Articles
Differences of molecular events driving pathological and
radiological progression of lung adenocarcinoma
Jun Shang,a,f He Jiang,a,f Yue Zhao,b,c,f Jinglei Lai,b,c,f Leming Shi,a,c Jingcheng Yang,a,d,∗ Haiquan Chen,b,c,e,∗∗ and Yuanting Zhenga,∗∗∗
a
State Key Laboratory of Genetic Engineering, Human Phenome Institute, School of Life Sciences and Shanghai Cancer Center, Fudan
University, Shanghai, China
b
Department of Thoracic Surgery, Shanghai Cancer Center, Fudan University, Shanghai, China
c
Institute of Thoracic Oncology, Fudan University, Shanghai, China
d
Greater Bay Area Institute of Precision Medicine, 115 Jiaoxi Road, Guangzhou, China
e
Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China
Summary
Background Ground-glass opacity (GGO)-like lung adenocarcinoma (LUAD) has been detected increasingly in the
clinic and its inert property and superior survival indicate unique biological characteristics. However, we do not know
much about them, which hampers identification of key reasons for the inert property of GGO-like LUAD.
Methods Using whole-exome sequencing and RNA sequencing, taking into account both radiological and pathological
classifications of the same 197 patients concomitantly, we systematically interrogate genes driving the progression
from GGO to solid nodule and potential reasons for the inertia of GGO. Using flow cytometry and IHC, we
validated the abundance of immune cells and activity of cell proliferation.
eBioMedicine
2023;94: 104728
Published Online xxx
https://doi.org/10.
1016/j.ebiom.2023.
104728
Findings Identifying the differences between GGO and solid nodule, we found adenocarcinoma in situ/minimally
invasive adenocarcinoma (AIS/MIA) and GGO-like LUAD exhibited lower TP53 mutation frequency and less active
cell proliferation-related pathways than solid nodule in LUAD. Identifying the differences in GGO between AIS/MIA
and LUAD, we noticed that EGFR mutation frequency and CNV load were significantly higher in LUAD than in AIS/
MIA. Regulatory T cell was also higher in LUAD, while CD8+ T cell decreased from AIS/MIA to LUAD. Finally, we
constructed a transcriptomic signature to quantify the development from GGO to solid nodule, which was an
independent predictor of patients’ prognosis in 11 external LUAD datasets.
Interpretation Our results provide deeper insights into the indolent nature of GGO and provide a molecular basis for
the treatment of GGO-like LUAD.
Funding This study was supported in part by the National Natural Science Foundation of China (32170657), the
National Natural Science Foundation of China (82203037), and Shanghai Sailing Program (22YF1408900).
Copyright © 2023 The Author(s). Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND
license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
Keywords: Lung adenocarcinoma; Ground-glass opacity; Pathological progression; Radiological progression;
Molecular events
Introduction
With the increasing concerns about personal health and
the popularity of chest low-dose computed tomography
(LDCT), more and more nodules with ground-glass
opacity (GGO) have been detected in the early
screening of lung cancer. GGO is a radiological
characteristic and appears on radiology as a dense,
ground-glass shadow with well-defined vascular and
bronchial texture within the lesion.1 According to the
consolidation-to-tumor ratio (CTR) on radiology, tumors
can be divided into pure GGO (pGGO, CTR = 0), mixed
GGO (mGGO, 0< CTR <1) and solid nodule (CTR = 1).2
*Corresponding author. State Key Laboratory of Genetic Engineering, Human Phenome Institute, School of Life Sciences and Shanghai Cancer
Center, Fudan University, Shanghai, China.
**Corresponding author. Department of Thoracic Surgery, Shanghai Cancer Center, Fudan University, Shanghai, China.
***Corresponding author. State Key Laboratory of Genetic Engineering, Human Phenome Institute, School of Life Sciences and Shanghai Cancer
Center, Fudan University, Shanghai, China.
E-mail addresses: yjcyxky@163.com (J. Yang), hqchen1@yahoo.com (H. Chen), zhengyuanting@fudan.edu.cn (Y. Zheng).
f
These authors contributed equally.
www.thelancet.com Vol 94 August, 2023
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Articles
Research in context
Evidence before this study
The underlying molecular mechanisms that lead to GGO-like
LUAD have attracted the attention of researchers in recent
years. Compared with solid nodule, GGO-like nodule showed
fewer genomic events, such as gene mutation load, driver
gene mutation frequency, and loss of heterozygosity of HLA.
In addition, higher immune cell activity, lower immune
suppression, and lower fibroblast activation were found in
GGO than in solid nodule. However, these studies may suffer
from two major problems. First, there were uncontrolled
variables when studying the molecular characteristics of
different types of GGO. These studies confused the effects of
pathological staging (AIS/MIA/LUAD) and radiological staging
(pGGO/mGGO/solid nodule), eventually making it impossible
to figure out which molecular alterations may lead to the
progression of pathological stage, and which may lead to the
transition from GGO to solid nodule. Therefore, we cannot
precisely identify reasons for GGO inertia after excluding the
influence of pathological period and it is also impossible to
find the molecular features that drive pathological
progression after excluding the influence of radiological
classification. Secondly, these studies were not
comprehensive. Therefore, it is impossible to see the LUAD
progression more completely from the earlier stage of the
disease and to identify the characteristics of the same GGO
type in different pathological stages. Only when we know the
relationship between molecular alterations and pathological/
radiological staging can we perform more precise intervention
in patients with different pathological periods or different
GGO types. To the best of our knowledge, no study has
comprehensively and separately evaluated the effect of GGO
From a pathological point of view, lung adenocarcinoma
(LUAD) can be divided into adenocarcinoma in situ
(AIS), minimally invasive adenocarcinoma (MIA) and
LUAD. Both AIS and MIA present as limited lesions
≤30 mm, with tumor cells growing along the alveolar
structures, except that AIS does not break through the
basement membrane, whereas MIA breaks through the
basement membrane, but the depth of invasion ≤5 mm.
Compared to the above stages, LUAD is a more malignant stage, which can be further divided into stage I, II,
III and IV according to the degree and site of invasion
and metastasis.3 pGGO is predominantly observed in
the pre/minimally invasive stage of LUAD including
adenocarcinoma in situ (AIS) and minimally invasive
adenocarcinoma (MIA). It consistently signifies a highly
favorable prognosis, with a 5-year relapse-free survival
(RFS) rate of 100%,4 even when it is observed in invasive
stage I non-small cell lung cancer (NSCLC). A lung
cancer screening in Shanghai indicated that lesions on
CT with GGO component appeared in 84.9% of cases
diagnosed with lung cancer.5 What is more, the results
2
components on molecular and immune characteristics in
AIS&MIA and LUAD.
Added value of this study
We comprehensively compared the molecular characteristics
of pGGO, mGGO, and solid nodule in the AIS&MIA and LUAD.
Firstly, we found that compared with solid nodule in LUAD,
GGO-like LUAD showed lower malignancy, which had similar
molecular characteristics to pre/minimally invasive, including
lower genomic events, cell proliferation, and matrix
remodeling activation. Additionally, we noticed that there
was almost no significant genomic difference between pGGO
and mGGO in AIS&MIA, but molecular characteristics
including EGFR mutation frequency and CNV load were
significantly higher in LUAD than AIS&MIA. Immunoreactiverelated cells were also more associated with pathological stage
than GGO components. Finally, we constructed a gene
signature associated with radiological, pathological and
histological progression, which can serve as a good predictor
of patient prognosis. To accurately identify whether a specific
molecular event was associated with GGO components or
pathological stage, one needs to control variables, i.e.
radiology or pathology. And it was what our study design
uniquely offered.
