Purpose: Liquid biopsies allow the tracking of clonal dynamics and detection of mutations during treatment.

Experimental Design: We evaluated under blinded conditions the ability of cell-free DNA (cfDNA) to detect RAS/BRAF mutations in the plasma of 42 metastatic colorectal cancer patients treated on a phase Ib/II trial of FOLFOX and dasatinib, with or without cetuximab.

Results: Prior to treatment, sequencing of archival tissue detected mutations in 25 of 42 patients (60%), while the cfDNA assay detected mutations in 37 of 42 patients (88%). Our cfDNA assay detected mutations with allele frequencies as low as 0.01%. After exposure to treatment, 41 of 42 patients (98%) had a cfDNA-detected RAS/BRAF mutation. Of 21 patients followed with serial measurements who were RAS/BRAF mutant at baseline, 11 (52%) showed additional point mutation following treatment and 3 (14%) no longer had detectable levels of another mutant allele. Of RAS/BRAF wild-type tumors at baseline, 4 of 5 (80%) showed additional point mutations. cfDNA quantitative measurements from this study closely mirrored changes in CEA and CT scan results, highlighting the importance of obtaining quantitative data beyond the mere presence of a mutation.

Conclusions: Our findings demonstrate the development of new RAS/BRAF mutations in patients regardless of whether they had preexisting mutations in the pathway, demonstrating a convergent evolutionary pattern. Clin Cancer Res; 23(16); 4578–91. ©2017 AACR.

Translational Relevance

Cell-free circulating DNA (cfDNA) provides a liquid biopsy alternative to tissue biopsies for monitoring cancer genetic changes over time. In this study, we used a recently developed multimarker assay enabling a qualitative and a quantitative cfDNA analysis for tracking acquired resistance by studying the real-time clonal evolution of the tumor. cfDNA of refractory mCRC patients to anti-EGFR therapy may harbor mutations at very low frequency down to 0.01% before initiation or during treatment, revealing the need of a highly sensitive technique. cfDNA demonstrates convergent evolution and common resistance mechanisms during treatment of colorectal cancer. Serial analysis of cfDNA provides a unique opportunity to study the evolving genomic landscape of a cancer during therapy, and to identify the early emergence of treatment resistance and guide targeted therapeutic decisions.

Targeted therapies are rapidly being integrated into the treatment of patients with cancer. However, the acquisition of resistance to such treatments is observed in virtually all cases. This resistance may arise as de novo mutations or as the expansion of a subclonal population of cells with preexisting resistance (1, 2). Detecting these resistant subclones and monitoring clonal populations over time is difficult and requires the development of more sensitive assays that are also convenient and safe for patients to have serially performed. Although the advancement of next-generation sequencing (NGS) coupled with pre and posttreatment biopsies may provide snapshots of clonal evolution, they are often difficult to implement and do not provide a longitudinal assessment of heterogeneity (2–8).

Not only do these biopsies not provide the ability to frequently assess heterogeneity and follow changes, they are also hindered by the fact that biopsies are inherently of a single location, often the safest lesion to biopsy, and this may not represent the entire clonal population of a tumor (8–10).

The ability to comprehensively and longitudinally characterize the clonal architecture of a tumor will have profound treatment implications. Early detection of acquired resistance may allow initiation of new therapies that suppress the expansion of clones that would otherwise result in disease progression. In addition, there may be clonal populations that were resistant to previous therapies that subsequently experience an extinction event, thus rendering the patient sensitive to reuse of a previous line of therapy and expanding potential treatment options. “Liquid biopsies” or cell-free DNA (cfDNA) may better capture the complexity of tumor heterogeneity, while at the same time minimizing patient risks and associated costs (11, 12). Recent studies have shown that liquid biopsies can be used to effectively genotype tumors (13, 14) and monitor the emergence of resistant clones during the course of treatment (15–17).

Use of the EGFR-directed mAbs cetuximab and panitumumab in the treatment of metastatic colorectal cancer (mCRC) is limited to RAS wild-type (WT) patients (18–20). Activating KRAS and NRAS (RAS) mutations in exons 2, 3, and 4 of each gene have been shown to result in a lack of benefit from EGFR-targeted therapy (21, 22). Other genetic alterations associated with resistance to EGFR0directed therapy include KRAS amplification (4), activating BRAF mutations (4, 23), activating PIK3CA mutations, and PTEN loss (4, 24). Despite the knowledge of these mechanisms of resistance, there are still RAS WT patients without these other perturbations who display primary resistance to cetuximab/panitumumab (1, 19). In addition, secondary resistance develops in all patients and is associated with the development of secondary KRAS, NRAS, EGFR or BRAF mutations (11, 25, 26). This demonstrated the ability to detect additional mutation of these secondary KRAS and NRAS mutations in the blood of patients receiving anti-EGFR therapies using a liquid biopsy approach and showed that resistant clones could be detected months before radiographic progression. Further reports have confirmed this technique and highlight the potential of cfDNA as a powerful way to longitudinally follow cancer patients (27, 28). Diaz and colleagues (15) described a mathematical model to suggest variants that lead to resistance are present prior to initiation of therapy. Thus, we can speculate that the mutations corresponding to those emerging clones may have initially been below the limit of detection of the platform before treatment. As a result, we will below refer to the “detection of additional mutation” rather than “developed” or “emerging mutation.”

In this study, we present IntPlex, a novel multimarker assay for the analysis of cfDNA and demonstrate its ability to sensitively detect the changing clonal dynamics of colorectal cancer. IntPlex is based on a refined allele-specific competitive blocker qPCR method specifically designed to improve quantification based on the structure and size of cfDNA (29–31). It enables simultaneous determination of five parameters: the total concentration of cfDNA, the presence of a point mutation, the mutant DNA concentration, the mutant allele fractions of cfDNA, and the cfDNA fragmentation index (32, 33). Using IntPlex, we evaluated cfDNA in the plasma of 42 mCRC patients treated on a phase Ib/II trial of FOLFOX and dasatinib with or without cetuximab and compared our findings with the results obtained from the archival tissue–based assay, currently considered the standard of care. The use of serial plasma collections from some of the patients on this trial allowed evaluation of the clonal dynamics of RAS and BRAF during therapy. Here, we present the evolutionary changes that occurred and compare our findings with standard clinical observation with radiographic imaging and carcinoembryonic antigen (CEA) of a select subset of patients.

Ethics statement, eligibility criteria, drug administration and study design, and treatment are described in Parseghian and colleagues (34).

Study design and treatment

We conducted a single-institution, open-label, investigator-initiated phase IB/II study in refractory mCRC patients treated with modified FOLFOX6, cetuximab, and dasatinib, with dasatinib dose escalation by cohort. This study is presented in a companion publication (34). The regimen consisted of cetuximab (400 mg/m2 week 1 followed by weekly doses of 250 mg/m2), oxaliplatin (85 mg/m2 once every 2 weeks), bolus 5-fluorouracil (5-FU; 400 mg/m2 once every 2 weeks), and leucovorin (400 mg/m2 once every 2 weeks), followed by a 46-hour infusion of 5-FU (2,400 mg/m2 once every 2 weeks). Dasatinib was dosed orally daily in cohorts of 100, 150, and 200 mg/day, administered without interruption. Dasatinib dose escalation was performed using a standard “3 + 3” design. Appropriate radiographic images were collected for all patients enrolled at baseline and at all postbaseline evaluations by the same radiological method, to assess response. All radiological tests that demonstrated tumor at baseline were repeated after every four cycles and at discontinuation of study treatment. For the phase II study, patients deemed to be KRAS codon 12/13 WT based on archival tissue received the MTD from the phase I study, while KRAS mutant patients received the same, without cetuximab.

Sample preparation

We retrospectively assessed archival tissue and plasma samples at baseline for mutational status, with repeat plasma samples occurring every four cycles prior to each restaging CT scan (2–6 hours after taking the daily dose of dasatinib). Investigators were blinded to the results of the clinical grade assay during the experiment. Plasma samples for cfDNA analysis were stored at −80°C and transferred between institutions on dry ice. The plasma isolation protocol we adapted from Chiu and colleagues (35) as well as handling and storage conditions were described previously (36). Briefly, plasma was first centrifuged at 1,600 × g at 4°C for 10 minutes, and supernatant was centrifuged at 16,000 × g at 4°C for 10 minutes. Supernatant was immediately used for DNA extraction and stored at −20°C. Total cfDNA was extracted from 1 mL of isolated plasma using the QIAamp DNA Mini Blood Kit (Qiagen) in accordance with the preanalytic guidelines we previously described (36) in an elution volume of 130 μL. DNA extracts were kept at −20°C until use or used immediately. In total, we analyzed 81 serial plasma samples from 46 mCRC patients (Supplementary Fig. S1).

Tumor tissue collection/storage/analysis

DNA was extracted from paraffin-embedded formalin-fixed tumor tissue. Genomic analysis samples were evaluated by using an NGS platform using the semiconductor-based ion PGM NGS platform with AmpliSeq Cancer HotSpot Panel v2 in 46 and 50 gene panels for the detection of frequently reported point mutations in human malignancies in Clinical Laboratory Improvement Amendments (CLIA)-certified molecular diagnostics laboratory.

IntPlex analysis of cfDNA

IntPlex analysis was performed as described previously (31, 32). Briefly, the qPCR assays were performed according the MIQE guidelines. qPCR amplifications were carried out at least in duplicate in a 25 μL volume on a CFX96 instrument using the CFX manager software (Bio-Rad). Each PCR reaction was composed of 12.5 μL of IQ Supermix Sybr Green (Bio-Rad), 2.5 μL of free water (Qiagen) or specific oligoblocker, 2.5 μL of forward and reverse primers (0.3 pmol/mL), and 5 μL of template. Thermal cycling comprised three repeated steps: a hot-start activation step at 95°C for 3 minutes, followed by 40 cycles of denaturation–amplification at 95°C for 10 seconds, then 60°C for 30 seconds. Melting curves were investigated by increasing the temperature from 60°C to 90°C with a plate reading every 0.2°C. Standard curves were performed for each run with a genomic extract of the DiFi cell line at 1.8 pg/μL of DNA. Each PCR run was carried out with no template control and positive control for each primer set. Positive controls were extracted from cell lines bearing KRAS/BRAF or NRAS point mutations or we purchased synthetic DNA (Horizon dx). In each single run, negative and positive controls for each tested mutation were included and one standard curve was prepared. Validation of qPCR amplification was made by melt curve differentiation. ctDNA mutation testing was made here without any sensitivity cutoff, while a threshold of >0.5% (mutant to WT ctDNA ratio) was used in our previous report (31). Consequently, ctDNA mutation testing here is much more sensitive (up to 500-fold) with a sensitivity ranging from 0.001% to 0.005% depending upon the mutations. When point mutations were found in only one of the two replicates, the point mutation was confirmed in triplicate.

