Front. Oncol. Frontiers in Oncology Front. Oncol. 2234-943X Frontiers Media S.A. 10.3389/fonc.2023.1207892 Oncology Systematic Review Detection of KRAS mutation using plasma samples in non-small-cell lung cancer: a systematic review and meta-analysis Cai Peiling 1 † Yang Bofan 2 † Zhao Jiahui 2 Ye Peng 1 Yang Dongmei 3 * 1 Department of Anatomy and Histology, School of Preclinical Medicine, Chengdu University, Chengdu, China 2 School of Clinical Medicine, Chengdu University, Chengdu, China 3 Clinical Laboratory & Clinical Research and Translational Center, Second People’s Hospital of Yibin City-West China Yibin Hospital, Sichuan University, Yibin, China

Edited by: Wouter H. Van Geffen, Medisch Centrum Leeuwarden, Netherlands

Reviewed by: Oke Dimas Asmara, University of Groningen, Netherlands; Hanxiao Chen, Beijing Cancer Hospital, China

*Correspondence: Dongmei Yang, dongmeiy2020@163.com

†These authors have contributed equally to this work and share first authorship

 06 07 2023 2023 13 1207892 18 04 2023 20 06 2023 Copyright © 2023 Cai, Yang, Zhao, Ye and Yang 2023 Cai, Yang, Zhao, Ye and Yang

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 Background

The aim of this study was to investigate the diagnostic accuracy of KRAS mutation detection using plasma sample of patients with non-small cell lung cancer (NSCLC).

 Methods

Databases of Pubmed, Embase, Cochrane Library, and Web of Science were searched for studies detecting KRAS mutation in paired tissue and plasma samples of patients with NSCLC. Data were extracted from each eligible study and analyzed using MetaDiSc and STATA.

 Results

After database searching and screening of the studies with pre-defined criteria, 43 eligible studies were identified and relevant data were extracted. After pooling the accuracy data from 3341 patients, the pooled sensitivity, specificity and diagnostic odds ratio were 71%, 94%, and 59.28, respectively. Area under curve of summary receiver operating characteristic curve was 0.8883. Subgroup analysis revealed that next-generation sequencing outperformed PCR-based techniques in detecting KRAS mutation using plasma sample of patients with NSCLC, with sensitivity, specificity, and diagnostic odds ratio of 73%, 94%, and 82.60, respectively.

 Conclusion

Compared to paired tumor tissue sample, plasma sample showed overall good performance in detecting KRAS mutation in patients with NSCLC, which could serve as good surrogate when tissue samples are not available.

 KRAS plasma non-small cell lung cancer diagnostic accuracy meta-analysis section-in-acceptance Thoracic Oncology Introduction

Lung cancer is a leading cause of cancer-related death worldwide (1). As its most prevalent subtype, non-small cell lung cancer (NSCLC) represents approximately 85% of lung cancer cases (2). Treatments of NSCLC include surgery, radiotherapy, chemotherapy, immunotherapy, and targeted therapy in tumors harboring certain oncogenetic variations, e.g., anti-epidermal growth factor receptor (EGFR) therapy (2).

Kirsten rat sarcoma viral oncogene homologue (KRAS) is the most frequently mutated oncogene in many types of cancer (3), with an overall prevalence of 27.5% in NSCLC (4). Mutation of KRAS gene is associated with resistance to anti-EGFR therapies (5–7). In addition, although KRAS was thought to be an “undruggable” target, it has become “druggable” after the successful approval of KRAS (G12C) inhibitor (Sotorasib) for the treatment of KRAS G12C-mutated metastatic NSCLC (8). Due to these important roles of KRAS mutation in targeted therapies, accurate detection of KRAS gene mutations, especially G12C, is crucial for the success of anti-EGFR therapies and KRAS inhibitors.

The detection of KRAS mutations in tumors is usually performed using tumor tissue samples, e.g., formalin-fixed paraffin-embedded (FFPE) tumor tissue samples. However, tissue samples are sometimes not available, or may not reflect the real-time mutation status of tumor due to the existence of cancer evolution (9). Research efforts were therefore made to find possible surrogates for tumor tissue samples, which are mainly cell-free DNA (cfDNA)-containing samples, such as plasma, urine, saliva, feces, exhaled breath condensate, and etc (10, 11). Before their clinical application, however, those surrogate sample types needs to be validated for their accuracy performance in detecting KRAS mutations. Many such studies have been conducted. A recently-published systemic review and meta-analysis by Palmieri (12) summarized the results of 40 relevant studies and reported an overall adequate accuracy of cfDNA-containing samples. This meta-analysis by Palmieri focused on cfDNA, and involved studies using plasma, urine, or sputum samples. However, cfDNA levels in the three sample types are quite different, which could potentially influence accuracy performance. In addition, compared to urine or sputum samples which could be highly concentrated or diluted, cfDNA levels in plasma samples are considered to be more stable and therefore had potentially better stability in accuracy performance. Considering these advantages, we chose to focus on plasma, and aimed to better understand the accuracy performance of plasma sample in KRAS mutation detection in NSCLC, including potential impact of patient characteristics.

