Next Article in Journal
Diagnostic Use of Selected Metalloproteinases in Endometrioid Ovarian Cancer
Previous Article in Journal
Encoding of Demographic and Anatomical Information in Chest X-Ray-Based Severe Left Ventricular Hypertrophy Classifiers
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Biomedicines
Volume 13
Issue 9
10.3390/biomedicines13092142
biomedicines-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
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Prognostic Impact of KRAS and SMARCA4 Mutations and Co-Mutations on Survival in Non-Small Cell Lung Cancer: Insights from the AACR GENIE BPC Dataset

by
Peter Manolakos
1,*,
Yu-Bo Wang
2,
Janice Withycombe
3,
Luigi Boccuto
3 and
Diana Ivankovic
3
1
Healthcare Genetics and Genomics PhD Program, Clemson University, Clemson, SC 29634, USA
2
School of Mathematical and Statistical Sciences, Clemson University, Clemson, SC 29634, USA
3
Department of Healthcare Genetics and Genomics, Clemson University, Clemson, SC 29634, USA
*
Author to whom correspondence should be addressed.
Biomedicines 2025, 13(9), 2142; https://doi.org/10.3390/biomedicines13092142
Submission received: 3 August 2025 / Revised: 26 August 2025 / Accepted: 27 August 2025 / Published: 2 September 2025
(This article belongs to the Section Molecular Genetics and Genetic Diseases)
Browse Figure
Versions Notes

Abstract

Background/Objectives: KRAS mutations are among the most prevalent oncogenic drivers in non-small cell lung cancer (NSCLC), with their impact on survival influenced by co-mutations. SMARCA4 mutations are increasingly associated with poor prognosis and can be classified as class 1 or class 2 mutations. This study evaluates the prognostic implications of KRAS and SMARCA4 mutations, including their co-mutations and their impact on NSCLC patients by utilizing real-world evidence. Methods: A retrospective analysis was conducted using the AACR GENIE Biopharma Collaborative (BPC) NSCLC 2.0 dataset. NSCLC patients with KRAS mutations, SMARCA4 mutations, or KRAS/SMARCA4 co-mutations were identified. Survival outcomes were assessed using univariate and multivariate Cox proportional hazards models, incorporating key clinical variables such as sex, race, smoking history, and stage. Results: Among 659 NSCLC patients with KRAS or SMARCA4 mutations analyzed, KRAS mutations were the most prevalent (79%, n = 518). SMARCA4 mutations were identified in 14% of cases (n = 95) across two classes. Six percent (n = 41) with class 1 mutations and 8% (n = 54) with class 2. Neither SMARCA4 class was associated with worse survival outcomes compared to KRAS-mutated patients (p = 0.438 & 0.720). Patients harboring KRAS/SMARCA4 class 1 co-mutations (3%, n = 18) had significantly worse overall survival compared to those with KRAS mutations alone (hazard ratio [HR] = 3.23, p < 0.001). In contrast, KRAS/SMARCA4 class 2 co-mutations (4%, n = 28) did not significantly impact survival compared to KRAS-mutated patients (HR = 1.34, p = 0.205). Conclusions: KRAS/SMARCA4 class 1 co-mutations are associated with significantly worse overall survival compared to KRAS-mutated NSCLC patients. Our multivariate analysis demonstrates the critical need to incorporate routine next-generation sequencing (NGS) testing in managing NSCLC patients at the time of metastatic diagnosis, with particular emphasis on identifying SMARCA4 mutation class as a potential prognostic biomarker in those with KRAS co-mutations.

