Abstract
Macrophages are abundant immune cells in the microenvironment of
diffuse large B-cell lymphoma (DLBCL). Macrophage estimation by
immunohistochemistry shows varying prognostic significance across
studies in DLBCL, and does not provide a comprehensive analysis of
macrophage subtypes. Here, using digital spatial profiling with whole
transcriptome analysis of CD68+ cells, we characterize macrophages in
distinct spatial niches of reactive lymphoid tissues (RLTs) and DLBCL.
We reveal transcriptomic differences between macrophages within RLTs
(light zone /dark zone, germinal center/ interfollicular), and between
disease states (RLTs/ DLBCL), which we then use to generate six
spatially-derived macrophage signatures (MacroSigs). We proceed to
interrogate these MacroSigs in macrophage and DLBCL single-cell
RNA-sequencing datasets, and in gene-expression data from multiple
DLBCL cohorts. We show that specific MacroSigs are associated with
cell-of-origin subtypes and overall survival in DLBCL. This study
provides a spatially-resolved whole-transcriptome atlas of macrophages
in reactive and malignant lymphoid tissues, showing biological and
clinical significance.
Subject terms: B-cell lymphoma, Cancer microenvironment, Tumour
immunology, Cancer genomics
__________________________________________________________________
Macrophages are abundant in the microenvironment of diffuse large
B-cell lymphoma (DLBCL). Here, the authors use spatial transcriptomics
to characterize macrophages in DLBCL and reactive lymphoid tissues, and
propose six spatially-derived macrophage signatures that are associated
with features like cell of origin and clinical outcomes.
Introduction
Diffuse large B-cell lymphoma (DLBCL) is the most common subtype of
non-Hodgkin lymphoma in adults^[80]1,[81]2. Combination therapy of
rituximab with cyclophosphamide, doxorubicin, vincristine, and
prednisolone (R-CHOP) is potentially curative, but 30%–40% of cases
relapse after initial therapy^[82]3. Elucidating mechanisms underlying
relapse following R-CHOP is critical for the development of therapeutic
strategies to improve DLBCL outcomes. An important and emerging area is
the role of the tumor microenvironment (TME) in mediating disease
progression and clearance of tumor cells after
chemotherapy^[83]4–[84]8. Understanding factors in the TME that mediate
DLBCL relapse following R-CHOP is also important in terms of
incorporating current immunotherapeutics such as bispecific CD20-CD3
T-cell engagers, CD19 CAR-T cells and anti-CD47 antibodies into
front-line treatment regimens for DLBCL.
Tumor-associated macrophages (TAMs) are abundant immune cells in the
DLBCL TME^[85]9,[86]10. TAMs are recognized as potential therapeutic
targets in oncology due to their role in tumor progression, metastasis,
and recurrence^[87]11. In DLBCL, TAM infiltration is associated with
poor prognosis after R-CHOP therapy^[88]9,[89]12,[90]13. However, there
are discrepancies observed between studies, with a lack of sufficient
reproducibility to identify consistent clinical prognostic
markers^[91]14. These discrepancies may partly result from the
simplified functional classification of macrophages into M1/M2
phenotypes^[92]15. Conventionally, M1 macrophages refer to a
pro-inflammatory phenotype with pathogen-killing abilities while M2
refers to an anti-inflammatory phenotype promoting proliferation,
tissue repair and tumorigenesis^[93]16. However, macrophages in several
physiological or pathological settings, including embryonic
macrophages, resolution-phase macrophages and even certain TAMs do not
clearly fall into either the M1 or M2 phenotype^[94]17. Furthermore,
macrophages show high phenotypic plasticity^[95]17, suggesting that the
M1/M2 dichotomy does not fully encompass the functional diversity of
macrophages^[96]17–[97]19. Indeed, with single-cell transcriptomic
approaches, the functional complexity of macrophages is now widely
appreciated^[98]20,[99]21. The nature of TAMs defined by comprehensive
transcriptomic approaches in DLBCL and their relationship with
treatment outcomes remain poorly understood^[100]22.
In this work, we comprehensively characterize TAMs in DLBCL and
reactive lymphoid tissues (RLTs) using digital spatial profiling (DSP),
an advanced technique for spatially resolved transcriptomics. The DSP
whole transcriptome atlas (WTA) provides an unbiased map of
18,000 + RNA targets throughout specific cell types of interest (chosen
based on a fluorescent protein marker- here CD68 for macrophages) in
formalin-fixed paraffin-embedded tissue sections. Here, using DSP, we
generate spatially-derived macrophage transcriptomic signatures and
explore their associations with previously described macrophage
subpopulations and clinical/ biological features of DLBCL.
Results
Digital spatial profiling (DSP) illuminates consistent profiles from distinct
masks in lymphoid tissue microregions
We profiled the whole transcriptome of macrophages, T cells, and B
cells using the GeoMx® DSP WTA assay (Fig. [101]1A), by selective
collection of UV-cleavable probes from distinct masks generated by
immunofluorescent staining of the morphology markers CD68, CD3 and CD20
(pertaining to macrophages, T-cells and B-cells, respectively), in RLTs
(n = 24) and DLBCL patients (n = 64) for a total of 702 areas of
interest (AOIs) (Supplementary Fig. [102]1A, representative images in
Fig. [103]1B, C and Supplementary Fig. [104]1B–D).
Fig. 1. Digital spatial profiling (DSP) illuminates consistent profiles from
distinct masks in lymphoid tissue microregions.
[105]Fig. 1
[106]Open in a new tab
A Schematic of GeoMx® DSP WTA workflow (created with BioRender.com). B,
C Immunofluorescence staining of DLBCL tissues (n = 87) and RLTs
(n = 24). In Group 1, CD68 stained macrophages (yellow), CD3 stained T
cells (cyan), CD20 stained B cells (magenta), and SYTO 13 stained
nuclei (blue). In Group 2, CD68 stained macrophages (yellow), NGFR
illuminated LZ (green) and SYTO 13 stains nuclei (blue). After ROI
selection, each cell type was segmented based on the staining signal
and their corresponding masks were generated. Representative images are
shown. Scale bar: 100 μm. Source data are provided as a [107]Source
Data file. D, E Cumulative density functions showed that the signatures
of macrophages (CD68, CD163, FCGR1A, and CSF1R), T cells (CD3D, CD3E,
UBASH3A, CD2, and TRBC2), and B cells (MS4A1, CD79A, CD79B, CD19, and
PAX5) were highly enriched in CD68+ regions, CD3+ regions, and CD20+
regions, respectively in RLTs and DLBCL tissues (Kolmogorov-Smirnov
P < 0.05). Digital spatial profiling, DSP; whole transcriptome
analysis, WTA; diffuse large B-cell lymphoma, DLBCL; reactive lymphoid
tissues, RLTs; regions of interest, ROIs; areas of interest, AOIs;
formalin-fixed paraffin-embedded, FFPE; light zone, LZ; dark zone, DZ;
nerve growth factor receptor, NGFR.