 Materials and methods Literature searching and selection of publication

Literature search was performed by BY and JZ in June 2022. Online literature databases (Pubmed, Embase, Cochrane Library, and Web of Science) were searched using keywords: “KRAS”, “plasma”, and “NSCLC”. Alternative spelling or abbreviations were also included in the literature search, e.g., non-small-cell lung cancer, non-small-cell lung carcinoma, NSCLCs, NSCLC’s, plasmas, and plasma’s (please see detailed searching strategy in Supplementary Material ). Searching results were exported from each database. Duplicated literatures were then identified by matching titles, names of first author, or identification numbers (e.g., Pubmed ID) of literatures from different databases. After removing the duplicated literatures, the abstracts of the searching results were firstly screened to exclude irrelevant literatures. The full texts of the rest literatures were then downloaded and screened for eligible studies. The criteria used for the two screening steps were as follows. Inclusion criteria: all original studies testing KRAS mutation in paired plasma and tumor tissue samples of NSCLC. Exclusion criteria: 1) not a human study; 2) missing plasma or tumor tissue samples; 3) plasma and tumor tissue samples were not paired; 4) not testing KRAS mutation in either plasma or tissue samples; 5) lacking KRAS wild-type or KRAS mutated samples; 6) not an original study; 7) un-interpretable data; 8) not NSCLC samples. Accuracy data were then extracted from the KRAS mutation testing results of paired plasma and tumor tissue samples in the eligible studies, including numbers of true positive, false positive, false negative, and true negative. In addition, characteristics of patients or techniques were also extracted, including region and population of studies, tumor stage, and techniques used to test KRAS mutation in plasma and in tissue samples. All the eligible studies were evaluated by quality assessment of diagnostic accuracy studies 2 (QUADAS-2) (13). Any disagreement between the two investigators (BY and JZ) were solved by a third investigator (PC). PRISMA 2009 Checklist is included in Supplementary Material .

 Statistical analysis

Statistical analysis was performed using Meta-DiSc 1.4 (14) and STATA 12.0 (STATA Corp.). Sensitivity, specificity, positive likelihood ratio (PLR), negative likelihood ratio (NLR), diagnostic odds ratio (DOR), and area under curve (AUC) of summary receiver operating characteristic (SROC) curve were pooled from the accuracy data extracted from the eligible studies. During the pooling, random effects model was used when significant heterogeneity was observed (I2 ≥ 50% and P < 0.05), and fixed effects model was used when no significant heterogeneity was observed (14). In case of significant heterogeneity, threshold analysis and meta-regression were performed to find its possible sources. Deek’s funnel plot asymmetry test was performed to find potential publication bias in the eligible studies. P < 0.05 was considered statistically significant.

 Results Search results

As shown in Figure 1 , a total of 622 publications were identified after the literature search (Pubmed: 114; Embase: 333; Cochrane Library: 29; Web of Science: 146). After removing 216 duplicated literatures, titles and abstracts of the rest 406 publications were screened, and 305 irrelevant studies were excluded. Full text of the rest 101 publications were downloaded and carefully evaluated for their eligibility, and another 58 publications were further excluded. From the 43 eligible studies, accuracy data and other relevant information were extracted.

PRISMA 2009 flow diagram.

 Review of eligible publications

Twenty-nine of the 43 eligible studies ( Table 1 ) used next-generation sequencing (NGS) to detect KRAS mutation in plasma samples. In the rest 14 studies, 12 studies used PCR-based techniques, 1 study used pyrosequencing, and 1 study used MassARRAY.

Summary of studies detecting KRAS mutation in paired plasma and tissue samples from NSCLC patients.

