Next Article in Journal
DCVax-L Vaccination in Patients with Glioblastoma: Real Promise or Negative Trial? The Debate Is Open
Previous Article in Journal
Targeted Therapies and Developing Precision Medicine in Gastric Cancer
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Cancers
Volume 15
Issue 12
10.3390/cancers15123250
cancers-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles thumb_up
...
Endorse textsms
...
Comment
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Genomic Mapping of Epidermal Growth Factor Receptor and Mesenchymal–Epithelial Transition-Up-Regulated Tumors Identifies Novel Therapeutic Opportunities

by
Lucía Paniagua-Herranz
1,
Bernard Doger
2,
Cristina Díaz-Tejeiro
1,
Adrián Sanvicente
1,3,
Cristina Nieto-Jiménez
1,
Víctor Moreno
4,
Pedro Pérez Segura
1,
Balazs Gyorffy
5,6,7,
Emiliano Calvo
2 and
Alberto Ocana
1,4,*
1
Experimental Therapeutics in Cancer Unit, Medical Oncology Department, Hospital Clínico San Carlos (HCSC), Instituto de Investigación Sanitaria (IdISSC) and CIBERONC, 28040 Madrid, Spain
2
START Madrid-HM Centro Integral Oncológico Clara Campal (CIOCC), Early Phase Program, HM Sanchinarro University Hospital, 28050 Madrid, Spain
3
Facultad de Ciencias Químicas, Universidad Complutense de Madrid, 28040 Madrid, Spain
4
START Madrid-Fundación Jiménez Díaz (FJD), Early Phase Program, Fundación Jiménez Díaz Hospital, 28040 Madrid, Spain
5
Department of Bioinformatics, Semmelweis University, 1094 Budapest, Hungary
6
Department of Pediatrics, Semmelweis University, 1094 Budapest, Hungary
7
TTK Cancer Biomarker Research Group, Institute of Enzymology, 1117 Budapest, Hungary
*
Author to whom correspondence should be addressed.
Cancers 2023, 15(12), 3250; https://doi.org/10.3390/cancers15123250
Submission received: 24 May 2023 / Revised: 13 June 2023 / Accepted: 16 June 2023 / Published: 20 June 2023
Browse Figures
Review Reports Versions Notes

Abstract

:

Simple Summary

The identification of proteins in the cellular membrane of the tumoral cell is a key to the design and guidance of therapeutic agents. Recently, a bi-specific antibody termed amivantamab, targeting epidermal growth factor receptor (EGFR) and mesenchymal–epithelial transition factor (MET), which are oncogenic membrane proteins, received regulatory approval for the treatment of adult patients with locally advanced or metastatic non-small cell lung cancer (NSCLC). In this study, the authors explored tumor types with high levels of expression of EGFR and MET and focused on prostate adenocarcinoma and pancreatic adenocarcinoma, where anti-PD(L)1 agents alone have not shown relevant signs of activity. In addition, the authors confirmed that high expression of either receptor is associated with poor response to anti-PD(L)1 therapy, independently of the expression of PD-L1, suggesting that blocking these receptors with bi-specific EGFR/MET antibodies could augment the efficacy of anti-PD(L)1 inhibitors.

Abstract

Background: The identification of proteins in the cellular membrane of the tumoral cell is a key to the design of therapeutic agents. Recently, the bi-specific antibody amivantamab, targeting the oncogenic membrane proteins EGFR and MET, received regulatory approval for the treatment of adult patients with locally advanced or metastatic NSCLC. Methods: The authors interrogated several publicly available genomic datasets to evaluate the expression of both receptors and PD-L1 in most of the solid and hematologic malignancies and focused on prostate adenocarcinoma (PRAD) and pancreatic adenocarcinoma (PAAD). Results: In PAAD, EGFR highly correlated with PD-L1 and MET, and MET showed a moderate correlation with PD-L1, while in PRAD, EGFR, MET and PD-L1 showed a strong correlation. In addition, in tumors treated with immune checkpoint inhibitors, including anti-PD(L)1 and anti-CTLA4, a high expression of EGFR and MET predicted detrimental survival. When exploring the relationship of immune populations with these receptors, the authors observed that in PAAD and PRAD, EGFR moderately correlated with CD8+ T cells. Furthermore, EGFR and MET correlated with neutrophils in PRAD. Conclusions: The authors identified tumor types where EGFR and MET were highly expressed and correlated with a high expression of PD-L1, opening the door for the future combination of bi-specific EGFR/MET antibodies with anti-PD(L)1 inhibitors.

1. Introduction

The identification of genomic alterations is a main task in cancer research [1]. Proteins expressed on the cell surface that are associated with kinase activity can be targeted either by kinase inhibitors or antibodies [1,2]. The action of membrane proteins, like HER2 and epidermal growth factor receptor (EGFR), with antibodies has demonstrated activity by inhibiting the oncogenic role of those proteins [3,4,5,6]. Examples include the anti-HER2 antibody trastuzumab or the anti-EGFR antibody cetuximab. Both have shown clinical activity in several solid tumors, like HER2-positive breast, colorectal cancer or head and neck cancer, among others [4,6,7]. In addition to the effect produced by the down-regulation of the receptor, these antibodies can stimulate the immune system through an induction of antibody-dependent cellular cytotoxicity (ADCC) mediated by the Fc fraction of the antibody [8]. On the other hand, kinase inhibitors against the kinase portion of the membrane receptor include, among others, osimertinib against EGFR or tucatinib against HER2 [9,10]. The action on membrane receptors with antibodies displays several potential benefits, including higher target specificity, therefore, avoiding undesired toxicities observed with chemical entities due to off-target effects [11,12].
More recently, novel antibody formats have been developed with the aim to improve clinical efficiency. Bi-specific antibodies were designed to bind two proteins through the Fab fraction, therefore, acting on both targets at the same time [11,13]. This approach has several potential benefits, including the fact that the antibody would be effective primarily in tumors when both proteins are present.
In solid tumors, only one antibody, amivantamab, that targets EGFR and MET, has received regulatory approval for the treatment of adult patients with locally advanced or metastatic non-small cell lung cancer (NSCLC) with EGFR exon 20 insertion mutations [14,15]. Other bi-specific agents against the same targets, like MCLA 129, are in clinical development at this moment [16]. Similarly, other bi-specific antibodies targeting different tumor-associated antigens (TAAs) in solid tumors are also in preclinical evaluation [8,11].
A further step to boost the efficacy of this family of agents is to exploit combinations of these compounds with antibodies that act on the immune response [8]. Immune checkpoint inhibitors (ICIs), like anti-PD(L)1 or anti-CTLA4 antibodies, could enhance the efficacy of bi-specific EGFR/MET antibodies by boosting T-cell activity in addition to ADCC and complement dependent cytotoxicity (CDC) [17,18].
Here, the authors interrogated genomic data to identify indications where both EGFR and MET receptors were up-regulated and, therefore, could be susceptible to be targetable with antibodies. In addition, the authors explored indications where these antibodies could be combined with immunotherapies, through the evaluation of the presence of immunomodulatory molecules or PD-L1 positivity within the selected tumors.