Given the heterogeneity and high cellularity of the DLBCL
microenvironment, we aimed at confirming that DSP-based cell selection
generated reliable cell-type specific profiles. We therefore
cross-validated our DSP output with publicly available scRNA-seq data
of RLTs and DLBCL specimens. To do this, we defined gene signatures
characteristic of macrophages, T cells and B cells using 4–5 hallmark
genes of these cell types (Supplementary Table [108]1). As expected,
these signatures projected to matching cell-types in scRNA-seq datasets
of RLTs (Supplementary Fig. [109]2) and DLBCL (Supplementary
Fig. [110]3). We then tested these signatures on our DSP based CD68+
(macrophage), CD20+ (B-cell) and CD3+ (T-cell) AOIs using cumulative
density functions. We show that the macrophage signature was enriched
in CD68+ AOIs, while the signatures of T and B cells were enriched in
CD3+ and CD20+ AOIs, respectively (adjusted P < 0.05, Fig. [111]1D, E).
This suggests that morphology marker-based AOIs accurately capture the
respective cell types of interest, enabling the collection of distinct
whole transcriptome profiles of macrophages, T cells and B cells in
their native tissue environment.
We also verified the robustness of our DSP experiment by
cross-comparing its output for differentially expressed genes (DEGs)
between the light zone (LZ) and dark zone (DZ) regions (all cells,
majority CD20 + ) in the germinal center (GC) of RLTs with previously
published information on transcriptional differences between the LZ and
DZ^[112]23 (Supplementary Fig. [113]4A, B and Supplementary
Data [114]1). Almost all DEGs of DZ and LZ regions from our DSP
experiment overlapped with previously reported LZ/DZ DEGs from ref.
^[115]23 (Supplementary Fig. [116]4C), further confirming the
reliability and adequate transcriptomic coverage of our DSP approach
for subsequent analyses and inferences.
Unique gene expression patterns differentiate macrophages in distinct spatial
locations within reactive lymphoid tissues
Having quality checked the DSP data, we next aimed to investigate
transcriptomic differences of CD68+ macrophages within different
spatial regions of RLTs. Through DEG analysis, we observed that 997 and
755 genes were differentially upregulated in the GC and IF macrophages,
respectively (adjusted P < 0.05 and |log[2]FC| >0.58, Fig. [117]2A),
suggesting highly distinct gene expression patterns. The heatmap in
Fig. [118]2B displays the top 10 DEGs highly expressed in the GC and IF
regions. Cell proliferation and metabolism-associated pathways such as
E2F targets, MYC targets, and oxidative phosphorylation were enriched
pathways in GC macrophages (adjusted P < 0.0001, Fig. [119]2C). These
proliferative pathways such as E2F transcription factors are critical
for a wide variety of cell-types including myeloid progenitors and
differentiated macrophages^[120]24. In contrast, enriched pathways in
IF were mostly associated with immune responses such as the interferon
γ response and TNF-α/NF-κB pathways (adjusted P < 0.0001,
Fig. [121]2C). Of interest, macrophages in the IF showed upregulation
of S100A family members (calcium binding proteins), such as S100A4,
S100A8, and S100A9, which are known Damage-Associated Molecular Pattern
(DAMP) molecules regulating macrophage biology^[122]25 (Supplementary
Data [123]2).
Fig. 2. Unique gene expression patterns differentiate macrophages in distinct
spatial locations within reactive lymphoid tissues.
[124]Fig. 2
[125]Open in a new tab
A Volcano plot showing the DEGs of macrophages between the GC and IF
based on adjusted P < 0.05 and |log[2]FC| ≥ 0.58. P values were
determined by two tailed moderated t test (BH corrected). B Top 20
macrophage DEGs (10 DEGs upregulated in GC and 10 DEGs upregulated in
IF) are displayed based on adjusted P value in the heatmap. C Pathway
enrichment analysis was performed on all DEGs between GC and IF. P
value calculated by two tailed Fisher exact test (BH corrected). The
top 10 pathways, based on BH adjusted P value, are shown. D The volcano
plot showed the macrophage DEGs between LZ and DZ based on adjusted
P < 0.05 and |log[2]FC| ≥ 0.58. P values were determined by two tailed
moderated t test (BH corrected). E Pathway enrichment analysis was
performed on all macrophage DEGs between LZ and DZ. P values were
calculated by two tailed Fisher exact test (BH corrected). F
MoMac-VERSE annotated 17 TAM subclusters using a compilation of 41
scRNA-seq datasets from 13 healthy and cancer tissues (Figure created
via [[126]https://macroverse.gustaveroussy.fr/2021_MoMac_VERSE/]). G–J
Top50 genes of each MacroSig1-4 were projected respectively onto
MoMac-VERSE. Germinal center, GC; interfollicular, IF; fold change, FC;
macrophage signatures, MacroSigs.
The GC consists of two functionally distinct compartments: dark zone
(DZ) and light zone (LZ)^[127]26,[128]27. This compartmentalization is
critical for dynamic differentiation of B cells within the
GC^[129]28,[130]29. We also compared the DEGs of macrophages in the LZ
and DZ. We also note significant differences (adjusted P < 0.05 and
|log[2]FC| > 0.58, Fig. [131]2D) in gene expression between macrophages
populating these anatomically distinct compartments of the germinal
center. The complement pattern recognition components C1QA, C1QB, and
C1QC were significantly upregulated in DZ macrophages (adjusted
P < 0.0001, Fig. [132]2D), as were other components of the Hallmark
gene set of the complement pathway (Fig. [133]2E). This suggests a
possible role for non-canonical complement system functions in
macrophage polarization in the DZ of RLTs.
Based on the above comparisons between macrophages in distinct spatial
locations, we derived macrophage signatures (termed henceforth as
MacroSigs) from the respective DEGs meeting the following criteria: a.
Benjamini-Hochberg adjusted P < 0.05; b. |log[2]FC|
> 0.58^[134]30–[135]32. MacroSigs (generated from upregulated DEGs)
corresponding to spatial compartments in RLTs were: MacroSig1 (GC),
MacroSig2 (IF), MacroSig3 (LZ), and MacroSig4 (DZ). We then evaluated
if these spatially-derived MacroSigs could be mapped to known
macrophage subclusters generated through single-cell RNA sequencing. We
utilized an integrative dataset named MoMac-VERSE^[136]33
(Fig. [137]2F), the current largest meta-analysis of human monocytes
and macrophages, with 17 annotated monocyte/macrophage subclusters from
41 scRNA-seq datasets comprising 13 healthy and pathological tissues.
