Access to this full-text is provided by MDPI.
Content available from Pharmaceuticals
This content is subject to copyright.
Citation: Wen, L.; Hu, W.; Hou, S.;
Luo, C.; Jin, Y.; Zeng, Z.; Zhang, Z.;
Meng, Y. GRB7 Plays a Vital Role in
Promoting the Progression and
Mediating Immune Evasion of
Ovarian Cancer. Pharmaceuticals 2024,
17, 1043. https://doi.org/10.3390/
ph17081043
Academic Editors: Gary J. Stephens
and Lawrence Marc Pfeffer
Received: 8 May 2024
Revised: 31 May 2024
Accepted: 30 July 2024
Published: 7 August 2024
Copyright: © 2024 by the authors.
Licensee MDPI, Basel, Switzerland.
This article is an open access article
distributed under the terms and
conditions of the Creative Commons
Attribution (CC BY) license (https://
creativecommons.org/licenses/by/
4.0/).
pharmaceuticals
Article
GRB7 Plays a Vital Role in Promoting the Progression and
Mediating Immune Evasion of Ovarian Cancer
Liang Wen 1,† , Wei Hu 2,† , Sen Hou 3, Ce Luo 4, Yiteng Jin 4, Zexian Zeng 4, Zhe Zhang 5,*
and Yuanguang Meng 1,5,*
1Chinese People’s Liberation Army (PLA) Medical School, Beijing 100853, China; 18392181763@163.com
2Department of Emergency, The Fifth Medical Center of Chinese PLA Hospital, Beijing 100039, China;
huwei301yy@163.com
3Department of Gastrointestinal Surgery, Peking University People’s Hospital, Beijing 100032, China;
housen95@163.com
4Center for Quantitative Biology, Academy for Advanced Interdisciplinary Studies, Peking University,
Beijing 100091, China; ce.luo@stu.pku.edu.cn (C.L.); 2101111816@pku.edu.cn (Y.J.);
zexianzeng@pku.edu.cn (Z.Z.)
5Department of Obstetrics and Gynecology, Seventh Medical Center of Chinese PLA General Hospital,
Beijing 100700, China
*Correspondence: tj.zhe.zhang@gmail.com (Z.Z.); meng6512@vip.sina.com (Y.M.)
†These authors contributed equally to this work.
Abstract: Background: Despite breakthroughs in treatment, ovarian cancer (OC) remains one of the
most lethal gynecological malignancies, with an increasing age-standardized mortality rate. This
underscores an urgent need for novel biomarkers and therapeutic targets. Although growth factor
receptor-bound protein 7 (GRB7) is implicated in cell signaling and tumorigenesis, its expression
pattern and clinical implications in OC remain poorly characterized. Methods: To systematically
investigate GRB7’s expression in OC, our study utilized extensive datasets from TCGA, GTEx, CCLE,
and GEO. The prognostic significance of GRB7 was evaluated by means of Kaplan–Meier and Cox
regression analyses. Using a correlation analysis and gene set enrichment analysis, relationships
between GRB7’s expression and gene networks, immune cell infiltration and immunotherapy re-
sponse were investigated.
In vitro
experiments were conducted to confirm GRB7’s function in the
biology of OC. Results: Compared to normal tissues, OC tissues exhibited a substantial upregulation
of GRB7. Reduced overall survival, disease-specific survival, and disease-free interval were all
connected with high GRB7 mRNA levels. The network study demonstrated that GRB7 is involved in
pathways relevant to the course of OC and has a positive connection with several key driver genes.
Notably, GRB7’s expression was linked to the infiltration of M2 macrophage and altered response to
immunotherapy. Data from single-cell RNA sequencing data across multiple cancer types indicated
GRB7’s predominant expression in malignant cells. Moreover, OC cells with GRB7 deletion showed
decreased proliferation and migration, as well as increased susceptibility to T cell-mediated cytotoxic-
ity. Conclusion: With respect to OC, our results validated GRB7 as a viable prognostic biomarker
and a promising therapeutic target, providing information about its function in tumorigenesis and
immune modulation. GRB7’s preferential expression in malignant cells highlights its significance in
the biology of cancer and bolsters the possibility that it could be useful in enhancing the effectiveness
of immunotherapy.
Keywords: ovarian cancer; GRB7; prognostic biomarker; immune infiltration; immunotherapy
1. Introduction
Ovarian cancer (OC) remains the most lethal gynecological malignancy. The Interna-
tional Agency for Research on Cancer estimated that there were more than 300,000 new
cases of ovarian cancer and more than 200,000 ovarian cancer deaths in the year 2022 [
1
].
Pharmaceuticals 2024,17, 1043. https://doi.org/10.3390/ph17081043 https://www.mdpi.com/journal/pharmaceuticals
Pharmaceuticals 2024,17, 1043 2 of 17
Between 2007 and 2017, the global age-standardized incidence and disability-adjusted
life-year rates of OC increased by 2.05% and 1.34%, respectively [
2
]. Additionally, the
age-standardized mortality rate of OC increased from 1.76 to 2.88 per 100,000 from 1990 to
2019 in China [
3
]. Ovarian cancer is the worst prognosis tumor among female reproductive
system tumors with an age-standardized 5-year net survival of 43.4% [
4
], largely due to late
diagnosis and frequent relapse after initial treatment. In addition, ovarian cancer responds
poorly to immune therapy, and the objective response rates were only 7.4% and 9.9% in
two different cohorts [
5
]. Therefore, we need more effective therapeutic regimens to treat
ovarian cancer. However, the complexity of tumor biology, including the interplay between
tumor cells and the immune microenvironment, poses a significant challenge to therapeutic
advancements.
The growth factor receptor-bound protein 7 (GRB7) belongs to a family of adapter
proteins implicating in various cellular processes and plays significant roles in cell sig-
naling pathways [
6
]. Due to its overexpression and possible role in the initiation and
spread of several malignancies, including breast and colon cancer, GRB7 has become a
noteworthy molecule [
7
,
8
]. Yet, the comprehensive role of GRB7 in ovarian cancer, in-
cluding its expression patterns, prognostic value, and therapeutic potential, has not been
fully elucidated.
Using omics data produced at several sequencing platforms, this study methodically
analyzed the expression patterns of GRB7 and examined the prognostic significance of GRB7
in OC and its relationship to patient prognosis. We conducted a thorough investigation
to investigate the expression of GRB7 in OC using datasets from The Cancer Genome
Atlas (TCGA), Genotype-Tissue Expression (GTEx), Cancer Cell Line Encyclopedia (CCLE),
and Gene Expression Omnibus (GEO). We conduct gene network construction, survival
analyses, and evaluations of GRB7’s impact on immune infiltration and immunotherapy
responsiveness. Importantly, our findings revealed that GRB7 is preferentially expressed
in malignant cells compared to immune cells across a variety of cancer types, suggesting
that GRB7 may be a promising target for future therapeutics. To support this, we assessed
the effect of GRB7 deletion on the migration, proliferation, and sensitivity of OC cells to T
cell-mediated cytotoxicity using in vitro tests.
2. Results
2.1. GRB7’s Expression Is Upregulated in Ovarian Cancer Tissue
Analyses of Cancer Cell Line Encyclopedia (CCLE) datasets showed that both mRNA
and protein levels of GRB7 were higher in OC than in most other cancers (Figure S1A,B).
