PIK3CA mutations contribute to acquired cetuximab resistance in metastatic colorectal cancer patients

Abstract

Purpose

Mutations in KRAS are considered to be the main drivers of acquired resistance to epidermal growth factor receptor (EGFR) blockade in patients with metastatic colorectal cancer (mCRC). However, the potential roles of other genes downstream of the EGFR signaling pathway in conferring acquired resistance has not been extensively investigated.

Experimental Design

Using circulating tumor DNA (ctDNA) from patients with mCRC and with acquired cetuximab resistance, we developed a targeted amplicon ultra-deep sequencing method to screen for low-abundance somatic mutations in a panel of genes that encode components of the EGFR signaling pathway. Mutations with significantly increased variant frequencies upon disease progression were selected by using quartile analysis. The functional consequences of the identified mutations were validated in cultured cells.

Results

We analyzed 32 patients with acquired cetuximab resistance in a development cohort. Of them, 7 (22%) carried five novel PIK3CA mutations, whereas 8 (25%) carried previously reported KRAS mutations. Functional studies showed that novel PIK3CA mutations (all in exon 19; p.K944N, p.F930S, p.V955G, p.V955I, and p.K966E) promote cell viability in the presence of cetuximab. Only one novel PIK3CA mutation (p.K944N) was verified in one of the 27 patients with acquired resistance in a validation cohort, simultaneous KRAS and PIK3CA hotspot mutations were detected in 2 patients. Among the above 59 acquired resistance patients, those with PIK3CA or RAS mutations detected in ctDNA showed a pronounced decrease in progression-free survival than patients with no mutation.

Conclusions

The PIK3CA mutations may potentially contribute to acquired cetuximab resistance in patients with mCRC.

INTRODUCTION

Epidermal growth factor receptor (EGFR) signaling promotes cell proliferation and migration, and the constitutive activation of this pathway is associated with tumor progression and metastasis in colorectal cancer. Anti-EGFR antibodies, such as cetuximab, improve the survival of metastatic colorectal cancer (mCRC) patients. Cetuximab is currently recommended for mCRC patients expressing wild-type RAS(1). However, in a large proportion of patients who respond, acquired resistance to cetuximab emerges despite the absence of detectable mutations in RAS.

It is now clear that embryologic origin of the colon contributes to the genomic profile of a colorectal cancer and has implications on prognosis and response to specific therapeutics. Right-sided colon cancers have embryologic origin in the midgut and have higher rates of microsatellite instability, gene promoter hypermethylation leading to gene silencing, and BRAF mutation, whereas left-sided tumors originate from the hindgut and have a genomic profile distinct from right-sided tumors. Recently, many studies(2–4) demonstrated that the predictive value of the location was mainly related to the response to biologic agents, cetuximab and bevacizumab, of which, cell proliferation and developmental pathways such as RAF/RAS/ERK/MEK and mTOR/PI3K/AKT pathways could play vital roles in response to anti-EGFR antibody, cetuximab.

Recent studies have implicated mutations in several other genes in the EGFR signaling pathway, such as BRAF, PIK3CA and PTEN, in the acquisition of resistance to cetuximab(5–7). However, it remains to be determined whether such resistance-causing mutations were present in the primary tumor or occurred during treatment. Diaz et al. have previously reported that KRAS mutations were responsible for acquired resistance to EGFR blockade in 9 of 24 mCRC patients(8). We hypothesized that in addition to KRAS, mutations in genes downstream of EGFR may also confer acquired resistance to cetuximab-based therapy.

Recent studies have used next generation sequencing (NGS) to test for specific mutations in circulating tumor DNA (ctDNA) obtained from patient plasma. However, these studies monitored mutations identified in tumor tissue from the same patient or known “hot-spot” mutations(9–14); hence, any other mutations emerging during treatment that result in acquired resistance are likely to have been missed.

Here, we have evaluated a panel of genes that are thought to play pivotal roles in the activation of the EGFR signaling pathway or frequently mutated in colorectal cancer. We overcome these previous limitations by employing ultra-deep amplicon sequencing technology and a newly developed data analysis approach, and we describe the identification of emergent resistant mutations in ctDNA from longitudinal plasma samples obtained from mCRC patients based on dynamic changes in allelic fractions. By analyzing structural and functional changes, as well as the clinical influence of these mutations, we extend our existing knowledge of mutations that contribute to acquired cetuximab resistance.

METHODS

Patients and Sample Collection for the Development Cohort

We performed a retrospective single-center study at the Affiliated Hospital, Academy of Military Medical Sciences, Beijing, China. Eligible patients (see Supplementary Materials and Methods) had pathologically confirmed mCRC harboring wild-type KRAS codons 12 and 13 and wild-type BRAF codon 600, as determined via Sanger sequencing of the tumor tissue DNA. NRAS was not recommended for routine testing at the onset of the study in 2011(15). Patients received cetuximab treatment with or without chemotherapy. Prior treatments were permitted except for cetuximab. Serum carcinoembryonic antigen (CEA) and cancer antigen 19-9 (CA 19-9) levels were measured during each therapeutic cycle. Computed tomography (CT) scans were performed and reviewed every 6–8 weeks to evaluate clinical response using the Response Evaluation Criteria in Solid Tumors (RECIST), version 1.1(16). Clinical data including response evaluation during the study were collected. Longitudinal blood samples (4 mL) from each patient were obtained at baseline and every 4 weeks until disease progression or until the last plasma sample was collected. Formalin-fixed paraffin-embedded (FFPE) tumor tissues and peripheral blood mononuclear cells were also collected before treatment. Blood specimens were frozen at -80°C and were linked to demographic, clinical, and genetic data stored in a secure research database. This study protocol was approved by the local ethics committee (KY-2011-8-3), and was conducted in accordance with International Ethical Guidelines for Biomedical Research Involving Human Subjects (CIOMS). Written informed consent was obtained from each patient.

