cancers Article Novel Somatic Genetic Variants as Predictors of Resistance to EGFR-Targeted Therapies in Metastatic Colorectal Cancer Patients Pau Riera 1,2,3,4,5, * , Benjamín Rodríguez-Santiago 1,4,5 , Adriana Lasa 1,4,5 , Lidia Gonzalez-Quereda 1,4,5 , Berta Martín 6 , Juliana Salazar 7 , Ana Sebio 6 , Anna C. Virgili 6 , Jordi Minguillón 1,4,5,8 , Cristina Camps 1,3,4 , Jordi Surrallés 1,4,5,8 and David Páez 5,6, * 1 Genetics Department, Hospital de la Santa Creu i Sant Pau, 08041 Barcelona, Spain; brodriguezs@santpau.cat (B.R.-S.); alasa@santpau.cat (A.L.); lgonzalezq@santpau.cat (L.G.-Q.); jminguillonp@santpau.cat (J.M.); ccampsf@santpau.cat (C.C.); jsurralles@santpau.cat (J.S.) 2 Pharmacy Department, Hospital de la Santa Creu i Sant Pau, 08041 Barcelona, Spain 3 Faculty of Pharmacy and Food Sciences, Universitat de Barcelona (UB), 08028 Barcelona, Spain 4 Join Research Unit on Genomic Medicine UAB-IR Sant Pau, Biomedical Research Institute, Hospital de la Santa Creu i Sant Pau, 08041 Barcelona, Spain 5 U705 and U745, ISCIII Center for Biomedical Research on Rare Diseases (CIBERER), 08041 Barcelona, Spain 6 Medical Oncology Department, Hospital de la Santa Creu i Sant Pau, 08041 Barcelona, Spain; bmartinc@santpau.cat (B.M.); asebio@santpau.cat (A.S.); avirgili@santpau.cat (A.C.V.) 7 Translational Medical Oncology Laboratory, Institut de Recerca Biomèdica Sant Pau (IIB-Sant Pau), 08041 Barcelona, Spain; jsalazar@santpau.cat 8 Department of Genetics and Microbiology, Universitat Autònoma de Barcelona, 08193 Bellaterra, Spain * Correspondence: priera@santpau.cat (P.R.); dpaez@santpau.cat (D.P.); Tel.: +34-935537464 (P.R.); +34-935535638 (D.P.); Fax: +34-935537287 (P.R.); +34-935535769 (D.P.)  Received: 18 June 2020; Accepted: 6 August 2020; Published: 11 August 2020  Abstract: Background: About 40% of RAS/BRAF wild-type metastatic colorectal cancer (mCRC) patients undergoing anti-EGFR-based therapy have poor outcomes. Treatment failure is not only associated with poorer prognosis but higher healthcare costs. Our aim was to identify novel somatic genetic variants in the primary tumor and assess their effect on anti-EGFR response. Patients and Methods: Tumor (somatic) and blood (germline) DNA samples were obtained from two well-defined cohorts of mCRC patients, those sensitive and those resistant to EGFR blockade. Genetic variant screening of 43 EGFR-related genes was performed using targeted next-generation sequencing (NGS). Relevant clinical data were collected through chart review to assess genetic results. Results: Among 61 patients, 38 were sensitive and 23 were resistant to treatment. We identified eight somatic variants that predicted non-response. Three were located in insulin-related genes (I668N and E1218K in IGF1R, T1156M in IRS2) and three in genes belonging to the LRIG family (T152T in LRIG1, S697L in LRIG2 and V812M in LRIG3). The remaining two variants were found in NRAS (G115Efs*46) and PDGFRA (T301T). We did not identify any somatic variants related to good response. Conclusions: This study provides evidence that novel somatic genetic variants along the EGFR-triggered pathway could modulate the response to anti-EGFR drugs in mCRC patients. It also highlights the influence of insulin-related genes and LRIG genes on anti-EGFR efficacy. Our findings could help characterize patients who are resistant to anti-EGFR blockade despite harboring RAS/BRAF wild-type tumors. Keywords: genetic variants; predictive biomarkers; anti-EGFR monoclonal antibodies; colorectal cancer; case-control study Cancers 2020, 12, 2245; doi:10.3390/cancers12082245 www.mdpi.com/journal/cancers Cancers 2020, 12, 2245 2 of 15 1. Introduction The epidermal growth factor receptor (EGFR), usually overexpressed in colorectal cancer (CRC), plays a pivotal role in tumor growth and progression [1,2]. Multiple proteins are involved in the EGFR signaling pathway, including other receptors, ligands, intracellular downstream effectors, and regulators [2–4]. In the metastatic setting, anti-EGFR targeted antibodies, cetuximab and panitumumab, are commonly used, but response rates are variable [5,6]. Several somatic mutations along the EGFR-triggered pathway, such as activating RAS mutations and the BRAF V600E mutation, are validated predictors of primary resistance to anti-EGFR-based therapies [7–12]. Other promising biomarkers of non-response are PIK3CA or PTEN mutations, although the level of evidence is lower [13,14]. In addition, some studies have observed that right-sided and mesenchymal tumors show worse outcomes to EGFR-targeted therapies regardless of RAS mutation status [15–18]. As about 40% of RAS-wild-type patients undergoing anti-EGFR therapy do not benefit from this treatment [19,20], we hypothesized that other mutations in the EGFR pathway could act as additional mechanisms of resistance to EGFR blockade. Next-generation sequencing (NGS) technologies have revolutionized research