1. Introduction

Lung cancer is the most prominent cause of cancer mortality globally among both men and women, with approximately 1.8 million deaths in 2023 [1,2]. In the US, approximately 350 people die each day from lung cancer, causing more deaths in 2020 than prostate, breast, and pancreatic cancers combined [3]. Non-small cell lung cancer (NSCLC) accounts for 84% of lung cancer cases, and the five-year survival rate was only 25% across all stages from 2013 to 2019 [4,5]. With the adoption of genomic testing and advances in targeted therapy treatment for patients with NSCLC, there has been some improvement compared to the standard of care in overall survival (OS) rates and progression-free survival [6,7]. However, there needs to be a better understanding of how established NSCLC driver mutations, passenger mutations, and co-mutations impact overall survival outcomes.
In Western countries, mutations in the Kirsten rat sarcoma viral oncogene homolog (KRAS) gene are the most identified driver mutation in non-squamous NSCLC, presenting in approximately 30% of adenocarcinoma cases [8]. KRAS mutations are associated with poor overall survival rates among patients diagnosed with NSCLC and have been determined to be a weak but valid prognostic biomarker [9]. In relation to the current standard of care in oncology treatments, KRAS mutations in NSCLC have shown mixed results as predictive clinical biomarkers for survival outcomes with immune checkpoint inhibitor (ICI) treatments [10]. More common KRAS co-mutations may impact therapy as retrospective analyses and critical reviews have posited that NSCLC patients with KRAS-Serine/Threonine Kinase 11 (STK11) co-mutations are likely to exhibit primary resistance to ICI treatments [11]. Questions arise when understanding the prognostic impact of other KRAS co-mutations with non-driver mutations in patients with NSCLC.
SWI/SNF-related, matrix-associated, actin-dependent regulator of chromatin, subfamily A, member 4 (SMARCA4) mutations are found in approximately 8–12% of NSCLC patients [12,13,14]. SMARCA4-mutated patients tend to present with adenocarcinoma, a smoking history, and a low frequency of epidermal growth factor receptor (EGFR)/SMARCA4 co-mutations [12,13,15]. In addition, a comprehensive critical review encompassing 21 studies concluded that overall survival outcomes were worse for NSCLC patients with SMARCA4 mutations than those with wild-type SMARCA4 alleles (tumors without SMARCA4 mutations) [16].
SMARCA4 mutations are categorized into two classes, as described by Schoenfeld et al., both of which are associated with epigenetic dysregulation but affect chromatin remodeling through distinct mechanisms [13,17]. For example, class 1 mutations involve truncating mutations (i.e., frameshift, nonsense), fusions, and homozygous deletions. SMARCA4 encodes Brahma-Related Gene 1 (BRG1) protein, and class 1 mutations are often associated with BRG1 loss and loss of function [16,18]. Class 1 mutations are mainly observed in NSCLC, and some cancer cases indicate that this loss of function leads to reduced chromatin accessibility and diminished remodeling activity [19]. Class 2 mutations include missense mutations, which are suggested to induce gain-of-function or dominant-negative effects, possibly leading to deleterious cellular processes.
Two of the most extensive SMARCA4 mutation datasets categorized these classes and types of mutations in a similar percentage of patients. For example, in the analysis conducted by Schoenfeld et al., including 408 SMARCA4-mutated NSCLC patients, 52% were class 1 and 48% were class 2 [13]. In this analysis, patients with SMARCA4 class 1 mutations had a worse prognosis and OS than patients with class 2 mutations or who were SMARCA4 wild-type (p < 0.001). Moreover, the landmark analysis conducted by Dagogo-Jack et al. of 3188 SMARCA4-mutated patients yielded similar distributions, with 49% of SMARCA4-mutated patients being categorized as class 1 and 51% of patients as class 2 [12].
In the American Association for Cancer Research (AACR) Project Genomics Evidence Neoplasia Information Exchange (GENIE) Biopharma Collaborative (BPC), SMARCA4-mutated NSCLC patients have an approximate 30% incidence of KRAS/SMARCA4 co-mutations [20]. Moreover, KRAS/SMARCA4 co-mutated patients across multiple NSCLC studies were found to have inferior survival outcomes throughout various types of cancer treatment analysis, including chemotherapy, ICI, and targeted therapy [21,22,23,24,25]. These inferior survival outcomes were also reinforced by the most extensive real-world evidence (RWE) retrospective NSCLC analysis of switch/sucrose non-fermenting (SWI/SWF) mutations, including SMARCA4 variants. Investigators found that the co-mutated KRAS/SMARCA4 NSCLC patients had worse survival vs. KRAS-mutated/SMARCA4 wild-type patients (hazard ratio [HR] = 1.882, p < 0.00001) [14]. In contrast, one large analysis has demonstrated superior OS for NSCLC patients with SMARCA4 mutations treated with ICI [13].
A limited number of SMARCA4-mutated NSCLC survival analyses have been completed, directly comparing class 1, class 2, and SMARCA4 wild-type patients [13,19,21]. As previously noted, there are several analyses comparing KRAS/SMARCA4 co-mutated to KRAS-mutated/SMARCA4 wild-type patients [21,22,23,24,25]. To our knowledge, only one large multivariate analysis has comprehensively assessed KRAS/SMARCA4 class 1 and KRAS/SMARCA4 class 2 vs. KRAS-mutated/SMARCA4 wild-type patients [13]. Analyzing high-quality observational RWE phenotypic data alongside genomic mutations and co-mutations in NSCLC may better elucidate how KRAS, SMARCA4 (class 1 and 2), and KRAS/SMARCA4 co-mutations intersect and influence overall patient outcomes and survival, thereby informing future drug development. This study aims to retrospectively analyze the prognostic impact on survival outcomes of KRAS-mutated and SMARCA4 (class 1 and 2) mutated or KRAS/SMARCA4 co-mutations in NSCLC patients via the AACR GENIE BPC dataset.

2. Materials and Methods

We utilized the AACR GENIE BPC NSCLC 2.0-public cohort, a publicly available dataset in cBioportal, to identify all NSCLC patients with KRAS mutations, SMARCA4 mutations, or KRAS/SMARCA4 co-mutations [26,27]. The NSCLC BPC cohort comprises 2004 samples from 1846 patients, randomly selected from samples in the GENIE 11.1-public release. The Dana–Farber Cancer Institute, Memorial Sloan Kettering Cancer Center, University Health Network, and Vanderbilt-Ingram Cancer Center contributed these samples. To be included in this analysis, patients were required to have at least 2 years of follow-up and a genomic sequencing report performed between 1 January 2014, and 31 December 2017. Clinical information was abstracted at each institution using the PRISSMM framework, deidentified, and provided to AACR for compilation [28]. The dataset of KRAS and SMARCA4 mutations included all NSCLC histological subtypes and stages in the BPC cohort (i.e., lung adenocarcinoma and lung squamous cell carcinoma). The study was reviewed by Clemson University (Institutional Review Board #2023-0636) and deemed exempt as it utilized a publicly available database containing de-identified patient data.
SMARCA4 class 1 and class 2 mutation categorizations were defined following the landmark analysis by Schoenfeld et al. [13]. SMARCA4-mutated patients with a frameshift deletion, frameshift insertion, nonsense mutation, fusion, splice site mutation, or splice region mutation were categorized as SMARCA4 class 1 mutations. Patients with missense mutations, in-frame insertions, or deletions were classified as SMARCA4 class 2 mutations. Tumors with concurrent SMARCA4 class 1 and SMARCA4 class 2 mutations were categorized as class 1. When multiple samples carrying the same mutation were attributed to the same patient ID, one sample was used for analysis, and the other samples were excluded. Similarly, patients with both KRAS and SMARCA4 mutations were merged under a single de-identified patient ID to avoid duplication in the analysis.
Smoking history was classified into three groups: Never, Current, or Former user. Former users were combined [(quit < 1 year), (quit > 1 year), or (quit = unknown time)]. Disease stages were classified into Stage IV vs. Stage I–III. Any patients that were Stage I–III Not Otherwise Specified (NOS) were classified as Stage I–III. Patients were either categorized as White or Non-White. Non-White patients included Black, Chinese, Other Asian, American Indian, or Other. Patients with “unknown” defined variables for categories in scope were excluded from the analysis to maintain the accuracy and interpretability of the results. A very low number of samples fell into the unknown category: race (n = 18), stage (n = 1), and smoking history (n = 2).

