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
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.
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.
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).
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.
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
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.
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.
Footnotes
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Enlarge this image.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.
Enlarge this image.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).
ctDNA-positive patient demographic information.
| ctDNA+ Population | |||
|---|---|---|---|
| Characteristics | <70 Years | ≥70 Years | Age Unknown |
| Median Age (Range) | 61 (18–69) | 77 (70–102) | N/A |
| Sex | |||
| Female | 8416 (50.23%) | 8307 (49.58%) | 33 (0.20%) |
| Male | 7079 (50.44%) | 6930 (49.38%) | 25 (0.18%) |
| Histologic Subtype | |||
| Adenocarcinoma | 14,530 (50.39%) | 14,247 (49.41%) | 58 (0.20%) |
| NSCLC NOS | 965 (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.
ctDNA+ population with alterations of interest according to sex.
| Alteration Detected | Total Patients (n = 30,790) | p-Value | ||
|---|---|---|---|---|
| Females | Males | |||
| 16,756 (54.42%) | 14,034 (45.58%) | |||
| EGFR | Exon 19 deletion | 1600 (9.55%) | 822 (5.86%) | <0.0001 |
| Exon 20 insertion | 209 (1.25%) | 133 (0.95%) | 0.0139 | |
| G719X | 161 (0.96%) | 83 (0.59%) | 0.0003 | |
| L858R | 1141 (6.81%) | 556 (3.96%) | <0.0001 | |
| T790M | 181 (1.08%) | 80 (0.57%) | <0.0001 | |
| S768I | 79 (0.47%) | 35 (0.25%) | 0.0013 | |
| L861Q | 98 (0.58%) | 48 (0.34%) | 0.002 | |
| KRAS | G12C | 1190 (7.10%) | 900 (6.41%) | 0.0169 |
| G12D | 413 (2.46%) | 357 (2.54%) | 0.6604 | |
| G12V | 520 (3.10%) | 395 (2.81%) | 0.1383 | |
| ALK | Fusion | 231 (1.38%) | 172 (1.23%) | 0.366 |
| ROS1 | Fusion | 25 (0.15%) | 29 (0.21%) | 0.274 |
| BRAF | V600E | 157 (0.94%) | 143 (1.02%) | 0.4848 |
| NTRK | NTRK 1 Fusion | 1 (0.01%) | 0 (0.00%) | >0.9999 |
| ERBB2 | Exon 20 insertions | 235 (1.40%) | 140 (1.00%) | 0.0012 |
| RET | Fusion | 13 (0.08%) | 6 (0.04%) | 0.2554 |
| MET | Exon 14 skipping | 188 (1.12%) | 153 (1.09%) | 0.827 |
| Amplification medium | 131 (0.78%) | 157 (1.12%) | 0.0024 | |
| Amplification high | 100 (0.60%) | 153 (1.09%) | <0.0001 | |
| PIK3CA | Mutant | 222 (1.32%) | 177 (1.26%) | 0.649 |
| STK11 | Mutant | 855 (5.10%) | 1068 (7.61%) | <0.0001 |
| TP53 | Mutant | 6370 (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.
ctDNA+ population with alterations of interest according to age.
| Alteration Detected | Total Patients (n = 30,732) | p-Value | ||
|---|---|---|---|---|
| Patients < 70 | Patients ≥ 70 | |||
| 15,495 (50.42%) | 15,237 (49.58%) | |||
| EGFR | Exon 19 deletion | 1571 (10.14%) | 851 (5.59%) | <0.0001 |
| Exon 20 insertion | 228 (1.47%) | 114 (0.75%) | <0.0001 | |
| G719X | 127 (0.82%) | 117 (0.77%) | 0.6528 | |
| L858R | 885 (5.71%) | 812 (5.33%) | 0.1475 | |
| T790M | 161 (1.04%) | 100 (0.66%) | 0.0003 | |
| S768I | 60 (0.39%) | 54 (0.35%) | 0.6405 | |
| L861Q | 50 (0.32%) | 96 (0.63%) | <0.0001 | |
| KRAS | G12C | 1174 (7.58%) | 916 (6.01%) | <0.0001 |
| G12D | 421 (2.72%) | 349 (2.29%) | 0.0176 | |
| G12V | 489 (3.16%) | 426 (2.80%) | 0.0649 | |
| ALK | Fusion | 326 (2.10%) | 77 (0.51%) | <0.0001 |
| ROS1 | Fusion | 40 (0.26%) | 14 (0.09%) | 0.0005 |
| BRAF | V600E | 169 (1.09%) | 131 (0.86%) | 0.0422 |
| NTRK | NTRK 1 Fusion | 1 (0.01%) | 0 (0.00%) | >0.9999 |
| ERBB2 | Exon 20 insertions | 230 (1.48%) | 145 (0.95%) | <0.0001 |
| RET | Fusion | 11 (0.07%) | 8 (0.05%) | 0.6477 |
| MET | Exon 14 skipping | 81 (0.52%) | 260 (1.71%) | <0.0001 |
| Amplification medium | 167 (1.08%) | 121 (0.79%) | 0.0108 | |
| Amplification high | 171 (1.10%) | 82 (0.54%) | <0.0001 | |
| PIK3CA | Mutant | 174 (1.12%) | 225 (1.48%) | 0.0065 |
| STK11 | Mutant | 1145 (7.39%) | 778 (5.11%) | <0.0001 |
| TP53 | Mutant | 6914 (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.