in cancer genomics. NGS allows for the simultaneous analysis from several genes to complete genomes with higher sensitivity and cost-effectiveness than the previously used Sanger sequencing methods. In addition, its high sensitivity has detected somatic variants in the tumor at a low allelic fraction. In this study we used NGS technology to analyze the exons and intron boundaries of 43 EGFR pathway-related genes. We genotyped both germline and tumor DNA samples to optimize the identification of somatic genetic variants. The main objective was to identify novel genetic variants in two cohorts of extreme responder patients with RAS wild-type metastatic CRC (mCRC). Extreme responders were either primary resistant or highly sensitive to anti-EGFR therapy. We also aimed to increase knowledge of EGFR-related genes as this could lead to the identification of new biomarkers of anti-EGFR response and promising therapeutic targets for mCRC. 2. Patients and Methods 2.1. Patient Population We conducted this case-control study with mCRC patients from the Hospital de la Santa Creu i Sant Pau (HSCSP, Barcelona, Spain). We retrospectively analyzed patients who underwent any anti-EGFR-containing regimen between 2012 and 2017. All tumor samples had been previously classified as RAS wild-type using the therascreen KRAS test (Qiagen, Hilden, Germany) or the TruSight Tumor 15 panel (Illumina, San Diego, CA, USA). Two well-defined cohorts of patients were studied, those sensitive (control group) and those resistant (case group) to EGFR blockade. Response to the anti-EGFR-based treatment was determined by total body CT scan. The first CT scan reassessment was performed 2–3 months after treatment. Patients showing disease progression in the first CT scan were classified as resistant. Conversely, those achieving a complete or partial response at this time point, or disease stabilization lasting at least 6 months were considered sensitive. Response to anti-EGFR treatment was assessed according to RECIST (Response Evaluation Criteria in Solid Tumors) v1.1 [21]. An Eastern cooperative oncology group (ECOG) performance status ≤ 2 and age ≥ 18 was required for inclusion in the study. We excluded patients for whom tumor DNA was not available. Clinical data collected from hospital records included gender, age, performance status (PS) according to the ECOG scale, smoking habit, primary tumor location, number of metastatic sites, time to metastases, resection of the primary tumor, previous lines of chemotherapy, type of anti-EGFR administered, and concomitant chemotherapy. Formalin-fixed paraffin-embedded (FFPE) primary tumor tissues were available from all patients. Germline DNA was also available for 92% of the patients. The study was approved by the Institutional Ethics Committee at HSCSP (ethical code: 22/2012) and all study participants gave written informed consent. Cancers 2020, 12, 2245 3 of 15 2.2. Gene Selection and Primer Design Two custom panels were created, one for blood samples and the other for tumor samples. Custom amplicon oligonucleotides were designed for each region of interest following the manufacturer’s instructions (Illumina, San Diego, CA, USA). As FFPE tumor DNA is more degraded and fragmented than germline DNA, amplicons of less length are needed to achieve good quality sequencing reads. The panel for germline DNA therefore contained 771 amplicons with an average size of 250–300 base pairs (bp) whereas the panel for tumor DNA contained 1124 amplicons of ~175 bp. Both panels included the 43 candidate genes related to the EGFR pathway and had an expected coverage >99% for all the coding regions (~200,000 bp). These genes mainly encoded receptors, ligands, intracellular downstream effectors or proteins involved in EGFR turnover. We included the most relevant genes of the pathway and also those related to anti-EGFR response in previous studies [3,22,23]. Table 1 provides information about the selected genes and the function of their corresponding encoded proteins. Table 1. Selected genes classified according to the function of their encoding proteins. Intracellular Proteins Involved in Ligands Receptors Others Downstream Effectors EGFR Turnover AREG EGFR (ERBB1 or HER1) AKT1 AGR2 TP53 BTC ERBB2 (HER2) BRAF CBL YAP1 EGF ERBB3 (HER3) HRAS LRIG1 EPGN ERBB4 (HER4) IRS2 LRIG2 EREG FGFR1 KRAS LRIG3 HBEGF IGF1R MAP2K1 NEDD8 HGF MET NRAS ERRFI1 (RALT or MIG6) IGF1-2 PDGFRA PIK3CA SOCS4 NRG1-4 PTEN SOCS5 TGFα SPRY2 Abbreviations: AGR2, Anterior Gradient 2, Protein Disulphide Isomerase Family Member; AKT1, AKT Serine/Threonine Kinase 1; AREG, Amphiregulin; BRAF, B-Raf Proto-Oncogene, Serine/Threonine Kinase; BTC, Betacellulin; CBL, Cbl Proto-Oncogene; EGF, Epidermal Growth Factor; EGFR, Epidermal Growth Factor Receptor; EPGN, Epithelial Mitogen; ERBB2, Erb-B2 Receptor Tyrosine Kinase 2; ERBB3, Erb-B3 Receptor Tyrosine Kinase 3; ERBB4, Erb-B4 Receptor Tyrosine Kinase 