Sample Processing and Amplicon Sequencing

Following DNA extraction and quantification, samples from plasma and tumor tissues were both subjected to amplicon deep sequencing (see Supplementary Materials and Methods) for the following target regions which are thought to play pivotal roles in the activation of the EGFR signaling pathway or frequently mutated in colorectal cancer: AKT1 (exon 3), BRAF (exon 15), EGFR (exons 10 and 12), KRAS (exons 2, 3 and 4), NRAS (exons 2, 3 and 4), PIK3CA (exons 8 and 19), PTEN (exons 5, 7 and 8), and TP53 (exons 5, 6 and 7) (6,17,18), as well as the splice site regions of these exons (i.e., intronic regions within 4 bp of an exon/intron boundary).

The amplicon libraries were subjected to deep sequencing using a Proton System (Life Technologies, Carlsbad, CA, US). The primer sequences are provided in Supplementary Materials and Methods. Targeted average sequencing depths were 1000× for FFPE tumor tissue or blood cell DNA samples and 10000× for cfDNA samples. All testing was conducted by an independent laboratory supplied with blinded samples. (QuestGenomics Biotechnology Co, Ltd. Nanjing, Jiangsu, China)

Identification of Mutations Associated with Acquired Resistance

After routine quality control and data processing steps, we called single nucleotide variants (SNVs) without filtering for any variant frequencies. Somatic mutations were identified by comparing SNVs from the genomic DNA extracted from peripheral blood mononuclear cells and FFPE samples. Using quartile analysis, mutations with significantly increased variant frequencies upon disease progression, or those that were present only during disease progression, were identified as candidate mutations associated with acquired resistance, Deep sequencing data processing, SNV calling, and quartile analysis are described in the Supplementary Materials and Methods. Mutations were annotated using SNPnexus(19). All non-synonymous mutations were further analyzed using PolyPhen-2 and SIFT to determine their potential effects on protein function(20,21). Variants predicted to be ‘damaging’ by both PolyPhen-2 and SIFT were selected for further analysis. The workflow was shown in Fig S1.

Functional Analysis of PIK3CA Mutations

Structure Modeling and functional Analysis

MODELLER (version 9v6)(22) was used for homology modeling. Three-dimensional (3D) structures of wild-type and mutant PIK3CA binding to PIK3R1 were modeled using the 3D structure of 4JPS in the PDB (Protein Data Bank) database as a template(23). Hydrogen atoms were added using CHARMM (version c32b2)(24). The protonation states of titratable residues were determined using an in-house CHARMM script(25). VMD (version 1.9.1)(26) was used to view and analyze the modeled structures.

In Vitro Functional Assays

The PIK3CA point mutations identified in ctDNA and FFPE tumor tissue were introduced into the full-length PIK3CA coding sequence using site-directed mutagenesis and were inserted into an expression vector (RC213112, Origene). Sequences of the primers used to construct PIK3CA mutation expression vectors are provided in Supplementary Materials and Methods. A human colorectal cancer cell line (DiFi) was transfected with the PIK3CA mutation expression vectors and assessed by Western blot to identify changes in the phosphorylation levels of AKT and other downstream EGFR targets. The following antibodies were used: Flag (Sigma-Aldrich, Cat. # F3165), p-AKT (Ser473) (Cell Signal, Cat. # 4060), pan-AKT (Cell Signal, Cat. # 4685), p-ERK1/2 (Cell Signal, Cat. # 4367), and total ERK1/2 (Cell Signal, Cat. # 9107). Cell viability analyses were performed to detect sensitivity to cetuximab and/or to 5-fluorouracil (5-FU). The hotspot mutations in PIK3CA exons 9 and 20 (p.E542K, p.E545K and p.H1047R) were included for comparison. Vector construction, western blot and cell viability analysis are described in the Supplementary Materials and Methods.

Validation Cohort Enrollment and Sample Collection

A validation cohort of patients was prospectively enrolled to verify the acquired resistance mutations detected only in ctDNA after the main data analyses were completed. These patients were enrolled on the same basis as the former patients, the exception being the inclusion of wild-type NRAS codons 12 and 13 in the eligibility criteria according to updated studies(1). Longitudinal plasma samples were collected every 4–8 weeks during the entire treatment period. Investigators blinded to clinical data conducted mutational analyses independently.

Target Gene Regions of Amplicon Sequencing

The following gene regions were added to the former panel for Amplicon deep sequencing: APC (exons 14, 15, 16), EGFR (exons 18 to 21), GNAS (exons 8), PIK3CA (exons 9 and 20) and TP53 (exons 4b and 8), as well as the splice site regions of these exons. All of these target regions were reported to be frequently mutated in colorectal cancer and are possibly implicated in drug resistance(27,28). The primer sequences are provided in Supplementary Materials and Methods. The remaining conditions for the next generation sequencing were the same as for the former experiments.

Statistical Analysis

Kaplan-Meier methods were used to estimate progression-free survival (PFS). A univariate Cox regression analysis was performed for each of the variables of interest. The hazard ratio (HR), 95% confidence intervals (CI) and Wald statistic P-values are reported for each model. A P-value of less than 0.05 was considered statistically significant, and an HR greater than 1.5 was considered clinically meaningful due to the small sample size. All statistical tests were performed using SPSS version 20.0 software (IBM).

RESULTS

Patient Information

In total, 59 patients from the initial development cohort were screened for eligibility between August 2011 and December 2013. Six patients were excluded from the study due to the lack of suitable tumor samples or of peripheral blood cell samples collected prior to therapy. The remaining 53 patients were enrolled in the study as the development cohort and received at least 4 weeks of cetuximab alone or in combination with chemotherapy. The patients were divided into two groups based on the response evaluation at week 12. We defined primary resistance as PFS < 12 weeks and acquired resistance as PFS ≥ 12 weeks(29).