We projected the top 50 genes of our MacroSigs (Supplementary
Data [138]2) onto MoMac-VERSE and noted that MacroSig1 (GC) overlapped
with TREM2+ macrophages (Fig. [139]2G). Interestingly, despite having
clear transcriptional distinctions from GC macrophages, the MacroSig2
(IF) was dispersed and did not overlay with one or more specific
subclusters of macrophages as defined by the MoMac-VERSE
(Fig. [140]2H). This raised the possibility that MacroSigs from
distinct regions of lymphoid tissue may denote hitherto unknown
macrophage subtypes (not represented in the MoMac-VERSE metanalysis,
which is not a spatially resolved approach). Additionally, MacroSig3
(LZ) localized to the MNP/T cell doublets (Fig. [141]2I), while
MacroSig4 (DZ) overlapped with the HES1/FOLR2 macrophage population
(Fig. [142]2J). These results indicated that these macrophage
subpopulations may play specific roles in distinct regions of lymphoid
tissues, deserving functional investigation.
Distinct transcriptomic profiles of macrophages between reactive and
malignant lymphoid tissue
We next compared the gene expression of macrophages from RLT germinal
center (GC) regions with that of macrophages from DLBCL samples. The
DEGs of these two macrophage subsets highlight that 895 and 468 unique
genes were differentially upregulated in RLTs GC and DLBCL macrophages,
respectively (Fig. [143]3A). The heatmap in Fig. [144]3B displays the
top DEGs highly expressed in both RLT and DLBCL. These DEGs are
referred to henceforth as MacroSig5 (RLT) and MacroSig6 (DLBCL) using
the above-mentioned criteria (adjusted P < 0.05 and |log[2]FC| > 0.58).
As the features of macrophages in distinct spatial locations of RLTs
have been detailed in the previous section, here we focus on features
of macrophages in DLBCL. We noted that CD163, a marker of
pro-tumorigenic macrophages, as well as complement pattern recognition
components (C1QA, C1QB, and C1QC) were markedly upregulated in DLBCL
macrophages (adjusted P < 0.0001, Supplementary Data [145]2). Enriched
pathways in the DLBCL MacroSig were mostly associated with immune
responses such as interferon response and complement pathways (adjusted
P < 0.005, Fig. [146]3C). Using the MoMac-VERSE, we see that MacroSig6
(DLBCL) projected to the IL4I1+ macrophage population (Fig. [147]3D), a
macrophage subset that embodies immunosuppressive functions in diverse
cancer types^[148]34–[149]36. These findings hint at a potential role
of IL4I1+ macrophages in DLBCL pathogenesis and offer the prospect of
exploring the targeting of these cells, although additional mechanistic
work will be required to confirm the feasibility of such putative
therapeutic interventions.
Fig. 3. Distinct transcriptomic profiles of macrophages between reactive and
malignant lymphoid tissue.
[150]Fig. 3
[151]Open in a new tab
A Volcano plot showing the macrophage DEGs between RLTs and DLBCL based
on adjusted P < 0.05 and |log[2]FC| ≥ 0.58. P values were determined by
two tailed moderated t test (BH corrected). B Top DEGs between RLTs and
DLBCL are displayed in the heatmap. C Pathway enrichment analysis was
performed on all macrophage DEGs between GC and DLBCL. P values were
calculated by two tailed Fisher exact test (BH corrected). The top 10
pathways, based on adjusted P are shown. D Top50 genes of MacroSig6
(DLBCL) were projected onto MoMac-VERSE.
Spatially-derived MacroSigs associate with COO DLBCL subclassifications
DLBCL patients are subtyped based on B cell-of-origin (COO) gene
expression profiling (GEP). As such, we sought to understand if our
spatially-derived MacroSigs were related to these subclassifications.
To perform this analysis, we explored the enrichment of our MacroSigs
in bulk RNA gene expression profiles of DLBCL patients across eight
publicly available transcriptomic datasets (n = 4594, 8 datasets).
Across all datasets, MacroSig1 (GC) was enriched in germinal center
B-cell like (GCB) DLBCL (adjusted P < 0.05, Fig. [152]4A, 8/8
datasets), MacroSig2 (IF) in unclassified (UNC) DLBCL (adjusted
P < 0.05, Figs. [153]4A, 6/8 datasets), and MacroSig6 (DLBCL) in
activated B-cell (ABC) subtype DLBCL (adjusted P < 0.05, Fig. [154]4B,
8/8 datasets). MacroSig3 (LZ) and MacroSig4 (DZ) were not distinctly
enriched in any COO category (Fig. [155]4C).
Fig. 4. Spatially-derived MacroSigs associate with COO DLBCL
subclassifications.
[156]Fig. 4
[157]Open in a new tab
The associations of MacroSigs with clinical categories (i.e., COO,
genetic subtypes) were evaluated through the Fisher exact test. The
overlap ratio refers to the number of patients classified as both a
certain MacroSig and COO category, divided by the total number of
patients classified in that particular COO category. A MacroSig1 (GC)
and MacroSig2 (IF) were enriched in DLBCL COO classifications in bulk
RNA gene expression profiles of DLBCL patients across eight publicly
available transcriptomic datasets (n = 4594, 8 datasets). B MacroSig5
(RLT) and MacroSig6 (DLBCL) were enriched in DLBCL COO classifications
in the above-mentioned eight datasets. C MacroSig3 (LZ) and MacroSig4
(DZ) were not distinctly enriched in any COO category in the
above-mentioned eight datasets. D All genes of each MacroSig, through
their respective module scores, were projected onto the
Monocyte/Macrophage and B cell subsets of DLBCL scRNA-seq datasets (Ye
et al; n = 17). The violin plot depicts, for each patient, the
percentage of B cells and macrophages expressing a given MacroSig
(module score > 0.1) The median and quartile bands are depicted. P
values were calculated by a paired t test (see also Supplementary
Figs. [158]5 and [159]6). Source data are provided as a [160]Source
Data file. Germinal center B-cell like, GCB; activated B-cell like,
ABC; unclassified, UNC; monocyte/macrophage, Mono/Mac.
