Abstract
Cancer immunoediting is a dynamic process of crosstalk between tumor
cells and the immune system. Herein, we explore the fast zebrafish
xenograft model to investigate the innate immune contribution to this
process. Using multiple breast and colorectal cancer cell lines and
zAvatars, we find that some are cleared (regressors) while others
engraft (progressors) in zebrafish xenografts. We focus on two human
colorectal cancer cells derived from the same patient that show
contrasting engraftment/clearance profiles. Using polyclonal xenografts
to mimic intra-tumor heterogeneity, we demonstrate that
SW620_progressors can block clearance of SW480_regressors.
SW480_regressors recruit macrophages and neutrophils more efficiently
than SW620_progressors; SW620_progressors however, modulate macrophages
towards a pro-tumoral phenotype. Genetic and chemical suppression of
myeloid cells indicates that macrophages and neutrophils play a crucial
role in clearance. Single-cell-transcriptome analysis shows a fast
subclonal selection, with clearance of regressor subclones associated
with IFN/Notch signaling and escaper-expanded subclones with enrichment
of IL10 pathway. Overall, our work opens the possibility of using
zebrafish xenografts as living biomarkers of the tumor
microenvironment.
Subject terms: Cancer microenvironment, Tumour heterogeneity
__________________________________________________________________
Zebrafish has emerged as an important vertebrate model to study tumor
biology and immune response. Here, using a zebrafish xenograft model,
the authors show that the engraftment capacity of human colorectal
cancer cell lines is influenced by innate immune response and
immunoediting.
Introduction
Clinically detectable tumors represent the ultimate consequence of
tumor immunoediting, which includes the detection and clearance of the
majority of the immunogenic clones by the immune system^[42]1. Clones
that escape immune detection further hijack immune cells to support
tumorigenesis^[43]2. Although the concept of immunoediting is well
established, the role of innate immune cells in shaping and selecting
subclones, as well as the mechanisms that allow for innate immune
evasion remain less explored^[44]3.
In recent years, there has been a major effort to uncover the role of
adaptive immunity on tumor immune surveillance/evasion/suppression,
which has been translated into promising new immunotherapies^[45]4.
Immune checkpoint therapies aim to remove inhibitory pathways that
block anti-tumor T-cell responses in the tumor microenvironment (TME).
However, therapy may fail because tumor cells do not express sufficient
neo-antigens (not immunogenic enough)^[46]5. Another major obstacle may
be the presence of a suppressive (cold) TME composed of stroma and a
variety of immune cells, such as regulatory T cells (Treg),
myeloid-derived suppressor cells (MDSC), alternative activated
pro-tumoral macrophages (“M2-like”), and neutrophils (“N2-like”), that
may block anti-tumor immune responses^[47]5,[48]6. In fact, innate
myeloid-derived cells effectively represent the major component of the
TME in most solid tumors^[49]5,[50]7, often outweighing lymphocytes or
even the tumor cells themselves. These populations of the immune system
are present in all tissues. However, their role in cancer-induced
immune suppression and immunotherapy remains less explored and
understood. Increasing evidence supports a crucial anti- and
pro-tumorigenic role for innate immune cells^[51]4. Importantly, innate
pro-tumorigenic states are highly dynamic and can be selectively
reverted^[52]8, creating the possibility for new and more effective
therapeutic approaches.
The zebrafish model has emerged as a powerful tool to study tumor
biology and interactions with the immune system^[53]9–[54]12. Zebrafish
have a highly conserved vertebrate innate immune system, including
complement, Toll-like receptors, neutrophils, and macrophages capable
of phagocytic activity. Another advantage is that the full maturation
of adaptive immunity only occurs at 2–3 weeks
post-fertilization^[55]13,[56]14. This offers a time window to study
exclusively innate immune response in vivo, independent of the adaptive
system. In addition, transparency allows for unprecedented real-time
imaging of cell–cell interactions and genetic tractability enables the
engineering of reporter lines and mutants^[57]14.
Recently, we have optimized zebrafish patient-derived xenografts
(zPDXs)—“zAvatars” for personalized medicine^[58]15. The assay is based
on the injection of labeled tumor cells into zebrafish embryos for
assessment of tumor behavior and response to therapy in just 4 days.
Although only innate immunity is active, we observed some heterogeneity
in tumor engraftment. Here we hypothesize that the zebrafish innate
immune system can be modulated by the tumor; itself capable of
generating an immunosuppressive TME or subjected to elimination. In the
present study, we apply a combination of zebrafish mono- and polyclonal
xenografts, “zAvatars”, zebrafish mutants and transgenics, mouse
xenografts, re-transplantation experiments, and single-cell
transcriptomics to test this hypothesis. We focus on a pair of human
colorectal cancer (CRC) cells derived from the same patient at
different stages of tumor progression: SW480 was derived from the
primary tumor, and SW620 from a lymph node metastasis isolated 6 months
later. SW480_regressors engraft poorly and most tumors are cleared
during the 4 days of the assay, whereas SW620_progressors engraft very
efficiently. Mixing SW480_regressors with SW620_progressors in
polyclonal tumors reduces clearance of SW480_regressors, suggesting
that progressors can induce an immune suppressive environment. Indeed,
not only SW620 progressor tumors recruit less neutrophils and
macrophages to the TME, but also polarize macrophages toward a M2-like
pro-tumoral phenotype. In addition, MIX polyclonal tumors show an
immune profile similar to the progressor tumors, i.e., reduced numbers
of innate cells and “M2-like” polarization. Genetic and chemical
depletion of myeloid cells confirms that macrophages and neutrophils
play a crucial role in this clearance process. To test whether innate
immunoediting is occurring in this short time frame, we perform
re-transplantation experiments of SW480 escaper tumors and show that
these tumors engraft more efficiently and generate bigger tumors with
reduced macrophage infiltration. Finally, single-cell RNA-seq reveals
the in vivo clearance and expansion of specific subclones.
Results
Zebrafish xenografts display differential engraftment profiles
Using a zebrafish xenograft model^[59]15, we investigated the
engraftment efficiency of multiple human breast and CRC cell lines. At
4 days post injection (4 dpi), we found that different cancer cell
lines display distinct engraftment profiles in zebrafish xenografts. Of
note, we describe engraftment as the frequency of xenografts that
present a tumor (at least 30 tumor cells) at 4 dpi (Fig. [60]1a) and
clearance as engraftment inhibition. We observed that some cancer cell
lines present a high engraftment rate—above 80%, while others engraft
poorly with an average engraftment rate of ~20–30%. We define here
these tumors as progressors and regressors, respectively, following
Schreiber nomenclature^[61]16 (Fig. [62]1a).
Fig. 1. Human cancer cells display differential engraftment profiles in
zebrafish.
[63]Fig. 1
[64]Open in a new tab
a Engraftment is the ratio between the number of zebrafish xenografts
that maintain a tumor at 4 days post injection (dpi) and the number of
total xenografts that were originally successful injected and survived
until day 4. MDA-MB-231 (MDA-231), MDA-MB-468 (MDA-468), and Hs578T are
breast cancer cell lines. SW480, SW48, HT29, SW620, HCT116, and Hke3
are colorectal (CRC) cancer cell lines. Tumor cells were labeled and
injected into the perivitelline space (PVS) of 2 days
post-fertilization (dpf) zebrafish embryos. Each dot represents one
independent experiment, number of independent experiments: 19 SW480,
3 SW48, 5 MDA-231 12 MDA-468, 5 HT29, 7 Hs578T, 22 SW620, 6 HCT116,
7 Hke3. Total number of xenografts analyzed (N) is depicted in the
charts. Error bars indicate mean ± S.D. b Engraftment of SW480 and
zebrafish patient-derived xenografts (zPDX-zAvatars) at 4 dpi, treated
with FOLFOX (FO) and radiotherapy (RAD) and their respective controls.
Each dot represents one independent experiment (3 SW480, 1 zPDX). Total
number of xenografts analyzed (N) is depicted in the charts. Error bars
indicate mean ± S.D. See also Supplementary Fig. [65]1. c–f
Comparative transcriptomic analysis between SW480 and SW620 xenografts.
c Schematic representation of the experiment where SW480 (in red) and
SW620 (in green) tumors were dissected at 2 dpi for RNA extraction (~30
tumors of each condition). d Heatmap presents a two-dimensional
dendogram (based on Pearson’s correlation coefficient distance) of log2
counts-per-million (logCPM), normalized expression values of
differentially expressed genes (N = 459, cut-off of FDR < 0.05 and
absolute log2FC > 1) in SW480 (low engraftment) versus SW620 (high
engraftment) comparison, where colors represent expression values
scaled by row (Z-scores). e, f GSEA of SW480 and SW620 xenografts.
Source data are provided as a Source data file.