The acquired resistance group consisted of 20 patients with longitudinal plasma samples (cases 1–20) and 18 patients without longitudinal plasma samples (with only 2 to 3 plasma samples including baseline and progression points) (cases 21–38). Of the 38 patients with acquired resistance, 6 patients without longitudinal plasma samples were excluded from further analysis due to the lack of plasma samples at progression. Therefore, a total of 32 patients with acquired resistance were included in the mutational analysis (Fig 1). As a comparison group, we included 15 primary resistance patients (cases 39–53). Detailed clinical information is provided in Table S1.

Figure 1. Workflow of the study.

Of the 59 patients screened for eligibility, 53 patients were enrolled in the study and received at least 4 weeks of cetuximab therapy. PD, progressive disease; PFS, progression-free survival; pts, patients.

Investigation of Somatic Variants in Circulating Tumor DNA

We sequenced 18 exons from 8 genes involved in EGFR signaling, covering approximately 3 thousand base pairs. The average sequencing depth (raw data) across all plasma samples reached 9,664×. We identified more than 20,000 SNVs among all samples processed. The vast majority of the SNVs were present in very low allelic fractions (<0.5%) (Fig 2A). We retained these variants in further analyses, as low-abundance mutations may be functionally relevant.

Figure 2. Screening and functional analysis of acquired resistance related mutations.

A, Distribution of the frequency of nonreference allele detected in circulating tumor DNA in a representative plasma sample. The majority of the single nucleotide variants (SNVs) were present in very low allelic frequency (<0.5%).

B, The frequencies of candidate mutations in four representative patients, before and after treatment with cetuximab therapy. Mutations frequencies were either significantly higher at disease progression or only present at disease progression. Quartile analysis was used to identify outliers.

C, Genetic mutations identified in the ctDNA of individual patients that were associated with acquired resistance to cetuximab. ctDNA, circulating tumor DNA.

D, Genetic mutations detected in tumor tissue and in pre-treatment plasma samples from individual patients.

E1, F930 can form a π interaction with Y836; E2, The F930S mutation abolishes the π interaction with Y836.

F1, K944 can form an ionic bond with D464 in the α-regulatory subunit; F2, The K944N mutation abolishes theionic bond interaction with D464,but it may form a hydrogen bond interaction with N453, Q457 and R577 in the α-regulatory subunit. Such interactions caused by these mutations would incur large conformational changes in the protein structure of PIK3CA.

G1, V952 and V955 both feature hydrophobic side chains; G2, G3, The V952A and V955G mutations result in smaller and less hydrophobic side chains; G4, The V955I mutation results in a larger side chain with greater hydrophobicity.

H1, K966 has a basic side chain, which can form ionic interactions with the acidic side chains of E976, D891 and D895. H2, K966E changes the basic side chain to acidic, resulting in repellent interactions with other acidic side chains.

I, Activation of AKT and ERK1/2 by PIK3CA mutations. Western blots depicting the phosphorylation levels of AKT and ERK1/2 in DiFi cells overexpressing wild-type and mutant PIK3CA (p.F930S, p.K944N, p.V955G, p.V955I, p.K966E, p.L938*, p.V952A, p.E542K, p.E545K and p.H1047R) cultured in serum-free media and treated with 100 nM cetuximab and/or 10 μM 5-FU.

J, Differential growth of DiFi cells transfected with wild-type PIK3CA or mutant PIK3CA in response to cetuximab and 5-FU, ordered by the relative sensitivity to cetuximab and 5-FU. The data are presented as the mean ± SD of six independent experiments. The assays were performed in serum-free media with increasing concentrations of cetuximab in combination with 5-FU at IC₅₀. wt PIK3CA: wild-type PIK3CA.

After screening for the allelic fractions of the SNVs by quartile analysis, we identified an average of 26 SNVs in each patient (Fig 2B). Only those mutations with allelic fractions significantly higher than the majority were eventually selected as candidate mutations. Overall, we identified 39 non-synonymous SNVs as candidates across all 32 patients with acquired resistance to cetuximab. Among these 39 non-synonymous SNVs, 20 mutations were selected as acquired resistance mutations using PolyPhen-2 and SIFT (Table S2). Some of these acquired resistance mutations, specifically p.W22G in AKT1; p.V600E in BRAF; p.K5N, p.G12D, p.G12V, p.G13D, and p.Q61H in KRAS; p.M134L and p.Q245* in PTEN, are described in the large intestine dataset of the COSMIC database (including in cecum, colon, and rectal cancers), whereas none of the mutations found in PIK3CA (p.K944N, p.F930S, p.V955G, p.V955I, and p.K966E) are described in COSMIC (Table S2).

Eventually, in the 32 patients with acquired resistance, 14 mutations were present in 10 of the 20 patients with longitudinal plasma samples, and 12 mutations were present in 8 of the 12 patients without longitudinal plasma samples. Twenty-five of the 26 mutations exhibited a highly significant difference (P-value < 0.01) from the background non-reference allelic fractions (Table 1; Table S3), whereas none of the candidate mutations were identified in the 15 patients with primary resistance.

Table 1.

Mutations related to acquired cetuximab resistance.

