Search

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

DLBCL is the most common lymphoma with high tumor heterogeneity. Treatment refractoriness and relapse from R-CHOP therapy in patients remain a clinical problem. Activation of the non-canonical NF-κB pathway is associated with R-CHOP resistance. However, downstream targets of non-canonical NF-κB mediating R-CHOP-induced resistance remains uncharacterized. Here, we identify the common mechanisms underlying both intrinsic and acquired resistance that are induced by doxorubicin, the main cytotoxic component of R-CHOP. We performed global transcriptomic analysis of (1) a panel of resistant versus sensitive and (2) isogenic acquired doxorubicin-resistant DLBCL cell lines following short and chronic exposure to doxorubicin respectively. Doxorubicin-induced stress in resistant cells activates a distinct transcriptional signature that is enriched in metabolic reprogramming and oncogenic signalling. Selective and sustained activation of non-canonical NF-κB signalling in these resistant cells exacerbated their survival by augmenting glycolysis. In response to doxorubicin, p52-RelB complexes transcriptionally activated multiple glycolytic regulators with prognostic significance through increased recruitment at their gene promoters. Targeting p52-RelB and their targets in resistant cells increased doxorubicin sensitivity in vitro and in vivo. Collectively, our study uncovered novel molecular drivers of doxorubicin-induced resistance that are regulated by non-canonical NF-κB pathway. We reveal new avenues of therapeutic targeting for R-CHOP-treated refractory/relapsed DLBCL patients. Nanyang Technological University National Medical Research Council (NMRC) National Research Foundation (NRF) Submitted/Accepted version This study is funded by the National Research Foundation (NRF) Singapore, under its Singapore NRF Fellowship (NRFNRFF2018-04). In addition, we thank the Nanyang Assistant Professorship (NAP) Startup-grant to Y.L. lab and National Medical Research Council (NMRC-OFLCG18May0028), Tanoto Foundation and Ling Foundation for their support.

With lowering costs of sequencing and genetic profiling techniques, genetic drivers can now be detected readily in tumors but current prognostic models for Natural-killer/T cell lymphoma (NKTCL) have yet to fully leverage on them for prognosticating patients. Here, we used next-generation sequencing to sequence 260 NKTCL tumors, and trained a genomic prognostic model (GPM) with the genomic mutations and survival data from this retrospective cohort of patients using LASSO Cox regression. The GPM is defined by the mutational status of 13 prognostic genes and is weakly correlated with the risk-features in International Prognostic Index (IPI), Prognostic Index for Natural-Killer cell lymphoma (PINK), and PINK-Epstein-Barr virus (PINK-E). Cox-proportional hazard multivariate regression also showed that the new GPM is independent and significant for both progression-free survival (PFS, HR: 3.73, 95% CI 2.07-6.73; p

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.

Summary of Patients and Clinical Data.

Supplementary Figure 1. Unsupervised Clustering on CCA samples. Supplementary Figure 2. Alterations Found in CCA Clusters. Supplementary Figure 3. MAP2K4 Homozygous Deletions and ERBB2 Amplifications. Supplementary Figure 4. Alterations Related to Structural Variations Found in CCAs. Supplementary Figure 5. FIREFLY Analysis of Pathways Systematically Dysregulated by Somatic Promoter Mutations that Alter Transcription Factor Binding. Supplementary Figure 6. Epigenetic Clusters and Mutation Signatures.

Structural Variations across 71 WGS CCAs.

PDF file - 110K, Non-synonymous somatic mutations identified in the Discovery set of four NKTCL cases

PDF file - 83K, JAK1 and JAK3 mutation status in NKTCL cases

PDF file - 75K, Sequence analysis summary of tumor and blood samples from four NKTCL cases

PDF file - 74K, Primer sets used for Sanger sequencing and HRM analysis

Summary of CCA Alterations.

Further details of methods, in addition to the supplementary figure and table legends.

PDF file - 185K, Representative HRM curves of a NKTCL case confirmed as heterozygous JAK3A573V mutation

Alterations in CCA Clusters.

Coverage Statistics and List of Genes for Targeted Sequencing.

Table S1. Patient Charactertistics and their mi-EP platform type

Purpose:Peripheral T-cell lymphoma (PTCL) is a heterogeneous group of non–Hodgkin lymphomas with aggressive clinical behavior. We performed comprehensive miRNA profiling in PTCLs and corresponding normal CD4+ Th1/2 and TFH-like polarized subsets to elucidate the role of miRNAs in T-cell lymphomagenesis.Experimental Design:We used nCounter (NanoString Inc) for miRNA profiling and validated using Taqman qRT-PCR (Applied Biosystems, Inc). Normal CD4+ T cells were polarized into effector Th subsets using signature cytokines, and miRNA significance was revealed using functional experiments.Results:Effector Th subsets showed distinct miRNA expression with corresponding transcription factor expression (e.g., BCL6/miR-19b, -106, -30d, -26b, in IL21-polarized; GATA3/miR-155, miR-337 in Th2-polarized; and TBX21/miR-181a, -331-3p in Th1-polarized cells). Integration of miRNA signatures suggested activation of TCR and PI3K signaling in IL21-polarized cells, ERK signaling in Th1-polarized cells, and AKT–mTOR signaling in Th2-polarized cells, validated at protein level. In neoplastic counterparts, distinctive miRNAs were identified and confirmed in an independent cohort. Integrative miRNA–mRNA analysis identified a decrease in target transcript abundance leading to deregulation of sphingolipid and Wnt signaling and epigenetic dysregulation in angioimmunoblastic T-cell lymphoma (AITL), while ERK, MAPK, and cell cycle were identified in PTCL subsets, and decreased target transcript abundance was validated in an independent cohort. Elevated expression of miRNAs (miR-126-3p, miR-145-5p) in AITL was associated with poor clinical outcome. In silico and experimental validation suggest two targets (miR-126→ SIPR2 and miR-145 → ROCK1) resulting in reduced RhoA-GTPase activity and T–B-cell interaction.Conclusions:Unique miRNAs and deregulated oncogenic pathways are associated with PTCL subtypes. Upregulated miRNA-126-3p and miR-145-5p expression regulate RhoA-GTPase and inhibit T-cell migration, crucial for AITL pathobiology.

Figure S6: Overall Survival in PTCL by miRNA expression. Kaplan-Meier curve representing overall survival of PTCL-NOS cases (n=24) by higher vs lower expression of miR-29c (A) and miR-106 (B) expression. (C) Correlation of n-Counter values between miR-126 and miR-145 expression (r=0.1)

Table S2: List of Antibodies used for the Western Blot Table S3: List of flow cytometry Antibodies used for the expression of different markers Table S4: List of genes and their Forward and Reverse Sequence for the evaluation of mRNA expression by performing Real time RT PCR Table S5: Mature Sequence of miR-126-3p and miR-145-5p taqman Probe used for Taqman base Real time qPCR assay