Abstract
Primary pulmonary lymphoepithelioma-like carcinoma (PPLELC) is a rare
subtype of non-small-cell lung cancer. Duo to the current lack of
precise targeted therapies, there is an urgent need to identify novel
therapeutic targets. In this study, we perform single-nucleus
transcriptome analysis on PPLELC samples to reveal the molecular tumor
heterogeneity and characterize the functional states of immune cells
within the tumor microenvironment. We identify a critical malignant
subpopulation of PPLELC characterized by elevated expression of AKT3
and FGFR2. Higher expression levels of AKT3 and FGFR2 are associated
with poorer patient outcomes. Moreover, treatment with either an AKT3
inhibitor or an FGFR2 inhibitor significantly attenuates tumor
progression in patient-derived xenograft models. Our findings highlight
AKT3 and FGFR2 as potential therapeutic targets and prognostic
biomarkers, providing valuable insights for the development of rational
targeted therapies and immunotherapeutic strategies.
Subject terms: Non-small-cell lung cancer, Molecular medicine,
Prognostic markers, Non-small-cell lung cancer
__________________________________________________________________
Single-cell transcriptome profiling reveals molecular tumor
heterogeneity, identifies prognostic markers, and uncovers therapeutic
targets for primary pulmonary lymphoepithelioma-like carcinoma, thereby
providing clinically relevant insights.
Introduction
PPLELC is a rare histological type of non-small-cell lung cancer
(NSCLC), which is strongly related to Epstein–Barr virus (EBV)
infection and accounts for 1% of all cases^[52]1. With continuous
progress in diagnostic techniques, the detection rate of PPLELCs from
NSCLC has gradually increased in recent years. PPLELC has unique
clinicopathological features, including younger age, non-smoker status
and abundant tumor-infiltrating lymphocytes^[53]2. Frequently mutated
driver genes such as epidermal growth factor receptor (EGFR) are rarely
detected in PPLELC, which indicates that the patients are less likely
to benefit from the current targeted therapy^[54]3. The first-line
treatment strategy for PPLELC is surgery and chemotherapy. Only a small
subset of PPLELC patients can benefit from anti-PD-1/ PD-L1
immunotherapy^[55]4. Nevertheless, post-operative chemotherapy has
improved the extremely limited survival time, and immunotherapy has led
to hyperprogression for PPLELC patients who are diagnosed in the IIIA
phase^[56]5. This finding highlights the need for personalized
therapeutic targets to improve the clinical outcomes of PPLELC
patients.
Previously, large-scale genomic studies reveal that the somatic
mutation rate is extremely low, but copy number variation (CNV) are
widespread in PPLELC according to whole exosome sequencing (WES).
However, the clinical application of WES remains less favourable for
substantially many PPLELC patients^[57]3,[58]6. Additionally, the
heterogeneity of the tumor microenvironment (TME), resistance to
therapies and interpatient discrepancies in clinical responses hinder
better prognosis of PPLELC patients. Single-nuclei RNA sequencing
(snRNA-seq) offers a unique opportunity to discern diverse arrays of
cell types in PPLELC TME. Intercellular interactions foster an
immunosuppressive environment to inhibit the eventual prognosis^[59]7.
Understanding the mechanism of these interactions has profound
implications for cancer treatment, especially immunotherapies^[60]8.
However, no study has revealed single-cell transcriptional profiles or
applied a PDX model to study PPLELC.
AKT3, which is a vital protein kinase B, modulates various cellular
activities such as proliferation, survival, apoptosis and tumorigenesis
via the PI3K-AKT pathway^[61]9. The PI3K-AKT pathway was previously
reported to be involved in EBV oncogenesis in multiple tumors.
Fibroblast growth factor receptor 2 (FGFR2) plays a vital role in lung
morphogenesis and contributes to the tumor angiogenesis in most
NSCLCs^[62]10. Inhibitors of AKT3 or FGFR2, which are Enzastaurin or
Erdafitinib, have been widely applied in the treatment of several types
of cancer as targeted therapies^[63]11–[64]14. Interestingly,
preclinical evidence of the application of Enzastaurin or Erdafitinib
in PPLELC patients remains scarce.
Here, we applied snRNA-seq of PPLELC tissues to characterize potential
therapeutic targets from a specific malignant subset, the efficacy of
which was validated in PDX models. Through single-cell transcriptome
profiling, we found a tumor-specific microenvironmental pattern and a
functional state of the immune cells. In summary, our work provides a
deep insight of the molecular tumor heterogeneity of PPLELC with
clinical implications.
Results
Single-cell transcriptional profiling in PPLELC
To systematically explore the tumor microenvironment features of
PPLELC, we performed snRNA-seq on samples from four independent
patients (Fig. [65]1a, Supplementary Data [66]1 & [67]2). After
implementing a series of quality controls (Supplementary Data [68]3)
and integrating two publicly available datasets from the GEO
database^[69]15 ([70]GSM4058912 and [71]GSM4058915), 50007 cells were
obtained for subsequent analysis, and 6 major cell types were
identified in 27 clusters based on the expression of canonical gene
markers. The major cell types were epithelial cells, T cells, B cells,
myeloid cells, fibroblasts and endothelial cells (Fig. [72]1b–[73]d,
and Supplementary Data [74]4), and these genes were annotated with the
KEGG pathway (Fig. [75]1e). We observed a significant increase in the
proportions of T cells and B cells, whereas the proportion of Myeloid
cells was markedly lower in tumors compared to adjacent normal tissues
(Fig. [76]1f). This indicates distinct immune landscapes in PPLELC.
Furthermore, although some clinicopathological features and pathogenic
factors were shared, the infiltrating immune cells of PPLELC patients
were obviously more heterogeneous than those of NPC patients and NSCLC
patients, as the previous single-cell publicly available data show
(Supplementary Fig. [77]1a). The malignant cell fraction was greater in
PPLELC patients than in NPC patients, whereas the B lymphocyte fraction
was greater than that in NSCLC patients. To distinguish malignant cells
from non-malignant cells, all epithelial cells were identified based on
the identified cell types, and large-scale chromosomal CNVs were
inferred in each epithelial cell (Supplementary Fig. [78]1c)^[79]16.
These CNVs were used to validate the quality of snRNA-seq, which is
consistent with the WES data (Fig. [80]1g).
Fig. 1. The single-cell transcriptional profiling of human PPLELC.