Further analyses of TCGA and GTEx databases showed significant upregulation of GRB7
in various cancers compared to their matched normal controls (Figure 1A). Notably, the
mRNA levels of GRB7 were significantly higher in ovarian cancer tissues than in normal
tissues, as confirmed by TCGA-GTEx (Figure 1B) and GEO data (Figure 1C–E). Protein
levels further validated significant upregulation in ovarian cancer tissues compared to the
controls (Figure 1F), with immunohistochemical staining from the Human Protein Atlas
(HPA) corroborating higher expression in OC tissue (Figures 1G and S2A,B).
2.2. GRB7’s Expression Is Independently Associated with a Poorer Outcome and Is Valuable for
Predicting OS in OC Patients
In OC patients, high GRB7 mRNA levels are associated with poor overall survival
(OS), disease-free interval (DFI), and disease-specific survival (DSS) but not significantly
with progress-free interval (PFI) based on TCGA (Figure 2A–D). To further verify the
predictive power of GRB7 in ovarian cancer overall survival, we conducted univariate
and multivariate regression analyses of GRB7 and clinicopathologic factors with OS in OC
patients from TCGA and found that it may be an independent risk factor for ovarian cancer
(Figure 2E). Subsequently, a nomogram predicting the 1-year, 2-year, and 3-year overall
survival probabilities was developed (Figure 2F), and the results showed that the AUC
Pharmaceuticals 2024,17, 1043 3 of 17
values for 1-year, 2-year, and 3-year overall survival probabilities were 0.705, 0.717, and
0.695, respectively (Figure S3).
Pharmaceuticals 2024, 17, x FOR PEER REVIEW 3 of 20
Figure 1. The expression of GRB7. (A,B) GRB7 mRNA levels in pan-cancer (A), OC (B), and the
corresponding normal tissues in TCGA and GTEx databases. (C–E) GRB7’s expression in OC and
normal tissues in GEO databases, GSE6008 (C), GSE36668 (D), and GSE66957 (E). (F) GRB7 protein
levels in OC and paired adjacent normal tissues from cProCite database. (G) Representative results
of immunochemically stained GRB7 proteins in OC and normal ovarian tissues from Human Protein
Atlas. * p < 0.05; *** p < 0.001 by unpaired Student’s t test (A–F). ns, not significant.
2.2. GRB7’s Expression Is Independently Associated with a Poorer Outcome and Is Valuable for
Predicting OS in OC Patients
In OC patients, high GRB7 mRNA levels are associated with poor overall survival
(OS), disease-free interval (DFI), and disease-specific survival (DSS) but not significantly
with progress-free interval (PFI) based on TCGA (Figure 2A–D). To further verify the
Figure 1. The expression of GRB7. (A,B) GRB7 mRNA levels in pan-cancer (A), OC (B), and the
corresponding normal tissues in TCGA and GTEx databases. (C–E) GRB7’s expression in OC and
normal tissues in GEO databases, GSE6008 (C), GSE36668 (D), and GSE66957 (E). (F) GRB7 protein
levels in OC and paired adjacent normal tissues from cProCite database. (G) Representative results
of immunochemically stained GRB7 proteins in OC and normal ovarian tissues from Human Protein
Atlas. * p< 0.05; *** p< 0.001 by unpaired Student’s ttest (A–F). ns, not significant.
Pharmaceuticals 2024,17, 1043 4 of 17
Pharmaceuticals 2024, 17, x FOR PEER REVIEW 5 of 20
Figure 2. The prognosis and diagnosis value of GRB7 in OC. (A–D) OS, DSS, DFI, and PFI curves
of lowly and highly expressed GRB7 in OC. (E) Univariate and multivariate regression analyses of
GRB7 and clinicopathologic factors with OS in OC patients from TCGA. (F) A nomogram to predict
OS probability at 1-year, 2-year, and 3-year overall survival probabilities for OC. * p< 0.05; ** p< 0.01;
*** p< 0.001 by unpaired Student’s t test (E, F). # Events represents the number of death cases.
Pharmaceuticals 2024,17, 1043 5 of 17
2.3. Network Establishment for GRB7-Correlated Genes in OC
We analyzed the genes associated with GRB7 in CCLE and in TCGA. The results
showed that GRB7 was positively correlated with HER2 and CDH1 in CCLE proteomics
data (Figure S4A,B), and GRB7 was positively correlated with HER2 and HER2_pY1248 in
pan-cancer and ovarian cancer (Figure S4C,D). To further investigate the gene networks
associated with GRB7 in OC from TCGA, we analyzed differentially expressed genes
(DEGs) in high and low groups based on the median GRB7 mRNA levels. The top 20
positively and top 20 negatively correlated genes with GRB7 were shown (Figure 3A). Next,
Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis was performed
based on DEGs between the GRB7 high and low groups, and the results showed that
the DEGs were mainly involved in several key pathways including the calcium signaling
pathway (Figure 3B), an important pathway associated with cancer cell proliferation and
invasiveness, and the ability of agents to induce cancer cell death. We also performed a gene
oncology (GO) enrichment analysis based on these DEGs and found that there were more
genes enriched in tumor progression associated pathways, such as the pattern specification
process, regionalization, and the transmembrane receptor protein serine/threonine kinase
signaling pathway (Figure 3C,D). Furthermore, we conducted a comprehensive analysis
of the association between GRB7’s expression and key driver gene mutations and found
that the expression levels of GRB7 were different in wild-type (WT) versus mutant (MUT)
genotypes of various driver genes, such as TP53 (Figure S5A,B).
2.4. GRB7’s Expression Correlates with Immune Infiltration and Immunotherapy Response and
Has Potential to Be a Therapeutic Target
To assess the immune infiltration of GRB7 in OC, we first evaluated the enrichment
of 20 immune cells with high and low expression of GRB7. The results illustrated that the
expression of GRB7 was positively related to M2 macrophages (Figure 4A), which was
accepted to be immunosuppressive. Given its immunosuppressive potential, we collected
syngeneic mouse models of immune checkpoint blockade (ICB) studies and clinical ICB-
treated cohorts and analyzed the expression level of GRB7 between ICB responders and
non-responders to evaluate whether GRB7 is associated with immunotherapy response.
In these studies, complete or partial response to treatment was defined as responder, and
stable or progressive disease was defined as non-response. We found that the GRB7’s
expression level tended to be lower in responders than non-responders from syngeneic
mouse models and vice versa (Figure 4B). Additionally, we established GRB7 signature,
which was calculated by the average expression of genes closely related to GRB7 according
to STRING database (Figure 4C) and found that the GRB7 signature tended to be lower in
responders than non-responders across two clinical immune checkpoint blockade (ICB)-
treated cohorts (Figure 4D,E). Furthermore, we collected various Clustered Regularly
Interspaced Short Palindromic Repeats (CRISPR)-associated protein 9 (CRISPR-Cas9) screen
datasets [
9
–
13
], in which the cancer cells were cocultured with T cells after being edited with
CRISPR, and negative log2 fold change in cell viability suggests that knocking out certain
genes may sensitize the tumor to T cell killing. These data showed that the deletion of GRB7
in tumor cells, including breast, colon, leukemia, and skin cancers, resulted in heightened
susceptibility of the tumor cells to T cell-mediated killing (Figure 4F). Given that GRB7
may affect immunotherapy, we desired to explore whether it could be a good therapeutic
target. Targeted therapies aim to interfere with specific cells without affecting others as
much as possible, thereby reducing side effects. Therefore, genes that are specifically
highly expressed in tumor cells may have potential for targeted therapy. In our quest
to determine the therapeutic potential of GRB7, we aggregated multiple single-cell RNA
sequencing (scRNA-seq) datasets from diverse studies. The analysis of these datasets
revealed a predominant expression of GRB7 in malignant cells as opposed to immune cells,
a pattern consistent across not only ovarian cancers but also a broad spectrum of other
cancer types (Figure 5).
