ATR expression as a prognostic biomarker in KRAS-mutated non-small cell lung cancer

Introduction

KRAS, a member of the Rat sarcoma (Ras) protein family, encodes a guanosine triphosphatase that binds to the cell membrane. When activated by mutations at codon 12, 13, or 61, KRAS promotes the development of cancer (1). When it binds to guanosine triphosphate (GTP), the active form of KRAS up-regulates several signaling pathways, including mitogen-activated protein kinase (MAPK), phosphatidylinositol-3-kinase, and Ras-like guanine nucleotide exchange factor. These pathways are responsible for various cellular processes such as cell proliferation, cell cycle regulation, metabolic changes, cell survival, and cell differentiation (2,3).

In RAS-mutated cancer, mutations are found most frequently in KRAS (85%), followed by NRAS (11%), and HRAS (3%) (4). Mutations in the KRAS gene are responsible for a significant number of cancer-related deaths globally, particularly in pancreatic cancer, colorectal cancer, and lung cancer. KRAS G12C is the most prevalent isoform in KRAS-mutant NSCLC (5,6). In China, KRAS is the second most common driver gene in non-small cell lung cancer (NSCLC), accounting for approximately 10–15% of cases (7,8). KRAS mutation was found to be a unique subtype that exclusively correlated with several blood parameters (9). This mutation is also recognized as a biomarker of a poor prognosis (7,10). KRAS mutations are associated with changes in the tumor microenvironment (TME). There is evidence that mutations in KRAS induce a pro-inflammatory state in the TME that accelerates tumor development. Within the TME, KRAS promotes tumor escape by promoting programmed cell death-ligand 1 (PD-L1) expression and decreasing major histocompatibility complex class I (MHC-I) expression in tumor cells, hindering the adhesion of leukocytes with anti-tumor activity (11).

Numerous therapeutic approaches have been developed to target KRAS mutations, including the direct targeting of the KRAS protein, post-translational modifications, membrane localization, synthetic lethality partners, and the blockage of downstream signaling cascades; however, the clinical benefits of these strategies remain limited (12). Sorafenib and mitogen-activated protein kinase (MEK) inhibitors have demonstrated some anti-tumor activity in patients with KRAS mutations, but more clinical trials are needed to establish their safety and efficacy more conclusively (13,14). Under the National Comprehensive Cancer Network guidelines (2025, version 3), sotorasib and adagrasib are recommended as second-line treatments for advanced NSCLC patients with the KRAS G12C mutation. However, their effectiveness is limited and have shown a median progression-free survival (PFS) of 5.6–6.5 months (15,16).

Currently, several ongoing clinical trials are investigating the clinical feasibility of sotorasib combined with chemotherapy (17-19), and the elementary effects of novel inhibitors in advanced NSCLC patients with KRAS G12C mutations (20-22). The lack of targeted therapies for KRAS mutations other than G12C in NSCLC has limited the therapeutic options for the KRAS-mutant population. In recent years, drugs targeting the KRAS G12D mutation have been under active development. In a phase-I clinical trial of HRS-4642 (KRAS G12D inhibitor), one NSCLC patient achieved a partial response after receiving a 200 mg dose, while among the overall study population, stable disease was observed in 11 patients (61.1%), 6 (33.3%) of whom showed target lesion shrinkage (23). The therapeutic effect of HRS-4642 may be linked to its modulation of the TME (24); however, the current findings are preliminary, and larger studies need to be conducted to validate its efficacy.

The RAMP 202 study that evaluated avutometinib (VS-6766) plus the focal adhesion kinase (FAK) inhibitor defactinib (VS-6063), showed no efficacy in advanced NSCLC patients harboring KRAS G12V mutations (25). Given the failure of drugs specifically designed for the KRAS mutation in ongoing clinical studies, this study aimed to identify an alternative target site associated with activated KRAS signaling pathways and further analyze drug sensitivity using the Genomics of Drug Sensitivity in Cancer (GDSC) database (https://www.cancerrxgene.org/). Our findings could provide new therapeutic strategies for KRAS-mutated NSCLC patients. We present this article in accordance with the REMARK reporting checklist (available at https://jtd.amegroups.com/article/view/10.21037/jtd-2025-1113/rc).

