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Article

Sex- and Age-Associated Differences in Genomic Alterations among Patients with Advanced Non-Small Cell Lung Cancer (NSCLC)

by
ErinMarie O. Kimbrough
1,2,
Julian A. Marin-Acevedo
2,
Leylah M. Drusbosky
3,
Ariana Mooradian
1,4,
Yujie Zhao
1,
Rami Manochakian
1 and
Yanyan Lou
1,*
1
Division of Hematology and Oncology, Mayo Clinic, Jacksonville, FL 32224, USA
2
Department of Hematology and Oncology, Division of Internal Medicine, Indiana University Melvin and Bren Simon Comprehensive Cancer Center, Indianapolis, IN 46202, USA
3
Guardant Health, Inc., Redwood City, CA 94063, USA
4
Division of Hematology and Medical Oncology, University of Florida, Jacksonville, FL 32209, USA
*
Author to whom correspondence should be addressed.
Cancers 2024, 16(13), 2366; https://doi.org/10.3390/cancers16132366
Submission received: 11 April 2024 / Revised: 15 June 2024 / Accepted: 21 June 2024 / Published: 27 June 2024
(This article belongs to the Special Issue Genetic, Epigenetic, and Epitranscriptomic Changes in Lung Cancer)
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Abstract

:

Simple Summary

Lung cancer is the leading cause of cancer-related death. While sex and age impact outcomes in non-small cell lung cancer (NSCLC), it is not clearly understood how these factors affect tumor biology. We believe that sex and age influence the distribution of genomic alterations in NSCLC and evaluated for differences in predictive and/or prognostic alterations in individuals with advanced NSCLC. Our study is the largest, to our knowledge, to evaluate and confirm that the genomic landscape in advanced NSCLC differs by both sex and age.

Abstract

Genomic mutations impact non-small cell lung cancer (NSCLC) biology. The influence of sex and age on the distribution of these alterations is unclear. We analyzed circulating-tumor DNA from individuals with advanced NSCLC from March 2018 to October 2020. EGFR, KRAS, ALK, ROS1, BRAF, NTRK, ERBB2, RET, MET, PIK3CA, STK11, and TP53 alterations were assessed. We evaluated the differences by sex and age (<70 and ≥70) using Fisher’s exact test. Of the 34,277 samples, 30,790 (89.83%) had a detectable mutation and 19,923 (58.12%) had an alteration of interest. The median age of the ctDNA positive population was 69 (18–102), 16,756 (54.42%) were female, and 28,835 (93.65%) had adenocarcinoma. Females had more alterations in all the assessed EGFR mutations, KRAS G12C, and ERBB2 ex20 ins. Males had higher numbers of MET amp and alterations in STK11 and TP53. Patients <70 years were more likely to have alterations in EGFR exon 19 del/exon 20 ins/T790M, KRAS G12C/D, ALK, ROS1, BRAF V600E, ERBB2 Ex20ins, MET amp, STK11, and TP53. Individuals ≥70 years were more likely to have alterations in EGFR L861Q, MET exon 14 skipping, and PIK3CA. We provided evidence of sex- and age-associated differences in the distribution of genomic alterations in individuals with advanced NSCLC.

1. Introduction

Lung cancer is the third most common cancer in the U.S., after breast and prostate, with an incidence of 50.3 per 100,000 women and 64.1 per 100,000 men [1,2,3]. The median age at diagnosis is 71 years, and non-small cell lung cancer (NSCLC) is uncommon among younger individuals (<50 years) [2,3,4]. Lung cancer is also the leading cause of death among cancer patients. The estimated five-year overall survival is 25.4%, with an estimated death rate of 29.3 per 100,000 women and 42.2 per 100,000 men [1,2,3].
Recent advances, including the discovery of predictive and prognostic biomarkers and the development of targeted therapies, have led to improved outcomes for some patients with lung cancer. Genomic testing, particularly on tumor samples, has become an integral part of the management of NSCLC and is considered a standard of care [5]. A tumor tissue biopsy offers precise histological and molecular information about the tumor, allowing for an accurate diagnosis and a comprehensive review of histology, mutational status, and tumor microenvironment. This approach is limited, however, by the need for invasive procedures and the potential sampling errors due to insufficient tissue or tumor heterogeneity, where a biopsy may fail to capture the complete genetic diversity of the tumor [5]. The concept of a “liquid biopsy” dates back to the 1940s when the molecules released by the primary tumor or metastatic lesions into the peripheral blood were first detected. Modern technologies have allowed us to detect small amounts of tumor in the blood [6]. Next-generation sequencing (NGS) assays are currently utilized to assess circulating-tumor DNA (ctDNA) in clinical practice. ctDNA is particularly helpful when the tumor is difficult to access safely or when repeat biopsies provide inadequate tissue for evaluation and diagnostic testing [6]. ctDNA is more representative of the mutational landscape of the cancer as a whole compared to tissue alone. The testing is less invasive and can be repeated to assess changes in the tumor mutational landscape over time, i.e., real-time monitoring [6]. Limitations of liquid biopsy include its inability to detect some rearrangements/fusions and to distinguish tumor alterations from contaminant leukocyte DNA. Some of the alterations detected may represent clonal hematopoiesis [6]. Detection of rearrangements and fusions using circulating tumor RNA (ctRNA) is thought to be more sensitive than ctDNA. The widespread use of ctRNA has been challenging due to its instability and rapid degradation by ribonucleases and immune cells. ctRNA can be broken down even before it enters circulation [7]. ctRNA may be utilized in conjunction with ctDNA in the future to enhance the detection of targetable alterations [8].
ctDNA is often utilized in NSCLC to detect predictive and/or prognostic mutations that help guide therapeutic decisions [6,9]. The current understanding of these predictive and/or prognostic mutations has improved with more frequent genomic testing and advanced sequencing technologies, but little is known about the factors that influence the distribution of these mutations. Sex and age are thought to impact the genomic landscape and clinical outcomes in patients with NSCLC [4,10,11]. Women with lung cancer have improved median overall survival (OS) compared to men with similar disease and treatment responses, while individuals ≤40 or >70 years tend to have a worse prognosis [4,12,13]. Most of the previously published studies evaluating sex and age differences were conducted using a limited mutation panel and often included a small number of patients [4,10,11].
We conducted a comprehensive analysis to examine how sex and age influence the genomic profiles of advanced NSCLC using one of the largest real-world datasets. We believe that with further characterization of these differences, we can better stratify those with mutations that portend worse outcomes and/or predict response to therapy and improve current treatment strategies.

2. Materials and Methods

We conducted a retrospective analysis utilizing de-identified data from the Guardant Health database (Palo Alto, CA, USA) from 1 March 2018 to 26 October 2020. Genomic profiles from patients with advanced NSCLC (stages IIIB and higher), who underwent molecular profiling using the plasma-based ctDNA NGS assay Guardant360, were included for the initial review. Single nucleotide variants (SNV), fusions, indels, and copy number variations (CNV) of up to 83 genes were analyzed. We focused on clinically relevant genomic alterations. Synonymous mutations, variants of undetermined significance (VUS), and sub-clonal mutations were excluded. While we did not have data regarding prior or current treatment, we included only the first serial sample in our analysis to limit the effects that any prior treatment may have on the genomic profiles and to capture newly diagnosed NSCLC patients. To minimize confounding factors that impact the accuracy of liquid biopsy (e.g., low tumor burden), samples without detectable alterations were excluded from the analysis [14,15].
We focused on alterations with known prognostic and/or predictive significance (“alterations of interest”) including EGFR exon 19 deletion (ex19 del), EGFR exon 20 insertion (ex20 ins), EGFR G719X, EGFR L858R, EGFR T790M, EGFR S7681, EGFR L861Q, KRAS G12C, KRAS G12D, KRAS G12V, ALK fusion, ROS1 fusion, BRAF V600E, NTRK 1 fusion, ERBB2 exon 20 insertion (ex20 ins), RET fusion, MET exon 14 (ex14) skipping, MET amplification (amp) medium, MET amp high, any PIK3CA mutation, any STK11 mutation, and any TP53 mutation. The frequency of these alterations was analyzed according to sex (female and male) and age (<70 and ≥70) using Fisher’s exact test. We selected a cut-off of 70 years of age based on the median age of NSCLC and worse outcomes reported in individuals >70 [4,12]. A p-value < 0.05 was considered statistically significant. We also assessed the co-occurrence and mutual exclusivity of these alterations using cBioPortal https://www.cbioportal.org, accessed on 9 January 2022 [16,17].