Design of patient's follow-up and detection of point mutations

Point mutations tested in this retrospective study with the IntPlex qPCR method are presented in the Supplementary Table S1. Total circulating cfDNA quantification was carried out on all serial plasma samples. However, for patients with KRAS c12/13, NRAS, or BRAF V600E point mutations at baseline, only these mutations were assessed for on-treatment samples. Final plasma samples at disease progression for all patients were assessed for all mutations in KRAS (exon 2, 3, 4), NRAS (exon 2, 3, 4), and BRAF V600E. In the case of a patient having no KRAS/NRAS/BRAF point mutation at baseline, we also assessed KRAS codons 61, 146 and NRAS point mutations in all on-treatment samples. EGFR S492R mutation testing was performed only on patients with prior cetuximab therapy (37, 38).

Prior to initiation of therapy, 25 of 42 patients (59.5%) had detectable KRAS 12/13, BRAFV600E, or NRAS mutations in archival tumor tissue, and 37 of 42 patients (88%) were found to have mutations in cfDNA (Fig. 1A). Among the 25 patients with mutations in archival tissue, 24 (96%) also had mutations in cfDNA (Fig. 1B). Considering archival tumor sequencing as the “gold standard,” the concordance between our cfDNA and tissue-based methodologies for KRAS exon2 testing was 71%, while the specificity and sensitivity of the cfDNA assay were 48% and 95%, respectively (Supplementary Table S2A). For BRAFV600E testing, the concordance between the two assays was 97%, while the specificity and sensitivity of the cfDNA was 97% and 100% respectively (Supplementary Table S2A), compared with the archival tissue-based assessment. As can be seen in Table 1, the two cohorts were well matched with no difference in age, gender, grade, presence of an intact primary, number of metastatic sites, or previous therapies. Fourteen patients in Cohort 2 had received prior anti-EGFR therapy, whereas only 1 patient in Cohort 1 had received prior anti-EGFR therapy.

Figure 1.

Comparison of mutational status of RAS/BRAF as determined by tumor tissue and plasma analysis at baseline (n = 42). A, Plasma analysis was carried out in Cohort 1 and 2 before initiation of treatments (baseline) accordingly with targeted genes. B, Comparison of the type of point mutation as determined by tumor tissue and plasma analysis before initiation of treatments and for Cohort 1 and 2. Extended RAS testing was only performed on plasma for patients who were KRAS exon 2 and BRAFV600E WT on all assays. ND, scored positive but the mutation is nondetermined; WT, wild type for mutations tested.

Figure 1.

Comparison of mutational status of RAS/BRAF as determined by tumor tissue and plasma analysis at baseline (n = 42). A, Plasma analysis was carried out in Cohort 1 and 2 before initiation of treatments (baseline) accordingly with targeted genes. B, Comparison of the type of point mutation as determined by tumor tissue and plasma analysis before initiation of treatments and for Cohort 1 and 2. Extended RAS testing was only performed on plasma for patients who were KRAS exon 2 and BRAFV600E WT on all assays. ND, scored positive but the mutation is nondetermined; WT, wild type for mutations tested.

Close modal
Table 1.

Patient's baseline clinicopathologic characteristics

Cohorts N (%)
Cohorts 1 + 2Cohort 1Cohort 2
N 42 21 (50) 21 (50) 
Age 
 Median 58.5 58 59 
 Min–Max 26–74 26–74 33–70 
Gender 
 Male 27 (64.3) 15 (35.7) 12 (28.6) 
 Female 15 (35.7) 6 (14.3) 9 (21.4) 
Grade of tumor 
 Moderate 34 (81) 17 (40.5) 17 (40.5) 
 Moderate–poor 6 (14) 3 (7) 3 (7) 
 Missing data 2 (4.8) 1 (2.4) 1 (2.4) 
Primary tumor in place 
 Yes 21 (50) 8 (19) 13 (31) 
 No 20 (47.6) 13 (31) 7 (16.6) 
 Missing data 1 (2.4) 1 (2.4) 
Delay between tumor tissue and blood collection 
 <1 year 3 (7) 2 (4.8) 1 (2.4) 
 1–2 years 15 (35.7) 10 (23.8) 5 (11.9) 
 >2 years 24 (57) 9 (21.7) 15 (35.7) 
No. of metastatic sites 
 1 26 (62) 13 (31) 13 (31) 
 >1 15 (36) 8 (19) 7 (17) 
 Missing data 1 (2.4) 1 (2.4) 
Previous chemotherapy 
 Oxaliplatin 38 (91) 20 (48) 18 (43) 
 Irinotecan 33 (78.6) 16 (38.1) 17 (40.5) 
 Any 3 (7.1) 1 (2.4) 2 (4.8) 
Previous targeted therapies 
 Bevacizumab 33 (78.6) 19 (45.3) 14 (33.3) 
 Cetuximab 14 (33.3) 1 (2.4) 13 (31) 
 Panitumumab 2 (4.8) 2 (4.8) 
 Any 3 (7.1) 1 (2.4) 2 (4.8) 
Cohorts N (%)
Cohorts 1 + 2Cohort 1Cohort 2
N 42 21 (50) 21 (50) 
Age 
 Median 58.5 58 59 
 Min–Max 26–74 26–74 33–70 
Gender 
 Male 27 (64.3) 15 (35.7) 12 (28.6) 
 Female 15 (35.7) 6 (14.3) 9 (21.4) 
Grade of tumor 
 Moderate 34 (81) 17 (40.5) 17 (40.5) 
 Moderate–poor 6 (14) 3 (7) 3 (7) 
 Missing data 2 (4.8) 1 (2.4) 1 (2.4) 
Primary tumor in place 
 Yes 21 (50) 8 (19) 13 (31) 
 No 20 (47.6) 13 (31) 7 (16.6) 
 Missing data 1 (2.4) 1 (2.4) 
Delay between tumor tissue and blood collection 
 <1 year 3 (7) 2 (4.8) 1 (2.4) 
 1–2 years 15 (35.7) 10 (23.8) 5 (11.9) 
 >2 years 24 (57) 9 (21.7) 15 (35.7) 
No. of metastatic sites 
 1 26 (62) 13 (31) 13 (31) 
 >1 15 (36) 8 (19) 7 (17) 
 Missing data 1 (2.4) 1 (2.4) 
Previous chemotherapy 
 Oxaliplatin 38 (91) 20 (48) 18 (43) 
 Irinotecan 33 (78.6) 16 (38.1) 17 (40.5) 
 Any 3 (7.1) 1 (2.4) 2 (4.8) 
Previous targeted therapies 
 Bevacizumab 33 (78.6) 19 (45.3) 14 (33.3) 
 Cetuximab 14 (33.3) 1 (2.4) 13 (31) 
 Panitumumab 2 (4.8) 2 (4.8) 
 Any 3 (7.1) 1 (2.4) 2 (4.8) 

NOTE: FOLFOX/dasatinib treatment (Cohort 1) and FOLFOX/dasatinib/cetuximab treatment (Cohort 2).

Mutation analysis and cfDNA assay sensitivity

Cohort 1: Patients with previously documented RAS mutation and treated with FOLFOX plus dasatinib.

Prior to treatment, 100% patients were found to have KRAS c12/13 point mutations in archival tissue, while 20 of 21 (95%) patients had cfDNA mutations detected (Fig. 2A). No BRAFV600E mutations were detected at baseline in tissue or cfDNA. Compared with tissue-detected mutations, 4 of 19 patients (21%) had different KRAS 12/13 point mutations noted in cfDNA, while 7 of 14 patients (50%) with concordant results had a second mutation detected in cfDNA (Fig. 1B). Of the 20 patients with cfDNA-detected mutations, 50% had a second concurrent mutation, while no patients with two concurrent mutations were detected in tissue (Fig. 2A).

Figure 2.

RAS/BRAF mutational status. Comparison of RAS/BRAF mutational status as determined by tumor tissue and plasma analysis. A, Before initiation of treatment of cohorts 1 and 2 (baseline). B, Longitudinal plasma genotyping during treatment (n = 26). C, Distribution of mutational status from cfDNA analysis of cohort 2 patients before and after EGFR-directed therapy. Percentage of mutant patients as determined by tumor tissue analysis and plasma analysis before initiation and during FOLFOX/dasatinib + cetuximab treatment as determined by serial plasma analysis. Results are shown according the target genes. An, acquired n additional mutation; CO, cooccurring mutations from plasma analysis; WT, wild type for mutations tested; single, one mutation was found in patient; multiple, at least two mutations were found in patient.

Figure 2.

RAS/BRAF mutational status. Comparison of RAS/BRAF mutational status as determined by tumor tissue and plasma analysis. A, Before initiation of treatment of cohorts 1 and 2 (baseline). B, Longitudinal plasma genotyping during treatment (n = 26). C, Distribution of mutational status from cfDNA analysis of cohort 2 patients before and after EGFR-directed therapy. Percentage of mutant patients as determined by tumor tissue analysis and plasma analysis before initiation and during FOLFOX/dasatinib + cetuximab treatment as determined by serial plasma analysis. Results are shown according the target genes. An, acquired n additional mutation; CO, cooccurring mutations from plasma analysis; WT, wild type for mutations tested; single, one mutation was found in patient; multiple, at least two mutations were found in patient.

