymphoma group and 11 [50%] of 22 patients in the adult T-cell leukaemia/lymphoma group), anaemia (25 [19%] and ten [46%]), and neutropenia (23 [17%] and four [18%]). Serious treatment-emergent adverse events were reported in 53 (40%) patients with peripheral T-cell lymphoma and 15 (68%) patients with adult T-cell leukaemia/lymphoma; nine (7%) patients and one (5%) patient had a serious treatment-emergent adverse event considered to be treatment related, respectively. No treatment-related deaths were reported., Interpretation: These data show that treatment with valemetostat leads to durable responses in patients with relapsed or refractory peripheral T-cell lymphoma, with a manageable safety profile., Funding: Daiichi Sankyo., Competing Interests: Declaration of interests PLZ has received consulting fees from lncyte, Novartis, BeiGene, and SOBI, and honoraria from Takeda, AstraZeneca, MSD, Bristol Myers Squibb (BMS), lncyte, Roche, Gilead, Recordati, Kyowa Kirin, Novartis, BeiGene, Janssen, and SOBI, unrelated to this study. KIz has received manuscript support from Daiichi Sankyo to their institution, related to this work; research funding Chugai, BMS, Incyte, Genmab, LOXO Oncology, Daiichi Sankyo, BeiGene, AbbVie, AstraZeneca, Regeneron, Yakult, Chugai, Otsuka, Novartis, Pfizer, MSD, Bayer, Kyowa Kirin, Eisai, Janssen, Ono Pharmaceutical, Gilead, Astellas, and Amgen to their institution, unrelated to this work; consulting fees from AstraZeneca, Ono Pharmaceutical, Mitsubishi Tanabe, Eisai, Chugai, BMS, AbbVie, Takeda, Zenyaku, Genmab, Kyowa Kirin, MSD, Carna Biosicences, Novartis, Yakult, Nihon Shinyaku, Novartis, and BeiGene, unrelated to this work; and honoraria from AstraZeneca, Ono Pharmaceutical, Eisai, Chugai, Janssen, Symbio, BMS, Daiichi Sankyo, Otsuka, AbbVie, Takeda, Eli Lilly, Genmab, Kyowa Kirin, MSD, Astellas, Pfizer, Meiji Seika Pharma, Novartis, Nihon Kayaku, and Gilead, unrelated to this work. NM-S has received research funding from AstraZeneca, BMS, C4 Therapeutics, Celgene, Corvus Pharmaceuticals, Daiichi Sankyo, Dizal Pharmaceuticals, Genetech/Roche, Incyte, Innate Pharmaceuticals, Secura Bio, Verastem, and Yingli Pharmaceuticals, to their institutions, unrelated to this work; consulting fees from AstraZeneca, Kyowa Hakka Kirin, Karyopharm, Ono Pharmaceuticals, Secura Bios, Daiichi Sankyo, Genentech, and Janssen, unrelated to this work; and participated on a data safety monitoring board and an advisory board for Daiichi Sankyo, unrelated to this work. SKB received manuscript support from Daiichi Sankyo, related to this work; consulting fees and honoraria from Acrotech, Kyowa Kirin, and Seagen, unrelated to this work; and participated on a data safety monitoring board or advisory board for Janssen, unrelated to this work. KIs has received manuscript support from Daiichi Sankyo, related to this work; research funding from Ono Pharmaceutical and Kyowa Kirin to their institutions, unrelated to this work; honoraria from Kyowa Kirin, Takeda, Chugai Pharmaceutical, Celgene, BMS, Daiichi Sankyo, Ono Pharmaceutical, Astellas, Eisai, Pfizer, Otsuka, Sanofi, CSL Behring, AbbVie, Yakult, Janssen Pharmaceutical, and Nippon Shinyaku, unrelated to this work; participated on data safety monitoring board or advisory board for Meiji Seika Pharma and Daiichi Sankyo, unrelated to this work; and received materials from Ono Pharmaceutical to their institution, unrelated to this work. SK has received support from Daiichi Sankyo, related to this work; research funding from Chugai, Kyowa Kirin, and Janssen, unrelated to this work; and honoraria from Daiichi Sankyo, Chugai, Kyowa Kirin, and Janssen, unrelated to this work. EB has received research funding from Amgen and BMS, to their