Strikingly, we observed differences in engraftment profiles between
cancer cells derived from the same patient at different stages of tumor
progression. While SW480 cells derived from the primary tumor present a
regressor behavior, SW620 cells isolated from a lymph node metastasis 6
months later^[66]17,[67]18 show a progressor phenotype (Fig. [68]1a).
These differences in engraftment rates between both tumor cells were
also originally reported in mouse xenografts^[69]18.
Importantly, engraftment/clearance capacity did not seem to correlate
to proliferation potential or basal cell death. This is exemplified by
the breast cancer cells Hs578T_progressors, which display a high
engraftment rate (~95% engraftment), despite their low proliferation
and high level of apoptosis in comparison, for instance, with breast
cancer MDA-MB-468, which display lower engraftment but are more
proliferative and less apoptotic (Supplementary Fig. [70]1a–d). Also,
although SW620_progressors are highly proliferative compared with
SW480_regressors, SW620_progressors present higher levels of apoptosis
(Supplementary Fig. [71]1e–h).
Moreover, paradoxically, we observed that SW480_regressors upon chemo-
(FOLFOX-FO) or radiotherapy (RAD), may increase their engraftment rate,
and this can also be observed in patient-derived xenografts (zAvatars)
(Fig. [72]1b). Given the fact that chemo/radiotherapy may elicit an
immunosuppressive effect, we hypothesized that this could reduce the
zebrafish host anti-tumor response, originally responsible for the
regressor (clearance) behavior.
Transcriptomic analysis between SW480 and SW620 xenografts
We next performed a general comparative transcriptomic analysis between
SW480_regressors and SW620_progressors. We sought to focus on the
SW480/SW620 pair of cell lines since they derived from the same patient
and therefore illustrate intra-patient heterogeneity and eventually the
original immunoediting process from primary to metastasis progression.
To this end, SW480 and SW620 tumors were dissected from zebrafish
xenografts at 2 dpi, a timepoint that corresponds to the timing when
clearance is actively taking place (Supplementary Fig. [73]2). A pool
of ~30 tumors from at least three independent experiments was collected
for RNA extraction (Fig. [74]1c). The remaining xenografts from the
same experiments were followed until 4 dpi to determine final
engraftment rates. We only used RNA samples from experiments where
SW480 engraftment was lower than ~30% and SW620 engraftment was higher
than 90%.
A differential expression analysis revealed 459 differentially
expressed genes (DEGs) between the two types of xenografts
(Fig. [75]1d, Supplementary Data [76]1). A gene set enrichment analysis
(GSEA)^[77]19 revealed an enrichment mainly in three biological
processes: immune response, metabolism and signaling (Fig. [78]1e, f).
Whereas genes involved in epithelial to mesenchymal transition (EMT)
were specifically enriched in SW480 xenografts, genes involved in
proliferation and hypoxia/angiogenesis were specifically represented in
SW620 tumors (Fig. [79]1e, f). The enrichment analysis is in accordance
with our earlier results, where SW480 showed an increased metastatic
potential and SW620 an increased capacity to recruit blood
vessels^[80]15. We also identified several immune-related pathways in
SW480 enriched DEGs, in particular those involved in graft-versus-host
disease, IL6 signaling, and allograft rejection pathways. In contrast,
SW620 DEGs were characterized by an enrichment in IFN and TNF
signaling, ROS and NOTCH pathways (Jagged1, MAML2), but not in
graft-versus-host disease or allograft rejection pathways (Fig. [81]1e,
f). These results suggest that SW480 tumors express signals that may
stimulate clearance, while SW620 tumors have reduced activity of
rejection-related pathways.
Progressors are able to protect regressors from being cleared
In order to test if progressors were able to induce a suppressive
environment, and thus avoid clearance of regressors, SW620_progressors
were mixed with SW480_regressors, thereby generating polyclonal
xenografts in vivo. To distinguish the two cell lines, SW480 cells were
labeled with red CM-DiI-dye and SW620 with green CMFDA-dye and mixed in
a 1:1 proportion (Fig. [82]2a, b). The three conditions were tested in
parallel—SW480 (red) alone, SW620 (green) alone, and MIX
(SW480 + SW620) and engraftment quantified at 4 dpi. As expected, SW480
cells presented a low average engraftment rate of ~20%, with the
majority of tumors being cleared from the zebrafish host; whereas SW620
had an average engraftment rate of ~90%. However, when mixed,
engraftment of SW480 increased to more than double, to ~60%
(P < 0.0001). In contrast, engraftment of SW620 decreased ~35%
(P < 0.0001) in relation to when it is alone (Fig. [83]2c). Analysis of
the relative proportions of each clone in each MIX xenograft, by
confocal microscopy, showed that both populations were always present,
with SW620 behaving as the dominant clone, making up ~70% of the tumor
(Fig. [84]2d). Interestingly, when we compared the size of each
population (number of cells) we found that the number of SW480 cells
increased in polyclonal xenografts, i.e., SW480 benefits from the
proximity of SW620 cells, suggesting that SW620 cells can protect SW480
cells from clearance and possibly provide some survival cues
(Fig. [85]2e).
Fig. 2. Progressor tumor cells are able to protect regressors from being
cleared.
[86]Fig. 2
[87]Open in a new tab
Tumor cells were labeled with lipophilic dyes and injected into the PVS
of 2dpf zebrafish embryos. a, b Representative images of SW480 (in
red), SW620 (in green), and MIX (1:1) polyclonal zebrafish xenografts
at 4 dpi. a Fluorescence stereoscope images. b Confocal images. c
Engraftment quantification at 4 dpi (Fisher exact test ****P < 0.0001).
Graph shows the mean ± S.D. Each dot represents one independent
experiments (5), and each set of independent experiments is represented
in a different gray color. d Representative quantification of the
proportions of each clone within each xenograft (N = 10) from four
independent experiment. e Quantification of tumor size (no. of tumor
cells) at 4 dpi (unpaired two-sided Mann–Whitney test ****P < 0.0001).
Graph shows the mean ± SEM from four independent experiments, each dot
represents one xenograft. f, g Representative images of SW480 (in red),
HCT116 (in green), and MIX (1:1) zebrafish xenografts at 4 dpi. f
Fluorescence stereoscope images. g Confocal images. h Engraftment
quantification at 4 dpi (Fisher exact test ****P < 0.0001,
***P = 0.0005). Graph shows the mean ± S.D. Each dot represents one
independent experiment (N = 3), and each set of independent experiments
is in a different gray color. i Representative quantification of the
cell proportions of each clone within each xenograft (N = 10) from one
independent experiment. j Quantification of tumor size (no. of tumor
cells) at 4 dpi (unpaired two-sided Mann–Whitney test **P = 0.0012,
Cohen’s D g = 4.88; ***P = 0.0002, Cohen’s D g = 4.32). Graph shows the
mean ± SEM from one independent experiment, each dot represents one
xenograft. Scale bars: 50 μm. Dashed lines encircle tumor areas. Nuclei
are stained with DAPI (blue). N is depicted in the charts. Source data
are provided as a Source data file.
Next, we engrafted a mixture of SW480_regressor with another CRC
progressor cell line derived from a different patient—HCT116
(Fig. [88]2f, g). In this instance, in the presence of HCT116, the
engraftment rate of SW480 was further increased, from ~20 to 90%
(P < 0.0001) (Fig. [89]2h), while analysis of each xenograft revealed
SW480:HCT116 frequencies of 30:70% (Fig. [90]2i). Once again, the size
of SW480 tumors increased in the presence of a progressor tumor cell
(Fig. [91]2j).
These results suggest that “regressors” can indeed lose their
“regression” profile in the presence of “progressors” and that the
latter might generate a protective immunosuppressive microenvironment.
These results are in accordance with mouse xenograft studies, which
show that advanced metastatic tumors engraft more efficiently and are
more immunosuppressive than primary tumors^[92]20,[93]21.
SW480 regressor TME is enriched in innate immune cells
To evaluate if regressors and progressors are able to generate
different tumor ecosystems, we analyzed the presence of neutrophils and
macrophages in the tumors, the main innate immune cells present at this
stage of development (2–6 days post fertilization-dpf)^[94]14,[95]22.
To this end, we injected SW480, SW620, and MIX tumor cells into
Tg(mpx:eGFP)^[96]23 and Tg(mpeg1:mcherry-F)^[97]24 zebrafish hosts,
which have neutrophils (Fig. [98]3a, b) and macrophages (Fig. [99]3e,
f) labeled, respectively.
Fig. 3. SW480_regressor TME is enriched in innate immune cells.