3. Results

Of the 34,277 samples reviewed, 30,790 (89.83%) had a somatic alteration and 19,923 (58.12%) had an alteration of interest (Figure 1). The median age of the ctDNA positive population was 69 (range: 18–102), 16,756 (54.42%) were female, and 28,835 (93.65%) had adenocarcinoma histology (Table 1). TP53 was the most commonly found mutation of interest (49.08%) followed by EGFR (19.92%), KRAS (14.39%), STK11 (7.33%), MET (3.36%), ALK (1.54%), PIK3CA (1.52%), ERBB2 ex20 ins (1.43%), BRAF (1.14%), ROS1 (0.21%), RET (0.07%), and NTRK1 (0.00%), Supplemental Figure S1.

3.1. Genomic Profile Differences in Advanced NSCLC by Sex

Females with advanced NSCLC had significantly more alterations in all assessed EGFR mutations [i.e., ex19 del (9.55% vs. 5.86%; p < 0.0001), ex20 ins (1.25% vs. 0.95%; p = 0.0139), G719X (0.96% vs. 0.59%; p = 0.0003), L858R (6.81% vs. 3.96%; p < 0.0001), T790M (1.08% vs. 0.57%; p < 0.0001), S768I (0.47% vs. 0.25%; p = 0.0013), and L861Q (0.58% vs. 0.34%; p = 0.002)], KRAS G12C (7.10% vs. 6.41%; p = 0.0169), and ERBB2 ex20 ins (1.40% vs. 1.00%; p = 0.0012) compared to the males. Males had significantly higher numbers of MET amp medium (1.12% vs. 0.78%; p = 0.0024) or high (1.09% vs. 0.60%; p < 0.0001), alterations in STK11 (7.61% vs. 5.10%; p < 0.0001) and TP53 (46.36% vs. 38.02%; p < 0.0001) than females. No significant differences between males and females were seen in the frequency of KRAS G12D (2.54% vs. 2.46%; p = 0.6604) and G12V (2.81% vs. 3.10%; p = 0.1383), ALK (1.23% vs. 1.38%; p = 0.366), ROS1 (0.21% vs. 0.15%; p = 0.274), BRAF V600E (1.02% vs. 0.94%; p = 0.4848), NTRK1 (0.00% vs. 0.01%; p > 0.9999), RET (0.04% vs. 0.08%; p = 0.2554), MET ex14 skipping (1.09% vs. 1.12%; p = 0.827), or PIK3CA alterations (1.26% vs. 1.32%; p = 0.649), Table 2.

3.2. Genomic Profile Differences in Advanced NSCLC by Age

Individuals <70 years of age were more likely to have mutations in EGFR ex19 del (10.14% vs. 5.59%; p < 0.0001), ex20 ins (1.47% vs. 0.75%; p < 0.0001), and T790M (1.04% vs. 0.66%; p = 0.0003). They were also more likely to have an alteration of KRAS G12C (7.58% vs. 6.01%; p < 0.0001) or G12D (2.72% vs. 2.29%; p = 0.0176), ALK fusion (2.10% vs. 0.51%; p < 0.0001), ROS1 fusion (0.26% vs. 0.09%; p = 0.0005), BRAF V600E (1.09% vs. 0.86%; p = 0.0422), ERBB2 ex20 ins (1.48% vs. 0.95%; p < 0.0001), MET amp medium (1.08% vs. 0.79%; p = 0.0108) or high (1.10% vs. 0.54%; p < 0.0001), STK11 (7.39% vs. 5.11%; p < 0.0001), and TP53 (44.62% vs. 39.13%; p < 0.0001) mutations. Those ≥70 were more likely to have EGFR L861Q (0.63% vs. 0.32%; p < 0.0001), MET ex14 skipping (1.71% vs. 0.52%; p < 0.0001), and PIK3CA (1.48% vs. 1.12%; p = 0.0065) alterations. There were no differences in those <70 versus individuals ≥70 in EGFR G719X (0.82% vs. 0.77%; p = 0.6528), L858R (5.71% vs. 5.33%; p = 0.1475), S768I (0.39% vs. 0.35%; p = 0.6405), KRAS G12V (3.16% vs. 2.80%; p = 0.0649), NTRK1 (0.01% vs. 0.00%; p > 0.9999), or RET alterations (0.07% vs. 0.05%; p = 0.6477), Table 3.

3.3. Sex and Age Differences in Advanced NSCLC

Females <70 years of age had significantly more alterations in EGFR ex19 del (12.07% vs. 7.03%; p < 0.0001), ex20 ins (1.62% vs. 0.88%; p < 0.0001), T790M (1.34% vs. 0.82%; p = 0.0013), KRAS G12C (8.15% vs. 6.07%; p < 0.0001), G12D (2.84% vs. 2.09%; p = 0.002), ALK (2.23% vs. 0.52%; p < 0.0001), ROS1 (0.21% vs. 0.08%; p = 0.0432), ERBB2 ex20 ins (1.65% vs. 1.16%; p = 0.007), MET amp high (0.84% vs. 0.35%; p < 0.0001), STK11 (6.33% vs. 3.88%; p < 0.0001), and TP53 (40.51% vs. 35.64%; p < 0.0001) than females ≥70. Females ≥70 had significantly higher number of alterations in EGFR L861Q (0.77% vs. 0.40%; p = 0.0022) and MET ex14 skipping (1.69% vs. 0.57%; p < 0.0001) compared to females <70. No statistically significant differences were seen in younger versus older females in the frequency of EGFR G719X (1.01% vs. 0.91%; p = 0.5795), L858R (7.05% vs. 6.60%; p = 0.2565), S768I (0.49% vs. 0.46%; p = 0.822), KRAS G12V (3.34% vs. 2.88%; p = 0.0904), BRAF V600E (1.07% vs. 0.81%; p = 0.0919), NTRK1 (0.01% vs. 0.00%; p > 0.9999), RET (0.08% vs. 0.07%; p > 0.9999), MET amp medium (0.91% vs. 0.65%; p = 0.0539), or PIK3CA (1.20% vs. 1.46%; p = 0.1561) alterations, Table 4.
Males < 70 were more likely to have alterations in EGFR ex19 del (7.84% vs. 3.85%; p < 0.0001), ex20 ins (1.30% vs. 0.59%; p < 0.0001), KRAS G12C (6.89% vs. 5.95%; p = 0.0229), ALK (1.95% vs. 0.49%; p < 0.0001), ROS1 (0.31% vs. 0.10%; p = 0.0082), ERBB2 ex20 ins (1.29% vs. 0.71%; p = 0.0006), MET amp high (1.41% vs. 0.76%; p = 0.0002), STK11 (8.65% vs. 6.58%; p < 0.0001), and TP53 (49.51% vs. 43.30%; p < 0.0001) compared to those ≥70. Males ≥70 had significantly more alterations in EGFR L861Q (0.46% vs. 0.23%; p = 0.0201), MET ex14 skipping (1.73% vs. 0.47%; p < 0.0001), and PIK3CA (1.50% vs. 1.03%; p = 0.0153) compared to those <70. There were no significant differences in alterations between males <70 and ≥70 in EGFR G719X (0.59% vs. 0.59%; p > 0.9999), L858R (4.12% vs. 3.81%; p = 0.3414), T790M (0.27% vs. 0.23%; p = 0.7361), S768I (0.68% vs. 0.46%; p = 0.0935), KRAS G12D (2.57% vs. 2.53%; p = 0.8724), G12V (2.94% vs. 2.70%; p = 0.4142), BRAF V600E (1.12% vs. 0.92%; p = 0.2749), NTRK1 (0.00% vs. 0.00%; p > 0.9999), RET (0.06% vs. 0.03%; p = 0.6875), or MET amp medium (1.27% vs. 0.97%; p = 0.0921), Table 4.

