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Cancers often overexpress multiple clinically relevant oncogenes, but it is not known if combinations of oncogenes in cellular subpopulations within a cancer influence clinical outcomes. Using quantitative multispectral imaging of the prognostically relevant oncogenes MYC, BCL2, and BCL6 in diffuse large B-cell lymphoma (DLBCL), we show that the percentage of cells with a unique combination MYC+BCL2+BCL6− (M+2+6−) consistently predicts survival across four independent cohorts (n = 449), an effect not observed with other combinations including M+2+6+. We show that the M+2+6− percentage can be mathematically derived from quantitative measurements of the individual oncogenes and correlates with survival in IHC (n = 316) and gene expression (n = 2,521) datasets. Comparative bulk/single-cell transcriptomic analyses of DLBCL samples and MYC/BCL2/BCL6-transformed primary B cells identify molecular features, including cyclin D2 and PI3K/AKT as candidate regulators of M+2+6− unfavorable biology. Similar analyses evaluating oncogenic combinations at single-cell resolution in other cancers may facilitate an understanding of cancer evolution and therapy resistance. Significance: Using single-cell–resolved multiplexed imaging, we show that selected subpopulations of cells expressing specific combinations of oncogenes influence clinical outcomes in lymphoma. We describe a probabilistic metric for the estimation of cellular oncogenic coexpression from IHC or bulk transcriptomes, with possible implications for prognostication and therapeutic target discovery in cancer. This article is highlighted in the In This Issue feature, p. 1027

Supplementary methods. Supplementary Figure 1. Phenotyping of B-cells in non-malignant tissues. A, Quantitation of marker positivity across ten tonsil and two reactive lymph node samples (rLN). Analysis is spatially resolved between the GC and extra-GC zones. B, Spatial map of cellular coordinates based on cell segmentation of images in Figure 1B. Marker-positivity is indicated, and a total proportion of positive and negative cells is depicted as a pie chart. These maps were used to derive sub-population phenotypes depicted in Figure 1C. Scale bar is 100μm. C, Proliferation analysis (i.e., Ki67-positivity) among sub-populations in five tonsil samples. Median with interquartile range, whiskers denote 10th and 90th percentile. Supplementary figure 2. Example pseudo-colored mfIHC images for MYC, BCL2, BCL6 cases in DLBCL. Images of a range of mean fluorescent intensities are shown with equal scaling for reference. Supplementary figure 3. Global distribution of MYC, BCL2 and BCL6 sub-populations within DLBCL cohorts. Heat-maps displaying the percentage extent of individual markers and each sub-population within the DLBCL NUH, CMMC, SGH and MDA cohorts. Hierarchical k-means clustering of patients according to sub-population extent is applied. Positivity shading for single markers ranges between 0-100% positivity, whereas shading for sub-populations reflects 0-50% positivity and remains fully saturated until 100%. IPI Risk Group - International Prognostic Index Risk Group, FISH - fluorescence in situ hybridization. Supplementary figure 4. Intra-tumor heterogeneity of sub-populations. A, Correlation of sub-population extent quantification between two biopsies of the same patient for which at least two tissue microarray (TMA) biopsies are available. Correlation is shown separately for lymph node and extranodal biopsies. Spearman rho is indicated for each correlation. Axes are in exponential and equivalent in all panels. B, Sub-population percentage extent quantification across multiple TMA cores (columns) of the same patient (rows). Pie charts are ordered according to decreasing cell numbers evaluated per core. All patients from the NUH cohort with at least five cores are evaluated. A heterogenous cluster is highlighted by the red box. Supplementary figure 5. Spatial heterogeneity of sub-population interactions. A, Conceptual schematic of pair correlation function (PCF) plots depicting a clustered distribution (left, green) and a random distribution (right, grey). Representative counterpart spatial maps are above each plot. B, PCF analysis for sub-populations to investigate spatial clustering (top). Mean results for two independent cohorts (shading is cohort standard deviation). An example tissue microarray core is shown as physical distance reference for spatial analyses (bottom left). Absolute number of neighboring cells expected within a given radius (data from 3500 randomly selected cells across all images, mean with standard deviation) (bottom right). C, Actual spatial map of sub-populations of an example DLBCL case (top). Extent of all sub-populations within the sample is shown on the left. Simulated, hypothetical random distribution of cells for the same case (middle). PCF analysis for the shown sample and its matched simulated random distribution (bottom). Scale bars in B and C are 100µm. D, Mean deviations from expected neighbor abundance (Δ%) summarizing cell-cell interactions between sub-populations for the sample shown in (C). E, Sub-population interaction matrices from spatially distinct biopsies (cores in tissue microarray) for example DLBCL patients. Biopsies of stable, spatially homogenous, sub-population interaction profiles are grouped (top), whereas biopsies of a differing, heterogenous, interaction profile are grouped separately (bottom). Supplementary figure 6. Global deviations from expected spatial neighbor abundance (Δ%). Hierarchical clustering (minimum variance method) of measured Δ% for all cases in the SGH and MDA cohorts. Extents of sub-populations are indicated for reference (top). For the MDA cohort, multiple biopsies (n = 1-3) from the same patient were included in the analysis to determine spatial interaction similarity across spatially distinct regions (bottom). Supplementary figure 7. Correlation of predicted MYC, BCL2 and BCL6 sub-population percentage extent based on single oncogene positivity and observed percentage extent in DLBCL cohorts. Spearman rho, axes are equivalent in all panels. Supplementary figure 8. Variance of M+2+6- percentage extent in the context of positivity calling across a 15% cut-off. A, M+2+6- scoring variance across multiple pathological imaging fields. All whole-tissue DLBCL sections from University of Palermo (UP), and samples from the NUH TMA with at least four fields scored per patient and a mean M+2+6- score above 5% are shown. Mean with SD. Ordinates between 50-100% are compressed for clarity. Dashed line denotes M+2+6- 15% positivity. B, Stability of M+2+6- case positivity calling across scoring increasing number of imaging fields. All cases from panel A with at least five fields scored in this study are shown. Only one case is called M+2+6- Low (