Case no.	Gene	Transcript Accession	Exon	Nucleotide (genomic)	Nucleotide (cDNA)	Amino acid (protein)	AF (%)	P Value
2	KRAS	NM_004985	3	Chr12:25380275 T>G	c.364T>G	Q61H	7.73	<0.0001
6	PIK3CA	NM_006218	19	Chr3:178948060 A>T	c.2832A>T	K944N	1.07	0.0005
7	PIK3CA	NM_006218	19	Chr3:178948091 G>A	c.2863G>A	V955I	1.21	<0.0001
12	EGFR	NM_005228	12	Chr7:55227909 G>A	c.1376G>A	G459E	1.24	0.032
12	KRAS	NM_004985	2	Chr12:25398284 C>T	c.35C>T	G12D	17.87	<0.0001
12	KRAS	NM_004985	2	Chr12:25398281 C>T	c.38C>T	G13D	16.14	<0.0001
13	KRAS	NM_004985	2	Chr12:25398284 C>T	c.35C>T	G12D	2.29	<0.0001
13	KRAS	NM_004985	2	Chr12:25398281 C>T	c.38C>T	G13D	2.31	<0.0001
14	BRAF	NM_004333	15	Chr7:140453136 A>T	c.1799T>A	V600E	4.2	<0.0001
16	PIK3CA	NM_006218	19	Chr3:178948060 A>T	c.2832A>T	K944N	1.52	0.0001
17	PIK3CA	NM_006218	19	Chr3:178948060 A>T	c.2832A>T	K944N	1.12	0.0081
19	KRAS	NM_004985	2	Chr12:25398284 C>T	c.35C>T	G12D	3.87	<0.0001
19	KRAS	NM_004985	2	Chr12:25398281 C>T	c.38C>T	G13D	2.92	<0.0001
20	PTEN	NM_000314	7	Chr10:89717708 C>T	c.733C>T	Q245*	1.15	<0.0001
21	KRAS	NM_004985	4	Chr12:25378686 C>A	c.312C>A	K104N	1.34	<0.0001
21	PIK3CA	NM_006218	19	Chr3:178948017 T>C	c.2789T>C	F930S	2.11	<0.0001
22	PIK3CA	NM_006218	19	Chr3:178948092 T>G	c.2864T>G	V955G	1.46	<0.0001
22	AKT1	NM_001014431	3	Chr14;105246536 A>C	c.64A>G	W22G	1.5	<0.0001
23	BRAF	NM_004333	15	Chr7:140453076 A>T	c.1859A>T	M620K	1.04	<0.0001
23	KRAS	NM_004985	2	Chr12:25398233 A>G	c.86A>G	V29A	1.65	<0.0001
27	TP53	NM_000546	5	Chr17:7578255 T>A	c.594T>A	E198D	1.57	<0.0001
27	PIK3CA	NM_006218	19	Chr3:178948124 A>G	c.2896A>G	K966E	1.72	<0.0001
28	KRAS	NM_004985	2	Chr12:25398304 T>G	c.15T>G	K5N	1.19	<0.0001
29	PTEN	NM_000314	5	Chr10:89692916 A>T	c.400A>T	M134L	1.15	<0.0001
33	BRAF	NM_004333	15	Chr7:140453181 T>C	c.1754T>C	H585R	1.13	<0.0001
34	KRAS	NM_004985	2	Chr12:25398284 C>A	c.35C>A	G12V	3.78	<0.0001

AF: mutant allelic fraction of the last plasma sample.

^*: Stop-gain mutation.

P-value: The differences of the identified mutant allelic fraction from the background non-reference allelic fractions were calculated using Student’s t-test. Twenty-five of the 26 mutations exhibited a highly significant difference (P-value < 0.01)

The detected acquired mutations were observed most commonly in PIK3CA and KRAS, with a total of 5 PIK3CA mutations identified in 7 of the 32 patients (22%), and 5 KRAS mutations in 8 of the 32 patients (25%; 3 of these patients harbored a double mutation in KRAS). In addition, 3 point mutations in BRAF, including the hotspot mutation c.1799T>A (p.V600E), and sporadic mutations in AKT1, EGFR, TP53, and PTEN were identified. Five patients harbored mutations in more than one gene (Fig 2C).

Analysis of Pre-existing Mutations in Tumor Tissues

Sixty-two somatic mutations were identified, in a total of 30 out of 53 patients. Thirty-nine of these mutations were identified in 22 out of 38 patients with acquired resistance (average 2 mutations per individual), which mainly clustered in KRAS and TP53 (30.8% each), whereas the remaining 23 mutations were identified in 8 out of 15 patients with primary resistance (average 3 mutations per individual), which were uniformly distributed (17.4% each in KRAS, NRAS, EGFR, and PTEN). Among these 62 mutations, only 14 were detected in plasma samples before treatment, 8 of which were in patients with acquired resistance (21% of 38 patients), with the remaining 6 mutations in patients with primary resistance (40% of 15 patients; Fig 2D; Table 2).

Table 2.