Author, year Sample size Detection method (plasma) Detection method (tissue) Region Tumor stage Race
Yin J et al., 2021 (15) 147 NGS (customized panel) NGS (customized panel) Asia I-IV Asian
Paweletz CP et al., 2016 (16) 48 NGS (customized panel) not specified America III-IV Caucasian
Narayan A et al., 2012 (17) 21 NGS (customized panel) Sanger sequencing/clinical lab America I-IV Caucasian
Couraud S et al., 2014 (18) 68 NGS (customized panel) NGS (customized panel) Europe I-IV Caucasian
Wang Z et al., 2017 (19) 103 NGS (cSMART) ARMS-PCR Asia III-IV Asian
Tran LS et al., 2019 (20) 40 NGS (Ultra-deep sequencing) NGS (Ultra-deep sequencing) Asia III-IV Asian
Yao Y et al., 2017 (21) 39 NGS (Agilent SureSelect) NGS (Agilent SureSelect) Asia III-IV Asian
Pritchett MA et al., 2019 (22) 147 NGS (Agilent SureSelect) NGS (Agilent SureSelect) America III-IV Caucasian
Liu L et al., 2018 (23) 65 NGS (customized panel) NGS (customized panel) Asia III-IV Asian
Li BT et al., 2019 (24) 110 NGS (customized panel) NGS (customized panel) America IV Caucasian
Chen Y et al., 2019 (25) 43 NGS (customized panel) NGS (customized panel) Asia I-IV Asian
Lin X et al., 2019 (26) 21 NGS (customized panel) NGS (customized panel) Asia III-IV Asian
Chen KZ et al., 2016 (27) 58 NGS (AmpliSeq Cancer Panel) NGS (AmpliSeq Cancer Panel) Asia I-II Asian
Xu S et al., 2016 (28) 42 NGS (AmpliSeq Cancer Panel) NGS (AmpliSeq Cancer Panel) Asia III-IV Asian
Pécuchet N et al., 2016 (29) 107 NGS (AmpliSeq Colon and Lung Cancer Research Panel v2) NGS (AmpliSeq Colon and Lung Cancer Research Panel v2) Europe III-IV Caucasian
Pasquale R et al., 2020 (30) 107 NGS (Oncomine Lung cfDNA assay) NGS (Oncomine Solid Tumor DNA) Europe not disclosed Caucasian
Mehta A et al., 2021 (31) 21 NGS (Oncomine Lung Cell-Free Total Nucleic Acid Assay) NGS (Tag sequencing) Asia III-IV Asian
Papadopoulou E et al., 2019 (32) 36 NGS (Oncomine Lung Cell-Free Total Nucleic Acid Assay) NGS (AmpliSeq Colon and Lung Cancer Research Panel v2) Europe not disclosed Caucasian
Nicolazzo C et al., 2021 (33) 38 NGS (Oncomine Lung Cell-Free Total Nucleic Acid Assay) NGS (AmpliSeq Colon and Lung Cancer Research Panel v2) Europe not disclosed Caucasian
Ma Y et al., 2020 (34) 28 NGS (AmoyDx Essential NGS panel) NGS (AmoyDx Essential NGS panel) Asia I-IV Asian
Garcia J et al., 2018 (35) 20 NGS (56G Oncology Panel Kit, Swift Biosciences) NGS (customized AmqliSeq panel) Europe not disclosed Caucasian
Remon J et al., 2019 (36) 88 NGS (InVisionSeq Lung, NeoGenomics) Sanger sequencing or allele-specific technique Europe III-IV Caucasian
Bauml JM et al., 2022 (37) 189 NGS (Guardant360) PCR (therascreen KRAS RGQ PCR Kit) America I-IV Caucasian
Thompson JC et al., 2016 (38) 50 NGS (Guardant360) NGS (Illumina TruSeq Amplicon - Cancer Panel, or Penn Precision Panel) America II-IV Caucasian
Leighl NB et al., 2019 (39) 282 NGS (Guardant360) Standard of care (NGS, PCR, FISH and/or IHC, Sanger sequencing America III-IV Caucasian
Lam VK et al., 2021 (40) 76 NGS (Guardant360) not specified America III-IV Caucasian
Qvick A et al., 2021 (41) 52 NGS (AVENIO ctDNA Surveillance kit) NGS (AmpliSeq Colon and Lung Cancer Research Panel v2, or AVENIO FFPE Surveillance kit (sufficient sample), or qPCR and FISH (insufficient samples) Europe I-IV Caucasian
Jiao XD et al., 2021 (42) 185 NGS (LungPlasma panel) NGS (OncoScreen Plus panel) Asia III-IV Asian
Guo N et al., 2016 (43) 41 NGS (SV-CA50-ctDNA panel, San Valley Biotech Inc.) NGS (SV-CA50-ctDNA panel, San Valley Biotech Inc.) Asia I-IV Asian
Michaelidou K et al., 2020 (44) 96 ddPCR Sanger sequencing Europe III-IV Caucasian
Oxnard GR et al., 2014 (45) 31 ddPCR Central lab America III-IV Caucasian
Sacher AG et al., 2016 (46) 87 ddPCR not specified America III-IV Caucasian
Mellert H et al., 2017 (47) 100 ddPCR not specified America III-IV Caucasian
Cho MS et al., 2020 (48) 36 PCR-based multiplex assay (PANAmutyper) PCR-based multiplex assay (PNAmutyper) Asia I-IV Asian
Han JY et al., 2016 (49) 135 PCR-based multiplex assay (PANAmutyper) PCR-based direct DNA sequencing Asia III-IV Asian
Wang S et al., 2010 (50) 273 PCR-RFLP Direct sequencing Asia I-IV Asian
Gautschi O et al., 2007 (51) 9 PCR-RFLP Sanger sequencing Europe I-IV Caucasian
Zhang H et al., 2013 (52) 86 Multiplex PCR (SurPlex MEL, SurExam Biotech, Inc) Multiplex PCR (SurPlex-xTAG70plex, SurExam Biotech, Inc) Asia III-IV Asian
Punnoose EA et al., 2012 (53) 18 Multiplex PCR (customized primers) + TaqMan assay or DxS kit not specified USA & Australia not disclosed Caucasian
Mack PC et al., 2009 (54) 49 ARMS ARMS America III-IV Caucasian
Campos CDM et al., 2018 (55) 3 solid phase extraction + PCR/LDR PCR/LDR America III-IV Caucasian
Kulasinghe A et al., 2021 (56) 103 MassARRAY (UltraSEEK lung panel, Agena Biosciences) not specified Australia I-IV Caucasian
Li XQ et al., 2014 (57) 43 pyrosequencing pyrosequencing Asia III-IV Asian