institution, unrelated to this work; honoraria from Novartis, Kite/Gilead, Roche, Takeda, Janssen, and AbbVie, unrelated to this work; support for attending meetings or travel from Roche and Kite/Gilead, unrelated to this work; and participated on data safety monitoring board or advisory board from Novartis, Kite/Gilead, Roche, Incyte, ADC Therapeutics, and AbbVie, unrelated to this work. KC received consulting fees from Roche, Takeda, Celgene, and AbbVie, unrelated to this work; honoraria from Roche and Takeda, unrelated to this work; support for attending meetings or travel from Roche, Takeda, and BMS, to their institution; and participated on data safety monitoring board or advisory board for Daiichi Sankyo, unrelated to this work. GG received honoraria from Ideogen and Takeda, unrelated to this work; support for attending meetings or travel from Roche, Kite-Gilead, Sandoz, BeiGene, and Janssen, unrelated to this work; and participated on data safety monitoring board or advisory board for AbbVie, Roche, Takeda, Kite-Gilead, Italfarmaco, Ideogen, and Genmab, unrelated to this work. EJ received research funding from Celgene, Merck, Pharmacyclics, and F Hoffman-LaRoche, unrelated to this work; honoraria from Merck, Daiichi, BMS, and Bayer, unrelated to this work; and has patents planned, issued, or pending with UpToDate, unrelated to this work. TF has received research funding from ADCT, AstraZeneca, BMS, Corvus, Daiichi, Genmab, Kymera, Merck, Seagen, TESSA, Trillium, Alexion, and Portola, unrelated to this work; consulting fees from ADCT, AstraZeneca, BMS, Epizyme, Genmab, Seagen, Pharmacyclics, and Celgene, unrelated to this work; honoraria from Takeda, Seagen, Genmab, Epizyme, ADCT, AstraZeneca, BMS, Pharmacyclics, AbbVie, and Pfizer, unrelated to this work; and owns stock in OMI and Genomic Testing Cooperative, unrelated to this work. DE received research funding from Nippon Shinyaku Pharmaceutical, Chugai Pharmaceutical, and Eisai Pharmaceutical, to their institution, unrelated to this work; and honoraria from Eisai Pharmaceutical, Chugai Pharmaceutical, SymBio Pharmaceuticals, BMS, Kyowa Kirin Pharmaceutical, and Nippon Shinyaku Pharmaceutical, unrelated to this work. EDD received consulting fees from Takeda, unrelated to this work; honoraria from Takeda and BeiGene, unrelated to this work; and funding for attending meetings or travel from Takeda, unrelated to this work. JZ received research funding from Secura Bio, Astex, CRSPR, Myeloid Therapeutics, and Daichi Sankyo, unrelated to this work; consulting fees from Seattle genetics, Secura Bio, Kyowa Kirin, and Myeloid Therapeutics, unrelated to this work; and honoraria from Kyowa Kirin, unrelated to this work. JJ, SW, TM, AI, and KN are employed by Daiichi Sankyo. TM has received support for attending meetings or travel from Daiichi Sankyo, unrelated to this work. AI, TM, and YC own stock options in Daiichi Sankyo, unrelated to this work. KN owns stock in Daiichi Sankyo, unrelated to this work. SH received research funding from the US National Institutes of Health/National Cancer Institute Cancer Center, (support grant P30 CA008748); research funding from ADC Therapeutics, Affimed, Aileron, Celgene, Crispr Therapeutics, Daiichi Sankyo, Forty Seven, Kyowa Hakko Kirin, Takeda, Seattle Genetics, Trillium Therapeutics, and SecuraBio; and honoraria from Abcuro, Autolus, Auxilius Pharma, Corvus, Cimeio Therapeutics, Daiichi Sankyo, Dren Bio, Kyowa Hakko Kirin, March, Bio, Ono Pharmaceuticals, SecuraBio, Shore line, Biosciences, Takeda, Tubulis and Yingli Pharma. All other authors declare no competing interests., (Copyright © 2024 Elsevier Ltd. All rights reserved, including those for text and data mining, AI training, and similar technologies.)