3.4. Co-Occurrence of Mutations of Interest

Most individuals (15,093 or 75.76%) had one alteration of interest; however, 4830 (24.24%) had two or more co-existing mutations of interest (Supplemental Figure S2). The individual mutation profiles are depicted in the Oncoprint (Supplemental Figure S3). The heatmap demonstrates co-occurring alterations among those with an alteration of interest (Figure 2). We found KRAS and STK11 alterations often co-occurred (p < 0.001). The other alterations tended to occur in isolation (Supplemental Table S1).

4. Discussion

Outcomes in patients with advanced NSCLC have improved in recent years due to the identification of targetable alterations and the development of novel therapies [2,3,18,19]. While 71% of patients with NSCLC have actionable alterations (e.g., EGFR, ALK, BRAF, ERBB2, MET, ROS1, RET, and KRAS), some have non-actionable mutations with prognostic and/or predictive implications (e.g., PIK3CA, STK11, and TP53) [20,21,22]. It is important that we understand the factors that influence the mutational landscape of NSCLC because these alterations play a critical role in therapeutic decision-making and affect outcomes. Smaller studies have suggested that sex and age impact the distribution of these alterations [4,10,11]. To our knowledge, our study represents the largest to date to investigate and provide evidence of sex- and age-associated differences in patients with advanced NSCLC. Clinicians can personalize treatment with better understanding of how these differences impact tumor biology.
Our study demonstrates that all evaluated EGFR mutations are more common among women, which is consistent with prior reports [4,23,24,25,26,27,28,29]. While some also found that EGFR mutations are more common among young individuals, except for EGFR L858R, we found that alterations in EGFR ex19 del, ex20 ins, and T790M are statistically more common in patients <70, while EGFR L861Q is statistically more common in adults ≥70 [4,23,24,25,26,27,28,29]. We found no association with age for mutations in EGFR G719X, L858R, or S768I.
There are conflicting data regarding the distribution of KRAS mutations in NSCLC. While some studies suggest that KRAS is more common in younger women, others have failed to demonstrate these differences [4,28,30,31,32,33,34,35]. In our study, alterations in KRAS G12C are more frequent in females and in younger individuals, while KRAS G12D mutations are more common in those <70 with no sex differences. We did not find differences in the distribution of KRAS G12V by sex or age.
As reported and confirmed in our study, ALK fusions are more common among young patients. Data regarding sex differences are mixed, and we found no difference by sex [4,10,36,37,38,39,40,41,42]. ROS1 fusions are reported more often among younger individuals and females [10,36,43]. In our study, we did not find any significant difference in the distribution by sex, but we confirmed that ROS1 fusions are significantly more common in younger individuals. Studies have suggested that BRAF mutations are more common among females without a clear association with age [4,44,45]. While we found no significant difference by sex in BRAF V600E mutations, we did note an association with age <70.
Given the rarity of NTRK fusions, sex and age differences are unknown [46,47,48]. We did not observe statistically significant differences by sex or age either, though the sample size was small. ERBB2 ex20 insertions are reported more frequently among females and younger individuals, and our findings are consistent with these reports [4,49,50]. Studies have failed to demonstrate sex differences in individuals with RET fusions, but a trend towards younger age has been reported [51,52]. We found no statistically significant differences by sex or age.
MET ex14 skipping mutations are reported more frequently among females and older individuals, while MET amplifications are reported more often in males without an association with age [53,54,55]. While we did not find any association with sex, we confirmed that MET ex14 skipping mutations are more frequent in individuals ≥70. In addition, we found that MET amplifications were associated with male sex and younger age. PIK3CA mutations are thought to occur more often in males and older individuals [38,41]. While our results confirm these mutations are more common among men, we did not find any differences by age.
Lastly, our findings are consistent with data suggesting that STK11 mutations are more common in males and younger individuals and TP53 mutations are more frequent in males [56,57,58,59,60]. We also found an association with TP53 mutations and younger age.
While many of our results are consistent with prior studies, several differences exist. Factors such as inclusion of early-stage disease, variability in the genomic testing (e.g., surgical specimens rather than ctDNA), inconsistency in the age cut-offs used to define older and younger populations, and differences in the populations evaluated (U.S. vs. non-U.S.; single institution vs. multi-center) may account for the discrepancies.
Our study highlights the influence sex and age have on the genomic landscape of NSCLC. We found that females were more likely to have alterations with a known targeted therapy (EGFR, KRAS G12C, ERBB2 ex20), while males were more likely to have mutations in STK11 or TP53 which are associated with poor response to therapy and worse outcomes [61,62]. Similarly, we found that younger patients are more likely to have targetable alterations than older individuals, but their tumors are also more likely to harbor poor prognostic mutations, including STK11 and TP53. These differences may impact the tumor biology, treatment, and outcomes for individuals with NSCLC and help explain why males and individuals ≤40 or >70 tend to have worse prognosis [4,10,11,12]. As we work to improve upon existing treatments, it is important to consider the co-occurrence of these alterations to predict response and/or to develop novel combination strategies. In general, these mutations are thought to be mutually exclusive [63]. While we confirmed that most of the evaluated alterations occur in isolation, we found that STK11 and KRAS were likely to co-occur. This has been described previously and is associated with worse clinical outcomes and poor response to immunotherapy [61,62].
We acknowledge several limitations of our study. Race and tobacco use are known to influence the genomic landscape of NSCLC [64,65]. For example, EGFR L858R is three times more common in Asian individuals, while KRAS G12C is most common among white individuals [64]. Tobacco use is more common among males and individuals <70) [65]. Tumor mutational burden increases with tobacco use, while driver mutations are more common in never-smokers [66,67]. Unfortunately, we did not have access to this demographic information and could not assess how race and tobacco use influenced the genomic landscape of our study population. In addition, information regarding the specific histology was limited and clinical outcome data were not available. While we attempted to identify patients with newly diagnosed advanced NSCLC and minimize the effect of prior treatment on the genomic profile by using only the first serial sample, we were unable to distinguish the mutational landscape differences between treatment naïve individuals and those who received therapy. We recognize that prior treatment may have influenced the tumor mutational landscape as a mechanism of resistance to therapy. Finally, cross comparison of our study to others in the literature was challenging given the different age cut-offs used to define older and younger adult populations, differences in the tissue testing (plasma, tumor, or other), inclusion of early-stage cancers, and differences in the populations evaluated (e.g., Asian or European vs. North American; single institution vs. multi-center).

5. Conclusions

To our knowledge, this is one of the largest studies to date evaluating the genomic landscape differences in individuals with advanced NSCLC by sex and age. We demonstrated significant differences in the distribution of predictive and/or prognostic alterations according to sex and age. This could explain differences in outcomes in otherwise similar patients (i.e., stage, histology, and performance status). Further research is needed to understand how these mutations interact with one another, affect response to therapy, and how they can be used to expand on existing therapies to improve outcomes. The influence that race, tobacco use, and other environmental stressors have on the distribution of these alterations by sex and age should also be evaluated.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/cancers16132366/s1. Supplemental Figure S1: Most Common Alterations of Interest. Supplemental Figure S2: Number of Mutations Per Individual with an Alteration of Interest. Supplemental Figure S3: Oncoprint. Supplemental Table S1: Analysis of Co-Occurrence of Alterations of Interest Using cBioPortal.