Implications of all the available evidence
These results will provide a molecular basis for the treatment
of GGO-like LUAD. Meanwhile, the relative balance of
malignant cancer cells and immune cells may lead to the inert
property of GGO.
of a previous study performing lung cancer screening in
Chinese hospital employees showed that 95.5% of the
lung cancer patients detected by screening were presented as GGO on radiological evaluation.6 Many
studies have demonstrated that GGO-like lung cancer is
an "inert" tumor with slow growth rate and good
prognosis.4,7
The underlying molecular mechanisms that lead to
lung cancer with or without the GGO component
remain to be explored, and have attracted the attention
of researchers in recent years. Compared with solid
nodule, GGO-like nodule showed fewer genomic events,
such as gene mutation load, driver gene mutation frequency and loss of heterozygosity of HLA.8,9 In addition,
the tumor microenvironment (TME) of GGO-like LUAD
has been of interest to many investigators. Higher immune cell activity, lower immune suppression, and
lower fibroblast activation were found in GGO than in
solid nodule.8,10 Meanwhile, malignant cells in GGO
suffered from thorough metabolic reprogramming and
immune stress.11 Therefore, the relative balance of
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Articles
malignant cancer cells and immune cells may lead to
the inert property of GGO.
However, these studies may suffer from two major
caveats. First, there were uncontrolled variables when
studying the molecular characteristics of different types
of GGO. “Uncontrolled variables” means most of the
previous studies performed genomic and transcriptomic
explorations did not thoughtfully take the pathological
stage into consideration. Some of these studies even
compared pGGO of AIS&MIA with solid nodule of
LUAD, which cannot tell whether the molecular differences found by such comparison which were originated
from pGGO vs solid, or AIS&MIA vs LUAD. To be more
specific, these studies confused the effects of pathological staging (AIS/MIA/LUAD) and radiological staging
(pGGO/mGGO/solid nodule), eventually making it
impossible to figure out which molecular alterations
may lead to the progression of pathological stage, and
which may lead to the transition from GGO to solid
nodule. Therefore, we cannot precisely identify reasons
for GGO inertia after excluding the influence of pathological period and it is also impossible to find the molecular features that drive pathological progression after
excluding the influence of radiological classification.
Secondly, these studies were not comprehensive.
Therefore, it is impossible to see the AIS/MIA and
LUAD progression more completely from the earlier
stage of the disease and to identify the characteristics of
the same GGO type in different pathological stages.
Only when we know the relationship between molecular
alterations and pathological/radiological staging can we
perform more precise intervention in patients with
different pathological periods or different GGO types.
To the best of our knowledge, no study has comprehensively and separately evaluated the effect of GGO
components on molecular and immune characteristics
in AIS&MIA and LUAD.
Based on our previous studies, which made a
comprehensive genomic and transcriptomic comparison between AIS&MIA and LUAD,12,13 we further
comprehensively compared the molecular characteristics of different GGO components in the AIS&MIA and
LUAD. First and foremost, we found that compared
with solid nodule in LUAD, GGO-like LUAD showed
low malignant, which had similar molecular characteristics to pre/minimally invasive, including lower
genomic events, cell proliferation, and matrix remodeling activation. Additionally, we noticed that there was
almost no significant genomic difference between
pGGO and mGGO in AIS&MIA, but molecular characteristics including EGFR mutation frequency and
CNV load were significantly higher in LUAD than
AIS&MIA. Immunoreactive-related cells, e.g. Treg and
CD8+, were also more associated with pathological stage
than GGO components. Finally, we constructed a gene
signature associated with radiological, pathological and
histological progression, which can serve as a good
www.thelancet.com Vol 94 August, 2023
predictor of patient prognosis. These results will provide
a molecular basis for the treatment of GGO-like LUAD.
Methods
Patient cohort and associated radiological
evaluation
We collected tumor-normal paired samples from a total
of 197 patients (24 AIS, 74 MIA, 83 I and 16 IIIA) and
associated RNA-seq and WES data were generated from
September 2011 to May 2016.12,13 Clinical characteristics
of our cohort including sex, age, tumor size, smoking
status, histology and pathological stage were shown in
Table 1. The sex of patients was self-reported by study
participants. Notably, lepidic histology pattern was
significantly plunged from pGGO to solid nodule in
LUAD. In contrast, other histology patterns gradually
increased. Based on the previous methods,14 we further
performed radiological evaluation based on enhanced
chest computed tomography (CT) scanning and positron emission tomography-CT (PET-CT) scanning images. The maximum diameter of the tumor is measured
on the lung window. Radiology-associated three subtypes including pGGO (CTR = 0), mGGO (0< CTR <1)
and pure solid (CTR = 1) were classified based on
consolidation to tumor ratio (CTR, ratio of the
maximum diameter of solid component to the
maximum diameter of whole tumor on CT scan). We
retrospectively collected IHC results of Ki67 based on
the pathological report of these patients. The Ki67
staining was performed using CONFIRM™ anti-Ki-67
(30-9) Rabbit Monoclonal Primary Antibody according
to the manufacturer’s instructions.
Gene mutation profiling and copy number variation
The WES data was generated with 150 bp paired-end
reads on Illumina HiSeq X Ten platform, and the
gene mutation files were from our previous study.12
TMB was calculated based on total non-synonymous
mutations and about 30 M of capture size. We reanalyzed the genomic mutation profiles of four subgroups (pGGO and mGGO in AIS/MIA, mGGO and
pure solid in LUAD) in this cohort. Differentially
mutated genes (DMGs) among four subgroups were
performed on each gene mutation frequency using
Fisher’s exact test with P < 0.05.
Gene variant allele frequency (VAF) refers to the
proportion of reads in a given gene that contains a specific variant allele, relative to the total number of reads in
that gene. Based on our previous study that utilized the
ABSOLUTE algorithm,12 we have a quantitative estimate
of the tumor purity for each sample analyzed.
Segment files of CNV were calculated using CNVkit
(v0.9.7) with default parameters.15 Amplification and
deletion region was marked by setting the mean
segment value to 0.1 and −0.1. GISTIC2 was used to
identify amplification and deletion peaks.16
3
Articles
Clinical variable
AIS/MIA
AIS/MIA
AIS/MIA
pGGO (n = 67)
mGGO (n = 30)
Solid (n = 1)
Female
46 (68.7)
21 (70.0)
Male
21 (31.3)
9 (30.0)
Sex, n (%)
P
LUAD
LUAD
LUAD
pGGO (n = 3)
mGGO (n = 32)
Solid (n = 64)
2 (66.7)
20 (62.5)
30 (46.9)
1 (33.3)
12 (37.5)
34 (53.1)
1 (33.3)
15 (46.9)
23 (35.9)
2 (66.7)
17 (53.1)
41 (64.1)
2 (66.7)
26 (81.2)
41 (64.1)
1 (33.3)
6 (18.8)
23 (35.9)
2 (66.7)
9 (28.1)
3 (4.7)
1
0.22
1 (100.0)
0 (0.0)
Age, n (%)
0.36
≤60
46 (68.7)
17 (56.7)
1 (100.0)
>60
21 (31.3)
13 (43.3)
0 (0.0)
Smoking, n (%)
0.42
0.97
Never
51 (76.1)
22 (73.3)
Ever
16 (23.9)
8 (26.7)
0.14
1 (100.0)
0 (0.0)
–
Histology, n (%)
<0.05
Lepidic
/
/
/
Acinar
/
/
/
1 (33.3)
17 (53.1)
40 (62.5)
Papillary
/
/
/
0 (0.0)
6 (18.8)
10 (15.6)
Solid
/
/
/
0 (0.0)
0 (0.0)
9 (14.1)
IMA
/
/
/
0 (0.0)
0 (0.0)
2 (3.1)
P stage, n (%)
0.32
5 (16.7)
0.15
AIS
19 (28.4)
0 (0.0)
/
MIA
48 (71.6)
25 (83.3)
IA
/
/
/
1 (100.0)
/
3 (100.0)
IB
/
/
/
IIIA
/
/
/
Tumor size, n (%)
P
/
/
/
/
22 (68.7)
31 (48.4)
0 (0.0)
7 (21.9)
20 (31.3)
0 (0.0)
3 (9.4)
13 (20.3)
0.27
<0.05
(0,1]
47
21
1
1
4
2
(1,2]
13
3
0
2
18
21
(2,3]
2
1
0
0
8
25
(3,6]
0
0
0
0
2
16
NA
5
5
0
0
0
0
AIS, adenocarcinoma in situ; MIA, minimally invasive adenocarcinoma; LUAD, lung adenocarcinoma; GGO, ground-glass opacity; pGGO, pure ground-glass opacity; mGGO,
mixed ground-glass opacity; IMA, invasive mucinous adenocarcinoma.