4; EREG, Epiregulin; ERRFI1, ERBB Receptor Feedback Inhibitor 1; FGFR1, Fibroblast Growth Factor Receptor 1; HBEGF, Heparin Binding EGF Like Growth Factor; HGF, Hepatocyte Growth Factor; HRAS, HRas Proto-Oncogene, GTPase; IGF1, Insulin Like Growth Factor 1; IGF2, Insulin Like Growth Factor 2; IGF1R, Insulin Like Growth Factor 1 Receptor; IRS2, Insulin Receptor Substrate 2; KRAS, KRAS Proto-Oncogene, GTPase; MAP2K1, Mitogen-Activated Protein Kinase Kinase 1; LRIG1, Leucine Rich Repeats And Immunoglobulin Like Domains 1; LRIG2, Leucine Rich Repeats And Immunoglobulin Like Domains 2; LRIG3, Leucine Rich Repeats And Immunoglobulin Like Domains 3; MET, MET Proto-Oncogene, Receptor Tyrosine Kinase; NEDD8, Neural Precursor Cell Expressed, Developmentally Down-Regulated 8; NRAS, NRAS Proto-Oncogene, GTPase; NRG1, Neuregulin 1; NRG2, Neuregulin 2; NRG3, Neuregulin 3; NRG4, Neuregulin 4; PDGFRA, Platelet Derived Growth Factor Receptor Alpha; PIK3CA, Phosphatidylinositol-4,5-Bisphosphate 3-Kinase Catalytic Subunit Alpha; PTEN, Phosphatase And Tensin Homolog; SOCS4, Suppressor Of Cytokine Signaling 4; SOCS5, Suppressor Of Cytokine Signaling 5; SPRY2, Sprouty RTK Signaling Antagonist 2; TGFα, Transforming Growth Factor Alpha; TP53, Tumor Protein P53; YAP1, Yes Associated Protein 1. 2.3. Isolation and Quantification of DNA Germline DNA was automatically extracted from peripheral whole-blood samples (Autopure, Qiagen, Hilden, Germany). Tumor DNA was extracted from primary tumor tissue samples using the GeneRead DNA FFPE Kit (Qiagen, Hilden, Germany). This kit purifies tumor DNA by removing artificial C > T mutations. DNA concentrations were measured using Qubit™ dsDNA HS Assay Kits with the Qubit 3.0 Fluorometer (Invitrogen, Carlsbad, CA, USA). Cancers 2020, 12, 2245 4 of 15 2.4. Library Preparation, Sequencing Runs, and NGS Analysis Library preparation was carried out following the manufacturer’s instructions (TruSeq Custom Amplicon Low Input Library Prep, Illumina, San Diego, CA, USA). We used ~15 ng of germline DNA or ~100 ng of tumor DNA, due to the low quality of DNA from FFPE samples. Library sizes were determined using QIAxcel DNA Screening Gel Cartridge on QIAxcel capillary electrophoresis system (Qiagen GmbH, Hilden, Germany). Library concentrations were measured with Qubit 3.0 Fluorometer (Invitrogen, Carlsbad, CA, USA). All libraries were subsequently diluted and pooled equimolarly. Germline DNA sequencing was performed on a MiSeq platform (Illumina) to obtain 150-bp paired-end reads. Samples were sequenced using the MiSeq v2-300 Reagent Kit. To achieve higher coverage, tumor DNA sequencing was performed on a NextSeq 500 platform (Illumina), also obtaining 150-bp paired-end reads. Samples were sequenced using the NextSeq Mid v2-300 Reagent Kit. Sequence reads were aligned against the human reference genome (version GRCh37) using the Burrows-Wheeler Aligner (BWA, version 0.7.12) [24]. Single nucleotide and indel variants were called by means of the Mutect2 tool (version 4.0.12.0) that can use both input tumor and germline data. It can also manage high coverage data from tumor sequences [25]. Alignment and calling were performed following software development best practices (Broad Institute, Cambridge, MA, USA). After alignment, a panel of normal variation (PoN) obtained from germline DNA sequencing data was used to exclude rare germline variants and individual-specific artifacts. The number of somatic variants per patient was calculated by filtering tumor DNA sequencing data with the PoN. We excluded variants located in intronic, intergenic or UTR regions, and also polymorphisms (GnomAD allele frequency >0.001). Results were inspected using the Integrative Genomics Viewer (IGV). Resistant patients whose tumors harbored a known non-response mutation (in KRAS, NRAS or BRAF V600E) of over 5% of mutated clones were excluded. The presence of these non-response mutations was confirmed by a second technique (Sanger sequencing or the TruSight Tumor 15 panel) in some patients. Finally, only those variants that were over 1% of mutant clones in all patients harboring them were kept for further examination. COSMIC cancer database v91 was used to assess whether the candidate somatic variants identified had been previously described [26]. Additional functional annotation of variants was performed using ANNOVAR [27]. Variant analyses and interpretation were performed using Alamut® Visual v2.15 software (SOPHiA GENETICS, Boston, MA, USA) and the Cancer Genome Interpreter platform (Institute for Research in Biomedicine, Barcelona, Spain), which is publicly available at http://www.cancergenomeinterpreter.org [28]. 