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The interaction between Chronic lymphocytic leukemia (CLL) cells and monocyte-derived nurse-like cells (NLCs) is fundamentally important to CLL biology. However, studies of how CLL cells and NLCs interact have been hampered by the need for freshly obtained CLL blood samples, coupled with wide variation in the number of monocytes present in the blood of individual patients. Here, we report the development and validation of a cell-line model of NLCs which overcomes these difficulties. Co-culture of primary CLL cells with THP-1 cells induced to differentiate into macrophages by phorbol 12-myristate 13-acetate (PMA) significantly reduced both spontaneous and fludarabine-induced cell death of leukemic cells. Furthermore, compared with their M1-polarized counterparts, M2-polarized macrophages derived from PMA-differentiated THP-1 cells conferred to CLL cells greater protection from spontaneous and fludarabine-induced apoptosis. Since NLCs resemble M2 tumor-associated macrophages, this cell-line model could be useful for investigating the mechanisms through which NLCs protect CLL cells from spontaneous and drug-induced apoptosis.

International audience; Simple Summary There have been huge improvements in both vaccination and the management of COVID-19 in patients with cancer. In addition, different variants may be associated with different presentations. Therefore, we examined whether indicators of the severity of COVID-19 in patients with cancer who present to hospital varied during different waves of the pandemic and we showed that these indicators remained predictive. We validated that the COVID-19 Risk in Oncology Evaluation Tool (CORONET), which predicts the severity of COVID-19 in cancer patients presenting to hospital, performed well in all waves. In addition, we examined patient outcomes and the factors that influence them and found that there was increased vaccination uptake and steroid use for patients requiring oxygen in later waves, which may be associated with improvements in outcome. Patients with cancer have been shown to have increased risk of COVID-19 severity. We previously built and validated the COVID-19 Risk in Oncology Evaluation Tool (CORONET) to predict the likely severity of COVID-19 in patients with active cancer who present to hospital. We assessed the differences in presentation and outcomes of patients with cancer and COVID-19, depending on the wave of the pandemic. We examined differences in features at presentation and outcomes in patients worldwide, depending on the waves of the pandemic: wave 1 D614G (n = 1430), wave 2 Alpha (n = 475), and wave 4 Omicron variant (n = 63, UK and Spain only). The performance of CORONET was evaluated on 258, 48, and 54 patients for each wave, respectively. We found that mortality rates were reduced in subsequent waves. The majority of patients were vaccinated in wave 4, and 94% were treated with steroids if they required oxygen. The stages of cancer and the median ages of patients significantly differed, but features associated with worse COVID-19 outcomes remained predictive and did not differ between waves. The CORONET tool performed well in all waves, with scores in an area under the curve (AUC) of >0.72. We concluded that patients with cancer who present to hospital with COVID-19 have similar features of severity, which remain discriminatory despite differences in variants and vaccination status. Survival improved following the first wave of the pandemic, which may be associated with vaccination and the increased steroid use in those patients requiring oxygen. The CORONET model demonstrated good performance, independent of the SARS-CoV-2 variants.

Diffuse large B-cell lymphoma (DLBCL) is the most common subtype of high-grade non-Hodgkin’s lymphoma, representing a group of heterogeneous diseases with varied responses and prognosis. Although prognostication tools exist such as the International Prognostic Index (IPI), they do not account for underlying tumour biology and therefore marked differences exist in outcomes within each group. With the advent of genetic profiling, new subtypes have been recognised; however, their application to the clinical setting has been limited due to cost of equipment and lack of expertise. To improve prognostication and account for variable response in DLBCL, the role of MYC and BCL2 oncogenes has been implicated in the pathogenesis of DLBCL1–5 using immunohistochemistry (IHC). Double-expresser lymphoma indicates all patients in which upregulation of these proteins is evidenced using IHC, typically at ≥40% for MYC and >50%–70% for BCL2 . There remains controversy about first, whether coexpression of MYC and BCL2 independent of their translocation status can predict prognosis1 6–8 and second, what cut-offs are clinically significant for MYC and BCL2 expression.1 6 8 We have therefore investigated these in our cohort of 43 patients. A comprehensive search was conducted on the local Merseyside Haemato-Oncology Diagnostic Service database to identify new diagnosis of DLBCL between May 2013 and December 2015. Patients with a diagnosis of ‘diffuse large B-cell lymphoma’, ‘high grade B-cell non-Hodgkin’s lymphoma’ or ‘Burkitt’s lymphoma’ were included. Due to exposure of rituximab therapy influencing IHC, 18 patients with relapsed DLBCL were excluded and therefore only new cases were considered. Data pertaining to patients’ age, gender and Ann Arbor staging were collected including clinical data relating to all components of the IPI score, performance status, therapy …