Table 1: Clinical characteristics of AIS/MIA and LUAD among pGGO, mGGO and solid nodule in our study cohort (n = 197).
APOBEC and COSMIC signature activity
APOBEC enrichment score was calculated using maftools (v2.10.0).17 Firstly, we generated a frequency matrix
of single nucleotide variants (SNVs) using the trinucleotideMatrix function. This matrix was then subjected
to non-negative matrix factorization (NMF) using the
extractSignatures function to identify the underlying
mutational processes, including those associated with
APOBEC activity. We used the signatureEnrichment
function to perform association analysis to determine
the APOBEC enrichment score in each sample, which
utilized the method described by Roberts et al.18 This
involved comparing the frequency of C > T mutations
occurring within the tCw trinucleotide context to the
background frequency of C > T mutations in the sample, as well as the frequency of tCw occurring around
±20 bp of the mutated bases.
The SigProfiler framework was used to identify the
underlying mutational processes that are driving the
observed patterns of mutations in different samples or
groups of samples. SigProfilerExtractor (v1.1.21) was
4
used to extract mutation signatures from the mutation
matrix. This includes both de novo identification of
mutation signatures and decomposition of these signatures into known COSMIC (v3.3). A comparison analysis is then performed between different groups based
on the mutation activity matrix of the SBS96 signature
of each sample.
Differentially expressed genes and gene expression
modules
We have obtained the gene expression matrix from raw
RNA-seq data in our previous study.13 The differentially
expressed genes (DEGs) among five groups (AIS&MIApGGO vs normal, AIS&MIA-mGGO vs pGGO, pGGO
of LUAD and AIS/MIA, mGGO and pGGO of LUAD,
solid and mGGO of LUAD) were performed on RNAseq using R package limma (v3.50.0). P < 0.05 and |
log2FC| > 1 were used to select DEGs.19 To identify the
gene modules associated with LUAD progression, we
identified genes with significant changes from normal
to pure GGO of AIS/MIA to pGGO, mGGO and solid of
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Articles
LUAD (P < 0.05 and |log2FC| ≥ 1) and further performed WGCNA on them. Each module was assigned at
least 50 genes, while the P-value ratio threshold of
reassigned genes between modules is 0.
Gene function and ontology annotation
Annotation GMT file of hallmark gene sets (v7.4) was
downloaded from Gene Set Enrichment Analysis
(GSEA, https://www.gsea-msigdb.org). Biological hallmarks significantly associated with gene expression
modules (M1, M2 and M3) were identified with GSVA
(v1.42.0). Significantly different enrichment biological
hallmarks (FDR <0.05) between GGO and pure solid
LUAD or lepidic-predominant LUAD and solidpredominant LUAD were analyzed by GSEA software
(v4.1.0).
Construction of GGO to solid (G2S) score
Differential expression analysis was performed between
GGO and solid of 99 LUAD patients in FUSCC dataset.
In LUAD, DEGs between GGO and solid were filtered
for |log2FC| ≥ 1.5 and P < 0.05. GGO and solid specific
genes were identified based on whether log2FC ≤ −1.5
or log2FC ≥ 1.5. Lepidic-predominant LUAD containing
GGO component usually has good prognosis, and vice
versa. Differential expression analysis was performed
between lepidic-predominant LUAD and solidpredominant LUAD in FUSCC and TCGA datasets to
identify lepidic- (close to GGO of radiology) and solid(close to solid of radiology) specific genes. Based on
intersection of the DEGs in the two datasets, 24 GGOspecific genes and 21 solid-specific genes were finally
identified. Then, G2S score (“TotalScore” from singscore) was calculated by R package singscore (v1.14.0)
with Upset (solid specific genes) and Downset (GGO
specific genes). Compared to solid, GGO samples would
have lower G2S score. In each dataset, patients were
divided into GGO-like group and solid-like group based
on the median value of G2S score.
The GGO-like and Solid-like groups can be further
divided into two subgroups based on the presence or
absence of EGFR or TP53 mutations. This would result
in a total of four subgroups based on the mutation status
of each gene, including GGO-like and EGFR wild type,
GGO-like and EGFR mutant, Solid-like and EGFR wild
type, Solid-like and EGFR mutant, GGO-like and TP53
wild type, GGO-like and TP53 mutant, Solid-like and
TP53 wild type, and Solid-like and TP53 mutant. Survival analysis can be conducted in the four subgroups
based on the G2S score and mutation status of EGFR
and TP53.
Tumor microenvironment analysis
Tumor microenvironment (TME) analysis was performed based on gene expression signatures. Proliferation rate and matrix remodeling for each sample were
www.thelancet.com Vol 94 August, 2023
evaluated with Fges.20 LM22 matrix was used to calculate
cell fraction of each sample.21
Flow cytometry
Thirty-two fresh tumor tissues and 11 distant normal
lung tissues were collected and stored in Tissue Storage
Solution (Miltenyi Biotec), and processed on the same
day of sample collection. The tissues were washed with
PBS until there was no visible blood on the surface, and
then minced into a homogenate. Subsequently, 10 mL
of type IV collagenase (Gibco) was used for digestion at
37 ◦ C for 1 h with agitation. The resulting single-cell
suspension was filtered through 70 μm cell strainer
(BD Bioscience), and the supernatant was discarded
after centrifugation at 500g for 10 min. After resuspension in PBS buffer, the suspension was centrifuged
at 800g for 2 min, and the supernatant was discarded.
This washing process was repeated twice. Fixable
Viability Dye eFluor780 (eBioscience) and Zombie Yellow Viability Kit (BioLegend) were used to distinguish
dead cells. Anti-human CD3 BUV395 (BD Bioscience),
Anti-human CD4 APC/Cy7 (BioLegend), Anti-human
CD8a Alexa Fluor700 (BioLegend), Anti-human Perforin FITC (BioLegend), and Anti-human FOXP3 PE/
Cy5 (eBioscience) were used to stain cell membrane
surface proteins and intracellular proteins. Samples
were acquired on the BD LSR Fortessa (BD Bioscience).
Flow cytometry data were analyzed using FlowJo
(TreeStar). The representative gating strategies for
Tregs, CD8+ T cells, and CD8+Perforin+ T cells were
shown in Supplementary Figure S1.
Published datasets
The published datasets used in this study were well
documented in the previous study.13 LUAD datasets
containing gene expression and prognostic information
were mainly from GEO and TCGA. The RNA-seq
expression data and clinical information of TCGA
LUAD datasets were downloaded from genomic data
commons (GDC). TCGA LUAD pathology information
such as lepidic predominant and solid predominant was
downloaded from UCSC Xena (https://tcga-xena-hub.
s3.us-east-1.amazonaws.com/download/TCGA.LUAD.
sampleMap%2FLUAD_clinicalMatrix). The microarray
gene expression data and clinical information were
downloaded from GEO databases (https://www.ncbi.
nlm.nih.gov/geo/).
Ethics statement
This study has been approved by the research ethics
review committee of Fudan University Shanghai Cancer
Center (FUSCC) Institutional Review Board (No.
090977-1). Informed consent was obtained from either
the patients themselves or their relatives for the donation of their samples to the tissue bank of Fudan University Shanghai Cancer Center.
5
Articles
Statistical analysis
All statistical analysis was performed with R (v4.1.2). R
package survminer (v0.4.9) and survival (v3.2-13) were
used to perform survival and cox analysis, respectively.
Cox analysis was performed with 95% confidence intervals (CIs) for overall survival (OS) and RFS. Kaplan–
Meier survival analysis in conjunction with the logrank test was employed to conduct assessments on
OS and RFS. Comparisons between independent two
groups used Welch’s t-test. Cell fraction comparisons
between paired tumor and normal were performed
with paired t-test. P-value was adjusted for multiple
comparisons using the False Discovery Rate (FDR).
Chi-squared test was used to perform hypothesis
testing on categorical clinical data. If the expected
count for each cell was less than five, a simulationbased test was performed.
Role of funders
The funders did not play a role in the study design, data
collection, data analyses, data interpretation, or manuscript writing.