[81]Fig. 1
[82]Open in a new tab
a Workflow diagram showing the processing of primary PPLELC tumor and
normal tissue for analysis. Created in BioRender. Xu, K. (2025)
[83]https://BioRender.com/k27t286. b UMAP plots of cells from snRNA-seq
with each color representing one cell types. c UMAP plot of 50007
single nucleuses grouped into 6 major cell types colored according to
cell type and each dot represents one single nucleus. d The expression
of canonical marker genes for the subsets defined in (c). Orange to
gray: high to low expression. e Heatmap plot showing the marker genes
in KEGG Pathway enrichment. f The fraction of cell types originating
from each sample. Each dot represents one cell type, colored according
to sample type. g Chromosomal landscape of inferred large-scale CNVs
distinguishing malignant epithelial cells from non-malignant epithelial
cells. The PPLELC02 tumor is shown with individual cells (y-axis) and
chromosomal regions (x-axis). Amplifications (red) or deletions (blue)
were inferred by averaging expression over 100-gene stretches on the
indicated chromosomes. CNVs are concordant with the calls from WES
(bottom). The CNV pattern of the normal epithelial cells from PPLELC04
is also shown (top).
A crucial cell cluster recurs across patients and is associated with
prognosis in PPLELC patients
Unsupervised clustering of the PPLELC epithelial cell compartment
identified 12 clusters. The epithelial clusters were categorized into 5
subsets based on the expressions of canonical marker genes: PPLELC
malignant cells, AT1, AT2, Club cells, basal cells and ciliated cells
(Fig. [84]2a, Supplementary Fig. [85]1b, and Supplementary Data [86]4).
Most malignant clusters were exclusive to single-tumor tissues, but
PPLELC Cluster 6 was strikingly recurrent across all tumor samples but
not present in normal samples (Mann-Whitney, p < 0.05) (Fig. [87]2b–d).
The cells in PPLELC Cluster 6 had the greatest CNV burden among all
epithelial cells, which is consistent with a malignant phenotype
(Supplementary Fig. [88]1c). A heatmap of the epithelial subsets
revealed that the DEGs in the epithelial clusters were consistent with
the findings of a previous study (Supplementary Fig. [89]1d).
Fig. 2. A subpopulation in malignant cell clusters related to prognosis of
PPLELC.
[90]Fig. 2
[91]Open in a new tab
a UMAP plot of 24,127 epithelial cells coloured by different clusters.
b Boxplot of subtype uncertainty of each SCLC cell stratified by
cluster (y axis; measured as entropy of subtype probabilities per cell
within each cluster; error bars span the 25th to 75th percentile). c
Stacked bar plot of sample fraction per cluster as in (b). d UMAP of
PPLELC epithelial cells with recurrent cluster 6 coloured in red. e
Pathways significantly enriched in cluster 6. Dot plot of ES from GSEA
for significantly enriched pathway (q-value < 0.05 and ES > 1). f
Volcano plot of DEGs between cluster 6 and other epithelial cluster
identified from snRNA-seq data. g Immunohistochemistry assay was
performed to detect expression of AKT3 and FGFR2 between tumor and
adjacent tissue (top, n = 42). Representative images of AKT3 and FGFR2
in PPLELC tumor and adjacent tissue are shown. Scale bars are 200 μm. h
Immunohistochemistry assay was performed to detect expression of p-AKT
and p-FGFR between tumor and adjacent tissue (top, n = 42).
Representative images of p-AKT and p-FGFR in PPLELC tumor and adjacent
tissue are shown. Scale bars are 200 μm. i, j Overall survival and
progression-free progression of 42 PPLELC patients with high or low
expression of AKT3 or FGFR2 with the medium Hscore as the threshold.
P-values are shown in figures. P-value of OS was calculated by the
log-rank test. P-value of FPS was calculated by the multivariate Cox
regression test. k Immunohistology comparison of expression of AKT3 and
FGFR2 in different clinical (left), T (middle) and N (right) stages of
PPLELC patients (n = 42). Representative images of p-AKT and p-FGFR in
different clinical (left), T (middle) and N (right) stages are shown.
Scale bars are 200 μm. Data are presented as means ± standard
deviation; *P < 0.05, **P < 0.01, ***P < 0.001.
The genes and gene programs related to viral carcinogenesis and innate
immunology, including Epstein-Barr virus infection, IL17signalling
pathway and PI3K-AKT signalling pathway, were enriched in the crucial
cluster according to the KEGG gene set analysis (Fig. [92]2e and
Supplementary Fig. [93]2a). A q value of less than 0.05 was used to
filter significant changes. The top DEGs between PPLELC Cluster 6 and
the other epithelial clusters were analysed to better understand their
potential functional differences using a volcano plot (Fig. [94]2f and
Supplementary Data [95]5). At the gene expression level, TRAF3, AKT3,
FGFR2, and TRAF5 were among the most up-regulated genes. The expression
levels of both AKT3 and FGFR2 were significantly upregulated in PPLELC
samples, as determined by immunohistochemical staining (Fig. [96]2g and
Supplementary Data [97]6). Consistently, the phosphorylation of AKT and
FGFR was also markedly elevated in the PPLELC tissues compared to the
control tissues (Fig. [98]2h and Supplementary Data [99]6). AKT3 was
up-regulated in the Epstein-Barr virus infected cancers progression,
such as the proliferation of malignant cells or lymphocytes and immune
escape, which might be triggered by EBV-encoded proteins LMP1 and LMP2
in the NPC^[100]17. FGFR2 pathway is also up-regulated, as implicated
in NSCLC, which supports the PPLELC in maintaining or increasing
fibroblast proliferation and differentiation^[101]18.
To investigate the clinical relevance of AKT3 and FGFR2 in PPLELC, we
used the median H score as the cut-off value to separate the PPLELC
samples into two groups. Notably, Kaplan-Meier survival analysis
revealed that the worse overall survival (OS) and progression-free
survival (PFS) rates were significantly associated with the increased
expression of AKT3 and FGFR2, which supports that patient who highly
expressed AKT3 and FGFR2 had a worse prognosis (Fig. [102]2i, j,
Table [103]1). In addition, the late TNM stage, large tumor size and
lymph node metastasis were associated with an increased expression of
AKT3 and FGFR2 (Fig. [104]2k and Supplementary Data [105]6).
Collectively, these results indicate that overexpression of AKT3 and
FGFR2 in PPLELC Cluster 6 was positively related to poor prognosis in
patients with PPLELC. To explore the specificity of these two
biomarkers in PPLELC, we further examined their correlation with
overall survival times and TNM stages in LUAD and LUSC. However, no
significant associations were observed between AKT3 or FGFR2 expression
levels and either the TNM stage or overall survival time in NSCLC
patients (Supplementary Fig. [106]2b & c), highlighting the
distinctiveness of PPLELC.
Table 1.