Chronic lymphocytic leukaemia (CLL) exhibits variable clinical course and response to therapy, but the molecular basis of this variability remains incompletely understood. Data independent acquisition (DIA)-MS technologies, such as SWATH (Sequential Windowed Acquisition of all THeoretical fragments), provide an opportunity to study the pathophysiology of CLL at the proteome level. Here, a CLL-specific spectral library (7736 proteins) is described alongside an analysis of sample replication and data handling requirements for quantitative SWATH-MS analysis of clinical samples. The analysis was performed on 6 CLL samples, incorporating biological (IGHV mutational status), sample preparation and MS technical replicates. Quantitative information was obtained for 5169 proteins across 54 SWATH-MS acquisitions: the sources of variation and different computational approaches for batch correction were assessed. Functional enrichment analysis of proteins associated with IGHV mutational status showed significant overlap with previous studies based on gene expression profiling. Finally, an approach to perform statistical power analysis in proteomics studies was implemented. This study provides a valuable resource for researchers working on the proteomics of CLL. It also establishes a sound framework for the design of sufficiently powered clinical proteomics studies. Indeed, this study shows that it is possible to derive biologically plausible hypotheses from a relatively small dataset.

Background Cancer patients are at increased risk of death from severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Cancer and its treatment affect many haematological and biochemical parameters, therefore we analysed these prior to and during coronavirus disease 2019 (COVID-19) and correlated them with outcome. Patients and methods Consecutive patients with cancer testing positive for SARS-CoV-2 in centres throughout the United Kingdom were identified and entered into a database following local governance approval. Clinical and longitudinal laboratory data were extracted from patient records. Data were analysed using Mann–Whitney U test, Fisher's exact test, Wilcoxon signed rank test, logistic regression, or linear regression for outcomes. Hierarchical clustering of heatmaps was performed using Ward's method. Results In total, 302 patients were included in three cohorts: Manchester (n = 67), Liverpool (n = 62), and UK (n = 173). In the entire cohort (N = 302), median age was 69 (range 19-93 years), including 163 males and 139 females; of these, 216 were diagnosed with a solid tumour and 86 with a haematological cancer. Preinfection lymphopaenia, neutropaenia and lactate dehydrogenase (LDH) were not associated with oxygen requirement (O2) or death. Lymphocyte count (P < 0.001), platelet count (P = 0.03), LDH (P < 0.0001) and albumin (P < 0.0001) significantly changed from preinfection to during infection. High rather than low neutrophils at day 0 (P = 0.007), higher maximal neutrophils during COVID-19 (P = 0.026) and higher neutrophil-to-lymphocyte ratio (NLR; P = 0.01) were associated with death. In multivariable analysis, age (P = 0.002), haematological cancer (P = 0.034), C-reactive protein (P = 0.004), NLR (P = 0.036) and albumin (P = 0.02) at day 0 were significant predictors of death. In the Manchester/Liverpool cohort 30 patients have restarted therapy following COVID-19, with no additional complications requiring readmission. Conclusion Preinfection biochemical/haematological parameters were not associated with worse outcome in cancer patients. Restarting treatment following COVID-19 was not associated with additional complications. Neutropaenia due to cancer/treatment is not associated with COVID-19 mortality. Cancer therapy, particularly in patients with solid tumours, need not be delayed or omitted due to concerns that treatment itself increases COVID-19 severity., Highlights • Pre-infection haematological/biochemical characteristics are not associated with COVID-19 severity. • Significant haematological/biochemical changes occur upon infection with heterogeneity in response observed. • High not low neutrophils were associated with oxygen requirement and COVID-19 mortality – GCSF should be used with caution. Age, haematological cancer, high neutrophil:lymphocyte ratio, CRP and low albumin were significant predictors of death in multivariable analysis. No significant complications requiring readmission were seen upon restart of cancer therapy following diagnosis of COVID-19.