Results
Study design
As shown in Fig. 1, a total of 197 patients were enrolled
in our study. RNA sequencing (RNA-seq) and wholeexome sequencing (WES) data from paired tumor and
normal samples of 197 patients were previously reported12 and used for this study. Based on CTR from
radiological imaging, 98 AIS&MIA patients were classified into 67 pGGO, 30 mGGO and 1 solid nodule,
meanwhile 99 LUAD patients were classified into 3
pGGO, 32 mGGO and 64 solid nodule (Supplementary
Figure S2a). Among them, most of the patients with
GGO
component
were
non-smoking
women
(Supplementary Figure S2b). The mean tumor purity was
over 0.2 (Supplementary Figure S2c) and the mean
sequencing depth of WES was over 170× (Supplementary
Figure S2d). We comprehensively explored the molecular
events driving radiological stage (pGGO, mGGO and
solid nodule) or pathological stage (AIS&MIA and
LUAD), such as the genomic profiling, gene expression
and tumor microenvironment. Finally, we successfully
constructed a gene signature associated with pathological,
radiological and histological stage to predict patient
prognosis.
Pathological progression and radiological
progression driven by different genomic events
Genomic profiling suggested that pathological stage and
GGO components were driven by different genomic
events. We revealed gene mutation profiles in pGGO of
AIS&MIA, mGGO of AIS&MIA, pGGO of LUAD,
mGGO of LUAD, and solid nodule of LUAD (Fig. 2a).
There was no significant difference in the TMB between
6
pGGO and mGGO within each stage (AIS&MIA or
LUAD). However, LUAD had higher TMB than AIS/
MIA (Fig. 2b, mGGO in AIS/MIA vs mGGO in LUAD,
Welch’s t-test, P.adj = 6.78e-05). In LUAD, TMB was
significantly increased in solid than in GGO (Fig. 2b,
mGGO in LUAD vs Solid in LUAD, Welch’s t-test,
P.adj = 9.14e-04). These results suggested that pathological progression and GGO components were codominated by the genomic mutation burdens. In our
datasets, there is a higher percentage of smoking patients in solid nodule compared to GGO nodule
(Supplementary Figure S2b). History of smoking was
closely associated with higher TMB (Supplementary
Figure S2e, Ever in Solid vs Never in Solid, Welch’s ttest, P.adj = 9.46e-04) only in solid nodule of LUAD.
APOBEC-related mutations, defined as C > G and C > T
mutations, are related to various cancer types, and
contributed to carcinogenesis.18 We found that patients
with solid nodule in LUAD had a significantly higher
proportion of APOBEC-enrichment score than GGO
nodule (Fig. 2c, mGGO in LUAD vs Solid in LUAD,
Welch’s t-test, P.adj = 0.037, mGGO in AIS/MIA vs
Solid in LUAD, Welch’s t-test, P.adj = 4.74e-04). However, smoking had little effect on APOBEC-enrichment
score in different GGO component groups in AIS/
MIA and LUAD (Supplementary Figure S2e).
The overall activity proportion of the SBS signature
that decomposed to COSMIC was comprehensively
explored in different pathological and radiological stage
(Supplementary Figures S3 and S4a and S4b). The results
revealed that smoking was associated with SBS4 in solid
nodules of LUAD in our datasets (Supplementary
Figure S4c, mGGO in LUAD vs Solid in LUAD,
Welch’s t-test, P = 0.025). SBS4 harbored single base
deletions of predominantly cytosine (C) and was closely
associated with tobacco smoking.22 Most mutations in
SBS4 originated from the mutations in TP53,23,24 therefore SBS4 may distinguish smoker and non-smoker
better in LUAD with more TP53 mutations. In our
cohort, the mutation frequency of TP53 in solid nodule of
LUAD was significantly higher than that of GGO-like
LUAD (Fig. 2d, mGGO in LUAD vs Solid in LUAD,
Fisher’s exact test, P.adj = 8.30e-03). Meanwhile,
compared to GGO-like LUAD, smokers were more likely
to appear as solid nodules (Supplementary Figure S2b).
Therefore, LUAD solid has the highest proportion of
smokers, and the increase in sample size may also be
helpful for the distinguishing performance of SBS4. Besides, we identified that patients with solid nodule in
LUAD had a significantly higher mutation burden of
APOBEC-related signatures (SBS2 and SBS13) than
GGO nodule (Supplementary Figure S4d; SBS2, mGGO
in LUAD vs Solid in LUAD, Welch’s t-test, P = 0.017;
SBS13, mGGO in LUAD vs Solid in LUAD, Welch’s ttest, P = 0.02). The APOBEC-related signatures (SBS2
and SBS13) were correlated with APOBEC-enrichment
score (Supplementary Figure S4e).
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Fig. 1: Study design and main findings of molecular events driven pathological and radiological progression of lung adenocarcinoma. (a) 197
patients with different pathological and radiological stages were enrolled to generate genomic and transcriptomic data. pGGO and mGGO of
AIS/MIA had low malignancy, mGGO of LUAD had moderate malignancy and solid nodule of LUAD had high malignancy. We couldn’t tell
malignancy of solid nodule of AIS/MIA and pGGO of LUAD because our cohort only had one solid nodule of AIS/MIA and three pGGO of LUAD,
which may lead to less convincing conclusions. (b) TP53 mutation frequency significantly increased in solid nodule of LUAD, EGFR mutation
frequency and CNV load in LUAD were significantly higher than those in AIS/MIA, while TMB was significantly higher in LUAD than AIS/MIA,
and also significantly higher in solid nodule of LUAD than GGO of LUAD. (c) Activity of cell proliferation pathway and E2F target pathway was
significantly higher in solid nodule of LUAD. (d) CD8+ T cell and Treg gradually elevated or decreased from pGGO of AIS/MIA to solid nodule of
LUAD. (e) GGO-to-Solid score from low to high represented the prognosis of patients from good to bad.
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Fig. 2: Genomic events which may drive AIS/MIA to LUAD and GGO to solid. (a) Gene mutation profiling of pGGO and mGGO in AIS/MIA and
pGGO, mGGO and solid nodule in LUAD. (b) Tumor mutation burden (TMB) in AIS/MIA divided into pGGO and mGGO and LUAD divided into
pGGO, mGGO and solid nodule (mGGO in AIS/MIA vs mGGO in LUAD, Welch’s t-test, P.adj = 6.78e-05; mGGO in LUAD vs Solid in LUAD,
Welch’s t-test, P.adj = 9.14e-04). (c) APOBEC enrichment score in AIS/MIA divided into pGGO and mGGO and LUAD divided into pGGO, mGGO
and solid nodule (mGGO in LUAD vs Solid in LUAD, Welch’s t-test, P.adj = 0.037, mGGO in AIS/MIA vs Solid in LUAD, Welch’s t-test,
P.adj = 4.74e-04). (d) Gene mutation frequency of eight genes in pGGO and mGGO of AIS/MIA and pGGO, mGGO and solid nodule of LUAD
(EGFR, pGGO in AIS/MIA vs Solid in LUAD, Fisher’s exact test, P.adj = 0.046; TP53, mGGO in LUAD vs Solid in LUAD, Fisher’s exact test,
P.adj = 8.30e-03). The VAF and purity distribution of EGFR (e) (mGGO in LUAD vs Solid in LUAD, Welch’s t-test, P.adj = 0.002; mGGO in AIS/MIA
vs mGGO in LUAD, Welch’s t-test, P.adj = 0.002) and TP53 (f) (mGGO in LUAD vs Solid in LUAD, Welch’s t-test, P.adj = 0.025) in LUAD among
different stages. P-values were adjusted by false discovery rate (FDR). *P < 0.05; **P < 0.01; ***P < 0.001.