Statistical Methods

Statistical analyses were performed under the R software 4.4.1, and the statistical significance level was set at α = 0.05. To assess the marginal association of each risk factor with survival status (the event is deceased), the t-test and chi-square test were first conducted separately for a continuous (i.e., age and categorical (i.e., sex) factor. The results were presented along with other vital descriptive statistics (i.e., sample means and frequencies). Then, the Cox proportional hazards model was fitted to present the final findings jointly for all these factors (i.e., their joint associations with the deceased).

3. Results

After applying inclusion and exclusion criteria, 659 patients were retained in the dataset.

3.1. Clinical and Genomic Characteristics

Table 1 describes the clinical and genomic patient characteristics of the 659 patients with NSCLC. The mean age for next-generation sequencing (NGS) was 67.4 years. There were more females (n = 397, 60%) than males (n = 262, 40%). The most prevalent race was White (n = 600, 91%) vs. non-White/Other (n = 59, 9%), with Black (n = 32) and Chinese/Other Asian (n = 16) as the most common non-White patients. Forty-one percent of patients (n = 267) had Stage IV NSCLC vs. 59% of patients (n = 392) having early or locally advanced disease (Stage I-III). For early or locally advanced disease, Stage I was the most common (n = 192), followed by Stage II (n = 74), Stage III (n = 120), and Stage I–III NOS (n = 6). The vast majority of patients had a smoking history: former (n = 496, 75%) and current (n = 113, 17%) vs. never having a smoking history (n = 50, 8%).
Regarding the KRAS and SMACAR4 mutation status for all 659 NSCLC patients, KRAS (n = 518, 79%) was the most common (Table 1). There were more SMARCA4 class 2 mutations (n = 54, 8%) than SMARCA4 class 1 mutations (n = 41, 6%). Similarly, slightly more KRAS/SMARCA4 class 2 co-mutations (n = 28, 4%) than KRAS/SMARCA4 class 1 co-mutations (n = 18, 3%) were reported. Seven SMARCA4 class 1 patients had more than one type of SMARCA4 mutation and were classified as class 1 due to one of the mutations being a frameshift or nonsense mutation. One KRAS/SMARCA4 co-mutated patient had more than one type of SMARCA4 mutation and was classified as KRAS/SMARCA4 class 1 because they had a SMARCA4 frameshift deletion and a SMARCA4 missense mutation.

3.2. Univariate OS Analysis

Table 2 provides the clinical and genomic univariate analysis of OS status (living vs. deceased) from cancer diagnosis. Factors of interest were evaluated for their marginal associations with survival status. Significant associations with the risk of death were found under α = 0.05 in the factors “Stage” (p < 0.001) and “Mutation Type” (p = 0.001). While the mean ages of NGS sequencing were 66.7 and 68.0 years for living and deceased patients, respectively, the difference in the mean ages is insignificant (p = 0.085). Additionally, no significant OS differences were found between the sexes (female vs. male, p = 0.327) or racial groups (White vs. Non-white/Other, p = 0.713) or even among patients with varied smoking histories (Never vs. Former vs. Current smokers, p = 0.901).

3.3. Multivariate Analysis

Table 3 presents the results of the Cox regression model for patients with KRAS, SMARCA4, or KRAS/SMARCA4-co-mutated NSCLC. In this joint modeling, three factors were identified as important predictors accounting for a significantly increased risk of death. Specifically, “Stage IV” patients had an approximately four-fold risk of death than the earlier stages (HR = 4.01; 95% CI: 3.21–5.01; p < 0.001). KRAS/SMARCA4 (class 1) co-mutated patients had a 3.2-fold risk of death compared to KRAS mutated patients (HR = 3.23; 95% CI: 1.90–5.51; p < 0.001). The age at sequencing was also associated with an increased risk of death (HR = 1.02; 95% CI: 1.01–1.03; p = 0.005).
Sex and race were not significantly associated with the risk of death [(Male vs. Female: HR = 1.18; 95% CI: 0.941–1.47; p = 0.153) and (White vs. Non-White/Other: HR = 0.978; 95% CI: 0.676–1.41; p = 0.905)]. Similarly, smoking history showed no significant association [(Former smoker vs. Never smoker: HR = 1.08; 95% CI: 0.702–1.66; p = 0.732) and (Current smoker vs. Never smoker: HR = 1.21; 95% CI: 0.745–1.98; p = 0.437)].
The other comparisons among the genomic mutation types were not associated with a significantly increased risk of death. Neither class 1 nor class 2 SMARCA4 mutations had significantly worse OS compared to KRAS mutations: [SMARCA4 (class 1) vs. KRAS (HR = 1.18; 95% CI: 0.779–1.78; p = 0.438)] and [SMARCA4 (class 2) vs. KRAS (HR = 0.932; 95% CI: 0.635–1.37; p = 0.720)]. The comparison between patients with KRAS/SMARCA4 (class 2) co-mutations vs. KRAS mutation was not significant (HR = 1.34; 95% CI: 0.851- 2.12; p = 0.205). To summarize these findings visually, the adjusted Kaplan–Meier curves (adjusted for other risk factors) for these five mutation types are shown in Figure 1.