Close modal

Pretreatment (FOLFOX/dasatinib) and postprogression plasma was available for 11 of 21 patients (52%) who had completed between 4 and 8 cycles of therapy. New point mutations were found in 8 of 11 (73%) patients (Fig. 2B), 6 of whom had additional KRAS mutations, 1 of whom developed two new KRAS mutations, and 3 of whom developed new BRAFV600E mutations. There was 1 patient with a baseline KRAS G12C mutation that was no longer detected after four cycles of therapy. As all plasma were scored KRAS exon2/BRAF mutant at baseline or during treatment, no extended RAS mutation testing was performed in this cohort.

Cohort 2: Patients without a previously documented RAS mutation and treated with FOLFOX, dasatinib plus cetuximab.

Eleven of 21 patients (52%) were KRAS 12/13 mutant using our cfDNA analysis before treatment (Fig. 2A), while 3 patients (14%) were BRAFV600E mutant using the cfDNA analysis. Archival testing detected only 2 patients (10%) with BRAFV600E mutations (Fig. 2A). Concordance of BRAFV600E mutation results using archival tissue results as the “gold standard” was 95%, with specificity of 95% and sensitivity of 100% (Supplementary Table S2C). Extended RAS mutation testing was performed on cfDNA only in patients with WT KRAS 12/13 and BRAF. No KRAS exon 3 or 4 mutations were noted in archival tissue, whereas 1 patient had a KRAS Q61H mutation by cfDNA testing prior to treatment. Two patients were NRAS 61 mutant on tissue, while 3 were NRAS 61 mutant in plasma (Fig. 2A). The same NRAS exon 61 mutation was detected from tumor tissue and plasma. Eight of 21 patients (38%) had concordant pretreatment results by archival tissue and cfDNA analysis. Conversely, 13 of 21 (62%) were scored mutant only using plasma cfDNA analysis: 11 were KRAS 12/13 mutant, 1 was KRAS Q61H mutant, and another was NRAS Q61R mutant. In Cohort 2, 5 of 17 patients (29%) were found to have multiple concurrent RAS mutations by plasma analysis at baseline, while none had multiple mutations in the tissue -based assay (Fig. 2A). One patient was KRAS 12/13 and BRAFV600E mutant before initiation of treatment.

Pretreatment and postprogression plasma was available for 15 of 21 patients who had completed between 4 and 16 cycles of FOLFOX, dasatinib plus cetuximab therapy. Plasma analysis showed new point mutations in 6 of 15 patients (40%), including detection of additional point mutations in patients who were WT extended RAS/RAF before treatment (Fig. 2B). Seven of 15 patients (47%) had no molecular change during treatment, including only 1 patient who remained RAS/BRAF WT during treatment and never developed a documented resistance mechanism in the RAS/BRAF pathway. In 2 of 15 patients (13%), the cfDNA point mutation detected at baseline was no longer detected during treatment. Compared with exon 2 RAS testing, extended RAS in plasma decreased the number of WT patients from 48% to 29% at baseline, and 43% to 19% upon completion of treatment (Fig. 2C). Our cfDNA assay reclassified even more patients to contain a mutation in the RAS/BRAF axis. Pretreatment, 19% of patients were deemed to be extended RAS and BRAF V600E WT, whereas posttreatment, only 5% lacked a mutation. Three of 15 patients (20%) with single baseline RAS mutations developed novel concurrent RAS mutations by the time of disease progression.

Combined cohorts assay analysis

The median interval between tumor tissue and plasma collection was longer for Cohort 2 patients than for Cohort 1 (1,074 days and 699 days, respectively; P = 0.038; Supplementary Fig. S2A). Delay between tissue/plasma collection was statistically different between patients with concordant and discordant data [692 vs. 1,074 days, respectively (P = 0.0163; Supplementary Fig. S2B)]. The median time between tumor tissue collection and enrollment on the clinical trial was 845 days in both arms compared with less than one day for plasma collection.

Eighty percent of patients (4/5) who were RAS/BRAF WT on both tissue and cfDNA assay became RAS or BRAF mutant during or at the end of the therapy. Only one patient (2%) among the 42 examined in the two arms was WT for both RAS/BRAF. No detection of EGFR S492R was found either before or during anti-EGFR–targeted therapy in the plasma of this patient.

Impact of cetuximab rechallenge on mutational status

Among patients examined in Cohort 2, 13 patients had previously received cetuximab in a prior line of therapy. Of these patients, 9 of 13 (69%) had a RAS mutation noted and another 1 of 13 (8%) had a BRAFV600E mutation at baseline. Thus, 77% of rechallenged patients previously determined to be WT by archival tissue analysis were indeed mutant. Notably, 30% (3/10) of the rechallenged RAS mutant patients had multiple cooccurring RAS point mutations at baseline. All plasma samples originating from cetuximab-rechallenged patients (n = 13) were tested for the EGFR S492R point mutation. No patients carried EGFR S492R mutations before, during, or after FOLFOX, dasatinib plus cetuximab treatment.

Temporal trends in cfDNA concentration during therapy

Circulating WT KRAS/BRAF cfDNA concentration.

The total concentration of WT cfDNA was determined by quantifying a WT sequence of both KRAS and BRAF genes and is shown in Table 2A and B, for Cohort 1 and 2, respectively. At baseline and as expected, data showed a linear positive relationship between the RefA values (ng/mL) of KRAS and BRAF WT sequences (Supplementary Fig. S3A) as previously observed (32). The KRAS:BRAF ratio was close to 1, both before and during treatment (Supplementary Fig. S3B). KRAS WT concentration did not statistically differ between cohorts (P = 0.36; Supplementary Fig. S4), and in most patients, KRAS WT concentration was higher at the end of treatment than at baseline (Fig. 3A and B). Of patients with serial measurements, 4 of 11 (36%) and 12 of 15 patients (80%) showed an increase of 50% (1.5-fold) above their baseline value at the end of FOLFOX plus dasatinib or FOLFOX, dasatinib plus cetuximab therapy, respectively (Fig. 3). No significant difference was seen in total concentration of WT cfDNA at baseline between patients whose treatment was stopped before cycle 4 and those treated with more than four cycles (P = 0.76; Supplementary Fig. S5).

Figure 3.

Impact of treatment on the evolution of cfDNA concentration. A and B, RefA values during FOLFOX/dasatinib treatment (A; Cohort 1, n = 11), and during FOLFOX/dasatinib/cetuximab treatment (B; Cohort 2, n = 15). RefA are expressed in fold change of baseline value. C, cfDNA mutant allele frequency of each detected mutant samples. mA% was determined by IntPlex as described in Materials and Methods before (blue, baseline) and after treatment (red, follow-up) in both cohorts. mA% varies from 0.009% to 60.28%. Twenty-one percent and 64% of all samples showed a mutant allele frequency below 0.1% and 1% (dotted lines), respectively. RefA, concentration of total cfDNA (ng/mL of plasma); BL, baseline; mA%, cfDNA mutation load.

Figure 3.

Impact of treatment on the evolution of cfDNA concentration. A and B, RefA values during FOLFOX/dasatinib treatment (A; Cohort 1, n = 11), and during FOLFOX/dasatinib/cetuximab treatment (B; Cohort 2, n = 15). RefA are expressed in fold change of baseline value. C, cfDNA mutant allele frequency of each detected mutant samples. mA% was determined by IntPlex as described in Materials and Methods before (blue, baseline) and after treatment (red, follow-up) in both cohorts. mA% varies from 0.009% to 60.28%. Twenty-one percent and 64% of all samples showed a mutant allele frequency below 0.1% and 1% (dotted lines), respectively. RefA, concentration of total cfDNA (ng/mL of plasma); BL, baseline; mA%, cfDNA mutation load.

Close modal
Table 2.

Impact of treatment on total cfDNA and mutant RAS/BRAF cfDNA concentration and mutant RAS/BRAF allele frequency