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Obviously, EGFR was the most frequently mutated
gene in LUAD (Fig. 2a). EGFR mutation may be associated with pathological stage, in that LUAD had higher
mutation frequency than AIS/MIA (Fig. 2d,
Supplementary Figure S5a and S5b, pGGO in AIS/MIA
vs Solid in LUAD, Fisher’s exact test, P.adj = 0.046). We
also noticed that the copy number variation (CNV)
sharply lifted from mGGO in AIS&MIA to mGGO in
LUAD (Supplementary Figure S6a, mGGO in AIS/MIA
vs mGGO in LUAD, Welch’s t-test, P.adj = 1.91e-07 and
Supplementary Figure S6b). Mutation of TP53 and
ERBB2 was found closely associated with GGO
component. Solid nodule had significantly higher TP53
mutation frequency (53%) than mGGO (13%) in LUAD
(Fig. 2d and Supplementary Figure S5b), suggesting
that TP53 non-synonymous mutation may drive the
transformation from part-solid to solid nodule. However, ERBB2 is at a specific low frequency of mutation in
GGO-like LUAD in our datasets (Fig. 2d). No other
recurrently mutated genes in LUAD were found to
associate with GGO component,12 such as RBM10,
KRAS, BRAF, MET and MAP2K1 (Fig. 2d). Besides, we
have analyzed and compared the variant allele frequency
(VAF) of EGFR, TP53, and ERBB2 (Fig. 2e and f and
Supplementary Figure S5c). We observed that the VAF
of tumors containing GGO component was significantly
lower than that of solid tumors in samples with EGFR
(Fig. 2e, mGGO in LUAD vs Solid in LUAD, Welch’s ttest, P.adj = 0.002) and TP53 (Fig. 2f, mGGO in LUAD
vs Solid in LUAD, Welch’s t-test, P.adj = 0.025) mutations. The VAF of EGFR mutation may be also related
with pathological stage (Fig. 2e, mGGO in AIS/MIA vs
mGGO in LUAD, Welch’s t-test, P.adj = 0.002). By
analyzing the correlation between tumor purity and
VAF, we found that the lower VAF in tumors with GGO
components was not totally due to low tumor purity
(Supplementary Figure S5d–S5f). This result suggests
that AIS/MIA and tumors with GGO components were
in an earlier stage of tumor growth compared to solid
tumors.
Proliferation-associated gene module was downregulated in both AIS&MIA and GGO-like LUAD
To identify the gene signatures associated with pathological stage and GGO components separately, we
calculated the differentially expressed genes (DEGs) in
the following four pairwise comparisons: mGGO vs
pGGO in AIS&MIA, GGO of LUAD vs GGO of AIS&MIA, mGGO vs pGGO in LUAD, and solid vs mGGO in
LUAD (|log2FC| ≥ 1 & P < 0.05) (Fig. 3a). DEGs identified in the above four comparisons suggested that both
pathological progression and GGO components had
significant effects on gene expression. PCA based on all
the DEGs in Fig. 3a demonstrated that gene expression
profiles of pGGO in LUAD were close to AIS/MIA
(Fig. 3b). Gene enrichment score, which was calculated
for each sample by applying gene set variant analysis
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(GSVA), indicated that the biological function of GGOlike LUAD was close to AIS&MIA (Supplementary
Figure S7a and S7b). This may explain why pGGO in
LUAD had excellent prognosis just like AIS/MIA.4
Using weighted gene co-expression network analysis
(WGCNA), we divided the 3948 pathologically and
radiologically associated DEGs (pGGO in AIS&MIA vs
normal, mGGO vs pGGO in AIS&MIA, GGO of LUAD
vs GGO of AIS&MIA, mGGO vs pGGO in LUAD, and
solid vs mGGO in LUAD) into three co-expression
modules (M1, M2 and M3) (Supplementary
Figure S7c). M0 contained 806 genes that were not
defined to a gene module (Supplementary Figure S7d).
It was obvious that M1 and M2 were DEGs modules
between tumor and normal, while M3 was a specific
high-expression gene module for solid nodule (Fig. 3c
and d). Gene functions in M1 and M2 were mainly
related to development, signaling and immune system
(Fig. 3e). The M3 genes, which were closely associated
with proliferation, may explain why GGO-like nodules
(down-regulated of M3 genes) progressed more slowly
than solid nodules (up-regulated of M3 genes)
(Supplementary Figure S7e–S7g). Moreover, we found
that most of the genes in M3 were strongly associated
with overall survival (OS) and RFS, and the distribution
of hazard ratio (HR) was significantly higher in M3 than
in M1 and M2 (Fig. 3f and g; HR of OS: M1 vs M2,
Welch’s t-test, P.adj = 3.27e-15, M2 vs M3, Welch’s ttest, P.adj = 3.67e-10; HR of RFS: M1 vs M2, Welch’s ttest, P.adj = 8.22e-43, M2 vs M3, Welch’s t-test,
P.adj = 2.97e-17). In brief, proliferation and poor prognosis related genes were relatively low expression in
GGO-like nodule. These results may explain why GGOlike LUAD grows more slowly and has a better prognosis than solid nodule in LUAD at the gene expression
level.
Pathological progression and radiological
progression were driven by different immune
responses
In the above analysis, we found significantly lower
expression of proliferative pathway-related genes in
GGO-like nodules than in solid nodule. To quantify the
proliferation trends, we further evaluated the proliferation rates of different GGO components at different
pathological stages. Using Fges method,20 we quantified
the proliferation rate in each sample. Compared to
paired normal lung tissue, proliferation rate significantly increased from mGGO to solid nodule in LUAD
(Supplementary Figure S8a and S8c; proliferation rate,
mGGO in LUAD vs normal, paired t-test, P.adj = 5.33e05, solid in LUAD vs normal, paired t-test, P.adj = 1.26e16; matrix remodeling, pGGO in AIS/MIA vs normal,
paired t-test, P.adj = 4.20e-4, mGGO in AIS/MIA vs
normal, paired t-test, P.adj = 4.77e-4, mGGO in LUAD
vs normal, paired t-test, P.adj = 1.02e-04, solid in LUAD
vs normal, paired t-test, P.adj = 2.57e-19). LUAD
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Fig. 3: Cell proliferation-related gene expression module was less active in GGO-like LUAD and associated with better prognosis. (a) Fold
change of gene expression in the following four groups: mixed GGO vs pure GGO in AIS&MIA; non-solid of LUAD vs non-solid of AIS&MIA;
mixed GGO vs pure GGO in LUAD; solid vs mixed GGO in AIS&MIA. Red dots represent relatively up-regulated genes and blue dots represent
relatively down-regulated genes. (b) PCA based on expression of 1999 DEGs in (a). The color of dot represents pure GGO, mixed GGO and
solid nodule in AIS&MIA and LUAD. (c) Three gene expression modules based on WGCNA. M1 consists of 1898 genes with gradual decrease
in expression from normal to AIS&MIA to LUAD. M2 consists of 890 genes with gradual increase in expression from normal to AIS&MIA to
LUAD. M3 consists of 354 genes that are specifically highly expressed in the solid nodule of the LUAD. (d) Heatmap showed the gene
expression trend in the three modules. (e) Function annotation of genes in the three modules. The genes of M1 were enriched in
development, signaling and immune. The genes of M2 were enriched in signaling. The genes of M3 were mainly enriched in proliferation
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containing GGO component has significantly lower
proliferation rate compared to solid LUAD (Fig. 4a;
mGGO in LUAD vs Solid in LUAD, Welch’s t-test,
P.adj = 2.86e-08; pGGO in AIS/MIA vs mGGO in
LUAD, Welch’s t-test, P.adj = 0.002; mGGO in AIS/MIA
vs mGGO in LUAD, Welch’s t-test, P.adj = 0.06).
Compared to GGO-like LUAD, the Ki67 positive rate
was significantly higher in solid nodule of LUAD, which
was consistent with the RNA-seq based proliferation
rate (Fig. 4a; mGGO in LUAD vs Solid in LUAD,
Welch’s t-test, P.adj = 8.82e-06; mGGO in AIS/MIA vs
mGGO in LUAD, Welch’s t-test, P.adj = 0.013). Meanwhile, using Fges method, we also found that the matrix
remodeling score lifted from AIS&MIA to LUAD
(Supplementary Figure S8a and S8c). In addition, the
matrix remodeling activity was significantly downregulated in GGO-like LUAD relative to solid LUAD
(Fig. 4d; mGGO in LUAD vs Solid in LUAD, Welch’s ttest, P.adj = 4.40e-04; mGGO in AIS/MIA vs Solid in
LUAD, Welch’s t-test, P.adj = 1.80e-07; Normal vs
pGGO in AIS/MIA, Welch’s t-test, P.adj = 3.41e-05).