ctDNA+ population with alterations of interest according to sex and age.
| Alteration Detected | Total Females (n = 16,723) | p-Value | Total Males (n = 14,009) | p-Value | |||
|---|---|---|---|---|---|---|---|
| Females < 70 | Females ≥ 70 | Males < 70 | Males ≥ 70 | ||||
| 8416 (27.39%) | 8307 (27.03%) | 7079 (23.03%) | 6930 (22.55%) | ||||
| EGFR | Exon 19 deletion | 1016 (12.07%) | 584 (7.03%) | <0.0001 | 555 (7.84%) | 267 (3.85%) | <0.0001 |
| Exon 20 insertion | 136 (1.62%) | 73 (0.88%) | <0.0001 | 92 (1.30%) | 41 (0.59%) | <0.0001 | |
| G719X | 85 (1.01%) | 76 (0.91%) | 0.5795 | 42 (0.59%) | 41 (0.59%) | >0.9999 | |
| L858R | 593 (7.05%) | 548 (6.60%) | 0.2565 | 292 (4.12%) | 264 (3.81%) | 0.3414 | |
| T790M | 113 (1.34%) | 68 (0.82%) | 0.0013 | 19 (0.27%) | 16 (0.23%) | 0.7361 | |
| S768I | 41 (0.49%) | 38 (0.46%) | 0.822 | 48 (0.68%) | 32 (0.46%) | 0.0935 | |
| L861Q | 34 (0.40%) | 64 (0.77%) | 0.0022 | 16 (0.23%) | 32 (0.46%) | 0.0201 | |
| KRAS | G12C | 686 (8.15%) | 504 (6.07%) | <0.0001 | 488 (6.89%) | 412 (5.95%) | 0.0229 |
| G12D | 239 (2.84%) | 174 (2.09%) | 0.002 | 182 (2.57%) | 175 (2.53%) | 0.8724 | |
| G12V | 281 (3.34%) | 239 (2.88%) | 0.0904 | 208 (2.94%) | 187 (2.70%) | 0.4142 | |
| ALK | Fusion | 188 (2.23%) | 43 (0.52%) | <0.0001 | 138 (1.95%) | 34 (0.49%) | <0.0001 |
| ROS1 | Fusion | 18 (0.21%) | 7 (0.08%) | 0.0432 | 22 (0.31%) | 7 (0.10%) | 0.0082 |
| BRAF | V600E | 90 (1.07%) | 67 (0.81%) | 0.0919 | 79 (1.12%) | 64 (0.92%) | 0.2749 |
| NTRK | NTRK 1 Fusion | 1 (0.01%) | 0 (0.00%) | >0.9999 | 0 (0.00%) | 0 (0.00%) | >0.9999 |
| ERBB2 | Exon 20 insertions | 139 (1.65%) | 96 (1.16%) | 0.007 | 91 (1.29%) | 49 (0.71%) | 0.0006 |
| RET | Fusion | 7 (0.08%) | 6 (0.07%) | >0.9999 | 4 (0.06%) | 2 (0.03%) | 0.6875 |
| MET | Exon 14 skipping | 48 (0.57%) | 140 (1.69%) | <0.0001 | 33 (0.47%) | 120 (1.73%) | <0.0001 |
| Amplification medium | 77 (0.91%) | 54 (0.65%) | 0.0539 | 90 (1.27%) | 67 (0.97%) | 0.0921 | |
| Amplification high | 71 (0.84%) | 29 (0.35%) | <0.0001 | 100 (1.41%) | 53 (0.76%) | 0.0002 | |
| PIK3CA | Mutant | 101 (1.20%) | 121 (1.46%) | 0.1561 | 73 (1.03%) | 104 (1.50%) | 0.0153 |
| STK11 | Mutant | 533 (6.33%) | 322 (3.88%) | <0.0001 | 612 (8.65%) | 456 (6.58%) | <0.0001 |
| TP53 | Mutant | 3409 (40.51%) | 2961 (35.64%) | <0.0001 | 3505 (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.
Supplementary Materials
The following supporting information can be downloaded at:
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; Drusbosky, Leylah M 3 ; Mooradian, Ariana 4 ; Zhao, Yujie 5 ; Manochakian, Rami 5 ; Lou, Yanyan 5 1 Division of Hematology and Oncology, Mayo Clinic, Jacksonville, FL 32224, USA; Department of Hematology and Oncology, Division of Internal Medicine, Indiana University Melvin and Bren Simon Comprehensive Cancer Center, Indianapolis, IN 46202, 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 Oncology, Mayo Clinic, Jacksonville, FL 32224, USA; Division of Hematology and Medical Oncology, University of Florida, Jacksonville, FL 32209, USA
5 Division of Hematology and Oncology, Mayo Clinic, Jacksonville, FL 32224, USA