2.5. Statistical Analyses We used the chi-square test to compare the baseline clinical characteristics between the two cohorts of patients and Fisher’s exact test to compare the prevalence of alterations between sensitive and resistant patients. The Mann-Whitney test was carried out to compare the number of somatic genetic variants between sensitive and resistant patients. All statistical tests were performed using R software (version 3.3.2., https://cran.r-project.org/bin/windows/base/old/3.3.2/) and IBM SPSS® statistics software (version 25, https://www.ibm.com/support/pages/downloading-ibm-spss-statistics-25). A 95% confidence level was set for all tests of significance. 3. Results 3.1. Patient Population A total of 168 mCRC patients were treated with anti-EGFR-containing regimens between 2012 and 2017. Sixty-one of the patients (38 sensitive and 23 resistant to EGFR blockade) were included in the study, as they met the inclusion criteria and quality of tumor DNA was good (Figure 1). They were all diagnosed with stage IV CRC (37.7% metachronous, 50.8% ≥ 2 metastatic locations). Regarding treatment, 33 patients received cetuximab and 28 received a panitumumab-containing regimen, with no significant differences between the two cohorts (p = 0.814). There were more females and more patients Cancers 2020, 12, x 5 of 17 the study, as they met the inclusion criteria and quality of tumor DNA was good (Figure 1). They Cancers were all 12, 2245 2020,diagnosed with stage IV CRC (37.7% metachronous, 50.8% ≥ 2 metastatic locations). 5 of 15 Regarding treatment, 33 patients received cetuximab and 28 received a panitumumab-containing regimen, with no significant differences between the two cohorts (p = 0.814). There were more females with worse ECOG PS in the non-responder cohort (p < 0.001 and p = 0.013, respectively). No differences and more patients with worse ECOG PS in the non-responder cohort (p < 0.001 and p = 0.013, were observed between the two cohorts in respect to primary tumor location (p = 0.634). A higher respectively). No differences were observed between the two cohorts in respect to primary tumor percentage of patients in the sensitive group received the anti-EGFR-containing regimen as first-line location (p = 0.634). A higher percentage of patients in the sensitive group received the anti-EGFR- treatment (47.4% in the sensitive group vs. 26.1% in the resistant group) and presented synchronous containing regimen as first-line treatment (47.4% in the sensitive group vs. 26.1% in the resistant metastases (71.1% in the sensitive group vs. 47.8% in the resistant group). Baseline clinical features are group) and presented synchronous metastases (71.1% in the sensitive group vs. 47.8% in the resistant described in Table 2. group). Baseline clinical features are described in Table 2. Flow chart Figure 1. Flow chart of of patient patient selection. selection. * *Inclusion Inclusioncriteria: patients≥ ≥ criteria:patients 1818years, years,with with ananEastern Eastern cooperative oncology group (ECOG) performance status ≤ 2 and RAS wild-type cooperative oncology group (ECOG) performance status ≤ 2 and RAS wild-type tumors. Patients had tumors. Patients hadbetosensitive to be sensitive (patients (patients achieving achieving complete complete or partial or partial response response at the at the first first CT-scan CT-scan orordisease disease stabilization lasting stabilization lastingat atleast least66months) months)or orresistant resistanttotoanti-EGFR anti-EGFRblockade blockade(patients (patientsshowing showing disease disease CT-scan). progression at the first CT-scan). Table 2. Baseline patient characteristics. Study Population Sensitive Patients Resistant Patients Characteristic (n = 61) (n = 38) (n = 23) p-value N (%) N (%) N (%) Cancers 2020, 12, 2245 6 of 15 Table 2. Baseline patient characteristics. Study Population Sensitive Patients Resistant Patients Characteristic (n = 61) (n = 38) (n = 23) p-Value N (%) N (%) N (%) Sex Male 40 (65.6%) 32 (84%) 8 (34.8%) <0.001 Female 21 (34.4%) 6 (16%) 15 (65.2%) Age <75 43 (70.5%) 26 (68.4%) 17 (73.9%) 0.649 ≥75 18 (29.5%) 12 (31.6%) 6 (26.1%) Mean Age 67.7 66.9 Performance status (ECOG) 0 31 (50.8%) 24 (63.2%) 7 (30.4%) 0.013 1–2 30 (49.2%) 14 (36.8%) 16 (69.6%) Smoking habit Never smokers 24 (39.3%) 13 (34.2%) 11 (47.8%) 0.291 Current or former smokers 37 (60.7%) 25 (65.8%) 12 (52.2%) Tumor side Right 24 (39.4%) 15 (39.5%) 9 (39.2%) Left 20 (32.7%) 13 (34.2%) 7 (30.4 %) 0.634 Rectal 16 (26.3%) 10 (26.3%) 6 (26.1%) Jejunum 1 (1.6%) 0 1 (4.3%) Number of metastatic sites 1 30 (49.2%) 18 (47.4%) 12 (52.3%) 0.716 ≥2 31 (50.8%) 20 (52.6%) 11 (47.7%) Time to metastases Synchronous 38 (62.3%) 27 (71.1%) 11 (47.8%) 0.070 Metachronous 23 (37.7%) 11 (28.9%) 12 (52.2%) Primary resected Yes 49 (80.3%) 32 (84.2%) 17 (73.9%) 0.327 No 12 (19.7%) 6 (15.8%) 6 (26.1%) Previous lines of treatment 0 24 (39.3%) 18 (47.4%) 6 (26.1%) 1 32 (52.5%) 17 (44.7%) 15 (65.2%) 0.246 ≥2 5 (8.2%) 3 (7.9%) 2 (8.7%) Type of anti-EGFR Cetuximab 33 (54.1%) 21 (55.3%) 12 (52.2%) 0.814 Panitumumab 28 (45.9%) 17 (44.7%) 11 (47.8%) Combination QT FOLFOX 18 (29.5%) 14 (36.8 %) 4 (17.4%) Irinotecan scheme 40 (65.6%) 23 (60.6%) 17 (73.9%) 