Given these interesting but unexpected correlations, we evaluated if
the genes composing our MacroSigs identified subpopulations of
macrophages in scRNA-seq data from DLBCL cases (n = 17, comprising
transcriptomes of 94,324 cells)^[161]37. We see that our MacroSigs were
enriched within distinct clusters of the monocyte-macrophage
populations in the DLBCL scRNA-seq dataset (Fig. [162]4D and
Supplementary Fig. [163]5). Most MacroSigs showed negligible module
scores in the DLBCL B-cell population (except for GC/RLT [Fig. [164]4D
and Supplementary Fig. [165]6], which share several cell-cycle/
proliferation genes with the GC/RLT B-cell signature from our DSP
experiment). This analysis suggests that MacroSigs are indeed likely to
represent macrophages in GEP analysis, even when evaluated in the
malignant setting with aberrant gene expression profiles as compared
with normal B cells. We further investigated if these MacroSigs could
relate to recently established DLBCL genetic and molecular subtypes.
Comparisons were conducted across 3 independent reference
datasets^[166]38–[167]40. Overall, we did not observe consistent
enrichment of MacroSigs with specific genetic subtypes (Supplementary
Fig. [168]7A). Similarly, there was no clear enrichment of our
MacroSigs with specific DLBCL TME categories proposed by ref. ^[169]5
(Supplementary Fig. [170]7B). These results point to convergent
mechanisms for tumor associated macrophage infiltration across broad
genetic and TME subgroups.
Spatially-derived MacroSigs stratify patients for survival in gene-expression
profiling datasets of DLBCL
Finally, we aimed to evaluate if spatially-derived MacroSigs could have
prognostic significance when evaluated in bulk gene expression data
from clinical samples of DLBCL, using the above-mentioned eight
clinically annotated DLBCL datasets (n = 4594, 8 datasets). Cases
enriched for MacroSig6 (DLBCL) had shorter OS than those with MacroSig5
(RLT) (Fig. [171]5A, adjusted P < 0.05, 6/8 datasets; hazard ratios
(HR) and 95% confidence intervals (CI) for each dataset in
Supplementary Table [172]2). This was corroborated in the Kaplan-Meier
plots across six datasets (Fig. [173]5B–I, adjusted P < 0.05). It is
possible that patients enriched in MacroSig5 (RLT) may represent those
with less immunosuppressive TMEs and fewer TAMs than cases enriched for
MacroSig6 (DLBCL). This result also highlights that the application of
a macrophage derived gene signature may remain clinically relevant in
bulk gene-expression data from these tumors. Of particular interest
however, we observed that DLBCL patients with MacroSig4 (DZ) had
significantly worse OS compared to those with MacroSig3 (LZ) across
multiple DLBCL datasets (Fig. [174]6A-I, adjusted P < 0.05, 7/8
datasets; HR and 95% CI refer to Supplementary Table [175]2). This
association was also validated in a multivariate analysis adjusted for
the IPI score and double hit lymphoma cases (Supplementary
Table [176]3, adjusted P < 0.05, 5/6 datasets; IPI scores not available
in 2 datasets; DHL only available in 2 datasets), confirming that
MacroSig4 (DZ) is prognostic factor independent of clinical high- risk
features for survival of DLBCL patients.
Fig. 5. Spatially-derived MacroSig5/6 (RLT/DLBCL) stratify for patient
survival in DLBCL datasets.
[177]Fig. 5
[178]Open in a new tab
A Forest plot depicting the univariate Cox proportional hazards model
analysis, comparing MacroSig5 (RLT) and MacroSig6 (DLBCL) (represented
as tertile groups, as described in Methods: Survival analysis).
Analysis applied to bulk RNA gene expression profiles of DLBCL patients
across eight publicly available transcriptomic datasets (n = 4594, 8
datasets). Data are presented as the 95% confidence interval of the
hazard ratio (plotted in log-scale). Source data are provided as a
[179]Source Data file. B–I Kaplan–Meier analyses showed that patients
with high expression of MacroSig6 (DLBCL) and low expression of
MacroSig5 (RLT) were associated with poor OS across six distinct DLBCL
datasets. P values generated by log-rank test. Overall survival, OS
(see Methods: Survival analysis).
Fig. 6. Spatially-derived MacroSig3/4 (LZ/DZ) stratify for patient survival
in DLBCL datasets.
[180]Fig. 6
[181]Open in a new tab
A Forest plot depicting the univariate Cox proportional hazards model
analysis, comparing MacroSig3 (LZ) and MacroSig4 (DZ) (represented as
tertile groups, as described in Methods: Survival analysis). Analysis
was applied to bulk RNA gene expression profiles of DLBCL patients
across eight publicly available transcriptomic datasets (n = 4594, 8
datasets). Data are presented as the 95% confidence interval of the
hazard ratio (plotted in log-scale). Source data are provided as a
[182]Source Data file. B–I Kaplan–Meier analyses showed that patients
with high expression of MacroSig4 (DZ) and low expression of MacroSig3
(LZ) were associated with poor OS in DLBCL patients across seven
distinct DLBCL datasets. P value generated by log-rank test.
Additional evaluation of the Dark Zone MacroSig in DLBCL
Gene expression signatures of B-cell dark-zone biology are now well
appreciated to be prognostic in DLBCL, overlapping with molecular
high-grade and double-hit like signatures^[183]41. We therefore
evaluated the prognostic impact of the dark zone signature of B-cells
obtained from our own DSP experiments (Supplementary Data [184]3), to
compare it with our results from the DZ-MacroSig4. The B-cell-based DZ
signatures were indeed prognostic (Fig. [185]7A, adjusted P < 0.05, 4/8
datasets), but interestingly with less consistency than the
DZ-MacroSigs (Fig. [186]6A, adjusted P < 0.05, 7/8 datasets). Of note,
only few genes were shared between the B-cell derived and
macrophage-derived LZ- and DZ- signatures (MacroSig3-4) (Fig. [187]7B),
highlighting that DLBCLs carrying features of these distinct aspects of
dark zone biology (B-cell and Macrophage) have adverse outcomes, but
likely through distinct biological mechanisms.
Fig. 7. Additional evaluation of the Dark Zone MacroSig hallmark C1Q in
DLBCL.
[188]Fig. 7
[189]Open in a new tab
A Forest plot depicting the univariate Cox proportional hazards model
analysis, comparing B cell-based LZ and DZ signatures (represented as
tertile groups, as described in Methods: Survival analysis). Analysis
applied to bulk RNA gene expression profiles of DLBCL patients across
eight publicly available transcriptomic datasets (n = 4594, 8
datasets). Data are presented as the 95% confidence interval of the
hazard ratio (plotted in log-scale). Source data are provided as a
[190]Source Data file. B Venn diagram displaying the overlapping genes
of LZ-, DZ-like B-cell signatures, and MacroSig3-4 (LZ and DZ). C
Immunochemistry staining of RLTs was shown (n = 3). Activation-induced
cytidine deaminase (AID) in magenta was used for illuminating the LZ
and DZ. C1Q in brown stained macrophages. Scale bar: 100 μm. Source
data are provided as a [191]Source Data file. D Immunofluorescence
stained CD68 + C1Q+ cells in DLBCL tissues (n = 86). Representative
images are shown. Scale bar: 100 μm. Source data are provided as a
[192]Source Data file. E Kaplan–Meier analyses showed that patients
with highly infiltrating levels of CD68 + C1Q+ cells were associated
with poor OS in DLBCL patients in CMMC cohort. P value generated by
log-rank test. F Graphical abstract summarizing the derivation of the
spatial derived MacroSigs and describing their associations with known
features of macrophage/ DLBCL biology and clinical outcome (created
with BioRender.com).