Pharmaceuticals 2024,17, 1043 6 of 17
Pharmaceuticals 2024, 17, x FOR PEER REVIEW 7 of 20
Figure 3. Gene expression and enrichment of GRB7-associated gene in OC from TCGA. (A)
Heatmap of top 20 genes positively correlated with GRB7 and top 20 negatively correlated genes in
OC. (B) KEGG enrichment results of all 225 different expressed genes. (C) Biological processes, cel-
lular components, and molecular functions from GO enrichment results. (D) Chord diagrams of
biological processes.
Figure 3. Gene expression and enrichment of GRB7-associated gene in OC from TCGA. (A) Heatmap
of top 20 genes positively correlated with GRB7 and top 20 negatively correlated genes in OC.
(B) KEGG enrichment results of all 225 different expressed genes. (C) Biological processes, cel-
lular components, and molecular functions from GO enrichment results. (D) Chord diagrams of
biological processes.
Pharmaceuticals 2024,17, 1043 7 of 17
Pharmaceuticals 2024, 17, x FOR PEER REVIEW 9 of 20
Figure 4. The immune infiltration and association with immunotherapy response of GRB7 in OC.
(A) Immune cell enrichment in low and high expression levels of GRB7 in OC from CIBERSORT.
(B) GRB7’s expression levels in responders and non-responders of ICB in syngeneic mouse models.
Figure 4. The immune infiltration and association with immunotherapy response of GRB7 in OC.
(A) Immune cell enrichment in low and high expression levels of GRB7 in OC from CIBERSORT.
(B) GRB7’s expression levels in responders and non-responders of ICB in syngeneic mouse models.
(C) Protein–protein interaction signature of GRB7 from string database. (D,E) The GRB7 signature
expression level in responders and non-responders of ICB-treated clinical cohorts, PD1 + CTLA4 in
Pharmaceuticals 2024,17, 1043 8 of 17
melanoma (D), and PDL1 in metastatic urothelial cancer (E). (F) GRB7 knockout in cancer cells
cocultured with T cells from several CRISPR-Cas9 screens. Box plots indicate median (middle line),
25th and 75th percentile (box), and 5th and 95th percentile (whiskers) (A,B,D,E), and each dot in the
scatter represents an individual patient sample (D,E). * p< 0.05; ** p< 0.01; *** p< 0.001 by unpaired
Student’s ttest (A,B,D,E). ns, not significant.
Pharmaceuticals 2024, 17, x FOR PEER REVIEW 10 of 20
(C) Protein–protein interaction signature of GRB7 from string database. (D,E) The GRB7 signature
expression level in responders and non-responders of ICB-treated clinical cohorts, PD1 + CTLA4 in
melanoma (D), and PDL1 in metastatic urothelial cancer (E). (F) GRB7 knockout in cancer cells co-
cultured with T cells from several CRISPR-Cas9 screens. Box plots indicate median (middle line),
25th and 75th percentile (box), and 5th and 95th percentile (whiskers) (A,B,D,E), and each dot in the
scaer represents an individual patient sample (D,E). * p < 0.05; ** p < 0.01; *** p < 0.001 by unpaired
Student’s t test (A,B,D,E). ns, not significant.
Figure 5. GRB7’s expression among different cell types and datasets. The cohorts highlighted in red
are the ovarian cancer single-cell datasets.
2.5. GRB7 Knockout Inhibits OC Cell Proliferation and Migration
To investigate the functional effect of GRB7 on ovarian cancer, we knocked out GRB7
in the ovarian cancer cell line OVCAR3 and conducted assays to assess proliferation and
migration. The knockout efficiency of GRB7 was confirmed by Western blot analyses (Fig-
ure 6A). The colony formation and CCK-8 assays results showed that the proliferation of
ovarian cancer cells was significantly reduced upon GRB7 knockout (Figure 6B,C). The
results of the transwell and wound healing assays showed that GRB7 knockout reduced
the migration of ovarian cancer cells (Figure 7A,B).
Figure 5. GRB7’s expression among different cell types and datasets. The cohorts highlighted in red
are the ovarian cancer single-cell datasets.
2.5. GRB7 Knockout Inhibits OC Cell Proliferation and Migration
To investigate the functional effect of GRB7 on ovarian cancer, we knocked out GRB7
in the ovarian cancer cell line OVCAR3 and conducted assays to assess proliferation and
migration. The knockout efficiency of GRB7 was confirmed by Western blot analyses
(Figure 6A). The colony formation and CCK-8 assays results showed that the proliferation
of ovarian cancer cells was significantly reduced upon GRB7 knockout (Figure 6B,C). The
results of the transwell and wound healing assays showed that GRB7 knockout reduced
the migration of ovarian cancer cells (Figure 7A,B).
Pharmaceuticals 2024,17, 1043 9 of 17
Pharmaceuticals 2024, 17, x FOR PEER REVIEW 11 of 20
Figure 6. Knockout GRB7 inhibits the proliferation of OVCAR3. (A) Western blot analysis of GRB7
knockout efficiency. (B) Colony formation capacity of GRB7 knockout and control. (C) CCK-8 assay
of GRB7 knockout and control. Data are represented as mean ± standard deviation (SD) (A–C). The
Shapiro–Wilk test confirmed normality, and Brown–Forsythe test confirmed homogeneity of vari-
ance (B,C). * p < 0.05; ** p < 0.01; *** p < 0.001 by one-way ANOVA (B) and two-way ANOVA (C).
Data are representative of three independent experiments (B,C).
Figure 6. Knockout GRB7 inhibits the proliferation of OVCAR3. (A) Western blot analysis of GRB7
knockout efficiency. (B) Colony formation capacity of GRB7 knockout and control. (C) CCK-8 assay
of GRB7 knockout and control. Data are represented as mean
±
standard deviation (SD) (A–C).
The Shapiro–Wilk test confirmed normality, and Brown–Forsythe test confirmed homogeneity of
variance (B,C).
*p< 0.05; ** p< 0.01; *** p< 0.001 by one-way ANOVA (B) and two-way ANOVA (C).
Data are representative of three independent experiments (B,C).
Pharmaceuticals 2024,17, 1043 10 of 17
Pharmaceuticals 2024, 17, x FOR PEER REVIEW 12 of 20
Figure 7. GRB7 knockout in OVCAR3 inhibits cell migration and sensitizes killing effect of CD8+ T
cells. (A) GRB7 knockout in OVCAR3 reduces migrating cell numbers in transwell assay. (B) GRB7
Pharmaceuticals 2024,17, 1043 11 of 17
knockout in OVCAR3 slows wound healings. (C) The representative FACS results and summary
of the log2 fold change in the ratio of GRB7 KO cells over the control after adding CD8+ T cells.
The pseudocolor in the figure represents the variation in cell density. Colors range from red to blue,
indicating a gradual decrease in cell density from high to low. Data are represented as mean
±
SD
(A–C). The Shapiro–Wilk test confirmed normality, and Brown–Forsythe test confirmed homogeneity
of variance (A–C). ** p< 0.01; *** p< 0.001 by one-way ANOVA (A–C). Data are representative of
three independent experiments (A–C).