Aa
Cohort 1- KRAS 12/13–mutant patients by tumor tissue analysis
RefA KRAS (ng/mL of plasma)mA (ng/mL of plasma)mA%
Time point treatmentTime point treatmentTime point treatment
Patient IDBaselineCycle 4Cycle 8MutationBaselineCycle 4Cycle 8BaselineCycle 4Cycle 8
#1 11.35 — — KRAS G12R 0.84 — — 7.42 — — 
— — — — KRAS G13D 0.15 — — 1.28 — — 
#2 16.61 11.29 14.91 KRAS G12C 0.2 ND ND 1.03 ND ND 
— — — — BRAF V600E ND ND 0.14 ND ND 2.27 
#3 13.18 — — KRAS G12V 0.08 — — 0.62 — — 
#4 0.05 — — KRAS G13D NQ — — NQ — — 
— — — — KRAS G12V NQ — — NQ — — 
#5 29.84 — — KRAS G12D 1.27 — — 4.27 — — 
#6 13.97 — — KRAS G13D 0.04 — — 0.3 — — 
— — — — KRAS G12D 0.37 — — 2.66 — — 
#7 37.97 52.19 — KRAS G13D 0.06 0.07 — 0.15 0.13 — 
— — — — KRAS G12V 3.76 4.59 — 9.9 8.8 — 
— — — — KRAS G12A ND 0.05 — ND 0.09 — 
— — — — KRAS G12C ND 0.05 — ND 0.09 — 
#8 90.04 11.55 — KRAS G13D 0.12 0.45 — 0.13 0.4 — 
— — — — KRAS G12A 11.87 18.61 — 13.18 16.68 — 
#9 37.13 — KRAS G13D 0.036 0.14 — 0.6 0.24 — 
— — — — KRAS G12D ND 0.054 — ND 0.15 — 
#10 34.35 27.98 — KRAS G13D 0.003 0.01 — 0.009 0.03 — 
#11 51.31 54.36 — KRAS G12S 16.33 32.77 — 31.83 60.28 — 
— — — — KRAS G13D 0.1 0.01 — 0.2 0.02 — 
— — — — KRAS G12A ND 0.13 — ND 0.24 — 
— — — — BRAF V600E ND 0.04 — ND 0.07 — 
#12 53.99 — — KRAS G12D 5.86 — — 10.86 — — 
— — — — KRAS G13D 0.09 — — 0.16 — — 
#13 13.46 17.95 — KRAS G12V 0.73 1.11 — 5.44 6.17 — 
— — — — KRAS G13D ND 0.05 — ND 0.3 — 
#14 4.77 18.25 — KRAS G12D 0.01 0.02 — 0.28 0.17 — 
#15 32.53 — — KRAS G12A 0.03 — — 0.09 — — 
#16 14.56 — — KRAS G13D 0.01 — — 0.1 — — 
— — — — KRAS G12D 0.07 — — 0.45 — — 
— — — — KRAS G12C 0.03 — — 0.2 — — 
#17 22.61 33.2 — KRAS G12D 0.97 0.54 — 4.3 1.63 — 
— — — — KRAS G12A ND 0.02 — ND 0.05 — 
#18 16.62 — — KRAS G13D 0.02 — — 0.12 — — 
— — — — KRAS G12V 0.12 — — 0.72 — — 
#19 2.46 6.89 — BRAF V600E ND 0.15 — ND 3.96 — 
#20 27.46 31.02 9.66 KRAS G12D 0.19 ND 0.02 0.67 ND 0.23 
— — — — KRAS G12A ND 0.08 0.02 ND 0.26 0.12 
#21 17.48 — — KRAS G12A 0.03 — — 0.19 — — 
— — — — KRAS G13D 0.01 — — 0.08 — — 
Bb 
Cohort 2 - KRAS 12/13 WT patients by tumor tissue analysis 
 RefA KRAS (ng/mL of plasma)  mA (ng/mL of plasma) mA% 
 Time point treatment  Time point treatment Time point treatment 
Patient ID Baseline Cycle 4 Cycle 8 Cycle 12 Cycle 16 Mutation Baseline Cycle 4 Cycle 8 Cycle 12 Cycle 16 Baseline Cycle 4 Cycle 8 Cycle 12 Cycle 16 
#22 8.02 39.53 — — — BRAF V600E 0.29 0.55 — — — 4.51 3.77 — — — 
#23 7.7 22.79 — — — NRAS Q61R 5.08 — — — 56 22.29 — — — 
#24 11.81 — — — — KRAS G12R 0.09 — — — — 0.72 — — — — 
#25 6.31 31.42 — — — WT WT WT — — — WT WT — — — 
#26 11.1 25.17 — — — KRAS G12A 0.07 0.7 — — — 0.64 2.78 — — — 
#27 12.12 13.34 21.25 — — KRAS G12A 0.28 0.05 ND — — 0.97 0.36 ND — — 
— — — — — — KRAS G12V 0.06 0.02 ND — — 0.21 0.16 ND — — 
#28 2.15 7.44 — 23.28 — KRAS G12A NQ 0.11 — 0.45 — NQ 1.43 — 1.9 — 
#29 14.92 33.35 — — — KRAS A146T ND NQ — — — ND NQ — — — 
#30 9.55 — — — — KRAS G12R NQ — — — — NQ — — — — 
— — — — — — KRAS G12D NQ — — — — NQ — — — — 
#31 116.29 26.27 45.01 47.45 350.48 KRAS G12A 0.052 0.03 0.015 0.17 0.16 0.04 0.11 0.03 0.45 0.04 
— — — — — — KRAS G13D ND ND ND 0.022 0.15 ND ND ND 0.05 0.03 
— — — — — — KRAS G12D ND ND ND ND 0.088 ND ND ND ND 0.01 
— — — — — — KRAS G12V ND ND ND ND 0.47 ND ND ND ND 0.11 
#32 27.68 19.62 — — — NRAS Q61R 0.46 0.43 — — — 0.76 3.4 — — — 
#33 94.78 — — — — BRAF V600E 19.58 — — — — 15.57 — — — — 
#34 110.14 — — — — KRAS G13D 0.03 — — — — 0.03 — — — — 
— — — — — — BRAF V600E 0.23 — — — — 0.16 — — — — 
#35 2.46 — — — — KRAS G12R NQ — — — — NQ — — — — 
#36 69.44 41.26 191.3 — — KRAS G12A 0.03 0.26 0.06 — — 0.05 0.26 0.03 — — 
— — — — — — KRAS G13D 0.16 3.41 4.57 — — 0.22 8.27 2.39 — — 
— — — — — — KRAS G12S ND ND 0.05 — — ND ND 0.02 — — 
#37 12.79 16.14 — — — BRAF V600E ND 0.15 — — — ND 1.4 — — — 
#38 5.43 9.19 — — — KRAS Q61H ND NQ — — — ND NQ — — — 
— — — — — — NRAS Q61K 0.85 3.94 — — — 18.87 18.74 — — — 
#39 14 27.45 — — — KRAS G12A ND 0.04 — — — ND 0.16 — — — 
#40 8.17 13.49 — — — KRAS G12A 0.05 0.04 — — — 0.58 0.28 — — — 
#41 106.37 95.17 — — — KRAS Q61H NQ ND — — — NQ ND — — — 
#42 11.24 — — — — KRAS G12A 0.12 — — — — 1.09 — — — — 
— — — — — — KRAS G12V 0.03 — — — — 0.24 — — — — 
Aa
Cohort 1- KRAS 12/13–mutant patients by tumor tissue analysis
RefA KRAS (ng/mL of plasma)mA (ng/mL of plasma)mA%
Time point treatmentTime point treatmentTime point treatment
Patient IDBaselineCycle 4Cycle 8MutationBaselineCycle 4Cycle 8BaselineCycle 4Cycle 8
#1 11.35 — — KRAS G12R 0.84 — — 7.42 — — 
— — — — KRAS G13D 0.15 — — 1.28 — — 
#2 16.61 11.29 14.91 KRAS G12C 0.2 ND ND 1.03 ND ND 
— — — — BRAF V600E ND ND 0.14 ND ND 2.27 
#3 13.18 — — KRAS G12V 0.08 — — 0.62 — — 
#4 0.05 — — KRAS G13D NQ — — NQ — — 
— — — — KRAS G12V NQ — — NQ — — 
#5 29.84 — — KRAS G12D 1.27 — — 4.27 — — 
#6 13.97 — — KRAS G13D 0.04 — — 0.3 — — 
— — — — KRAS G12D 0.37 — — 2.66 — — 
#7 37.97 52.19 — KRAS G13D 0.06 0.07 — 0.15 0.13 — 
— — — — KRAS G12V 3.76 4.59 — 9.9 8.8 — 
— — — — KRAS G12A ND 0.05 — ND 0.09 — 
— — — — KRAS G12C ND 0.05 — ND 0.09 — 
#8 90.04 11.55 — KRAS G13D 0.12 0.45 — 0.13 0.4 — 
— — — — KRAS G12A 11.87 18.61 — 13.18 16.68 — 
#9 37.13 — KRAS G13D 0.036 0.14 — 0.6 0.24 — 
— — — — KRAS G12D ND 0.054 — ND 0.15 — 
#10 34.35 27.98 — KRAS G13D 0.003 0.01 — 0.009 0.03 — 
#11 51.31 54.36 — KRAS G12S 16.33 32.77 — 31.83 60.28 — 
— — — — KRAS G13D 0.1 0.01 — 0.2 0.02 — 
— — — — KRAS G12A ND 0.13 — ND 0.24 — 
— — — — BRAF V600E ND 0.04 — ND 0.07 — 
#12 53.99 — — KRAS G12D 5.86 — — 10.86 — — 
— — — — KRAS G13D 0.09 — — 0.16 — — 
#13 13.46 17.95 — KRAS G12V 0.73 1.11 — 5.44 6.17 — 
— — — — KRAS G13D ND 0.05 — ND 0.3 — 
#14 4.77 18.25 — KRAS G12D 0.01 0.02 — 0.28 0.17 — 
#15 32.53 — — KRAS G12A 0.03 — — 0.09 — — 
#16 14.56 — — KRAS G13D 0.01 — — 0.1 — — 
— — — — KRAS G12D 0.07 — — 0.45 — — 
— — — — KRAS G12C 0.03 — — 0.2 — — 
#17 22.61 33.2 — KRAS G12D 0.97 0.54 — 4.3 1.63 — 
— — — — KRAS G12A ND 0.02 — ND 0.05 — 
#18 16.62 — — KRAS G13D 0.02 — — 0.12 — — 
— — — — KRAS G12V 0.12 — — 0.72 — — 
#19 2.46 6.89 — BRAF V600E ND 0.15 — ND 3.96 — 
#20 27.46 31.02 9.66 KRAS G12D 0.19 ND 0.02 0.67 ND 0.23 
— — — — KRAS G12A ND 0.08 0.02 ND 0.26 0.12 
#21 17.48 — — KRAS G12A 0.03 — — 0.19 — — 
— — — — KRAS G13D 0.01 — — 0.08 — — 
Bb 
Cohort 2 - KRAS 12/13 WT patients by tumor tissue analysis 
 RefA KRAS (ng/mL of plasma)  mA (ng/mL of plasma) mA% 
 Time point treatment  Time point treatment Time point treatment 
Patient ID Baseline Cycle 4 Cycle 8 Cycle 12 Cycle 16 Mutation Baseline Cycle 4 Cycle 8 Cycle 12 Cycle 16 Baseline Cycle 4 Cycle 8 Cycle 12 Cycle 16 
#22 8.02 39.53 — — — BRAF V600E 0.29 0.55 — — — 4.51 3.77 — — — 
#23 7.7 22.79 — — — NRAS Q61R 5.08 — — — 56 22.29 — — — 
#24 11.81 — — — — KRAS G12R 0.09 — — — — 0.72 — — — — 
#25 6.31 31.42 — — — WT WT WT — — — WT WT — — — 
#26 11.1 25.17 — — — KRAS G12A 0.07 0.7 — — — 0.64 2.78 — — — 
#27 12.12 13.34 21.25 — — KRAS G12A 0.28 0.05 ND — — 0.97 0.36 ND — — 
— — — — — — KRAS G12V 0.06 0.02 ND — — 0.21 0.16 ND — — 
#28 2.15 7.44 — 23.28 — KRAS G12A NQ 0.11 — 0.45 — NQ 1.43 — 1.9 — 
#29 14.92 33.35 — — — KRAS A146T ND NQ — — — ND NQ — — — 
#30 9.55 — — — — KRAS G12R NQ — — — — NQ — — — — 
— — — — — — KRAS G12D NQ — — — — NQ — — — — 
#31 116.29 26.27 45.01 47.45 350.48 KRAS G12A 0.052 0.03 0.015 0.17 0.16 0.04 0.11 0.03 0.45 0.04 
— — — — — — KRAS G13D ND ND ND 0.022 0.15 ND ND ND 0.05 0.03 
— — — — — — KRAS G12D ND ND ND ND 0.088 ND ND ND ND 0.01 
— — — — — — KRAS G12V ND ND ND ND 0.47 ND ND ND ND 0.11 
#32 27.68 19.62 — — — NRAS Q61R 0.46 0.43 — — — 0.76 3.4 — — — 
#33 94.78 — — — — BRAF V600E 19.58 — — — — 15.57 — — — — 
#34 110.14 — — — — KRAS G13D 0.03 — — — — 0.03 — — — — 
— — — — — — BRAF V600E 0.23 — — — — 0.16 — — — — 
#35 2.46 — — — — KRAS G12R NQ — — — — NQ — — — — 
#36 69.44 41.26 191.3 — — KRAS G12A 0.03 0.26 0.06 — — 0.05 0.26 0.03 — — 
— — — — — — KRAS G13D 0.16 3.41 4.57 — — 0.22 8.27 2.39 — — 
— — — — — — KRAS G12S ND ND 0.05 — — ND ND 0.02 — — 
#37 12.79 16.14 — — — BRAF V600E ND 0.15 — — — ND 1.4 — — — 
#38 5.43 9.19 — — — KRAS Q61H ND NQ — — — ND NQ — — — 
— — — — — — NRAS Q61K 0.85 3.94 — — — 18.87 18.74 — — — 
#39 14 27.45 — — — KRAS G12A ND 0.04 — — — ND 0.16 — — — 
#40 8.17 13.49 — — — KRAS G12A 0.05 0.04 — — — 0.58 0.28 — — — 
#41 106.37 95.17 — — — KRAS Q61H NQ ND — — — NQ ND — — — 
#42 11.24 — — — — KRAS G12A 0.12 — — — — 1.09 — — — — 
— — — — — — KRAS G12V 0.03 — — — — 0.24 — — — — 