4. Discussion

Our retrospective RWE analysis provides a comprehensive view of the prognostic impact of KRAS and SMARCA4 mutations, including their co-mutations, on OS in patients with NSCLC. Utilizing the AACR GENIE BPC dataset, we identified distinct differences in genomic profiles influencing prognostic outcomes across a multivariate analysis. Notably, our findings highlight how these mutations and their co-mutations are significantly associated with increased patient risk, offering potential insights into how they may influence overall survival outcomes in NSCLC.
The results of the Cox-regression multivariate analysis demonstrated that KRAS/SMARCA4 class 1 co-mutations are associated with worse OS compared to KRAS mutations for patients with NSCLC (HR = 3.23; 95% CI: 1.90–5.51; p < 0.001). In contrast, KRAS/SMARCA4 class 2 co-mutations did not appear to confer a significantly worse OS than KRAS mutations (HR = 1.34; 95% CI: 0.851–2.12; p = 0.205). In addition, SMARCA4 class 1 and class 2 mutations alone were not associated with worse OS when compared to KRAS-mutated NSCLC patients.
Our findings support the possible need to determine the SMARCA4 mutation class as a prognostic factor when a patient carries a KRAS/SMARCA4 co-mutation. Moreover, a recent meta-analysis outside of the co-mutation scope posited that SMARCA4-mutated class 1 was associated with worse OS for NSCLC patients (HR = 1.63; 95% CI: 1.44–1.85; p < 0.00001) in contrast to SMARCA4-mutated class 2 where no OS association was observed (HR = 1.34; 95% CI: 0.87–2.06; p = 0.18) [29]. This meta-analysis by Wankhede et al. differs from our OS analysis because it did not directly compare KRAS/SMARCA4 co-mutated patients across both SMARCA4 mutation classes.
Although multiple NSCLC studies demonstrate worse OS in patients with KRAS/SMARCA4 co-mutations compared to KRAS-mutated patients, most do not analyze the impact of SMARCA4 class 1 versus class 2 on OS within this co-mutated population [22,24,25]. For example, in the most extensive RWE analysis to date, Herzberg underscored the unfavorable prognostic effect of KRAS/SMARCA4 co-mutations compared to KRAS mutations (HR = 1.882, p < 0.00001), and the SMARCA4 mutation class effect was not directly noted [14]. They took a different approach and categorized SWI/SWF mutations as likely pathogenic or pathogenic (LP/P) per OncoKB, a precision oncology mutation database that provides the clinical significance of cancer-related mutations. Fifty-two percent of the SMARCA4 mutations used for their analysis were deemed LP/P and most likely included some missense mutations, as OncoKB does categorize specific types of missense mutations as LP/P. In contrast, our analysis categorized all missense mutations as class 2 regardless of LP/P status following the methodology applied by Schoenfeld et al. [13].
Similar to our results, Schoenfeld et al. reported that NSCLC patients with KRAS/SMARCA4 class 1 co-mutations were significantly associated with poorer survival (HR = 1.59; 95% CI: 1.04–2.41; p < 0.001) compared to KRAS-mutated/SMARCA4 wild-type tumors [13]. Additionally, their NSCLC analysis showed that KRAS/SMARCA4 class 2 co-mutations were associated with worse OS (HR = 2.75; 95% CI: 1.84–4.11; p < 0.001) compared to KRAS/SMARCA4 wild-type tumors. These results remained prognostic after accounting for variables such as age, sex, histology, smoking status, TMB, and the presence of STK11 or Kelch-like ECH-associated protein 1 (KEAP1) co-mutations.

4.1. Biological Insights

These survival findings reinforce the hypothesis that the loss-of-function nature of SMARCA4 class 1 mutations may lead to diminished chromatin accessibility and remodeling, impairing gene regulation and potentially contributing to tumor progression [30]. As demonstrated in the Dagogo-Jack et al., Alessi et al., and Schoenfeld et al. studies, most of the class 1 SMARCA4 truncating mutations result in a loss of BRG1 protein function [12,13,21]. For example, in these three studies, 84% (26 of 31), 100% (11 of 11), and 81% (50 of 62) NSCLC samples with truncating SMARCA4 mutations lacked BRG1 immunohistochemistry (IHC) protein expression, respectively. These variants leading to BRG1 protein loss co-mutated with KRAS may lead to cancer treatment resistance by creating an immunosuppressive tumor microenvironment and impairing the effectiveness of DNA repair mechanisms. Liu et al. found that KRAS/SMARCA4 co-mutated patients had significantly lower activated Cluster of Differentiation (CD4+) memory T cells (p = 0.0035) and (CD8+) T-cells proportions (p = 0.015) than KRAS-mutated/SMARCA4 wild-type NSCLC patients [25].

4.2. Emerging Therapeutic Strategies for SMARCA4-Mutated NSCLC

Currently, there are several therapeutic approaches targeting SMARCA4 mutations. SWI/SNF-related, matrix-associated, actin-dependent regulator of chromatin, subfamily A, member 2 (SMARCA2) selective protein degraders, such as PRT3789, offer a unique approach by exploiting synthetic lethality in SMARCA4-deficient tumors with >1000-fold selectivity for SMARCA4-mutated cancer cells compared to wild-type cells [31]. Two patients with NSCLC treated with PRT3789 had a confirmed partial response [32]. In treatment combination approaches, Ataxia–Telangiectasia and Rad3-related protein (ATR) inhibitors are being studied in combination with ICI treatment in previously treated NSCLC patients [33]. Tuvusertib (M1774) is a selective ATR inhibitor with antitumor activity as monotherapy in preclinical models with DNA damage repair pathway gene mutations. It is being studied with cemiplimab in a cohort of NSCLC patients with SMARCA4 mutations [33,34].

4.3. Clinical Implications

Our multivariate analysis underscores the critical need to incorporate routine NGS testing in managing NSCLC patients at the time of metastatic diagnosis, with particular emphasis on determining SMARCA4 mutation class in those with KRAS co-mutations. We demonstrated that NSCLC patients with KRAS/SMARCA4 class 1 co-mutations were significantly associated with a worse prognosis and overall survival compared to KRAS-mutated patients. We also confirmed that patients diagnosed with Stage IV disease, across the entire cohort, had significantly worse prognosis and survival compared to those with Stage I–III disease.