[100]Fig. 3
[101]Open in a new tab
a, b Representative confocal projection images of neutrophils in SW480,
SW620, and MIX tumors from Tg(mpx:eGFP) zebrafish xenografts at 4 dpi.
c, d Quantification of neutrophils percentage (no. of neutrophils/no.
of tumor cells x 100) within SW480, SW620, and MIX TME, at 1 dpi (c,
****P < 0.0001, ***P = 0.0002, **P = 0.0094) and 4 dpi (d,
****P < 0.0001, ns = 0.39). e, f Representative confocal projection
images of macrophages in SW480, SW620, and MIX tumors from
Tg(mpeg1:mcherry-F) zebrafish xenografts at 4 dpi. g, h Quantification
of macrophage percentage (no. of macrophages/no. of tumor cells x 100)
within SW480, SW620, and MIX tumors, at 1 dpi (g, ***P = 0.0009,
**P = 0.0011, ns = 0.45) and 4 dpi (h, 480 vs 620 **P = 0.0089, 480 vs
MIX **P = 0.0025, *P = 0.024). SW480 (red) and SW620 (green),
neutrophils (white) and macrophages (white) fake colors. Scale bars:
50 μm. Dashed lines encircle tumor areas. Nuclei are stained with DAPI.
N is depicted in the chart. Each dot represents one xenograft. Error
bars indicate mean ± SEM (from three independent experiments). All data
were analyzed using unpaired two-sided Mann–Whitney test. See also
Supplementary Fig. [102]3. Source data are provided as a Source data
file.
As early as 24 hpi (1 dpi), we could detect a significant higher
recruitment of neutrophils and macrophages to the SW480 tumors in
comparison to SW620 (neutrophils P < 0.0001, macrophages P = 0.0011), a
difference that was maintained and reinforced at 4 dpi (neutrophils
P < 0.0001, macrophages P = 0.0089) (Fig. [103]3c, d, g, h).
Interestingly, MIX tumors showed a TME similar to SW620, with
significant lower recruitment of neutrophils and macrophages than SW480
tumors (Fig. [104]3, SW480 vs MIX neutrophils P[4dpi] < 0.0001,
macrophages P[4dpi] = 0.0025). These results suggest that the presence
of SW620 in the MIX is able to block the recruitment of immune cells
toward the tumor. We next questioned whether immune cell recruitment
was associated with the total number of tumor cells within the tumoral
mass. Linear regression analysis of the tumor size vs immune cell
counts suggests a weak correlation between tumor size and immune cell
infiltrates in SW480 tumors, but moderate in SW620 tumors
(Supplementary Fig. [105]3).
SW480 and SW620 tumors modulate zebrafish macrophage polarization
In the TME, tumor-associated macrophages (TAMs) and neutrophils (TANs)
can either adopt an anti-(M1/N1-like) or pro-tumoral (M2/N2-like)
phenotype, known to be modulated by multiple tumor-derived
signals^[106]24,[107]25. To investigate the polarization state of
macrophages in both TMEs, SW480, and SW620 cells were injected into
double transgenic animals Tg(mpeg1:mCherry-F; tnfa:eGFP-F)^[108]24 and
each population was analyzed at 1 and 4 dpi (Fig. [109]4a, b and
Supplementary Fig. [110]4a, b). Quantification of the immune cell
populations showed that SW480_regressors are able to recruit a
significantly higher number of inflammatory cells (TNFa positive cells
and M1-like TNFa+mpeg+), than SW620_progressors, since 1 dpi
(Supplementary Fig. [111]4a, b, M1-like P[1dpi] = 0.0003;
P[4dpi] = 0.001). Moreover, when we analyzed the proportions of M1-like
(TNFa+) versus M2-like (TNFa−) macrophages, we observed that the SW480
TME presented ~57% M1-like to 43% M2-like- macrophages at 4 dpi
(Fig. [112]4c, d). In clear contrast, the TME of SW620_progressors
cells presented a ratio of ~35% M1-like to ~65% M2-like
macrophages (Fig. [113]4c). Interestingly, a progressive increase in
M2-like-(TNFa−) macrophages in the TME of SW620 from 1 to 4 dpi was
observed (Fig. [114]4c). This result suggests that SW620_progressor
cells can polarize macrophages to a M2-like pro-tumoral state. In
addition, the MIX xenografts again show similar dynamics to SW620
xenografts (M2- > M1-like macrophages), from 1 to 4 dpi (Fig. [115]4c,
d).
Fig. 4. SW480 and SW620 human tumor cells modulate zebrafish macrophage
polarization.
[116]Fig. 4
[117]Open in a new tab
a, b Representative confocal images of SW480, SW620, and MIX xenografts
injected in Tg(mpeg1:mcherry-F, tnfa:GFP-F) at 1 and 4 dpi. Red:
macrophages; green: TNFa+ cells; yellow: overlay of macrophages in red
and TNFa+ cells in green—M1-like macrophages. c Proportion of M1- and
M2-like macrophages in the TME at 1 and 4 dpi (paired two-sided t test,
**P = 0.0033, ns = 0.1833, ns = 0.1160, ****P < 0.0001, *P = 0.0116,
****P < 0.0001). d Quantification of absolute numbers of M1- and
M2-like macrophages in the TME at 1 and 4 dpi (paired two-sided
Wilcoxon rank test **P = 0.0016, ns = 0.3086, ns = 0.1473,
***P < 0.0002, *P = 0.0205, ***P < 0.0005). Scale bars: 50 μm. Dashed
lines encircle tumor areas. N is depicted in the images. In c and d,
the number of xenografts analyzed is: SW480_1 dpi N = 21, SW480_4 dpi
N = 11, SW620_1 dpi N = 31, SW620_4 dpi N = 31, MIX_1 dpi N = 12, and
MIX_4 dpi N = 12. Images are maximum intensity projections. Each dot
represents one xenograft. Error bars indicate mean ± SEM (from 2
independent experiments in SW480, 3 in SW620, and 1 experiment for the
MIX). Source data are provided as a Source data file.
Moreover, as expected, we detected a higher phagocytic activity
(displayed by M1-like TNFa+/mpeg+ and TNFa+/mpeg− cells) in SW480 TME
than in SW620 (Supplementary Fig. [118]4c–i, P < 0.0001). In summary,
these results show that human tumor cells are able to modulate the
zebrafish TME toward a more anti- or pro-tumoral state, through
macrophage polarization and consequent phagocytic properties.
Engraftment of MIX tumors correlates with SW620 ratio
Next, we questioned whether clonal proportions could affect tumor
engraftment and TME modulation. Zebrafish embryos were injected with
mixtures of regressors with progressors at different ratios (1:3, 1:1,
and 3:1) (Supplementary Fig. [119]5a). We found that a proportional
increase in the number of SW620_progressors cells in polyclonal tumors
correlates with higher engraftment rates (Supplementary Fig. [120]5b,
c, R2 = 0.78, P < 0.0001). Interestingly, instead of a steady reduction
of the immune infiltrate into the polyclonal tumors, the presence of
SW620, even in a 3:1 ratio (SW480:SW620), was sufficient to block
neutrophil recruitment (P = 0.0066) (Supplementary Fig. [121]5d).
However, macrophage recruitment is only reduced when SW620 increases up
to 50% of tumor cells (Supplementary Fig. [122]5e), suggesting that
neutrophil and macrophage recruitment have different dynamics, possibly
modulated by different mechanisms.
Zebrafish innate immune cells regulate SW480 clearance
The above results show that SW620_progressors protect SW480_regressors
from being cleared and that SW480 cells are able to recruit more
efficiently innate immune cells. Moreover, increasing amounts of SW620
in MIX xenografts correlate with increased engraftment of SW480 and the
presence of SW620 seems sufficient to reduce immune cell infiltration.
All together, these results suggest that innate immunity plays an
active role in clearance/engraftment.
To directly test this, we injected both CRC cell lines into mutant
zebrafish embryos that have either a transient downregulation of
neutrophils (runx1^w84x mutant)^[123]26 or of macrophages (M-CFS
receptor/fms mutant csf1ra^j4blue panther)^[124]27 (Fig. [125]5a–b).
The results show that runx1^w84x and panther mutants present a
significant increase in the engraftment of SW480 regressors cells
(Fig. [126]5c). In runx1^w84x mutants, we observed a significant
3.2-fold increase of engraftment (P < 0.0001), whereas in panther
mutants we observed a 2.8-fold of increase (P < 0.0001) (Fig. [127]5c).
In contrast, downregulation of neutrophils or macrophages had no
significant impact on SW620_progressors’ engraftment rate.
Interestingly, quantification of tumor size in each background shows
that SW480 regressors increase their size in panther mutants, which
have reduced number of macrophages (Fig. [128]5d, P = 0.0013).
Fig. 5. Zebrafish innate immune cells regulate clearance of SW480 tumor
cells.
[129]Fig. 5
[130]Open in a new tab
a, b Representative confocal images of SW480 and SW620 xenografts in
runx1^w84x and csf1ra^j4blue (panther) mutants. SW480 were labeled in
red and SW620 in green. c Quantification of engraftment in runx1^w84x
and csf1ra^j4blue (panther) mutants and respective controls (Fisher
exact test, ****P < 0.0001, SW620 wt vs SW620 runx1^w84x ns = 0.62,
SW620 wt vs SW620 panther ns = 0.09). Error bars represent mean ± S.D.