Abbreviations: ND, nondetected; NQ, scored positive but nonquantified; mA, concentration of mutant cfDNA (ng/mL of plasma); mA%, mutation load; RefA, concentration of total cfDNA (ng/mL of plasma); WT, wild type for mutations tested.

aThe total cfDNA concentration was determined by IntPlex as described in Materials and Methods, for each patient of Cohort 1 before initiation of treatment up to progression and at the end of treatment. Also, the concentration of mutant cfDNA and mutation loads was determined by IntPlex as described in Materials and Methods, for all point mutations found in each mutant patient of Cohort 1 before initiation of treatments up to the end of treatment.

bThe total cfDNA concentration was determined by IntPlex as described in Materials and Methods, for each patient of Cohort 2 before initiation of treatment up to progression and at the end of treatment. Also, the concentration of mutant cfDNA and mutation loads was determined by IntPlex as described in Materials and Methods, for all point mutations found in each mutant patient of Cohort 2 before initiation of treatment up to the end of treatment.

Circulating mutant RAS/BRAF cfDNA concentration.

Concentration of mutant cfDNA (Table 2A and B) did not differ at baseline when comparing Cohorts 1 and 2 (P = 0.65; Supplementary Fig. S6). No significant difference was observed in the baseline concentration of mutant cfDNA between patients whose treatment was stopped before the fourth cycle and patients who continued more than four cycles without progression (P = 0.18; Supplementary Fig. S7). In both cohorts combined, 7 of 46 point mutations (15.2%) found at baseline had concentrations higher than 1 ng/mL of plasma, 19 of 46 mutations (41.3%) had concentrations less than 0.1 ng/mL, and 4 of 46 (8.7%) were noted at 0.01 ng/mL (Table 2A and B).

Mutant allele frequency

The mutant allele frequency represents the proportion of detected alleles that are mutant for a particular point mutation. The distribution of mutant allele fraction (Table 2A and B) for both cohorts highly varied before and during treatment, ranging from 0.009% to 60% (Fig. 3C). Pretreatment, 65% (30/46) of detected mutations were below an allele frequency of 1%, including 45.68% (21/46) below 0.5% and 15% (7/46) below 0.1% (Table 2A and C). At baseline, mutant allele fraction varied from 0.009% to 31.83%. In patients with concordant results between archival tissue and cfDNA, 56% (10/18) had a mutant allele fraction >1%, 33% (6/18) were between 0.01% and 1%, and 11% (2/18) were <0.01% (Supplementary Table S3). One mutation was detectable but not quantifiable by our qPCR assay, as its allele fraction was <0.01%.

During treatment, 65% (31/48) of variants were found below 0.5% allele frequency, including 27% (13/48) below 0.1% (Table 2A and B). We noted that 83% (15/18) of new point mutations during treatment occur with a mutant allele fraction less than 0.5% and 50% (9/18) and are first noted at fractions ≤0.1% (Supplementary Table S4). We observed no difference between mutant allele fraction at baseline between Cohorts 1 and 2 (P = 0.77; Supplementary Fig. S8).

Ability of cfDNA to monitor disease progression and track therapeutic resistance

We collected data on imaging CT scan and CEA biomarker in most of the patients from baseline to the end of the treatment and compared them with mutant cfDNA parameters, such as mA%, mA, or refA (Supplementary Table S5). To illustrate the clinical application of circulating cfDNA to track detection of additional mutant subclones and assess disease response/progression, we present 4 example patients. Patient #8 was treated with FOLFOX plus dasatinib, while patients #28, #31, and #36 were treated with FOLFOX, dasatinib plus cetuximab. Treatment of patients #8, #36, #28, and #31 was stopped at cycles 4, 8, 12, and 16, respectively (Fig. 4A and B). RAS WT and mutant allele concentration and mutant allele fraction were compared with standard clinical assessment with CT scan and CEA level. For all patients except #8, RAS WT concentration, RAS-mutant concentration, and mutant allele fraction closely matched laboratory and radiological indicators of disease status. For patient #36, cfDNA may have provided a signal of disease progression earlier than standard clinical assessments when a rising KRAS G12S clone was noted.

Figure 4.

Quantitative cfDNA analysis in the course of treatments in comparison with serum CEA and CT scan measurements. Patients #8, #28, #31, and #36 illustrate impact of treatment on those biomarkers. A, Patients #8, #28, and #36 are patients who responded to treatment with stable disease. B, Patient #31 was the single patient in both cohort showing initial response to therapy (cetuximab). CEA, carcinoembryonic antigen (expressed in ng/mL); CT scan, computed tomography scan (expressed as percentage change of baseline value); RefA, concentration of total cfDNA (ng/mL of plasma); mA, concentration of mutant cfDNA (ng/mL of plasma); mA%, mutation load; PR, partial response; SD, stable disease (RECIST criteria); PD, progressive disease (RECIST criteria).

Figure 4.

Quantitative cfDNA analysis in the course of treatments in comparison with serum CEA and CT scan measurements. Patients #8, #28, #31, and #36 illustrate impact of treatment on those biomarkers. A, Patients #8, #28, and #36 are patients who responded to treatment with stable disease. B, Patient #31 was the single patient in both cohort showing initial response to therapy (cetuximab). CEA, carcinoembryonic antigen (expressed in ng/mL); CT scan, computed tomography scan (expressed as percentage change of baseline value); RefA, concentration of total cfDNA (ng/mL of plasma); mA, concentration of mutant cfDNA (ng/mL of plasma); mA%, mutation load; PR, partial response; SD, stable disease (RECIST criteria); PD, progressive disease (RECIST criteria).

Close modal

Interestingly, differential evolution of mutant subclones may be occurring in patients #31 and #36 (Fig. 4A and B), with some clones increasing in mutant allele frequency, while others decrease over time. In patient #36, there was detection of additional KRAS G12S–mutant subclone and a decrease of a KRAS G12A subclone at cycle 8. Similarly in patient #31, there are KRAS G12A and G12V clones that have inverse dynamics noted.

Although mAbs to EGFR improve mCRC patient survival, they are limited by primary and secondary resistance (39). Standard-of-care clinical biomarkers poorly track treatment resistance during early tumor progression. The selective pressure from targeted therapy combined with spatial and temporal clonal heterogeneity leads to a complex clinical challenge. Early detection of secondary resistance may impact clinical outcomes. Bertotti and colleagues (40) characterized secondary resistance to EGFR blockade in mCRC and observed that alterations in KRAS, NRAS, and BRAF were important mechanisms of resistance. With two recent reports showing the ability of cfDNA to detect secondary resistance to anti-EGFR therapies with using liquid biopsy (15, 17), this study further explored cfDNA and expanded upon work in the field by examining both the ability of cfDNA assays to assess for the presence of a mutation, and the ability of quantitative parameters associated with this assay, such as RAS WT concentration, RAS mutant concentration, and mutant allele fraction, to impact outcome.

cfDNA assay performance

Approximately half (52%) of the patients from Cohort 2 who initially tested WT for KRAS exon 2 mutations from archival tumor tissue analysis were actually noted to have KRAS exon 2 mutations in their plasma, and even more patients were found to have extended RAS or BRAF mutations. While concordance between tissue and plasma appeared better for BRAFV600E mutations, discordance for KRAS exon 2 mutations was notable. Concordance between cfDNA and archival tissue seems to be lower in patients who do not have their primary tumor in situ at the point of study entry as well in case of long delay between tumor tissue and blood collection (Supplementary Table S6). Our study suggests that cfDNA may be more sensitive than archival tumor tissue analysis in detecting mutations. We can speculate that discordance in 10 patients from Cohort 2 might result from the fact that blood was collected following prestudy targeted therapy, causing selection of mutant clones potentially detectable by plasma analysis at baseline before study treatment of Cohort 2. (Fig. 2A). We recently reported similar findings in another prospective and blinded study where we found a much higher proportion of mCRC patients with RAS or BRAF mutations using a cfDNA assay compared with tissue (76% instead of 55%; ref. 41). Various factors may explain this discordance. Tissue biopsies provide a static snapshot of the tumor at the time of tissue collection and do not necessarily represent the entire tumor genome due to tumor heterogeneity and clonal evolution (9, 10, 42). Temporal clonal evolution (43) may also impact concordance as we noted that time between collection of archival tissue and blood draw differed significantly between those with concordant and discordant results (692 vs. 1,074 respectively; P = 0.016; Supplementary Figs. S11 and S12). This suggests that therapeutic pressures and tumor evolution over time make archival tissue less reliable for determining the genotype of a tumor and highlights the advantage of real-time genotyping with cfDNA.