2.6. Enhanced Susceptibility of OC Cells to T Cell-Mediated Cytotoxicity Post-GRB7 Knockout
Our computational analyses implicated that GRB7 knockout in cancer cells would
enhance their susceptibility to T cell-mediated cytotoxicity. Therefore, we embarked on
a coculture experiment involving tumor cells and T cells. Our results showed that the
ablation of GRB7 in the OVCAR3 cell line significantly enhanced the vulnerability of these
tumor cells to the cytotoxic effects of CD8+ T cells (Figure 7C).
3. Discussion
The GRB7 family of adaptor molecules, which consists of GRB7, GRB10, and GRB14,
interact with receptor tyrosine kinases like EGF, HER2, and insulin receptors to play signifi-
cant roles in cellular signaling [
14
–
16
]. GRB7 has been identified as a biomarker and found
to be important in regulating the cellular signaling pathways involved in proliferation,
tumorigenesis, and metastasis. It is implicated in various malignancies, including breast
cancer, colon cancer, bladder cancer, and thyroid cancer [
7
,
8
,
17
,
18
]. Additionally, GRB7
shows promise as a potential therapeutic target in breast cancer as well [
19
]. According
to recent reports, GRB7 was overexpressed in OC and may regulate angiogenesis through
the VEGFA/VEGFR2 signaling pathway [
20
,
21
]. However, the scope of these findings of
OC, derived from a limited number of cell lines and small tumor tissue samples, calls for
a more comprehensive analysis of GRB7’s expression and its impact on OC. To address
this gap, we conduct rigorous validation through
in vitro
experiments and use substantial
datasets from TCGA, GTEx, CCLE, and GEO, allowing for a comprehensive examination
of GRB7 expression patterns in ovarian cancer and their correlation with prognosis.
The overexpression of GRB7 or the co-overexpression of GRB7 and members of the
ERBB family play essential roles in advanced human cancers and are associated with
decreased survival and recurrence of cancers [
6
]. In breast cancer, GRB7 was overexpressed
and co-amplified with HER2, which promoted cell migration, invasion, and tumorige-
nesis [
8
,
22
]. Additionally, a shorter breast cancer-free interval has been linked to GRB7
overexpression [
23
]. Moreover, co-treatment of the HER2 inhibitor Herceptin and GRB7
inhibitor in breast cancer resulted in a decrease in the Herceptin EC 50 value [
19
]. Recently,
it was proved that GRB7 was stabilized by circCDYL2 through preventing its ubiquitination
degradation and enhanced its interaction with FAK, which thus sustained the activities
of downstream AKT and ERK1/2 and contributed to trastuzumab resistance in HER2+
breast cancer patients [
24
]. HER2 and GRB7 were the two genes most observed in fusion
events that were most frequently observed in glioblastoma, breast cancer, and ovarian
cancer [
25
,
26
]. Additionally, EGF-induced GRB7 tyrosine phosphorylation activates Ras-
GTPases and extracellular signal-regulated kinases 1/2 (ERK1/2) contributes to cancer
proliferation [
27
]. Moreover, it was reported that specific GRB7 peptides targeting the SH2
domain of GRB7 blocks EGF/EGFR signal-mediated ERK activation [
8
] and deletion of
GRB7 ablates MMP-9 expression in cervical cancer [
28
], which suggest that GRB7 may
modulate cancer invasion by mediating EGF/EGFR signal-mediated ERK activation or
matrix metallopeptidase 9 (MMP-9) expression. In colon cancer, GRB7-PLK1 was also
discovered to be a pivotal axis mediating tolerance to MEK inhibitor tolerance [
7
]. GRB7
also modulated the proliferation, cell cycle, migration and invasion of bladder and thyroid
cancer via the AKT pathway and GRB7/ERK/FOXM1 signaling cascade [
17
,
18
]. MiR-193a-
3p has been reported to target the GRB7 and MAPK/ERK pathways in ovarian cancer,
hence promoting the aggressiveness of the malignancy [
14
]. This work demonstrated that
Pharmaceuticals 2024,17, 1043 12 of 17
GRB7 is overexpressed in ovarian cancer tissue based on omics data and its overexpression
is linked to poor patient outcomes based on TCGA data, consistent with earlier studies on
other types of malignancies [
8
]. From the CCLE and TCGA datasets, it was discovered that
GRB7 and HER2 are co-amplified in OC, which is congruent with that in breast cancer [
22
].
Via TCGA data, we found that the calcium signaling pathway and transmembrane receptor
protein serine/threonine kinase signaling pathway were enriched from GRB7 associated
genes, echoing the
in vitro
evidence of reduced proliferation and migration upon GRB7
knockout in OC cells, which involve in cancer progression and resistance to cell death
inducers [
29
,
30
]. Furthermore, it appears that GRB7 may play a crucial role in carcinogenic
pathways due to its variable expression in response to mutations in driver genes like
TP53. High-grade serous ovarian cancer is characterized by TP53 mutations in almost all
tumors [
31
]. GRB7 may therefore be particularly important in TP53-mutated OC. Indeed,
the data indicate that GRB7 could impact ovarian cancer development via a variety of
signaling channels. The possible involvement of GRB7 in vital processes like cell migration
and proliferation is highlighted by this interaction.
Although immunotherapy has advanced significantly in the treatment of many can-
cers, its effectiveness in the management of ovarian cancer is still restricted, with objective
response rates under 10% [
5
,
32
]. The Ovarian Tumor Tissue Analysis Consortium [
33
]
has demonstrated a clear dose–response relationship between CD8+ tumor-infiltrating
lymphocytes (TILs) and improved survival rates in ovarian cancer patients. This under-
scores the potential of enhancing the effectiveness of immunotherapy in ovarian cancer by
further investigating strategies to mobilize pre-existing immunity and boost the activation
of T cells within the tumor’s microenvironment [
34
,
35
]. The association of GRB7 and
HER2 with Type 2 T-helper cells [
36
], and the role of local Th2 inflammation in fostering
an immunosuppressive environment that promotes tumor progression [
37
,
38
], has been
noted. Our research revealed that the positive association of GRB7’s expression with M2
macrophages infiltration, which may involve immunosuppression [
39
]. Furthermore, we
found that in syngeneic mice or clinical ICB-treated cohorts, the expression level of GRB7
or GRB7 signature tended to be lower in ICB responders than non-responders. The afore-
mentioned details shed light on GRB7’s putative function in the tumor microenvironment
and how it affects immune evasion. It’s interesting to note that GRB7-knockout cells have
demonstrated enhanced vulnerability to T cell-mediated cytotoxicity, suggesting that tar-
geting GRB7 may improve immunotherapies’ efficacy. This suggests that GRB7 may be a
viable target for immunomodulatory strategies targeted at enhancing the effectiveness of
immunotherapy in ovarian cancer patients, in addition to serving as a possible prognostic
biomarker for the disease.
Molecularly targeted therapies are becoming increasingly attractive due to their speci-
ficity for cancer cells while preserving normal cells [
40
]. In addition to helping to un-
derstand the clonal composition and heterogeneity within tumors and identifying the
characteristics of tumor cells and immune cells, single-cell sequencing technology of-
fers previously unheard-of opportunities for molecularly targeted therapies, which can
provide a crucial basis for identifying new therapeutic targets and drug resistance mech-
anisms [
41
,
42
]. GRB7 may be a promising therapeutic target for ovarian cancer, since
our scRNA-seq data analysis showed that it is primarily expressed in malignant ovarian
cells. Furthermore, based on TCGA data, we discovered that GRB7 is overexpressed in the
majority of tumor tissues and is primarily expressed in malignant cells across a range of
cancer types. These results imply that GRB7 may possibly be a potential therapeutic target
in multiple tumors.