Each dot represents one independent experiment. d Quantification of
tumor size in runx1^w84x and csf1ra^j4blue (panther) mutants and
respective controls (unpaired two-sided Mann–Whitney test—SW480 wt vs
SW480 runx1^w84x ns = 0.22, **P = 0.0013, SW620 wt vs SW620 runx1^w84x
ns = 0.44, SW620 wt vs SW620 panther ns = 0.18). Error bars represent
mean ± SEM, each dot represents one xenograft from 3 independent
experiments. e–j Zebrafish embryos with 2 dpf were injected
simultaneously with SW480 tumor cells (in green) with PBS (control),
with L-PBS or with L-Clodronate liposomes into Tg(mpeg1:mcherry)
background (macrophages in red). e–g Representative fluorescence
stereoscope images of SW480 xenografts at 1 dpi in the different
conditions. h–j Representative confocal images of SW480 xenografts at
4 dpi. k Quantification of engraftment: Fisher exact test ns = 0.83,
****P < 0.0001; error bars represent mean ± S.D.; each dot represents
one independent experiment, and each set of independent experiment is
represented in a different gray color. l Quantification of tumor
size—no. of tumor cells (unpaired two-sided Mann–Whitney test
ns = 0.062, ****P < 0.0001, *P = 0.022, error bars represent
mean ± SEM) in the different experimental conditions at 4 dpi, each dot
represents one xenograft from 3 independent experiments. Scale bars:
50 μm. White dashed lines encircle tumor areas. Nuclei are stained with
DAPI. N is depicted in the chart. See also Supplementary Fig. [131]6.
Source data are provided as a Source data file.
Overall, our results suggest that both myeloid cells play a crucial
role in the SW480_regressors’ clearance and that SW620_progressors are
able to evade and/or suppress the host innate immune system.
Resident and definitive macrophages are required for SW480 clearance
Recent studies have shown differential functions for resident
macrophages and hematopoietic monocyte-derived macrophages in
tumorigenesis^[132]28–[133]30. In 3 dpf zebrafish larvae, macrophages
are distributed in several peripheral tissues, such as the brain,
heart, retina, and muscle, and in the caudal hematopoietic tissue
(CHT), a transient hematopoietic tissue^[134]9. In panther mutants
(csf1ra^j4blue), it has been shown that there is an overall ~40%
reduction of the macrophage population and impairment of their
migration. However, the tissue-resident macrophages (derived from the
primitive and transient waves of hematopoiesis) show a stronger
reduction (~60%) than macrophages derived from the second-monocytic
definitive wave (CHT-20%)^[135]31–[136]33. The results observed in
panther mutants thus reflect mostly the contribution of the resident
macrophages^[137]32. To further investigate the role of the different
macrophages in tumor clearance, we depleted most macrophage population
by using Liposome-Clodronate (L-clodronate), which targets macrophages
regardless of their embryonic origin. Strikingly, upon almost complete
macrophage depletion (without affecting neutrophil numbers^[138]32, see
Fig. [139]5e–g and Supplementary Fig. [140]6a, b), SW480 engraftment
reaches almost 100% (Fig. [141]5k, P < 0.0001), contrasting with the
significant but less pronounced engraftment increase in panther mutants
(~60%, Fig. [142]5c). Moreover, quantification of the tumor size also
shows that SW480 tumor size increases by almost 2-fold (Fig. [143]5h–j,
l, L-PBS vs L-Clodro, P = 0.02). In summary, our results highlight a
major role for both tissue-resident and peripheral macrophages in tumor
clearance.
Conservation of phenotypes in mouse xenografts
Zebrafish has become a relevant animal model to study cancer. This is
only possible due to the major conservation of genes and signaling
pathways between human and zebrafish^[144]9,[145]13. Nevertheless, as
mouse is the gold-standard model in cancer and immunology, we tested if
the phenotypes unveiled in zebrafish were conserved across species.
SW480, SW620, and MIX mouse xenografts were generated using as host the
immunocompromised mice strain Rag1−/− C57BL6/N, lacking mature B and T
cells^[146]34.
However, in contrast with previous mouse xenografts^[147]18 and our
zebrafish studies using SW480 and SW620 cells, we could not detect
major differences in engraftment capacity between SW480 and SW620. This
discrepancy between our mouse experiments with zebrafish engraftment
and the previous published mouse studies may be a reflection of our use
of different mutations and background strains, both of which were
immunodeficient but with different immunological repertoires. Instead
of using mouse Rag1−/− C57BL6/N, Hewitt et al.^[148]18, used BalbC nude
(Foxn1 mutation) mice, which lack a thymus and functional B cells,
whereas Rag1−/− mutants lack mature B and T cells^[149]34.
Nevertheless, analysis of the F4/80^+CD80^+ macrophages, showed that
SW480 tumors were more enriched in “M1-like” anti-tumoral macrophage
population than SW620 or in MIX (Fig. [150]6a–c, d, P = 0.016, see
Supplementary Fig. [151]7 for gating strategy). Also, like our previous
zebrafish results, SW620 cells became the dominant clone in MIX mouse
xenografts (Fig. [152]6e, *P = 0.029).
Fig. 6. Mice xenografts display similar TME behavior as zebrafish.
[153]Fig. 6
[154]Open in a new tab
a–c Representative graphs of flow cytometry analysis of the TME of
SW480, SW620, and MIX mouse (Rag1^−/−C57BL/6J) xenografts at 3 weeks
post inoculation. d Quantification of double positive F4/80, CD80
macrophage in each TME, SW620 vs SW480. *P = 0.016, Cohen’s D g = 6.2;
SW620 vs MIX ns = 0.29, Cohen’s D g = 0.83; SW480 vs MIX *P = 0.029,
Cohen’s D g = 1.28). e Quantification of the percentage of each clone
in MIX mice xenografts, *P = 0.029, Cohen’s D g = 7.68. d, e Data from
quantification of flow cytometry analysis. Error bars represent
mean ± S.D. f Growth curves of SW480 tumors treated with PBS, L-PBS, or
L-Clodronate mice (f ns = 0.43, Cohen’s D g = 0.12, **P = 0.007,
Cohen’s D g = 2.16, *P = 0.017, Cohen’s D g = 1.92). Error bars
represent mean ± SEM. All data were analyzed using unpaired two-sided
Mann–Whitney test. To avoid macrophage repopulation, mice were injected
every 4 days (see “Methods”). N is depicted in the chart. Each dot
represents one mouse xenograft. Source data are provided as a Source
data file.
To test whether mouse macrophages can actively modulate SW480 tumors,
macrophages were depleted with L-clodronate. Results show that
similarly to zebrafish, macrophage depletion leads to an increase in
tumor size (Fig. [155]6f, **P = 0.007, *P = 0.017), suggesting that the
role of macrophages in SW480 TME is conserved across species.
As a comment on the differences of the models, the analysis of the
murine model was performed ~24 days post tumor injection, while our
zebrafish TME analysis was performed at 1 and 4 dpi, i.e., a
discrepancy of ~20 days. We believe that the zebrafish model allows for
an immediate snapshot of the “tumor state”, but the murine model allows
to study how these tumor-TME interactions evolve along time. Therefore,
this “timing” issue can account for some differences, and does not
necessarily undermine one model or the other.
Innate immunoediting in zebrafish xenografts
Next, we aimed at analyzing if engrafted zebrafish SW480 tumors were
undergoing innate immunoediting, and therefore, would be able to escape
host innate immunity.
To this end, seven SW480 tumors were dissected at 4 dpi, from an
experiment that yielded ~12% engraftment. Dissected tumors were then
expanded in vitro for three passages (Fig. [156]7a) and these
(SW480zEscapers cells) were next injected into 2 dpf zebrafish embryos.
Engraftment, tumor size, proliferation, apoptosis, and macrophage
infiltration were quantified and compared to parental cells.
Strikingly, SW480zEscapers engrafted much more efficiently (from an
average of ~20% in parental to ~60% in SW480zEscapers, P < 0.0001)
(Fig. [157]7b, c) and tumor size increased in relation to parental
tumors (Fig. [158]7d, P < 0.0001). Interestingly, we could not detect a
higher proliferation rate in these tumors (Fig. [159]7e) and apoptosis
levels were slightly increased (Fig. [160]7f). Thus, these results
reinforce the idea that proliferation and apoptosis are not the main
drivers of engraftment/clearance. Importantly, the macrophage
infiltrate was significantly reduced in these tumors (Fig. [161]7g,
<0.0001). These results suggest that innate immunity plays a critical
role in immunoediting cancer cells toward tumorigenesis.
Fig. 7. Innate immunoediting in zebrafish xenografts.