These results also indicated that proliferation and matrix
remodeling may play an important role in the progression from AIS/MIA to LUAD as well as from GGO to
solid nodule.
The growth of tumor cells depends on their own
ability to proliferate on one hand, and on the other hand
it depends on whether they can defeat immune cells. To
explore the changes in the immune microenvironment
in GGO-like LUAD, we deconvoluted the proportion of
various immune cells for each sample using gene
expression data (Fig. 4c). Compared with normal tissue,
regulatory T cells (Tregs) significantly increased and
CD8 T cells significantly decreased in tumor sample
(Supplementary Figure S8b and S8d, Tregs, pGGO in
AIS/MIA vs normal, paired t-test, P.adj = 0.02, mGGO
in AIS/MIA vs normal, paired t-test, P.adj = 0.01,
mGGO in LUAD vs normal, paired t-test, P.adj = 3.73e04, solid in LUAD vs normal, paired t-test, P.adj = 4.34e09; CD8 T cells, pGGO in AIS/MIA vs normal, paired ttest, P.adj = 0.027, mGGO in AIS/MIA vs normal,
paired t-test, P.adj = 1.89e-05, mGGO in LUAD vs
normal, paired t-test, P.adj = 1.89e-05, solid in LUAD vs
normal, paired t-test, P.adj = 1.72e-03). No significant
difference in Tregs was observed between different
GGO components, but Tregs significantly increased
from AIS&MIA to LUAD (Fig. 4b; mGGO in AIS/MIA
vs mGGO in LUAD, Welch’s t-test, P.adj = 0.003;
mGGO in AIS/MIA vs Solid in LUAD, Welch’s t-test,
P.adj = 5.96e-06). CD8 T cells showed a downward trend
from pGGO to solid nodule in LUAD and reached the
lowest point in solid nodule of LUAD, although no
statistical significance was observed (Fig. 4e, solid in
LUAD vs normal, Welch’s t-test, P.adj = 7.58e-06; pGGO
in AIS/MIA vs normal, Welch’s t-test, P.adj = 1.42e-04).
The above results were successfully confirmed by
flow cytometry. Consistent with RNA-seq results, the
number of Tregs steadily increased during the disease
progression and significantly increased in solid nodule
of LUAD (Fig. 4b; pGGO in AIS/MIA vs Solid in LUAD,
Welch’s t-test, P.adj = 0.007; mGGO in LUAD vs Solid
in LUAD, Welch’s t-test, P.adj = 0.043). Conversely,
CD8+ T cells decreased with the progression of the
disease. Importantly, there was no significant trend both
in deconvolution and flow cytometry (Fig. 4e). We found
that the ability of CD8+ T cells to secrete perforin was
almost significantly inhibited (Fig. 4f; Normal vs Solid
in LUAD, Welch’s t-test, P.adj = 0.02; mGGO in LUAD
vs Solid in LUAD, Welch’s t-test, P.adj = 0.053). Another
study has found that Treg promotes the occurrence and
development of tumors by inhibiting the endogenous
secretion of killer factors by CD8+ T cells.25 This may
suggest that one of the reasons for immune escape in
LUAD may be through upregulating Tregs (Fig. 4b) to
weaken the killing ability of CD8+ T cells (Fig. 4f), rather
than directly reducing their numbers (Fig. 4e). The
representative flow cytometry plots of CD8+ T cells and
Tregs expressing perforin were shown in Fig. 4g and h.
Prognosis prediction based on gene signatures
associated with pathological and radiological
progression
The aforementioned results about gene modules and
tumor microenvironment suggested that genes associated with GGO-to-solid progression in the LUAD play an
important role in patients’ prognosis. Therefore, quantification of the process based on genes significantly varied
between GGO and solid in the LUAD may be helpful in
the prognostic stratification of LUAD patients. Meanwhile, histological progression related features also can
predict histology patterns and prognosis of tumor samples. LUAD can be classified into lepidic-predominant,
acinar-predominant,
papillary-predominant,
micropapillary-predominant, and solid-predominant adenocarcinoma according to the main growth patterns.26,27
Among them, lepidic-predominant nodule always contains GGO component and has a superior prognosis,
whereas the solid-predominant adenocarcinoma usually
does not have a GGO component and is associated with
poor prognosis.28–30 We speculated that the biological
differences between the GGO and solid nodule in radiology and the lepidic-predominant and solid-
and also contain development and metabolic. (f) Proportional distribution of genes significantly associated with OS or RFS in M1, M2 and
M3. (g) Distribution of OS and RFS hazard ratios of genes in M1, M2 and M3 (HR of OS: M1 vs M2, Welch’s t-test, P.adj = 3.27e-15, M2 vs
M3, Welch’s t-test, P.adj = 3.67e-10; HR of RFS: M1 vs M2, Welch’s t-test, P.adj = 8.22e-43, M2 vs M3, Welch’s t-test, P.adj = 2.97e-17). Pvalues were adjusted by false discovery rate (FDR). ***P < 0.001.
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Fig. 4: Dynamic changes in the tumor microenvironment evaluation. (a) Proliferation (RNA-seq, mGGO in LUAD vs Solid in LUAD, Welch’s ttest, P.adj = 2.86e-08; pGGO in AIS/MIA vs mGGO in LUAD, Welch’s t-test, P.adj = 0.002; mGGO in AIS/MIA vs mGGO in LUAD, Welch’s t-test,
P.adj = 0.06) (IHC, mGGO in LUAD vs Solid in LUAD, Welch’s t-test, P.adj = 8.82e-06; mGGO in AIS/MIA vs mGGO in LUAD, Welch’s t-test,
P.adj = 0.013), (b) Tregs (RNA-seq, mGGO in AIS/MIA vs mGGO in LUAD, Welch’s t-test, P.adj = 0.003; mGGO in AIS/MIA vs Solid in LUAD,
Welch’s t-test, P.adj = 5.96e-06) (Flow cytometry, pGGO in AIS/MIA vs Solid in LUAD, Welch’s t-test, P.adj = 0.007; mGGO in LUAD vs Solid in
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predominant adenocarcinoma in histology may be
similar. To validate our hypothesis, we performed gene
set enrichment analysis (GSEA) of cancer hallmarks for
the two groupwise comparisons: GGO vs solid and
lepidic-predominant vs solid predominant. As a result,
eight hallmarks were significantly enriched in radiological solid and five hallmarks were significantly enriched
in histological solid-predominant, with four hallmarks
including G2/M checkpoint, E2F targets, mitotic spindle,
and mTORC1 signaling, significantly enriched in both
groups (Fig. 5a–d). We also identified four hallmarks that
were significantly enriched in solid-predominant histology from the TCGA dataset, which were also identified in
the FUSCC dataset (Fig. 5e and f).
We conducted a simulation of radiological and histological progression using genes significantly altered
from GGO to solid as well as lepidic-predominant to
solid-predominant. To identify GGO/lepidic predominant and solid/solid predominant associated features,
we performed differential expression analysis in the
following three groups: solid vs GGO in the FUSCC
dataset, solid-predominant vs lepidic-predominant in
FUSCC dataset, and solid-predominant vs lepidicpredominant in the TCGA dataset. By setting |log2FC|
≥ 1.5 and P-value <0.05, we obtained 61 genes significantly up-regulated in GGO/lepidic-predominant
(GGO/lepidic-specific genes) and 36 genes significantly up-regulated in solid/solid predominant (solidspecific genes) (Fig. 6a and b). 24/61 GGO & lepidicpredominant specific genes and 21/36 solid & solidpredominant specific genes were further identified in
8108 genes from the intersection of 12 gene expression
datasets (Fig. 6c). Then, we constructed a signature
named G2S (lepidic/GGO to solid score) for quantification of GGO to solid or lepidic predominant to solidpredominant using the 24 GGO/lepidic-predominant
specific and 21 solid-predominant specific genes. It
was significant that G2S increased during radiological
or histological progression patterns in both the FUSCC
(Fig. 6d and e; 6d: mGGO in LUAD vs Solid in LUAD,
Welch’s t-test, P.adj = 5.14e-09; mGGO in AIS/MIA vs
mGGO in LUAD, Welch’s t-test, P.adj = 0.007; Normal
vs pGGO in AIS/MIA, Welch’s t-test, P.adj = 0.002; 6e:
Papillary vs Solid, Welch’s t-test, P.adj = 0.007; Lepidic
vs Acinar, Welch’s t-test, P.adj = 3.24e-06; Normal vs
AIS, Welch’s t-test, P.adj = 0.007) and TCGA datasets
(Fig. 6f, Micropapillary vs Solid, Welch’s t-test,
P.adj = 0.001; Papillary vs Micropapillary, Welch’s t-test,
P.adj = 2.16e-04; Lepidic vs Acinar, Welch’s t-test,
P.adj = 0.003). Solid and solid-predominant had the
highest score on average in the FUSCC and TCGA
datasets. In the FUSCC dataset, we classified patients
into GGO-like group and solid-like group based on the
median of G2S. Better prognosis in terms of overall
survival (OS) and RFS was found in GGO-like group
than in the solid-like group (Fig. 6g). The other 11 independent datasets were also classified into GGO-like
and solid-like based on the median of G2S and validated the prognosis predictive performance of G2S
(Fig. 6h and i, Supplementary Figure S9a and S9b).