4.4. Limitations

In addition to our study’s strengths, several limitations exist. This study was retrospective, hypothesis-generating, and subject to potential biases associated with real-world evidence. The multivariate analysis focuses on prognosis and overall survival risk and does not include specific treatment regimens or other potential KRAS co-mutations that could affect survival outcomes.

5. Conclusions

This analysis provides valuable insights into the prognostic impact of KRAS and SMARCA4 mutations, including their co-mutations, on survival outcomes in metastatic NSCLC. Utilizing the AACR GENIE BPC NSCLC 2.0-public dataset, we demonstrated that KRAS/SMARCA4 class 1 co-mutations were significantly associated with worse overall survival compared to KRAS mutations alone in NSCLC patients. These findings reinforce the importance of routine genomic profiling in NSCLC patients to optimize precision oncology treatment strategies further.
Our results highlight the biological complexity of SMARCA4 class 1 versus class 2 mutations and their role in tumor progression when co-mutated with KRAS in patients with NSCLC. Future prospective studies should include KRAS/SMARCA4 class 1 and KRAS/SMARCA4 class 2 cohorts to validate the prognostic effects and potential predictive value of these co-mutations on survival outcomes in this high-risk NSCLC population.

Author Contributions

Conceptualization, P.M., J.W., L.B., and D.I.; Methodology, Y.-B.W., P.M., and J.W.; Software, Y.-B.W.; Formal Analysis, Y.-B.W.; Investigation, P.M.; Resources, P.M.; Data Curation, P.M.; Writing—Original Draft Preparation, P.M.; Writing—Review and Editing, P.M., J.W., L.B., Y.-B.W., and D.I.; Supervision, J.W., L.B., and D.I. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Institutional Review Board Statement

The study was reviewed by Clemson University (IRB#2023-0636) and deemed exempt as it utilized a publicly available database containing de-identified patient data.

Informed Consent Statement

This study utilized de-identified data from the AACR Project GENIE Biopharma Collaborative database. The original data providers obtained written informed consent, and all human investigations were approved by Institutional Review Boards (IRB) at respective institutions. This study was deemed exempt from IRB review by Clemson University (IRB#2023-0636).

Data Availability Statement

All data utilized in this analysis were obtained from the AACR Project GENIE BPC database. The dataset is publicly available through the AACR GENIE Data Commons (https://genie.cbioportal.org/). The specific dataset version employed is [Version 2.0-public]. Researchers can access the dataset following the instructions provided on the AACR GENIE website.

Acknowledgments

A special thank you to A.M. for all her support. The authors would like to acknowledge the American Association for Cancer Research and its financial and material support in the development of the AACR Project GENIE registry, as well as members of the consortium for their commitment to data sharing. Interpretations are the responsibility of the study authors.

Conflicts of Interest

P.M. is an employee of and a stockholder in Novartis®. Novartis® had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, and in the decision to publish the results. Y-B.W., J.S.W., L.B., and D.I. declare no conflicts of interest.