Pre-existing mutations in tumor tissue and presence in plasma before treatment

Case no.	Gene	Transcript Accession	Exon	Nucleotide (genomic)	Nucleotide (cDNA)	Amino acid (protein)	AFa in tumor tissue (%)	AF in plasma before treatment (%)	Source of tumor tissueb
1	KRAS	NM_004985	4	chr12:25378688 T>C	c.310T>C	p.K104E	7.41	–c	P
	KRAS	NM_004985	3	chr12:25380231 T>C	c.227T>C	p.E76G	5.13	1.09
2	KRAS	NM_004985	2	chr12:25398245 T>G	c.74T>G	p.Q25P	5.41	–	P
3	TP53	NM_000546	7	chr17:7577121 G>A	c.817G>A	p.R273C	35.15	1.09	P
	KRAS	NM_004985	3	chr12:25380275 T>G	c.183T>G	p.Q61H	27.19	1.83
5	TP53	NM_000546	5	chr17:7578263 G>A	c.586G>A	p.R196*	30.82	1.59	P
	KRAS	NM_004985	4	chr12:25378670 G>A	c.328G>A	p.P110S	8.11	–
	NRAS	NM_002524	4	chr1:115252209 G>A	c.431G>A	p.T144I	6.45	–
6	PTEN	NM_000314	8	chr10:89720654 A>C	c.805A>C	p.K269Q	6	–	P
7	PTEN	NM_000314	7	chr10:89717661 C>A	c.686C>A	p.S229*	6.67	–	P
	KRAS	NM_004985	2	chr12:25398285 C>T	c.34C>T	p.G12S	7.69	–
8	TP53	NM_000546	5	chr17:7578266 T>A	c.583T>A	p.I195F	14.17	–	P
10	KRAS	NM_004985	4	chr12:25378609 G>A	c.389G>A	p.A130V	5.13	–	P
	EGFR	NM_005228	12	chr7:55227843 C>T	c.1310C>T	p.S437F	6.25	–
11	TP53	NM_000546	6	chr17:7577566 T>C	c.715T>C	p.N239D	6.18	–	P
	TP53	NM_000546	6	chr17:577555 G>T	c.726G>T	p.C242*	5.85	–
	TP53	NM_000546	7	chr17:7577085 C>T	c.853C>T	p.E285K	5.37	–
	KRAS	NM_004985	2	chr12:25398314 G>A	c.5G>A	p.T2I	6.4	–
	BRAF	NM_004333	15	chr7:140453124 C>T	c.1811C>T	p.W604*	5.1	–
	NRAS	NM_002524	3	chr1:115256508 C>T	c.203C>T	p.R68K	6.86	–
	KRAS	NM_004985	4	chr12:25378577 A>G	c.421A>G	p.F141L	5.75	–
	NRAS	NM_002524	3	chr1:115256445 G>T	c.266G>T	p.S89*	6.98	–
16	KRAS	NM_004985	2	chr12:25398285 C>T	c.34C>T	p.G12S	28.15	–	P
17	KRAS	NM_004985	2	chr12:25398284 C>A	c.35C>A	p.G12V	40.6	–	P
	PTEN	NM_000314	7	chr10:89717672 C>T	c.697C>T	p.R233*	13.73	–
18	TP53	NM_000546	7	chr17:7577138 C>T	c.800C>T	p.R267Q	14.34	–	P
19	TP53	NM_000546	5	chr17:7578217 G>A	c.632G>A	p.T211I	23.08	–	P
22	BRAF	NM_004333	15	chr7:140453136 A>T	c.1799A>T	p.V600E	6.25	–	P
	BRAF	NM_004333	15	chr7:140453139 G>A	c.1796G>A	p.T599I	6.25	–
	NRAS	NM_002524	2	chr1:115258685 C>T	c.97C>T	p.D33N	6.25	–
24	TP53	NM_000546	5	chr17:7578263 G>A	c.586G>A	p.R196*	54.55	–	P
27	EGFR	NM_005228	10	chr7:55224507 A>G	c.1189A>G	p.T397A	5	–	P
32	TP53	NM_000546	5	chr17:7578262 C>T	c.587C>T	p.R196Q	8.16	–	M
34	PIK3CA	NM_006218	19	chr3:178948041 T>A	c.2813T>A	p.L938*	6.9	–	P
35	BRAF	NM_004333	15	chr7:140453136 A>T	c.1799A>T	p.V600E	44.35	3.77	M
36	BRAF	NM_004333	15	chr7:140453136 A>T	c.1799A>T	p.V600E	15	–	P
37	TP53	NM_000546	5	chr1:7578263 G>A	c.586G>A	p.R196*	25	17.54	M
	KRAS	NM_004985	2	chr12:25398284 C>T	c.35C>T	p.G12D	18.03	11.06
38	TP53	NM_000546	7	chr17:7577121 G>A	c.817G>A	p.R273C	33.58	1.39	P
39	PTEN	NM_000314	8	chr10:89720654 A>T	c.805A>T	p.K269*	17.39	–	P
	PTEN	NM_000314	5	chr10:89693000 G>T	c.484G>T	p.D162Y	5.34	–
	NRAS	NM_002524	2	chr1:115258744 C>T	c.38C>T	p.G13D	5.15	–
	EGFR	NM_005228	10	chr7:55224522 A>G	c.1204A>G	p.T402A	6.22	–
	EGFR	NM_005228	10	chr7:55224507 A>G	c.1189A>G	p.T397A	5.78	–
	BRAF	NM_004333	15	chr7:140453182 G>A	c.1753G>A	p.H585Y	5.35	–
40	TP53	NM_000546	7	chr17:7577120 C>T	c.818C>T	p.R273H	81.16	24.5	P
	NRAS	NM_002524	3	chr1:115256529 T>A	c.182T>A	p.Q61L	41.67	15.28
42	TP53	NM_000546	7	chr17:7577138 C>T	c.800C>T	p.R267Q	7.64	–	M
	PTEN	NM_000314	5	chr10:89692890 A>G	c.374A>G	p.K125R	5.47	–
	PIK3CA	NM_006218	19	chr3:178948083 T>C	c.2855T>C	p.V952A	7.44	–
	KRAS	NM_004985	4	chr12:25378598 C>T	c.400C>T	p.A134T	5.49	–
	KRAS	NM_004985	2	chr12:25398285 C>T	c.34C>T	p.G12S	27.27	11.93
	EGFR	NM_005228	10	chr7:55224490 A G	c.1172A>G	p.E391G	6.25	–
43	KRAS	NM_004985	3	chr12:25380276 T>C	c.182T>C	p.Q61R	6.47	–	M
	KRAS	NM_004985	3	chr12;25380275 T>A	c.183T>A	p.Q61H	5.04	17.57
	EGFR	NM_005228	12	chr7:55227963 G>A	c.1430G>A	p.W477*	7.25	–
	AKT1	NM_001014431	3	chr14:105246551 C>T	c.49C>T	p.E17K	17.24	25
44	BRAF	NM_004333	15	chr7:140453136 A>T	c.1799A>T	p.V600E	24.42	–	P
45	NRAS	NM_002524	2	chr1:115258745 C>G	c.37C>G	p.G13R	34.82	37.97	P
48	PTEN	NM_000314	5	chr10:89692905 G>A	c.389G>A	p.R130Q	54.55	–	P
	NRAS	NM_002524	2	chr1:115258747 C>T	c.35C>T	p.G12D	30.43	–
50	BRAF	NM_004333	15	chr7:140453136 A>T	c.1799A>T	p.V600E	18.28	–	P

^aAF: mutant allelic fraction of the last plasma sample.

^bSource of tumor tissue: P, primary tumor; M, metastatic lesion

^c—: Mutant allele under the detectable level

^*: Stop-gain mutation.