2. Materials and Methods

2.1. Gene Expression and Genomic Alteration Analysis

RNA-sequencing expression data to evaluate the transcriptomic levels of EGFR and MET in tumor and normal tissue were obtained from the GTEx (Genotype-Tissue Expression) and TCGA (The Cancer Genome Atlas) databases [19], using the bio informatics’ tool Gene Expression Profiling Interactive Analysis (http://gepia2.cancer-pku.cn/#index; last accessed on 6 February 2023) [20]. TCGA data contained at cBioportal [21,22] (https://www.cbioportal.org/; last accessed on 20 February 2023) [23] were considered to evaluate the amplification of EGFR and MET genes in patients with different tumors. For some indications, authors used additional sources including for: PRAD (MSK, [24]), CHOL (ICGC, [25]), PAAD (QCMG, [26]), SARC (MSK, [27]) and LUAD (MSK, [28]).

2.2. Correlation between Gene Expression and Immune Cell Infiltration

Tumor Immune Estimation Resource (TIMER 2.0) web server [29] (https://timer.cistrome.org/; last accessed on 8 March 2023) [30] was employed to investigate the co-expression pattern of EGFR, MET and PD-L1 across TGCA cancer types through the Gen_Corr Module. We assessed the association of EGFR and MET with immune checkpoints and tumor-infiltrating immune cells, such as CD8+ and CD4+ T cells, B cells and neutrophils, among others, across different cancer types. TIMER2.0 uses six state-of-the-art algorithms to acquire a better estimation of immune infiltration and includes 10,897 samples over 32 cancer types [31,32]. The purity and the infiltration level of immune cells were represented with a log2 TPM scale and illustrated through the “Immune Association” module of TIMER2.0. The Rho and p-values were obtained from Spearman’s correlation test.

2.3. Outcome and Prognosis Analysis

The Kaplan–Meier Plotter Online Tool [33] (https://kmplot.com/analysis/; last accessed on 22 March 2023) [34] was utilized to determine the prognostic value in solid tumors, in relation to the expression of a particular gene. The authors investigated the relationship between clinical outcome and EGFR and MET expression levels in cancer patients treated with anti-PD1 or anti-PD-L1. Overall survival (OS) was utilized as endpoint for outcome analysis. OS time was defined as the time from diagnosis to patient death or last follow-up. The KM plots are represented with the longrank p-value (p), the hazard ratio (HR) and the false-discovery rate (FDR). Genes with an HR < 1 and p < 0.05 predict a favorable clinical outcome.

3. Results

3.1. Expression of EGFR and MET in Cancer

The authors first evaluated the expression of EGFR and MET across a wide number of solid tumors using publicly available human genomic datasets, as described in the Materials and Methods section. EGFR was highly expressed with ≥32 transcripts per million (TPM) in glioblastoma (GBM), low-grade glioma (LGG), kidney renal clear cell carcinoma (KIRC), esophageal carcinoma (ESCA) and head and neck squamous cell carcinoma (HNSC) (Figure 1A). MET was highly expressed (≥32 TPM) in thyroid carcinoma (THCA), kidney renal papillary cell carcinoma (KIRP), KIRC and kidney chromophobe (KICH) (Figure 1B).
Indications with the highest fold change between tumor and non-transformed tissue for EGFR included thymoma (THYM) (>600-fold change), GBM (>20-fold change) and LGG (≥5-fold change) (Figure 1C,E), followed by KIRC, stomach adenocarcinoma (STAD), lung squamous cell carcinoma (LUSC), ESCA, pancreatic adenocarcinoma (PAAD), HNSC and sarcoma (SARC) (all ≥1.5-fold change) (Figure 1C). MET expression in THYM and diffuse large B-cell lymphoma (DLBC) comprised > 100-fold change (Figure 1E). ESCA, PAAD, rectum adenocarcinoma (READ), THCA, STAD, ovarian serous cystadenocarcinoma (OV), colon adenocarcinoma (COAD), testicular germ cell tumors (TGCT) and KIRP displayed a ≥5-fold change. Tumors with a ≥1.5-fold change are described in Figure 1D. Amplification of EGFR in more than 10% was observed in GBM and ESCA, and for MET, in more than 5% in OV (Figure 2A,B).

3.2. Co-Occurrent Expression of EGFR and MET with PD-L1 in Solid Tumors

The authors next explored the correlation between the expression of EGFR and MET individually with the expression of PD-L1 to identify indications where combinations of both therapeutic strategies could be of interest. As can be seen in Figure 3A, a positive correlation was observed for EGFR and PD-L1 in THYM, PRAD, diffuse large B-cell lymphoma (DLBC) and PAAD (Rho > 0.6), followed by THCA (Rho > 0.5) and breast-invasive carcinoma, luminal A (BRCA LumA), cholangiocarcinoma (CHOL), liver hepatocellular carcinoma (LIHC), KIRC and BRCA-basal (Rho > 0.4). For MET and PD-L1, a strong correlation was observed for uveal melanoma (UVM) (Rho > 0.6), followed by THCA, PRAD and adrenocortical carcinoma (ACC) (Rho > 0.5), as well as BLCA, THYM and lung adenocarcinoma (LUAD) (Rho > 0.4) (Figure 3B). Indications where anti-PD1 or anti-PD-L1 are approved by the US Food and Drug Administration (FDA) are marked with an arrow. Tumor types where anti-PD-(L)1 is approved was very limited and showed a modest correlation (Rho > 0.4 and ≤0.5), including for EGFR: CHOL, LIHC, KIRC and BRCA-basal, and for MET: bladder cancer (BLCA) and LUAD (Figure 3A,B). Among tumors where ICIs have not been approved, a strong correlation for both receptors was observed in PRAD and THCA, as well as a moderate correlation in PAAD and THYM.