In conclusion, the data suggest that GRB7 has a great deal of potential as a target for
improving the effectiveness of immunotherapy for ovarian cancer. Despite the fact that
our study offers compelling evidence, it has certain shortcomings. Large-scale clinical
validation studies are needed to assess the expression levels of GRB7 in ovarian cancer
patient samples and the association of GRB7 expression with clinicopathological parameters
such as tumor stage, grade, even if our systematic analysis of multiomics data has helped
Pharmaceuticals 2024,17, 1043 13 of 17
us understand the expression pattern of GRB7. Subsequent research endeavors ought to
concentrate on elucidating the molecular mechanisms underlying GRB7’s role in ovarian
cancer progression and immune evasion, explore how GRB7 interacts with key signaling
pathways involved in tumor growth, invasion, and immune modulation within the tumor
microenvironment. Additionally, further research is necessary to validate the predictive
value of GRB7 and examine the therapeutic benefit of GRB7 targeting in pre-clinical models
of OC.
4. Materials and Methods
4.1. Expression of GRB7 and Clinicopathological Character Analysis
We obtained GRB7’s expression data across various cancers and normal tissues
from TCGA [
43
] (https://portal.gdc.cancer.gov) (accessed on 16 February 2024) and
GTEx [
44
] (https://www.gtexportal.org/home/-index.html) (accessed on 16 February
2024) databases, including clinical samples of ovarian cancer patients from TCGA. A total
of 419 OC samples, which included detailed pathology and prognosis information, and
88 normal ovarian tissue RNA sequencing samples from the GTEx database were ana-
lyzed. Additionally, GRB7’s expression profiles were obtained from the GEO database
(https://www.ncbi.nlm.nih.gov/gds) (accessed on 16 February 2024), and GRB7 pro-
tein abundance data were obtained from the cProSite [
45
], in which the Tandem Mass
Tagging (TMT) log2 ratio was used to present the relative protein abundance (https:
//cprosite.ccr.cancer.gov/) (accessed on 16 February 2024). Immunohistochemistry data of
GRB7 protein in human ovarian cancer and normal tissue were retrieved from The Human
Protein Atlas [46] (https://www.proteinatlas.org/) (accessed on 16 February 2024).
4.2. Correlation Analysis of GRB7 and Prognosis
Patients from TCGA were categorized into two groups according to the median GRB7
mRNA expression level. A Kaplan–Meier survival analysis was performed using the R
package survival (v 3.5-8) and survminer (v0.4.9) to assess correlations between GRB7’s
expression and OS, PFI, DSS, and DFI of patients with ovarian cancer.
4.3. Analyses of Univariate and Multivariate Cox Regression
Univariate and multivariate analyses were conducted with Cox proportional hazards
regression models using the R package survival (v3.5-8). A univariate Cox regression analy-
sis and multivariate analysis were used to construct a prognostic classifier to determine the
impact of GRB7’s expression levels on the clinical outcomes of OC patients. A nomogram
was also constructed incorporating race, stage, age, and GRB7’s expression to predict OS
probability. ROC curves were applied to assess the multivariate model’s predictive ability.
4.4. Correlation of Related Genes and Gene Set Enrichment Analysis
The differential expression genes were analyzed using DESeq2 (v1.40.2). GO and
KEGG enrichment analyses were performed with clusterProfiler (v4.8.3). All visualizations
were performed using ggplot2 (v3.5.0), enrichplot (v1.20.3), and GOplot (v1.0.2).
4.5. Immune Cell Infiltration and Association with Immunotherapy
The association between immune cell infiltration and GRB7’s expression was evaluated
using CIBERSORT algorithm [
47
]. Additionally, RNA-seq samples of syngeneic mouse
tumors treated with ICB
in vivo
were obtained from the TISMO Database [
48
] (https:
//tismo.cistrome.org) (accessed on 8 March 2024) and were used to analyze the association
between immunotherapy response and GRB7’s expression. The protein–protein interaction
signature of GRB7 was mined from the STRING database [
49
] (https://string-db.org)
(accessed on 8 March 2024), and we compared the signature expression level between
responders and non-responders from clinical ICB-treated cohorts [
50
]. Furthermore, we
explored the association of GRB7 knockout with the immune cell killing effect from several
CRISPR screen datasets.
Pharmaceuticals 2024,17, 1043 14 of 17
4.6. GRB7’s Expression Level in Single Cells of Tumor Tissue
We collected various scRNA-seq datasets from the GEO database, including ovarian
cancer, non-small cell lung cancer, and breast cancer. We filtered low-quality cells or genes,
corrected for batch effects, and normalized all the data. We identified different cell types
with their markers and calculated the expression level of GRB7 in the different cell types.
4.7. Cell Culture
The human ovarian cancer cell line (OVCAR3) and human embryonic kidney cell line
(293T) were obtained from the Cell Bank of the Chinese Academy of Sciences (Shanghai,
China). We follow the CRISPR-Cas9 genome engineering protocol to knockout the target
gene [
51
]. Sequences of sgRNAs targeting GRB7 included GCCTTGAGCGACGAGACCTG
and GAAGCGGCTATCTCCGCCCA. Following transfection, the cells were selected using
puromycin. We validated the knockout efficiency through Western blot analysis, stained
with anti-GRB7 (Proteintech, Wuhan, China) and performed relative quantification of GRB7
protein levels based on results from three technical replicates. 293T cells were cultured in
Dulbecco’s Modified Eagle’s Medium (DMEM; Gibco, Rockville, MD, USA) supplemented
with 10% fetal bovine serum (FBS; Gibco, Rockville, MD, USA) and 1% penicillin and
streptomycin (PS; Gibco, Rockville, MD, USA) for lentivirus production for gene editing.
RPMI-1640 medium supplemented with 20% FBS (Gibco, Rockville, MD, USA), 1% PS, and
0.01 mg/mL bovine insulin (Coolaber, Beijing, China) was used to culture OVCAR3 cells.
The cell lines were cultured in a 37 ◦C, 5% CO2incubator.
4.8. Cell Proliferation
We conducted a CCK-8 assay and colony formation assay to assess cell proliferation.
For the CCK-8 assay, 10
µ
L CCK-8 solution (Dojindo, Ube City, Japan) was added and
incubated for 2 h after culturing OVCAR3 cells for 24, 48, 72, 96, and 120 h. The absorbance
was then measured at 450 nm using a microplate reader (Sunrise; Tecan, Männedorf,
Switzerland). For the colony formation assay, the cells were seeded into 6-well plates at
a density of 10,000 cells/well and cultured at 37
◦
C in a humidified 5% CO
2
incubator
for 10–14 days. The cells were fixed with methanol (Solarbio, Beijing, China) and stained
with 0.1% crystal violet (Solarbio, Beijing, China) for 10 min. The number of colonies
was counted using ImageJ software (1.54f). All the experiments were performed in three
technical replicates.
4.9. Cell Migration
We evaluated cell migration using transwell and wound healing assays. For the
transwell assay, cells were cultured in an upper chamber (Becton, Dickinson and Company,
Franklin Lakes, NJ, USA) with 200
µ
L of medium containing 2% FBS, while the lower
chamber was filled with 500
µ
L of medium containing 20% FBS. After a 48 h incubation, cells
were fixed with methanol and stained with 0.1% crystal violet for 10 min; the experiment
was performed in three technical replicates and images from three random fields of each
replicate were then acquired via a microscope and counted using ImageJ software, and the
average number of cells in the three fields was used for a quantitative analysis. For the
wound healing assay, after confirming the ideal cell density under a microscope, a pipette
tip was used to create a wound scratch; then, the cells were cultured for 48 h. Images were
taken at 0, 24, and 48 h; the experiment was performed in three technical replicates.