Author Contributions

Conceptualization: E.O.K., J.A.M.-A. and Y.L.; methodology: E.O.K. and L.M.D.; validation: L.M.D.; formal analysis: L.M.D.; investigation: E.O.K., J.A.M.-A. and L.M.D.; resources, Y.L.; data curation: L.M.D.; writing—original draft preparation: E.O.K. and J.A.M.-A.; writing—review and editing: E.O.K., J.A.M.-A., L.M.D., A.M., Y.Z., R.M. and Y.L.; visualization: Y.L.; supervision: Y.L.; funding acquisition: Y.L. 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 conducted in accordance with the Declaration of Helsinki and approved by the Institutional Review Board of Advarra (protocol code Advarra IRB Pro00034566/CR00218935 on 9 September 2019 and was last renewed 7 July 2023).

Informed Consent Statement

The generation of de-identified datasets by Guardant Health for research purposes was approved by the Advarra Institutional Review Board (Pro00034566). All patients signed consent for molecular testing and for the use of this testing for research purposes. Patient identity was maintained throughout the study in a deidentified database.

Data Availability Statement

The datasets presented in this article are not readily available. The data analyzed in this study were obtained from Guardant Health database. Requests to access these datasets will require a data use agreement and can be requested by contacting [email protected].

Acknowledgments

The authors would like to give a special thank you to Hiba Dada and Dong Yang for their contributions to the data analysis and figure generation.

Conflicts of Interest

Leylah M. Drusbosky is an employee and stockholder of Guardant Health. Yujie Zhao discloses research funding support from PDS Biotechnology, Zai Lab, Incyte, Mirati, Alpine, Pfizer, Merck, and Elucida Oncology. Rami Manochakian has previously participated on an advisory board for AstraZeneca, Takeda, Guardant Health, Janssen, Novocure, Turning Point, and OncoHost. Yanyan Lou has participated on an advisory board for AstraZeneca, Takeda, Guardant Health, Janssen, Novocure, Turning Point Therapeutics, Cardinal Health, Clinical Education Alliance, Oncohost, Mirati Therapeutics Honorarium to Mayo Clinic; Research Funding Support: Merck, Tolero Pharmaceuticals, AstraZeneca, Blueprint Medicines, Sun Pharma, Mirati Therapeutics, Genmab, EMD Serono, Jacobio pharma, TOPALLIAN, Daiichi Sankyo. ErinMarie O. Kimbrough, Julian A. Marin-Acevedo, and Ariana Mooradian declare no conflicts of interest.