0.192 Monotherapy 3 (4.9%) 1 (2.6 %) 2 (8.7%) PFS (months) 18.8 4.7 OS (months) 41.2 17.2 Abbreviations: ECOG, Eastern Cooperative Oncology Group; OS, overall survival; PFS, progression-free survival. p-values below 0.05 are highlighted in bold. 3.2. Genetic Analyses The mean target coverage for tumor samples was 3600, achieving 100× or greater coverage for 88% of the bases. For germline samples, the mean target coverage was 640, achieving 30× or greater coverage for 87% of the bases. As shown in Figure 2, we identified 46,512 somatic genetic variants. We found a mean of 707 variants per sample in sensitive patients and 854 variants per sample in resistant patients (p = 0.19). Cancers 2020, 12, 2245 7 of 15 Cancers 2020, 12, x 7 of 17 3000 p=0.1929 2500 Somatic genetic variants 2000 1500 1000 500 0 Sensitive Resistant Mean = 707 854 n= 38 23 Figure Figure 2. 2. Number Number of of somatic somatic genetic genetic variants variants and and response response to to anti-EGFR anti-EGFR agents. agents. 3.3. KRAS, 3.3. KRAS, NRAS, NRAS, BRAF, BRAF, and and PIK3CA PIK3CA Assessment Assessment and and Patient Patient Selection Selection Five anti-EGFR Five anti-EGFR resistant resistant patients patients were were finally finally excluded excluded from from thethe analyses analyses because because aa validated validated mutation of non-response (in RAS or BRAF V600E ) was detected at an allelic fraction over 5%. One of of mutation of non-response (in RAS or BRAF V600E) was detected at an allelic fraction over 5%. One the five patients (P11) had the KRAS G12C mutation. The remaining four the five patients (P11) had the KRAS G12C mutation. The remaining four patients (P3, P39, P55, andpatients (P3, P39, P55, and P64), P64), all with all with right-sided right-sided tumors, tumors, presented presented thethe BRAF BRAF V600Emutation. V600E mutation.InInoneonepatient patient(P64) (P64) this this mutation had not been previously detected by Sanger. Patient P55 presented mutation had not been previously detected by Sanger. Patient P55 presented the mutations BRAF the mutations BRAF V600E and V600E NRAS G13D and NRAS G13D at at aa frequency frequency over over 5% 5% for for both both mutations. mutations. As As for for patient patient P39, BRAF V600E P39, BRAF V600E KRAS Q61L mutations coexisted, although the allelic fraction for KRAS and KRAS Q61L mutations coexisted, although the allelic fraction for KRAS Q61L was only 0.8%.0.8%. and Q61L was only No No patients patients harbored harbored somatic somatic mutations mutations in both in both KRAS KRAS and NRAS and NRAS genes. genes. One sensitive One sensitive patient patient (P28) (P28) who who underwent underwent FOLFIRI FOLFIRI plusplus panitumumab panitumumab as aassecond-line a second-line treatmentpresented treatment presentedthe KRAS A146V theKRAS A146V mutation at an allelic fraction of 5.0%. This mutation had not been identified previously. Table 33 and mutation at an allelic fraction of 5.0%. This mutation had not been identified previously. Table and Figure S1 Figure S1 show show allall the the somatic somatic mutations mutations found found inin these these genes genes and and their their allelic allelic fractions. fractions. We also analyzed PIK3CA mutations and BRAF mutations other than V600E. Two patients, one resistant (P63) and one sensitive (P66), presented the PIK3CA E545K mutation, with an allelic fraction of 10.1% and 6.1%, respectively. In addition, two BRAF mutations previously related to favorable prognosis (D594N and G466A) [29–32] were identified at around 20% in two sensitive patients (P45 and P59). Table 3 shows all the findings concerning BRAF and PIK3CA assessment. Cancers 2020, 12, 2245 8 of 15 Table 3. Assessment of KRAS, NRAS, BRAF, and PIK3CA mutational status by next-generation sequencing. Treatment % of Mutational Status Prior to Gene Mutation Patient Coverage Outcome Mutation Anti-EGFR Prescription KRAS G12C P11 45.7% 2316 KRAS wild-type KRAS Q61L P39 0.8% 14,637 KRAS exon 3 not tested NRAS G12S P51 2.6% 5093 NRAS wild-type NRAS G13D P55 8.1% 12,265 NRAS not tested Resistant NRAS G13D P57 0.5% 41,758 NRAS wild-type BRAF V600E P3 24.4% 10,995 BRAF V600E mutated BRAF V600E P39 10.6% 7906 BRAF V600E mutated BRAF V600E P55 20.8% 22,185 BRAF V600E mutated BRAF V600E P64 12.1% 1316 BRAF V600E wild-type PIK3CA E545K P63 10.1% 17,051 PIK3CA not tested KRAS A146V P28 5.0% 2743 KRAS wild-type Sensitive BRAF D594N P45 19.7% 23,584 BRAF codon 594 not tested BRAF G466A P59 21.0% 10,234 BRAF codon 466 not tested PIK3CA E545K P66 6.1% 12,888 PIK3CA not tested Abbreviations: BRAF, B-Raf Proto-Oncogene, Serine/Threonine Kinase; KRAS, KRAS Proto-Oncogene, GTPase; NRAS, NRAS Proto-Oncogene, GTPase; PIK3CA, Phosphatidylinositol-4,5-Bisphosphate 3-Kinase Catalytic Subunit Alpha. 