Structure Modeling and Functional Analysis of the PIK3CA Mutations

Structural modeling revealed that all of the PIK3CA point mutations identified in ctDNA (p.K944N, p.F930S, p.V955G, p.V955I, and p.K966E) and in FFPE tumor tissue (p.V952A and p.L938*) affected the structural configuration of the PIK3CA protein and the kinase activity. p.F930 is located in the ATP-binding pocket, p. K944, p.V952 and p.V955 are in the activation-loop, and p.K966 is in the coil region close to the c-lobe, which is thought to stabilize the kinase conformation (Fig 2E–H).

In Vitro Functional Assays of the PIK3CA Mutations

To confirm the role of PIK3CA mutations in cetuximab resistance, DiFi cells were transfected with PIK3CA mutations p.K944N, p.V955G, p.V955I, p.K966E p.F930S and p.V952A, as well as the truncation mutation p.L938* were generated. We performed western blot assays for pAKT (phosphorylated AKT) and pERK in DiFi cells transfected with these PIK3CA mutations. Compared with non-targeting control cells, PIK3CA mutations p.K944N, p.V955G, p.V955I and p.K966E markedly increased the phosphorylation levels of AKT and MAPK3/MAPK1 (ERK1/ERK2), which were not affected by the addition of cetuximab alone or in combination with 5-FU. While the PIK3CA mutations p.F930S and p.V952A, as well as the truncation mutation p.L938*, had a less effect on AKT and MAPK3/MAPK1 phosphorylation, especially in the presence of cetuximab and/or 5-FU (Fig 3I).

Figure 3. Comparing circulating biomarkers with acquired resistant mutations used to monitor tumor dynamics and comparing different scenarios to predict progression-free survival.

Each panel represents data from a different patient. A, case 2; B, case 12; C, case 16; D, case 17.

Upper panels show the plasma levels of circulating biomarkers: CEA (ng per milliliter, red lines) and CA19-9 (U per milliliter, blue lines), over time (22 weeks). Lower panels show the allelic fractions (AF) of mutations in the patient’s circulating tumor DNA (ctDNA). Each color represents a specific mutation. Vertical dotted lines show the tumor response evaluation assessed according to RECIST criteria. PD, progressive disease; SD, stable disease; PR, partial response.

E to G each panel represents Kaplan-Meier survival functions for PFS, based on 65 patients with acquired cetuximab resistance. PFS, progression-free survival. ctDNA, circulating tumor DNA.

E, Present formalin-fixed paraffin-embedded samples. Patients with mutations detected in ctDNA before treatment exhibit a significantly reduced PFS compared to those without any mutation in ctDNA (median PFS 14 w vs. 38 w; HR, 2.69; 95% CI, 1.12 to 6.49; P = 0.027).

F, Patients with the presence of a residual primary tumor have a trend of shorter PFS compared with those without a residual primary tumor (mPFS, 20 weeks vs. 41 weeks; HR, 1.71; 95% CI, 0.94 to 3.13; P =0.08).

G, Comparing to patients with no mutation detected in ctDNA, patients with PIK3CA or RAS mutations showed a pronounced mPFS decrease, although without significant difference due to very small sample size (40 weeks vs. 38 weeks vs. 29 weeks; HR, 1.26; 95% CI, 0.79 to 2.01; P = 0.34).

Cell viability analysis suggested that in the presence of cetuximab in combination with 10 μM 5-FU, compared with mock transfected cells, DiFi cells overexpressing p.K944N, p.V955G, p.V955I, p.K966E, p.E542K, p.E545K and p.H1047R exhibited a high degree of resistance. Wild-type PIK3CA, as well as its p.F930S and p.V952A mutants, resulted in moderate resistance to cetuximab, and the truncation mutation p.L938* did not exhibit resistance. In the presence of 10 μg/ml cetuximab, growth rates in cells with novel PIK3CA mutations (p.K944N, p.F930S, p.V955G, p.V955I, and p.K966E) and with wild-type PIK3CA were 59.18±14.72% vs. 31.37%, respectively (Fig 2J; Fig S2). These results demonstrate that these candidate mutations are likely to be involved in conferring resistance because of their effects on the activation of the phosphorylation of AKT and MAPK3/MAPK1 (ERK1/ERK2).

Correlations among Variant Frequency, CEA and CA19-9 Levels, and Tumor Response evaluation

Among the 20 patients with longitudinal plasma samples, 10 carried candidate mutations. We compared changes in the allelic fraction of the identified mutations in each patient with serum levels of corresponding tumor biomarkers and response ascertained using CT (as defined by RECIST) (Fig 3A–D; Fig S3). PIK3CA mutations were identified in 4 patients (Nos. 6, 7, 16, and 17), and KRAS mutations were identified in an additional 4 patients (Nos. 2, 12, 13 and 19). Only 2 of the 10 patients harbored mutations in other genes. Patient 14 harbored the BRAF p.V600E mutation, whereas patient 20 carried the PTEN p.Q245* mutation. These mutations exhibited an increasing allelic fraction during treatment (Table 1; Table S3). Whereas, only 5 of the 10 patients had elevated CEA or CA19-9 levels that correlated well with the tumor response. The levels of candidate resistant mutations increased prior to clinical progression, as determined by CT scans. A median of 15.0 weeks of lead time (range 0–35 weeks) was observed. In patients carried PIK3CA mutations, the average lead time was 10.8 weeks, whereas in patients with KRAS mutations, the average lead time was 21.8 weeks (Table S1).

Additional Mutation Analysis in the Validation Cohort

An independent validation cohort of 32 patients (cases v1-v32) was prospectively enrolled between January 2014 and June 2016 to verify the acquired resistance mutations detected in the ctDNA of patients from the development cohort. Five patients (cases v1, v3, v9, v17 and v18) were placed in the primary resistance group, while the remaining 27 patients were classed as having acquired resistance (Table S4).