The relations between clinical characteristic and the expression of
AKT3 or FGFR2 in PPLELC patients
AKT3 FGFR2
Clinical characteristics Low High P value Low High P value
Age (y)
≥ 60 5 10 0.107 4 11 0.024
< 60 16 11 17 10
Gender
Male 5 14 0.005 7 12 0.121
Female 16 7 14 9
Clinical stage
I 10 0 0.001 9 2 0.031
II 3 1 2 2
III 8 16 10 13
IV 1 3 0 4
T classification
T1 13 1 0.001 12 2 0.001
T2 7 8 8 7
T3 0 7 0 7
T4 1 5 1 5
N classification
N0 12 1 0.002 10 3 0.013
N1 2 2 2 2
N2 5 10 6 9
N3 2 8 3 7
Metastasis
No 21 17 0.115 21 17 0.115
Yes 0 4 0 4
[107]Open in a new tab
Targeted therapies of AKT3 or FGFR2 in PDXs
To determine whether AKT3 or FGFR2 can serve as potential targets, we
first constructed the F0 generation PDX tumor models with biospecimens
from PPLELC patients and transplanted into F1 generation PDXs for
efficacy experiments. To directly explore the efficiency of the AKT3
inhibitor (Enzastaurin) and FGFR2 inhibitor (Erdafitinib), they were
applied to treat PPLELC tumors in vivo (Fig. [108]3a). The tumor
volumes decreased by 93.21% and 90.41% due to Enzastaurin and
Erdafitinib compared to the NC group, respectively (p < 0.05,
ES = 11.90 and 9.13, Fig. [109]3b, c, and Supplementary Data [110]7).
The tumor weights decreased by 85.26% and 82.11%, whereas no obvious
difference was detected in the body weights of the PDX models
(p < 0.05, ES = 8.30 and 9.13, Fig. [111]3d, Supplementary
Fig. [112]2d, and Supplementary Data 8 & [113]9). These results
revealed that the tumor growth rate and tumor mass were dramatically
inhibited by Enzastaurin or Erdafitinib. To detect the expression of
targeted molecules and their downstream proteins, IHC and Western
blotting were performed on the tumor tissues. The IHC results showed
that the relative phosphorylation over total AKT and relative
phosphorylation over total FGFR were significantly suppressed, but the
expressions of AKT3 and FGFR2 were unaltered (Fig. [114]3e, f, and
Supplementary Data [115]10, [116]11). In addition, the Western blotting
results showed that the phosphorylation of downstream proteins
including GSK3β, P70S6K, ERK1/2 and p-FRS2 was suppressed compared to
the NC group, but there was no significant change in their total
expressions (Fig. [117]3g and Supplementary Fig. [118]6). Thus, we
discovered that both Enzastaurin and Erdafitinib shrank the PPLELC
tumor in PDX models, which implies their potential benefit for PPLELC
patients.
Fig. 3. The targeted therapies of AKT3 or FGFR2 shrunk the PPLELC tumor in
PDXs.
[119]Fig. 3
[120]Open in a new tab
(a) Workflow diagram showing the processing of targeting AKT3 and FGFR2
treatments in PPLELC models. Created in BioRender. Xu, K. (2025)
[121]https://BioRender.com/p86s709. (b) Images of Xenograft tumors in
each group of PDXs at the end of experiment (n = 6 per group).
(c)Volume growth curves of xenograft tumor in PDXs (n = 6 per group).
(d) The weight of xenograft tumors in PDXs were measured at the end of
the experiment (n = 6 per group). P value was calculated using unpaired
T tests. (e) Immunohistologic staining of AKT3 and phosphorylated AKT
in xenograft tumors between Enzastaurin and NC groups (n = 6 per group)
(top). Diagram showing the Hscore results of AKT3 and phosphorylated
AKT (n = 6 per group) (below). Scale bars are 100 μm. (f)
Immunohistologic staining of FGFR2 and phosphorylated FGFR in xenograft
tumors between Enzastaurin and NC groups (n = 6 per group) (top).
Diagram showing the Hscore results of FGFR2 and phosphorylated FGFR
(n = 6 per group) (below). Scale bars are 100 μm. (g) The expression of
total and phosphorylated AKT, GSK3β and P70S6K between Enzastaurin and
NC group (left). The expression of total and phosphorylated of FGFR and
ERK1/2 and phosphorylated FRS2 between Erdafitinib and NC group
(right). NC, nonsense control. Data are presented as means ± standard
deviation; *P < 0.05, **P < 0.01, ***P < 0.001.
T-cell clustering and state analysis in PPLELC
To better elucidate the heterogeneity of T lymphocyte infiltration in
PPLELC, we analysed the gene expression of 7117 T cells from both tumor
(6615 cells) and adjacent normal tissues (502 cells). We categorized
all T lymphocytes into 10 clusters and annotated these clusters based
on their expression of canonical T cell markers and gene molecules
among the ten clusters (Fig. [122]4a). We identified the following
clusters: naïve, cytotoxic, exhausted, follicular helper T (Tfh),
regulator T (Treg), helper T1 (Th1) and helper T17 (Th17) cells and
exhibited their gene profiles (Fig. [123]4b, c, and Supplementary Data
[124]4). The distribution of T cells was mainly derived from tumors
(Fig. [125]4d). To evaluate the functional state of the major T cell
subsets, we removed the clusters from non-malignant tissues with fewer
than 150 cells and obtained 5 major T cell subsets, including naïve T
cells, exhausted CD8^+ T cells, Tregs, cytotoxic CD8^+ T cells and
Tfhs.
Fig. 4. Assessing the functional states of tumor-infiltrating T cells in
PPLELC.
[126]Fig. 4
[127]Open in a new tab
(a) Subclustering of tumor-infiltrating T cells on the UMAP plots of
the scRNA-seq datasets. (b) Expression and distribution of canonical T
cell marker genes among cells. Red to gray: high to low expression. (c)
Average expression of T cell-specific markers across different
clusters. The dot size is proportional to the relative expression level
of each gene. (d) T cell distribution from tumors and normal tissues.
(e, f) Violin plots showing the signature scores of cytotoxic and
exhaustion gene sets for each tumor-infiltrating T cell cluster in the
snRNA-seq. Signature scores for each cell were calculated by the VISION
method. (g, h) Violin plots showing the signature scores of progenitors
and terminal exhaustion gene sets for each tumor-infiltrating T cell
cluster in the scRNA-seq. Signature scores for each cell were
calculated by the VISION method. (i, j) GSEA of significantly enriched
pathways for DEGs of cytotoxic (left) and exhausted (right) T cell
cluster in the snRNA-seq data. Top significant results ranked by their
NES are illustrated. (k) Representative images of multiplex IHC
staining in PPLELC tumor tissues. Proteins detected using respective
antibodies in the assays are indicated on top. The green, yellow, and
red arrows indicated the representative cells positive for LMP1, FOXP3
and CD8 proteins in PPLELC tissue, respectively. Scale bars are 200 μm.