NGS, next generation sequencing; PCR, polymerase chain reaction; PCR-RFLP, PCR-restriction fragment length polymorphism; ddPCR, digital droplet PCR; ARMS, Amplification Refractory Mutation System.

 NGS

In the eligible studies using NGS, sensitivities ranged from 25% to 100%, and specificities and concordance rates were relatively higher, ranging from 64% to 100% and from 52.63% to 100%, respectively.

Twelve studies used customized NGS panels, in which 5 studies used amplicon-based targeted sequencing (15–19). In the study by Yin (15), KRAS mutation detected in tumor tissue samples were all detected in paired plasma samples, resulting in 100% sensitivity. The specificity and concordance rate were 99.24% and 99.32%, respectively. Similarly, study by Narayan (17) showed perfect matching (100% concordance rate) of KRAS mutation results between plasma and tissue samples. However, study by Paweletz (16) and by Couraud (18) showed much lower sensitivity (54.55% and 75%, respectively), although high specificity (100%) was observed. In the study by Wang Z (19), circulating single-molecule amplification and resequencing technology (cSMART) showed sensitivity of 58.82%, specificity of 100%, and concordance rate of 93.20%. The large variations in the sensitivity of KRAS mutation detection in plasma samples may be due to the small number of patients included in these studies.

The rest 7 studies used hybridization-based targeted sequencing (20–26). A customized panel from xGen (Integrated DNA Technologies) showed perfect match between plasma and tumor tissue results (100% concordance rate) (20). Studies by Yao (21) and Pritchett (22) used a hybridization-based target enrichment method from Agilent Technologies (SureSelect). The two studies showed similar concordance rates (91.16% and 97.44%). Studies by Liu (23), Li BT (24), Chen Y (25), and Lin (26) also used hybridization-based capture methods to enrich customized gene panels for NGS sequencing of plasma samples. The concordance rates of those studies were all high, ranging from 93.02% to 96.92%.

Besides customized NGS panels, several commercial NGS panels were also used, such as AmpliSeq panels, Oncomine panels, AmoyDx Essential NGS panel, 56G Oncology Panel, InVisionSeq Lung, Guardant360, AVENIO ctDNA Surveillance kit, LungPlasma panel, and SV-CA50-ctDNA panel. AmpliSeq Cancer Panel (Thermo Fisher Scientific) was used in two studies (27, 28). However, the results varied greatly between them. Sensitivity, specificity, and concordance rate were 60%, 96.23%, and 93.10% in Chen KZ’s study (27), and 100%, 83.33%, and 85.71% in Xu’s study (28). AmpliSeq Colon and Lung Cancer Research Panel v2 showed sensitivity of 62.96%, specificity of 100%, and concordance rate of 90.65% (29). Oncomine Lung cfDNA Assay (Thermo Fisher Scientific) showed sensitivity, specificity, and concordance rate of 61.54%, 93.83%, and 85.98%, respectively (30). Oncomine Lung Cell-Free Total Nucleic Acid Assay (Thermo Fisher Scientific) was used in three studies, and accuracy results varied greatly: sensitivity from 30.77% to 81.82%, specificity from 64% and 100%, and concordance rate from 52.63% to 94.44% (31–33). AmoyDx Essential NGS panel (Amoy Diagnostics) was used in a 28-patient cohort, and the sensitivity, specificity, and concordance rate were 66.67%, 96%, and 92.86%, respectively (34). Studies by Garcia (35) and Remon (36) also used amplicon-based targeted sequencing techniques, including 56G Oncology Panel (Swift Biosciences), InVisionSeq Lung (NeoGenomics), respectively. Results showed sensitivity of 64.29% and 88%, specificity of 83.33% and 88.89%, and concordance rate of 70% and 88.64%.