References

  1. US Department of Health and Human Services, Centers for Disease Control and Prevention, Division of Cancer Prevention and Control. An Update on Cancer Deaths in the United States; US Department of Health and Human Services, Centers for Disease Control and Prevention, Division of Cancer Prevention and Control: Atlanta, GA, USA, 2022.
  2. National Cancer Institute. SEER Cancer Stat Facts: Lung and Bronchus Cancer; National Cancer Institute: Bethesda, MD, USA, 2024. Available online: https://seer.cancer.gov/statfacts/html/lungb.html (accessed on 9 January 2024).
  3. Siegel, R.L.; Miller, K.D.; Wagle, N.S.; Jemal, A. Cancer statistics, 2023. CA Cancer J. Clin. 2023, 73, 17–48. [Google Scholar] [CrossRef] [PubMed]
  4. Sacher, A.G.; Dahlberg, S.E.; Heng, J.; Mach, S.; Janne, P.A.; Oxnard, G.R. Association between Younger Age and Targetable Genomic Alterations and Prognosis in Non-Small-Cell Lung Cancer. JAMA Oncol. 2016, 2, 313–320. [Google Scholar] [CrossRef] [PubMed]
  5. Russano, M.; Napolitano, A.; Ribelli, G.; Iuliani, M.; Simonetti, S.; Citarella, F.; Pantano, F.; Dell’Aquila, E.; Anesi, C.; Silvestris, N.; et al. Liquid biopsy and tumor heterogeneity in metastatic solid tumors: The potentiality of blood samples. J. Exp. Clin. Cancer Res. 2020, 39, 95. [Google Scholar] [CrossRef] [PubMed]
  6. Bonanno, L.; Dal Maso, A.; Pavan, A.; Zulato, E.; Calvetti, L.; Pasello, G.; Guarneri, V.; Conte, P.; Indraccolo, S. Liquid biopsy and non-small cell lung cancer: Are we looking at the tip of the iceberg? Br. J. Cancer 2022, 127, 383–393. [Google Scholar] [CrossRef] [PubMed]
  7. Kasi, P.M.; Lee, J.K.; Pasquina, L.W.; Decker, B.; Vanden Borre, P.; Pavlick, D.C.; Allen, J.M.; Parachoniak, C.; Quintanilha, J.C.F.; Graf, R.P.; et al. Circulating Tumor DNA Enables Sensitive Detection of Actionable Gene Fusions and Rearrangements Across Cancer Types. Clin. Cancer Res. 2024, 30, 836–848. [Google Scholar] [CrossRef] [PubMed]
  8. Chen, Y.-H.; Hancock, B.A.; Solzak, J.P.; Radovich, M. Abstract 2280: Co-detection of circulating tumor DNA and RNA in the plasma of patients with breast cancer increases the detectable number of mutated molecules. Cancer Res. 2019, 79, 2280. [Google Scholar] [CrossRef]
  9. Swanton, C.; Govindan, R. Clinical Implications of Genomic Discoveries in Lung Cancer. N. Engl. J. Med. 2016, 374, 1864–1873. [Google Scholar] [CrossRef] [PubMed]
  10. Dang, A.H.; Tran, V.U.; Tran, T.T.; Thi Pham, H.A.; Le, D.T.; Nguyen, L.; Nguyen, N.V.; Thi Nguyen, T.H.; Nguyen, C.V.; Le, H.T.; et al. Actionable Mutation Profiles of Non-Small Cell Lung Cancer patients from Vietnamese population. Sci. Rep. 2020, 10, 2707. [Google Scholar] [CrossRef]
  11. Xiao, D.; Pan, H.; Li, F.; Wu, K.; Zhang, X.; He, J. Analysis of ultra-deep targeted sequencing reveals mutation burden is associated with gender and clinical outcome in lung adenocarcinoma. Oncotarget 2016, 7, 22857–22864. [Google Scholar] [CrossRef]
  12. Hsu, C.L.; Chen, K.Y.; Shih, J.Y.; Ho, C.C.; Yang, C.H.; Yu, C.J.; Yang, P.C. Advanced non-small cell lung cancer in patients aged 45 years or younger: Outcomes and prognostic factors. BMC Cancer 2012, 12, 241. [Google Scholar] [CrossRef]
  13. Wakelee, H.A.; Wang, W.; Schiller, J.H.; Langer, C.J.; Sandler, A.B.; Belani, C.P.; Johnson, D.H.; Eastern Cooperative Oncology Group. Survival differences by sex for patients with advanced non-small cell lung cancer on Eastern Cooperative Oncology Group trial 1594. J. Thorac. Oncol. 2006, 1, 441–446. [Google Scholar] [CrossRef] [PubMed]
  14. Kasi, P.M.; Fehringer, G.; Taniguchi, H.; Starling, N.; Nakamura, Y.; Kotani, D.; Powles, T.; Li, B.T.; Pusztai, L.; Aushev, V.N.; et al. Impact of Circulating Tumor DNA-Based Detection of Molecular Residual Disease on the Conduct and Design of Clinical Trials for Solid Tumors. JCO Precis. Oncol. 2022, 6, e2100181. [Google Scholar] [CrossRef]
  15. Deveson, I.W.; Gong, B.; Lai, K.; LoCoco, J.S.; Richmond, T.A.; Schageman, J.; Zhang, Z.; Novoradovskaya, N.; Willey, J.C.; Jones, W.; et al. Evaluating the analytical validity of circulating tumor DNA sequencing assays for precision oncology. Nat. Biotechnol. 2021, 39, 1115–1128. [Google Scholar] [CrossRef] [PubMed]
  16. 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]
  17. 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]
  18. Politi, K.; Herbst, R.S. Lung cancer in the era of precision medicine. Clin. Cancer Res. 2015, 21, 2213–2220. [Google Scholar] [CrossRef] [PubMed]
  19. Yuan, M.; Huang, L.L.; Chen, J.H.; Wu, J.; Xu, Q. The emerging treatment landscape of targeted therapy in non-small-cell lung cancer. Signal Transduct. Target. Ther. 2019, 4, 61. [Google Scholar] [CrossRef]
  20. Suh, J.H.; Johnson, A.; Albacker, L.; Wang, K.; Chmielecki, J.; Frampton, G.; Gay, L.; Elvin, J.A.; Vergilio, J.A.; Ali, S.; et al. Comprehensive Genomic Profiling Facilitates Implementation of the National Comprehensive Cancer Network Guidelines for Lung Cancer Biomarker Testing and Identifies Patients Who May Benefit from Enrollment in Mechanism-Driven Clinical Trials. Oncologist 2016, 21, 684–691. [Google Scholar] [CrossRef]
  21. Pavan, A.; Bragadin, A.B.; Calvetti, L.; Ferro, A.; Zulato, E.; Attili, I.; Nardo, G.; Dal Maso, A.; Frega, S.; Menin, A.G.; et al. Role of next generation sequencing-based liquid biopsy in advanced non-small cell lung cancer patients treated with immune checkpoint inhibitors: Impact of STK11, KRAS and TP53 mutations and co-mutations on outcome. Transl. Lung Cancer Res. 2021, 10, 202–220. [Google Scholar] [CrossRef]
  22. Jiao, X.D.; Qin, B.D.; You, P.; Cai, J.; Zang, Y.S. The prognostic value of TP53 and its correlation with EGFR mutation in advanced non-small cell lung cancer, an analysis based on cBioPortal data base. Lung Cancer 2018, 123, 70–75. [Google Scholar] [CrossRef]
  23. Shigematsu, H.; Lin, L.; Takahashi, T.; Nomura, M.; Suzuki, M.; Wistuba, I.I.; Fong, K.M.; Lee, H.; Toyooka, S.; Shimizu, N.; et al. Clinical and biological features associated with epidermal growth factor receptor gene mutations in lung cancers. J. Natl. Cancer Inst. 2005, 97, 339–346. [Google Scholar] [CrossRef] [PubMed]
  24. Marchetti, A.; Martella, C.; Felicioni, L.; Barassi, F.; Salvatore, S.; Chella, A.; Camplese, P.P.; Iarussi, T.; Mucilli, F.; Mezzetti, A.; et al. EGFR mutations in non-small-cell lung cancer: Analysis of a large series of cases and development of a rapid and sensitive method for diagnostic screening with potential implications on pharmacologic treatment. J. Clin. Oncol. 2005, 23, 857–865. [Google Scholar] [CrossRef] [PubMed]
  25. Xu, H.; Yang, G.; Li, W.; Li, J.; Hao, X.; Xing, P.; Yang, Y.; Wang, Y. EGFR Exon 18 Mutations in Advanced Non-Small Cell Lung Cancer: A Real-World Study on Diverse Treatment Patterns and Clinical Outcomes. Front. Oncol. 2021, 11, 713483. [Google Scholar] [CrossRef]