To strengthen the relationship between genomic and
transcriptional data, we integrated gene mutation and
gene expression to construct a prognostic model. EGFR
and TP53 were chosen for their significantly increased
mutation frequency observed from AIS&MIA to LUAD,
or from GGO-like LUAD to solid nodule in LUAD. We
analyzed the correlation between EGFR/TP53 mutation
status and prognosis, and found that EGFR mutations
were associated with a favorable prognosis in the LUAD
(Supplementary Figure S10a and S10b), with the best
prognosis observed in patients with GGO characteristics
and EGFR mutations, and the worst prognosis observed
in patients with solid features and wild-type EGFR
(Supplementary Figure S10c and S10d). The same
conclusion can be observed in the Asian LUAD dataset,
GSE31210, which includes a high frequency of EGFR
mutations (Supplementary Figure S10e–S10h). EGFRmutated patients exhibited a better prognosis
compared to EGFR wild-type patients, even in the
absence of tyrosine-kinase inhibitor (TKI) administration (Supplementary Figure S10i–S10n). There was a
trend towards a correlation between mutations of tumor
suppressor gene TP53 and poor prognosis (P = 0.15 and
0.35) (Supplementary Figure S11a and S11b). We integrated the gene expression-based G2S model with TP53
mutations and found that prognosis stratification was
mainly driven by G2S, and TP53 mutation information
did not significantly improve the prognostic performance of the model (Supplementary Figure S11c and
S11d).
Discussion
GGO-like often appears in pre-invasive or early stage,
with inert growth and good prognosis.4,14,31 Many
studies, including our previous findings, have indicated
LUAD, Welch’s t-test, P.adj = 0.043), (c) Immune cell content from gene expression showed immune response, (d) Matrix Remodeling (mGGO in
LUAD vs Solid in LUAD, Welch’s t-test, P.adj = 4.40e-04; mGGO in AIS/MIA vs Solid in LUAD, Welch’s t-test, P.adj = 1.80e-07; Normal vs pGGO in
AIS/MIA, Welch’s t-test, P.adj = 3.41e-05), (e) CD8+ T cells (RNA-seq, solid in LUAD vs normal, Welch’s t-test, P.adj = 7.58e-06; pGGO in AIS/MIA
vs normal, Welch’s t-test, P.adj = 1.42e-04), and (f) CD8+Perforin+ T cells (Flow cytometry, Normal vs Solid in LUAD, Welch’s t-test, P.adj = 0.02;
mGGO in LUAD vs Solid in LUAD, Welch’s t-test, P.adj = 0.053) distribution in normal, AIS&MIA divided into pGGO and mGGO and LUAD
divided into pGGO, mGGO and solid nodule. (g) Representative flow cytometry plot of CD8+ T cells expressing perforin. (h) Representative flow
cytometry plot of T regulatory cells (Tregs). P-values were adjusted by false discovery rate (FDR). *P < 0.05; **P < 0.01; ***P < 0.001.
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Fig. 5: GSEA of the FUSCC and TCGA datasets. (a) Eight significantly enriched hallmarks between solid and GGO in the FUSCC datasets. (b) Five
significantly enriched hallmarks between solid predominant and lepidic predominant in the FUSCC datasets. (c) Enrichment score and FDR of
the eight gene sets significantly enriched in solid. (d) Enrichment score and FDR of the five gene sets significantly enriched in solid predominant.
(e) Four significantly enriched hallmarks between solid predominant and lepidic predominant in TCGA datasets. (f) Enrichment score and FDR of
the four gene sets significantly enriched in solid predominant.
that there are many significant molecular differences
between AIS&MIA and LUAD.12 In this study, we
further classified AIS&MIA and LUAD into three
14
subtypes based on CTR which was used to define GGO
proportion in tumor. Then, molecular characteristics of
pGGO and mGGO in AIS&MIA as well as pGGO,
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Fig. 6: Prognosis prediction features associated with pathological, radiological and histological in LUAD. (a) Venn plot showed up-regulated genes in
GGO or lepidic predominant group by following three comparing groups: solid vs GGO in FUSCC dataset, solid predominant vs lepidic predominant
in FUSCC dataset and solid predominant vs lepidic predominant in TCGA dataset. (b) Venn plot showed up-regulated genes in solid or solid
predominant group by following three comparing groups: solid vs GGO in FUSCC dataset, solid predominant vs lepidic predominant in FUSCC
dataset and solid predominant vs lepidic predominant in TCGA dataset. (c) Volcano plot, which derived from comparison between solid and GGO in
FUSCC dataset, showed the log2FC and P-value distributions for 8108 genes from the intersection of 12 expression datasets. 24/61 GGO or lepidic
predominant specific genes were marked with blue dots and 21/36 solid or solid predominant specific genes were marked with red dots. (d) G2S
score distribution from normal to AIS&MIA and LUAD divided into pGGO, mGGO and solid in FUSCC dataset (mGGO in LUAD vs Solid in LUAD,
Welch’s t-test, P.adj = 5.14e-09; mGGO in AIS/MIA vs mGGO in LUAD, Welch’s t-test, P.adj = 0.007; Normal vs pGGO in AIS/MIA, Welch’s t-test,
P.adj = 0.002). (e) G2S score distribution from normal to different histological stages in FUSCC dataset (Papillary vs Solid, Welch’s t-test,
P.adj = 0.007; Lepidic vs Acinar, Welch’s t-test, P.adj = 3.24e-06; Normal vs AIS, Welch’s t-test, P.adj = 0.007). (f) G2S score distribution of different
histological stage in TCGA dataset (Micropapillary vs Solid, Welch’s t-test, P.adj = 0.001; Papillary vs Micropapillary, Welch’s t-test, P.adj = 2.16e-04;
Lepidic vs Acinar, Welch’s t-test, P.adj = 0.003). (g) Overall survival (OS) and relapse-free survival (RFS) between GGO-like and solid-like divided by
median value of G2S score. Hazard ratio and 95% confidence intervals (CIs) for OS (h) and RFS (i) calculated based on G2S score in 12 datasets. Pvalues were adjusted by false discovery rate (FDR). **P < 0.01; ***P < 0.001.
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15
Articles
mGGO and solid in LUAD were comprehensively
described.
Our results showed consistent and different points of
molecular characteristics in driving the pathological and
radiological progression of GGO-like LUAD. Although
relatively lower tumor mutation burden (TMB) and less
copy number variation have been reported in subsolid
nodule of lung,8,9 our study provided a more detailed
comparison of different GGO components in AIS&MIA
and LUAD. AIS&MIA and GGO-like LUAD exhibited
similar level of TMB, suggesting that GGO-like LUAD
may be the early stage in the development of tumor
invasion (Fig. 2b). Meanwhile, our analysis revealed no
significant difference in TMB and no DMG between
pGGO of AIS&MIA and mGGO of AIS&MIA, suggesting that the effect of solid component on genomic
events in AIS&MIA might be negligible (Fig. 2b and
Supplementary Figure S5a). However, the genomic
differences between pure solid and GGO nodule in
AIS&MIA remained to be further explored, as the
FUSCC dataset consisted of only one pure solid nodule
in AIS&MIA.