3.4. Identification of Novel Genetic Variants Related to Anti-EGFR Response We identified eight potential somatic variants of non-response at a frequency of over 1% in 12 out of 18 (66.7%) non-responder patients (Table 4). In 8 cases, two or more of these variants coexisted. Mutant allele fractions differed substantially among the patients (Figure S2). No potential resistance variants were found in 6 non-responders. Additionally, no variants of good response were identified. Table 4. Genetic variations significantly associated with lack of response to anti-EGFR blockade. Patients % of the Presence in COSMIC Gene Genetic Variant with the Somatic Coverage p- Value * Cancer Database v91 Variant Variant P4 3.5% 8008 NM_001291858.1: c.2003T > A; P9 4.2% 1745 0.029 Not described p.(I668N) P10 10.6% 3093 IGF1R P1 6.3% 2740 Mutation Id: 6919417 NM_001291858.1: P2 11.8% 1866 Patient with a c.3652G > A; 0.008 P9 2.4% 1238 leiomyosarcoma (n = 1) p.(E1218K) [33] P61 1.5% 10,017 P12 7.0% 743 Mutation Id: 6974893 NM_003749.2: Patient with colon IRS2 c.3467C > T; P14 5.1% 1093 0.029 cancer (n = 1) p.(T1156M) P57 3.4% 11,118 [33] Cancers 2020, 12, 2245 9 of 15 Table 4. Cont. Patients % of the Presence in COSMIC Gene Genetic Variant with the Somatic Coverage p- Value * Cancer Database v91 Variant Variant P12 89.3% 196 Mutation Id: 4005617 NM_015541: Patient with colon LRIG1 c.456G > A; P57 2.7% 12,073 0.008 cancer (n = 1) p.(T152T) P63 7.9% 6106 Patient with bladder cancer (n = 1) P67 4.0% 3218 [34] P1 2.2% 8802 NM_014813.2: LRIG2 c.2090C > T; P10 4.2% 1464 0.029 Not described p.(S697L) P12 7.9% 1794 P1 1.7% 12,378 NM_001136051.2: LRIG3 c.2434G > A; P2 4.8% 4477 0.029 Not described p.(V812M) P9 4.6% 2266 P9 1.6% 2273 NM_002524.3: NRAS c.344del; P10 3.1% 3140 0.029 Not described p.(G115Efs*46) P63 2.2% 20,507 P52 22.0% 19,078 NM_001347828: PDGFRA c.903G > A; P63 3.8% 26,774 0.029 Not described p.(T301T) P67 1.2% 55,158 * p-value was obtained by Fisher’s exact test. Abbreviations: IGF1R, Insulin-like Growth Factor 1 Receptor; IRS2, Insulin Receptor Substrate 2; LRIG1, Leucine-rich Repeats and Immunoglobulin-like Domains 1; LRIG2, Leucine-rich Repeats and Immunoglobulin-like Domains 2; LRIG3, Leucine-rich Repeats and Immunoglobulin-like Domains 3; NRAS, NRAS Proto-Oncogene, GTPase; PDGFRA, Platelet-derived Growth Factor Receptor Alpha. Three of the eight variants detected were missense variants located in insulin-related genes, such as IGF1R I668N and E1218K or IRS2 T1156M. Three others were found in genes belonging to the LRIG family: LRIG1 (T152T), LRIG2 (S697L), and LRIG3 (V812M). The remaining two variants were found in NRAS (G115Efs*46) and PDGFRA (T301T). All variants were non-synonymous, except for LRIG1 and PDGFRA variants. According to Alamut software, these two variants may create a novel cryptic acceptor site identifiable by the splicing complex. 4. Discussion We sought to investigate the existence of novel somatic variants in EGFR-related genes as predictive markers of response to anti-EGFR antibodies. We found eight potential somatic variants that could explain the lack of response to these agents, highlighting the variants in the insulin-related and LRIG family genes. In contrast, we did not find somatic variants related to good response. Accurate identification of somatic variants is challenging. In the past, Sanger sequencing was the only technique available to detect somatic mutations in tumor samples. Currently, NGS technology is replacing Sanger method as it allows the sequencing of hundreds of genes simultaneously and shows high sensitivity [35]. These advantages have enabled the detection of concomitant mutations in several genes of interest, including low-allele-fraction variants not previously found by Sanger sequencing. Mutant allele fractions may influence the response to targeted therapies. In this sense, the mutant allele fraction that determines anti-EGFR response continues to be debated. A major point of discussion is whether the optimal threshold of the RAS mutant allele fraction to identify patients likely to benefit from anti-EGFR drugs should be 1% or 5%. We used a threshold of 5% as it has been reported that reducing the threshold to 1% does not improve outcomes [36,37]. We found one sensitive patient (P28) who harbored the A146V KRAS mutation at 5%. The good response in this patient could be Cancers 2020, 12, 2245 10 of 15 related to the chemotherapy scheme concomitantly given with the anti-EGFR drug. We also found a novel somatic variant of non-response in the NRAS gene. NRAS variants routinely tested prior to anti-EGFR initiation are normally missense mutations (≈95%) [7]. Conversely, the new NRAS variant we found, G115Efs*46, is a truncating mutation. It consists of a deletion located in exon 4 that leads to a premature stop codon and a truncated protein [26]. The Cancer Genome Interpreter predicts that it is a passenger mutation with a highly deleterious effect, but its role in anti-EGFR resistance should be further explored before a solid conclusion can be reached. Similarly