To further validate the DZ signature, we aimed to perform a protein
based multiplex staining approach. As complement pattern recognition
component genes (C1QA, C1QB, and C1QC) appeared as top ranking genes in
the DZ signature (MacroSig4, Supplementary Data [193]2), and since C1Q
expressing macrophages are a defined entity^[194]42, we evaluated the
immunofluorescence staining of C1Q in RLTs and also in an additional
independent set of DLBCL tissues in tissue microarray format from the
Chi-Mei Medical Center (CMMC), Taiwan. Using a co-stain with the dark
zone marker AID, we note that C1Q expressing macrophages were indeed
enriched in the dark zone (DZ) when compared to the light zone (LZ) in
germinal centers from RLTs (Fig. [195]7C). In the CMMC cohort of DLBCL
cases, we observed that patients with high C1Q expressing macrophages
had poorer survival compared to those with low C1Q in their macrophages
(Log-rank P < 0.05, Fig. [196]7D, E). As C1Q was also a differentially
expressed hit in the DLBCL MacroSig (which was also prognostic in
DLBCL), additional comparative staining of proteins coded within the
MacroSigs will be required to tease apart different subtypes of C1Q
expressing macrophages and their relationship to the poor prognostic
dark-zone B-cell signature. Nonetheless, these results suggest that C1Q
expressing macrophages populating the germinal center dark zone will be
an important area of research of biology/ therapeutic relevance in
DLBCL. Overall, our findings provide insights to the landscape of the
DLBCL TME, and highlight the importance of dark zone biology in the
prognosis of DLBCL.
Discussion
Using DSP, through the application of a mask for CD68 (a broad marker
for cells of macrophage-monocyte lineage)^[197]43,[198]44, we defined
macrophage transcriptomic signatures in reactive and malignant lymphoid
tissue (termed MacroSig1-6; corresponding to biological/clinical
categories listed in Table [199]1) and described their associations
with known features of macrophage/DLBCL biology and clinical outcome
(Fig. [200]7F). Macrophages are highly plastic, acquiring diverse
phenotypes and functions that enable their role in a variety of
physiological or pathological settings^[201]45. We demonstrate the
spatial transcriptional diversity of macrophages within different
regions of RLTs and between macrophages populating RLTs and DLBCL.
While certain MacroSigs (GC, LZ, DZ, DLBCL) overlap with distinct and
previously defined macrophage groups based on scRNA-seq^[202]33,
indicating that macrophage subpopulations in different spatial niches
do have specific roles or functions within their respective regions, it
is of interest that certain others do not (interfollicular [IF], RLT).
As the reference scRNAseq datasets are not spatially resolved, it will
be of interest to evaluate these MacroSigs in the future in spatially
resolved reference datasets of macrophages. Distinguishing these
relationships and further orthogonal validation of these findings at
single cell resolution would aid in the understanding of macrophage
biology and the development of macrophage targeted therapies.
Table 1.
Six distinct MacroSigs based on biological/clinical characteristics
MacroSigs Abbreviations Associated biological/clinical feature
MacroSig1 GC-MacroSig Macrophages from GC of RLTs
MacroSig2 IF-MacroSig Macrophages from IF of RLTs
MacroSig3 LZ-MacroSig Macrophages from LZ of RLTs
MacroSig4 DZ-MacroSig Macrophages from DZ of RLTs
MacroSig5 RLT-MacroSig Macrophages from RLTs
MacroSig6 DLBCL-MacroSig Macrophages from DLBCL patients
[203]Open in a new tab
MacroSigs macrophage signatures, GC germinal center, IF
interfollicular, LZ light zone, DZ dark zone, RLTs reactive lymphoid
tissues, DLBCL diffuse large B-cell lymphoma.
A key finding from our study is the association of certain MacroSigs
with established clinically-relevant DLBCL
subclassifications^[204]5,[205]46–[206]48. GEP identifies two
prognostically distinct clusters of DLBCL based on COO, but there
remains a consistent cluster of unclassified cases. We report a strong
correlation of the signature of IF-macrophages (MacroSig2) with this
unclassified cluster, along with clear correlations of MacroSig1 (GC)
to the germinal center B-cell like (GCB) DLBCL cluster and MacroSig6
(DLBCL) to the ABC cluster. While the association of the germinal
center (GC) MacroSig to the GCB B-cell COO signature may be explained
by a high degree of similarity in cell-cycle/proliferative genes
between these, the associations of the IF and DLBCL MacroSigs to COO
subtypes are particularly striking and supported by a negligible
expression of genes from these MacroSigs in malignant B-cells. These
results suggest that DLBCL gene expression signatures may be entwined
with the nature of underlying macrophage infiltrates, and may indeed
co-evolve during tumorigenesis. The role of IF-like macrophages in
potentially determining the overall gene-expression status of the
unclassified COO subcluster, and its links to TNFR signaling and
inhibitory immune checkpoint overexpression^[207]49 will be an
interesting avenue of research.
We also identify MacroSigs that are prognostic for poor clinical
outcomes in multiple independent DLBCL datasets. Though it was not the
main objective of this study to define new predictors of DLBCL outcome
to be used in routine practice, our results lend credence to the
clinical significance of our signatures. For example, both MacroSig4
(DZ) and MacroSig6 (DLBCL) were negatively prognostic, and were
associated with higher expression of M2-related markers such as CD209,
CSF1R, IL10, and TGFβ-associated genes (TGFBI, TGFB1). Additionally, it
is now recognized that macrophage phagocytosis checkpoint molecules are
associated with resistance to rituximab in DLBCL^[208]11. We also found
that macrophage checkpoints such as SIRPα, LRLRB1, SIGLEC10, and PDCD1
are enriched in patients categorized to have MacroSig 6 (DLBCL) in
comparison to MacroSig 5 (RLT) in eight DLBCL publicly available
datasets (Supplementary Fig. [209]8). Future mechanistic work
elucidating their underlying relationship could contribute to the
development of potential therapeutic strategies to overcome rituximab
resistance.