Four studies validated the accuracy of Guardant360 in detecting KRAS mutation in plasma samples (37–40). Sensitivity ranged from 66.67% to 87.50%. Specificity ranged from of 74.81% to 100%, and concordance rate ranged from 75.89% to 98%. AVENIO ctDNA Surveillance kit (Roche) is also a commercial panel using hybridization-based target enrichment. A study using AVENIO ctDNA Surveillance kit showed sensitivity of 72.73%, specificity of 100%, and concordance rate of 94.23% (41).

In the rest two studies using commercial NGS panels, detailed target enrichment method was not disclosed. Studies by Jiao (42) used LungPlasma NGS panel (Burning Rock Biotech), and sensitivity, specificity, and concordance rate were 68.97%, 99.36%, and 94.59%. Guo (43) used SV-CA50-ctDNA panel (San Valley Biotech), and results showed 50% sensitivity, 97.44% specificity, and 95.12% concordance rate.

 PCR-based techniques

A total of 4 studies used digital droplet PCR (ddPCR) to detect KRAS mutation in plasma samples (44–47). Although ddPCR is a sensitive technique which could detect genetic mutations as low as 0.01%, the results of these studies did not show high accuracy of ddPCR in plasma-based KRAS mutation detection. Sensitivity ranged from 51.43% to 87.88%, and specificity ranged from 88.52% to 100%, resulting in concordance rates from 75% to 96%.

Other than ddPCR, several PCR-based techniques were also used to detect KRAS mutation in plasma samples, such as PANAmutyper, PCR-restriction fragment length polymorphism (PCR-RFLP), multiplex PCR, Amplification Refractory Mutation System (ARMS), and PCR/ligase detection reaction (LDR) technique. Overall, those PCR-based techniques were mostly used in early studies, which showed sensitivity ranging from 33.33% to 100%, specificity from 50% to 100%, and concordance rate from 55.56% to 100%.

PANAmutyper is a multiplex PCR method which increases sensitivity through suppressing amplification of wild-type DNA using specific peptide nucleic acids (PNA) (48). In the two studies using PANAmutyper, the sensitivity was 33.33% and 50%, and specificity was 100% and 89.43%, resulting in concordance rates of 88.89% and 85.93%, respectively (48, 49).

In the two studies using PCR-RFLP, accuracy results varied greatly. In Wang S’s study (50), the sensitivity, specificity, and concordance rate were 76.67%, 95.06%, and 93.04%, respectively. In the study of Gautschi (51), these numbers were 50%, 66.67%, and 55.56%, respectively.

Multiplex PCR was used in two studies. Study by Zhang (52) used SurExam MEL (SurExam Biotech), a typical commercial multiplex PCR, to detect KRAS mutation in plasma samples, and sensitivity, specificity, and concordance rate were 33.33%, 98.80%, and 96.51%. In the study by Punnoose (53), the KRAS mutation results of plasma samples matched perfectly with tissue samples (100% concordance rate).

An early study by Mack (54) used KRAS Scorpion-ARMS test kit (DxS Ltd), and results showed 50% sensitivity, 100% specificity, and 97.96% concordance rate.

Campos (55) and colleagues developed a microfluidic solid-phase extraction device to extract cfDNA, which were then analyzed using PCR/LDR technique. Only 3 NSCLC samples were tested in the study, and the results showed 100% sensitivity, 50% specificity, and 66.67% concordance rate.

 MassARRAY and pyrosequencing

UltraSEEK lung panel (Agena Biosciences), a commercial MassARRAY panel, was used in a 103-patient cohort, and sensitivity, specificity, and concordance rate were 62.96%, 92.11%, and 84.47%, respectively (56). Pyrosequencing was used in an early study (57), and sensitivity and specificity were 75% and 100%, respectively, resulting in a concordance rate of 97.67%.

In all, the 43 eligible studies compared KRAS mutation status in paired plasma and tissue samples from 3341 NSCLC patients. Thirty-nine of the 43 eligible studies (39/43) showed high specificity (≥ 80%), and 37 studies showed high concordance rate (≥ 80%). However, high sensitivity (≥ 80%) was only observed in 14 out of 43 studies.

 Quality assessment of eligible studies

Quality assessment of eligible studies was performed using QUADAS-2. As shown in Table 2 , the 43 eligible studies showed overall good quality, with high risk observed in only 2 studies (both in flow and timing). In the assessment of risk of bias, percentage of low risk ranged from 46.51% (n = 20, Index test) to 69.77% (n = 30, both patient selection and reference standard). In the application concerns, no high risk was observed, and percentage of low risk ranged from 83.72% (n = 36, reference standard) to 86.05% (n = 37, both patient selection and index test).

QUADAS-2 assessment of eligible studies.