Data are presented as means ± standard deviation from three independent
experiments; *P < 0.05, **P < 0.01, ***P < 0.001.
We next investigated the functional properties of these T cell subsets
using multiple functional gene sets and the VISION method^[128]19
(Supplementary Data 12). The cytotoxic CD8^+ T cell subsets had the
highest cytotoxic score and exhausted the CD8^+ T cell subsets with the
highest exhaustion score and second highest cytotoxic score (p < 0.05,
Fig. [129]4e, [130]f), but there were no significant differences in the
cell stress signature (Supplementary Fig. [131]3a). Compared with the
other subsets, the exhausted CD8^+ T cell subset had a greater
progenitor exhaustion signature but no obvious difference in the
terminal exhaustion signature (Fig. [132]4g, [133]h). Moreover,
pseudo-time analysis was separately applied to CD4^+ and CD8^+ T cells
to evaluate the conversion. CD4^+ T cells gradually transformed into
Treg and exhausted CD4^+ T cells (Supplementary Fig. [134]3b). CD8^+ T
cells exhibited increased cytotoxic and exhausted scores along the
trajectory (Supplementary Fig. [135]3c). Pathway enrichment analysis
was performed on the T cell subsets via GSEA (q value < 0.05 and log[2]
(FC) > 1). We found that the T cytotoxicity-related pathways, including
the Wnt / β-catenin signalling, inflammatory response and Notch
signalling pathways, were significantly enriched in the cytotoxic CD8^+
T cell subset, whereas Epstein-Barr virus infection might play an
important role^[136]20 (Fig.[137]4i). In addition, the TGF-β catenin
pathway, PI3K-AKT pathway and Interferon γ / α pathway^[138]21 were
strongly enriched in the exhausted T cell subset (Fig. [139]4j),
whereas Tregs were strongly positively correlated with the inflammatory
response pathway and JAK-STAT3 signalling pathway, which might be
induced by lymphocryptoviral latent membrane protein 1 (LMP1), a
pathogenic protein encoded by EBV^[140]17,[141]22 (Supplementary
Fig. [142]3d). Next, paraffin sections from 6 PPLELC patients were
subjected to mIHC staining to explore the relationships of LMP1, Treg
and CD8^+ T cells in the PPLELC microenvironment (Fig. [143]4k and
Supplementary Data [144]13). The results show that the expression of
LMP1 protein was significantly positively related to the expression of
FOXP3, but negatively related to the expression of CD8 in patient
sections.
B and myeloid cell clustering and state analysis in the PPLELC
Furthermore, B lymphocytes support T-cell function and sustain adaptive
immunity by generating specific antibodies; thus, they play a prominent
role in anti-tumor immunity^[145]23. We obtained 2907 B cells and
divided them into 9 subsets. To accurately explore the B cell
clustering and function in PPLELC, we removed the clusters with fewer
than 150 cells and applied the canonical B cell markers to define the
subsets (Fig. [146]5a, b, and Supplementary Data 4). Most B lymphocytes
were derived from the PPLELC tumor (Fig. [147]5c and Supplementary
Fig. [148]4a) and defined into three subsets: plasma (SDC1), follicular
B cells (FCER2) and germinal centre (GC) B cells (BCL6). GC B cells
tend to convert into plasma cells^[149]24 (Supplementary Fig. [150]4b).
Notably, the plasma subset was significantly down-regulated in the
inflammatory pathway (e.g., inflammatory response, Wnt-β and TGF-β
pathway, Supplementary Fig. [151]4c), and IGHM and BACH2 were the
highest DEGs. BACH2 has been reported as the immunosuppressive factor
in the lung cancer, which indicates poor prognosis^[152]25.
Fig. 5. Assessing the functional states of tumor infiltrating-B cells and
TAMs in PPLELC.
[153]Fig. 5
[154]Open in a new tab
a Subclustering of tumor-infiltrating B cells on the UMAP plots of the
snRNA-seq datasets. b Expression and distribution of canonical B cell
marker genes among cells. Red to gray: high to low expression. c Dot
plot showing the highest expressed genes of each tumor-infiltrating B
cell cluster in the snRNA-seq. Dot size indicates the fraction of
expressing cells, colored based on normalized expression or activity. d
Subclustering of TAMs on the UMAP plots of the scRNA-seq datasets. e
Expression and distribution of canonical TAM marker genes among cells.
Red to gray: high to low expression. f Dot plot showing the highest
expressed genes of each TAM cluster in the snRNA-seq. Dot size
indicates the fraction of expressing cells, colored based on normalized
expression or activity. g Heatmap showing the expression of MHC
molecules, chemokines, cytokines, and other related genes in each TAM
cluster. h Boxplots showing the M1 and M2 signature scores for each TAM
cluster in the snRNA-seq data. The signature score was calculated by
the VISION method. i, j Boxplot showing the angiogenesis and
phagocytosis signature scores for each TAM cluster in the snRNA-seq
data. The signature score was calculated by the VISION method. k Dot
plot showing the expression of the immune costimulatory, checkpoint,
and evasion genes for each TAM cluster in the snRNA-seq dataset.
Tumor-associated macrophages (TAMs) have emerged as a focus in the
immune-oncology field because of the vital role of macrophages in tumor
progression^[155]26. Similar to T lymphocyte analysis, we identified
three TAM clusters and a cDC2 cluster by applying previously published
markers^[156]27 (Fig. [157]5d–f, Supplementary Fig. [158]4d and
Supplementary Data [159]4). The TAM-C1QC cluster highly expressed
complement genes (e.g., C1QC, C1QA and C1QB), NRG1 and IL21R.
Furthermore, MHC class II molecules, including HLA-DRA, HLA-DRB1 and
HLA-DQA1, were highly expressed in the TAM-C1QC cluster, which
indicates their vital role in strong antigen presentation ability
(Fig. [160]5g). PPARG^[161]28 and the lung tissue-resident marker
MARCO^[162]27 were highly expressed in the TAM-PPARG cluster, along
with the supersessive genes APOC1^[163]29, FN1^[164]30, and
CCL18^[165]31. Moreover, the TAM-PPARG cluster expressed high levels of
cytokine and chemokine molecules, which suggests a strong
immunosuppressive ability. In the third cluster, TAM-IL2RA highly
expressed the innate immune response markers IL2RA^[166]32 and CD163.