  26. Xu, H.; Yang, G.; Liu, R.; Yang, Y.; Li, W.; Li, J.; Hao, X.; Xing, P.; Wang, Y. EGFR uncommon alterations in advanced non-small cell lung cancer and structural insights into sensitivity to diverse tyrosine kinase inhibitors. Front. Pharmacol. 2022, 13, 976731. [Google Scholar] [CrossRef] [PubMed]
  27. Imianitov, E.; Demidova, I.; Gordiev, M.; Filipenko, M.; Kekeeva, T.; Moliaka, Y.; Gervas, P.; Kozhemyako, V.; Vodolazhsky, D.I.; Aleksakhina, S.; et al. EGFR analysis of 21,039 patients with NSCLC: Age-related gradual increase of the L858R mutation frequency in adenocarcinomas and high occurrence of ex19del/L858R mutations in squamous cell carcinomas from females and/or nonsmokers. J. Clin. Oncol. 2017, 35, 9040. [Google Scholar] [CrossRef]
  28. Dogan, S.; Shen, R.; Ang, D.C.; Johnson, M.L.; D’Angelo, S.P.; Paik, P.K.; Brzostowski, E.B.; Riely, G.J.; Kris, M.G.; Zakowski, M.F.; et al. Molecular Epidemiology of EGFR and KRAS Mutations in 3026 Lung Adenocarcinomas: Higher Susceptibility of Women to Smoking-Related KRAS-Mutant Cancers. Clin. Cancer Res. 2012, 18, 6169–6177. [Google Scholar] [CrossRef] [PubMed]
  29. Chiu, C.H.; Yang, C.T.; Shih, J.Y.; Huang, M.S.; Su, W.C.; Lai, R.S.; Wang, C.C.; Hsiao, S.H.; Lin, Y.C.; Ho, C.L.; et al. Epidermal Growth Factor Receptor Tyrosine Kinase Inhibitor Treatment Response in Advanced Lung Adenocarcinomas with G719X/L861Q/S768I Mutations. J. Thorac. Oncol. 2015, 10, 793–799. [Google Scholar] [CrossRef] [PubMed]
  30. Araujo, J.M.; Rosas, G.; Belmar-López, C.; Raez, L.E.; Rolfo, C.D.; Schwarz, L.J.; Infante-Huaytalla, U.; Paez, K.J.; García, L.R.; Alvarado, H.; et al. Influence of Sex in the Molecular Characteristics and Outcomes of Malignant Tumors. Front. Oncol. 2021, 11, 752918. [Google Scholar] [CrossRef] [PubMed]
  31. Judd, J.; Abdel Karim, N.; Khan, H.; Naqash, A.R.; Baca, Y.; Xiu, J.; VanderWalde, A.M.; Mamdani, H.; Raez, L.E.; Nagasaka, M.; et al. Characterization of KRAS Mutation Subtypes in Non–small Cell Lung Cancer. Mol. Cancer Ther. 2021, 20, 2577–2584. [Google Scholar] [CrossRef]
  32. Stapelfeld, C.; Dammann, C.; Maser, E. Sex-specificity in lung cancer risk. Int. J. Cancer 2020, 146, 2376–2382. [Google Scholar] [CrossRef]
  33. Nassar, A.H.; Adib, E.; Kwiatkowski, D.J. Distribution of KRASG12C Somatic Mutations across Race, Sex, and Cancer Type. New Engl. J. Med. 2021, 384, 185–187. [Google Scholar] [CrossRef]
  34. Araujo, L.H.; Souza, B.M.; Leite, L.R.; Parma, S.A.F.; Lopes, N.P.; Malta, F.S.V.; Freire, M.C.M. Molecular profile of KRAS G12C-mutant colorectal and non-small-cell lung cancer. BMC Cancer 2021, 21, 193. [Google Scholar] [CrossRef] [PubMed]
  35. Ricciuti, B.; Alessi, J.V.; Elkrief, A.; Wang, X.; Cortellini, A.; Li, Y.Y.; Vaz, V.R.; Gupta, H.; Pecci, F.; Barrichello, A.; et al. Dissecting the clinicopathologic, genomic, and immunophenotypic correlates of KRASG12D-mutated non-small-cell lung cancer. Ann. Oncol. 2022, 33, 1029–1040. [Google Scholar] [CrossRef] [PubMed]
  36. Bergethon, K.; Shaw, A.T.; Ou, S.H.; Katayama, R.; Lovly, C.M.; McDonald, N.T.; Massion, P.P.; Siwak-Tapp, C.; Gonzalez, A.; Fang, R.; et al. ROS1 rearrangements define a unique molecular class of lung cancers. J. Clin. Oncol. 2012, 30, 863–870. [Google Scholar] [CrossRef] [PubMed]
  37. Inamura, K.; Takeuchi, K.; Togashi, Y.; Hatano, S.; Ninomiya, H.; Motoi, N.; Mun, M.Y.; Sakao, Y.; Okumura, S.; Nakagawa, K.; et al. EML4-ALK lung cancers are characterized by rare other mutations, a TTF-1 cell lineage, an acinar histology, and young onset. Mod. Pathol. 2009, 22, 508–515. [Google Scholar] [CrossRef] [PubMed]
  38. Liu, J.; Liu, Y. Molecular diagnostic characteristics based on the next generation sequencing in lung cancer and its relationship with the expression of PD-L1. Pathol. Res. Pr. 2020, 216, 152797. [Google Scholar] [CrossRef] [PubMed]
  39. Nagashima, O.; Ohashi, R.; Yoshioka, Y.; Inagaki, A.; Tajima, M.; Koinuma, Y.; Iwakami, S.; Iwase, A.; Sasaki, S.; Tominaga, S.; et al. High prevalence of gene abnormalities in young patients with lung cancer. J. Thorac. Dis. 2013, 5, 27–30. [Google Scholar] [CrossRef] [PubMed]
  40. VandenBussche, C.J.; Illei, P.B.; Lin, M.T.; Ettinger, D.S.; Maleki, Z. Molecular alterations in non-small cell lung carcinomas of the young. Hum. Pathol. 2014, 45, 2379–2387. [Google Scholar] [CrossRef] [PubMed]
  41. Zhou, J.X.; Yang, H.; Deng, Q.; Gu, X.; He, P.; Lin, Y.; Zhao, M.; Jiang, J.; Chen, H.; Lin, Y.; et al. Oncogenic driver mutations in patients with non-small-cell lung cancer at various clinical stages. Ann. Oncol. 2013, 24, 1319–1325. [Google Scholar] [CrossRef]
  42. Wu, S.-G.; Kuo, Y.-W.; Chang, Y.-L.; Shih, J.-Y.; Chen, Y.-H.; Tsai, M.-F.; Yu, C.-J.; Yang, C.-H.; Yang, P.-C. EML4-ALK Translocation Predicts Better Outcome in Lung Adenocarcinoma Patients with Wild-Type EGFR. J. Thorac. Oncol. 2012, 7, 98–104. [Google Scholar] [CrossRef]
  43. Bi, H.; Ren, D.; Ding, X.; Yin, X.; Cui, S.; Guo, C.; Wang, H. Clinical characteristics of patients with ROS1 gene rearrangement in non-small cell lung cancer: A meta-analysis. Transl. Cancer Res. 2020, 9, 4383–4392. [Google Scholar] [CrossRef] [PubMed]
  44. Marchetti, A.; Felicioni, L.; Malatesta, S.; Sciarrotta, M.G.; Guetti, L.; Chella, A.; Viola, P.; Pullara, C.; Mucilli, F.; Buttitta, F. Clinical Features and Outcome of Patients with Non–Small-Cell Lung Cancer Harboring BRAF Mutations. J. Clin. Oncol. 2011, 29, 3574–3579. [Google Scholar] [CrossRef] [PubMed]
  45. Marin-Acevedo, J.A.; Hicks, J.K.; Thapa, R.; Chen, D.-T.; Kimbrough, E.; Gray, J.E. Assessment of BRAF class I/II/III mutations, demographics, and treatment outcomes in NSCLC. J. Clin. Oncol. 2021, 39, e21016. [Google Scholar] [CrossRef]
  46. Farago, A.F.; Taylor, M.S.; Doebele, R.C.; Zhu, V.W.; Kummar, S.; Spira, A.I.; Boyle, T.A.; Haura, E.B.; Arcila, M.E.; Benayed, R.; et al. Clinicopathologic Features of Non–Small-Cell Lung Cancer Harboring an NTRK Gene Fusion. JCO Precis. Oncol. 2018, 2, 1–12. [Google Scholar] [CrossRef] [PubMed]
  47. Westphalen, C.B.; Krebs, M.G.; Le Tourneau, C.; Sokol, E.S.; Maund, S.L.; Wilson, T.R.; Jin, D.X.; Newberg, J.Y.; Fabrizio, D.; Veronese, L.; et al. Genomic context of NTRK1/2/3 fusion-positive tumours from a large real-world population. NPJ Precis. Oncol. 2021, 5, 69. [Google Scholar] [CrossRef] [PubMed]
  48. Ou, S.H.I.; Sokol, E.S.; Trabucco, S.E.; Jin, D.X.; Frampton, G.M.; Graziano, S.L.; Elvin, J.A.; Vergilio, J.A.; Killian, J.K.; Ngo, N.; et al. 1549P—NTRK1-3 genomic fusions in non-small cell lung cancer (NSCLC) determined by comprehensive genomic profiling. Ann. Oncol. 2019, 30, v638. [Google Scholar] [CrossRef]
  49. Guinee, D.G., Jr.; Travis, W.D.; Trivers, G.E.; De Benedetti, V.M.; Cawley, H.; Welsh, J.A.; Bennett, W.P.; Jett, J.; Colby, T.V.; Tazelaar, H.; et al. Gender comparisons in human lung cancer: Analysis of p53 mutations, anti-p53 serum antibodies and C-erbB-2 expression. Carcinogenesis 1995, 16, 993–1002. [Google Scholar] [CrossRef] [PubMed]