Molecular high-grade (MHG)^[210]50/double-hit gene expression signature
(DHITsig)^[211]51 is another salient molecular subgroup of interest in
DLBCL. This subgroup carries a uniformly poor prognosis and is thought
to be reflective of GC-dark zone (DZ) biology^[212]52,[213]53, and was
renamed the DZ signature^[214]52. It is intriguing that the strongest
prognostic signature in our study was also of DZ-cells, but that of
macrophages and not B-cells. Importantly, we found that genes
comprising MacroSig4 (DZ), collected from CD68 + DZ macrophages, are
distinct from published B-cell DZ signatures and from those collected
in our own experiments from CD20 + DZ B-cells. Notably, both MacroSig4
(DZ) and B-cell DZ signatures were highly prognostic in multiple DLBCL
datasets, with higher effect sizes and more consistent statistical
significance for the MacroSig4 (DZ). None of genes constituting
MacroSig4 (DZ) were noted to be highly expressed within CD20+ cells in
DLBCL in our dataset, indicating that the signature is likely to confer
its prognostic significance because of macrophage infiltration and not
aberrant expression of these genes in tumor cells. Pathway enrichment
analysis indicated that the cell proliferation/cycle-associated
pathways were activated in DZ macrophages. A recent transcriptomic
analysis of the circulating monocyte-derived macrophages (MDMs)
identified a proliferative macrophage subcluster which influence
MDMs-mediated inflammation and regeneration^[215]54. It is tempting to
speculate a similar biology exists in these proliferative DZ
macrophages, the molecular (proteomic/secreted) characteristics of
which require further investigation.
A potential limitation of DSP is its reliance on average values across
a group of cells defined by a cellular “mask”, and its resolution which
likely extends beyond the size of a single cell. There could exist
heterogeneity within each mask, even within spatially defined regions,
and this can hopefully be clarified through future advances in
single-cell resolved spatial transcriptomic methods. Furthermore, the
active scavenging function of macrophages may contribute to
non-macrophage transcript contamination^[216]55, which will be a
general limitation of any spatial transcriptomic study of macrophages.
Nonetheless, we have demonstrated that there is minimal overlap of gene
sets between the CD68+ and CD20+ masks profiled regions, suggesting
that neither phagocytosis nor the technical spill-over of transcripts
between the CD68 and CD20 photocleaved DSP regions are likely to
significantly contribute to the observed phenotypes associated with
different macrophage spatial profiles. Moreover, we also generated
signatures by filtering transcripts potentially linked to close
interactions between macrophages and T cells, to evaluate and exclude
the contribution of transcripts related to the interaction with T
lymphocytes, obtaining analogous results in terms of prognostic ability
(Supplementary Fig. [217]9 and Supplementary Table. [218]4).
Nevertheless, further refinements to spatial transcriptomics at
single-cell resolution will provide more detailed dissection of
intrinsic vs scavenged transcripts, and also allow direct evaluation of
spatial interactions between different cell types, contributing to a
deeper understanding of the actual relationships between macrophage
subtypes and other TME components.
In summary, through the use of DSP, we present a spatially resolved
transcriptomic characterization of macrophages in RLTs and DLBCL,
showing diverse characteristics of macrophage landscapes in different
spatial localizations. Spatially-derived MacroSigs of lymphoid tissue
can complement existing genetic and molecular DLBCL subclassifications,
contributing to our understanding of the DLBCL TME and the
ever-evolving classification of this heterogenous disease, and are also
prognostic across numerous distinct DLBCL datasets. These data provide
a framework to further evaluate the biological and clinical relevance
of macrophage subtypes in lymphoid biology and disease.
Methods
DSP study population
Our research complies with all relevant ethical regulations. All biopsy
samples were pre-treatment samples and obtained from the Department of
Pathology, National University Hospital, with IRB approved waiver of
consent in accordance with the ethical guidelines of the National
Healthcare Group domain specific review board (NHG DSRB) approved
protocol 2015/00176. This waiver of consent applies to all samples
obtained between 1st January 1990 and 30th April 2020 on the basis that
there is no longer patient contact (patient is deceased or lost to
follow-up) and that this study poses minimal risk to the patient. The
overall DSP study population was made up of two patient groups. Group 1
comprised of two tissue microarrays (TMAs) of de novo DLBCL samples
derived from pre-treatment biopsies of 64 patients between 2010 and
2017 subsequently treated with 6x R-CHOP with a follow-up time more
than three years (for relapsed patients, the follow-up time is at least
1 year), at the National University Hospital in Singapore
(Supplementary Fig. [219]10). 23 patients had duplicate cores between
both TMAs, meaning a total of 87 biopsies were profiled. Patient
details and characteristics from the aforementioned cohorts are
summarized in Supplementary Table [220]5. Group 2 (non-malignant RLT
samples) comprised of a TMA containing 12 tonsil samples and 11 tonsil
whole-slide samples, obtained from patients with tonsillectomies at the
National University Hospital for non-cancer indications. Also included
into Group 2 was a whole-slide tonsil section retrieved from the
archives of the Tumor Immunology Laboratory of the University of
Palermo and approved by the University of Palermo Institutional Review
Board 09/2018. All tissues were formalin-fixed and paraffin-embedded at
the time of resection and stored as paraffin blocks. An additional
DLBCL TMA from the Chi-Mei Medical Center, Taiwan (CMMC cohort; n = 86)
with OS more than 6 months, was used as a validation cohort for
quantitative immunofluorescence analyses. Usage of tissues from all
providing institutions is incorporated into the framework of an NUS IRB
approved translational study (H-19-055E).
GeoMx® DSP WTA assay
Spatial transcriptomics of RLTs and DLBCL tissues was conducted using
the GeoMx® WTA kit (GMX-RNA-NGSHuWTA-4, NanoString, Seattle,
Washington, USA), according to manufacturer’s instructions
(Fig. [221]1A).
GeoMx® DSP sample preparation on Bond Max
The formalin-fixed paraffin-embedded sections were freshly cut (5 µm
thick) and placed on Bond plus slides (S21.2113.A, Leica Biosystems,
Germany). The slides were baked at 60 °C for 1 h and loaded, with
covertiles, into the slide tray on Bond Max Fully Automated IHC and ISH
Staining System for deparaffinization, rehydration, antigen retrieval
(ER2 solution [AR9640, Leica Biosystems] at 100 °C for 20 min), RNA
digestion (Proteinase K 1 μg/ml for 15 min) and post-fixation (10%
neutral buffered formalin [NBF, HT501128, Sigma-Aldrich, St. Louis,
Missouri, USA] for 5 min, NBF stop buffer for 5 min twice). The NBF
stop buffer was prepared using Tris base (H5133, Promega, Madison,
Wisconsin, USA) and Glycine (15527013, Thermo Fisher Scientific,
Waltham, Massachusetts, USA) in DEPC-treated water. Upon run
completion, the covertiles were removed and the slides were soaked in
PBS for subsequent hybridization.