Thirty-seven mutations were identified in 14 of the 27 patients with acquired resistance, including one novel PIK3CA mutation (p.K944N) and the hotspot mutations p.V600E in BRAF; p.G12V in KRAS; p.G12V, p.G13V and p.Q61H in NRAS; p.H1047R and p.E545K in PIK3CA; and p.T790M in EGFR (Table S5, Fig S4). Among these 14 patients, 5 patients carried multiple gene mutations, including 2 of them harbored both KRAS and PIK3CA hotspot mutations (case v5: p.H1047R in PIK3CA and p.M72V in KRAS; case v20: p.E545K, p.Q546K in PIK3CA and p.G12V in KRAS). Other novel mutations in PIK3CA (p.F930S, p.V955G, p.V955I, and p.K966E) identified in the initial cohort were not detected in patients in this validation cohort. Although, p.V600E in BRAF and p.G12V in KRAS were the only mutations verified in both cohorts due to the limited sample size and increased gene regions for sequencing in validation cohort, the identification of aforementioned hotspot mutations testified the reliability of the results in this validation cohort. No acquired resistance mutations were detected in the 5 patients with primary resistance in the validation cohort.

Clinical Features and Prognostic Value of Mutation Profiles and the Clinical Implications

The relationship between tumor location and RAS or PIK3CA mutations were compared. We first analyzed RAS and PIK3CA mutations in tumor tissue before cetuximab treatment. In the 13 cases with right-sided colon, RAS mutation rate was 38.5% (5/13) and PIK3CA mutation rate was 7.7% (1/13); In the 38 cases with left-sided colon, RAS mutation rate was 31.6% (12/38) and PIK3CA mutation rate was 2.6% (1/38) (Table S6).

We also analyzed differences of acquired RAS or PIK3CA mutations in ctDNA between left- and right- colon. In 17 patients with right-sided colon, RAS mutation rate was 23.5% (4/17) and PIK3CA mutation rate was 11.8% (2/17); in 47 patients with left-sided colon, RAS mutation rate was 19.1% (9/47), PIK3CA mutation rate was 17% (8/47). No significant differences were observed between left- and right- colon both in RAS and PIK3CA mutation rate (Table S7).

Next, we examined progression-free survival (PFS) from the clinical data of 65 patients with acquired resistance, to determine the relative prognostic value of mutation profiles and other clinical features. These patients consisted of 38 patients from the development cohort and 27 patients from the validation cohort.

Patients from the development cohort who had pre-existing mutations in FFPE samples that were also present in ctDNA prior to treatment, showed significantly shorter PFS compared to those without pre-existing mutations in ctDNA (mPFS, 14 weeks vs. 38 weeks; HR, 2.69; 95% CI, 1.12 to 6.49; P =0.027) (Fig 3E), respectively. We did not perform this analysis in the validation cohort due to the unavailability of FFPE samples.

A trend towards a shorter PFS was noted in patients with a residual primary tumor compared to those with no residual primary tumor (mPFS, 20 weeks vs. 41 weeks; HR, 1.71; 95% CI, 0.94 to 3.13; P =0.08) (Fig 3F).

When comparing to patients with no mutation (n=27) detected in ctDNA, patients with PIK3CA (n=7) or RAS (n=10) mutations showed a pronounced mPFS decrease, although without significant difference due to very small sample size (40 weeks vs. 38 weeks vs. 29 weeks; HR, 1.26; 95% CI, 0.79 to 2.01; P = 0.34)(Fig 3G). Taken together, these results suggest that pre-existing mutations present in ctDNA, residual primary tumor and RAS mutations will be potentially negative factors for cetuximab treatment.

DISCUSSION

In this study, our purpose was to investigate the potentially acquired resistance mutations in 8 targeted genes after clinical benefit from cetuximab-based regimens. The prerequisite concerning the acquired resistance during the cetuximab therapy is the PFS ≥ 12 weeks. For this reason we enrolled all mCRC patients with no previous anti-EGFR antibody treatment prior to study participation who would be treated with cetuximab-based regimen, no matter how many previous treatment lines patients experienced.

We sequenced targeted gene areas with deep coverage to identify emergent mutations in longitudinal plasma samples collected from mCRC patients. Our quartile-based selection strategy helped us to identify mutations that started at extremely low allelic fractions but increased in frequency during treatment, independently of the identification of existing somatic mutations in tumor tissues or in known “hot-spots”. We were therefore not only able to identify novel mutations associated with acquired resistance, but also to build an understanding of resistance in each patient, even those with diverse mutation profiles.

Previous studies have demonstrated that mutations in the EGFR signal transduction pathway, such as RAS, contribute resistance to cetuximab(30). However, whether the PIK3CA mutations at exon 9 and 20 that have been identified in tumor tissue are correlated with drug resistance remains controversial(5,17,31,32). As in the previous study, Bettegowda et al. analyzed known “hot-spot” mutations in NRAS, BRAF, EGFR and PIK3CA and concluded that KRAS mutation is the most important factor for acquiring resistance to anti-EGFR therapy. However, they did not identify any known PIK3CA mutations associated with the acquisition of resistance(13,14).

For PIK3CA, we examined exons 8 and 19 instead of previously reported hotspot mutations in exon 9 and 20, because the hotspot mutations in exons 9 and 20 were not identified in previous reports of acquired cetuximab resistance(13,14). Bettegowda et al had examined the acquired mutations of key genes in EGFR signaling pathway including KRAS, NRAS, BRAF, EGFR and PIK3CA (exon 9 and 20) from ctDNA of 24 mCRC patients during EGFR blockade. They observed 70 acquired mutations including KRAS, NRAS, BRAF and EGFR(13). Siravegna et al had monitored the acquired mutations of 226 genes including PIK3CA (exon 20) in 16 mCRC patients during Anti-EGFR antibody therapy. They identified only KRAS and EGFR mutations. These two studies did not identify treatment-related mutations in exons 9 and 20 of PIK3CA gene(14). Exon 8 encodes the region responsible for plasma membrane binding and exon 19 encodes part of the catalytic domain, including the ATP-binding pocket(33,34). We reasoned that mutations in these PIK3CA sites might cause the constitutive activation of the kinase, similar to previously identified hotspot mutations(35,36), although no any mutations in exons 8 and 19 had been reported in previous studies.