[162]Fig. 7
[163]Open in a new tab
a Schematic illustration of SW480 escaper cells selection from SW480
parental xenografted (see “Methods” for more info). b Representative
confocal images of tumoral masses of SW480 parental and SW480zEscapers
xenografts at 4 dpi. c Quantification of engraftment at 4 dpi (Fisher
exact test ****P < 0.0001). Error bars represent mean ± S.D. Each dot
represents one independent experiment. d Quantification of tumor
size—no. of tumor cells, at 4 dpi (unpaired two-sided Mann–Whitney test
****P < 0.0001). e Quantification of mitotic tumor cells at 4 dpi
(unpaired two-sided Mann–Whitney test ns = 0.25). f Quantification of
apoptotic tumor cells at 4 dpi (unpaired two-sided Mann–Whitney test
*P = 0.01). g Quantification of macrophage present in the TME of SW480
parental versus SW480Zesc at 4 dpi (unpaired two-sided Mann–Whitney
test ****P < 0.0001). In dot plots, error bars represent mean ± SEM.
Scale bars: 50 μm. Dashed lines encircle tumor areas. Nuclei are
stained with DAPI. N is depicted in the chart. Each dot represents one
xenograft. Data of SW480zEscapers results from three independent
injections. Source data are provided as a Source data file.
Clearance and expansion of different SW480 subclones
To investigate the molecular alterations that might underlie the
emergence of SW480 escapers (as well as the subclones that get
cleared), we performed single-cell transcriptomic profiling. We
injected SW480 parental cells (GFP transfected) and then dissected
tumors at 2 time-points for single-cell RNA-seq (scRNAseq): 1 dpi
(where all subclones should be present) and at 4 dpi (where only the
subclones that escape clearance are present) (Fig. [164]8a).
Dissociated single cells were sorted by fluorescence-activated cell
sorting (FACS) into 384-well plates for scRNAseq SORT-seq^[165]35: 3
plates for the first timepoint and 2 plates for the second
(Fig. [166]8a, b and see Supplementary Fig. [167]8a for quality
control). Cells were pooled and clustered according to their gene
expression profiles using Seurat^[168]36, resulting in six different
cell clusters (cell states), which were visualized using the uniform
manifold approximation and projection (UMAP) approach^[169]37
(Fig. [170]8b, c, see Supplementary Fig. [171]8 for PCA and heatmap
showing the differential gene expression between different clusters).
Fig. 8. Single-cell transcriptome profiling reveals the clearance and
expansion of different SW480 subclones.
[172]Fig. 8
[173]Open in a new tab
a Schematic illustration of the design of the experiment. SW480 cells
were injected into 2 dpf zebrafish embryos, and at 1 and 4 dpi, tumors
were dissected and processed for scRNAseq. b Relative frequencies of
the cell clusters present in each library replicate. c Uniform Manifold
Approximation and Projection (UMAP), representing the relative
similarity between individual cells, colored by cell cluster and
divided by timepoints 1 and 4 dpi. d Heatmap representation of
normalized enrichment scores (NES) of representative pathways with
statistically significant (adjusted P-value < 0.05) enrichment in gene
set enrichment analysis (GSEA), comparing the gene expression of each
cellular subgroup to all the others. Red colors mean that genes in that
pathway tend to be more expressed in that cellular subcluster, while
blue means that genes tend to be less expressed. Significant NES values
are marked with asterisk (Fisher exact test *: adjusted P-value < 0.05;
**: adjusted P-value < 0.01; ***: adjusted P-value < 0.001). Gray
colors are cases where a NES value could not be obtained and should be
considered non-significant (see Supplementary Data [174]2 for GSE
values). e Schematic illustration of expansion/reduction of each
cluster from 1 to 4 dpi with the most representative pathways and
genes.
Comparing the clusters’ frequency between 1 and 4 days, it was possible
to follow how the various tumor clusters changed (Fig. [175]8) but also
the dynamics of the signaling pathways (Supplementary Fig. [176]8d).
Interestingly, two cell clusters (1 and 4) almost disappear in just 3
days, whereas others maintain their frequency (0 and 2) but others
clearly expand (3 and 5). These results suggest that some clusters were
cleared (1 and 4), while others were able to evade innate immune
detection and were therefore maintained (0, 2, 3, and 5) (Fig.
[177]8e).
In cluster 1, whose frequency was strikingly reduced, enrichment
pathway analysis showed the activation of innate immune-related
pathways as the interferon pathway (Myd88 independent TLR cascade and
DNA-dependent activation of IFN-regulatory factors) as well as several
inflammatory cytokines (e.g., CX3CL1, CXCL1) (Fig. [178]8d, e
Supplementary Fig. [179]9a-c, Supplementary Data [180]2). These
cytokines are known to act as chemoattractants for various immune
cells; the large CX3CL1/fractalkine attracting T cells and
monocytes^[181]38, whereas the small chemokine CXCL1 acts in particular
to attract neutrophils during inflammation^[182]39. Their increased
expression in subclones that decrease frequency in the tumor might
contribute to this clearance.
In contrast, an enrichment of IL10 immunosuppressive related signaling
was observed in cluster 3 (which is expanded at 4 dpi), suggesting that
IL10 signaling might protect SW480_zEscapers from clearance
(Fig. [183]8d, e and Supplementary Fig. [184]9d).
Comparison analysis between expanding cluster 3 vs cleared clusters (1
and 4) reveals the opposing dynamics between IL10 signaling and IFNγ
(Supplementary Fig. [185]9e).
SW480 and SW620 cells have been previously ascribed to a stem-like
subtype, with high expression of Wnt signaling targets as well as other
stem cell and mesenchymal genes, together with low expression of
differentiation markers^[186]40. As expected, we could identify
enrichment of Wnt and Notch pathways (Supplementary Fig. [187]10), as
these are major players in the maintenance of the stem cell state and
the regulation of differentiation of transit-amplifying (TA)
progenitors^[188]41.
Wnt signaling seemed to be highly active in cluster 3, as highlighted
by the higher expression of various pathway components (NOTUM, APCDD1,
and AXIN2, see Supplementary Fig. [189]10a and Fig. [190]8e). In
contrast, Notch activation was uniquely predominant in cluster 1, as
evidenced by the high expression of HES1, HES5, and HEY2/L genes
(Supplementary Fig. [191]10b), which are canonical downstream targets
and effectors of the pathway^[192]42. Notch signaling, besides
contributing to the stem cell state, is essential in the decision
between absorptive TA progenitors (NOTCH_ON) vs secretory TA
progenitors (NOTCH_OFF)^[193]41. Since NOTCH-ON cluster 1 was mostly
cleared, we wondered if the other expanding clones had markers for the
“opposing” secretory-like fate. Indeed, we observed that expression of
ASCL-2, TFF3, and PROX1^[194]43,[195]44 were highly enriched in cluster
3 (Supplementary Fig. [196]10c, d). Interestingly, the expanded cluster
5 seemed to have an enrichment in Tert and Dll4, suggesting that this
cluster may represent the quiescent-like progenitor pool known as
+4^[197]45 (Fig. [198]8e, Supplementary Fig. [199]10c, d).
In summary, our results show the clearance of specific regressors’
subclones expressing IFN related signaling and Notch activation, as
well as the expansion of subclones that express IL10 suppressive
pathway with expansion of Wnt and secretory-like “states” (cluster 3),
as well as a putative “quiescent”-like progenitor state (cluster 5)
(Fig. [200]8e).
Discussion
In the present study, we take advantage of the fast zebrafish larvae
xenograft model to study the crosstalk between human cancer cells and
the innate immune system. Previous work^[201]15 suggested that although
most human tumors engraft well, some are cleared from the zebrafish
host. Here, we studied a pair of human CRC cells derived from the same
patient at different stages of tumor progression—SW480 from the primary
tumor, and SW620 from a lymph node metastasis isolated 6 months later.
While SW480_regressors engraft poorly and are mostly cleared in 4 days,
SW620_progressors have high engraftment rates. Gene expression assessed
by RNA-seq of these tumors revealed the involvement of innate
immune-related pathways that may contribute to this phenotype. Indeed,
we found that SW480 cells recruit neutrophils and macrophages more
efficiently than SW620. However, SW620 can polarize macrophages toward
a M2-like pro-tumoral phenotype. Genetic and chemical depletion of
myeloid cells demonstrate that macrophages and neutrophils play a
crucial role in tumor clearance. We also performed re-transplantation
experiments of in vivo selected tumors and showed that these tumors
engrafted more efficiently and generated bigger tumors with reduced
macrophage infiltrates. These results suggest that zebrafish innate
immunity can immune-shape tumors toward tumorigenesis.
Finally, single-cell transcriptome analysis clearly shows a fast tumor
selection process, with clearance and expansion of specific subclones
or “cell states” in just 3 days. In accordance with our hypothesis, the
“cleared”-regressor subclones were associated with activation of
immune-inflammatory pathways and escaper-expanded subclones with an
enrichment of IL10 immune suppressor pathway and a secretory-like fate.