  50. Singh, V.; Feldman, R.; Sukari, A.; Kim, C.; Mamdani, H.; Spira, A.I.; Bepler, G.; Kim, E.S.; Raez, L.E.; Pai, S.G.; et al. Characterization of ERBB2 alterations in non-small cell lung cancer. J. Clin. Oncol. 2020, 38, e21553. [Google Scholar] [CrossRef]
  51. Wang, R.; Hu, H.; Pan, Y.; Li, Y.; Ye, T.; Li, C.; Luo, X.; Wang, L.; Li, H.; Zhang, Y.; et al. RET fusions define a unique molecular and clinicopathologic subtype of non-small-cell lung cancer. J. Clin. Oncol. 2012, 30, 4352–4359. [Google Scholar] [CrossRef]
  52. Hess, L.M.; Han, Y.; Zhu, Y.E.; Bhandari, N.R.; Sireci, A. Characteristics and outcomes of patients with RET-fusion positive non-small lung cancer in real-world practice in the United States. BMC Cancer 2021, 21, 28. [Google Scholar] [CrossRef]
  53. Champagnac, A.; Bringuier, P.-P.; Barritault, M.; Isaac, S.; Watkin, E.; Forest, F.; Maury, J.-M.; Girard, N.; Brevet, M. Frequency of MET exon 14 skipping mutations in non-small cell lung cancer according to technical approach in routine diagnosis: Results from a real-life cohort of 2369 patients. J. Thorac. Dis. 2020, 12, 2172–2178. [Google Scholar] [CrossRef] [PubMed]
  54. Vuong, H.G.; Ho, A.T.N.; Altibi, A.M.A.; Nakazawa, T.; Katoh, R.; Kondo, T. Clinicopathological implications of MET exon 14 mutations in non-small cell lung cancer—A systematic review and meta-analysis. Lung Cancer 2018, 123, 76–82. [Google Scholar] [CrossRef] [PubMed]
  55. Schubart, C.; Stöhr, R.; Tögel, L.; Fuchs, F.; Sirbu, H.; Seitz, G.; Seggewiss-Bernhardt, R.; Leistner, R.; Sterlacci, W.; Vieth, M.; et al. MET Amplification in Non-Small Cell Lung Cancer (NSCLC)-A Consecutive Evaluation Using Next-Generation Sequencing (NGS) in a Real-World Setting. Cancers 2021, 13, 5023. [Google Scholar] [CrossRef] [PubMed]
  56. Pécuchet, N.; Laurent-Puig, P.; Mansuet-Lupo, A.; Legras, A.; Alifano, M.; Pallier, K.; Didelot, A.; Gibault, L.; Danel, C.; Just, P.A.; et al. Different prognostic impact of STK11 mutations in non-squamous non-small-cell lung cancer. Oncotarget 2017, 8, 23831–23840. [Google Scholar] [CrossRef] [PubMed]
  57. Pons-Tostivint, E.; Lugat, A.; Fontenau, J.F.; Denis, M.G.; Bennouna, J. STK11/LKB1 Modulation of the Immune Response in Lung Cancer: From Biology to Therapeutic Impact. Cells 2021, 10, 3129. [Google Scholar] [CrossRef] [PubMed]
  58. Yuan, Y.; Liu, L.; Chen, H.; Wang, Y.; Xu, Y.; Mao, H.; Li, J.; Mills, G.B.; Shu, Y.; Li, L.; et al. Comprehensive Characterization of Molecular Differences in Cancer between Male and Female Patients. Cancer Cell 2016, 29, 711–722. [Google Scholar] [CrossRef] [PubMed]
  59. Hao, F.; Gu, L.; Zhong, D. TP53 Mutation Mapping in Advanced Non-Small Cell Lung Cancer: A Real-World Retrospective Cohort Study. Curr. Oncol. 2022, 29, 7411–7419. [Google Scholar] [CrossRef]
  60. Jiang, W.; Cheng, H.; Yu, L.; Zhang, J.; Wang, Y.; Liang, Y.; Lou, F.; Wang, H.; Cao, S. Mutation patterns and evolutionary action score of TP53 enable identification of a patient population with poor prognosis in advanced non-small cell lung cancer. Cancer Med. 2023, 12, 6649–6658. [Google Scholar] [CrossRef]
  61. Shire, N.J.; Klein, A.B.; Golozar, A.; Collins, J.M.; Fraeman, K.H.; Nordstrom, B.L.; McEwen, R.; Hembrough, T.; Rizvi, N.A. STK11 (LKB1) mutations in metastatic NSCLC: Prognostic value in the real world. PLoS ONE 2020, 15, e0238358. [Google Scholar] [CrossRef]
  62. 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]
  63. Pao, W.; Girard, N. New driver mutations in non-small-cell lung cancer. Lancet Oncol. 2011, 12, 175–180. [Google Scholar] [CrossRef] [PubMed]
  64. Shi, H.; Seegobin, K.; Heng, F.; Zhou, K.; Chen, R.; Qin, H.; Manochakian, R.; Zhao, Y.; Lou, Y. Genomic landscape of lung adenocarcinomas in different races. Front. Oncol. 2022, 12, 946625. [Google Scholar] [CrossRef] [PubMed]
  65. Cornelius, M.E.; Loretan, C.G.; Jamal, A.; Davis Lynn, B.C.; Mayer, M.; Alcantara, I.C.; Neff, L. Tobacco Product Use Among Adults—United States, 2021. Morb. Mortal. Wkly. Rep. 2023, 72, 475–483. [Google Scholar] [CrossRef] [PubMed]
  66. Govindan, R.; Ding, L.; Griffith, M.; Subramanian, J.; Dees, N.D.; Kanchi, K.L.; Maher, C.A.; Fulton, R.; Fulton, L.; Wallis, J.; et al. Genomic landscape of non-small cell lung cancer in smokers and never-smokers. Cell 2012, 150, 1121–1134. [Google Scholar] [CrossRef] [PubMed]
  67. Kuśnierczyk, P. Genetic differences between smokers and never-smokers with lung cancer. Front. Immunol. 2023, 14, 1063716. [Google Scholar] [CrossRef]
Cancers 16 02366 g001
Figure 1. Study Population. The basic study design and population are depicted above. We evaluated the individual profiles of patients with advanced NSCLC who underwent molecular profiling using ctDNA from the Guardant Health database. Individuals without an alteration detected were excluded. Among those with an alteration, we focused on individuals with variants of predictive or prognostic significance including EGFR exon 19 deletion, EGFR exon 20 insertion, EGFR G719X, EGFR L858R, EGFR T790M, EGFR S7681, EGFR L861Q, KRAS G12C, KRAS G12D, KRAS G12V, ALK fusion, ROS1 fusion, BRAF V600E, NTRK1 fusion, ERBB2 exon 20 insertion, RET fusion, MET exon 14 skipping, MET amplification medium, MET amplification high, any PIK3CA mutation, any STK11 mutation, or any TP53 mutation. Of the 34,277 samples reviewed, 30,790 (89.83%) had a somatic alteration and 19,923 (58.12%) had an alteration of interest. Abbreviations: NSCLC, non-small cell lung cancer; ctDNA, circulating tumor DNA.
Figure 1. Study Population. The basic study design and population are depicted above. We evaluated the individual profiles of patients with advanced NSCLC who underwent molecular profiling using ctDNA from the Guardant Health database. Individuals without an alteration detected were excluded. Among those with an alteration, we focused on individuals with variants of predictive or prognostic significance including EGFR exon 19 deletion, EGFR exon 20 insertion, EGFR G719X, EGFR L858R, EGFR T790M, EGFR S7681, EGFR L861Q, KRAS G12C, KRAS G12D, KRAS G12V, ALK fusion, ROS1 fusion, BRAF V600E, NTRK1 fusion, ERBB2 exon 20 insertion, RET fusion, MET exon 14 skipping, MET amplification medium, MET amplification high, any PIK3CA mutation, any STK11 mutation, or any TP53 mutation. Of the 34,277 samples reviewed, 30,790 (89.83%) had a somatic alteration and 19,923 (58.12%) had an alteration of interest. Abbreviations: NSCLC, non-small cell lung cancer; ctDNA, circulating tumor DNA.
Cancers 16 02366 g001
Cancers 16 02366 g002
Figure 2. Heatmap of Alterations of Interest. This heatmap shows the co-occurring mutation profiles for patients with an alteration of interest (n = 19,923).
Figure 2. Heatmap of Alterations of Interest. This heatmap shows the co-occurring mutation profiles for patients with an alteration of interest (n = 19,923).
Cancers 16 02366 g002
Table 1. ctDNA-positive patient demographic information.
Table 1. ctDNA-positive patient demographic information.
ctDNA+ Population
n = 30,790
Characteristics<70 Years