References

  1. World Health Organization. Lung Cancer. Available online: https://www.who.int/news-room/fact-sheets/detail/lung-cancer (accessed on 13 November 2024).
  2. Thandra, K.; Barsouk, A.; Saginala, K.; Aluru, J.S.; Barsouk, A. Epidemiology of lung cancer. Contemp. Oncol./Współczesna Onkol. 2021, 25, 45–52. [Google Scholar] [CrossRef]
  3. Siegel, R.L.; Miller, K.D.; Fuchs, H.E.; Jemal, A. Cancer statistics, 2022. CA Cancer J. Clin. 2022, 72, 7–33. [Google Scholar] [CrossRef] [PubMed]
  4. Ganti, A.K.; Klein, A.B.; Cotarla, I.; Seal, B.; Chou, E. Update of Incidence, Prevalence, Survival, and Initial Treatment in Patients With Non-Small Cell Lung Cancer in the US. JAMA Oncol. 2021, 7, 1824–1832. [Google Scholar] [CrossRef]
  5. Surveillance Research Program, National Cancer Institute. SEER*Explorer: An Interactive Website for SEER Cancer Statistics. Available online: https://seer.cancer.gov/statistics-network/explorer/ (accessed on 23 November 2024).
  6. Soria, J.C.; Ohe, Y.; Vansteenkiste, J.; Reungwetwattana, T.; Chewaskulyong, B.; Lee, K.H.; Dechaphunkul, A.; Imamura, F.; Nogami, N.; Kurata, T.; et al. Osimertinib in Untreated EGFR-Mutated Advanced Non-Small-Cell Lung Cancer. N. Engl. J. Med. 2018, 378, 113–125. [Google Scholar] [CrossRef]
  7. Jänne, P.A.; Riely, G.J.; Gadgeel, S.M.; Heist, R.S.; Ou, S.I.; Pacheco, J.M.; Johnson, M.L.; Sabari, J.K.; Leventakos, K.; Yau, E.; et al. Adagrasib in Non-Small-Cell Lung Cancer Harboring a KRASG12C Mutation. N. Engl. J. Med. 2022, 387, 120–131. [Google Scholar] [CrossRef]
  8. Jordan, E.J.; Kim, H.R.; Arcila, M.E.; Barron, D.; Chakravarty, D.; Gao, J.; Chang, M.T.; Ni, A.; Kundra, R.; Jonsson, P.; et al. Prospective Comprehensive Molecular Characterization of Lung Adenocarcinomas for Efficient Patient Matching to Approved and Emerging Therapies. Cancer Discov. 2017, 7, 596–609. [Google Scholar] [CrossRef]
  9. Pan, W.; Yang, Y.; Zhu, H.; Zhang, Y.; Zhou, R.; Sun, X. KRAS Mutation Is a Weak, but Valid Predictor for Poor Prognosis and Treatment Outcomes in NSCLC: A Meta-Analysis of 41 Studies. Oncotarget 2016, 7, 8373–8388. [Google Scholar] [CrossRef]
  10. Manolakos, P.; Ward, L.D. A Critical Review of the Prognostic and Predictive Implications of KRAS and STK11 Mutations and Co-Mutations in Metastatic Non-Small Lung Cancer. J. Pers. Med. 2023, 13, 1010. [Google Scholar] [CrossRef] [PubMed]
  11. Skoulidis, F.; Goldberg, M.E.; Greenawalt, D.M.; Hellmann, M.D.; Awad, M.M.; Gainor, J.F.; Schrock, A.B.; Hartmaier, R.J.; Trabucco, S.E.; Gay, L.; et al. STK11/LKB1 Mutations and PD-1 Inhibitor Resistance in KRAS-Mutant Lung Adenocarcinoma. Cancer Discov. 2018, 8, 822–835. [Google Scholar] [CrossRef] [PubMed]
  12. Dagogo-Jack, I.; Schrock, A.B.; Kem, M.; Jessop, N.; Lee, J.; Ali, S.M.; Ross, J.S.; Lennerz, J.K.; Shaw, A.T.; Mino-Kenudson, M.; et al. Clinicopathologic Characteristics of BRG1-Deficient NSCLC. J. Thorac. Oncol. 2020, 15, 766–776. [Google Scholar] [CrossRef]
  13. Schoenfeld, A.J.; Bandlamudi, C.; Lavery, J.A.; Montecalvo, J.; Namakydoust, A.; Rizvi, H.; Egger, J.; Concepcion, C.P.; Paul, S.; Arcila, M.E.; et al. The Genomic Landscape of SMARCA4 Alterations and Associations with Outcomes in Patients with Lung Cancer. Clin. Cancer Res. 2020, 26, 5701–5708. [Google Scholar] [CrossRef]
  14. Herzberg, B.; Gandhi, N.; Henick, B.; Xiu, J.; Vanderwalde, A.; Reuss, J.; Murray, J.; Concepcion-Crisol, C. Effects of Mutations in SWI/SNF Subunits on Context-Specific Prognosis in Driver Positive and Driver Negative NSCLC. In Proceedings of the 2023 American Society of Clinical Oncology Annual Meeting, Chicago, IL, USA, 2–6 June 2023. Abstract 9039. [Google Scholar]
  15. Liang, X.; Gao, X.; Wang, F.; Li, S.; Zhou, Y.; Guo, P.; Meng, Y.; Lu, T. Clinical Characteristics and Prognostic Analysis of SMARCA4-Deficient Non-Small Cell Lung Cancer. Cancer Med. 2023, 12, 14171–14182. [Google Scholar] [CrossRef]
  16. Manolakos, P.; Boccuto, L.; Ivankovic, D.S. A Critical Review of the Impact of SMARCA4 Mutations on Survival Outcomes in Non-Small Cell Lung Cancer. J. Pers. Med. 2024, 14, 684. [Google Scholar] [CrossRef]
  17. Hodges, H.; Stanton, B.; Cermakova, K.; Chang, C.; Miller, E.; Kirkland, J.; Ku, W.; Veverka, V.; Zhao, K.; Crabtree, G. Dominant-Negative SMARCA4 Mutants Alter the Accessibility Landscape of Tissue-Unrestricted Enhancers. Nat. Struct. Mol. Biol. 2018, 25, 61–72. [Google Scholar] [CrossRef] [PubMed]
  18. Mardinian, K.; Adashek, J.J.; Botta, G.P.; Kato, S.; Kurzrock, R. SMARCA4: Implications of an Altered Chromatin-Remodeling Gene for Cancer Development and Therapy. Mol. Cancer Ther. 2021, 20, 2341–2351. [Google Scholar] [CrossRef] [PubMed]
  19. Fernando, T.M.; Piskol, R.; Bainer, R.; Sokol, E.S.; Trabucco, S.E.; Zhang, Q.; Trinh, H.; Maund, S.; Kschonsak, M.; Chaudhuri, S.; et al. Functional Characterization of SMARCA4 Variants Identified by Targeted Exome-Sequencing of 131,668 Cancer Patients. Nat. Commun. 2020, 11, 5551. [Google Scholar] [CrossRef] [PubMed]
  20. de Bruijn, I.; Kundra, R.; Mastrogiacomo, B.; Tran, T.N.; Sikina, L.; Mazor, T.; Li, X.; Ochoa, A.; Zhao, G.; Lai, H.; et al. Analysis and Visualization of Longitudinal Genomic and Clinical Data from the AACR Project GENIE Biopharma Collaborative in cBioPortal. Cancer Res. 2023, 83, 3861–3867. [Google Scholar] [CrossRef]
  21. Alessi, J.V.; Ricciuti, B.; Spurr, L.F.; Gupta, H.; Li, Y.Y.; Glass, C.; Nishino, M.; Cherniack, A.D.; Lindsay, J.; Sharma, B.; et al. SMARCA4 and Other SWItch/Sucrose Non-Fermentable Family Genomic Alterations in NSCLC: Clinicopathologic Characteristics and Outcomes to Immune Checkpoint Inhibition. J. Thorac. Oncol. 2021, 16, 1176–1187. [Google Scholar] [CrossRef]
  22. Alessi, J.V.; Elkrief, A.; Ricciuti, B.; Wang, X.; Cortellini, A.; Vaz, V.R.; Lamberti, G.; Frias, R.L.; Venkatraman, D.; Fulgenzi, C.A.; et al. Clinicopathologic and Genomic Factors Impacting Efficacy of First-Line Chemoimmunotherapy in Advanced NSCLC. J. Thorac. Oncol. 2023, 18, 731–743. [Google Scholar] [CrossRef]
  23. Negrao, M.V.; Araujo, H.A.; Lamberti, G.; Cooper, A.J.; Akhave, N.S.; Zhou, T.; Delasos, L.; Hicks, J.K.; Aldea, M.; Minuti, G.; et al. Comutations and KRASG12C Inhibitor Efficacy in Advanced NSCLC. Cancer Discov. 2023, 13, 1556–1571. [Google Scholar] [CrossRef]
  24. Boiarsky, D.; Lydon, C.A.; Chambers, E.S.; Sholl, L.M.; Nishino, M.; Skoulidis, F.; Heymach, J.; Luo, J.; Awad, M.; Janne, P.; et al. Molecular Markers of Metastatic Disease in KRAS-Mutant Lung Adenocarcinoma. Ann. Oncol. 2023, 34, 589–604. [Google Scholar] [CrossRef] [PubMed]