Chronic lymphocytic leukemia (CLL) is a B-cell-derived hematologic malignancy whose progression depends on active B-cell receptor (BCR) signaling. Despite the spectacular efficacy of Ibrutinib, an irreversible inhibitor of Bruton tyrosine kinase (BTK), resistance can develop in CLL patients, and alternative therapeutic strategies are therefore required. Cancer immunotherapy has revolutionized cancer care and may be an attractive approach in this context. We speculated that characterizing the immune responses of CLL patients may highlight putative immunotherapeutic targets. Here, we used high-dimensional spectral flow cytometry to compare the distribution and phenotype of non-B-cell immune populations in the circulating blood of CLL patients treated with Ibrutinib displaying a complete response or secondary progression. Although no drastic changes were observed in the composition of their immune subsets, the Ibrutinib-resistant group showed increased cycling of CD8+ T cells, leading to their overabundance at the expense of dendritic cells. In addition, the expression of 11 different surface checkpoints was similar regardless of response status. Together, this suggests that CLL relapse upon Ibrutinib treatment may not lead to major alterations in the peripheral immune response.

In the 2022, WHO and ICC classifications, myeloid/lymphoid neoplasms with eosinophilia (M/LN-eo) and tyrosine kinase gene fusions represent rare hematologic malignancies driven by rearrangements of PDGFRA, PDGFRB, FGFR1, JAK2, FLT3, and ETV6::ABL1 fusion. Eosinophilia is the most constant finding, whereas the clinicopathological features are quite heterogeneous, presenting as Chronic eosinophilic leukemia (CEL) NOS, myelodysplastic/myeloproliferative neoplasm (MDS/MPN), MDS, MPN, systemic mastocytosis (SM), T or B cell acute lymphoblastic leukemia/lymphoblastic lymphoma (ALL/LBL), acute myeloid leukemia (AML), blastic phase of MPN, or mixed phenotype acute leukemia (MPAL). Extramedullary involvement at diagnosis or during progression is common. Here, we report a very unusual case of myeloid/lymphoid neoplasm with ETV6::FLT3 fusion with a nodal presentation without associated eosinophilia. Our case draws attention to diagnostic pitfalls in these rare entities., (© 2023. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

Background: t-AML occurs after a primary malignancy treatment and retains a poor prognosis., Aims: To determine the impact of primary malignancies, therapeutic strategies, and prognostic factors on clinical outcomes of t-AML., Results: A total of 112 adult patients were included in this study. Fifty-Five patients received intensive chemotherapy (IC), 33 non-IC, and 24 best supportive care. At t-AML diagnosis, 42% and 44% of patients presented an unfavorable karyotype and unfavorable 2010 ELN risk profile, respectively. Among treated patients (n = 88), 43 (49%) achieved complete remission: four out of 33 (12%) and 39 out of 55 (71%) in non-IC and IC groups, respectively. With a median follow-up of 5.5 months, the median overall survival (OS) and disease-free survival (DFS) for the whole population were 9 months and 6.3 months, respectively, and for the 88 treated patients 13.5 months and 8.2 months, respectively. Univariate analysis on OS and DFS showed a significant impact of high white blood cells (WBC) and blast counts at diagnosis, unfavorable karyotype and ELN classification. Multivariate analysis showed a negative impact of WBC count at diagnosis and a positive impact of chemotherapy on OS and DFS in the whole population. It also showed a negative impact of previous auto-HCT and high WBC count on OS and DFS and of IC on OS in treated patients which disappeared when we considered only confounding variables (age, previous cancers, marrow blasts, and 2010 ELN classification). In a pair-matched analysis comparing IC treated t-AML with de novo AML, there was no difference of OS and DFS between the two populations., Conclusion: We showed, in this study that t-AML patients with unfavorable features represented almost half of the population. Best outcomes obtained in patients receiving IC must be balanced by known confounding variables and should be improved by using new innovative agents and therapeutic strategies., (© 2023 The Authors. Cancer Medicine published by John Wiley & Sons Ltd.)