Author, year Risk of bias Applicability concerns
Patient selection Index test Reference standard Flow and timing Patient selection Index test Reference standard
Yin J et al., 2021 (15) low unclear low low low low low
Paweletz CP et al., 2016 (16) low low low low low low low
Narayan A et al., 2012 (17) low unclear low unclear unclear low low
Couraud S et al., 2014 (18) low unclear unclear unclear low low unclear
Wang Z et al., 2017 (19) low low low unclear low unclear unclear
Tran LS et al., 2019 (20) low unclear unclear low low low low
Yao Y et al., 2017 (21) unclear unclear low low low low low
Pritchett MA et al., 2019 (22) low unclear low unclear low low low
Liu L et al., 2018 (23) low unclear unclear low low low low
Li BT et al., 2019 (24) low low low unclear low low low
Chen Y et al., 2019 (25) low unclear low unclear unclear low low
Lin X et al., 2019 (26) unclear low low unclear low low low
Chen KZ et al., 2016 (27) unclear unclear low low low unclear low
Xu S et al., 2016 (28) low low low low low low low
Pécuchet N et al., 2016 (29) low low low high low unclear unclear
Pasquale R et al., 2020 (30) low low low low low low low
Mehta A et al., 2021 (31) unclear unclear low unclear low low low
Papadopoulou E et al., 2019 (32) low unclear low unclear low low low
Nicolazzo C et al., 2021 (33) unclear unclear low unclear low low low
Ma Y et al., 2020 (34) unclear low unclear unclear low low low
Garcia J et al., 2018 (35) low unclear low low low unclear unclear
Remon J et al., 2019 (36) low low unclear unclear unclear low low
Bauml JM et al., 2022 (37) low unclear low low low low low
Thompson JC et al., 2016 (38) low low low low low low unclear
Leighl NB et al., 2019 (39) low low low unclear low unclear unclear
Lam VK et al., 2021 (40) unclear unclear unclear unclear unclear low low
Qvick A et al., 2021 (41) unclear low unclear unclear low low low
Jiao XD et al., 2021 (42) low unclear low low low unclear low
Guo N et al., 2016 (43) low low low low low low low
Michaelidou K et al., 2020 (44) low unclear low low low low low
Oxnard GR et al., 2014 (45) unclear unclear unclear unclear low low low
Sacher AG et al., 2016 (46) low low low low low low low
Mellert H et al., 2017 (47) unclear unclear unclear unclear low low low
Cho MS et al., 2020 (48) low low low unclear low low low
Han JY et al., 2016 (49) low unclear low low low low unclear
Wang S et al., 2010 (50) low low low low low low low
Gautschi O et al., 2007 (51) low low unclear unclear low low low
Zhang H et al., 2013 (52) low low low low low low low
Punnoose EA et al., 2012 (53) unclear unclear unclear high unclear low low
Mack PC et al., 2009 (54) unclear low unclear low low low low
Campos CDM et al., 2018 (55) unclear unclear low unclear unclear low low
Kulasinghe A et al., 2021 (56) low unclear low low low low low
Li XQ et al., 2014 (57) low low unclear low low low low

low, low risk; unclear, unclear risk; high, high risk.

 Meta-analysis

From the 43 eligible studies, we pooled the KRAS mutation detection results from paired plasma and tissue samples of 3341 patients with NSCLC. The overall sensitivity and specificity were 0.71 [95% confidence interval (CI): 0.68-0.75] and 0.94 (95%CI: 0.93-0.95), respectively. The pooled DOR was 59.28 (95%CI: 34.37-102.25), and AUC of SROC curve was 0.8883. Please see Table 3 and Figure 2 for details.

Meta-analysis results.

No. of studies Sensitivity Specificity PLR NLR DOR AUC of SROC curve
Overall 43 0.71(0.68-0.75) 0.94(0.93-0.95) 16.27(10.08-26.25) 0.36(0.30-0.43) 59.28(34.37-102.25) 0.8883
Techniques used for plasma sample
NGS 29 0.73(0.69-0.77) 0.94(0.93-0.95) 20.99(10.68-41.23) 0.33(0.26-0.41) 82.60(40.62-167.96) 0.9162
PCR-based techniques 12 0.66(0.59-0.74) 0.95(0.94-0.97) 9.88(4.60-21.19) 0.42(0.31-0.58) 31.58(11.88-83.95) 0.7888
 ddPCR 4 0.68(0.59-0.77) 0.97(0.93-0.99) 26.46(2.68-261.05) 0.33(0.18-0.59) 85.60(6.80-1078.05) 0.2741
 Other PCR-based techniques 8 0.63(0.50-0.75) 0.95(0.93-0.97) 7.61(3.16-18.31) 0.40(0.29-0.55) 22.01(11.18-43.33) 0.8147
Region
Asia 18 0.71(0.63-0.78) 0.97(0.95-0.98) 18.00(9.96-32.53) 0.32(0.25-0.40) 63.84(38.95-104.65) 0.9381
America 13 0.76(0.71-0.81) 0.92(0.90-0.94) 31.28(5.36-182.47) 0.25(0.20-0.30) 111.35(56.05-221.20) 0.9272
Europe 10 0.63(0.56-0.71) 0.93(0.91-0.95) 7.42(3.17-17.41) 0.43(0.29-0.62) 22.62(6.69-76.49) 0.7013
Tumor stage
I-IV 13 0.71(0.65-0.77) 0.97(0.96-0.98) 22.11(13.39-36.52) 0.39(0.28-0.54) 64.59(34.43-121.17) 0.9273
III-IV 24 0.73(0.69-0.78) 0.93(0.92-0.94) 18.68(9.26-37.69) 0.29(0.25-0.34) 54.70(36.59-81.75) 0.9086
Race of patients
Asian 18 0.71(0.63-0.78) 0.97(0.95-0.98) 18.00(9.96-32.53) 0.32(0.25-0.40) 63.84(38.95-104.65) 0.9381
Caucasian 25 0.72(0.68-0.75) 0.92(0.91-0.94) 14.85(7.39-29.84) 0.34(0.27-0.42) 53.73(24.95-115.69) 0.8445