MTSS1^[167]33, which is a novel protein important in tumor progression,
was up-regulated in the TAM-IL2RA cluster.
To evaluate the functional state of the TAM subsets in PPLELC, the
expression or activity of multiple functional gene sets were detected
in their profiles^[168]27 (Supplementary Data 14). The results revealed
that the TAM-C1QC cluster had the highest M1 signature score
(p < 0.05), followed by the cDC2 cluster (Fig. [169]5h). Reciprocally,
the highest alternative M2 macrophage signature was expressed in the
TAM-PPARG cluster compared with the other clusters (p < 0.05,
Fig. [170]5h). In addition, the angiogenesis signature and FN1^[171]27,
which are linked with a survival disadvantage in kidney cancer, were
more likely associated with the TAM-PPARG cluster, whereas the
phagocytosis signature was not significantly different among the three
TAM subsets (Fig. [172]5i, j). In parallel, we investigated the
differences among these subpopulations at the pathway level and found
that various pathways related to cellular immunity were significantly
enriched in the TAM-C1QC cluster (Supplementary Fig. [173]4e). The
TAM-PPARG cluster was enriched in hypoxia and angiogenesis pathways but
inversely correlated with inflammation pathways (such as the IFN-γ,
IFN-α and JAK-STAT signalling pathways; Supplementary Fig. [174]4f).
Furthermore, we detected the expression of immune checkpoint genes and
costimulatory molecules. Multiple costimulatory signals and
presentation molecules were detected in the TAM-C1QC cluster but not in
the TAM-PPARG or TAM-IL2RA cluster (Fig. [175]5k). The ligands
(VSIR^[176]34, PD-L1, PD-L2, and SIGLEC10^[177]35) that mediated the T
lymphocyte immune checkpoint were highly expressed in the cluster
TAM-C1QC, but ICOSLG^[178]36 was exclusively enriched in the cluster
TAM-PPARG.
Complex intercellular communication networks in the PPLELC TME
Cellchat^[179]37 was used to identify the ligand-receptor pairs and
molecular interactions among major cell types (Fig. [180]6a,
Supplementary Fig. [181]5a, b and Supplementary Data [182]15). The TAM
subsets had the largest number of ligand-receptor interactions, whereas
the T subpopulations had the fewest pairs in the immune cell
populations. Tumor cells played crucial roles in the entire TME, and
the interactions between tumor cells and other immune cells served
instructive functions in tumor immunotherapy. The malignant cells were
mainly associated with naïve T, Treg, plasma and TAM clusters via
multiple ligand-receptor interactions.
Fig. 6. Ligand-receptor-based interaction between tumor and immune cells.
[183]Fig. 6
[184]Open in a new tab
a Circo plots of the numbers of interaction between cell types in the
snRNA-seq data. b, c Chord plots displaying the pathways enriched in
the putative ligand-receptor interactions among malignant cell clusters
and other cell types. d Dot plots showing the mean interaction strength
for selected ligand–receptor pairs between tumor cells and
tumor-infiltrating T clusters. Dot size indicates the p value, colored
by the average expression level of interaction. e Dot plots showing the
mean interaction strength for selected ligand–receptor pairs between
tumor cells and TAM clusters. Dot size indicates the p value, colored
by the average expression level of interaction. f Dot plots showing the
mean interaction strength for selected ligand–receptor pairs between
tumor cells and tumor-infiltrating B clusters. Dot size indicates the p
value, colored by the average expression level of interaction. g
Representative images of mIHC in PPLELC to prove the interaction
between tumor cells and T cells. CD8, CD44, LAMA3 and DAPI were labeled
with different colors. h Representative images of mIHC in PPLELC to
prove the interaction between tumor cells and macrophages. CD68,
COL4A5, CD44 and DAPI were labelled with different colors. i
Representative images of mIHC in PPLELC to prove the interaction
between tumor cells and B cells. CD19, CD74, APP and DAPI were labelled
with different colors.
In addition, specific signalling pathways regulated by several
ligand-receptor pairs between PPLELC and immune cells are shown in the
chord plots (Fig. [185]6b, c, and Supplementary Fig. [186]5c, d).
Notably, the FGF pathway and ncWnt pathway were the major signalling
pathways among the malignant PPLELC subsets. Tumor cells that
communicated with other immune cells were massively enriched in the
BMP, Notch and SEMA3 pathways. To further study the crosstalk between
malignant cells and immune cells in the PPLELC TME, we detected major
subpopulations of lymphocytes and TAM cells with the greatest number of
interactions with malignant PPLELC Cluster 6. Most of the interactions
between PPLELC Cluster 6 and T lymphocytes were involved in the
ECM-receptor interaction (LAMA3 and CD44)^[187]38 and cytotoxic
function (GZMA and PARD3) (Fig. [188]6d). In addition, the interactions
between malignant PPLELC Cluster 6 and TAM subsets were prominent in
terms of macrophage migration inhibitory factor (MIF) and receptors
(COL4A5 and CD44), which indicates its vital functions in tumor
progression, angiogenesis and immune escape^[189]39 (Fig. [190]6e).
Unlike the T cell subsets, the three B subpopulations highly expressed
CD74 with the ligand APP overexpressed in the tumor cells, which
suggests their potential immunosuppressive relation (Fig. [191]6f). To
confirm the reliability of the significantly overexpressed
ligand-receptor interactions, multiplex fluorescent immunohistochemical
staining was performed on the paraffin sections from PPLELC patients.
The results showed that PPLELC tumor cells exhibited notable
chemoattraction for CD8^+ T cells via the crucial interactions of
CD44-LAMA3, towards tumor-infiltrating macrophages through COL4A5-CD44,
and for B cells through APP-CD74 (Fig. [192]6g–[193]i). Collectively,
our results suggest that the abundant interactions among diverse immune
cells and tumor cells may determine the prognosis and reflect
therapeutic responses in patients with PPLELC.