Interestingly, we observed a clearance of subclones with active Notch
signaling. The role of Notch signaling and of the identified
immune-related pathways will be the subject of future investigation.
The concept of immunoediting has been mainly focused on adaptive
immunity^[202]46,[203]47, and only one study, to our knowledge^[204]16,
has shown that innate immunity on its own is able to perform
immunoediting. O’Sullivan describes the role of NK cells in educating
macrophages toward an anti-tumoral state that act as crucial effectors
in immunoediting in a RAG2−/− x γc (−/−) mouse model of induced
sarcoma. In the present study, we show that not only macrophages play
an essential role but also that neutrophils contribute to clearance and
therefore to immunoediting. We may speculate that, as NK cells educate
macrophages in the O’Sullivan model, neutrophils might also interact
with macrophages, possibly “re-educating” them toward an anti-tumoral
phenotype.
These results, which show that neutrophils and macrophages may have an
active anti-tumoral role, are in accordance with CRC clinical data that
suggest that TAMs and TANs are associated with a favorable prognosis,
especially in early stages^[205]48–[206]52. On the other hand, in other
types of cancer such as lung, gastric, gynecological, and breast
cancer, high infiltration of TAMs correlates with a poor clinical
prognosis^[207]6,[208]53.
Although the presence of macrophages might indicate a poor prognosis,
macrophages can be in an anti-tumoral state and therefore tumoricidal,
becoming instead a good prognosis. Or on the other hand, if the tumor
has been able to communicate with the nearby macrophages and “talked
them” into becoming pro-tumoral and immunosuppressive then it will be
indeed an indicative of a bad prognosis. Thus, identifying the
functional state of the TME cells becomes fundamental to anticipate
prognosis and also response to immunotherapy, i.e., defining a hot
(permissive) or cold (immunosuppressed) TME. Many different markers
have emerged to classify immune cell types as pro- or anti-tumoral.
However, no universal robust marker which may help guide treatment
decisions, has been found so far. Most studies use a battery of
different molecules to identify the macrophage
subtypes^[209]52–[210]57, although this battery also varies between
studies. This is probably due to the amazing plasticity and diversity
of the different macrophage populations. There are not two static
states but multiple states that are dynamic and
interchangeable^[211]58–[212]60. Also, there are numerous types of
macrophages with different embryonic origins, leading to a huge
heterogeneity of phenotypes and functions. Consequently, to find a good
and universal marker of the innate functional status has been a major
hurdle, that has not been yet achieved^[213]59.
Numerous studies have shown that the zebrafish model can respond to
human tumor angiogenic cues and therefore be used as a reporter of the
angiogenic potential of tumor cells^[214]61,[215]62. Here, we show that
zebrafish can also “read” the innate immune cues and reconstitute an
innate microenvironment in just 4 days. We propose that by analyzing
the engraftment/clearance in wild-type (wt) and mutants as well as
using reporters, such as TNFa, it is possible to infer the function of
these innate immune cells.
Our results are opening the possibility of using the zebrafish Avatar
model as a living biomarker to infer the innate TME state, i.e., reveal
an anti-tumoral state (immune permissive/hot) or pro-tumoral (and
immune suppressive/cold). Importantly, this could have a prognostic
value and possibly help select patients that can benefit from TME-based
therapies, such as immunotherapy. In addition, we also propose that
future experiments can use zebrafish xenografts to study innate immune
suppressive mechanisms and possibly find new therapeutics to enhance
immunotherapy.
Methods
Zebrafish welfare and handling
Zebrafish (Danio rerio) model was handled and maintained according to
the standard protocols of the European Animal Welfare Legislation,
Directive 2010/63/EU (European Commission, 2016) and Champalimaud Fish
Platform. All protocols were approved by the Champalimaud Animal
Ethical Committee and Portuguese institutional organizations—ORBEA
(Órgão de Bem-Estar e Ética Animal/Animal Welfare and Ethics Body) and
DGAV (Direção Geral de Alimentação e Veterinária/Directorate General
for Food and Veterinary).
Zebrafish transgenic and mutant lines
According to the purpose of each experiment, different genetically
modified zebrafish lines were used in this study: Tg(mpx:eGFP)^[216]23,
Tg(mpeg1:mCherry-F)^[217]24, Tg(mpeg1:mCherry-F; tnfa:GFP-F)^[218]24,
runx1^w84x mutant^[219]26, and csf1ra^j4blue (panther) mutant^[220]27.
Wild-type Tubingen strain or Casper mutants were used as control and as
a background line for the experiments.
Human tissue processing
Human samples used for zebrafish patient-derived xenograft (zAvatars)
establishment were obtained from Champalimaud Hospital and Prof
Fernando Fonseca Hospital with written informed consent. The study was
approved by both Hospital Ethics Committees.
Neoplastic colorectal tissues were obtained from surgically resected
specimens. Human tissue processing protocol was performed as previously
described^[221]15. In brief, samples were washed in ice-cold 1X-PBS,
chopped into small pieces, and cryopreserved in 90% FBS 10% DMSO.
Cryopreserved human primary tumor tissue was defrosted, further washed,
and minced in mix1 (DMEM-F12 (Gibco), 60%FBS (Sigma), Y-27632 10 μM
(Cliniscience), Primocin 100 μg/ml (Invivogen), Putrescin 10 μg/ml
(Sigma-Aldrich), Nicotinamide 10 mM (Sigma-Aldrich), and digested with
Liberase (Roche) for 5–10 min at 37 °C. Tumor cell suspension was
passed through a 70 μm cell strainer and centrifuged at 250 × g for
4 min at 4 °C. For cell labeling, tumor cells were incubated with
CM-DiI (1:100) in mix1 but without FBS and supplemented with DNase I
5 U/ml (Fermentas), for 15 min at 37 °C. Cells were resuspended in mix1
supplemented with human EGF (50 ng·mL^−1, Peprotech) at final
concentration of ~0.25 × 10^6 cells per milliliter.
Human cancer cell lines and culture
Human breast cancer cell lines Hs578T, MDA-MB-231, and MDA-MB-468 were
kindly provided by Mónica Bettencourt Dias’ Lab (Instituto Gulbenkian
da Ciência).
Human colorectal cancer cell lines SW480, SW620, and HT29 were
purchased from American Type Culture Collection (ATCC), whereas HCT116
and Hke3 isogenic cell lines were kindly provided by Ângela Relógio
(Charité Medical University of Berlin). SW48 cell line was provided by
Luis Costa Lab (ATCC, Instituto de Medicina Molecular). All cell lines
were kept and grown in Dulbecco’s modified Eagle medium (DMEM) High
Glucose (Biowest) and supplemented with 10% fetal bovine serum (FBS)
(Sigma-Aldrich) and antibiotics (100 U ml^−1 penicillin and
100 μg ml^−1 streptomycin, Hyclone) in a humidified 5% CO[2] atmosphere
at 37 °C. All cell lines were authenticated through short tandem repeat
(STR) profile analysis and tested routinely for mycoplasma
contamination.
Cell staining
Tumor cells were grown to 70% confluence, washed with Dulbecco’s
phosphate-buffered saline (DPBS) 1X (Biowest) and stained in a flask
with lipophilic dyes—Vybrant CM-DiI (4 μl/ml in DPBS 1X), green CMFDA
(1 μl/ml in DPBS 1X, 1 mM stock), or Deep Red Cell Tracker (1 μl/ml in
DPBS 1X, 10 mM stock) (Life Technologies), for 10 min at 37 °C, in
darkness. Cells were washed with DPBS and detached with 2 mM EDTA by
scrapping. Cell suspension was collected to 1.5 ml eppendorfs,
centrifuged at 250 × g, for 4 min at 4 °C, and resuspended in DMEM.
Cell viability was assessed by trypan blue exclusion method, and cell
number was determined by hemocytometer counting. Cells were resuspended
in DPBS 1X to a final concentration of 0.25 × 10^6 cells/μl.
Zebrafish xenografts
Fluorescently labeled cancer cells were injected using borosilicate
glass microcapillaries under a fluorescence scope (Zeiss Axio Zoom.V16)
with a mechanical micropipetor attached (World Precision Instruments,
Pneumatic Pico pump PV820). Approximately 500–1000 cells were injected
into the periviteline space (PVS) of 2 dpf zebrafish embryo, previously
anesthetized with Tricaine 1X (Sigma-Aldrich). After injection,
zebrafish xenografts remained for ~10 min in Tricaine 1X and then
transferred to E3 medium and kept at 34 °C. At 1 dpi, zebrafish
xenografts were screened according to the presence or absence of
tumoral mass. Xenografts with cells in the yolk sac or cellular debris
were discarded, whereas successfully ones were grouped according to
their tumor size, which was classified by comparison with eye’s size.