  25. Liu, L.; Ahmed, T.; Petty, W.J.; Grant, S.; Ruiz, J.; Lycan, T.W.; Topaloglu, U.; Chou, P.; Miller, L.D.; Hawkins, G.A.; et al. SMARCA4 Mutations in KRAS-Mutant Lung Adenocarcinoma: A Multi-Cohort Analysis. Mol. Oncol. 2021, 15, 462–472. [Google Scholar] [CrossRef]
  26. Choudhury, N.J.; Lavery, J.A.; Brown, S.; de Bruijn, I.; Jee, J.; Tran, T.N.; Rizvi, H.; Arbour, K.C.; Whiting, K.; Shen, R.; et al. The GENIE BPC NSCLC Cohort: A Real-World Repository Integrating Standardized Clinical and Genomic Data for 1,846 Patients with Non-Small Cell Lung Cancer. Clin. Cancer Res. 2023, 29, 3418–3428. [Google Scholar] [CrossRef]
  27. AACR Project GENIE Consortium. AACR Project GENIE: Powering Precision Medicine Through an International Consortium. Cancer Discov. 2017, 7, 818–831. [Google Scholar] [CrossRef] [PubMed] [PubMed Central]
  28. Lavery, J.A.; Lepisto, E.M.; Brown, S.; Rizvi, H.; McCarthy, C.; LeNoue-Newton, M.; Yu, C.; Lee, J.; Guo, X.; Yu, T.; et al. A Scalable Quality Assurance Process for Curating Oncology Electronic Health Records: The Project GENIE Biopharma Collaborative Approach. JCO Clin. Cancer Inform. 2022, 6, e2100105. [Google Scholar] [CrossRef]
  29. Wankhede, D.; Grover, S.; Hofman, P. SMARCA4 Alterations in Non-Small Cell Lung Cancer: A Systematic Review and Meta-Analysis. J. Clin. Pathol. 2024, 77, 457–463. [Google Scholar] [CrossRef]
  30. Alver, B.H.; Kim, K.H.; Lu, P.; Wang, X.; Manchester, H.E.; Wang, W.; Haswell, J.R.; Park, P.J.; Roberts, C.W. The SWI/SNF Chromatin Remodeling Complex Is Required for Maintenance of Lineage Specific Enhancers. Nat. Commun. 2017, 8, 14648. [Google Scholar] [CrossRef] [PubMed]
  31. Ito, K.; Hulse, M.; Agarwal, A.; Carter, J.; Sivakumar, M.; Vykuntam, K.; Fultang, N.; Schwab, A.; Wang, M.; Coward, M.; et al. Discovery of PRT3789, a Selective SMARCA2 Degrader. In Proceedings of the AACR-NCI-EORTC International Conference on Molecular Targets and Cancer Therapeutics, Hynes Convention Center, Boston, MA, USA, 11–15 October 2023. [Google Scholar]
  32. Dagogo-Jack, I.; Dowlati, A.; Guo, R.; Awad, M.M.; Swalduz, A.; Calvo, E.; Moreno Garcia, V.; Adjei, A.A.; Lorusso, P.; Punekar, S.; et al. A Phase 1 Study of PRT3789, a Potent and Selective Degrader of SMARCA2 in Patients with Advanced or Metastatic Solid Tumors and a SMARCA4 Mutation. In Proceedings of the Annual European Society for Medical Oncology Congress (ESMO), Madrid, Spain, 20–24 October 2023. [Google Scholar]
  33. Yap, T.A.; Tolcher, A.W.; Plummer, R.; Mukker, J.K.; Enderlin, M.; Hicking, C.; Grombacher, T.; Locatelli, G.; Szucs, Z.; Gounaris, I.; et al. First-in-Human Study of the Ataxia Telangiectasia and Rad3-Related (ATR) Inhibitor Tuvusertib (M1774) as Monotherapy in Patients with Solid Tumors. Clin. Cancer Res. 2024, 30, 2057–2067. [Google Scholar] [CrossRef]
  34. Paz-Ares, L.; Cappuzzo, F.; Yamamoto, N.; Vokes, N.; Gray, J.E.E.; Owonikoko, T.K.; Ariyasu, R.; Ishii, H.; Kang, J.H.; Lee, S.; et al. 104TiP—Phase Ib/IIa Study of ATR Inhibitor Tuvusertib + Anti-PD-1 Cemiplimab in Patients with Advanced Non-Squamous Non-Small Cell Lung Cancer (NSCLC) That Has Progressed on Prior Anti-PD-(L)1 and Platinum-Based Therapies. Ann. Oncol. 2024, 9 (Suppl. 3), 102683. [Google Scholar] [CrossRef]
Biomedicines 13 02142 g001
Figure 1. Adjusted Kaplan–Meier overall survival analysis among NSCLC patients with KRAS, SMARCA4, and KRAS/SMARCA4 co-Mutations.
Figure 1. Adjusted Kaplan–Meier overall survival analysis among NSCLC patients with KRAS, SMARCA4, and KRAS/SMARCA4 co-Mutations.
Biomedicines 13 02142 g001
Table 1. Clinical and genomic characteristics of KRAS, SMARCA4, and KRAS/SMARCA4 co-mutated NSCLC patients.
Table 1. Clinical and genomic characteristics of KRAS, SMARCA4, and KRAS/SMARCA4 co-mutated NSCLC patients.
ClinicalN = 659, n (%)
Age at NGS sequencing (mean years)67.4
Sex
Female397 (60%)
Male262 (40%)
Race
Non-White/Other59 (9%)
White600 (91%)
Stage
I–III (early and locally advanced)392 (59%)
IV (metastatic)267 (41%)
Smoking History
Never50 (8%)
Former496 (75%)
Current113 (17%)
Mutation Type
KRAS518 (79%)
SMARCA4 (class 1)41 (6%)
SMARCA4 (class 2)54 (8%)
KRAS + SMARCA4 (class 1)18 (3%)
KRAS + SMARCA4 (class 2)28 (4%)
Abbreviations: KRAS, Kirsten rat sarcoma viral oncogene homolog; NGS, next-generation sequencing; NSCLC, non-small cell lung cancer; SMARCA4, SWI/SNF-related, matrix-associated, actin-dependent regulator of chromatin, subfamily A, member 4.
Table 2. Univariate analysis of OS status from cancer diagnosis for KRAS, SMARCA4, and KRAS/SMARCA4 co-mutated NSCLC patients.
Table 2. Univariate analysis of OS status from cancer diagnosis for KRAS, SMARCA4, and KRAS/SMARCA4 co-mutated NSCLC patients.
OS Status from Cancer DiagnosisTotal LivingDeceasedp-Value
ClinicalN = 659N = 311N = 348
Age at NGS sequencing (mean) 66.768.00.085
Sex 0.327
Female 397194203
Male 262117145
Race 0.713
Non-White/Other 592633
White 600285315
Stage <0.001
I–III (early and locally advanced)392253139
IV (metastatic)26758209
Smoking History 0.901
Never 502525
Former 496232264
Current 1135459
GenomicN = 659
Mutation Type 0.001
KRAS518264254
SMARCA4 (class 1) 411526
SMARCA4 (class 2) 542232
KRAS + SMARCA4 (class 1) 18315
KRAS + SMARCA4 (class 2) 28721
Abbreviations: KRAS, Kirsten rat sarcoma viral oncogene homolog; NGS, next-generation sequencing; NSCLC, non-small cell lung cancer; OS, overall survival; SMARCA4, SWI/SNF-related, matrix-associated, actin-dependent regulator of chromatin, subfamily A, member 4.
Table 3. Multivariate analysis of overall survival in NSCLC patients with KRAS, SMARCA4, and KRAS/SMARCA4 co-mutations.
Table 3. Multivariate analysis of overall survival in NSCLC patients with KRAS, SMARCA4, and KRAS/SMARCA4 co-mutations.
FactorHR95% CIp-Value
Sex
(Reference group:
Female)
Male1.180.94–1.470.153
Age1.021.01–1.030.005
Race
(Reference group:
Non-White/Other)
White0.980.68–1.410.905
Smoking History
(Reference group:
Never smoker)
Former smoker1.080.70–1.660.732
Current smoker1.210.75–1.980.437
Stage
(Reference group: Stage I–III)
Stage IV4.013.21–5.01<0.001
Genomic
(Reference group: KRAS)
SMARCA4 (class 1)1.180.78–1.780.438
SMARCA4 (class 2)0.930.64–1.370.720
KRAS + SMARCA4 (class 1)3.231.90–5.51<0.001
KRAS + SMARCA4 (class 2)1.340.85–2.120.205
Abbreviations: CI, Confidence Interval; HR, Hazard Ratio; KRAS, Kirsten rat sarcoma viral oncogene homolog; NSCLC, non-small cell lung cancer; OS, overall survival; SMARCA4, SWI/SNF-related, matrix-associated, actin-dependent regulator of chromatin, subfamily A, member 4.
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