Cancers often overexpress multiple clinically relevant oncogenes, but it is not known if combinations of oncogenes in cellular subpopulations within a cancer influence clinical outcomes. Using quantitative multispectral imaging of the prognostically relevant oncogenes MYC, BCL2, and BCL6 in diffuse large B-cell lymphoma (DLBCL), we show that the percentage of cells with a unique combination MYC+BCL2+BCL6− (M+2+6−) consistently predicts survival across four independent cohorts (n = 449), an effect not observed with other combinations including M+2+6+. We show that the M+2+6− percentage can be mathematically derived from quantitative measurements of the individual oncogenes and correlates with survival in IHC (n = 316) and gene expression (n = 2,521) datasets. Comparative bulk/single-cell transcriptomic analyses of DLBCL samples and MYC/BCL2/BCL6-transformed primary B cells identify molecular features, including cyclin D2 and PI3K/AKT as candidate regulators of M+2+6− unfavorable biology. Similar analyses evaluating oncogenic combinations at single-cell resolution in other cancers may facilitate an understanding of cancer evolution and therapy resistance.Significance:Using single-cell–resolved multiplexed imaging, we show that selected subpopulations of cells expressing specific combinations of oncogenes influence clinical outcomes in lymphoma. We describe a probabilistic metric for the estimation of cellular oncogenic coexpression from IHC or bulk transcriptomes, with possible implications for prognostication and therapeutic target discovery in cancer.This article is highlighted in the In This Issue feature, p. 1027