3.3. Association of EGFR and MET Expression with Clinical Outcome in PD(L)1-Treated Population

The authors aimed to explore if the expression of EGFR or MET could predict outcome in patients treated with ICIs. To do so, the authors used an extensive dataset that is described in detail in Supplementary Table S1 and in Section 2. As shown in Figure 4A, a high expression of EGFR and MET predicted detrimental survival in the whole population of patients treated with ICIs, including both agents anti-PD(L)1 and anti-CTLA4 (EGFR: HR = 1.76 CI = 1.48–2.09, p = 7.4 × 10−11; FDR = 1% and MET: HR = 1.42 CI = 1.2–1.69, p = 4.5 × 10−5; FDR = 2%). When selecting patients treated only with anti-PD1, the authors also observed a detrimental outcome: HR = 1.76 CI = 1.33–2.32, p = 5.9 × 10−5; FDR = 1% and HR = 1.36 CI = 1.02–1.82, p = 0.035; FDR 50% for EGFR and MET, respectively (Figure 4B). A similar unfavorable prognosis was identified in patients treated with anti-PD-L1 alone, if they harbored a high expression of EGFR and MET: HR = 1.25 CI = 0.99–1.58, p = 0.062; FDR = 100% and HR = 1.42 CI = 1.08–1.88, p = 0.011; FDR = over 50%, respectively (Figure 4C). Unfortunately, an analysis in some specific indications could not be performed due to the limited number of patients in those cohorts (Supplementary Table S1). However, globally, these data demonstrate that a high expression of either receptor is associated with a poor response to anti-PD(L)1 therapy, independently of the expression of PD-L1, suggesting that blocking these receptors could augment the efficacy of ICIs.

3.4. Co-Occurrent Expression of EGFR and MET in Solid Tumors

Next, the authors explored the correlation between EGFR and MET in all of the evaluated tumors. The authors’ aim was to identify indications where both receptors were highly present to facilitate the delivery of the antibody to the tumor. EGFR and MET strongly correlated in THYM (Rho = 0.759, p = 7.76 × 10−23), BRCA-LumA (Rho = 0.754, p = 2.62 × 10−96), PAAD (Rho = 0.699, p = 2.31 × 10−26), BRCA (Rho = 0.686, p = 2.25 × 10−139), THCA (Rho = 0.675, p = 3.33 × 10−66), HNSC-HPV+ (Rho = 0.672, p = 5.78 × 10−13), KIRP (Rho = 0.633, p = 3.02 × 10−30); and with less strength PRAD (Rho = 0.599, p = 8.48 × 10−42), HNSC (Rho = 0.588, p = 3.99 × 10−47), HNSC-HPV- (Rho = 0.566, p = 2.48 × 10−35), READ (Rho = 0.565, p = 4.12 × 10−13), BLCA (Rho = 0.548, p = 2.67 × 10−30), KICH (Rho = 0.531, p = 5.37 × 10−6), MESO (Rho = 0.527, p = 2.16 × 10−7), KIRC (Rho = 0.518, p = 5.17 × 10−33) and BRCA-basal (Rho = 0.513, p = 4.35 × 10−13) (Figure 5A).

3.5. Therapeutic Opportunities to Exploit Anti-EGFR/MET with Anti-PD-(L)1 Antibodies

Taking into consideration the data described before, the authors observed some potential opportunities for clinical development in indications where anti-PD(L)1 agents alone have not shown relevant signs of activity. In this context, we selected the tumor types that also showed a moderate or high correlation between EGFR, MET and PD-L1. Among them, PAAD displayed a high correlation of EGFR and PD-L1 (Rho = 0.619, p = 1.79 × 10−19) and a moderate correlation between MET and PD-L1 (Rho = 0.398, p = 6.87 × 10−8) (Figure 5B). In addition, in this tumor, the authors observed a high correlation between EGFR and MET (Rho = 0.698, p = 2.31 × 10−26). As in Figure 5A,B, PRAD showed an association between EGFR and PD-L1 (Rho = 0.665, p = 1.47 × 10−54), as well as between MET and PD-L1 (Rho = 0.542, p = 3.42 × 10−33) and EGFR and MET (Rho = 0.598, p = 8.48 × 10−42). We also noted that EGFR, MET and PD-L1 correlated among them in THCA and THYM, as shown in Figure 5A,B. Nevertheless, the authors selected PAAD and PRAD for further analysis. Therefore, the authors decided to exclude THCA, as no information about the different histological and genomic subtypes was available (papillar, follicular, anaplastic, etc.) for a specific and detailed characterization and THYM due to the fact that this tumor originates from a lymphoid gland involved in the development of the immune system.

3.6. Expression of Immune Populations and Co-Inhibitors in PAAD and PRAD

Next, the authors analyzed the association of EGFR and MET expression with immune infiltration levels in PAAD and PRAD. For PAAD, a positive but moderate correlation was seen between EGFR and CD8+ T cells (Rho = 0.403, p = 4.57 × 10−8), neutrophils (Rho = 0.408, p = 2.92 × 10−8) and myeloid dendritic cells (DCs) (Rho = 0.426, p = 6.50 × 10−9) (Figure 6A,B). No association with CD4+ T cells, B cells and macrophages was observed. On the other hand, the presence of MET did not correlate with any of the evaluated immune cells (Figure 6A,B). The authors also observed a positive correlation in PRAD between EGFR and CD8+ T cells (Rho = 0.457, p = 8.19 × 10−23) (Figure 6A). In addition, EGFR and MET correlated both with neutrophils (Rho = 0.538, p = 1.59 × 10−23 and Rho = 0.638, p = 5.94 × 10−49, respectively) and myeloid dendritic cells (Rho = 0.449, p = 4.48 × 10−22 and Rho = 0.534, p = 5.53 × 10−32, respectively) (Figure 6B). In this tumor, MET also had a positive correlation with CD4+ T cells (Rho = 0.438, p = 6.42 × 10−21) (Figure 6A). No correlation was found for EGFR and MET with CD4+ T cells and macrophages (Figure 6A,B).
Apart from the correlation observed between both receptors and PD-L1, the authors analyzed the association with co-inhibitor immune checkpoint inhibitors, detecting only a moderate correlation (Rho = 0.460, p = 3.21 × 10−23) between MET and HAVCR2 in PRAD (Figure 6C).