to RAS genes, BRAF and PIK3CA are driver oncogenes involved in colorectal carcinogenesis. It has been reported that BRAF mutations implying a high kinase activity (such as V600E) confer a poor prognosis, whereas those implying a low kinase activity (such as those located in codons 594 and 596) confer a favorable prognosis [29–31]. Our results reinforce the differential prognostic role of BRAF mutations as we only detected mutations implying a high kinase activity (V600E) in patients who were resistant to treatment, and we only found mutations causing low kinase activity in sensitive patients (D594N and G466A). As for location, several studies show that V600E mutations are more common in right colon cancers [38,39]. Accordingly, we only found V600E mutations in right-sided tumors. In contrast, we found D594N and G466A mutations in left-sided tumors. This differential distribution of BRAF mutations may affect the prognosis of left-sided tumors vs. right-sided tumors and define a clinically distinct subtype of CRC with an excellent prognosis. In addition, one resistant patient harbored NRAS and BRAF mutations over 5%, indicating that they are not always mutually exclusive. Regarding PIK3CA, the role of its activating mutations on anti-EGFR response remains under discussion [14,40,41]. In the present study, two patients, one responder and one non-responder, harbored the PIK3CA E545K mutation with an allelic fraction over 5%. Our results therefore strengthen the notion that this mutation is not critical for anti-EGFR response. Our findings suggest some insulin-related genes (IGF1R and IRS2) have a substantial influence on anti-EGFR outcomes. A growing amount of evidence indicates that the insulin-like growth factor-1 receptor (IGF1R) is frequently overexpressed in CRC and that its activation is related to poorer outcomes [42]. IGF1R is a tyrosine kinase receptor that can also activate the RAS pathway and promote proliferation of cancer cells, resistance to apoptosis, and epithelial-mesenchymal transition [43]. Furthermore, it has been reported that a functional IGF1R receptor is required for EGFR-mediated growth and transformation [44] and that IGF1R expression modulates anti-EGFR efficacy in mCRC patients [45]. Consequently, in the same way that activating mutations in the EGFR receptor potentiate the RAS pathway, IGF1R activating mutations could play a similar role, with worse responses to EGFR-targeted therapies. To our knowledge, this is the first time that the missense IGF1R variants identified in our study (I668N and E1218K) have been related to anti-EGFR resistance. The variant E1218K has been previously described in a patient with leiomyosarcoma [33], but the variant I668N has not been reported previously [26]. We also identified a missense variant in the IRS2 gene (T1156M) associated with a lack of response to anti-EGFR drugs. Interestingly, Bertotti et al. demonstrated that IRS2 knockdown reduced sensitivity to cetuximab [23]. Truncating variants in this gene could therefore imply a lack of response to this drug. In our study, we found a missense variant in this gene harbored by three non-responders (Table 4). This variant had already been reported in a patient with colon cancer [33]. Like EGFR and IGF1R signaling pathways, the PDGFRA pathway is also involved in cell proliferation and migration [46]. The platelet-derived growth factor receptor A (PDGFRA) is a tyrosine kinase receptor that is frequently mutated in gastrointestinal stromal tumors [47]. In the present study, we found a splicing variant located in PDGFRA. Mutations in this gene have been correlated with a lack of response to anti-EGFR agents, although evidence is still scarce. In this line, Bertotti et al. described novel missense mutations located in/near the catalytic domain as mechanisms of anti-EGFR resistance [23]. We also found that LRIG1-3 genes could play a role in anti-EGFR response. Little is known about the role of somatic variants in these genes in responsiveness to anti-EGFR agents. Several studies have demonstrated that LRIG1 acts as a tumor suppressor by down-regulating ErbB and Met receptors, including EGFR [48–52]. In contrast, no definitive conclusions concerning the contribution of LRIG2 Cancers 2020, 12, 2245 11 of 15 and LRIG3 to EGFR levels can be drawn as results reported to date are contradictory [48,53–56]. Gelfo et al. found that a lower LRIG1 expression predicted resistance to cetuximab therapy in CRC xenopatients, an effect that was not observed with LRIG3 [57]. In our study, one somatic variant in each LRIG gene was significantly associated with resistance to anti-EGFR blockade. Interestingly, the alteration located in LRIG1 had already been described in two cancer patients, one with a colon adenocarcinoma and the other with bladder cancer [34]. Conversely, the variants found in LRIG2 and LRIG3 have not been previously reported [26]. We wish to emphasize that