Methods

Data sources and processing methods

Transcriptome sequencing data and mutation data from The Cancer Genome Atlas Program-Lung Adenocarcinoma (TCGA-LUAD) dataset and the GSE72094 dataset were analyzed to identify genes that function closely with KRAS in LUAD.

For the TCGA-LUAD dataset and the GSE72094 dataset, we respectively utilized data from 504 patients and 398 patients with available RNA-seq and survival information. The primary prognostic outcome utilized was overall survival (OS), defined as the time from diagnosis to death from any cause. Survival status (deceased or censored) and survival time were obtained from the clinical data files. For survival analysis, we considered for the following covariates, where available and relevant to our analysis: age at diagnosis, gender, TNM stage (Tumor-Node-Metastasis stage) and KRAS mutation status.

For the TCGA-LUAD dataset, the “level 3” RNA sequencing data and clinical information of LUAD tumor tissues and adjacent normal tissues were downloaded using the GDC-client tool (https://portal.gdc.cancer.gov/). Gene Identifiers (IDs) were converted into official gene symbols according to the Genome Reference Consortium Human Build 38 assembly. Mutation data of TCGA-LUAD dataset were downloaded and analyzed via the cBioPortal pipeline (https://www.cbioportal.org/). For the GSE72094 dataset, microarray gene expression profiles and patients’ clinical information were downloaded from Gene Expression Omnibus (https://www.ncbi.nlm.nih.gov/geo/) database, and the probe names were converted to gene symbols according to the platform file GPL15048. In both datasets, the mutation status of the KRAS gene, clinical pathological information, and OS data of each LUAD patient were integrated with the transcriptome sequencing data to create a merged matrix for downstream analyses. In total, 504 LUAD samples from the TCGA-LUAD dataset and 398 LUAD samples from the GSE72094 dataset were included for analysis in this study. The study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments.

Identification of differentially expressed genes (DEGs)

In the present study, we used the EdgeR package (Version 3.40.2) (26) as the initial step to identify DEGs between the tumor tissues and adjacent normal tissues in TCGA-LUAD dataset. Our focus was primarily on identifying significantly upregulated genes in the tumor samples. The DEGs were considered significantly upregulated if they met the following criteria: had an average transcript per million (TPM) expression level >10 in tumor tissues, a fold change (FC) >1.5, and an adjusted P value <0.001 when compared to normal tissues. The genes identified through this process were designated as candidate genes, and subsequent investigations into their association with KRAS mutations in LUAD were conducted.

Screening for KRAS function-sensitive genes

In this study, we defined a KRAS function-sensitive gene as a gene whose effect on cell survival, as quantified by knockout-induced gene dependency scores obtained from the DepMap portal of the Cancer Cell Line Encyclopedia (CCLE) database (https://depmap.org/portal/ccle/), was significantly correlated with the mutation status of the KRAS gene in LUAD. To be identified as a KRAS function-sensitive gene, the gene had to have a mean dependency score >0.9 in the 13 KRAS-mutated cell lines, thereby indicating the vital role played by the identified gene in cell survival. Additionally, in patients with KRAS mutations, a significant difference in OS was expected to be found between the high- and low-expression groups based on the expression levels of the identified genes. Conversely, no such difference was expected among the wild-type patients, thus providing additional evidence of the collaborative involvement of the KRAS-mutated gene in the development of LUAD. The schematic diagram illustrating the research design process is depicted in Figure 1.

Figure 1 Flow chart of study design. CCLE, Cancer Cell Line Encyclopedia; DEGs, differentially expressed genes; GEO, Gene Expression Omnibus; TCGA-LUAD, The Cancer Genome Atlas Program-Lung Adenocarcinoma.