Every day xenografts were checked—dead ones removed and E3 medium
refreshed. Four days after injection the engraftment rate was
calculated (formula below) and zebrafish xenografts were sacrificed,
fixed with 4% (v/v) Formaldehyde (FA) (Thermo Scientific) at 4 °C
overnight and preserved at −20 °C in 100% (v/v) methanol.
Xenograft engraftment calculation
[MATH: Engraftment(%)=no.ofxenograftswithtumorat4dpitotalno.ofxenograftsat4dpi×100
:MATH]
Ratios of SW480 and SW620
SW480 and SW620, prepared for injection as mentioned above, were mixed
in different proportions right before injection to generate the
following ratios:
Mix 3:1—75% SW480 + 25% SW620
Mix 1:1—50% SW480 + 50% SW620
Mix 1:3—25% SW480 + 75% SW620
Chemo/radio treatment of zebrafish xenografts and zAvatars
At 1 dpi, zebrafish xenografts with the same tumor size were randomly
distributed in the treatment groups: control E3 medium and FOLFOX in E3
(4.2 mM 5-FU, 0.18 mM folinic acid, 0.08 mM oxaliplatin) for three
consecutive days, replaced daily. Maximum tolerated concentration in
zebrafish larvae was determined as previously described^[222]15. Single
dose of 25 Gy was delivered to zAvatars at 1 dpi as described in ref.
^[223]63 and 3 days after the experiment ended. In brief, Irradiation
procedures and regimens were adapted for zebrafish xenografts by the
Champalimaud Foundation Radiation Oncology Department. The 6MV X-rays
beams with 25 Gy were calculated with the same algorithm used in
clinical practice (ECLIPSE, Varian Medical System, CA) and was
delivered via a linear accelerator (Truebeam, Varian Medical Systems,
CA). Irradiation was targeted to the center of a defined area of
30 × 30 cm from a 6-well plate with the anesthetized zebrafish (6 mL of
E3 medium per well, ~12 xenografts per well). The well plates were
positioned with a source-to-surface distance of 100 cm.
Zebrafish macrophage ablation with clodronate liposomes
For the selective depletion of macrophages, Liposomes-encapsulated PBS
(L-PBS) and Liposomes-encapsulated clodronate (L-Clodronate) were
purchased from Liposoma. At the time of cell resuspension immediately
prior to cell microinjection into zebrafish, cells were resuspended
either in PBS, L-PBS, or L-Clodronate at a final concentration of
0.25 × 10^6 cells/μL.
Imaging and analysis of zebrafish xenografts
All images were obtained using a Zeiss LSM 710 fluorescence confocal
microscope, generally with a 5 μm interval in a total of ~60 μm stack
using the z-stack function. Generated images were processed using the
FIJI/ImageJ software. Some of the acquired z-stacks were projected
using maximum intensity projection. Number of cells was quantified with
ImageJ software Cell counter plugin.
To assess tumor size, three representative slices of the tumor, from
the top (Zfirst), middle (Zmidle), and bottom (Zlast), per z-stack per
xenograft were analyzed and a proxy of total cell number of the entire
tumor (DAPI nuclei) was estimated as follows:
[MATH: tumorsize=no.ofDAPIcellsZfirst+no.ofDAPIcellsZmidle+no.ofDAPIcellsZlasttotalnumberofslices×1.5
:MATH]
The 1.5 correction number was estimated to these CRC cells that have a
nuclei with an average of 10–12 μm of diameter. Number of mitotic
figures, activated caspase-3, macrophages, neutrophils, M1 and M2-like
TNFa+ and TNFa− macrophages, as well as other inflammatory cells were
counted in every slice, starting in the first and finishing in the last
slice of the tumor. To get the percentage of each, raw number was
divided by tumor size.
Bulk RNA-seq sample preparation
SW480 and SW620 tumors were dissected from zebrafish xenografts at
2 dpi. A pool of ~30 tumors from each type of tumor and independent
experiments was collected in RNAlater solution (#AM7020, Ambion) and
kept at −20 °C until RNA extraction. The engraftment rate (determined
at 4 dpi with remaining zebrafish xenografts) of SW480 and SW620 used
for gene expression analysis was the following: SW480_B—8%,
SW480_A—30%, and SW480_7—31.5%; SW620_1—92.4%, SW620_2—92%,
SW620_5—97.2%, and SW620_7—98.3%. To study the genetic signatures of
the underlying observed phenotypes (regressors and progressors), total
RNA was extracted from the dissected tumors using Trizol reagent
(Invitrogen Life Technologies, Carlsbad, CA, USA) and further purified
with RNeasy Plus Micro Kit (Qiagen), in accordance with the
manufacturer’s instructions.
RNA-seq analysis
mRNA-libraries were prepared using the Smart-seq2 protocol (Illumina,
USA). Samples were sequenced by Next-Seq 500 Illumina sequencer and
unstranded single-end mRNA-seq libraries of 76 bp were obtained. An
average of ~38 million reads per sample. These RNA-seq libraries
contain a mixture of human and zebrafish RNA derived from the xenograft
as well as the host cells infiltrating it. After quality control
assessment with FastQC^[224]64 (v0.11.7) and low quality reads
filtering with Trimmomatic^[225]65 (v0.38), all sequenced libraries
were quantified with Salmon^[226]66 (v0.13.1 using the respective
transcript human annotations (Hg38) from the Ensembl genome database
project). For downstream analysis package Tximport^[227]67 was used, to
import transcript lengths and abundance estimates and export
(estimated) count matrices. And differential expression analysis was
performed using Limma^[228]68. Genes with a FDR < 0.05 and absolute
log2 foldchange >1 were considered significant.
Pathway enrichment analysis of a ranked gene list using GSEA
Pathway enrichment analysis helps us gain biological insight into large
gene lists typically resulting from high throughput experiments. It
identifies biological pathways that are enriched in the gene list more
than expected by chance. A ranked gene list obtained from SW480 low
engraftment versus SW620 high engraftment differential expression
analysis was input to GSEA PreRank^[229]19 (v4.0.2, Broad Institute,
Cambridge, MA) as RNK file. We used curated gene sets from Molecular
Signatures Database^[230]69 (v7.0, Hallmarks and Canonical Pathways
including KEGG and REACTOME). We then ran GSEA PreRank using the
default weighted statistic. The thresholds for significance were
determined by permutation analysis (1000 permutations), selecting the
enriched pathways with a false discovery rate (FDR) < 0.07.
Single-cell RNA-seq preparation
SW480 tumors were dissected from zebrafish xenografts at 1 and 4 dpi. A
pool of ~100 tumors was collected to 1 ml of DMEM High Glucose
(Biowest), 10 µM of Anoikis inhibitor Y-27632 2HCl (#S1049,
Selleckchem) and 5U of Dnase (#EN0521, Thermofisher). Tumors were then
digested by adding 5 µl of liberase (5 mg/ml, #05401020001, Roche), for
2–3′ at 37 °C and centrifuged at 300 × g, for 4′ at 4 °C. Cell
suspension was resuspended in DPBS 1X (Biowest), EDTA 2 mM (Sigma), FBS
2% (Sigma) and Hepes (Fisher Scientific) 25 mM, filtered with 70 µm
strainer, and DAPI (5 µg/ml) was added as a control for live-cell
selection in FACS (stained DAPI cells were dead and DAPI negative_live
were sorted). Cells were kept on ice and FACS sorted into 384-well
plates containing 384 primers and Mineral oil (Sigma).
Single-cell RNA-seq analysis
During sequencing, Read 1 was assigned 26 base pairs and was used for
identification of the Illumina library barcode, cell barcode and Unique
Molecular Identifiers (UMI). R2 was assigned 60 base pairs and used to
map the Human reference transcriptome with BWA^[231]70. Unique barcode
gene counts were used for further processing in Seurat^[232]36. Only
genes present in at least 10 cells were considered. Moreover, we only
considered barcodes with counts in more than 2000 genes and with <25%
of counts in mitochondrial genes. After quality control and filtering,
we were left with a total of 533 human GFP positive cells from the
first timepoint and 293 human GFP positive cells from the second. After
normalization, the 2000 most variable genes were used for
dimensionality reduction and clustering. We chose clustering parameters
empirically to provide a balance between the number of clusters and
their size. We then ran GSEA analysis for each cluster by ranking genes
according to their differences in gene expression to the other
clusters. Cells were displayed in a 2-dimensional plot using uniform
manifold approximation and projection (UMAP)^[233]37. Normalized
expression values of genes from selected enriched pathways were also
displayed as violin plots or heatmaps. The Normalized expression values
of genes used for visualization correspond to the log2 of total cell
counts divided by 10,000 (very similar to the traditional CPM). For
heatmaps, expression values were displayed scaled by gene (row
z-score).