The COVID-19 pandemic has been a disruptive event for cancer patients, especially those with haematological malignancies (HM). They may experience a more severe clinical course due to impaired immune responses. This multi-center retrospective UK audit identified cancer patients who had SARS-CoV-2 infection between 1 March and 10 June 2020 and collected data pertaining to cancer history, COVID-19 presentation and outcomes. In total, 179 patients were identified with a median age of 72 (IQR 61, 81) and follow-up of 44 days (IQR 42, 45). Forty-one percent were female and the overall mortality was 37%. Twenty-nine percent had HM and of these, those treated with chemotherapy in the preceding 28 days to COVID-19 diagnosis had worse outcome compared with solid malignancy (SM): 62% versus 19% died [HR 8.33 (95% CI, 2.56-25), p p = 0.001). Patients with haematological malignancies and those who acquire nosocomial transmission are at increased risk of death. Therefore, there is an urgent need to reassess shielding advice, reinforce stringent infection control, and ensure regular patient and staff testing to prevent nosocomial transmission.

BackgroundCancer patients are at increased risk of severe COVID-19. As COVID-19 presentation and outcomes are heterogeneous in cancer patients, decision-making tools for hospital admission, severity prediction and increased monitoring for early intervention are critical.ObjectiveTo identify features of COVID-19 in cancer patients predicting severe disease and build a decision-support online tool; COVID-19 Risk in Oncology Evaluation Tool (CORONET)MethodData was obtained for consecutive patients with active cancer with laboratory confirmed COVID-19 presenting in 12 hospitals throughout the United Kingdom (UK). Univariable logistic regression was performed on pre-specified features to assess their association with admission (≥24 hours inpatient), oxygen requirement and death. Multivariable logistic regression and random forest models (RFM) were compared with patients randomly split into training and validation sets. Cost function determined cut-offs were defined for admission/death using RFM. Performance was assessed by sensitivity, specificity and Brier scores (BS). The CORONET model was then assessed in the entire cohort to build the online CORONET tool.ResultsTraining and validation sets comprised 234 and 66 patients respectively with median age 69 (range 19-93), 54% males, 46% females, 71% vs 29% had solid and haematological cancers. The RFM, selected for further development, demonstrated superior performance over logistic regression with AUROC predicting admission (0.85 vs. 0.78) and death (0.76 vs. 0.72). C-reactive protein was the most important feature predicting COVID-19 severity. CORONET cut-offs for admission and mortality of 1.05 and 1.8 were established. In the training set, admission prediction sensitivity and specificity were 94.5% and 44.3% with BS 0.118; mortality sensitivity and specificity were 78.5% and 57.2% with BS 0.364. In the validation set, admission sensitivity and specificity were 90.7% and 42.9% with BS 0.148; mortality sensitivity and specificity were 92.3% and 45.8% with BS 0.442. In the entire cohort, the CORONET decision support tool recommended admission of 99% of patients requiring oxygen and of 99% of patients who died.Conclusions and RelevanceCORONET, a decision support tool validated in hospitals throughout the UK showed promise in aiding decisions regarding admission and predicting COVID-19 severity in patients with cancer presenting to hospital. Future work will validate and refine the tool in further datasets.