Purpose of the Report: We aimed to evaluate the role of 18F-FDG PET/CT in predicting patient outcome following chimeric antigen receptor T (CAR T) cells infusion in aggressive B-cell lymphoma., Methods: 18F-FDG PET/CT data before leukapheresis, before CAR T-cell infusion and 1 month (M1) after CAR T-cell infusion, from 72 patients were retrospectively analyzed. SUVmax, total lesion glycolysis (TLG), metabolic tumor volume (MTV), and parameters describing tumor kinetics were calculated for each 18F-FDG PET/CT performed. The aim was to evaluate the prognostic value of 18F-FDG PET/CT metabolic parameters for predicting progression-free survival (PFS) and overall survival (OS) following CAR T-cell therapy., Results: Regarding PFS, ∆MTVpre-CAR and ∆TLGpre-CAR were found to be more discriminating compared with metabolic parameters at preinfusion. Median PFS in patients with a ∆MTVpre-CAR of less than 300% was 6.8 months (95% confidence interval [CI], 2.8 months to not reached) compared with 2.8 months (95% CI, 0.9-3.0 months) for those with a value of 300% or greater (P = 0.004). Likewise, median PFS in patients with ∆TLGpre-CAR of less than 420% was 6.8 months (95% CI, 2.8 months to not reached) compared with 2.7 months (95% CI, 1.3-3.0 months) for those with a value of 420% or greater (P = 0.0148). Regarding OS, metabolic parameters at M1 were strongly associated with subsequent outcome. SUVmax at M1 with a cutoff value of 14 was the most predictive parameter in multivariate analysis, outweighing other clinicobiological variables (P < 0.0001)., Conclusions: Disease metabolic volume kinetics before infusion of CAR T cells seems to be superior to initial tumor bulk itself for predicting PFS. For OS, SUVmax at M1 might adequately segregate patients with different prognosis., Competing Interests: Conflicts of interest and sources of funding: P. Sesques: honoraria, advisory/consultancy from Novartis and Kite/Gilead; F.W.: honoraria, advisory/consultancy from Novartis and Kite/Gilead; V. Schwiertz: honoraria, advisory/consultancy from Kite/Gilead and Novartis; G.S.: advisory board/consulting for Gilead, Kite, Novartis; E.B.: honoraria, consultancy from Gilead, Novartis. The other authors have none declared., (Copyright © 2021 Wolters Kluwer Health, Inc. All rights reserved.)

Introduction: Older adults with cancer suffer from the combined effects of ageing, cancer disease and treatment side effects. The main treatment for patients with haematological malignancies is chemotherapy, associated with significant toxicities. Chemotherapy can alter patients' physical function and quality of life which are often already diminished in older patients due to ageing and comorbidities. It therefore seems essential to develop and to evaluate interventions capable of preventing physical and psychosocial decline and its consequences. Promoting physical activity is a promising approach to improve physical function and quality of life in older adults with cancer, but there are limited data on the feasibility of such interventions among older patients with haematological malignancies, concomitant to chemotherapy., Methods and Analysis: OCAPI (OnCogeriatric and Individualized Physical Activity) is a single-arm, interdisciplinary, prospective, interventional, feasibility study. It is intended to include 40 patients (20 patients with acute myeloid leukaemia and 20 patients with non-Hodgkin's lymphoma) over 65 years in an individualised 6-month physical activity programme. The programme consists of individually supervised exercise sessions with an increasing volume of physical activity either at home and/or in a laminar airflow room (depending on the disease and treatment regimen) followed by unsupervised sessions and phone follow-ups. Patients will receive an activity tracker during the 6 months of the programme. Evaluations will take place at inclusion and at 3, 6 and 12 months to assess the feasibility of the programme and to explore potential changes in physical, psychosocial and clinical outcomes. The results will generate preliminary data to implement a larger randomised controlled trial., Ethics and Dissemination: The study protocol was approved by the French ethics committee (Comité de protection des personnes Est I, N°ID-RCB 2019-A01231-56, 12 July 2019). All participants will have to sign and date an informed consent form. The findings will be disseminated in peer-reviewed journals and academic conferences., Trial Registration Number: NCT04052126., Competing Interests: Competing interests: None declared., (© Author(s) (or their employer(s)) 2021. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.)

High throughput sequencing (HTS) is increasingly important in determining cancer diagnoses, with subsequent prognostic and therapeutic implications. The biology of cancer is becoming increasingly deciphered and it is clear that therapy needs to be individually tailored. Whilst translational research plays an important role in lymphoid malignancies, few guidelines exist to guide biologists and routine laboratories through this constantly evolving field. In this article, we review the challenges of interpreting HTS in lymphoid malignancies and provide a toolkit to interpret single nucleotide variants obtained from HTS. We define the pre-analytical issues such as sequencing DNA obtained from formalin-fixed and paraffin-embedded tissue (FFPE), the acquisition of germline DNA, or the bioinformatic pitfalls, the analytical issues encountered and how to manage them. We describe the main constitutional and cancer databases, their characteristics and limitations, with an emphasis on variant interpretation in lymphoid malignancies. Finally, we discuss the challenges of predictions that one can make using in silico or in vitro modelling, pharmacogenomic screening, and the limits of those prediction tools. This description of the current status in genomic interpretation highlights the need for new large databases and international collaboration in the lymphoma field.