There are only a few other prospective studies examining the concordance of RAS mutation in tissue and cfDNA under blinded conditions (44–46).

Many other retrospective or small studies are available in the literature (45, 47). In these studies, specificity, sensitivity, and overall concordance in comparison with archival tissue were highly variable (55%–97%, 26%–92%, 64%–96%, respectively), likely due to assay characteristics coupled with time between tissue and plasma collection. When using an ultrasensitive method (48), such as employed in this study, another group noted a significant level of discordance (15%–25%), mainly associated with an increase in tumors classified as mutation positive by mutant cfDNA assessment. Our results are in agreement with Kuo and colleagues (49) who observed that 21% of patients who were WT by archival tissue were actually cfDNA positive for a KRAS mutation for an overall concordance of 79%. As detection of actionable mutations by plasma analysis is not limited by (i) intratumoral, (ii) intertumoral, and (iii) temporal clonal heterogeneity, in contrast to tumor tissue analysis, cfDNA testing has clear advantages that may lead to the earlier and more sensitive detection of mutations. Tumor tissue in now challenged as the gold standard in screening for actionable mutations in oncology.

Sensitivity is a crucial requirement for analyzing cfDNA due to the potentially low mutant allele frequency noted during sampling. To address this, we developed IntPlex, an allele-specific qPCR technique that uses several nested primer sets and takes into account the low fragment size of cfDNA (mean size of 100 bp) to optimize assay performance. IntPlex enables detection of variant alleles down to a sensitivity of 0.005% mutant to WT ratio that greatly surpasses conventional NGS-based methods (0.5%–2%; refs. 4, 50, 51). Spiking experiments showed clearly single-copy detection in highly diluted PCR mixture under Poisson law distribution (Supplementary Fig. S9). Using this technique, one mutant fragment may be detected among 10 billion cfDNA fragments providing a sensitivity level (0.005%, mutant/WT ratio) This targeted method is limited by the number of mutant fragments existing in the plasma, with a limit of 26 mutant cfDNA fragments/mL of plasma. Despite the fact that plasma for this study was stored frozen for more than 4 years (4–6 years of storage range) and the preanalytic conditions were not optimal, IntPlex was still able to detect mutations at very low frequencies and performed well. Note, no KRAS exon 2 and BRAFV600E mutation could be detected in 29 certified healthy volunteers in an ad hoc study of our previous report, regardless of the threshold of detection used (31).

There are some limitations to our assay. IntPlex enables the detection of only a restricted set of common mutations (i.e., known point mutations). Only 15 KRAS, 9 NRAS, and one BRAF mutations were tested in this study; however, these hotspot mutations represent more than 95%–99%, 92%–99% and 96%–97% of all mutations found in these three genes in colorectal patients, respectively.

Significance of low mutant allele frequency mutations

The impressive sensitivity of our assay and its ability to quantify mutations noted at as low as 0.01 ng/mL allowed the identification of many mutations with very small allelic fractions. The ability to confidently detect these low frequency mutations likely explains part of the discrepancy between the results from the initial clinical assay used for trial enrollment and our results. CLIA-certified laboratory often has a mutant allele fraction limit of detection of 5%. Positive results below this threshold are often suppressed from clinicians because a false positive result due to sequencing errors cannot be ruled out. We retrospectively sequenced archival tissue with a high-sensitivity assay to confirm mutations (Fig. 1B) and note significant discordance regardless of whether a clinical grade or high-sensitivity research-based platform is used (Fig. 1).

We observed subclones with very low allele frequency (down to 0.009%) either at baseline or during the course of the treatment (Supplementary Fig. S10). There were 30% of patients with a mutation at an allele frequency below 0.1% at baseline, and 20% of patients with a mutation noted below 0.1% during the course of the treatment. These rare subclones may exist before treatment but be undetectable by clinical grade assays and subsequently emerge under anti-EGFR treatment. Secondary RAS mutations have been previously shown to develop in up to 60% of tumors progressing during anti-EGFR therapy, and we noted that they could be found in all but 1 patient on this study; however, many of these mutations may have been present yet undetectable at baseline. Retrospective studies have suggested that many of these mutations may be detected in cfDNA prior to initiation of therapy (13), whereas other work using mathematic models of tumor kinetics and growth has shown that these mutations must be present prior to initiation of therapy (15).

As these low frequency mutations may result in treatment resistance, very sensitive methods to initially select patients for anti-EGFR therapies are needed. Tougeron and colleagues (52) recently demonstrated that low-prevalence KRAS mutations detected by an advanced pyrosequencing method in tumors previously defined as KRAS WT were associated with shorter PFS. Our data revealed that 52% (22/42) of patients had an allele fraction below 1%, 31% (13/42) were below 0.5%, and 19% (8/42) were below 0.1% (Supplementary Table S7). These low allelic values may not perfectly correlate to the allele frequency that would be detected in a tumor. Plasma analysis relies on the total number of cfDNA fragments shed into the blood stream from normal and tumor cells. In contrast, tumor tissue analysis relies on the number of malignant cells in a biopsy and may be enriched by pathologist selection of section with higher tumor content. As a consequence, the value used to estimate cfDNA sensitivity does not correspond directly to that of tumor tissue analysis, which precludes use of a universal sensitivity threshold for detection of mutations. We illustrated this by showing that in the 16% (4/25) of patients with cfDNA allele frequency below 0.5%, mutations could be readily detected in matched tumor tissue (Supplementary table S8; ref. 41).

Impact of therapy on clonal dynamics and development of additional RAS mutations

Following EGFR-directed therapy, we noted that 98% of patients had at least one mutation in RAS or BRAF that likely contributed to treatment resistance. More interestingly, of the 21 patients defined as RAS/BRAF mutant at baseline, 11 (50%) showed additional point mutation that was not present at baseline, while 3 (14%) lost previously detected mutations. There was even a single patient with a baseline KRAS G12A mutation that subsequently developed additional G12D, G12V, and G13D mutations after receiving 16 cycles of cetuximab-containing therapy. These findings demonstrate that even if a single clonal population with a resistance mechanism is present in a tumor, treatment pressures may drive the development of additional alternative resistance mechanisms in a pattern of convergent evolution. These multiple convergent mutations were noted to develop whether patients received FOLFOX plus dasatinib or FOLFOX, dasatinib plus cetuximab. Another important clonal dynamic that we noted was that some RAS mutations appeared to be more strongly associated with secondary mutation than primary resistance. Our data show that KRAS codon 61 was expressed more frequently in acquired resistance mutations (5.5%) than in the general mCRC population (1%–4%; 2,51), suggesting that mutations at this codon might provide a selective growth advantage under cetuximab treatment. This was also observed for KRAS codon 146 mutations by Morelli and colleagues (48). Our findings also reinforce the importance of BRAF mutations as mechanisms of resistance, as we demonstrated that 4 of 42 patients (10%) had additional BRAFV600E mutations during therapy.

cfDNA to follow disease progression

cfDNA quantitative measurements from this study closely mirrored changes in CEA and CT scan results as evaluated in 18 and 16 patients in Cohort 1 and 2, respectively. We noted that both the qualitative detection of a mutation and quantitative information such as RAS-mutant and WT cfDNA concentrations and mutant allele fraction appeared to be useful in following disease course. The level of RAS/BRAF mutations expressed as the percentage of mutant cfDNA as well as ng/mL plasma of mutant cfDNA did not correlate with the treatment response, highlighting the necessity of detecting low level and low frequency mutations (Supplementary Table S9). The circulating WT cfDNA concentration appeared to closely approximate other clinical parameters, and it was not only the detection of a mutant cfDNA sample that was related to disease progression (Fig. 4B). The patient in Fig. 4B had an interesting molecular profile that demonstrated falling levels of RAS DNA, both WT and mutant during response to therapy. Subsequently, after 16 cycles of therapy, this patient had developed three additional RAS mutations beyond the baseline KRAS G12A mutation. Mutant allele frequency of the single baseline mutation was very low (0.04%), and the subsequent new clones all remained subclonal with allelic frequencies ranging from 0.01% to 0.11%, potentially explaining why the patient continued to respond for over several cycles. There was likely a large proportion of this tumor that lacked RAS mutations.

Siravegna and colleagues (53) elegantly demonstrated the utility of following cfDNA to measure KRAS-mutant subclones as a potential for reusing prior targeted therapies. Upon withdrawal of the selective pressure of anti-EGFR treatment, KRAS-mutant clones declined (53). These patients subsequently regained sensitivity to anti-EGFR therapy. In a similar manner, following the patient in Fig. 4B serially may allow continuation of therapy until a resistance clone becomes more dominant, highlighting the importance of obtaining quantitative data beyond the mere presence of a mutation (12, 54). If therapy had been stopped at the first incidence of a new resistance mutation, that patient would have switched therapy 2 months earlier than was required clinically.

Limitations

Although this study demonstrates the potential applications of highly sensitive cfDNA to follow tumor dynamics, it must be interpreted within the contexts of its limitations. The small sample size of our study makes it difficult to draw firm conclusions. Our assessment of mutation status was blinded and samples were prospectively collected, both features that add to the rigor of experimental design but do not obviate the need for larger trials for validation. We were also limited by the infrequent plasma samples that were available for cfDNA analysis, occurring at baseline and once every 4 cycles. Although our study does not provide evidence that cfDNA detects mutant clones and progression earlier than clinical assessment, this is likely due to the fact that we only assessed cfDNA once every four cycles.