Discussion
In recent years, an increasing number of PPLELC cases have been
diagnosed, and there is a lack of effective therapeutic strategies. Our
data revealed a unique subset of malignant cells that were recurrent in
the tumor tissues of PPLELC patients and presented the highest CNV
signals and Shannon entropy, which indicates the distinct
characteristics of this malignancy. This malignant subset was
characterized by profound expressions of AKT3 and FGFR2. Previous
reports have shown that signalling by AKT3 is related to EBV infection
in NPC^[194]17. The vital role of FGFR2 in NSCLC tumorigenesis has been
validated by several studies but has not been reported in NPC, so FGFR2
may be an individualized signature for PPLELC. Furthermore, the AKT3
and FGFR2 overexpression is significantly correlated with the late
clinical stage, large tumor size, increased lymph node metastasis and
poor prognosis. To date, sequencing technology has been applied in the
PPLELC with a focus on WES and bulk RNA sequencing^[195]40. The WES
results suggested that mutated genes were rare in PPLELC tissues, which
indicates that new therapeutic targets must be urgently explored. In
addition, bulk RNA sequencing revealed only down-regulated genes such
as ZBTB16, PPARG, TGFBR2 and PIK3R5, in PPLELC patients, and few
up-regulated genes were reported. These results indicate the clinical
utility of AKT3 or FGFR2 as prognostic markers, but the relationship
between AKT3 and FGFR2 in PPLELC and whether EBV infection is the
initial factor of the AKT3 and FGFR2 overexpression remain unknown.
This AKT3- and FGFR2-overexpressing subcluster constitutes only a small
fraction of the malignant cells that comprise PPLELC, but it is
strongly associated with survival, which highlights the practical value
of single-cell analysis.
Our results revealed that the overexpression of AKT3 and FGFR2 was
positively related to poor prognosis in PPLELC patients, which
indicates its potential utility as a prognostic biomarker for these
patients. However, to further substantiate the clinical application of
AKT3 and FGFR2 as biomarkers, strategic validation studies should be
conducted in the future. Our study has confirmed the prognostic value
of AKT3 and FGFR2 at the pathological level. Future studies will focus
on evaluating their clinical validity. To further substantiate the role
of these markers, we propose validating the expression levels of AKT3
and FGFR2 in a larger PPLELC cohort, including those from
non-research-oriented hospitals. This validation will examine the
associations between marker expression and patients’ outcomes (OS, PFS,
and EFS), identify appropriate evaluation metrics and diagnostic
thresholds, and ultimately establish a standardized evaluation process.
Additionally, distinguishing PPLELC from NPC and NSCLC represents a
significant challenge for enhancing antitumor therapy. Given the
elevated expression levels of AKT3 and FGFR2 in specific subsets of
PPLELC cells, which are distinct from NSCLC and NPC, these markers may
serve as potential biomarkers for differentiating PPLELC from
metastatic NPC and NSCLC, thereby improving clinical outcomes. In
future study, we plan to collaborate with other centers to collect
specimens of NPC and NSCLC, aiming to further investigate the
diagnostic utility of AKT3 and FGFR2.
Currently, surgical resection and chemotherapy are the primary
treatments for PPLELC^[196]41. Resection of the primary tumor is an
effective treatment modality for early-stage PPLELC; however, surgery
alone is not an independent predictor of OS in these patients. Notably,
a previous study reported that traditional staging systems for lung
carcinoma may not be appropriate for PPLELC, as the sensitivity to
chemotherapy and radiotherapy significantly influences
outcomes^[197]42. Moreover, there is no consensus on the optimal
selection of chemotherapy regimens for advanced PPLELC patients,
although platinum-based chemotherapy has been identified as an
independent predictor for OS. Nevertheless, metastasis and
chemoresistance remain significant challenges in PPLELC. Consequently,
the development of novel, specific targeted therapeutic drugs is
essential. To our knowledge, this study represents the first attempt to
establish a PDX model for PPLELC to evaluate therapeutic efficacy.
Enzastaurin and Erdafitinib, which have been FDA-approved for clinical
use in treating large B-cell lymphoma, colorectal cancer, bladder
cancer, urothelial carcinoma and mantle cell lymphoma, demonstrated
significant tumor reduction in PPLELC PDX models. Additionally, our
sequencing results revealed that CD8^+ T cells tend to transform into
cytotoxic or exhausted T cells, suggesting a potential effective
response to immunotherapy. However, while single-agent targeted
therapies are generally more effective and less toxic than
chemotherapy, they often lead to resistance in advanced NSCLC. A
rational combination of targeted therapy, chemotherapy, or
immunotherapy may enhance therapeutic efficacy and reduce the
development of acquired drug resistance in PPLELC. Taking NSCLC as an
example, in the FLAURA2 study^[198]43, the combination of EGFR-TKI and
chemotherapy demonstrated a significant PFS benefit compared to the
EGFR-TKI monotherapy group. This suggests that combining targeted
therapy with chemotherapy can effectively delay the development of
tumor resistance. In future animal model experiments, we intend to
establish several groups: monotherapy with targeted agents, combination
of targeted therapy and chemotherapy, combination of targeted therapy
and immunotherapy, as well as dual-targeted therapy. This will allow us
to systematically investigate the optimal utilization of targeted
drugs.
Despite promising results in our study, several limitations should be
acknowledged. First, due to the challenges associated with preoperative
diagnosis and obtaining fresh PPLELC specimens, further cellular
mechanistic and functional experiments on tumor cells, as well as
additional analyses of immune cells, are warranted. Second, in this
study, the patient cohort was exclusively derived from the Chinese
population, which may limit the statistical power and generalizability
of our findings. For instance, in NSCLC, the mutation frequency of EGFR
is markedly higher in Asian populations compared to Caucasian
populations, resulting in a greater utilization rate of EGFR-TKIs in
Asian NSCLC patients. This ethnic disparity in mutation rates may also
be observed in PPLELC. Therefore, future studies should aim to include
larger and more diverse populations to validate the correlation between
AKT3 and FGFR2 expression levels and prognosis across different
ethnicities. Additionally, to balance the sample sizes between tumor
and adjacent tissues, we incorporated external single-cell sequencing
data of adjacent tissues from GEO datasets. The integration of external
datasets may introduce technical and biological biases. To minimize
these potential biases, we selected sequencing data generated on the
same platform, controlled for confounding factors such as age and
gender, and validated key findings in additional samples. In future
multi-center clinical studies, we aim to expand the patient cohort size
to further reduce the impact of these biases. Third, while AKT3 and
FGFR2 show potential as specific prognostic biomarkers for PPLELC
patients rather than NSCLC patients, their validity requires
confirmation through independent PPLELC cohorts. Finally, prolonged
monotherapy with AKT3 or FGFR2 inhibitors may lead to drug resistance,
potentially through the activation of alternative signalling pathways
and adverse effects on normal cell function. Future studies should
explore rational combination therapies that mitigate resistance
development and enhance therapeutic efficacy.
In conclusion, our work has revealed that AKT3 and FGFR2 may serve as
therapeutic targets and shows the transcriptomic landscape and cell
state of the PPLELC microenvironment. Our findings have potential
implications for the design of rational targeted therapy and
immunotherapeutic approaches.