In this study, by analyzing different exons, we discovered 5 novel mutations in PIK3CA exon 19 in the development cohort and 1 novel mutation in exon 19, 2 “hot-spot” mutations in exon 9 and 20 in the validation cohort are closely associated with the development of resistance. Protein structure and functional assays revealed that all of these mutations would affect the structural configuration of PIK3CA, resulting in functional changes of protein. In vitro studies also demonstrated that novel PIK3CA mutations (p.K944N, p.V955G, p.V955I, and p.K966E) exhibited a high degree of resistance to cetuximab because of their effects on the activation of the phosphorylation of AKT and MAPK3/MAPK1 (ERK1/ERK2).

Analyses of molecular pathology and its interactions with environment are increasingly important in cancer research (37–42). As for the comparison of RAS or PIK3CA mutation rate of left- and right- sided colon, Both RAS and PIK3CA mutation rates in tumor tissue were higher in the right- sided colon, which is consistent with findings in the literature reports(43–48), although there were no significant differences due to small sample size. While acquired RAS mutation is more frequent in the right colon, acquired PIK3CA mutation is more frequent in the left colon. Also, there were no significant differences due to small sample size.

We further compared the dynamics of acquired RAS and PIK3CA mutations in ctDNA and their influence on PFS. As compared to those with KRAS mutations, patients with PIK3CA mutations had a shorter lead time of presence of acquired mutations in ctDNA to clinical progression (21.8 vs.10.8 weeks). Patients harboring PIK3CA mutations exhibited increased median PFS, although the difference was not statistically significant due to the limited sample size. Because RAS is the upstream activator of the PI3K pathway(18,49), we speculate that PIK3CA mutations in sites other than the RAS-binding domain are late-onset molecular events, relative to RAS mutations, that occur after exposure to cetuximab. These mutations might potentially exert a complementary effect in tumor progression and acquired resistance to cetuximab.

Another negative prognostic factor revealed in our study is the presence of residual primary tumor, which few studies in cetuximab treatment have mentioned. It is possible that when clones with wild-type genes are eliminated under the selective pressure of cetuximab, the remaining clones with preexisting and other emergent mutations in primary tumor would expand and display resistance over a relatively short time period, resulting in a shorter PFS. In the COIN clinical trial(50), which failed to demonstrate the survival benefit with the addition of cetuximab to chemotherapy, one underlying factor affect the survival benefit could be a substantial proportion of patients (> 40%) with advanced CRC enrolled had unresected primary tumors. This independently supports our finding.

One limitation of this study is that the sample size in both cohorts was small. Larger sample size might help to discover more novel mutations, especially to distinguish the impact of clinical characters, such as tumor location(45,48) and pre-existing mutations on PFS between patients with and without acquired resistance mutations. Second, our present study focused only on the genomic variations analysis. Future investigation will be warranted to explore the potential mechanism beyond EGFR signaling pathway.

In summary, our data indicate that the PIK3CA mutations contribute to acquired cetuximab resistance in patients with mCRC. Compared to RAS mutations, which were reported to present before cetuximab treatment(8,13,14), PIK3CA mutations probably occur after exposure to cetuximab, and may potentially exert a complementary effect in acquired resistance to cetuximab.

Table 3.

Clinical characteristics of acquired resistance patients in development cohort and validation cohort

Patient Characteristics	Development cohort	Validation cohort	P* value
Age (year)			1
<65	32	23
>=65	6	4
Gender			1
Male	20	15
Female	18	12
ECOG			0.533
0	9	7
1	24	19
2	5	1
Primary tumor location			0.775
Right side	9	8
Left side	29	19
Prior surgery			0.894
Radical resection	20	14
Palliative resection	10	6
None	8	7
Residue of primary tumor			1
Yes	15	10
No	23	17
Line of prior therapy			0.002
<2 lines	17	23
>=2 lines	21	4
Regimen			<0.001
Cetuximab	19	1
Cetuximab+ chemotherapy	19	26

^*Fisher’s exact test

Translational Relevance.

Acquired resistance to cetuximab limits their application in clinical practice. In addition to RAS mutations, other genes downstream of the EGFR signaling pathway might be involved in acquired resistance to cetuximab.

We have designed an innovative strategy to identify low-frequency candidate mutations that are associated with acquired cetuximab resistance, by sequencing circulating tumor DNA taken from metastatic colorectal cancer (mCRC) patient plasma samples, obtained before and during treatment. This quartile-based selection strategy helped us to identify mutations that started at extremely low allelic fractions but increased in frequency during treatment, independently of the identification of existing somatic mutations in tumor tissues or in known “hot-spots”.

We have identified 5 novel mutations in exon 19 of PIK3CA, which contribute to acquired cetuximab resistance. The importance of PIK3CA mutations in acquired cetuximab resistance highlights the potential therapeutic benefit of combining a PIK3CA inhibitor with an anti-EGFR antibody in the treatment of mCRC.

Abbreviations

CA19-9

cancer antigen 19-9

CEA

carcinoembryonic antigen

CI

confidence intervals

CNV

copy number variation

COSMIC

catalogue of somatic mutations in cancer

CT

computed tomography

ctDNA

circulating tumor DNA

3D

three-dimensional

EGFR

epidermal growth factor receptor

FFPE

formalin fixed paraffin embedded

5-FU

5-fluorouracil

HR

hazard ratio

mCRC

metastatic colorectal cancer

NGS

next generation sequencing

PD

progressive disease

PFS

progression-free survival

RECIST

response evaluation criteria in solid tumors

SNVs

single nucleotide variants