An important finding in this study was the convergent evolution of clones with redundant resistance mechanisms in the EGFR pathway. We were only able to test three clinically relevant genes and did not test genes involved in EGFR downstream signaling pathways or the mTOR pathway. PIK3CA mutation, PTEN loss (55), EGFR mutations, as well as MET and ERBB2 amplifications may predict a lack of response to anti-EGFR agents. Thus, it would be interesting to see whether there were redundant resistance pathways that also developed in these genes in addition to the multiple concurrent KRAS clones that were noted (40, 48). Because EGFR S492R has been described as an alternate mechanism of resistance, we did test the 13 plasma samples of patients who had previously received anti-EGFR therapy for EGFR S492R mutations at baseline; however, none of the 13 plasma samples were positive for this mutation.

With our assay, we were able to uncover the development of secondary resistance mutations in RAS and BRAF in all but one of 42 patients. A significant fraction of these mutations detected either before or in the course of treatment from plasma analysis harbors low mutant allele frequency in cfDNA. This shows the need of an ultrasensitive method to detect additional point mutation when tracking secondary resistance. Periodic liquid biopsies require a simple blood draw, and if optimized for clinical use would optimize patient safety and convenience. We note that not only was the qualitative presence of a mutant clone important, but the additional quantitative measurements of WT KRAS concentration, mutant KRAS concentration, and mutant allele fraction may be useful parameters to follow. We also noted that many patients experienced convergent evolution that resulted in redundant mutations that would provide multiple mechanisms of resistance to EGFR-directed therapy.

No potential conflicts of interest were disclosed.

Conception and design: A.R. Thierry

Development of methodology: A.R. Thierry, A.D. Katsiampoura

Acquisition of data (provided animals, acquired and managed patients, provided facilities, etc.): Z.-Q. Jiang, S. Kopetz

Analysis and interpretation of data (e.g., statistical analysis, biostatistics, computational analysis): A.R. Thierry, B. Pastor, Z.-Q. Jiang, A.D. Katsiampoura, J.M. Loree, M.J. Overman, S. El Messaoudi, M. Ychou

Writing, review, and/or revision of the manuscript: A.R. Thierry, B. Pastor, Z.-Q. Jiang, A.D. Katsiampoura, C. Parseghian, J.M. Loree, M.J. Overman, S. El Messaoudi, M. Ychou, S. Kopetz

Administrative, technical, or material support (i.e., reporting or organizing data, constructing databases): A.R. Thierry, B. Pastor, Z.-Q. Jiang, A.D. Katsiampoura, J.M. Loree, C. Sanchez

Study supervision: A.R. Thierry

The authors thank Thibault Mazard, Frederic Bibeau, Philippe Blache, Corinne Prevostel, Celine Gongora, and Evelyne Crapez for helpful discussion, Vanessa Guillaumon for help in supporting this work, Peter Gahan for editing the manuscript, and Amaelle Otandault and Baptiste Auzemery for technical assistance.

This study was supported by the SIRIC Montpellier grant «INCa-DGOS-Inserm 6045» and BMS and Roche France. A.R. Thierry was supported by INSERM.

The costs of publication of this article were defrayed in part by the payment of page charges. This article must therefore be hereby marked advertisement in accordance with 18 U.S.C. Section 1734 solely to indicate this fact.