4.10. In Vitro Cancer-Killing Assay by Antigen-Specific T Cells
Primary CD8+ T cells were isolated from a donor’s PBMC (Biosource, Waltham, MA,
USA) following the protocols of the CD8+ T isolation kit (Stemcell, Vancouver, BC, Canada);
then, we overexpressed 1G4 TCR, specific to the tumor antigen NY-ESO-1, in primary
CD8+ T cells, and OVCAR3 cells were engineered to express NY-ESO-1 simultaneously,
as described in a prior study [
52
]. We pre-plated the Cell Trace Violet (CTV; Invitrogen,
Waltham, MA, USA) stained control and GRB7 knockout cells at a 1:1 ratio and cocultured
Pharmaceuticals 2024,17, 1043 15 of 17
them with 1G4 CD8+ T cells at a 1:6 E:T ratio in triplicates. After 24–48 h of coculturing,
cells were collected and analyzed using Cytoflex (Beckman Coulter, Brea, CA, USA) and
FlowJo software (v10.8.1). The experiment was performed in three technical replicates.
4.11. Statistical Analyses
The data were analyzed using GraphPad Prism (v9.5.1) and R software (v4.3.0). Statis-
tical analyses were performed using parametric tests where appropriate. The normality
of the data was assessed using the Shapiro–Wilk test, and the homogeneity of variance
was evaluated using the Brown–Forsythe test. The data were considered to follow a nor-
mal distribution if the Shapiro–Wilk test p-value was greater than 0.05. Homogeneity
of variance was confirmed if the Brown–Forsythe test p-value was greater than 0.05. If
these assumptions were not met, non-parametric tests were used. p< 0.05 was considered
statistically significant.
5. Conclusions
In conclusion, GRB7 emerges from our study as a potential biomarker for ovarian
cancer prognosis and a promising target for therapeutic intervention. Our integrative
approach spanning transcriptomic and proteomic data, complemented by functional assays,
provides a robust framework for understanding GRB7’s multifaceted role in OC. As we
progress toward precision oncology, the insights from this research lay the groundwork for
identifying therapeutic targets for ovarian cancer.
Supplementary Materials: The following supporting information can be downloaded at https:
//www.mdpi.com/article/10.3390/ph17081043/s1, Figure S1: The expression level of GRB7 in
different cancer cell lines from CCLE; Figure S2: Quantification of immunochemically stained GRB7
proteins in OC and normal ovarian tissue from Human Protein Atlas; Figure S3: The time-dependent
ROC curves of the risk score model for predicting 1-year, 3-year, and 5-year overall survival in OC;
Figure S4: Volcano plots depicting the association of GRB7 with other proteins in cancer; Figure S5:
Comprehensive analysis of the association between GRB7’s expression and key driver gene mutations.
Author Contributions: L.W., Methodology, Formal analysis, Data curation, Visualization, and
Writing—original draft; W.H., Methodology, Formal analysis, and Writing—original draft; S.H.,
Methodology, Data curation, and Visualization; C.L., Methodology and Formal analysis; Y.J., Method-
ology and Data curation; Z.Z. (Zexian Zeng), Methodology, Formal analysis, and Writing—review
and editing; Z.Z. (Zhe Zhang), Conceptualization, Methodology, Supervision, and Writing—review
and editing; Y.M., Conceptualization, Methodology, Supervision, and Writing—review and editing.
All authors have read and agreed to the published version of the manuscript.
Funding: This research received no external funding.
Institutional Review Board Statement: Not applicable.
Informed Consent Statement: Not applicable.
Data Availability Statement: The data presented in this study are available in this article and
supplementary material.
Acknowledgments: We thank the flow cytometry core at the National Center for Protein Sciences at
Peking University, particularly Huan Yang and Zeng Fan, for their technical help.
Conflicts of Interest: The authors declare no conflicts of interest.
References
1.
Bray, F.; Laversanne, M.; Sung, H.; Ferlay, J.; Siegel, R.L.; Soerjomataram, I.; Jemal, A. Global cancer statistics 2022: GLOBOCAN
estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J. Clin. 2024,74, 229–263. [CrossRef]
[PubMed]
2.
Yang, D.; Huang, H.; Ruan, T.; Yang, H.; Hu, J.; Xu, S.; Ling, S.; Yu, Y. Global, regional, and national burden of ovarian cancer and
the attributable risk factors in all 194 countries and territories during 2007–2017: A systematic analysis of the Global Burden of
Disease Study 2017. J. Obstet. Gynaecol. Res. 2021,47, 4389–4402. [CrossRef] [PubMed]
Pharmaceuticals 2024,17, 1043 16 of 17
3.
Wang, Z.; Guo, E.; Yang, B.; Xiao, R.; Lu, F.; You, L.; Chen, G. Trends and age-period-cohort effects on mortality of the three major
gynecologic cancers in China from 1990 to 2019: Cervical, ovarian and uterine cancer. Gynecol. Oncol. 2021,163, 358–363. [CrossRef]
4.
Caetano Dos Santos, F.L.; Wojciechowska, U.; Michalek, I.M.; Didkowska, J. Progress in cancer survival across last two decades: A
nationwide study of over 1.2 million Polish patients diagnosed with the most common cancers. Cancer Epidemiol. 2022,78, 102147.
[CrossRef]
5.
Matulonis, U.A.; Shapira-Frommer, R.; Santin, A.D.; Lisyanskaya, A.S.; Pignata, S.; Vergote, I.; Raspagliesi, F.; Sonke, G.S.; Birrer,
M.; Provencher, D.M.; et al. Antitumor activity and safety of pembrolizumab in patients with advanced recurrent ovarian cancer:
Results from the phase II KEYNOTE-100 study. Ann. Oncol. 2019,30, 1080–1087. [CrossRef] [PubMed]
6.
Chu, P.Y.; Tai, Y.L.; Shen, T.L. Grb7, a Critical Mediator of EGFR/ErbB Signaling, in Cancer Development and as a Potential
Therapeutic Target. Cells 2019,8, 435. [CrossRef]
7.
Yu, C.; Luo, D.; Yu, J.; Zhang, M.; Zheng, X.; Xu, G.; Wang, J.; Wang, H.; Xu, Y.; Jiang, K.; et al. Genome-wide CRISPR-cas9
knockout screening identifies GRB7 as a driver for MEK inhibitor resistance in KRAS mutant colon cancer. Oncogene 2022,41,
191–203. [CrossRef]
8.
Giricz, O.; Calvo, V.; Pero, S.C.; Krag, D.N.; Sparano, J.A.; Kenny, P.A. GRB7 is required for triple-negative breast cancer cell
invasion and survival. Breast Cancer Res. Treat. 2012,133, 607–615. [CrossRef]
9.
Dufva, O.; Koski, J.; Maliniemi, P.; Ianevski, A.; Klievink, J.; Leitner, J.; Pölönen, P.; Hohtari, H.; Saeed, K.; Hannunen, T.; et al. Integrated
drug profiling and CRISPR screening identify essential pathways for CAR T-cell cytotoxicity. Blood 2020,135, 597–609. [CrossRef]
10.