Gene set enrichment analysis (GSEA)

A GSEA was conducted to investigate the functional differences between high- and low-expression groups of the KRAS-mutated patients with LUAD in TCGA dataset. In the GSEA, the expression levels of all the protein-coding genes were used as input for the GSEA software (version 4.0.3) (27). The Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway gene sets (c2.cp.kegg.v7.0.symbols, https://www.kegg.jp) (28) were selected to examine the phenotypic patterns. For each pathway analyzed, an enrichment score (ES) and the significance of the ES were calculated. Additionally, normalized ESs and false discovery rates (FDRs) were calculated to assess the significance of the functional enrichment outcomes. In this analysis, a cut-off FDR of 0.05 was considered statistically significant.

Drug sensitivity analysis

In this study, we used the GDSC database to conduct a drug sensitivity analysis of the identified genes. The GDSC database provides comprehensive information on the sensitivity of diverse cancer cell lines to a wide range of anti-cancer drugs, including data on drug sensitivity in both KRAS wild-type and KRAS-mutant LUAD cell lines. This extensive dataset enabled us to evaluate potential correlations between the drug sensitivity exhibited by the identified genes and the presence of KRAS mutations.

Statistical analysis

The survival analysis was performed using a non-parametric Kaplan-Meier analysis, and A multivariate Cox proportional hazards model was utilized to assess the independent prognostic value of ATR expression, adjusting for clinically relevant covariates including age, sex, TNM stage. All reported P values were obtained from two-tailed tests. A P value <0.05 was considered statistically significant. The statistical analysis for this study was conducted using R 4.3.0 (R Foundation for Statistical Computing, Vienna, Austria), SPSS 25.0 (IBM Corp., Armonk, NY, USA), and Prism 7.0 (GraphPad Software, San Diego, CA, USA).

Results

Summary of the KRAS mutation in LUAD patients

In TCGA-LUAD dataset, 154 LUAD patients exhibited KRAS mutations in their tumor tissues, yielding a mutation rate of 29.7%, ranking this gene in the top 10 among all the mutated genes in the dataset (Figure 2A). The majority of these mutations were missense mutations (157/159; Figure 2B), and the most prevalent single nucleotide variant (SNV) types included p.G12C (65/157, 41%), p.G12V (36/157, 23%), p.G12D (19/157, 12%), and p.G12A (16/157, 10%) (Figure 2C).

Figure 2 Distribution of KRAS mutation in TCGA-LUAD dataset. (A) Gene mutation frequency in TCGA-LUAD dataset. (B) KRAS mutation type in TCGA-LUAD dataset. (C) KRAS SNV mutation type in TCGA-LUAD dataset. SNV, single nucleotide variant; TCGA-LUAD, The Cancer Genome Atlas Program-Lung Adenocarcinoma.

Identification of upregulated KRAS function-sensitive genes

We initially performed a differential expression analysis using TCGA-LUAD expression profiles and identified a total of 2,399 DEGs that exhibited significant upregulation in LUAD tissues (FC >1.5, FDR <0.001, mean TPM >1) (table available at https://cdn.amegroups.cn/static/public/jtd-2025-1113-1.docx and Figure S1). Subsequently, we conducted a more comprehensive investigation into the dependency scores of these genes using data from the CCLE database. Among the 2,399 DEGs, 235 were found to have a mean dependency score >0.9 in the KRAS-mutated LUAD cell lines (table available at https://cdn.amegroups.cn/static/public/jtd-2025-1113-1.docx). These genes were not only significantly upregulated in the cancerous tissues but also exerted a notable effect on the cell survival status of the KRAS-mutated LUAD cell lines. As a result, they were considered candidate KRAS function-sensitive genes for further analysis.