PLR, positive likelihood ratio; NLR, negative likelihood ratio; DOR, diagnostic odds ratio; AUC, area under curve; SROC, summary receiver operating characteristic; PCR, polymerase chain reaction; NGS, next generation sequencing; ddPCR, digital droplet PCR.

Pooled sensitivity, specificity, DOR, and SROC curve of eligible studies.

Since significant heterogeneity (I2 ≥ 50% and P < 0.05) was observed, we further analyzed its possible sources. Analysis of diagnostic threshold showed no significant threshold effect (spearman correlation coefficient = 0.058, P = 0.714). Meta-regression revealed that inter-study heterogeneity was associated with techniques used for plasma sample (P = 0.0388), but not with techniques used for tissue sample (P = 0.1280), region of study (P = 0.3299), tumor stage (P = 0.3049), or race of patients (P = 0.7798).

Subgroup analysis was then performed on different techniques used for plasma sample. The 43 eligible studies were grouped into three subgroups: NGS, PCR-based techniques, and other techniques. Meta-analysis was performed in each subgroup except other techniques due to limited number (only two) of studies in that subgroup. As shown in Table 3 , compared to PCR-based techniques, NGS showed overall better accuracy: sensitivity of 0.73 (95%CI: 0.69-0.77), specificity of 0.94 (95%CI: 0.93-0.95), DOR of 82.60 (95%CI: 40.62-167.96), and AUC of SROC curve of 0.9162. After further dividing the group of PCR-based techniques into two subgroups (ddPCR and other PCR-based techniques), ddPCR showed higher sensitivity [0.68 (95%CI: 0.59-0.77)], specificity [0.97 (95%CI: 0.93-0.99)], and DOR [85.60 (95%CI: 6.80-1978.05)], but much lower AUC of SROC curve (0.2741).

Subgroup analysis was also performed on the region of studies, including Asia, America, Australia, and Europe. Australia was excluded from the subgroup analysis due to limited number of studies in the subgroup. In the other three subgroups, studies performed in America showed overall best accuracy, with pooled sensitivity of 0.76 (95%CI: 0.71-0.81), specificity of 0.92 (95%CI: 0.90-0.94), DOR of 111.35 (95%CI: 56.05-221.20), and AUC of SROC curve of 0.9272.

Twenty-four of the 43 eligible studies used late-stage (stage III and IV) NSCLC samples, and 13 studies used NSCLC samples of any stage (stage I to IV). As shown in Table 3 , pooled accuracy results of the two subgroups (stage III-IV versus stage I-IV) did not differ much from each other. However, this result should be treated carefully because although early-stage NSCLC samples were involved, majority of the samples were still late-stage in stage I-IV subgroup. The rest 6 studies were not involved in the subgroup analysis, including 1 study using early-stage (I and II) NSCLS samples only, and 5 studies which did not disclose the tumor stage of samples.

Majority of the 43 eligible studies were conducted using samples from Caucasian patients, and the rest studies used samples of Asian patients. Between the two subgroups, pooled accuracy data were similar (see Table 3 ).

Publication bias was evaluated using Deek’s funnel plot ( Figure 3 ). The results indicated no significant publication bias (P = 0.097).

Deek’s funnel plot.

 Discussion

Before anti-EGFR therapies are given to NSCLC patients, it is important to determine whether the tumor carries KRAS mutation since it may lead to resistance to anti-EGFR therapies. Moreover, determination of KRAS mutation status is also required before the usage of KRAS (G12C) inhibitor, e.g., Sotorasib. Tumor tissue samples are the “gold standard” in the determination of KRAS mutation. However, tumor tissue samples are sometimes not available, and cfDNA-containing samples (e.g., plasma, urine, saliva, etc.) have been intensively investigated as surrogates for tissue samples. A recently-published systemic review and meta-analysis by Palmieri summarized the performance of cfDNA-containing samples in detecting KRAS mutation in NSCLC (12). Due to the higher and more stable levels of cfDNA in plasma compared to other cfDNA-containing sample types, we focused solely on plasma in this systemic review and meta-analysis, and investigated its accuracy in determining tumor KRAS mutation status in NSCLC.