Quantitative single-cell analysis of MYC, BCL2, and BCL6 protein expression in B cells in nonmalignant tissues and diffuse large B-cell lymphoma. A, Schematic workflow of a quantitative digital pathology experiment. B, Spectrally unmixed multiplexed fluorescent images for CD20, MYC, BCL2, and BCL6 and nuclear counterstaining in tonsil tissue. The germinal center (GC) and extragerminal center (extra-GC) zones are indicated. C, Spatial map of MYC/BCL2/BCL6 subpopulations, i.e., possible permutations of MYC/BCL2/BCL6-positivity and -negativity within the CD20-positive cell population in a tonsil image. D, Quantitation of subpopulation extent within CD20-positive cells in tonsils and reactive lymph nodes resolved spatially between the GC and extra-GC zones. E, Example of pseudocolored MYC/BCL2/BCL6/CD20 mfIHC staining in diffuse large B-cell lymphoma (DLBCL; left). Cell segmentation and single oncogene positivity masks are shown within the CD20-positive cell population (right). F, Summary of percentage extent of subpopulations across patients from National University Hospital (NUH), Chi-Mei Medical Center (CMMC), MD Anderson (MDA), and Singapore General Hospital (SGH). Relevant clinicopathologic features are indicated; see also Supplementary Fig. S3. Patients were ordered arbitrarily according to extent of the triple-positive M+2+6+ subpopulation extent. IPI Risk Group, International Prognostic Index Risk Group; FISH, fluorescence in situ hybridization. G, Intrapatient spatial stability of subpopulations – proportion of subpopulations measured across four spatially distinct biopsies from the same patient (rows). Biopsy comparison overview is shown across 11 representative example DLBCL patients (columns). See also Supplementary Fig. S4A and S4B for a correlation analysis for all patients with multiple biopsies available. H, Proliferation analysis (i.e., Ki-67-positivity) among subpopulations in DLBCL samples. Proliferative BCL6-positive subpopulations are grouped. Median with interquartile range, whiskers denote 10th and 90th percentile. Mann–Whitney test (BCL6-positive vs. -negative subpopulations). All scale bars, 100 μm.

Transcriptomic analysis of M+2+6− high cases and potential role of CCND2. A, Correlation of observed M+2+6− percentage extent in the BCA cohort with the cell-of-origin (COO) DLBCL90-COO signature. Bonferroni corrected Kruskal–Wallis test for ABC vs. others. B, Correlation of M+2+6− metric in GEP cohorts with COO signatures. Mean M+2+6− metric value per group per cohort is shown. Bonferroni corrected paired-samples t test. C, Correlation of the M+2+6− percentage extent and metric evaluated by mfIHC and mRNA inference, respectively, with genetic subtypes (LymphGen classification). D, Sankey plot of M+2+6− dichotomized grouping matched with molecular features. E, Volcano plot of pooled direct correlation of gene mRNA expression and M+2+6− metric across seven GEP cohorts. Genes highly correlated with M+2+6− metric across datasets at absolute Spearman rho ≥0.2 and FDR≤0.001 are shown (see also Supplementary Table S11). The abscissa is scaled exponentially. F, Differential gene expression between primary GC B cells overexpressing M+2+ and M+2+6+ (see also Supplementary Table S12). Analysis is generated from 4 biological replicates from each condition, from cells of independent donors. G, Genes highly enriched in M+2+6− cells: correlation of results from E and F. H,CCDN2 gene expression in GEP cohorts in patients dichotomized by M+2+6− 15% metric (left) and in primary B cells (right). Paired t test (left); mean with standard deviation and FDR (FDR as per Supplementary Table S12) for t test (right). I, Single-cell RNA-seq of GC primary B cells transduced either with BCL2 and MYC (MYC-transduced) or BCL2 and BCL6 (BCL6-transduced). Untransduced GC primary B cells are also included. Expression of CCND2 is indicated in color. J, Proliferation analysis of M+2+6+ primary GC B cells overexpressing cyclin D2 (CCND2) compared with M+2+6+ primary GC B cells transduced with an empty vector (EV). Analysis performed with 3 biological replicates for each condition, using cells from 3 independent patients; mean with standard deviation; t test. UMAP, Uniform Manifold Approximation and Projection.