Prognostic value of candidate KRAS function-sensitive genes in TCGA-LUAD dataset

A further analysis was conducted to investigate the prognostic value of the 235 candidate genes in the TCGA-LUAD dataset. In both the KRAS-mutated and wild-type subgroups, the samples were stratified into high- and low-expression groups based on the median gene expression values. Subsequently, a Kaplan-Meier survival analysis was performed to compare the survival differences between the high- and low-expression groups. The findings indicated that among the 235 genes, differential expression of only 4 genes (NOP16, RUVBL1, ATR, and BOP1) exhibited prognostic differences in the KRAS-mutated patients but not in the KRAS wild-type patients (Table 1). Specifically, in the KRAS-mutated patients (n=151), the group in which these four genes were highly expressed (the high-expression group) had significantly worse survival outcomes than the group in which these four genes were lowly expressed (the low-expression group) (ATR: P=0.008; BOP1: P=0.009; NOP16: P<0.001; RUVBL1: P=0.007; Figure 3A and Figure S2A); conversely, no significant survival differences were observed between the high- and low-expression groups in the wild-type patients (n=353) (ATR: P=0.95; BOP1: P=0.07; NOP16: P=0.13; RUVBL1: P=0.34; Figure 3B and Figure S2B). Further, the multivariate Cox regression analysis revealed that ATR expression served as an independent unfavorable prognostic factor with adjustment for age, gender and TNM stage [hazard ratio (HR) =2.192; 95% confidence interval (CI): 1.187–4.048; P=0.01] for the KRAS-mutated LUAD patients (Figure 3C,3D).

Figure 3 Prognostic value of ATR in KRAS-mutated (A,C) and KRAS wild-type (B,D) LUAD patients from TCGA-LUAD dataset. CI, confidence interval; TCGA-LUAD, The Cancer Genome Atlas Program-Lung Adenocarcinoma; TNM, Tumor-Node-Metastasis.

External evaluation in the GSE72094 dataset

To further validate the prognostic significance of these four genes in KRAS-mutated LUAD patients, we conducted external validation analyses using the GSE72094 dataset. The GSE72094 dataset comprises gene expression profiles and survival data from 139 KRAS-mutated LUAD patients and 259 KRAS wild-type LUAD patients. The clinical and pathological information for both TCGA-LUAD and GSE72094 datasets is summarized in Table 2. There were no significant differences in the distribution of the clinical and pathological characteristics between the two datasets (age: P=0.32; gender: P=0.51; TNM stage: P=0.23; KRAS mutation: P=0.11). The results regarding the prognostic value of the ATR gene obtained from the GSE72094 dataset were consistent with those obtained from the TCGA-LUAD dataset. Specifically, in the KRAS-mutated patients, those with high ATR gene expression had significantly worse OS than those with low expression (P=0.02, Figure 4A). However, this survival difference was not observed in the wild-type patients (P=0.54, Figure 4B). Additionally, the expression level of the ATR gene remained an independent unfavorable prognostic factor with adjustment for age, gender and TNM stage (HR =2.060; 95% CI: 1.133–3.746; P=0.02) in the KRAS-mutated patients (Figure 4C,4D). However, similar results were not found for the other three candidate genes (Figure S3).

Figure 4 Prognostic value of ATR in KRAS-mutated (A,C) and KRAS wild-type (B,D) LUAD patients from the GSE72094 dataset. CI, confidence interval; LUAD, lung adenocarcinoma; TNM, Tumor-Node-Metastasis.

Functional exploration of the ATR gene in KRAS-mutated LUAD patients

As described above, the ATR gene exhibited significantly higher expression in LUAD tissues and was closely associated with cell survival in the KRAS-mutated LUAD cell lines. Additionally, its prognostic significance differed between the KRAS-mutated and wild-type patients. To further investigate the potential biological functions of the ATR gene in KRAS-mutated LUAD, we performed a GSEA of the KRAS-mutated patients in the TCGA-LUAD dataset specifically. The analysis revealed significant differences in pathway enrichment between the ATR high- and low- expression groups in KRAS-mutated patients (Figure 5 and table available at https://cdn.amegroups.cn/static/public/jtd-2025-1113-1.docx). Some of them included well-known cancer-related pathways such as “ubiquitin mediated proteolysis”, “Insulin signaling pathway”, “pathways in cancer”, “MAPK signaling pathway”, “non-small cell lung cancer”, and “apoptosis”. Moreover, several immune-related pathways were also identified, including “T cell receptor signaling pathway”, “Fc gamma r-mediated phagocytosis”, and “B cell receptor signaling pathway”. These findings suggested that in KRAS-mutated LUAD, the ATR gene may play a role in regulating multiple pathways associated with cancer development and may influence the immune status of the TME, thereby affecting patient prognosis.