In order to investigate the accuracy of KRAS mutation detection using plasma samples, several previous studies compared KRAS mutation results in paired plasma and tissue samples from patients with NSCLC. After database searching and screening, we identified 43 eligible studies. After pooling the KRAS mutation status from 3341 patients with NSCLC, the results showed overall moderate sensitivity (0.71) and high specificity (0.94). Other important indicators of diagnostic accuracy, DOR and AUC of SROC curve, were also high (59.28 and 0.8883, respectively). Although with moderate sensitivity, these results indicated overall high accuracy of plasma samples in detecting KRAS mutation. In the systemic review and meta-analysis by Palmieri (12), the pooled sensitivity and specificity were 0.71 and 0.93, respectively, and DOR was 35.24, which were similar to the findings of our study.

Since significant inter-study heterogeneity was observed during the pooling (I2 ≥ 50% and P < 0.05), we investigated its possible sources. Analysis of diagnostic threshold did not indicate significant threshold effect. Meta-regression revealed significant association between inter-study heterogeneity and techniques used for plasma sample. This is different from Palmieri’s study, in which detection method did not contribute to heterogeneity (12). No significant association was shown between heterogeneity and other covariates (techniques used for tissue sample, region of study, tumor stage, and race of patients).

Different from Palmieri’s study, we further conducted subgroup analysis. Subgroup analysis on technique used for plasma sample was firstly performed. After pooling the accuracy results, we found that NGS outperformed PCR-based techniques in many accuracy parameters, including sensitivity (0.73), DOR (82.60), and AUC of SROC curve (0.9162). We further divided the group of PCR-based techniques into two groups: ddPCR and other PCR-based techniques. Compared to NGS, ddPCR showed similar sensitivity (0.68), specificity (0.97), and DOR (85.60), except for surprisingly low AUC of SROC curve (0.2741) which was possibly due to the limited number of studies in this subgroup ( Table 3 ).

We also performed subgroup analysis on region of study. Studies performed in Asia showed the highest AUC of SROC curve (0.9381). Studies performed in America showed the highest sensitivity (0.76) and DOR (111.35), and similar AUC of SROC curve with Asia (0.9272), indicating overall the highest accuracy of the studies from America.

Late-stage tumors was reported to be associated with significantly higher fraction of circulating tumor DNA (ctDNA) in cfDNA (58), which may indicate potentially better performance of genetic testing using these samples. In the 43 eligible studies, involvement of early-stage samples did not significantly influence the accuracy results. However, this result should be treated with care because numbers of early-stage samples were much smaller than late-stage samples in a large proportion of these studies. Race of patients also did not show significant impact on the accuracy results. The performance of KRAS mutation testing using plasma was similar between Asian and Caucasian patients. Significant publication bias was not observed using Deek’s funnel plot asymmetry test.

In summary, results of this systemic review and meta-analysis indicated overall high accuracy of plasma samples in predicting KRAS mutation results of paired NSCLC tumor tissue samples. Plasma could serve as surrogates when tissue samples are not available, although it may miss a small proportion of patients carrying KRAS mutation considering its moderate sensitivity. Among different techniques, NGS showed the best accuracy. Although majority of accuracy results were comparable to NGS, ddPCR suffered from its low AUC of SROC curve. Therefore, NGS is recommended in the detection of KRAS mutations in plasma samples of patients with NSCLC, especially when multiple genetic variations are tested considering the high-throughput of the technology. Limitation of this study may be the small number of studies in the ddPCR subgroup and limited numbers of early-stage tumor samples used in some studies, which must be treated carefully. In addition, although different techniques are generally thought to have similar performance in tumor samples considering the high abundance of DNA, it may still cause potential bias. Large prospective studies are required to further validate the results of this study.

 Data availability statement

The original contributions presented in the study are included in the article/ Supplementary Material . Further inquiries can be directed to the corresponding author.

 Author contributions

PC, BY, and DY contributed to conception and design of the study. BY and JZ organized the database. PY performed the statistical analysis. PC wrote the first draft of the manuscript. BY, JZ, and PY wrote sections of the manuscript. All authors contributed to manuscript revision, read, and approved the submitted version.

 Funding

A Project Supported by Center for Early Childhood Education Research, Sichuan (grant number CECER-2022-B01) and Chengdu Municipal Health Commission, 2022 Chengdu Medical Research Project (grant number 2022582).

 Conflict of interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

 Publisher’s note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

 Supplementary material

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fonc.2023.1207892/full#supplementary-material

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