Lawson, K.A.; Sousa, C.M.; Zhang, X.; Kim, E.; Akthar, R.; Caumanns, J.J.; Yao, Y.; Mikolajewicz, N.; Ross, C.; Brown, K.R.; et al.
Functional genomic landscape of cancer-intrinsic evasion of killing by T cells. Nature 2020,586, 120–126. [CrossRef]
11.
Kearney, C.J.; Vervoort, S.J.; Hogg, S.J.; Ramsbottom, K.M.; Freeman, A.J.; Lalaoui, N.; Pijpers, L.; Michie, J.; Brown, K.K.; Knight,
D.A.; et al. Tumor immune evasion arises through loss of TNF sensitivity. Sci. Immunol. 2018,3, eaar3451. [CrossRef] [PubMed]
12.
Vredevoogd, D.W.; Kuilman, T.; Ligtenberg, M.A.; Boshuizen, J.; Stecker, K.E.; de Bruijn, B.; Krijgsman, O.; Huang, X.; Kenski,
J.C.N.; Lacroix, R.; et al. Augmenting Immunotherapy Impact by Lowering Tumor TNF Cytotoxicity Threshold. Cell 2019,178,
585–599. [CrossRef] [PubMed]
13.
Young, T.M.; Reyes, C.; Pasnikowski, E.; Castanaro, C.; Wong, C.; Decker, C.E.; Chiu, J.; Song, H.; Wei, Y.; Bai, Y.; et al. Autophagy
protects tumors from T cell-mediated cytotoxicity via inhibition of TNF
α
-induced apoptosis. Sci. Immunol. 2020,5, eabb9561.
[CrossRef] [PubMed]
14.
Chen, K.; Liu, M.X.; Mak, C.S.; Yung, M.M.; Leung, T.H.; Xu, D.; Ngu, S.F.; Chan, K.K.; Yang, H.; Ngan, H.Y.; et al. Methylation-
associated silencing of miR-193a-3p promotes ovarian cancer aggressiveness by targeting GRB7 and MAPK/ERK pathways.
Theranostics 2018,8, 423–436. [CrossRef] [PubMed]
15.
Kasus-Jacobi, A.; Béréziat, V.; Perdereau, D.; Girard, J.; Burnol, A.F. Evidence for an interaction between the insulin receptor and
Grb7. A role for two of its binding domains, PIR and SH2. Oncogene 2000,19, 2052–2059. [CrossRef] [PubMed]
16.
Han, D.C.; Shen, T.L.; Guan, J.L. The Grb7 family proteins: Structure, interactions with other signaling molecules and potential
cellular functions. Oncogene 2001,20, 6315–6321. [CrossRef] [PubMed]
17.
Zheng, Y.; Pei, Y.; Yang, L.; Zeng, Z.; Wang, J.; Xie, G.; Wang, L.; Yuan, J. Upregulated GRB7 promotes proliferation and
tumorigenesis of Bladder Cancer via Phospho-AKT Pathway. Int. J. Biol. Sci. 2020,16, 3221–3230. [CrossRef] [PubMed]
18.
Tang, H.; Yang, P.; Yang, X.; Peng, S.; Hu, X.; Bao, G. Growth factor receptor bound protein-7 regulates proliferation, cell cycle, and
mitochondrial apoptosis of thyroid cancer cells via MAPK/ERK signaling. Mol. Cell Biochem. 2020,472, 209–218. [CrossRef] [PubMed]
19.
Pero, S.C.; Shukla, G.S.; Cookson, M.M.; Flemer, S., Jr.; Krag, D.N. Combination treatment with Grb7 peptide and Doxorubicin or
Trastuzumab (Herceptin) results in cooperative cell growth inhibition in breast cancer cells. Br. J. Cancer 2007,96, 1520–1525.
[CrossRef] [PubMed]
20.
Zeng, M.; Yang, Z.; Hu, X.; Liu, Y.; Yang, X.; Ran, H.; Li, Y.; Li, X.; Yu, Q. Grb7 gene amplification and protein expression by FISH
and IHC in ovarian cancer. Int. J. Clin. Exp. Pathol. 2015,8, 11296–11304.
21.
Xu, Q.; Liu, Z.; Zhu, Z.Q.; Fan, Y.; Chen, R.; Xie, X.H.; Cheng, M. Knockdown of growth factor receptor bound protein 7 suppresses
angiogenesis by inhibiting the secretion of vascular endothelial growth factor A in ovarian cancer cells. Bioengineered 2021,12,
12179–12190. [CrossRef] [PubMed]
22.
Stein, D.; Wu, J.; Fuqua, S.A.; Roonprapunt, C.; Yajnik, V.; D’Eustachio, P.; Moskow, J.J.; Buchberg, A.M.; Osborne, C.K.; Margolis,
B. The SH2 domain protein GRB-7 is co-amplified, overexpressed and in a tight complex with HER2 in breast cancer. Embo. J.
1994,13, 1331–1340. [CrossRef] [PubMed]
23.
Ramsey, B.; Bai, T.; Hanlon Newell, A.; Troxell, M.; Park, B.; Olson, S.; Keenan, E.; Luoh, S.W. GRB7 protein over-expression and
clinical outcome in breast cancer. Breast Cancer Res. Treat. 2011,127, 659–669. [CrossRef] [PubMed]
24.
Ling, Y.; Liang, G.; Lin, Q.; Fang, X.; Luo, Q.; Cen, Y.; Mehrpour, M.; Hamai, A.; Liu, Z.; Shi, Y.; et al. circCDYL2 promotes
trastuzumab resistance via sustaining HER2 downstream signaling in breast cancer. Mol. Cancer 2022,21, 8. [CrossRef] [PubMed]
25.
Schubert, L.; Elliott, A.; Le, A.T.; Estrada-Bernal, A.; Doebele, R.C.; Lou, E.; Borghaei, H.; Demeure, M.J.; Kurzrock, R.; Reuss, J.E.;
et al. ERBB family fusions are recurrent and actionable oncogenic targets across cancer types. Front. Oncol. 2023,13, 1115405.
[CrossRef] [PubMed]
26.
Lesurf, R.; Griffith, O.L.; Griffith, M.; Hundal, J.; Trani, L.; Watson, M.A.; Aft, R.; Ellis, M.J.; Ota, D.; Suman, V.J.; et al. Genomic
characterization of HER2-positive breast cancer and response to neoadjuvant trastuzumab and chemotherapy-results from the
ACOSOG Z1041 (Alliance) trial. Ann. Oncol. 2017,28, 1070–1077. [CrossRef] [PubMed]
Pharmaceuticals 2024,17, 1043 17 of 17
27.
Chu, P.Y.; Li, T.K.; Ding, S.T.; Lai, I.R.; Shen, T.L. EGF-induced Grb7 recruits and promotes Ras activity essential for the
tumorigenicity of Sk-Br3 breast cancer cells. J. Biol. Chem. 2010,285, 29279–29285. [CrossRef] [PubMed]
28.
Zhao, H.B.; Zhang, X.F.; Jia, X.L.; Wang, H.B. Grb7 is over-expressed in cervical cancer and facilitate invasion and inhibit apoptosis
in cervical cancer cells. Pathol. Res. Pract. 2017,213, 1180–1184. [CrossRef] [PubMed]
29.
Roberts-Thomson, S.J.; Chalmers, S.B.; Monteith, G.R. The Calcium-Signaling Toolkit in Cancer: Remodeling and Targeting. Cold
Spring Harb. Perspect. Biol. 2019,11, a035204. [CrossRef]
30.