Methods
PPLELC samples
All PPLELC patients were pathologically diagnosed and received on
surgery from 2019 to 2022. PPLELC patient were followed-up from 1 to 58
months (median 27.37 months). All tissue specimens from individuals
with primary pulmonary lymphoepithelioma-like carcinoma were confirmed
by Epstein–Barr virus-encoded small RNA (EBER) staining. We summarized
their clinicopathological parameters in Supplementary Data 16. Informed
consent was obtained from each patient, and the study protocol was
approved by the Ethics Committee of Sun Yat-Sen Memorial Hospital
(SYSKY-2024-061-01). All ethical regulations relevant to human research
participants were followed. This study is compliant with the Guidance
of the Ministry of Science and Technology for the Review and Approval
of Human Genetic Resources (2025BAT00067).
Sample processing, cDNA amplification and library construction
Tween with salts and Tris (TST) buffer were used to isolate PPLELC
tissues. PPLELC tissues were mechanically dissociated using fine
scissors and nucleus suspension was pelleted by centrifuging at 500 g
for 5 min at 4 °C. We added RNase inhibitor (Thermo Fisher Scientific,
N8080119) in all TST buffer for all washing steps. Nucleus were
resuspended in 200–1000 µl TST buffer, filtered through a 40 µm cell
strainer and attached to a fluorescence-activated cell sorting tube
(Corning, 352235). The resulting nucleus were counted by disposable
counting chambers (Bulldog Bio, DCS-S01), and processed for sequencing.
We prepared single-nucleus RNA libraries per Chromium Next GEM Single
Cell 3ʹ v3.3 User Guide (10× Genomic) and applied TapeStation D1000
screening tapes (Agilent) and Qubit HS DNA quantification kit (Thermo
Fisher Scientific, [199]Q32854) to construct and analyze libraries.
Processing of snRNA-seq
We generated and processed snRNA-seq data from 10× Genomics platform by
CellRanger (version 3.1.0). Firstly, raw base call files were
demultiplexed into FASTQ files. Secondly, we performed alignment,
quantified the gene expression levels of single nucleus, and align
transcripts to the human GRCh38 reference genome. In each sample, cells
without gene expression would be removed at the beginning. Next, we
applied UMI < 1 and expressed genes < 500 as the filter of low-quality
cell. TPM-like value was consequently performed on each cellular gene
by the UMI counts divided from the sum of the cellular UMI counts and
multiplying by 10,000. Log[2] (TPM + 1) was set to be the final
expression value. Lastly, we applied Seurat package (version 3.2.3) to
transform the output file into Seurat object for further analyses.
Processing of whole-exome sequencing
Whole-Exome Sequencing (WES) from the PPLELC tumor was extracted with
Tissue and Blood Kit (Qiagen, 69504). We applied the Agilent SureSelect
Human All Exon V6 Kit (Agilent Technologies, 5190-8863) to capture
whole-exome and sequenced the resulting libraries by a HiSeq X Ten
platform (Illumina). Next, we filtered the sequencing reads with
contained adapter reads, low-qualityreads, too many nitrogen atoms
(>10%), or low-quality bases (>60% bases with quality < 6). Then we
subjected high quality paired-end reads to gap alignment to a UCSC
human reference genome (hg19) using BWA-MEM (v0.7.15). Duplicate reads
were sorted and marked by Picard (v1.84). Local realignment and base
quality score recalibration of the BWA-aligned reads was then conducted
using the Genome Analysis Toolkit (GATK; v3.4).
Inferring CNV based on whole-exome sequencing
PCR duplicates were removed by Samtools rmdup and the coverage depth
were calculated at each covered base. The whole exome was segmented
into small windows (0.5 Mb in size) and calculated total depth of each
window followed by normalization of the sequencing data volume. Using
Loess normalization, we corrected the bias from the genomic GC content
and calculated depth ratios across the whole genome of each tumor
sample. Then we used DNACopy R package to merge windows with similar
depth ratios into genomic segments. MergeLevels were further performed
to join adjacent segments.
Integration of individual samples
Batch correction was performed in combining clinical samples reduced to
the top 50 PCs using fastMNN with pseudocount of 1. The dataset
included three PPLELC samples (PPLELC-1, PPLELC-2, PPLELC-3), one
control sample labelled as Normal, and two additional normal samples
from the GEO database ([200]GSM4058912 and [201]GSM4058915). An
entropy-based measure was performed to evaluate the effect of batch
correction, which quantifies the normalized expression mixes across
patients^[202]44. A k-nearest neighbors graph (k = 30) was constructed
by Euclidean distance on the normalized dataset and computed the
fraction of cells qT derived from each tumor sample T in the
neighborhood of each cell j. Then Shannon entropy Hj of sample
frequencies were calculated within each cell’s neighborhood as:
[MATH:
Hj=∑T−qTlogqT :MATH]
High entropy suggests that the most similar cells originate from a
diverse set of tumors, while low entropy indicates that most similar
cells derive from the same tumor. Harmony was run on the PCA matrix
above with patient ID as the batch key by default parameters^[203]45.
Identification of cell cluster
We identified major cell types using manually annotating of the
differentially expressed gene (DEG) between clusters. Cluster-specific
markers for each cluster were identified by the “FindAllMarkers”
function with fold change threshold as 0.25. The subsets of epithelial,
T/NK, B lymphocyte and myeloid cells were identified by canonical
markers (Supplementary Data 4). We performed UMAP embeddings to
visualize cluster-specific expression scores.
Differential expressed genes in snRNA-seq and enriched gene pathways
FindAllMarkers were performed on normalized count data to analyze
population-specific DEGs. Expression value of each gene in given
cluster were compared with other clusters by Wilcoxon rank sum test.
Gene set enrichment analysis (GSEA) was performed on DEGs by the GSEA
tool (version 4.0.3) based on the Molecular Signatures Database
(MSigDB, C5, gene ontology set, version 7.2). The enrichment scores
(ES) were calculated based on this ranked DEG list.
Validation of cancer cells using single-cell CNV calling
Single-cell CNV calling was performed to distinguish cancer cells from
non-malignant epithelial compartment. First, all putative cancer
subsets were confirmed to separate from the cells originated from
normal lung subsets. Additionally, CNVs harboured from cancer cell
clusters were identified based on matched bulk DNA-sequencing from our
WES data^[204]16. Meanwhile, we exhibited CNV at the single-cell level
with a diploid mean and set available adjacent normal samples as
standard deviation. At least two standard deviations from the diploid
mean were considered to be a copy number change.