1.
Fakih
M
. 
Biologic therapies in colorectal cancer: indications and contraindications
.
Am Soc Clin Oncol Educ Book
2015
;
e197
206
.
2.
Li
Z-Z
,
Wang
F
,
Zhang
Z-C
,
Wang
F
,
Zhao
Q
,
Zhang
D-S
, et al
Mutation profiling in chinese patients with metastatic colorectal cancer and its correlation with clinicopathological features and anti-EGFR treatment response
.
Oncotarget
2016
;
7
:
28356
68
.
3.
Blanco-Calvo
M
,
Concha
Á
,
Figueroa
A
,
Garrido
F
,
Valladares-Ayerbes
M
. 
Colorectal cancer classification and cell heterogeneity: a systems oncology approach
.
Int J Mol Sci
2015
;
16
:
13610
32
.
4.
Haley
L
,
Tseng
L-H
,
Zheng
G
,
Dudley
J
,
Anderson
DA
,
Azad
NS
, et al
Performance characteristics of next-generation sequencing in clinical mutation detection of colorectal cancers
.
Mod Pathol
2015
;
28
:
1390
9
.
5.
Westwood
M
,
van Asselt
T
,
Ramaekers
B
,
Whiting
P
,
Joore
M
,
Armstrong
N
, et al
KRAS mutation testing of tumours in adults with metastatic colorectal cancer: a systematic review and cost-effectiveness analysis
.
Health Technol Assess
2014
;
18
:
1
132
.
6.
Laurent-Puig
P
,
Pekin
D
,
Normand
C
,
Kotsopoulos
SK
,
Nizard
P
,
Perez-Toralla
K
, et al
Clinical relevance of KRAS-mutated subclones detected with picodroplet digital PCR in advanced colorectal cancer treated with anti-EGFR therapy
.
Clin Cancer Res
2015
;
21
:
1087
97
.
7.
Ciardiello
F
,
Normanno
N
,
Maiello
E
,
Martinelli
E
,
Troiani
T
,
Pisconti
S
, et al
Clinical activity of FOLFIRI plus cetuximab according to extended gene mutation status by next-generation sequencing: findings from the CAPRI-GOIM trial
.
Ann Oncol
2014
;
25
:
1756
61
.
8.
Caldas
C
. 
Cancer sequencing unravels clonal evolution
.
Nat Biotechnol
2012
;
30
:
408
10
.
9.
Gerlinger
M
,
Rowan
AJ
,
Horswell
S
,
Larkin
J
,
Endesfelder
D
,
Gronroos
E
, et al
Intratumor heterogeneity and branched evolution revealed by multiregion sequencing
.
N Engl J Med
2012
;
366
:
883
92
.
10.
Swanton
C
. 
Intratumor heterogeneity: evolution through space and time
.
Cancer Res
2012
;
72
:
4875
82
.
11.
Diaz
LA
,
Bardelli
A
. 
Liquid biopsies: genotyping circulating tumor DNA
.
J Clin Oncol
2014
;
32
:
579
86
.
12.
Thierry
AR
,
El Messaoudi
S
,
Gahan
PB
,
Anker
P
,
Stroun
M
. 
Origins, structures, and functions of circulating DNA in oncology
.
Cancer Metastasis Rev
2016
;
35
:
347
76
.
13.
Bettegowda
C
,
Sausen
M
,
Leary
RJ
,
Kinde
I
,
Wang
Y
,
Agrawal
N
, et al
Detection of circulating tumor DNA in early- and late-stage human malignancies
.
Sci Transl Med
2014
;
6
:
224ra24
.
14.
Newman
AM
,
Bratman
SV
,
To
J
,
Wynne
JF
,
Eclov
NCW
,
Modlin
LA
, et al
An ultrasensitive method for quantitating circulating tumor DNA with broad patient coverage
.
Nat Med
2014
;
20
:
548
54
.
15.
Diaz
LA
,
Williams
RT
,
Wu
J
,
Kinde
I
,
Hecht
JR
,
Berlin
J
, et al
The molecular evolution of acquired resistance to targeted EGFR blockade in colorectal cancers
.
Nature
2012
;
486
:
537
40
.
16.
Murtaza
M
,
Dawson
S-J
,
Tsui
DWY
,
Gale
D
,
Forshew
T
,
Piskorz
AM
, et al
Non-invasive analysis of acquired resistance to cancer therapy by sequencing of plasma DNA
.
Nature
2013
;
497
:
108
12
.
17.
Misale
S
,
Yaeger
R
,
Hobor
S
,
Scala
E
,
Janakiraman
M
,
Liska
D
, et al
Emergence of KRAS mutations and acquired resistance to anti-EGFR therapy in colorectal cancer
.
Nature
2012
;
486
:
532
6
.
18.
Lièvre
A
,
Bachet
J-B
,
Le Corre
D
,
Boige
V
,
Landi
B
,
Emile
J-F
, et al
KRAS mutation status is predictive of response to cetuximab therapy in colorectal cancer
.
Cancer Res
2006
;
66
:
3992
5
.
19.
Van Cutsem
E
,
Lenz
H-J
,
Köhne
C-H
,
Heinemann
V
,
Tejpar
S
,
Melezínek
I
, et al
Fluorouracil, leucovorin, and irinotecan plus cetuximab treatment and RAS mutations in colorectal cancer
.
J Clin Oncol
2015
;
33
:
692
700
.
20.
Allegra
CJ
,
Rumble
RB
,
Hamilton
SR
,
Mangu
PB
,
Roach
N
,
Hantel
A
, et al
Extended RAS gene mutation testing in metastatic colorectal carcinoma to predict response to anti-epidermal growth factor receptor monoclonal antibody therapy: American Society of Clinical Oncology Provisional Clinical Opinion Update 2015
.
J Clin Oncol
2016
;
34
:
179
85
.
21.
Sorich
MJ
,
Wiese
MD
,
Rowland
A
,
Kichenadasse
G
,
McKinnon
RA
,
Karapetis
CS
. 
Extended RAS mutations and anti-EGFR monoclonal antibody survival benefit in metastatic colorectal cancer: a meta-analysis of randomized, controlled trials
.
Ann Oncol
2015
;
26
:
13
21
.
22.
Douillard
JY
,
Siena
S
,
Cassidy
J
,
Tabernero
J
,
Burkes
R
,
Barugel
M
, et al
Final results from PRIME: randomized phase III study of panitumumab with FOLFOX4 for first-line treatment of metastatic colorectal cancer
.
Ann Oncol
2014
;
25
:
1346
55
.
23.
Boissière-Michot
F
,
Lopez-Crapez
E
,
Frugier
H
,
Berthe
M-L
,
Ho-Pun-Cheung
A
,
Assenat
E
, et al
KRAS genotyping in rectal adenocarcinoma specimens with low tumor cellularity after neoadjuvant treatment
.
Mod Pathol
2012
;
25
:
731
9
.
24.
De Roock
W
,
Claes
B
,
Bernasconi
D
,
De Schutter
J
,
Biesmans
B
,
Fountzilas
G
, et al
Effects of KRAS, BRAF, NRAS, and PIK3CA mutations on the efficacy of cetuximab plus chemotherapy in chemotherapy-refractory metastatic colorectal cancer: a retrospective consortium analysis
.
Lancet Oncol
2010
;
11
:
753
62
.
25.
Garcia-Murillas
I
,
Schiavon
G
,
Weigelt
B
,
Ng
C
,
Hrebien
S
,
Cutts
RJ
, et al
Mutation tracking in circulating tumor DNA predicts relapse in early breast cancer
.
Sci Transl Med
2015
;
7
:
302ra133
.
26.
Reinert
T
,
Schøler
LV
,
Thomsen
R
,
Tobiasen
H
,
Vang
S
,
Nordentoft
I
, et al
Analysis of circulating tumour DNA to monitor disease burden following colorectal cancer surgery
.
Gut
2016
;
65
:
625
34
.
27.
Douillard
J-Y
,
Oliner
KS
,
Siena
S
,
Tabernero
J
,
Burkes
R
,
Barugel
M
, et al
Panitumumab-FOLFOX4 treatment and RAS mutations in colorectal cancer
.
N Engl J Med
2013
;
369
:
1023
34
.
28.
Kim
M-J
,
Lee
HS
,
Kim
JH
,
Kim
YJ
,
Kwon
JH
,
Lee
J-O
, et al
Different metastatic pattern according to the KRAS mutational status and site-specific discordance of KRAS status in patients with colorectal cancer
.
BMC Cancer
2012
;
12
:
347
.
29.
Thierry
AR
,
Mouliere
F
,
Gongora
C
,
Ollier
J
,
Robert
B
,
Ychou
M
, et al
Origin and quantification of circulating DNA in mice with human colorectal cancer xenografts
.
Nucleic Acids Res
2010
;
38
:
6159
75
.
30.
Mouliere
F
,
Robert
B
,
Arnau Peyrotte
E
,
Del Rio
M
,
Ychou
M
,
Molina
F
, et al
High fragmentation characterizes tumour-derived circulating DNA
.
PLoS One
2011
;
6
:
e23418
.
31.
Thierry
AR
,
Mouliere
F
,
El Messaoudi
S
,
Mollevi
C
,
Lopez-Crapez
E
,
Rolet
F
, et al
Clinical validation of the detection of KRAS and BRAF mutations from circulating tumor DNA
.
Nat Med
2014
;
20
:
430
5
.
32.
Mouliere
F
,
El Messaoudi
S
,
Pang
D
,
Dritschilo
A
,
Thierry
AR
. 
Multi-marker analysis of circulating cell-free DNA toward personalized medicine for colorectal cancer
.
Mol Oncol
2014
;
8
:
927
41
.
33.
El Messaoudi
S
,
Mouliere
F
,
Du Manoir
S
,
Bascoul-Mollevi
C
,
Gillet
B
,
Nouaille
M
, et al
Circulating DNA as a strong multimarker prognostic tool for metastatic colorectal cancer patient management care
.
Clin Cancer Res
2016
;
22
:
3067
77
.
34.
Parseghian
C
,
Parikh
NU
,
Wu
JY
,
Jiang
Z-Q
,
Henderson
LD
,
Tian
F
, et al
Dual Inhibition of EGFR and c-Src by cetuximab and dasatinib combined with FOLFOX chemotherapy in patients with metastatic colorectal cancer
.
Clin Cancer Res
2017
Mar 9.
[Epub ahead of print]
.
35.
Chiu
RW
,
Poon
LL
,
Lau
TK
,
Leung
TN
,
Wong
EM
,
Lo
YM
. 
Effects of blood-processing protocols on fetal and total DNA quantification in maternal plasma
.
Clin Chem
2001
;
47
:
1607
13
.
36.
El Messaoudi
S
,
Rolet
F
,
Mouliere
F
,
Thierry
AR
. 
Circulating cell free DNA: preanalytical considerations
.
Clin Chim Acta
2013
;
424
:
222
30
.
37.
Arena
S
,
Bellosillo
B
,
Siravegna
G
,
Martínez
A
,
Cañadas
I
,
Lazzari
L
, et al
Emergence of multiple EGFR extracellular mutations during cetuximab treatment in colorectal cancer
.
Clin Cancer Res
2015
;
21
:
2157
66
.
38.
Esposito
C
,
Rachiglio
AM
,
La Porta
ML
,
Sacco
A
,
Roma
C
,
Iannaccone
A
, et al
The S492R EGFR ectodomain mutation is never detected in KRAS wild-type colorectal carcinoma before exposure to EGFR monoclonal antibodies
.
Cancer Biol Ther
2013
;
14
:
1143
6
.
39.
Waring
P
,
Tie
J
,
Maru
D
,
Karapetis
CS
. 
RAS mutations as predictive biomarkers in clinical management of metastatic colorectal cancer
.
Clin Colorectal Cancer
2016
;
15
:
95
103
.
40.
Bertotti
A
,
Papp
E
,
Jones
S
,
Adleff
V
,
Anagnostou
V
,
Lupo
B
, et al
The genomic landscape of response to EGFR blockade in colorectal cancer
.
Nature
2015
;
526
:
263
7
.
41.
Thierry
AR
,
Messaoudi
SE
,
Mollevi
C
,
Pastor
B
,
Sanchez
C
,
Raoul
J-L
, et al
Abstract 2632: clinical validation of circulating DNA analysis for the detection of point mutations and of the longitudinal metastatic colorectal patient follow up for detecting emergence of resistance to targeted therapy
.
Cancer Res
2016
;
76
(
14 Suppl
):
2632
.
42.
Ulz
P
,
Auer
M
,
Heitzer
E
. 
Detection of circulating tumor DNA in the blood of cancer patients: an important tool in cancer chemoprevention
.
Methods Mol Biol
2016
;
1379
:
45
68
.
43.
Uchi
R
,
Takahashi
Y
,
Niida
A
,
Shimamura
T
,
Hirata
H
,
Sugimachi
K
, et al
Integrated multiregional analysis proposing a new model of colorectal cancer evolution
.
PLoS Genet
2016
;
12
:
e1005778
.
44.
Sasaki
Y
,
Akasu
T
,
Saito
N
,
Kojima
H
,
Matsuda
K
,
Nakamori
S
, et al
Prognostic and predictive value of extended RAS mutation and mismatch repair status in stage III colorectal cancer
.
Cancer Sci
2016
;
107
:
1006
12
.
45.
Yen
L-C
,
Yeh
Y-S
,
Chen
C-W
,
Wang
H-M
,
Tsai
H-L
,
Lu
C-Y
, et al
Detection of KRAS oncogene in peripheral blood as a predictor of the response to cetuximab plus chemotherapy in patients with metastatic colorectal cancer
.
Clin Cancer Res
2009
;
15
:
4508
13
.
46.
Spindler
K-LG
,
Pallisgaard
N
,
Appelt
AL
,
Andersen
RF
,
Schou
JV
,
Nielsen
D
, et al
Clinical utility of KRAS status in circulating plasma DNA compared to archival tumour tissue from patients with metastatic colorectal cancer treated with anti-epidermal growth factor receptor therapy
.
Eur J Cancer
2015
;
51
:
2678
85
.
47.
Ryan
BM
,
Lefort
F
,
McManus
R
,
Daly
J
,
Keeling
PWN
,
Weir
DG
, et al
A prospective study of circulating mutant KRAS2 in the serum of patients with colorectal neoplasia: strong prognostic indicator in postoperative follow up
.
Gut
2003
;
52
:
101
8
.
48.
Morelli
MP
,
Overman
MJ
,
Dasari
A
,
Kazmi
SMA
,
Mazard
T
,
Vilar
E
, et al
Characterizing the patterns of clonal selection in circulating tumor DNA from patients with colorectal cancer refractory to anti-EGFR treatment
.
Ann Oncol
2015
;
26
:
731
6
.
49.
Kuo
Y-B
,
Chen
J-S
,
Fan
C-W
,
Li
Y-S
,
Chan
E-C
. 
Comparison of KRAS mutation analysis of primary tumors and matched circulating cell-free DNA in plasmas of patients with colorectal cancer
.
Clin Chim Acta
2014
;
433
:
284
9
.
50.
Altimari
A
,
de Biase
D
,
De Maglio
G
,
Gruppioni
E
,
Capizzi
E
,
Degiovanni
A
, et al
454 next generation-sequencing outperforms allele-specific PCR, Sanger sequencing, and pyrosequencing for routine KRAS mutation analysis of formalin-fixed, paraffin-embedded samples
.
OncoTargets Ther
2013
;
6
:
1057
64
.
51.
Gao
J
,
Wu
H
,
Wang
L
,
Zhang
H
,
Duan
H
,
Lu
J
, et al
Validation of targeted next-generation sequencing for RAS mutation detection in FFPE colorectal cancer tissues: comparison with Sanger sequencing and ARMS-Scorpion real-time PCR
.
BMJ Open
2016
;
6
:
e009532
.
52.
Tougeron
D
,
Lecomte
T
,
Pagès
JC
,
Villalva
C
,
Collin
C
,
Ferru
A
, et al
Effect of low-frequency KRAS mutations on the response to anti-EGFR therapy in metastatic colorectal cancer
.
Ann Oncol
2013
;
24
:
1267
73
.
53.
Siravegna
G
,
Mussolin
B
,
Buscarino
M
,
Corti
G
,
Cassingena
A
,
Crisafulli
G
, et al
Clonal evolution and resistance to EGFR blockade in the blood of colorectal cancer patients
.
Nat Med
2015
;
21
:
827
.
54.
Perkins
G
,
Yap
TA
,
Pope
L
,
Cassidy
AM
,
Dukes
JP
,
Riisnaes
R
, et al
Multi-purpose utility of circulating plasma DNA testing in patients with advanced cancers
.
PLoS One
2012
;
7
:
e47020
.
55.
Therkildsen
C
,
Bergmann
TK
,
Henrichsen-Schnack
T
,
Ladelund
S
,
Nilbert
M
. 
The predictive value of KRAS, NRAS, BRAF, PIK3CA and PTEN for anti-EGFR treatment in metastatic colorectal cancer: a systematic review and meta-analysis
.
Acta Oncol
2014
;
53
:
852
64
.

Supplementary data