Heldin, C.H.; Lu, B.; Evans, R.; Gutkind, J.S. Signals and Receptors. Cold Spring Harb. Perspect. Biol. 2016,8, a005900. [CrossRef]
31.
Cancer Genome Atlas Research Network. Integrated genomic analyses of ovarian carcinoma. Nature 2011,474, 609–615. [CrossRef]
32. Sharma, P.; Allison, J.P. Immune checkpoint targeting in cancer therapy: Toward combination strategies with curative potential.
Cell 2015,161, 205–214. [CrossRef]
33.
Goode, E.L.; Block, M.S.; Kalli, K.R.; Vierkant, R.A.; Chen, W.; Fogarty, Z.C.; Gentry-Maharaj, A.; Tołoczko, A.; Hein, A.; Bouligny,
A.L.; et al. Dose-Response Association of CD8+ Tumor-Infiltrating Lymphocytes and Survival Time in High-Grade Serous
Ovarian Cancer. JAMA Oncol. 2017,3, e173290. [CrossRef]
34.
Rajtak, A.; Ostrowska-Le´sko, M.; ˙
Zak, K.; Tarkowski, R.; Kotarski, J.; Okła, K. Integration of local and systemic immunity in
ovarian cancer: Implications for immunotherapy. Front. Immunol. 2022,13, 1018256. [CrossRef]
35.
Kandalaft, L.E.; Dangaj Laniti, D.; Coukos, G. Immunobiology of high-grade serous ovarian cancer: Lessons for clinical translation.
Nat. Rev. Cancer 2022,22, 640–656. [CrossRef]
36.
López-Cade, I.; García-Barberán, V.; Cabañas Morafraile, E.; Díaz-Tejeiro, C.; Saiz-Ladera, C.; Sanvicente, A.; Pérez Segura, P.;
Pandiella, A.; Gy˝orffy, B.; Ocaña, A. Genomic mapping of copy number variations influencing immune response in breast cancer.
Front. Oncol. 2022,12, 975437. [CrossRef]
37.
Feng, Q.; Wei, H.; Morihara, J.; Stern, J.; Yu, M.; Kiviat, N.; Hellstrom, I.; Hellstrom, K.E. Th2 type inflammation promotes the
gradual progression of HPV-infected cervical cells to cervical carcinoma. Gynecol. Oncol. 2012,127, 412–419. [CrossRef]
38.
Chen, Y.; Sun, J.; Luo, Y.; Liu, J.; Wang, X.; Feng, R.; Huang, J.; Du, H.; Li, Q.; Tan, J.; et al. Pharmaceutical targeting Th2-mediated
immunity enhances immunotherapy response in breast cancer. J. Transl. Med. 2022,20, 615. [CrossRef]
39.
Liu, J.; Geng, X.; Hou, J.; Wu, G. New insights into M1/M2 macrophages: Key modulators in cancer progression. Cancer Cell Int.
2021,21, 389. [CrossRef]
40.
Hou, J.; He, Z.; Liu, T.; Chen, D.; Wang, B.; Wen, Q.; Zheng, X. Evolution of Molecular Targeted Cancer Therapy: Mechanisms of
Drug Resistance and Novel Opportunities Identified by CRISPR-Cas9 Screening. Front. Oncol. 2022,12, 755053. [CrossRef]
41.
Lei, Y.; Tang, R.; Xu, J.; Wang, W.; Zhang, B.; Liu, J.; Yu, X.; Shi, S. Applications of single-cell sequencing in cancer research:
Progress and perspectives. J. Hematol. Oncol. 2021,14, 91. [CrossRef]
42.
Mustachio, L.M.; Roszik, J. Single-Cell Sequencing: Current Applications in Precision Onco-Genomics and Cancer Therapeutics.
Cancers 2022,14, 657. [CrossRef] [PubMed]
43. Blum, A.; Wang, P.; Zenklusen, J.C. SnapShot: TCGA-Analyzed Tumors. Cell 2018,173, 530. [CrossRef] [PubMed]
44. Consortium, G. The Genotype-Tissue Expression (GTEx) project. Nat. Genet. 2013,45, 580–585. [CrossRef]
45.
Wang, D.; Qian, X.; Du, Y.-C.N.; Sanchez-Solana, B.; Chen, K.; Park, B.; Chen, B.; Jenkins, L.; Luo, J.; Tripathi, B.K.; et al. Abstract
3912: cProSite: A web based interactive platform for on-line proteomics and phosphoproteomics data analysis. Cancer Res. 2022,
82, 3912. [CrossRef]
46.
Uhlén, M.; Fagerberg, L.; Hallström, B.M.; Lindskog, C.; Oksvold, P.; Mardinoglu, A.; Sivertsson, Å.; Kampf, C.; Sjöstedt, E.;
Asplund, A.; et al. Proteomics. Tissue-based map of the human proteome. Science 2015,347, 1260419. [CrossRef] [PubMed]
47.
Newman, A.M.; Steen, C.B.; Liu, C.L.; Gentles, A.J.; Chaudhuri, A.A.; Scherer, F.; Khodadoust, M.S.; Esfahani, M.S.; Luca, B.A.;
Steiner, D.; et al. Determining cell type abundance and expression from bulk tissues with digital cytometry. Nat. Biotechnol. 2019,
37, 773–782. [CrossRef] [PubMed]
48.
Zeng, Z.; Wong, C.J.; Yang, L.; Ouardaoui, N.; Li, D.; Zhang, W.; Gu, S.; Zhang, Y.; Liu, Y.; Wang, X.; et al. TISMO: Syngeneic
mouse tumor database to model tumor immunity and immunotherapy response. Nucleic Acids Res. 2022,50, D1391–D1397.
[CrossRef] [PubMed]
49.
Szklarczyk, D.; Kirsch, R.; Koutrouli, M.; Nastou, K.; Mehryary, F.; Hachilif, R.; Gable, A.L.; Fang, T.; Doncheva, N.T.; Pyysalo, S.;
et al. The STRING database in 2023: Protein-protein association networks and functional enrichment analyses for any sequenced
genome of interest. Nucleic Acids Res. 2023,51, D638–D646. [CrossRef]
50.
Mariathasan, S.; Turley, S.J.; Nickles, D.; Castiglioni, A.; Yuen, K.; Wang, Y.; Kadel, E.E., III.; Koeppen, H.; Astarita, J.L.; Cubas, R.; et al.
TGFβattenuates tumour response to PD-L1 blockade by contributing to exclusion of T cells. Nature 2018,554, 544–548. [CrossRef]
51.
Ran, F.A.; Hsu, P.D.; Wright, J.; Agarwala, V.; Scott, D.A.; Zhang, F. Genome engineering using the CRISPR-Cas9 system. Nat.
Protoc. 2013,8, 2281–2308. [CrossRef] [PubMed]
52.
Joung, J.; Kirchgatterer, P.C.; Singh, A.; Cho, J.H.; Nety, S.P.; Larson, R.C.; Macrae, R.K.; Deasy, R.; Tseng, Y.Y.; Maus, M.V.; et al.
CRISPR activation screen identifies BCL-2 proteins and B3GNT2 as drivers of cancer resistance to T cell-mediated cytotoxicity.
Nat. Commun. 2022,13, 1606. [CrossRef] [PubMed]
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual
author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to
people or property resulting from any ideas, methods, instructions or products referred to in the content.
Available via license: CC BY 4.0
Content may be subject to copyright.