In-situ hybridization
An overnight in-situ hybridization was performed with GeoMx® Human NGS
WTA (GMX-RNA-NGSHuWTA-4, NanoString) that contained probes for 18,000+
protein-coding genes. After which, the slides were washed twice with
equal parts of 4X SSC (15557044, Thermo Fisher Scientific) and 100%
formamide (AM9342, Sigma-Aldrich) at 37 °C for 25 min to remove
off-target probes.
GeoMx® DSP sample collection
The slides were incubated with blocking Buffer W (200 μL/slide,
GMX-PREP-RNAFFPE-12, NanoString) for 30 min in the humidity chamber at
room temperature after hybridization. For Group 1, slides of DLBCL TMA
were stained with macrophage marker CD68 (sc-20060 AF594, Santa Cruz
biotechnology, Texas, USA), T-cell marker CD3 (A0452, Dako, California,
USA), B-cell marker CD20 (NBP2-47840 AF647, Novus Biologicals,
Colorado, USA) and the nuclear stain SYTO 13. For Group 2
(Fig. [222]1B), the slide of RLT TMA was visualized with CD68, the
follicular dendritic cell marker nerve growth factor receptor (Ab52987,
NGFR, Abcam, Cambridge, UK) and SYTO 13. Individual RLT sections of
Group 2 (Supplementary Fig. [223]1C) were stained with similar markers
as Group 1. Additionally, corresponding serial sections were stained
with NGFR to identify light zone (LZ) and dark zone (DZ) regions. After
immunofluorescent staining, the slides were visualized using the GeoMx®
DSP instrument (software: 2.4.0.421) to select regions of interest
(ROIs). To acquire representative regions, ROI selection was performed
by an expert pathologist. Based on the intensity of respective
morphology marker’s fluorescent staining (CD68, CD3, and CD20), we
adjusted the thresholds of each channel until the desired masks are
generated to fit the corresponding cell types precisely. Then each ROI
was segmented into corresponding areas of interest (AOIs) - CD68+
regions, CD3+ regions, and CD20+ regions based on the respective masks.
Subsequently, each AOI- defined by the cellular masks within an ROI
(e.g., CD68)- was exposed to UV light and photocleaved oligos were
aspirated from the solution into the wells of a collection plate
(Fig. [224]1A) for downstream sequencing and data processing. We
therefore obtain cell-type specific transcriptome data for all cells
marked by the mask, across a range of AOI sizes. The gene counts
obtained from each AOI are the aggregate from all cells of a given cell
type within an ROI (normalized as described in Methods: DSP data
processing and harmonization). The size and cell numbers of each AOI
are supplied in Supplementary Data [225]4.
Library preparation and sequencing
Collected photocleaved oligos were PCR amplified with the corresponding
GeoMx® Seq Code Primer Plate and Master Mix (GMX-NGS-SEQ, NanoString).
PCR products were pooled and cleaned with AMPure XP beads (A63880,
Beckman Coulter, Brea, California, USA) twice to obtain the libraries.
The quality and concentration of libraries were assessed using a high
sensitivity DNA Kit (5067-4626, Agilent, Santa Clara, California, USA)
and Bioanalyzer. Subsequently, libraries were sequenced on an illumina
sequencing platform (HiSeq 3000 or NovaSeq 6000) with standard workflow
specifications (dual-indexing and paired-end reads [2 × 27 bp]).
DSP data processing and harmonization
Raw reads were trimmed, stitched, aligned, and deduplicated to generate
digital counts data for unique target genes. AOIs with fewer than
10,000 raw reads or sequencing saturation <50% were filtered out of the
analysis. AOIs with less than 5% of all target genes (18,000 + ) and
target genes that did not achieve the limit of quantitation were
removed. The data was then processed with the Q3 normalization method
for all the remaining targets according to NanoString guidelines. Q3
normalization divides the counts in one AOI by the 3rd quartile value
for that AOI, then subsequently multiplies that value by the geometric
mean of the 3rd quartile values of all AOIs. Q3 normalization rescales
the gene expression data such that all AOIs have similar gene
expression ranges. It reduces variance from AOI size, AOI cellularity,
and other technical factors. Underlying the Q3 method is an assumption
that, despite biological variation, AOIs should have similar gene
expression count distributions. Thus, Q3 is only appropriate when a
probe panel is large and diverse, such as the one used here, that
targets the full transcriptome.
As the spatial profiling was conducted as two individual experiments,
the sequencing data was harmonized using the removeBatchEffect function
of the limma (3.56.2) package based on the respective masks (CD68,
CD20, and CD3)^[226]56. Principal component analysis (PCA) analysis was
conducted using the FactoMinR (2.9) package, and outlier AOI were
removed according to the PCA projections. In addition, the
Kolmogorov-Smirnov test was applied to further assess the overall
distribution of the data obtained from each mask: Using a procured set
of genes representing macrophages (CD68, CD163, FCGR1A, and CSF1R), T
cells (CD3D, CD3E, UBASH3A, CD2, and TRBC2), and B cells (MS4A1, CD79A,
CD79B, CD19, and PAX5) the cumulative expression for each signature was
evaluated and compared within each mask (Fig. [227]1D, E). Following
which, formal analyses were conducted as detailed in subsequent
sections. The overall data analysis workflow is provided in
Supplementary Fig. [228]11. All statistical analyses were performed
using R statistical software (v 4.3.0) ([229]http://www.R-project.org).
Clustering analysis
The Euclidean distance metric across AOIs was considered for the sample
hierarchical clustering analysis, and the ward.D2 aggregation method
was used to build the heatmap dendrogram within the R package pheatmap
(1.0.12).
Differential expression analyses (DEAs) and derivation of MacroSigs
DEAs were conducted using moderated t test from the limma
package^[230]56. The presence of DLBCL samples that belong to the same
patient was considered using the duplicateCorrelation option of the
limma package. Upregulated/downregulated genes were selected by
applying the Benjamini-Hochberg correction on the P values (BH adjusted
P < 0.05) and considering the |log[2]FC| > 0.58. Based on both
criteria, MacroSigs1-6 were derived from the DEAs between GC/IF, LZ/DZ,
and RLT/DLBCL To establish the representative genes of our MacroSigs
and filter out contaminant genes derived from B-cells, we removed those
DEGs whose percentile rank of average expression, as determined after
Q3 normalization, was greater in the CD20 mask than in the CD68 mask.