4. Discussion

In this article, the authors mapped the transcriptomic expression of EGFR and MET in solid tumors and their association with PD-L1 expression. The authors’ goal was to identify indications where combinations of bi-specific antibodies against EGFR/MET could be utilized in combination with anti-PD-L1 antibodies.
First, the authors identified indications where EGFR and MET were highly and differentially expressed compared to normal tissues, including for EGFR: THYM, GBM and LGG, and for MET: THYM and DLBC. On the other hand, gene amplification was not significantly observed, apart from GBM and ESCA for EGFR (more than 10%) and OV for MET (more than 5%).
When exploring the contribution to the anti-PD (L) 1 response of EGFR and MET receptors, the authors confirmed a clear detrimental effect. A high expression of EGFR and MET in a dataset of patients treated with ICIs was associated with poor survival. These data verified the authors’ previous observations describing the lack of activity of anti-PD (L) 1 antibodies in lung cancer patients with activation of EGFR due to kinase mutations [35,36,37,38]. Little information is available regarding the contribution of MET alterations, mainly MET exon 14 skipping mutations, in relation to the response to anti-PD(L)1 therapies. Data in this regard are restricted to a very small number of patients [39,40]. Of note, for other membrane tirosine kinase (TK) receptors, including ErbB2, activation of their pathway has been associated with a lack of response to ICIs [41,42]. In line with this, tumors with alterations in the ErbB family genes (and particularly EGFR) develop a non-inflamed tumor microenvironment, which could be due to a low tumor mutational burden (TMB) [43]. In our study, it is unknown if tumors with a high presence of EGFR and MET associate with low TMB.
A clear example that was not evaluated previously includes PAAD, where EGFR and MET correlated with each other and with PD-L1. This tumor also had a mild correlation between EGFR and CD8+ T cells and dendritic cells. Likewise, in PRAD, EGFR and MET correlated with the presence of PD-L1, and in addition to that, EGFR had a mild correlation with CD8+ T cells, neutrophils and DCs, while MET correlated strongly with neutrophils.
In these two indications, PAAD and PRAD, the activity of ICI was limited. For instance, in PRAD, anti-PD1 antibodies have shown a lack of efficacy in monotherapy or in combination with hormonotherapy, suggesting the necessity for the evaluation in combination with other immune-modulating agents [44,45]. In addition, in this indication, a relevant role for ErbB receptors and ligands has been reported in different studies, suggesting potential use as a therapeutic target [46]. In line with this, classical MET expression has been associated with androgen-resistant prostate cancer tumors [47]. In PAAD, therapeutic actions against EGFR either with kinase inhibitors or with antibodies have shown disappointing results [48], and it is known that this tumor harbors an immune-suppressive environment [49]. Very recently published preclinical data suggest augmented antitumoral activity using in vivo models of the dual targeting of EGFR and MET in this particular tumor type [50].
A combination of antibodies with anti-PD-(L)1 inhibitors can potentiate the activity of each agent alone through the activation of ADCC and CDC immunologic mechanisms [8]. Antibodies with FC modifications to augment ADCC and CDC activity have demonstrated clinical activity, like tafasitamab in DLBC [51]. In this context, it is expected that combinations of bi-specific antibodies against EGFR/MET would enhance the effect when combined with anti-PD-(L)1 agents. Finally, examples of bi-specific PD-L1 and EGFR antibodies are currently in preclinical development [52]. Similar examples have been reported for MET and PD1 bi-specific antibodies [53].
The authors’ study has limitations. First, this is a bioinformatic analysis using publicly available genomic datasets. Therefore, the authors may expect that in the near future, the findings described here could be confirmed with immunohistochemistry studies using human samples. Secondly, the combinations suggested here should also be evaluated preclinically. In addition, we acknowledge that genetic variations, including single-nucleotide polymorphisms (SNPs) or EGFRex20 insertions, which can lead to differences in ethnicities or therapeutic efficacies, could not be explored due to the lack of available datasets.

5. Conclusions

In summary, the authors have described tumor types, where the presence of EGFR/MET is associated with detrimental response to anti-PD(L)1 therapies and with an immune-suppressive environment. Therefore, the authors suggest that co-targeting EGFR/MET and PD(L)1 with antibodies could enhance treatment efficacy, particularly in those tumors with high correlation among receptors, like PAAD and PRAD.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/cancers15123250/s1, Table S1: Kaplan–Meier survival values of EGFR and MET in patients treated with anti-PD1 and anti-PD-L1.

Author Contributions

Conceptualization, A.O. and L.P.-H.; methodology, L.P.-H.; software, L.P.-H.; validation, C.D.-T., A.S. and C.N.-J.; formal analysis, B.D.; investigation, L.P.-H.; resources, A.O.; data curation, L.P.-H.; writing—original draft preparation, A.O. and L.P.-H.; writing—review and editing, C.D.-T., A.S. and C.N.-J.; visualization, V.M., P.P.S., B.G. and E.C.; supervision, A.O. project administration, A.O.; funding acquisition, A.O. All authors have read and agreed to the published version of the manuscript.

Funding

These results have been supported by Instituto de Salud Carlos III (PI19/00808), ACEPAIN (000/2023) and CRIS Cancer Foundation (PPS.C02CRIS) (to A.O.). The work carried out in our laboratories received support from the European Community through the regional development funding program (FEDER).

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

All data generated or analyzed during this study are included in this published article.

Conflicts of Interest

A.O. is currently an employee of Symphogen, Copengahen, Denmark. There are no conflicts of interest to declare in relation to this work. V.M. reports personal fees from Bristol-Myers Squibb, Bayer, Janssen, and Pieris outside the submitted work. E.C. reports grants and personal fees from Astellas, Novartis, Nanobiotix, Pfizer, Janssen-Cilag, PsiOxus Therapeutics, Merck, BristolMyers Squibb, Seattle Genetics, Boehringer Ingelheim, AstraZeneca, Roche/Genentech, Servier, Celgene, AbbVie, Amcure, Alkermes, PharmaMar, and BeiGene, outside the submitted work. P.P. reports grant/research support from Bristol-Myers Squibb, AstraZeneca, MSD, outside the submitted work. The other authors declare that they have no competing interests.