n = 15,495
(50.32%)		≥70 Years
n = 15,237
(49.49%)		Age Unknown
n = 58
(0.19%)
Median Age (Range)61 (18–69)77 (70–102)N/A
Sex
    Female8416 (50.23%)8307 (49.58%)33 (0.20%)
    Male7079 (50.44%)6930 (49.38%)25 (0.18%)
Histologic Subtype
    Adenocarcinoma14,530 (50.39%)14,247 (49.41%)58 (0.20%)
    NSCLC NOS965 (49.36%)990 (50.64%)0 (0.00%)
Basic demographic information for the ctDNA positive study cohort is provided in this table. The median age of the ctDNA positive population was 69 (range 18–102), 16,756 (54.42%) were female, and 28,835 (93.65%) had adenocarcinoma histology. Abbreviations: ctDNA, circulating tumor DNA; N/A, not applicable; NOS, not otherwise specified; NSCLC, non-small cell lung cancer.
Table 2. ctDNA+ population with alterations of interest according to sex.
Table 2. ctDNA+ population with alterations of interest according to sex.
Alteration DetectedTotal Patients (n = 30,790)p-Value
FemalesMales
16,756 (54.42%)14,034 (45.58%)
EGFRExon 19 deletion1600 (9.55%)822 (5.86%)<0.0001
Exon 20 insertion209 (1.25%)133 (0.95%)0.0139
G719X161 (0.96%)83 (0.59%)0.0003
L858R1141 (6.81%)556 (3.96%)<0.0001
T790M181 (1.08%)80 (0.57%)<0.0001
S768I79 (0.47%)35 (0.25%)0.0013
L861Q98 (0.58%)48 (0.34%)0.002
KRASG12C1190 (7.10%)900 (6.41%)0.0169
G12D413 (2.46%)357 (2.54%)0.6604
G12V520 (3.10%)395 (2.81%)0.1383
ALKFusion231 (1.38%)172 (1.23%)0.366
ROS1Fusion25 (0.15%)29 (0.21%)0.274
BRAFV600E 157 (0.94%)143 (1.02%)0.4848
NTRKNTRK 1 Fusion1 (0.01%)0 (0.00%)>0.9999
ERBB2Exon 20 insertions235 (1.40%)140 (1.00%)0.0012
RETFusion13 (0.08%)6 (0.04%)0.2554
METExon 14 skipping188 (1.12%)153 (1.09%)0.827
Amplification medium131 (0.78%)157 (1.12%)0.0024
Amplification high100 (0.60%)153 (1.09%)<0.0001
PIK3CAMutant 222 (1.32%)177 (1.26%)0.649
STK11Mutant855 (5.10%)1068 (7.61%)<0.0001
TP53Mutant6370 (38.02%)6506 (46.36%)<0.0001
Differences in the genomic alterations by sex in the ctDNA-positive population are depicted above. A p-value < 0.05 indicates statistical significance. Abbreviations: ctDNA, circulating tumor DNA.
Table 3. ctDNA+ population with alterations of interest according to age.
Table 3. ctDNA+ population with alterations of interest according to age.
Alteration DetectedTotal Patients (n = 30,732)p-Value
Patients < 70Patients ≥ 70
15,495 (50.42%)15,237 (49.58%)
EGFRExon 19 deletion1571 (10.14%)851 (5.59%)<0.0001
Exon 20 insertion228 (1.47%)114 (0.75%)<0.0001
G719X127 (0.82%)117 (0.77%)0.6528
L858R885 (5.71%)812 (5.33%)0.1475
T790M161 (1.04%)100 (0.66%)0.0003
S768I60 (0.39%)54 (0.35%)0.6405
L861Q50 (0.32%)96 (0.63%)<0.0001
KRASG12C1174 (7.58%)916 (6.01%)<0.0001
G12D421 (2.72%)349 (2.29%)0.0176
G12V489 (3.16%)426 (2.80%)0.0649
ALKFusion326 (2.10%)77 (0.51%)<0.0001
ROS1Fusion40 (0.26%)14 (0.09%)0.0005
BRAFV600E 169 (1.09%)131 (0.86%)0.0422
NTRKNTRK 1 Fusion1 (0.01%)0 (0.00%)>0.9999
ERBB2Exon 20 insertions230 (1.48%)145 (0.95%)<0.0001
RETFusion11 (0.07%)8 (0.05%)0.6477
METExon 14 skipping81 (0.52%)260 (1.71%)<0.0001
Amplification medium167 (1.08%)121 (0.79%)0.0108
Amplification high171 (1.10%)82 (0.54%)<0.0001
PIK3CAMutant 174 (1.12%)225 (1.48%)0.0065
STK11Mutant1145 (7.39%)778 (5.11%)<0.0001
TP53Mutant6914 (44.62%)5962 (39.13%)<0.0001
Differences in the genomic alterations by age in the ctDNA-positive population are depicted above. Only n = 30,732 instead of n = 30,790 were included in this analysis because of missing age data for 58 patients. A p-value < 0.05 indicates statistical significance. Abbreviations: ctDNA, circulating tumor DNA.
Table 4. ctDNA+ population with alterations of interest according to sex and age.
Table 4. ctDNA+ population with alterations of interest according to sex and age.
Alteration DetectedTotal Females (n = 16,723)p-ValueTotal Males (n = 14,009)p-Value
Females < 70Females ≥ 70Males < 70Males ≥ 70
8416 (27.39%)8307 (27.03%) 7079 (23.03%)6930 (22.55%)
EGFRExon 19 deletion1016 (12.07%)584 (7.03%)<0.0001555 (7.84%)267 (3.85%)<0.0001
Exon 20 insertion136 (1.62%)73 (0.88%)<0.000192 (1.30%)41 (0.59%)<0.0001
G719X85 (1.01%)76 (0.91%)0.579542 (0.59%)41 (0.59%)>0.9999
L858R593 (7.05%)548 (6.60%)0.2565292 (4.12%)264 (3.81%)0.3414
T790M113 (1.34%)68 (0.82%)0.001319 (0.27%)16 (0.23%)0.7361
S768I41 (0.49%)38 (0.46%)0.82248 (0.68%)32 (0.46%)0.0935
L861Q34 (0.40%)64 (0.77%)0.002216 (0.23%)32 (0.46%)0.0201
KRASG12C686 (8.15%)504 (6.07%)<0.0001488 (6.89%)412 (5.95%)0.0229
G12D239 (2.84%)174 (2.09%)0.002182 (2.57%)175 (2.53%)0.8724
G12V281 (3.34%)239 (2.88%)0.0904208 (2.94%)187 (2.70%)0.4142
ALKFusion188 (2.23%)43 (0.52%)<0.0001138 (1.95%)34 (0.49%)<0.0001
ROS1Fusion18 (0.21%)7 (0.08%)0.043222 (0.31%)7 (0.10%)0.0082
BRAFV600E 90 (1.07%)67 (0.81%)0.091979 (1.12%)64 (0.92%)0.2749
NTRKNTRK 1 Fusion1 (0.01%)0 (0.00%)>0.99990 (0.00%)0 (0.00%)>0.9999
ERBB2Exon 20 insertions139 (1.65%)96 (1.16%)0.00791 (1.29%)49 (0.71%)0.0006
RETFusion7 (0.08%)6 (0.07%)>0.99994 (0.06%)2 (0.03%)0.6875
METExon 14 skipping48 (0.57%)140 (1.69%)<0.000133 (0.47%)120 (1.73%)<0.0001
Amplification medium77 (0.91%)54 (0.65%)0.053990 (1.27%)67 (0.97%)0.0921
Amplification high71 (0.84%)29 (0.35%)<0.0001100 (1.41%)53 (0.76%)0.0002
PIK3CAMutant101 (1.20%)121 (1.46%)0.156173 (1.03%)104 (1.50%)0.0153
STK11Mutant533 (6.33%)322 (3.88%)<0.0001612 (8.65%)456 (6.58%)<0.0001
TP53Mutant3409 (40.51%)2961 (35.64%)<0.00013505 (49.51%)3001 (43.30%)<0.0001
Differences in the genomic alterations by sex and age in the ctDNA-positive population (n = 30,790) are depicted above. Only n = 30,732 were included in this analysis because of missing age data for 58 patients. A p-value < 0.05 indicates statistical significance. Abbreviations: ctDNA, circulating tumor DNA.
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Kimbrough, E.O.; Marin-Acevedo, J.A.; Drusbosky, L.M.; Mooradian, A.; Zhao, Y.; Manochakian, R.; Lou, Y. Sex- and Age-Associated Differences in Genomic Alterations among Patients with Advanced Non-Small Cell Lung Cancer (NSCLC). Cancers 2024, 16, 2366. https://doi.org/10.3390/cancers16132366

AMA Style

Kimbrough EO, Marin-Acevedo JA, Drusbosky LM, Mooradian A, Zhao Y, Manochakian R, Lou Y. Sex- and Age-Associated Differences in Genomic Alterations among Patients with Advanced Non-Small Cell Lung Cancer (NSCLC). Cancers. 2024; 16(13):2366. https://doi.org/10.3390/cancers16132366

Chicago/Turabian Style

Kimbrough, ErinMarie O., Julian A. Marin-Acevedo, Leylah M. Drusbosky, Ariana Mooradian, Yujie Zhao, Rami Manochakian, and Yanyan Lou. 2024. "Sex- and Age-Associated Differences in Genomic Alterations among Patients with Advanced Non-Small Cell Lung Cancer (NSCLC)" Cancers 16, no. 13: 2366. https://doi.org/10.3390/cancers16132366

APA Style

Kimbrough, E. O., Marin-Acevedo, J. A., Drusbosky, L. M., Mooradian, A., Zhao, Y., Manochakian, R., & Lou, Y. (2024). Sex- and Age-Associated Differences in Genomic Alterations among Patients with Advanced Non-Small Cell Lung Cancer (NSCLC). Cancers, 16(13), 2366. https://doi.org/10.3390/cancers16132366

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