Manolakos, P.; Wang, Y.-B.; Withycombe, J.; Boccuto, L.; Ivankovic, D. Prognostic Impact of KRAS and SMARCA4 Mutations and Co-Mutations on Survival in Non-Small Cell Lung Cancer: Insights from the AACR GENIE BPC Dataset. Biomedicines 2025, 13, 2142. https://doi.org/10.3390/biomedicines13092142

AMA Style

Manolakos P, Wang Y-B, Withycombe J, Boccuto L, Ivankovic D. Prognostic Impact of KRAS and SMARCA4 Mutations and Co-Mutations on Survival in Non-Small Cell Lung Cancer: Insights from the AACR GENIE BPC Dataset. Biomedicines. 2025; 13(9):2142. https://doi.org/10.3390/biomedicines13092142

Chicago/Turabian Style

Manolakos, Peter, Yu-Bo Wang, Janice Withycombe, Luigi Boccuto, and Diana Ivankovic. 2025. "Prognostic Impact of KRAS and SMARCA4 Mutations and Co-Mutations on Survival in Non-Small Cell Lung Cancer: Insights from the AACR GENIE BPC Dataset" Biomedicines 13, no. 9: 2142. https://doi.org/10.3390/biomedicines13092142

APA Style

Manolakos, P., Wang, Y.-B., Withycombe, J., Boccuto, L., & Ivankovic, D. (2025). Prognostic Impact of KRAS and SMARCA4 Mutations and Co-Mutations on Survival in Non-Small Cell Lung Cancer: Insights from the AACR GENIE BPC Dataset. Biomedicines, 13(9), 2142. https://doi.org/10.3390/biomedicines13092142

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