References

  1. Ocana, A.; Pandiella, A.; Siu, L.L.; Tannock, I.F. Preclinical development of molecular-targeted agents for cancer. Nat. Rev. Clin. Oncol. 2010, 8, 200–209. [Google Scholar] [CrossRef] [PubMed]
  2. Rudolph, J.; Settleman, J.; Malek, S. Emerging Trends in Cancer Drug Discovery-From Drugging the “Undruggable” to Overcoming Resistance. Cancer Discov. 2021, 11, 815–821. [Google Scholar] [CrossRef] [PubMed]
  3. Gandullo-Sanchez, L.; Ocana, A.; Pandiella, A. HER3 in cancer: From the bench to the bedside. J. Exp. Clin. Cancer Res. 2022, 41, 310. [Google Scholar] [CrossRef] [PubMed]
  4. Esparis-Ogando, A.; Montero, J.C.; Arribas, J.; Ocana, A.; Pandiella, A. Targeting the EGF/HER Ligand-Receptor System in Cancer. Curr. Pharm. Des. 2016, 22, 5887–5898. [Google Scholar] [CrossRef]
  5. Ocana, A.; Amir, E. Irreversible pan-ErbB tyrosine kinase inhibitors and breast cancer: Current status and future directions. Cancer Treat. Rev. 2009, 35, 685–691. [Google Scholar] [CrossRef] [PubMed]
  6. Ocana, A.; Pandiella, A. Targeting HER receptors in cancer. Curr. Pharm. Des. 2013, 19, 808–817. [Google Scholar] [CrossRef]
  7. Hynes, N.E. ErbB2: From an EGFR Relative to a Central Target for Cancer Therapy. Cancer Res. 2016, 76, 3659–3662. [Google Scholar] [CrossRef] [Green Version]
  8. Musolino, A.; Gradishar, W.J.; Rugo, H.S.; Nordstrom, J.L.; Rock, E.P.; Arnaldez, F.; Pegram, M.D. Role of Fcγ receptors in HER2-targeted breast cancer therapy. J. Immunother. Cancer 2022, 10, e003171. [Google Scholar] [CrossRef]
  9. Lin, N.U.; Murthy, R.K.; Abramson, V.; Anders, C.; Bachelot, T.; Bedard, P.L.; Borges, V.; Cameron, D.; Carey, L.A.; Chien, A.J.; et al. Tucatinib vs. Placebo, Both in Combination with Trastuzumab and Capecitabine, for Previously Treated ERBB2 (HER2) Positive Metastatic Breast Cancer in Patients with Brain Metastases: Updated Exploratory Analysis of the HER2CLIMB Randomized Clinical Trial. JAMA Oncol. 2023, 9, 197–205. [Google Scholar] [CrossRef]
  10. Ramalingam, S.S.; Vansteenkiste, J.; Planchard, D.; Cho, B.C.; Gray, J.E.; Ohe, Y.; Zhou, C.; Reungwetwattana, T.; Cheng, Y.; Chewaskulyong, B.; et al. Overall Survival with Osimertinib in Untreated, EGFR-Mutated Advanced NSCLC. N. Engl. J. Med. 2020, 382, 41–50. [Google Scholar] [CrossRef]
  11. Kaplon, H.; Crescioli, S.; Chenoweth, A.; Visweswaraiah, J.; Reichert, J.M. Antibodies to watch in 2023. MAbs 2023, 15, 2153410. [Google Scholar] [CrossRef]
  12. Ocana, A.; Garcia-Alonso, S.; Amir, E.; Pandiella, A. Refining Early Antitumoral Drug Development. Trends Pharmacol. Sci. 2018, 39, 922–925. [Google Scholar] [CrossRef]
  13. Runcie, K.; Budman, D.R.; John, V.; Seetharamu, N. Bi-specific and tri-specific antibodies—The next big thing in solid tumor therapeutics. Mol. Med. 2018, 24, 50. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  14. Park, K.; Haura, E.B.; Leighl, N.B.; Mitchell, P.; Shu, C.A.; Girard, N.; Viteri, S.; Han, J.Y.; Kim, S.W.; Lee, C.K.; et al. Amivantamab in EGFR Exon 20 Insertion-Mutated Non-Small-Cell Lung Cancer Progressing on Platinum Chemotherapy: Initial Results from the CHRYSALIS Phase I Study. J. Clin. Oncol. 2021, 39, 3391–3402. [Google Scholar] [CrossRef]
  15. Yun, J.; Lee, S.H.; Kim, S.Y.; Jeong, S.Y.; Kim, J.H.; Pyo, K.H.; Park, C.W.; Heo, S.G.; Yun, M.R.; Lim, S.; et al. Antitumor Activity of Amivantamab (JNJ-61186372), an EGFR-MET Bispecific Antibody, in Diverse Models of EGFR Exon 20 Insertion-Driven NSCLC. Cancer Discov. 2020, 10, 1194–1209. [Google Scholar] [CrossRef] [PubMed]
  16. Ou, S.H.; Moreno Garcia, V.; Gil Bazo, I.; Prenen, H.; Moreno, I.; Johnson, M.; Castañón Álvarez, E.; Nagasaka, M.; Adeyemi, S.; Barasa, B.; et al. MCLA-129, a human anti-EGFR and anti-c-MET bispecific antibody, in patients with advanced NSCLC and other solid tumors: An ongoing phase 1/2 study. Eur. J. Cancer 2022, 174, S122. [Google Scholar] [CrossRef]
  17. Andre, P.; Denis, C.; Soulas, C.; Bourbon-Caillet, C.; Lopez, J.; Arnoux, T.; Blery, M.; Bonnafous, C.; Gauthier, L.; Morel, A.; et al. Anti-NKG2A mAb Is a Checkpoint Inhibitor that Promotes Anti-Tumor Immunity by Unleashing Both T and NK Cells. Cell 2018, 175, 1731–1743.e1713. [Google Scholar] [CrossRef] [Green Version]
  18. La Corte, C.M.; Fasano, M.; Ciaramella, V.; Cimmino, F.; Cardnell, R.; Gay, C.M.; Ramkumar, K.; Diao, L.; Di Liello, R.; Viscardi, G.; et al. Anti-tumor activity of cetuximab plus avelumab in non-small cell lung cancer patients involves innate immunity activation: Findings from the CAVE-Lung trial. J. Exp. Clin. Cancer Res. 2022, 41, 109. [Google Scholar] [CrossRef]
  19. Tang, Z.; Kang, B.; Li, C.; Chen, T.; Zhang, Z. GEPIA2: An enhanced web server for large-scale expression profiling and interactive analysis. Nucleic Acids Res. 2019, 47, W556–W560. [Google Scholar] [CrossRef] [Green Version]
  20. Zhang, Z. Available online: http://gepia2.cancer-pku.cn/#index (accessed on 6 February 2023).
  21. Cerami, E.; Gao, J.; Dogrusoz, U.; Gross, B.E.; Sumer, S.O.; Aksoy, B.A.; Jacobsen, A.; Byrne, C.J.; Heuer, M.L.; Larsson, E.; et al. The cBio cancer genomics portal: An open platform for exploring multidimensional cancer genomics data. Cancer Discov. 2012, 2, 401–404. [Google Scholar] [CrossRef] [Green Version]
  22. Gao, J.; Aksoy, B.A.; Dogrusoz, U.; Dresdner, G.; Gross, B.; Sumer, S.O.; Sun, Y.; Jacobsen, A.; Sinha, R.; Larsson, E.; et al. Integrative analysis of complex cancer genomics and clinical profiles using the cBioPortal. Sci. Signal. 2013, 6, pl1. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  23. Gao, C. Available online: https://www.cbioportal.org (accessed on 20 February 2023).
  24. Nguyen, B.; Mota, J.M.; Nandakumar, S.; Stopsack, K.H.; Weg, E.; Rathkopf, D.; Morris, M.J.; Scher, H.I.; Kantoff, P.W.; Gopalan, A.; et al. Pan-cancer Analysis of CDK12 Alterations Identifies a Subset of Prostate Cancers with Distinct Genomic and Clinical Characteristics. Eur. Urol. 2020, 78, 671–679. [Google Scholar] [CrossRef] [PubMed]
  25. Jusakul, A.; Cutcutache, I.; Yong, C.H.; Lim, J.Q.; Huang, M.N.; Padmanabhan, N.; Nellore, V.; Kongpetch, S.; Ng, A.W.T.; Ng, L.M.; et al. Whole-Genome and Epigenomic Landscapes of Etiologically Distinct Subtypes of Cholangiocarcinoma. Cancer Discov. 2017, 7, 1116–1135. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  26. Bailey, P.; Chang, D.K.; Nones, K.; Johns, A.L.; Patch, A.M.; Gingras, M.C.; Miller, D.K.; Christ, A.N.; Bruxner, T.J.; Quinn, M.C.; et al. Genomic analyses identify molecular subtypes of pancreatic cancer. Nature 2016, 531, 47–52. [Google Scholar] [CrossRef]