GGO-like nodule was closely associated with relatively low TP53 mutation frequency compared to solid
nodule. TP53 is a tumor suppressor gene that is usually
mutated in cancer, and its mutation can promote tumor
proliferation and invasion, and regulate tumor
metabolism.32–35 Our results suggested that TP53 mutation may contribute to solid component emergence in
early stage of LUAD because significantly higher TP53
mutation frequency was found in solid nodule of LUAD
than in GGO-like nodule (Fig. 2d). Even though TP53
mutation frequency of mGGO in AIS&MIA was not
significantly higher than that of pGGO, there was an
increasing trend in mGGO (Fig. 2d). EGFR mutation
may be closely related to pathological progression, but
not to GGO components. Although one study reported
that the EGFR mutated group had significantly higher
frequency of GGO than EGFR non-mutated group.36
Consistent with several studies,37,38 EGFR mutation
was almost not significantly connected with GGO
component in AIS&MIA and LUAD (Fig. 2d). ERBB2
(also known as HER2) is an oncogene, whose mutation
is associated with EGFR tyrosine kinase inhibitor
resistance, poor prognosis, and tumor invasion.39,40
However, GGO-associated nodule of AIS&MIA had
higher ERBB2 mutation frequency than solid nodule of
LUAD in our dataset (Fig. 2d). This conclusion was
consistent with previous study,41 which showed a higher
frequency of mutations in ERBB2 in AIS&MIA
compared to LUAD. However, the low frequency of
ERBB2 mutations and the small sample size of each
subgroup after dividing our dataset into AIS&MIA and
LUAD may lead to less robustness of our conclusions. A
larger sample size is needed to validate this conclusion.
AIS/MIA and GGO-like LUAD tumor exhibit lower
proliferative activity and are less densely clustered
16
compared to pure solid LUAD. Consequently, accurate
analysis of AIS/MIA and GGO-like LUAD requires
higher tumor purity and sequencing depth. Previous
studies have employed micro-dissection technique to
enhance tumor purity for AIS/MIA and utilized target
sequencing to enhance sequencing depth.42–44 Fortunately, our findings were consistent with these studies.
For example, TMB, EGFR and TP53 mutations were
more common in LUAD compared to AAH/AIS/MIA,
while ERBB2 had higher mutation frequency in the
earlier stage, and EGFR-mutated cases showed a favorable prognosis.
Cell proliferation and matrix remodeling were less
activated in GGO nodule compared to solid nodule of
LUAD. Proliferation-related genes, which were closely
associated with patients’ prognosis, were expressed
lower in GGO nodule than solid nodule in LUAD
(Fig. 3c and d). The activity of proliferation-related
pathways, including G2M checkpoint, E2F targets, and
mitotic spindle pathway, which contributed to early lung
cancer progression,45 was significantly lower in GGO
nodules than in solid nodules (Figs. 3e and 5a and c).
Aberrantly activated G2M checkpoint pathway and
mitotic spindle pathway cause more tumor cells to enter
the mitotic period (M period), while overexpression of
several E2F targets (e.g. CCNE and CDC6) will induce
replicative stress and decreases genomic stability,46 and
all these can result in tumor cell proliferation. It was
clear that tumor proliferation rate also increased
significantly from GGO nodule to solid nodule (Fig. 4a).
Therefore, we speculated that the relatively low activity
of proliferation-related pathway may lead to slower
growth of tumor cells in GGO of LUAD, which may
explain why GGO nodule exhibits inert property and
good prognosis. Consistent with proliferation rate, matrix remodeling, which has been demonstrated closely
associated with tumor invasion,47 increased in the
pathological course of LUAD and reached its highest in
solid of LUAD (Fig. 4d). It has been reported that matrix
remodeling plays an important role in the process of
pre-invasive to invasive.10,13 We further demonstrated
that matrix remodeling activity of GGO nodule was
significantly lower than solid nodule in LUAD (Fig. 4d).
The low malignancy and slow progression of GGO in
LUAD compared to solid nodules is partly due to its own
weak cell proliferative capacity and also depends on the
strong immune activity of immune cells in tumor
microenvironment. We found that immunosuppression
was significantly enhanced from AIS&MIA to LUAD,
reaching the highest in the solid of LUAD. Consistent
with previous report,48 we also observed increase in
immune inhibition from pre-invasive to invasive LUAD.
Besides, the immunosuppression was significantly
increased from GGO to solid nodule in LUAD (Fig. 4b).
To quantify transition from GGO in LUAD with low
malignancy to solid nodules in LUAD with high malignancy, we constructed a gene signature called G2S
www.thelancet.com Vol 94 August, 2023
Articles
originated from the comparison of pure GGO nodule
and pure solid nodule. Gene signature (G2S) reflecting
GGO to solid or lepidic-predominant to solidpredominant process was an independent predictor of
patients’ OS and RFS in 11 independent LUAD cohorts
(Fig. 6g and i). Radiological subtype (pGGO, mGGO and
solid) and histological subtype (lepidic, acinar, papillary,
micropapillary, solid, etc.) both were vital to guide the
prognostic stratification and therapy for LUAD
patients.2,4,49–51 Therefore, G2S could be a good complement to radiological and histological subtypes.
It should be noted that there were some limitations
in our study. First, our cohort contained only one case
of solid nodule in AIS&MIA and only three cases of
pGGO in LUAD, which may lead to less robust conclusions. Secondly, we could not obtain authentic
mGGO containing both GGO and solid parts from
fresh tumor samples. Consequently, the specific contributions of the solid and subsolid components in the
analysis of mGGO remain unclear. However, these
samples harbored molecular alterations in overall
mGGO nodules or the patients who exhibit mGGO
radiologically, which was also worth studying, as the
GGO part in mGGO may be different from pGGO, and
the solid part in mGGO may also be different from the
solid nodules. We hope there are methods in the future
that can be used to identify the radiological subtypes of
fresh tumor tissue.
In summary, we comprehensively depicted the
genomic profiling of different GGO component nodules
of AIS&MIA and LUAD. We found that the molecular
characteristics of GGO in LUAD are similar to AIS/MIA
with low malignancy, which may be one of the reasons
for the indolent state and good prognosis of GGO.
Therefore, studies about GGO-like nodule should
exclude the influence of pathological differences. Our
results demonstrated that even though both AIS&MIA
and LUAD were GGO-like nodule, there were significant differences in TMB, APOBEC-related mutation,
gene mutation frequency, CNV frequency and gene
expression between AIS&MIA and LUAD. In LUAD,
lower TMB and TP53 mutation frequency were found in
GGO nodule than in solid nodule. Almost no significant
difference of genomic events in pGGO and mGGO of
AIS&MIA. Proliferation rate and matrix remodeling
were lower active in GGO nodule than solid nodule. To
accurately identify whether a specific molecular event
was associated with GGO components or pathological
stage, one needs to control variables, i.e. radiology or
pathology. And it was what our study design uniquely
offered.
Contributors
YZheng, HC, LS, JS and HJ conceived the study. JS, HJ and JY analyzed
the data. YZhao and JL collected and interpreted clinical data. JL and HJ
performed flow cytometry experiment analyzed the data. JS and HJ
drafted the manuscript. LS, HC and YZheng revised the manuscript and
www.thelancet.com Vol 94 August, 2023
supervised the work. All authors reviewed and approved the manuscript.
HJ and JS have accessed and verified the data.
Data sharing statement
WES and RNA-seq raw data have been deployed in the National Omics
Data Encyclopedia (NODE) (https://www.biosino.org/node) with the
accession number OEP000325. RNA-seq data of TCGA lung adenocarcinoma were downloaded from GDC (https://portal.gdc.cancer.gov/).
All gene expression microarray data and corresponding clinical phenotypes were obtained from GEO (https://www.ncbi.nlm.nih.gov/geo/).
Declaration of interests
The authors declare no competing financial interests.
Acknowledgements
This study was supported in part by the National Natural Science Foundation of China (32170657), the National Natural Science Foundation of
China (82203037), and Shanghai Sailing Program (22YF1408900). Fig. 1
was created with Biorender.com.
Appendix A. Supplementary data
Supplementary data related to this article can be found at https://doi.
org/10.1016/j.ebiom.2023.104728.
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