Mouse welfare and strains
Mice experiments and corresponding protocols were approved by the
Champalimaud Animal Ethical Committee and portuguese institutional
organizations—ORBEA (Órgão de Bem-Estar e Ética Animal/Animal Welfare
and Ethics Body) and DGAV (Direção Geral de Alimentação e
Veterinária/Directorate General for Food and Veterinary).
Rag1−/− C57BL/6J mice were bred at 23 °C, with 40–60% relative
humidity, 12 h light cycle (8 am–8 pm) by the animal facility of
Champalimaud Vivarium, Lisbon, Portugal.
SW480 and SW620 transduction by lentiviral infection
To facilitate posterior quantification of each population on mice
experiments, we first generated SW480 and SW620 cell expressing GFP and
tomato fluorescent proteins, respectively (false colors in the
figures).
SW480 and SW620 cells were seeded (1 × 10^6 cells per well) in 6-well
plates and incubated at 37 °C ON. In the following day, a range of
dilutions (1:50 up to 1:1000) of lentivirus vectors were added to each
well. DMEM supplemented with 10%FBS, 1%P/S, and 8 μg/mL polybrene was
used to enhance transduction efficiency. Twenty-four hours later,
medium was replaced to obtain stable transduced cells and maintained at
37 °C. Untransduced cells with the same antibiotics were used as
controls.
Cells were expanded and the transduction efficiency was measured by
flow cytometry (BD LSRFortessa™ X-20 cell analyser—Biosciences). Cells
were then sorted (BD FACSAria Fusion) using FACS Diva software v8, with
a 99% of purity.
Mouse SW480 xenografts and macrophage ablation
Approximately, 1 × 10^6 SW480 cells were resuspended with 1:1 matrigel
(reduced growth factors, Corning) and one of the following conditions:
PBS, L-PBS, or L-Clodronate. SW480 were injected subcutaneously in the
right flank of 8-week-old Rag1−/− C57BL/6J mice (N = 5 per group).
For macrophage depletion, L-Clodronate was administrated right upon
tumor cell inoculation through intravenous injection (retro-orbital
injection). The same protocol was performed for PBS or L-PBS controls.
To avoid macrophage repopulation, the same treatment conditions were
injected every 4 days. Tumor size was measured once a week using
caliper measurements and tumor volumes were calculated according to a
standard formula:
[MATH:
43π
×(Shortaxisofthetumor/2)2×(Longaxisofthetumor/2) :MATH]
At the end of the experiment, mice were euthanized with carbon dioxide,
and tumor size was measured and immediately fixed in 4% FA.
Mouse SW480, SW620, or MIX 1:1 xenografts
Approximately, 1 × 10^6 tumor cells (SW480, SW620, or MIX 1:1) were
resuspended with 1:1 matrigel (reduced growth factors, Corning) and
subcutaneously injected in the right flank of 7–10-week-old Rag1−/−
C57BL/6J mice.
Tumor size was measured every 3–4 days using caliper measurements and
tumor volumes were calculated according to a standard formula of:
[MATH:
43π
×(Shortaxisofthetumor/2)2×(Longaxisofthetumor/2) :MATH]
At the end of the experiment, mice were euthanized with carbon dioxide
and tumor size was measured and immediately fixed in 4% (v/v) FA.
Mouse xenografts tumor isolation and staining
Tumor-bearing mice were euthanized according to approved guidelines
with carbon dioxide three weeks (~24 days) after inoculation of cancer
cells. Subcutaneous tumors were resected and measured with a caliper.
Tumors were thoroughly minced with scalpels, transferred to 1.5 mL
Eppendorf tubes, and digested in 1 ml of PBS 1X containing 10 μl of
Liberase TM (5 mg/ml) (Sigma) and 3 μl of DNAse I (Thermo Scientific)
for 30 min at 37 °C. Digested suspension was filtered through a 40 μm
mesh into a 15 mL Falcon tube. Digestion was then blocked by addition
of buffer containing 800 ml of HBSS (Corning), 2 ml of EDTA 0.5 M, and
0.1 g of BSA. Tubes were centrifuged 10 min at 300 × g. Pellet was
resuspended in FACS buffer (PBS 1 × wo Ca/Mg, EDTA 2 mM, and FBS 2%).
Total viable cell yield per volume was determined using Trypan Blue and
an automated cell counter. Tumor single-cell suspension was then
stained for FACS analysis.
Flow cytometry antibodies
The following monoclonal antibodies were used for flow cytometric
analysis of tumors: Live/dead discrimination (LIVE/DEAD® Fixable Aqua
Dead Cell Stain Kit, 1:500, Thermo Scientific, #[234]L34957),
anti-human EpCAM CD326 (9C4, Biolegend, 5:100), APC/Cyanine7 anti-mouse
CD45.2 (104, Biolegend, 1:200), anti-mouse F4/80 PE-Cyanine7 (BM8,
eBioscience, 1:200), FITC anti-mouse CD80 (16-10A1, Biolegend, 1:200).
Flow cytometry gating strategy for mouse xenografts
Data were acquired using the BD LSRFortessa™ X-20 cell analyzer
(Biosciences) and analyzed using FlowJo™ v10.6.1 software. Populations
were determined as follows: Human cells (LIVE/DEAD^−EpCAM^+), SW620
cells (LIVE/DEAD−EpCAM^+PE^+FITC^−), SW480 cells
(LIVE/DEAD^−EpCAM^+PE^−FITC^+), mouse cells (LIVE/DEAD^−EpCAM^−),
Macrophages (LIVE/DEAD^−EpCAM^–CD45^+F4/80^+), anti-tumoral M1-like
macrophages (LIVE/DEAD^−EpCAM^−CD45^+F4/80^+CD80^+). See Supplementary
Fig. [235]7.
Generation of SW480zEscapers
Parental SW480 cells expressing GFP (red false color in Fig. [236]7)
were injected into the PVS of 2 dpf zebrafish embryos. At 1 dpi,
zebrafish xenografts were scored according to the tumor size and kept
in E3 medium at 34 °C until day 4. Tumors that persisted until 4 dpi
were dissected and expanded in vitro for three passages in multi-well
plates—which we named SW480zEscapers. SW480zEscapers were injected in
new zebrafish embryos with 2 dpf and engraftment rate was quantified at
4 dpi.
Immunofluorescence
Whole-mount immunofluorescence was performed starting hydration through
methanol series (75% > 50% > 25%). Next, xenografts were permeabilized
with 0.1% (w/v) Triton in PBS and blocked with a mixture of PBS 1X,
BSA, DMSO, Triton 1% (w/v), and goat serum, for 1 h at room
temperature. The xenografts were then incubated with primary antibody
Anti-Cleaved caspase-3 (Asp175) (rabbit, Cell Signaling, 1:100, #9661)
or Mpx (rabbit, GeneTex, 1:50, #gtx128379) overnight and followed by
incubation of the secondary antibody goat anti-rabbit IgG (H+L) 650
(Dylight, 1:400, #84546) and 50 μg/ml DAPI (for nuclear
counterstaining), again overnight.
Wash and fixation steps were performed, and xenografts mounted between
two coverslips, allowing double side acquisition using Mowiol mounting
media (Sigma).
Statistical analysis
Statistical analysis was performed using GraphPad Prism 8.0 software.
All data sets were challenged by D’Agostino & Pearson and Shapiro–Wilk
normality tests. In general, data sets with a Gaussian distribution
were analyzed by parametric unpaired t test and data sets that did not
pass the normality tests were analyzed by nonparametric unpaired
Mann–Whitney test. All were two-sided tests with a confidence interval
of 95%. The exception was related to Fig. [237]4 where data were
analyzed by paired tests either by a parametric paired t test (in
Fig. [238]4g) or by paired nonparametric Wilcoxon test (Fig. [239]4h).
Differences were considered significant at P < 0.05 and statistical
output was represented as follows: non-significant (ns) ≥0.05, *<0.05,
**<0.01, ***<0.001, ****<0.0001. The graphs indicate the results as
AVG ± standard error of the mean (SEM) or standard deviation (SD). In
addition, for small number of samples (<10), we performed an effect
size analysis—Cohen’s D with a Hedges’ g correction (g), using Cohen’s
D 1988 scale (g): g > 0.2 low; g > 0.5 moderate; g > 0.8 high.
Reporting summary
Further information on research design is available in the [240]Nature
Research Reporting Summary linked to this article.
Supplementary information
[241]Supplementary Information^ (12.6MB, pdf)
[242]Peer Review File^ (1.5MB, pdf)
[243]41467_2021_21421_MOESM3_ESM.pdf^ (326KB, pdf)
Descriptions of Additional Supplementary Files
[244]Supplementary Data 1^ (26.5KB, xlsx)
[245]Supplementary Data 2^ (7.4KB, xlsx)
[246]Supplementary Software 1^ (8.1KB, zip)
[247]Reporting Summary^ (653.4KB, pdf)
Acknowledgements