  27. Nacev, B.A.; Sanchez-Vega, F.; Smith, S.A.; Antonescu, C.R.; Rosenbaum, E.; Shi, H.; Tang, C.; Socci, N.D.; Rana, S.; Gularte-Mérida, R.; et al. Clinical sequencing of soft tissue and bone sarcomas delineates diverse genomic landscapes and potential therapeutic targets. Nat. Commun. 2022, 13, 3405. [Google Scholar] [CrossRef]
  28. Caso, R.; Sanchez-Vega, F.; Tan, K.S.; Mastrogiacomo, B.; Zhou, J.; Jones, G.D.; Nguyen, B.; Schultz, N.; Connolly, J.G.; Brandt, W.S.; et al. The Underlying Tumor Genomics of Predominant Histologic Subtypes in Lung Adenocarcinoma. J. Thorac. Oncol. 2020, 15, 1844–1856. [Google Scholar] [CrossRef]
  29. Li, T.; Fu, J.; Zeng, Z.; Cohen, D.; Li, J.; Chen, Q.; Li, B.; Liu, X.S. TIMER2.0 for analysis of tumor-infiltrating immune cells. Nucleic Acids Res. 2020, 48, W509–W514. [Google Scholar] [CrossRef]
  30. Taiwen, L. Available online: http:/timer.cistrome.org/ (accessed on 8 March 2023).
  31. Li, T.; Fan, J.; Wang, B.; Traugh, N.; Chen, Q.; Liu, J.S.; Li, B.; Liu, X.S. TIMER: A Web Server for Comprehensive Analysis of Tumor-Infiltrating Immune Cells. Cancer Res. 2017, 77, e108–e110. [Google Scholar] [CrossRef] [Green Version]
  32. Li, B.; Severson, E.; Pignon, J.C.; Zhao, H.; Li, T.; Novak, J.; Jiang, P.; Shen, H.; Aster, J.C.; Rodig, S.; et al. Comprehensive analyses of tumor immunity: Implications for cancer immunotherapy. Genome Biol. 2016, 17, 174. [Google Scholar] [CrossRef] [Green Version]
  33. Kovacs, S.A.; Gyorffy, B. Transcriptomic datasets of cancer patients treated with immune-checkpoint inhibitors: A systematic review. J. Transl. Med. 2022, 20, 249. [Google Scholar] [CrossRef]
  34. Gyorffy, B. Available online: https://kmplot.com/analysis/ (accessed on 24 March 2023).
  35. To, K.K.W.; Fong, W.; Cho, W.C.S. Immunotherapy in Treating EGFR-Mutant Lung Cancer: Current Challenges and New Strategies. Front. Oncol. 2021, 11, 635007. [Google Scholar] [CrossRef] [PubMed]
  36. Borghaei, H.; Paz-Ares, L.; Horn, L.; Spigel, D.R.; Steins, M.; Ready, N.E.; Chow, L.Q.; Vokes, E.E.; Felip, E.; Holgado, E.; et al. Nivolumab versus Docetaxel in Advanced Nonsquamous Non-Small-Cell Lung Cancer. N. Engl. J. Med. 2015, 373, 1627–1639. [Google Scholar] [CrossRef] [PubMed]
  37. Herbst, R.S.; Baas, P.; Kim, D.W.; Felip, E.; Perez-Gracia, J.L.; Han, J.Y.; Molina, J.; Kim, J.H.; Arvis, C.D.; Ahn, M.J.; et al. Pembrolizumab versus docetaxel for previously treated, PD-L1-positive, advanced non-small-cell lung cancer (KEYNOTE-010): A randomised controlled trial. Lancet 2016, 387, 1540–1550. [Google Scholar] [CrossRef] [PubMed]
  38. Lee, C.K.; Man, J.; Lord, S.; Links, M.; Gebski, V.; Mok, T.; Yang, J.C. Checkpoint Inhibitors in Metastatic EGFR-Mutated Non Small Cell Lung Cancer—A Meta-Analysis. J. Thorac. Oncol. 2017, 12, 403–407. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  39. Dantoing, E.; Piton, N.; Salaun, M.; Thiberville, L.; Guisier, F. Anti-PD1/PD-L1 Immunotherapy for Non-Small Cell Lung Cancer with Actionable Oncogenic Driver Mutations. Int. J. Mol. Sci. 2021, 22, 6288. [Google Scholar] [CrossRef]
  40. Mazieres, J.; Drilon, A.; Lusque, A.; Mhanna, L.; Cortot, A.B.; Mezquita, L.; Thai, A.A.; Mascaux, C.; Couraud, S.; Veillon, R.; et al. Immune checkpoint inhibitors for patients with advanced lung cancer and oncogenic driver alterations: Results from the immunotarget registry. Ann. Oncol. 2019, 30, 1321–1328. [Google Scholar] [CrossRef]
  41. Esteva, F.J.; Hubbard-Lucey, V.M.; Tang, J.; Pusztai, L. Immunotherapy and targeted therapy combinations in metastatic breast cancer. Lancet Oncol. 2019, 20, e175–e186. [Google Scholar] [CrossRef]
  42. Kumagai, S.; Koyama, S.; Nishikawa, H. Antitumour immunity regulated by aberrant ERBB family signalling. Nat. Rev. Cancer 2021, 21, 181–197. [Google Scholar] [CrossRef]
  43. Dong, Z.Y.; Zhang, J.T.; Liu, S.Y.; Su, J.; Zhang, C.; Xie, Z.; Zhou, Q.; Tu, H.Y.; Xu, C.R.; Yan, L.X.; et al. EGFR mutation correlates with uninflamed phenotype and weak immunogenicity, causing impaired response to PD-1 blockade in non-small cell lung cancer. Oncoimmunology 2017, 6, e1356145. [Google Scholar] [CrossRef] [Green Version]
  44. Petrylak, D.P.; Loriot, Y.; Shaffer, D.R.; Braiteh, F.; Powderly, J.; Harshman, L.C.; Conkling, P.; Delord, J.P.; Gordon, M.; Kim, J.W.; et al. Safety and Clinical Activity of Atezolizumab in Patients with Metastatic Castration-Resistant Prostate Cancer: A Phase I Study. Clin. Cancer Res. 2021, 27, 3360–3369. [Google Scholar] [CrossRef]
  45. Powles, T.; Yuen, K.C.; Gillessen, S.; Kadel, E.E., III; Rathkopf, D.; Matsubara, N.; Drake, C.G.; Fizazi, K.; Piulats, J.M.; Wysocki, P.J.; et al. Atezolizumab with enzalutamide versus enzalutamide alone in metastatic castration-resistant prostate cancer: A randomized phase 3 trial. Nat. Med. 2022, 28, 144–153. [Google Scholar] [CrossRef] [PubMed]
  46. Wiesehöfer, M.; Raczinski, B.B.G.; Wiesehöfer, C.; Dankert, J.T.; Czyrnik, E.D.; Spahn, M.; Kruithof-de Julio, M.; Wennemuth, G. Epiregulin expression and secretion is increased in castration-resistant prostate cancer. Front. Oncol. 2023, 13, 1107021. [Google Scholar] [CrossRef]
  47. Verras, M.; Lee, J.; Xue, H.; Li, T.H.; Wang, Y.; Sun, Z. The androgen receptor negatively regulates the expression of c-Met: Implications for a novel mechanism of prostate cancer progression. Cancer Res. 2007, 67, 967–975. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  48. Qian, Y.; Gong, Y.; Fan, Z.; Luo, G.; Huang, Q.; Deng, S.; Cheng, H.; Jin, K.; Ni, Q.; Yu, X.; et al. Molecular alterations and targeted therapy in pancreatic ductal adenocarcinoma. J. Hematol. Oncol. 2020, 13, 130. [Google Scholar] [CrossRef] [PubMed]
  49. Li, X.; Gulati, M.; Larson, A.C.; Solheim, J.C.; Jain, M.; Kumar, S.; Batra, S.K. Immune checkpoint blockade in pancreatic cancer: Trudging through the immune desert. Semin. Cancer Biol. 2022, 86, 14–27. [Google Scholar] [CrossRef]
  50. Shi, X.; Wang, M.; Zhang, Y.; Guo, X.; Liu, M.; Zhou, Z.; Zhao, Y.; He, R.; Gao, Y.; Liu, Y.; et al. Hypoxia activated HGF expression in pancreatic stellate cells confers resistance of pancreatic cancer cells to EGFR inhibition. EBioMedicine 2022, 86, 104352. [Google Scholar] [CrossRef]