Analysis of the signatures score in clusters of T lymphocytes and myeloid
cells
The expressions of eight signatures (Cytotoxic, Exhausted, Progenitor
Exhausted, Terminal Exhausted, M1, M2, Angiogenesis and Phagocytosis,
Supplementary Data 12 & [205]14) were analysed in T lymphocytes and
myeloid cells. For each signature, the normalized expression levels of
each cellular gene were extracted as a gene expression matrix. Then,
for each cell, the average expressed value of all genes in the
signature in that cell was regarded as cluster-specific signature
score. Finally, a mean overall expression score for the signature was
calculated based on all cells of a particular group. Box plots and
violin plots were used to compare the signature scores of the cell
groups. Wilcoxon-tests were used to analyze the differences in
signature scores.
Cell-cell interaction analysis
We performed Cellchat package to investigate cell–cell interaction
among different cell clusters, focusing on those interactions among
tumor cells and other immune cells, in PPLELC^[206]37. To capture
interaction, we integrated previously published multiple
ligand–receptor resources, resulting in 3190 human ligand–receptor
pairs (Supplementary Data 15). We input cell type annotation
information, gene-cell raw matrix and these ligand–receptor pairs to
cellchat with a threshold set as 0.1. Mean value ≥ 1 and p-value < 0.05
were considered as significant ligand–receptor pairs.
PDX generation of PPLELC and in vivo drug testing
The female NOD-SCID mice aged 5–7 weeks (GemPharmatech, T001492) were
randomly divided into 3 groups, housed under pathogen-free conditions,
and engrafted with fresh PPLELC tumor fragments measuring 1–8 mm^3.
Enzastaurin (Selleckchem, S1055) was resuspended in 0.05% Tween80, and
administered at 100 mg/kg/d for 5 d/week by oral gavage. Erdafitinib
(Selleckchem, S8401) was resuspended in 1% Tween 80, and administered
at 25 mg/kg/d for 5 d/week by oral gavage. The volume was calculated as
(length × width^2)/2. In animal experiments, the tumor was excised
before reaching a maximum volume of 2000 mm³. We ensured that the
maximum allowed tumor volume was not exceeded during the experiment. We
have complied with all relevant ethical regulations for animal use.
Animal studies were approved by the Animal Care and Use Committee of
the Guangdong Laboratory Animals Monitoring Institute (IACUC 2023129).
Immunochemistry
Paraffin-embedded samples were cut into 4 µm consecutive sections, and
antigen retrieval by a pressure cooker for 10 min in citric acid buffer
(pH 6.0). The FFPE sections were incubated with AKT3 antibody (1:200,
Abcam, ab152157), or FGFR2 antibody (1:200, Abcam, ab10648). Hscore
were applied to evaluate the protein expression in tumor cells
including intensity and percentage of stained tumor cells (Intensity: 0
for no, 1 for weak, 2 for moderate, and 3 for strong; Percentage: 0 for
0%, 1 for 1%–25%, 2 for 26%–50%, 3 for 51%–75%, and 4 for 76%–100%).
The Hscore of IHC result was generated as follows: intensity score ×
percentage score. All PPLELC patients were divided into two subgroups
(High and Low) compared to the median Hscore of the AKT3 and FGFR2
immunostaining, respectively. Paraffin-embedded sections of PDXs were
obtained after the endpoint of in vivo drug testing. Then, the FFPE
samples were incubated with AKT3 antibody (1:200, Abcam, ab152157),
FGFR2 antibody (1:200, Abcam, ab10648), p-AKT antibody (1:100, Cell
Signaling Technology, 4060) and p-FGFR antibody (1:50, Thermo Fisher,
PA5-105880).
Multi-IHC
FFPE slides from 6 PPLELC primary tumors from a cohort of 42 PPLELC
patients including snRNA-seq samples, were subjected to multi-IHC and
multispectral imaging using a PANO Multiplex IHC kit (Panovue,
10004100100) to examine LMP1 (1:100, Santa Cruz, sc-71023), CD8 (1:200,
Abcam, ab237709), FOXP3 (1:200, Abcam, ab20034), CD19 (1:500, Abcam,
ab134114), CD68 (1:1000, Abcam, ab303565), LAMA3 (1:200, Abcam,
ab151715), CD44 (1:50, Cell Signaling Technology, 3570), CD74 (1:200,
Cell Signaling Technology, 77274), APP (1:500, Cell Signaling
Technology, 29765 T) and COL4A5 (1:200, Abcam, ab157779). The whole
steps of mIHC were followed the manufacture’s protocol^[207]46.
Western blotting
Western blotting was performed as described in ref. ^[208]43 using AKT3
(1:5000, Abcam, ab152157), FGFR2 (1:2000, Abcam, ab10648), p-AKT
(1:1000, Cell Signaling Technology, 4060), p-FGFR (1:1000, Thermo
Fisher, PA5-105880), GSK3β (1:5000, Abcam, ab32391), p-GSK3β (1:1000,
Thermo Fisher, 44-604 G), P70S6K (1:1000, Cell Signaling Technology,
2708), p-P70S6K (1:1000, Thermo Fisher, PA5-104842), ERK1/2 (1:1000,
Thermo Fisher, 13-6200), p-ERK1/2 (1:1000, Thermo Fisher, 44-680 G),
p-FRS2 (1:1000, Thermo Fisher, PA5-118578) and β-Actin (1:1000, Cell
Signaling Technology, 4967).
Statistics and reproducibility
Statistical analysis was performed and plots were generated using
GraphPad Prism 9 and SPSS Statistics 26. Q value Correction were
applied to correct the calculated p value in differential expressed
genes, and q value < 0.05 was considered significant. P-value ≥ 0.05
were written as ns., p-value < 0.05, < 0.01, and < 0.001 are
represented as *, **, and ***, respectively. The overall survival
analyses were performed using the Log-rank test, and p < 0.05 was
considered significant. The p value of progression-free survival was
calculated using the multivariate Cox regression test, and p < 0.05 was
considered significant. The effect size in PDX models was calculated by
Cohen’s d formula. To ensure reproducibility, four PPLELC tumor samples
for snRNA-seq and 42 PPLELC patients’ tumor samples served as
biological replicates. Other quantification methods and analysis had
been described for each method.
Reporting summary
Further information on research design is available in the [209]Nature
Portfolio Reporting Summary linked to this article.
Supplementary information
[210]Supplementary Information^ (2.4MB, pdf)
[211]Supplementary Data 1–16^ (311KB, xlsx)
[212]42003_2025_7819_MOESM3_ESM.pdf^ (58.7KB, pdf)
Description of Additional Supplementary Materials
[213]Reporting Summary^ (2.4MB, pdf)
Acknowledgements