Figure 5 Functional enrichment analysis between ATR high- and ATR low-expression KRAS-mutated LUAD patients from TCGA-LUAD dataset. KEGG, Kyoto Encyclopedia of Genes and Genomes; TCGA-LUAD, The Cancer Genome Atlas Program-Lung Adenocarcinoma.

Drug sensitivity of ATR inhibitors in KRAS-mutated LUAD cell lines

The GDSC database provides a compilation of ATR inhibitors along with their half-maximal inhibitory concentration (IC50) values in LUAD cell lines. Figure 6 illustrates a comparative analysis of the IC50 values of these ATR inhibitors between the KRAS-mutated and KRAS wild-type LUAD cell lines, and the results revealed significant differences in the IC50 values for the AZ20 and AZD6738 compounds between the two groups. Specifically, in the KRAS-mutated cell lines, these ATR inhibitors had significantly lower IC50 values, indicating their ability to inhibit cell growth at lower concentrations and potentially exert more potent anti-tumor effects in KRAS-mutated LUAD.

Figure 6 Drug sensitivity of ATR inhibitors in KRAS-mutated and KRAS wild-type LUAD cell lines. IC₅₀, half-maximal inhibitory concentration; LUAD, lung adenocarcinoma.

Discussion

Currently, the development of targeted therapeutic strategies for patients with activating mutations in KRAS is challenging (29). In comparison to other driver genes in NSCLC, targeting the KRAS mutation often yields unfavorable outcomes and presents a treatment dilemma. In our study, we identified a novel drug target, ATR, in KRAS-mutated LUAD cell lines. This target is associated with a poor clinical prognosis and has an effect on the survival status of KRAS-mutated LUAD cells. Further, our drug sensitivity analysis indicated that ATR inhibitors may exert potential anti-tumor effects in KRAS-mutated LUAD. There is a promising possibility that ATR could potentially serve as a novel therapeutic target for KRAS-mutated NSCLC in the future.

Consistent with previous literature (4), the majority of KRAS mutations observed in our study of patients with LUAD were missense mutations, and p.G12C was the most prevalent SNV type. Through the GSEA and Kaplan-Meier survival analysis, we identified four genes (i.e., NOP16, RUVBL1, ATR and BOP1) that exhibited prognostic differences in the KRAS-mutated LUAD patients. Further, the multivariate Cox regression analysis showed that ATR expression was the sole independent unfavorable prognostic factor in both TCGA-LUAD dataset and the external validation dataset.

ATR, a member of the phosphatidylinositol 3-kinase-related kinase (PIKK) family, is a prominent kinase with a significant role in the activation of the replication stress response (RSR) (30). Replication stress is caused by endogenous or exogenous obstacles to DNA replication that slow or prevent replication fork progression. Cells relieve replication stress through the RSR mechanism (30-32). On encountering stalled DNA replication forks, ATR initiates a cascade of events, including the phosphorylation of downstream substrates such as checkpoint kinase 1 (CHK1) and checkpoint kinase 2 (CHK2), which in turn induce replication checkpoints and facilitate fork repair (31,33). Notably, ATR signaling has emerged as a promising target for the development of cancer therapeutics (34,35). Preliminary findings indicate that the combined use of an ATR inhibitor, such as berzosertib, can synergize the anti-tumor effect of other inhibitors, with a phase-II clinical trial meeting its primary endpoint (36,37). Currently, the combination of berzosertib (M6620) and programmed death-ligand 1 inhibitors in NSCLC is under investigation (38). Our research indicates that the synergistic effect may also exist in KRAS-mutated NSCLC patients.