no somatic variants related to good response were found in our study. This result is not striking as resistant mCRC phenotypes tend to be more heterogeneous than sensitive phenotypes [58]. Our results are in keeping with most papers to date describing mutations of non-response to anti-EGFR antibodies [59–61]. Only three of the eight somatic variants identified had been previously described [26], strengthening the extreme phenotype approach as a useful strategy to identify rare alterations. However, our study has some limitations. First, the two cohorts of patients are relatively small. The low number of patients showing an extremely poor response could be the result of good selection by clinicians after assessing RAS and BRAF mutational status and considering patient’s characteristics. The lack of statistically significant differences in the number of somatic mutations between the two cohorts of patients and between sidedness and drug response could also be attributable to the small sample size. Likewise, the association observed between sex and responsiveness could be a false positive predictive marker. Second, patients were included over a five-year period (2012–2017), during which time scientific knowledge regarding RAS and BRAF mutations increased significantly. This could explain why some patients with a BRAF V600E mutation already detected in the first assessment received an anti-EGFR agent. Larger, prospective and functional studies are needed to confirm the validity of our findings. Therapies targeting different members and downstream effectors of the EGFR signaling pathway, such as KRAS or BRAF, have previously been explored [62,63]. However, most patients develop resistance to these agents. Our findings reveal novel mechanisms of resistance to anti-EGFR targeted therapies in mCRC patients that could provide new avenues for therapeutic intervention involving insulin-related and/or LRIG proteins. The utility of IGF1R as a possible therapeutic target has already been evaluated. In non-small cell lung cancer, IGF1R hyperactivity is related to acquired resistance to erlotinib, and simultaneous inhibition of EGFR and IGF1R is effective to prevent and also to overcome erlotinib resistance [64]. In CRC, the simultaneous inhibition of EGFR together with the IGF1R antagonist dalotuzumab has been tested in KRAS wild-type mCRC patients showing negative results [65]. Our results, together with the evidences thus far reported, could promote studies assessing insulin-related and also LRIG1-3 proteins as possible therapeutic targets in CRC. The impact of somatic variants in the genes encoding these proteins on anti-EGFR efficacy should also be confirmed. 5. Conclusions In conclusion, this study shows that novel genetic variants along the EGFR-triggered pathway could affect the response to anti-EGFR drugs in mCRC patients. Our findings may help to better identify patients who are resistant to anti-EGFR drugs despite harboring RAS/BRAF-wild-type tumors. The eight genetic variants predictive of non-response could help guide clinical decision-making and improve outcomes by tailoring anti-EGFR drugs. Supplementary Materials: The following are available online at http://www.mdpi.com/2072-6694/12/8/2245/s1, Figure S1: KRAS, NRAS, BRAF, and PIK3CA mutations, visualized with the Integrative Genomics Viewer (IGV), Figure S2: Genetic variations significantly associated with lack of response to anti-EGFR blockade, visualized with the Integrative Genomics Viewer (IGV). Author Contributions: Conceptualization, P.R., J.S. (Jordi Surrallés), and D.P.; Formal analysis, P.R., B.R.-S., A.L., L.G.-Q., B.M. and D.P.; Funding acquisition, J.S. (Jordi Surrallés) and D.P.; Investigation, P.R., L.G.-Q. and C.C.; Methodology, P.R., B.R.-S., J.S. (Jordi Surrallés) and D.P.; Project administration, J.M., J.S. (Jordi Surrallés) and D.P.; Resources, P.R., B.R.-S., A.L., L.G.-Q., B.M., J.S. (Juliana Salazar), A.S., A.C.V., C.C. and D.P.; Software, B.R.-S.; Supervision, J.M., J.S. (Jordi Surrallés) and D.P.; Validation, P.R.; Writing–original draft, P.R.; Writing–review & Cancers 2020, 12, 2245 12 of 15 editing, P.R., B.R.-S., A.L., L.G.-Q., B.M., J.S. (Juliana Salazar), A.S., A.C.V., J.M., C.C., J.S. (Jordi Surrallés) and D.P. All authors have read and agreed to the published version of the manuscript. Funding: This work was supported by the Asociación Española Contra el Cáncer (Beca Campus CIMA de Sanitas Hospitales) and the Instituto de Salud Carlos III (grant CM18/00207 to Pau Riera]. Jordi Surrallés is supported by ICREA Academia Programme. Acknowledgments: The authors thank Carolyn Newey for English language editing. Conflicts of Interest: Ana Sebio reports receiving travel grants from Merck, Amgen, Sanofi, and Roche. Anna C. 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Natl. Cancer Inst. 2015, 107, djv258. [CrossRef] [PubMed] © 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).