  51. Salles, G.; Duell, J.; Gonzalez Barca, E.; Tournilhac, O.; Jurczak, W.; Liberati, A.M.; Nagy, Z.; Obr, A.; Gaidano, G.; Andre, M.; et al. Tafasitamab plus lenalidomide in relapsed or refractory diffuse large B-cell lymphoma (L-MIND): A multicentre, prospective, single-arm, phase 2 study. Lancet Oncol. 2020, 21, 978–988. [Google Scholar] [CrossRef]
  52. Rubio-Perez, L.; Lazaro-Gorines, R.; Harwood, S.L.; Compte, M.; Navarro, R.; Tapia-Galisteo, A.; Bonet, J.; Blanco, B.; Lykkemark, S.; Ramirez-Fernandez, A.; et al. A PD-L1/EGFR bispecific antibody combines immune checkpoint blockade and direct anti-cancer action for an enhanced anti-tumor response. Oncoimmunology 2023, 12, 2205336. [Google Scholar] [CrossRef]
  53. Yuan, Q.; Liang, Q.; Sun, Z.; Yuan, X.; Hou, W.; Wang, Y.; Wang, H.; Yu, M. Development of bispecific anti-c-Met/PD-1 diabodies for the treatment of solid tumors and the effect of c-Met binding affinity on efficacy. Oncoimmunology 2021, 10, 1914954. [Google Scholar] [CrossRef]
Cancers 15 03250 g001 550
Figure 1. EGFR and MET expression profile across all tumor samples and paired normal tissues. (A) EGFR expression levels in different cancers validated using GEPIA2 database. EGFR was highly expressed (TPM > 32) in GBM, LGG, KIRC, ESCA and HNSC. (B) MET expression levels across different cancers validated using GEPIA2 database. MET was highly expressed (TPM > 32) in THCA, KIRP, KIRC and KIRCH. (C) Heat map depicting fold change between tumor and non-transformed tissue for EGFR and MET (D). (E) Heat maps displaying information from those indications with a fold change >20 between tumor and non-transformed tissue for EGFR and MET.
Figure 1. EGFR and MET expression profile across all tumor samples and paired normal tissues. (A) EGFR expression levels in different cancers validated using GEPIA2 database. EGFR was highly expressed (TPM > 32) in GBM, LGG, KIRC, ESCA and HNSC. (B) MET expression levels across different cancers validated using GEPIA2 database. MET was highly expressed (TPM > 32) in THCA, KIRP, KIRC and KIRCH. (C) Heat map depicting fold change between tumor and non-transformed tissue for EGFR and MET (D). (E) Heat maps displaying information from those indications with a fold change >20 between tumor and non-transformed tissue for EGFR and MET.
Cancers 15 03250 g001
Cancers 15 03250 g002 550
Figure 2. Amplification of EGFR (A) and MET (B) across tumor indications.
Figure 2. Amplification of EGFR (A) and MET (B) across tumor indications.
Cancers 15 03250 g002
Cancers 15 03250 g003 550
Figure 3. Co-occurrent expression of EGFR and MET with PD-L1 expression in tumor indications. Heat map displaying the Spearman correlation coefficient of EGFR (A) and MET (B) with PD-L1. Green arrows indicate tumors where anti PD1 or anti PD-L1 antibodies are approved by the US food and drug administration (FDA).
Figure 3. Co-occurrent expression of EGFR and MET with PD-L1 expression in tumor indications. Heat map displaying the Spearman correlation coefficient of EGFR (A) and MET (B) with PD-L1. Green arrows indicate tumors where anti PD1 or anti PD-L1 antibodies are approved by the US food and drug administration (FDA).
Cancers 15 03250 g003
Cancers 15 03250 g004 550
Figure 4. Association between EGFR or MET expression levels and overall survival (OS) in patients treated with immune check point inhibitors (ICIs). (A) Kaplan-Meier survival curves comparing low and high expression of EGFR (OS; n = 954) and MET (OS; n = 933) in the whole population of patients treated with ICIs, including both anti-PD(L)1 and anti-CTLA4. (B) Kaplan-Meier survival curves comparing low and high expression of EGFR (left panel) and MET (right panel) in PD1-treated population (OS; n = 423 and OS; n = 402, respectively) and (C) in PD-L1-treated population (OS; n = 459 and OS; n = 459, respectively).
Figure 4. Association between EGFR or MET expression levels and overall survival (OS) in patients treated with immune check point inhibitors (ICIs). (A) Kaplan-Meier survival curves comparing low and high expression of EGFR (OS; n = 954) and MET (OS; n = 933) in the whole population of patients treated with ICIs, including both anti-PD(L)1 and anti-CTLA4. (B) Kaplan-Meier survival curves comparing low and high expression of EGFR (left panel) and MET (right panel) in PD1-treated population (OS; n = 423 and OS; n = 402, respectively) and (C) in PD-L1-treated population (OS; n = 459 and OS; n = 459, respectively).
Cancers 15 03250 g004
Cancers 15 03250 g005 550
Figure 5. Correlation between EGFR and MET expression levels in TCGA tumors. (A) Heat map depicting the Spearman correlation coefficient between EGFR and MET expression levels using TIMER2.0. (B) Correlation between PD-L1, EGFR and MET expression levels estimated using TIMER2.0 in PAAD, PRAD, THCA and THYM.
Figure 5. Correlation between EGFR and MET expression levels in TCGA tumors. (A) Heat map depicting the Spearman correlation coefficient between EGFR and MET expression levels using TIMER2.0. (B) Correlation between PD-L1, EGFR and MET expression levels estimated using TIMER2.0 in PAAD, PRAD, THCA and THYM.
Cancers 15 03250 g005
Cancers 15 03250 g006 550
Figure 6. Association of EGFR and MET expression levels with immune infiltrates and co-inhibitor immune checkpoints in PAAD and PRAD. Heat map depicting the Spearman correlation coefficient between EGFR and MET expression and the presence of (A) CD8+ T cells, CD4+ T cells, B cells and (B) neutrophils, dendritic cells (DCs) and macrophages. Numbers inside the heat maps indicate the rho value. (C) Heat map depicting the Spearman correlation coefficient between EGFR and MET with inhibitory checkpoint molecules.
Figure 6. Association of EGFR and MET expression levels with immune infiltrates and co-inhibitor immune checkpoints in PAAD and PRAD. Heat map depicting the Spearman correlation coefficient between EGFR and MET expression and the presence of (A) CD8+ T cells, CD4+ T cells, B cells and (B) neutrophils, dendritic cells (DCs) and macrophages. Numbers inside the heat maps indicate the rho value. (C) Heat map depicting the Spearman correlation coefficient between EGFR and MET with inhibitory checkpoint molecules.
Cancers 15 03250 g006
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.