The resulting DEGs constituted the corresponding MacroSigs. This
filtering process was not applied to the MacroSig5 (RLT) due to the
absence of data from corresponding CD20 masks within whole-region
germinal center ROIs.
Pathway enrichment analyses
The pathway enrichment analyses were performed considering the Hallmark
gene sets downloaded from Human Molecular Signature Database
(MSigDB)^[231]57. The Fisher exact test P values were calculated using
the phyper function of the R software and adjusted for multiple
comparisons applying the BH correction. The dot-plots were generated
using clusterProfiler (3.12), org.Hs.eg.db (3.18.0), and ggplot2 (2
3.4.4) R packages. Count refers to the number genes present in the
overlap between the MacroSigs and the Hallmark gene sets. The gene
ratios were obtained by dividing the count by the total number of genes
in that respective Hallmark gene set.
Single-cell sequencing data analysis
Seurat (2.3.0)^[232]58 was used for the analysis of the single-cell RNA
sequencing (scRNA-seq) datasets. All functions were run with default
parameters, unless specified otherwise. Low quality cells (< 200
genes/cell and >10% mitochondrial genes) and genes that were present in
fewer than 3 cells were excluded. Clusters were defined based on
annotations provided by original authors (refs.
^[233]33,[234]37,[235]59). Refinement and validation of annotation was
conducted by projecting and evaluating a curated B cell, T cell and
macrophage marker list (see Methods: DSP data processing and
harmonization).
For characterization and feature expression analysis of the MacroSigs,
each MacroSig was further refined. A module score
([236]https://satijalab.org/seurat/reference/addmodulescore) using the
top 50 statistically significant DEGs, ranked by their log fold change,
was created for each MacroSig. This score was then projected onto the
uniform manifold approximation and projection (UMAP) space named
MoMac-VERSE. Moreover, to determine that all genes of our MacroSigs
belong to macrophages, all genes of each MacroSig, through their
respective module scores, were projected onto the Monocyte/Macrophage
and B cell subsets of DLBCL scRNA-seq datasets (n = 17)^[237]37 and
subsequently compared (Fig. [238]4D and Supplementary Figs. [239]5,
[240]6).
Survival analysis
To evaluate the predictive power of our MacroSigs from a clinical
standpoint, we applied them to eight distinct DLBCL bulk gene
expression cohorts^[241]38–[242]40,[243]46,[244]50,[245]60–[246]62. The
MacroSigs were tested in pairs: GC/IF, LZ/DZ, RLT/DLBCL. In line with
the percentile strategy commonly used in the literature^[247]63, we
divided the patients into tertiles based on MacroSig scores calculated
by the following formula:
[MATH: score=∑i=1n−log10pi⋅xi⋅Ii :MATH]
1
[MATH: Ii=−1,iflog2FCi
<0+1,iflog2FCi
>0 :MATH]
2
Where p[i] and FC[i] are the moderated t-test P value and FC of gene-i
obtained from the DEAs, x[i] is the expression of gene-i from the DLBCL
bulk RNA-seq data, and n is the number of genes within the gene
signature. The log[10](p[i]) quantity was introduced to weigh the genes
in relation to their significance level. Comparing high and low tertile
groups increases the chance of detecting biomarker related differences
between the two groups. Therefore, based on the score derived from a
paired MacroSig, e.g., DZ [MacroSig4]/LZ[MacroSig3]: the aggregate of
genes upregulated in the DZ (MacroSig4) and downregulated in the DZ
(MacroSig3 [LZ]), each DLBCL cohort was divided into three groups based
on tertile values: (1) patients having high expression of downregulated
genes and low expression of upregulated genes [e.g., patients classed
as MacroSig3]; (2) patients having an intermediate gene signature
expression. (3) patients having high expression of upregulated genes
and low expression of downregulated genes [e.g., patients classed as
MacroSig4]. The extreme groups (i.e., group 1 [e.g., MacroSig3] and
group 3 [e.g., MacroSig4]) have been compared in terms of OS, COO,
genetic subtypes categories, and DEA of macrophage checkpoints.
For a comprehensive survival analysis, we applied both the Cox
proportional hazards model and the Kapan-Meier method. Before fitting
the Cox model and conducting the log-rank test, the cox.ph test was
used to test the proportional hazard assumption. We applied the Cox
model to MacroSigs as a discrete variable, comparing the top tertile
group with the bottom tertile group, as described above, to determine
the hazard ratio associated with each MacroSig (displayed as forest
plots). The Kaplan-Meier method was used to estimate the survival
functions among tertile groups, and the log-rank test was used to test
the differences in the OS between groups. The survival (3.5–7) and
survminer (0.4.9) R packages were used for the survival analysis
estimations.
Association analysis
The associations between patient groups and clinical categories (i.e.,
COO, genetic subtypes, microenvironment categories) were evaluated
through the Fisher exact test. The P values were calculated using the
phyper function of the R software and adjusted for multiple comparisons
applying the BH correction. The COO association analyses are presented
as dot plots, where the overlap ratio refers to the number of patients
classified as both a certain MacroSig and COO category, divided by the
total number of patients classified in that particular COO category
(Fig. [248]4A–C). The genetic subtypes association analysis is
presented as an integrated bar graph, where the strength of association
between MacroSigs and genetic subtypes is represented by an enrichment
score calculated by: -log10 (adjusted Fisher P value) (Supplementary
Fig. [249]7A). The microenvironment categories association analysis is
displayed in the dot plot (Supplementary Fig. [250]7B). Count refers to
the number genes present in the overlap between the MacroSigs and DLBCL
microenvironment categories generated by ref. ^[251]5. The overlap
ratios were obtained by dividing the count by the total number of genes
in that respective DLBCL microenvironment category.
Software
Unless otherwise stated, graphs were constructed with GraphPad Prism
(9.5.0; GraphPad Software, Massachusetts, USA).
Reporting summary
Further information on research design is available in the [252]Nature
Portfolio Reporting Summary linked to this article.
Supplementary information
[253]Supplementary information^ (3.3MB, pdf)
[254]Peer Review File^ (5.2MB, pdf)
[255]41467_2024_46220_MOESM3_ESM.pdf^ (89.1KB, pdf)
Description of Additional Supplementary Files
[256]Supplementary Data 1^ (69.7KB, xlsx)
[257]Supplementary Data 2^ (149.6KB, xlsx)
[258]Supplementary Data 3^ (55.1KB, xlsx)
[259]Supplementary Data 4^ (37.3KB, xlsx)
[260]Reporting Summary^ (3.2MB, pdf)
Source data
[261]Source Data^ (128.8MB, xlsx)
Acknowledgements