To further elucidate the potential biological functions of ATR in KRAS-mutated LUAD, we conducted a GSEA, which showed the effect of ATR on cell survival and immune status. A previous study has reported a correlation between the ATR/CHK1 pathway and sensitivity to radiation and cisplatin in NSCLC (39). The inhibition of ATR has been shown to enhance cell death induced by cisplatin (40) and potentiate radiation-induced tumor-infiltrating lymphocytes (41), which aligns with the findings of our functional analysis. Additionally, several preclinical experiments have indicated that the combined inhibition of AXL (anexelekto, a TAM family receptor tyrosine kinase) or Pol η (DNA polymerase eta, a Y-family translesion synthesis polymerase) with ATR can significantly decrease cell proliferation and reverse the drug resistance to cisplatin in NSCLC cells (42,43).

Despite the development of drugs targeting ATR signaling pathways, the majority of relevant studies have primarily focused on cell experiments and have paid less attention to the KRAS-mutated NSCLC group (44-47). Directly blocking KRAS is challenging due to the shallow and smooth surface of the KRAS protein, as well as its strong affinity to GTP, which limits the molecules that can effectively inhibit it (6). Additionally, alternative pathways such as RAS/RAF/MEK have been explored for their potential in down-signaling KRAS mutations, but the outcomes have been unsatisfactory (48). Considering the findings of our study, it is crucial to emphasize the importance of ATR inhibitors in the therapeutic approach for KRAS-mutated NSCLC.

Our study had several inevitable limitations that should be acknowledged. First, the lack of large real-world cohorts limited our ability to validate the prognostic significance of ATR. Second, in vivo experimental verification is necessary to confirm the specific biological function of ATR in KRAS-mutated cells. Finally, the sample size might not be sufficient to draw definitive conclusions. Given that KRAS is the most challenging target gene in NSCLC from a pharmacological standpoint, further research needs to be conducted to fully understand its role. Distinct KRAS mutation subtypes may differentially influence ATR expression and clinical outcomes. Given the current therapeutic limitations, future studies will prioritize elucidating subtype-specific ATR dependencies. Simultaneously, Comprehensive investigation into the interplay of ATR with key KRAS effector pathways (such as MAPK and PI3K) and immune checkpoint inhibitors (ICIs) will be pursued, as understanding these interactions is essential for advancing rational combinatorial strategies in lung cancer treatment.

Our findings, which identified ATR as a novel surrogate target, has the potential to contribute to the development of a treatment strategy for KRAS-mutated NSCLC. Additional clinical trials need to be conducted to validate these findings.

Conclusions

Our study screened four KRAS function-related genes and identified ATR as an independent predictor of prognosis in KRAS-mutated NSCLC patients. Our drug sensitivity analysis revealed that ATR was a promising druggable target and was sensitive to two compounds. These findings provide valuable insights into the development of new targeted and chemotherapeutic drugs for KRAS-mutated NSCLC.

Acknowledgments

None.

Footnote

Reporting Checklist: The authors have completed the REMARK reporting checklist. Available at https://jtd.amegroups.com/article/view/10.21037/jtd-2025-1113/rc

Peer Review File: Available at https://jtd.amegroups.com/article/view/10.21037/jtd-2025-1113/prf

Funding: This work was supported by a grant from the National Natural Science Foundation of China (No. 81972190).

Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://jtd.amegroups.com/article/view/10.21037/jtd-2025-1113/coif). A.T.A. reports honoraria from MSD, Pfizer, Janssen and Astra Zeneca; and support for attending meeting by Janssen. All honoraria paid to institution. She participated in an Advisory Board of Janssen and Astra Zeneca. The authors have no other conflicts of interest to declare.

Ethical Statement: The authors are accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved. The study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments.

Open Access Statement: This is an Open Access article distributed in accordance with the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License (CC BY-NC-ND 4.0), which permits the non-commercial replication and distribution of the article with the strict proviso that no changes or edits are made and the original work is properly cited (including links to both the formal publication through the relevant DOI and the license). See: https://creativecommons.org/licenses/by-nc-nd/4.0/.

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(English Language Editor: L. Huleatt)