Multivariate analysis of continuous M+2+6− percentage extent at 5% increments as a predictor of OS in the NUH, SGH, and MDA cohorts of DLBCL (Cox proportional hazards model)

Supplementary table 1. Per-patient mfIHC MYC, BCL2 and BCL6 single oncogene and subpopulation scores for normal tonsil tissue and reactive lymph node tissue. Supplementary table 2. Per-patient mfIHC MYC, BCL2 and BCL6 single oncogene and subpopulation scores for DLBCL tissue (NUH, CMMC, SGH, MDA, BCA and UP). Supplementary table 6. Inferred percentage extents of MYC, BCL2, BCL6 and sub-population metrics in GEP cohorts. Supplementary table 11. Correlation of M+2+6- metric with gene expression in GEP cohorts. Supplementary table 12. Differential gene expression analysis of primary germinal center (GC) B-cells with M+2+ and M+2+6+ overexpression. Supplementary table 13. Differentially expressed genes between M+2+6- and all other malignant cells in scRNA-seq samples of DLBCL. Dichotomized non-parametric comparison, Wilcoxon rank sum test. Supplementary table 14. Analysis of positive enrichment of Wikipathways terms between M+2+6- and all other malignant cells in scRNA-seq samples of DLBCL by gprofiler2.

MYC, BCL2, and BCL6 protein coexpression in DLBCL can be inferred from individual marker data. A, Schematic of possible relationships between expression of three oncogenes in a population of cells. The distribution of these oncogenes can either reflect interdependent expression, independent/stochastic expression, or mutually exclusive expression. These relationships result in the percentage extent of oncogenes in the tumor being either strongly positively correlated, not correlated, or strongly negatively correlated, respectively. Created with BioRender.com. B, Correlation of MYC, BCL2, and BCL6 percentage extent across patients in DLBCL cohorts. Spearman correlation; axes are equivalent in all panels. C, Good correlation between probabilistically predicted subpopulation percentage extent based on single oncogene positivity and observed percentage extent in the NUH cohort. Cases of double-hit lymphoma (DHL, MYC+BCL2+ translocations or MYC+BCL6+ translocations) or triple-hit lymphoma (THL) are highlighted. Spearman rho for each correlation is shown; axes are equivalent in all panels. Correlation for other cohorts can be found in Supplementary Fig. S7. D, Prospective evaluation of an optimal dichotomization cutoff for M+2+6− percentage extent in the BCA cohort. Univariate Cox PH model at 1% extent positivity increment, HR for death with 95% CI. HR scale is exponential. Optimal dichotomization cutoff is highlighted in blue. E, Kaplan–Meier OS analysis of the chromogenic IHC BCA cohort evaluation stratified into M+2+6− metric high and low groups across an absolute value of 15% M+2+6−metric. Log-rank test, shading denotes 95% confidence interval. CMMC, Chi-Mei Medical Center.

Validation of prognostic significance of the M+2+6− subpopulation metric in gene-expression datasets. A, Distribution of single oncogene positivity in DLBCL cohorts as assessed by mfIHC (see Supplementary Fig. S9A and S9B). B, Impact of subpopulation metrics in GEP datasets on OS. Pooled univariate Cox PH model analysis; metric was used as a continuous variable in the model at 5% increments. HR per 5% increment with 95% CI is shown; CI are proportional on both tails but are capped at the graph's edges. Pooled P values were Bonferroni corrected to adjust for multiple testing and are shown for each subpopulation. C, Kaplan–Meier OS analysis of GEP cohorts stratified uniformly across absolute 15% M+2+6− metric into -high and -low groups. Log-rank test, shading denotes 95% CI. Total patient numbers in each group are shown. CMMC, Chi-Mei Medical Center.

Prognostic significance of subpopulations after R-CHOP therapy. A, Pooled univariate Cox PH model analysis for MYC, BCL2, and BCL6 single oncogene and subpopulations percentage extent as predictors of OS across multiple DLBCL cohorts. Percentage extent was used as a continuous variable in the model at 5% increments (see Survival Analysis) for an unbiased comparison between the variables. Pooled P values were Bonferroni corrected for single oncogenes and subpopulations independently to adjust for multiple testing and are shown for each variable. Hazard ratio (HR) with 95% confidence interval (CI) per 5%-positivity increment is shown (see also Supplementary Table S4). B, Kaplan–Meier OS analysis of dichotomized into M+2+6− high and low groups. Log-rank test, shading denotes 95% CI. An optimal dichotomization cutoff was used for stratification; total patient numbers in each group are shown.

Contemporary data of peripheral T-cell lymphoma (PTCL) and natural-killer/T-cell lymphoma (NKTL) patients treated with ifosfamide, carboplatin and etoposide (ICE) are limited.We performed a retrospective analysis to estimate outcomes of ICE-treated PTCL and NKTL patients at three tertiary cancer centres in Singapore.Patients were identified through lymphoma databases from National Cancer Centre Singapore (NCCS), National University Hospital, Singapore (NUHS), and Singapore General Hospital (SGH). Responses and survival outcomes were determined from electronic medical records. A total of 75 patients with a median age of 50 were included. ICE was used as first-line treatment in 14 patients (19%) and as subsequent lines of treatment in 61 patients (81%). The overall response rates (ORR) for all patients was 63% (40% complete response [CR]). The ORR and CR in the first line were 86% and 64% respectively. At a median follow-up duration of 71.0 months, the median progression-free (PFS) and overall survival (OS) for all patients were 4.4 months (95%CI, 2.7-6.0) and 16 months (95%CI, 8.3-45.4) respectively.In summary, ICE showed high ORR but poor PFS in relapsed/refractory PTCL and NKTL. ORR of ICE in the first line setting appears better than real-world CHOP data and warrants further study.

Treatment with ABVD (doxorubicin, bleomycin, vinblastine, and dacarbazine) or escalated(e)-BEACOPP (bleomycin, etoposide, doxorubicin, cyclophosphamide, vincristine, procarbazine, and prednisolone) remains the international standard of care for advanced-stage classical Hodgkin lymphoma (HL). We performed a retrospective, multicentre analysis of 221 non-trial (“real-world”) patients, aged 16–59 years, diagnosed with advanced-stage HL in the Anglia Cancer Network between 2004 and 2014, treated with ABVD or eBEACOPP, and compared outcomes with 1088 patients in the Response-Adjusted Therapy for Advanced Hodgkin Lymphoma (RATHL) trial, aged 18–59 years, with median follow-up of 87.0 and 69.5 months, respectively. Real-world ABVD patients (n=177) had highly similar 5-year progression-free survival (PFS) and overall survival (OS) compared with RATHL (PFS 79.2% vs 81.4%; OS 92.9% vs 95.2%), despite interim positron-emission tomography-computed tomography (PET/CT)-guided dose-escalation being predominantly restricted to trial patients. Real-world eBEACOPP patients (n=44) had superior PFS (95.5%) compared with real-world ABVD (HR 0.20, p=0.027) and RATHL (HR 0.21, p=0.015), and superior OS for higher-risk (international prognostic score ≥3 [IPS 3+]) patients compared with real-world IPS 3+ ABVD (100% vs 84.5%, p=0.045), but not IPS 3+ RATHL patients. Our data support a PFS, but not OS, advantage for patients with advanced-stage HL treated with eBEACOPP compared with ABVD and suggest higher-risk patients may benefit disproportionately from more intensive therapy. However, increased access to effective salvage therapies might minimise any OS benefit from reduced relapse rates after frontline therapy.

Advances in the treatment of acute myocardial infarction have led to increased survival but also an increase in those suffering from heart failure. The concept of adult progenitor cell therapy has grown from observations that cardiac function improves after injection of bone marrow-derived cells into infarcted regions in animal models. The mechanism of action is unclear and is probably multifactorial. Different subpopulations of autologous cells have been utilised in humans with acute myocardial infarction and heart failure, as well different routes of delivery. These small Phase I clinical trials suggest that this approach is safe and leads to clinical improvement, and larger scale randomised control studies are underway. We believe that cell therapy heralds a revolutionary new era in the treatment of disease.

Introduction: The majority of young patients presenting with advanced-stage Hodgkin lymphoma (HL) in the UK are managed with ABVD. However, from 2009, escalated BEACOPP (escBEACOPP) was introduced as a treatment option in certain UK cancer centres. Within the East of England Cancer Network (EECN), 6 hospitals introduced escBEACOPP as a treatment option, while 2 hospitals continued to use ABVD as standard first-line therapy for all patients. During this time period, recruitment to the RATHL (Response Adjusted Therapy for Hodgkin Lymphoma) trial was also a first-line treatment option across the UK and 4 of the EECN centres. Analysing data from all patients treated in the EECN centres and the RATHL trial has therefore enabled outcomes of patients treated with either escBEACOPP or ABVD within a non-trial setting to be compared to patients treated with ABVD in a clinical trial. Methods: We performed a retrospective multicentre analysis of patients treated for advanced-stage HL, aged 16-59 years, diagnosed between 2004-2014 in the EECN and compared outcomes with age and international prognostic score (IPS)-matched patients in the RATHL trial. Results: Over this 10-year period, 250 patients were diagnosed with advanced-stage HL in the EECN; 202 commenced treatment with ABVD (23 within RATHL), 44 escBEACOPP, 3 other regimens, and 1 died before treatment. Five-year progression-free survival (PFS) for all patients was 82% and overall survival (OS) was 93%. In RATHL, 1088 patients aged 18-59 years commenced treatment with ABVD, with 5-year PFS of 81% and OS of 95%. Within the EECN, there was a clinician-patient preference to treat higher-risk patients (IPS 3+) with more intensive therapy; a higher proportion of escBEACOPP patients were IPS 3+ (IPS 3+: escBEACOPP 75% vs. ABVD 39%, p ABVD-treated EECN patients had highly similar 5-year PFS and OS rates compared with age-matched RATHL patients (PFS 80% vs 81%; HR: 1.1 (95% CI: 0.8-1.5), p=0.60: OS 93% vs 95%; HR: 1.5 (95% CI: 0.8-2.7), p=0.21; Figure 1A-B). Despite being enriched with higher-risk patients, the 5-year PFS for all escBEACOPP-treated EECN patients was superior to ABVD in both the EECN and RATHL cohorts (PFS 95% vs 80% (EECN); HR 0.2 (95% CI: 0.1-0.9), p=0.04; Figure 1C; Table 1), but there was no significant OS advantage (5-year OS 97% vs. 93% (EECN); HR 0.4 (95% CI: 0.0-2.8), p=0.34; Figure 1D; Table 1). However, subgroup analysis of higher-risk (IPS 3+) patients found that escBEACOPP-treated EECN patients had both a 5-year PFS and OS advantage when compared with IPS 3+ ABVD-treated EECN patients (PFS 97% vs 74%; HR 0.1 (95% CI: 0.0-0.8), p=0.03; OS 100% vs 85%; p=0.03). However, when these higher-risk escBEACOPP patients were compared with IPS 3+ patients from RATHL, the significance of the OS advantage was lost (Table 1). Despite similar relapse/refractory rates for IPS 3+ ABVD-treated patients in the EECN and RATHL datasets, the OS for relapsed/refractory patients was inferior in the EECN. One explanation could be the time period over which the EECN data was collected, as the majority (7/9) of higher-risk ABVD-treated EECN patients who died from progressive disease died prior to 2011, when brentuximab vedotin was unavailable to treat relapsed HL in the UK. Conclusion: Our data support recently published European data showing that first-line HL patients treated in the non-trial setting achieve similar outcomes to those treated in contemporary clinical trials. Our data also reflect prospective trial results which show a first-remission PFS, but not OS, advantage for advanced-stage HL patients treated with escBEACOPP compared with ABVD, but suggest that higher-risk patients might benefit disproportionately from more intensive first-line therapy with escBEACOPP. However, improved access to more effective salvage treatments for relapsed/refractory patients may have diminished any OS benefit from the use of first-line escBEACOPP in higher-risk patients. Disclosures Follows: Abbvie: Other: advisory board and lecturing; Janssen: Other: advisory board and lecturing; Gilead: Other: advisory board and lecturing; Roche: Other: advisory board and lecturing.

A proportion of MYC translocation positive diffuse large B‐cell lymphomas (DLBCL) harbour a BCL2 and/or BCL6 translocation, known as double‐hit DLBCL, and are clinically aggressive. It is unknown whether there are other genetic abnormalities that cooperate with MYC translocation and form double‐hit DLBCL, and whether there is a difference in clinical outcome between the double‐hit DLBCL and those with an isolated MYC translocation. We investigated TP53 gene mutations along with BCL2 and BCL6 translocations in a total of 234 cases of DLBCL, including 81 with MYC translocation. TP53 mutations were investigated by PCR and sequencing, while BCL2 and BCL6 translocation was studied by interphase fluorescence in situ hybridization. The majority of MYC translocation positive DLBCLs (60/81 = 74%) had at least one additional genetic hit. In MYC translocation positive DLBCL treated by R‐CHOP (n = 67), TP53 mutation and BCL2, but not BCL6 translocation had an adverse effect on patient overall survival. In comparison with DLBCL with an isolated MYC translocation, cases with MYC/TP53 double‐hits had the worst overall survival, followed by those with MYC/BCL2 double‐hits. In MYC translocation negative DLBCL treated by R‐CHOP (n = 101), TP53 mutation, BCL2 and BCL6 translocation had no impact on patient survival. The prognosis of MYC translocation positive DLBCL critically depends on the second hit, with TP53 mutations and BCL2 translocation contributing to an adverse prognosis. It is pivotal to investigate both TP53 mutations and BCL2 translocations in MYC translocation positive DLBCL, and to distinguish double‐hit DLBCLs from those with an isolated MYC translocation.

Summary points About 8000 people in the United Kingdom are diagnosed with leukaemia each year, and in 2010, 4504 people died in the UK from this disease.1 Leukaemia encompasses a clinically and pathologically diverse set of conditions whose incidence and prevalence are rising. In the past, leukaemia was classified on the basis of the morphological characteristics of abnormally proliferating leucocytes in the blood and bone marrow. Emerging genetic data, however, have shown genomic heterogeneity in what were thought to be homogeneous disorders, prompting the World Health Organization to revise the classification.2 Despite these advances, the profound immunological and haematological disturbances inherent in most leukaemias and the systemic side effects of chemotherapy remain complex challenges. This is a two part review with the first part focusing on the current diagnosis and management of leukaemia. The second part will consider the types of support patients need in the community. #### Sources and selection criteria We searched PubMed for clinical trials and the Cochrane Library for meta-analyses. We also sought expert opinion from experienced consultant haematologists. Keywords used were leuk(a)emia, chemotherapy, supportive care, and community care. We also reviewed guidelines from the British Committee for Standards in Haematology and the National Institute for Health and Clinical Excellence. Leukaemia is a cancer of circulating white blood cells. Leukaemias are divided into acute and chronic types. When immature white blood cells or …

Summary points Although curative chemotherapy is typically delivered in the inpatient setting, patients are at home for substantial periods of time while they progress through treatment. Patients with chronic leukaemia are usually treated entirely as outpatients. Almost all of these patients will develop complications from their disease or its treatment, and after completing chemotherapy, some patients will experience late complications. A balance between hospital and community based care is vital to optimise treatment efficacy and avoid unnecessary hospital admissions. This article reviews some of the adverse effects of leukaemia chemotherapy that non-specialists should be aware of and considers the longer term problems seen in survivors. #### Sources and selection criteria We searched PubMed for clinical trials and the Cochrane Library for meta-analyses. We also sought expert opinion from experienced consultant haematologists. Keywords used were leuk(a)emia, chemotherapy, supportive care, and community care. We also reviewed guidelines from the British Committee for Standards in Haematology and the National Institute for Health and Clinical Excellence. ### Treatment and prevention of infection including neutropenic sepsis Neutropenic sepsis is a medical emergency, and prompt treatment with intravenous antibiotics is usually life saving.1 Although incidence depends on regimen intensity, all patients on chemotherapy are at risk. Patients may present with non-specific symptoms, rigors, or symptoms associated with the infection site. Patients are advised to contact the acute oncology service available at their local treatment centre, and to monitor …