Share and Cite

MDPI and ACS Style

Paniagua-Herranz, L.; Doger, B.; Díaz-Tejeiro, C.; Sanvicente, A.; Nieto-Jiménez, C.; Moreno, V.; Pérez Segura, P.; Gyorffy, B.; Calvo, E.; Ocana, A. Genomic Mapping of Epidermal Growth Factor Receptor and Mesenchymal–Epithelial Transition-Up-Regulated Tumors Identifies Novel Therapeutic Opportunities. Cancers 2023, 15, 3250. https://doi.org/10.3390/cancers15123250

AMA Style

Paniagua-Herranz L, Doger B, Díaz-Tejeiro C, Sanvicente A, Nieto-Jiménez C, Moreno V, Pérez Segura P, Gyorffy B, Calvo E, Ocana A. Genomic Mapping of Epidermal Growth Factor Receptor and Mesenchymal–Epithelial Transition-Up-Regulated Tumors Identifies Novel Therapeutic Opportunities. Cancers. 2023; 15(12):3250. https://doi.org/10.3390/cancers15123250

Chicago/Turabian Style

Paniagua-Herranz, Lucía, Bernard Doger, Cristina Díaz-Tejeiro, Adrián Sanvicente, Cristina Nieto-Jiménez, Víctor Moreno, Pedro Pérez Segura, Balazs Gyorffy, Emiliano Calvo, and Alberto Ocana. 2023. "Genomic Mapping of Epidermal Growth Factor Receptor and Mesenchymal–Epithelial Transition-Up-Regulated Tumors Identifies Novel Therapeutic Opportunities" Cancers 15, no. 12: 3250. https://doi.org/10.3390/cancers15123250

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop