cancers Article Usefulness of Two Independent DNA and RNA Tissue-Based Multiplex Assays for the Routine Care of Advanced NSCLC Patients Elba Marin 1,2,3 , Cristina Teixido 2,3,4 , Elena Carmona-Rocha 1 , Roxana Reyes 1,3 , Ainara Arcocha 1,3 , Nuria Viñolas 1,3 , MªCarmen Rodríguez-Mues 1,3 , Carlos Cabrera 5 , Marcelo Sánchez 3,6 , Ivan Vollmer 3,6 , Sergi Castillo 7 , Silvia Muñoz 7 , Ivana G. Sullivan 8 , Adela Rodriguez 1 , Mireia Garcia 4 , Silvia Alos 4 , Pedro Jares 4,9 , Antonio Martinez 4 , Aleix Prat 1,2,10 , Miguel Ángel Molina-Vila 11 and Noemi Reguart 1,2,3, * 1 Division of Medical Oncology, Hospital Clínic, 08036 Barcelona, Spain; ELMARIN@clinic.cat (E.M.); elecr6@gmail.com (E.C.-R.); rmreyes@clinic.cat (R.R.); aarcocha@clinic.cat (A.A.); nvinolas@clinic.cat (N.V.); mrodrigm@clinic.cat (M.R.-M.); adrodriguez2@clinic.cat (A.R.); alprat@clinic.cat (A.P.) 2 Translational Genomics and Targeted Therapeutics in Solid Tumors, Institut d’Investigacions Biomèdiques August Pi I Sunyer, 08036 Barcelona, Spain; teixido@clinic.cat 3 Unitat Funcional de Tumors Toràcics, Hospital Clínic, 08036 Barcelona, Spain; msanche@clinic.cat (M.S.); vollmer@clinic.cat (I.V.) 4 Division of Pathology, Hospital Clínic, 08036 Barcelona, Spain; garcia01@clinic.cat (M.G.); salos@clinic.cat (S.A.); pjares@clinic.cat (P.J.); antonmar@clinic.cat (A.M.) 5 Division of Medical Oncology, Instituto Oncologico Dr. Rosell, Teknon Hospital, 08028 Barcelona, Spain; ccabrera@oncorosell.com 6 Division of Thoracic Radiology, Hospital Clínic Barcelona, 08036 Barcelona, Spain 7 Division of Medical Oncology, Hospital General de Granollers, 08402 Barcelona, Spain; sergimed77@gmail.com (S.C.); smunoz@fphag.org (S.M.) 8 Division of Medical Oncology, Hospital de la Santa Creu i Sant Pau, 08041 Barcelona, Spain; ISullivan@santpau.cat 9 Molecular Biology Core Facility, Hospital Clínic, 08036 Barcelona, Spain 10 Department of Medicine, University of Barcelona, 08036 Barcelona, Spain 11 Laboratory of Oncology, Pangaea Oncology, Quirón Dexeus University Hospital, 08028 Barcelona, Spain; mamolina@panoncology.com * Correspondence: nreguart@clinic.cat; Tel.: +34-93-227-54-02  Received: 3 April 2020; Accepted: 23 April 2020; Published: 30 April 2020  Abstract: Personalized medicine is nowadays a paradigm in lung cancer management, offering important benefits to patients. This study aimed to test the feasibility and utility of embedding two multiplexed genomic platforms as the routine workup of advanced non-squamous non-small cell lung cancer (NSCLC) patients. Two parallel multiplexed approaches were performed based on DNA sequencing and direct digital detection of RNA with nCounter® technology to evaluate gene mutations and fusions. The results were used to guide genotype-directed therapies and patient outcomes were collected. A total of 224 advanced non-squamous NSCLC patients were prospectively included in the study. Overall, 85% of samples were successfully characterized at DNA and RNA levels and oncogenic drivers were found in 68% of patients, with KRAS, EGFR, MET∆ex14, BRAF, and ALK being the most frequent (31%, 19%, 5%, 4%, and 4%, respectively). Among all patients with complete genotyping results and follow-up data (n = 156), the median overall survival (OS) was 1.90 years (confidence interval (CI) 95% 1.69–2.10) for individuals harbouring an actionable driver treated with a matched therapy, compared with 0.59 years (CI 95% 0.39–0.79) in those not eligible for any targeted therapy and 0.61 years (CI 95% 0.12–1.10) in patients with no drivers identified (p < 0.001). Integrating DNA and RNA multiplexing technologies into the routine molecular testing of advanced NSCLC patients is feasible and useful and highlights the necessity of widespread integrating comprehensive molecular diagnosis into lung cancer care. Cancers 2020, 12, 1124; doi:10.3390/cancers12051124 www.mdpi.com/journal/cancers Cancers 2020, 12, 1124 2 of 14 Keywords: advanced non-small cell lung cancer; molecular diagnostics; oncogenic drivers; mutations; targeted therapies 1. Introduction The emergence of targeted therapy has revolutionized the management of patients with lung cancer by incorporating tumour molecular analyses into the routine diagnostic and therapeutic process [1]. At this time, non-small cell lung cancer (NSCLC) has undergone a paradigm shift from a purely histological to a molecular-based treatment approach, allowing for the selection of patients that would most benefit from targeted therapies, and thus impacting lung cancer care overall. Adenocarcinoma is probably the lung cancer subtype that has most benefited from this molecular-based strategy, with an estimated frequency of oncogenic drivers higher than 50% [2]. About 25%–30% of these patients can benefit from a targeted therapy that has already been approved, and another 25%–30% are able to enroll in clinical trials. In order to personalize treatment decisions, several guidelines currently endorse routine genetic testing of four leading oncogenic drivers—EGFR, ALK, ROS1, and BRAF—in newly metastatic non-squamous NSCLC patients [3–7], while a number of other emerging molecular targets, such as RET and NTRK gene rearrangements, MET exon 14 skipping mutations (MET∆ex14), and activating HER2 mutations, are likely approaching clinical practice [8]. However, real-world data indicate that comprehensive biomarker testing is still not universal and remains suboptimal in daily clinical practice [8,9]. This is the case of Europeans, who face extreme inequality in access to appropriate molecular diagnosis, even within the same country, with huge differences in the national policies for reimbursement [10]. Our understanding of the molecular events that drive lung cancer pathogenesis has come from the progress made in highly efficient next-generation sequencing (NGS) technologies, which allow for a blanket testing of multiple potentially actionable driver genes from a single sample [11]. Several groups have already demonstrated the practicality of routine genetic testing in large cohorts of patients nationwide and the potential use of this information to guide treatment decisions [4,12–22]. However, the vast majority of those groups used DNA workflows to identify somatic variants—deletions, insertions, inversions, and substitutions—present in selected regions, whereas RNA-based multiplex analysis, which enables testing of several rearrangements and fusion partners, is still not in such common use [8,14] (Table S1). In a previous study, we retrospectively validated a multiplexed RNA-based nCounter codeset for the detection of ALK, ROS1, and RET fusion transcripts in formalin-fixed and paraffin embedded (FFPE) samples from patients with advanced NSCLC and proved its advantage compared with standard diagnostic assays—immunohistochemistry (IHC) and fluorescent in situ hybridization (FISH) [23]. Here, we aimed to prospectively demonstrate the feasibility and usefulness of embedding two DNA and RNA tissue-based multiplex technologies in the real-life context of advanced lung cancer patients. The co-primary objectives set for this work were to determine the frequency of oncogenic alterations in advanced non-squamous NSCLC patients from our community and to measure overall survival (OS) in major molecular subgroups (driver/no-driver). The secondary objectives were to describe the characteristics of genotyped patients (age, gender, Eastern Cooperative Oncology Group (ECOG) performance status, and smoking history) and compare the outcomes according to treatment strategy (targeted/no-targeted therapy). 2. Results 2.1. Patient Cohort and Clinical Data Between June 2017 and August 2019, a total of 224 advanced NSCLC patients were prospectively diagnosed at our institution. The last follow-up and update of the database was done in November Cancers 2020, 12, x doi: 3 of 14 Cancers 2020, 12, 1124 3 of 14 Between June 2017 and August 2019, a total of 224 advanced NSCLC patients were prospectively diagnosed at our institution. The last follow-up and update of the database was done in November 2019. Molecular 2019. Molecular DNA DNAand/or and/orRNA RNAtesting testingyielded yielded an informativeresult an informative resultinin207 207patients patients (92%). (92%). Among Among them, 191 (85%) had both DNA and RNA informative results. All successfully genotyped them, 191 (85%) had both DNA and RNA informative results. All successfully genotyped patients patients had available clinicalclinical had available data and datafollow-up (Figure and follow-up 1). 1). (Figure Figure 1. Flow diagram. NSCLC: non-small-cell lung cancer; OS: overall survival; FISH: fluorescence Figure 1. Flow diagram. NSCLC: non-small-cell lung cancer; OS: overall survival; FISH: fluorescence in situ hybridization; IHC: immunohistochemistry. in situ hybridization; IHC: immunohistochemistry. The primary characteristics of the patients are shown in Table 1. Median age was 70 years The primary characteristics of the patients are shown in Table 1. Median age was 70 years (interquartile range of 60–77), 63% of patients were men, and 57% had an ECOG performance status of 0 (interquartile range of 60–77), 63% of patients were men, and 57% had an ECOG performance status or of 1 at0 metastatic disease disease or 1 at metastatic diagnosis. Most patients diagnosis. (81%) were Most patients (81%)former were or current former smokerssmokers or current and 72%and were diagnosed at advanced stage IV disease. The histology was adenocarcinoma for 92% 72% were diagnosed at advanced stage IV disease. The histology was adenocarcinoma for 92% ofof patients, while thepatients, remaining casesthe while included not otherwise remaining specified cases included not (NOS) or mixed otherwise NSCLC specified histologies. (NOS) or mixedTheNSCLC majority of histologies. patients (84%) were treatment-naïve at the time of molecular assessment. The majority of patients (84%) were treatment-naïve at the time of molecular assessment. Table1.1. Patient Table Patient characteristics. characteristics. Total, Total, Targeted Therapy, Targeted Therapy, No NoTargeted Targeted Therapy, Therapy, Variables Variables N.(%) N. (%) (n = 224) N. (%) N. (%) (n (n == 45) 45) N.N. (n = (%)(%) (n179) = 179) (n = 224) AgeAge at metastatic at metastatic disease,median disease, median(IQR) (IQR) 70(60–77) 70 (60–77) 70 (59–77) 70 (59–77) 6969 (60–76) (60–76) Sex Sex Women 82 (37) 30 (67) 52 (29) WomenMen 82 (37) 142 (63) 30 (67) 15 (33) 52 (29) 127 (71) Men 142 (63) Histology 15 (33) 127 (71) Adenocarcinoma Histology 206 (92) 44 (98) 162 (91) Other 1 Adenocarcinoma 20618(92) (8) 441 (2) (98) 17 (9)(91) 162 Performance 2 Other 1 18 (8) Status 1 (2) 17 (9) 0 32 (14) Status 2 11 (24) Performance 21 (12) 1 96 (43) 23 (51) 73 (41) 0 32 (14) 11 (24) 21 (12) 2 40 (18) 7 (16) 33 (18) 13 9648(43) (22) 234 (9) (51) 4473 (41) (25) 24 405(18) (2) 70(16) (0) 533 (3)(18) Unknown 3 483(22) (1) 40 (0) (9) 344 (1)(25) Cancers 2020, Cancers 12,12, 2020, 1124 x doi: 4 of 14 4 of 14 4 5 (2) 0 (0) 5 (3) Unknown 3 (1) 1. Cont. Table 0 (0) 3 (1) Smoking history Total, Never 42 (19) 26 (58)Therapy, Targeted 16 (9)Therapy, No Targeted Variables N. (%) Former 3 99(n(44) N.15 (%) (n = 45) (33) N. (%) (n = 179) 84 (47) = 224) Current 82 (37) 4 (9) 78 (43) Smoking history Unknown 1 (<1%) 0 (0) 1 (< 1) Never 42 (19) 26 (58) 16 (9) 3 Stage at diagnose 99 (44) 15 (33) 84 (47) Former I Current 882(3)(37) 2 (4) 4 (9) 6 (3) 78 (43) II Unknown 221(10) (<1%) 3 (7) 0 (0) 19 (11) 1 (< 1) III 33 (15) Stage at diagnose 6 (13) 27 (15) IV I 161 8(72)(3) 2 (4) 34 (76) 1276 (3) (71) II Treatment setting22 at (10) molecular diagnosis 3 (7) 19 (11) TreatmentIIInaïve 18833(84)(15) 35 6(78) (13) 27 (15) 153 (85) IV 161 (72) 34 (76) 127 (71) Previously treated 36 (16) 10 (22) 26 (15) Treatment setting at molecular diagnosis IQR: interquartile range.naïve Treatment 1 Other: includes not otherwise specified (NOS) or mixed non-small-cell lung 188 (84) 35 (78) 153 (85) cancer (NSCLC) Previously treated histologies. 2 The ECOG (Eastern 36 Cooperative (16) 10 (22) Group) performance Oncology 26 (15)status of interquartile IQR: 0 (fully active); 1 (restricted range. 1 Other: in physically includes strenuous specified not otherwise activity, but (NOS)ambulatory or mixedand able to carry non-small-cell outcancer lung (NSCLC) work of a light or 2sedentary histologies. The ECOG (Eastern nature); Cooperative Oncology 2 (ambulatory and capableGroup) of allperformance status self-care, but unableof 0to(fully carryactive); 1 (restricted in physically out any work activities;strenuous up and aboutactivity, more but ambulatory than and able 50% of waking to carry hours); out work 3 (capable of a limited of only light or self- sedentary nature); 2 (ambulatory and capable of all self-care, but unable to carry out any work activities; up and about more care; than 50%confined of wakingto hours); bed or chair moreof 3 (capable than only50% of waking limited hours); self-care; and 4to(completely confined bed or chairdisabled). 3 more than 50% Patients of waking who and hours); have4 not (completely for the last3 year smoked disabled). or more. Patients who have not smoked for the last year or more. 2.2.2.2. MolecularCharacterization Molecular Characterization Among the 191 genotyped patients with a complete DNA and RNA molecular characterization, Among the 191 genotyped patients with a complete DNA and RNA molecular characterization, a genetic alteration was recorded in 90% of patients (Table 2). Driver alterations were found in 129 a genetic alteration was recorded in 90% of patients (Table 2). Driver alterations were found in 129 patients (68%), with KRAS (n = 59, 31%), EGFR (n = 36, 19%), METΔex14 (n = 9, 5%), BRAF (n = 7, 4%), patients and ALK(68%), 4%) KRAS with (n = 7, (n = being the 31%), EGFR 59, commonly most (n = 36, detected. 19%), Other less-common (n = 9, 5%), MET∆ex14oncogenic driver genes(n = 7, BRAF 4%), and ALK identified (n =ERBB4, were 7, 4%) ERBB2, being the most commonly PIK3CA, NRAS, NTRK1,detected. OtherNon-driver and ROS1. less-common oncogenic alterations weredriver genes identified identified in 43were ERBB4, patients ERBB2, (22%), PIK3CA, whereas NRAS, NTRK1, 19 full-wild-type (WT) and ROS1. patients Non-driver (10%) alterations did not harbour any were identified in 43 patients alteration (Figure 2A). (22%), whereas 19 full-wild-type (WT) patients (10%) did not harbour any alteration (Figure 2A). (A) (B) (C) Full WT 3% KRAS 8% No driver 8% NTRK1 3% Full-WT ROS1 3% Full WT 14% 10% KRAS 31% ALK 8% No driver 22% KRAS 39% BRAF 3% No driver 17% EGFR (E19del/L858R) 48% NTRK1 <1% EGFR ROS1 <1% NRAS 1% NRAS 1% (E19del/L858R) METΔex14 15% PIK3CA 4% ERBB4 1% 16% ERBB2 1% ALK 4% PIK3CA 2% ALK 4% BRAF 5% EGFR (E19del/L858R) 11% BRAF 4% EGFR (not E19del/L858R) 3% METΔex14 2% EGFR (not E19del/L858R) 5% EGFR (not E19del/L858R) 4% METΔex14 5% (D) (E) (F) Full-WT 7% Full WT Full WT 9% 12% KRAS 18% No driver 16% KRAS 35% KRAS 37% NTRK1 1% No driver 26% ROS1 1% No driver 39% NRAS 1% EGFR ERBB2 3% (E19del/L858R) 30% ALK 7% EGFR (E19del/L858R) 5% NRAS 1% METΔex14 2% BRAF 4% ERBB4 2% BRAF 3% EGFR (E19del/L858R) 8% PIK3CA 3% EGFR (not E19del/L858R) 1% METΔex14 NRAS 2% ALK 2% ALK 2% EGFR (not E19del/L858R) 5% 7% ERBB4 3% ERBB2 2% BRAF 3% METΔex14 3% Figure 2. Genetic alterations identified. Pie charts depicting frequencies of driver alterations (EGFR, Figure BRAF, 2. Genetic KRAS, alterations MET∆ex14, ALK,identified. Pie charts ERBB2, ERBB4, depicting PIK3CA, frequencies NTRK1, ROS1, ofand driver alterations NRAS) from (EGFR, 191 patients BRAF, KRAS, MET∆ex14, ALK, ERBB2, ERBB4, PIK3CA, NTRK1, ROS1, and NRAS) from 191 patients included in the prospective cohort (expressed as the percentage of positive samples for each molecular included in the prospective cohort (expressed as the percentage of positive samples for each molecular alteration relative to the total number of cases with an informative molecular result). Non-driver alterations include mutations at AKT1, TP53, STK11, CTNNB1, SMAD4, DDR2, MAPK, METnon∆ex14, ALK mutations, and cases with concomitant mutations (see Table 2 for further details). Wild-type (full-WT) Cancers 2020, 12, x doi: 5 of 14 alteration Cancers relative to the total number of cases with an informative molecular result). Non-driver5 of 14 2020, 12, 1124 alterations include mutations at AKT1, TP53, STK11, CTNNB1, SMAD4, DDR2, MAPK, METnon∆ex14, ALK mutations, and cases with concomitant mutations (see Table 2 for further details). tumours refer Wild-type to those (full-WT) in which tumours no genetic refer to thosealteration in which was identified. no genetic (A) Overall alteration population, was identified. (A)(B) never Overall smokers, (C) former smokers, (D) smokers, (E) male, and (F) female. population, (B) never smokers, (C) former smokers, (D) smokers, (E) male, and (F) female. Sensitizing mutations (E19del and L858R) represented the most common EGFR subtype (86%), Sensitizing mutations (E19del and L858R) represented the most common EGFR subtype (86%), corresponding to 13% of all genotyped patients, whereas G12C was the most frequent KRAS variant corresponding to 13% of all genotyped patients, whereas G12C was the most frequent KRAS variant identified (39%), accounting for 12% of all genotyped patients. Co-mutations were found in 51% identified (39%), accounting for 12% of all genotyped patients. Co-mutations were found in 51% of of cases, with TP53 (38%), PIK3CA (9%), and STK11 (5%) being the most common. Co-mutations cases, with TP53 (38%), PIK3CA (9%), and STK11 (5%) being the most common. Co-mutations with with TP53 (32%) and STK11 (10%) were the most common in KRAS-mutant, whereas TP53 (39%) TP53 (32%) and STK11 (10%) were the most common in KRAS-mutant, whereas TP53 (39%) and and CTNNB1 (8%) were the most frequently described in EGFR-mutant patients. The most common CTNNB1 (8%) were the most frequently described in EGFR-mutant patients. The most common oncogenic driver mutations were mutually exclusive except for PIK3CA. A co-mutation plot from the oncogenic driver mutations were mutually exclusive except for PIK3CA. A co-mutation plot from the full sequencing of genotyped patients is shown in Figure 3. full sequencing of genotyped patients is shown in Figure 3. KRAS 30% EGFR 18% MET∆14 5% ALK 4% BRAF 4% PIK3CA 6% ERBB2 1% ERBB4 2% NRAS 1% ROS1 <1% NTRK1 <1% METnon∆14 <1% ALKmut 1% STK11 6% AKT1 <1% CTNNB1 3% SMAD4 2% DDR2 1% FGFR2 <1% MAPK <1% TP53 44% 0 Cases 191 Figure 3. Co-mutation Co-mutation plot plot of of genetic alterations identified. The The percentage percentage of of samples samples with an alteration detected detectedis is noted noted on right. on the the right. Samples Samples are displayed are displayed as columns as columns and to and arranged arranged to emphasize emphasize mutual exclusivity mutual exclusivity among mutations. among mutations. The The frequencies frequencies of of the the molecular molecular alterations alterations according according toto smoking smoking habits habits and gender are and gender are shown shown in Figure 2B–F. Oncogenic drivers were more commonly found in women in Figure 2B–F. Oncogenic drivers were more commonly found in women (77%), non-smokers (90%), (77%), non-smokers (90%), and former and former smokers smokers (69%), (69%), compared compared with menwithand mensmokers and smokers (61% (61% and 51%, and 51%, respectively). respectively). The The frequency of KRAS mutation was consistently related to smoking exposure frequency of KRAS mutation was consistently related to smoking exposure (8% vs. 39% vs. 35% in (8% vs. 39% vs. 35% in never-, former-, and current-smokers, respectively), while EGFR EGFR mutations were inversely related to smoking exposure exposure (53%(53% vs. 5%, respectively). vs. 15% vs. 5%, respectively). InIn our our series, series, two two out out of of five five women (35%) and two out of four non-smoking patients (53%) harboured an EGFR mutation. out of four non-smoking patients (53%) harboured an EGFR mutation. METΔex14 MET∆ex14 mutations were mutations more more were frequent in never-smokers frequent (15%(15% in never-smokers vs. 2%vs.in 2%patients withwith in patients anyanysmoking smoking history). TheThe history). subset of subset full-WT of full-WTtumours tumours(10%, n = 19) (10%, n =did 19)not diddisplay differences not display between differences the different between phenotypes the different studied, phenotypes except for studied, the non-smoking except group, ingroup, for the non-smoking which inthewhich percentage was slightly the percentage waslower slightly(3%). lower (3%). The The complete completemolecular molecularresults (DNA results (DNA andandRNA) RNA)were used were to decide used first-line to decide treatments first-line for most treatments for patients (172 out most patients (172of out 191,of 90%), 191,and 45 out 90%), and of45191 outpatients of 191 (24%) were patients selected (24%) were for targeted selected fortherapies targeted (Table 2). (Table therapies Women, 2).non-smokers, and ECOG Women, non-smokers, and0–2ECOG patients 0–2were significantly patients associated were significantly with targeted associated with therapies,therapies, targeted as well as asthewell presence EGFR, MET∆ex14, of presence as the of EGFR,NTRK1, BRAF,NTRK1, METΔex14, and ALK gene alterations. BRAF, and ALK gene alterations. Table 2. Genes with mutational or structural changes identified and targeted treatments received for patients with full genotyping (n = 191). Cancers 2020, 12, 1124 6 of 14 Table 2. Genes with mutational or structural changes identified and targeted treatments received for patients with full genotyping (n = 191). Molecular Alteration Genotyping, N. (%) 1 (n = 191) Targeted therapy, N. (%) 2 (n = 45) Driver oncogene 3 129 (68) 45 (35) EGFR 36 (19) 27 (75) EGFR E21 1 (<1) 0 (0) EGFR E19del 25 (13) 22 (88) EGFR E20 4 (2) 1 (25) EGFR L858R 6 (3) 5 (83) BRAF 7 (4) 3 (43) BRAF V600E 2 (1) 1 (50) BRAF non-V600E 5 (3) 2 (40) KRAS 59 (31) 0 (0) KRAS G12C 23 (12) 0 (0) KRAS G12V 14 (7) 0 (0) KRAS G12 (various) 17 (9) 0 (0) KRAS G13 2 (1) 0 (0) KRAS (other) 3 (2) 0 (0) MET∆ex14 9 (5) 7 (78) ALK 7 (4) 7 (100) ERBB2 (HER2) 2 (1) 0 (0) ERBB4 2 (1) 0 (0) PIK3CA 3 (2) 0 (0) NTRK1 1 (<1) 1 (100) ROS1 1 (<1) 0 (0) NRAS 2 (1) 0 (0) Non-Driver oncogene 4 43 (22) 0 (0) TP53 31 (16) 0 (0) STK11 3 (2) 0 (0) CTNNB1 1 (<1) 0 (0) >1 gene alteration 8 (4) 0 (0) Full-WT 19 (10) 0 (0) WT: wild-type 1 For percentages in this column, only patients with an evaluable molecular test are being considered, thus 191 is used as the denominator instead of the total 224 analysed. 2 Percentage among cases with detected mutation. 3 Genes considered driver oncogenes include EGFR, BRAF, KRAS, MET∆ex14, ALK, ERBB2, ERBB4, PIK3CA, ROS1, NTRK1, and NRAS. 4 Non-driver alterations included mutations at TP53, STK11, CTNNB1, AKT1, SMAD4, DDR2, FGFR2, MAPK, ALK mutations, and METnon∆ex14. 2.3. Treatment Strategies and Overall Survival (OS) Of the 36 patients carrying EGFR mutations, 27 (75%) were treated with a targeted agent, including first-generation (n = 14), second-generation (n = 8), and third-generation (n = 5) EGFR tyrosine-kinase inhibitors (TKIs). All patients with ALK rearrangements (n = 7) were treated with ALK inhibitors (four patients with crizotinib and three patients with new generation TKI). Three out of seven patients (43%) with BRAF mutations were treated with dabrafenib-trametinib (one BRAF-V600E and two non-V600E). Seven out of nine patients (78%) with the MET∆ex14 alteration were treated with MET inhibitors (four in a clinical trial and three with crizotinib as compassionate use). The only patient diagnosed with an NTRK1 gene rearrangement was treated with entrectinib in a compassionate use program. At the time of analysis, 87 patients were alive. The median OS of the whole cohort of 156 patients successfully evaluated through April 2019 was 0.93 years (95% confidence interval (CI) 0.63–1.24) (Figure 4A). Median OS was 1.40 years (95% CI 0.75–2.02) in the cohort of patients with an oncogenic driver identified (n = 107) and 0.61 years (95% CI 0.12–1.10) in the cohort of patients with no drivers identified (n = 49) (log-rank p = 0.008) (Figure 5A). Regarding the most common oncogenic drivers, median OS was significantly increased in EGFR-mutant patients (n = 35) compared with KRAS-mutant patients (n = 41) (median OS of 2.02 vs. 0.55 years, respectively, p = 0.001) (Figure 4B). Among MET∆ex14, the third most common oncogenic driver alteration, the median OS was 1.77 years (95% CI 0.94–2.60). Cancers Cancers2020, 2020,12, 12,xxdoi: doi: 77ofof14 14 METΔex14, METΔex14, Cancers the thethird 2020, 12, 1124 thirdmost mostcommon commononcogenic oncogenicdriver driveralteration, alteration,the themedian medianOS OSwas was1.77 1.77years years(95% (95% 7 of 14 CI CI0.94–2.60). 0.94–2.60). (A) (A) (B) (B) 1.0 1.0 1.0 1.0 EGFR (n=35) EGFR (n=35) EGFR EGFR (n=35) (n=35) KRAS KRAS KRAS(n=41) (n=41) (n=41) KRAS (n=41) 0.8 0.8 0.8 0.8 0.6 0.6 0.6 0.6 0.4 0.4 0.4 0.4 0.2 0.2 0.2 0.2 0.0 0.0 0.0 Log-rank 0.0 Log-rank p=0.001 p=0.001 Log-rank p=0.001 Log-rank p=0.001 0.0 0.0 0.5 0.5 1.0 1.0 1.5 1.5 2.0 2.0 2.5 2.5 0.0 0.0 0.5 0.5 1.0 1.0 1.5 1.5 2.0 2.0 2.5 2.5 Figure Figure4. Overall survival (OS). (A) Entire cohort of successfully successfully genotypedpatients patients diagnosed with Figure4.4.Overall Overallsurvival survival(OS). (OS).(A) (A)Entire Entirecohort cohortof of successfullygenotyped genotyped patientsdiagnosed diagnosedwith with metastatic metastaticdisease through April 2019 (n = 156). Median OS (95% CI): 0.93 years (0.63–1.24); (B) Patients metastatic disease disease through through April April 2019 2019 (n (n == 156). 156). Median Median OSOS (95% (95% CI): CI): 0.93 0.93 years years (0.63–1.24); (0.63–1.24); (B) (B) harbouring Patients EGFR (n = 35) or KRAS (n = 41) oncogenic driver mutations. For KRAS-mutated patients, Patientsharbouring harbouringEGFREGFR(n (n==35) 35)ororKRAS KRAS(n (n==41) 41)oncogenic oncogenicdriver drivermutations. mutations.For ForKRAS-mutated KRAS-mutated 54% received patients, patients,54% chemotherapy, 54%received 12% immunotherapy, receivedchemotherapy, chemotherapy,12% and 34% and 12%immunotherapy, immunotherapy,and best34% supportive 34%best care. For bestsupportive supportivecare. EGFR-mutated care.For ForEGFR- EGFR- patients, mutated 77% received mutatedpatients, patients,77% a matched 77%received therapy, receivedaamatched 6% matchedtherapy,chemotherapy, therapy,6% and 6%chemotherapy, 17% chemotherapy,and best and17% supportive 17%best care. Median bestsupportive supportive care. care. OSMedian (95%CI): Median OS OSEGFR 2.02EGFR (95%CI): (95%CI): years (1.86–2.19); EGFR2.022.02years KRAS 0.55KRAS years(1.86–2.19); (1.86–2.19); years (0.36–0.74), KRAS0.55 0.55years years(0.36–0.74), (0.36–0.74), = log-rank plog-rank 0.001. pVertical log-rank tick p==0.001. 0.001. marks are tick Vertical Vertical censoring tickmarks events. marksare arecensoring censoringevents. events. (A) (A) (B) (B) 1.0 1.0 1.0 1.0 Driver Driver(n=107) (n=107) Driver-Targeted Driver-Targeted(n=44) (n=44) No-Driver Driver-No Driver-Notargeted targeted(n=63) No-Driver(n=49) (n=49) (n=63) No-Driver No-Driver(n=49) (n=49) 0.8 0.8 0.8 0.8 0.6 0.6 0.6 0.6 0.4 0.4 0.4 0.4 0.2 0.2 0.2 0.2 0.0 Log-rank 0.0 Log-rankp=0.008 p=0.008 0.0 Log-rank 0.0 Log-rankp<0.001 p<0.001 0.0 0.0 0.5 0.5 1.0 1.0 1.5 1.5 2.0 2.0 2.5 2.5 0.0 0.0 0.5 0.5 1.0 1.0 1.5 1.5 2.0 2.0 2.5 2.5 Figure Figure5. 5.Comparison Figure Comparisonofof 5.Comparison ofoverall overallsurvival overall survival(OS). survival (OS). (A) (OS).(A) Cohort (A)Cohort Cohortof of patients ofpatients patients(n (n===156) (n 156) 156) with with with any any any oncogenic oncogenic oncogenic driver identified driver driveridentified (n identified(n = 107) (n==107) and 107)and patients andpatients with patientswith withnono driver nodriver driver(n(n = 49). (n==49). Median 49).Median MedianOS OS OS(95% (95% (95%CI): CI): driver, CI):driver, 1.40 driver,1.40 1.40 years years years (0.75–2.02); (0.75–2.02); no-driver, (0.75–2.02);no-driver, 0.61 no-driver,0.61 years 0.61years (0.12–1.10), years(0.12–1.10), log-rank (0.12–1.10),log-rank p = 0.008. log-rankpp==0.008. 0.008.(B)(B) Patients (B)Patients Patientswith with withno no driver nodriver driver(n (n = 49)49) (n==49) compared compared compared with withpatients with patients patients with withany with anyoncogenic any oncogenic driver oncogenicdriver treated drivertreated with treatedwith targeted(n(n withtargeted targeted (n=== 44) 44) 44) and and and patients patients patients with with with anyanyoncogenic any oncogenic oncogenic driver driver driver not not nottreated treatedwith treated withtargeted with targetedtherapy targeted therapy therapy(n (n== (n 63). =63). Allpatients 63).All All patients patients inin in thethe the driver-targeted driver-targeted driver-targeted group group group were werewere treated treated treated with with withaaagenotype-directed genotype-directed genotype-directedtherapy. therapy.No-driver No-driverand No-driver anddriver-no and driver-no driver-no targeted targeted targeted patients were patients patients were were treated treated treated with with with chemotherapy chemotherapy chemotherapy (51% (51% and (51%and 48%), 48%),immunotherapy and48%), immunotherapy(12% immunotherapy (12%andand11%), and 11%),and 11%), and and best best best supportive supportive supportive care care care (37% (37%(37% and andand 41%), 41%), 41%), respectively. respectively. Median respectively.MedianMedianOS OS (95%CI): OS(95% CI):driver-targeted CI): driver-targeted1.90 driver-targeted 1.90years 1.90 years(1.69–2.10); years (1.69–2.10); (1.69–2.10); driver-no driver-no driver-no targeted targeted targeted 0.59 0.590.59 years years years (0.39–0.79); (0.39–0.79); (0.39–0.79);no no driver nodriver 0.61 driver0.61 years years(0.12–1.10), 0.61years (0.12–1.10), log-rankppp<<< (0.12–1.10),log-rank log-rank 0.001. 0.001. 0.001. Vertical tick Vertical Vertical marks tick tick marks marks are areare censoring censoring censoring events. events. events. When When When outcomes outcomes outcomes were were compared compared were in in accordance in accordance compared with with accordance the with the the treatment treatment received treatment received (targeted/no- (targeted/no-targeted), received (targeted/no- targeted), thetargeted), median OSthe median thewas median OS 1.90OS was 1.90 was(95% years years 1.90 years (95% CI 1.69–2.10) (95% CI 1.69–2.10) CI 1.69–2.10) in patients in patientsinwith patientswith a with who a driver driver a driver who whoreceived received received aa a matched matched matchedgenotype-directed genotype-directed genotype-directed therapy, therapy,compared therapy, compared 0.59 with compared with with0.59 years 0.59 (95%years years (95% (95%CICI0.39–0.79) CI 0.39–0.79) 0.39–0.79) in patientsin patients inwith patients with with oncogenic oncogenic drivers but not amenable to any targeted therapy (log-rank p = 0.002). Outcomes drivers but not amenable to any targeted therapy (log-rank p = 0.002). Outcomes in this latter group oncogenic drivers but not amenable to any targeted therapy (log-rank p = 0.002). Outcomes in inthis thisare very similar to those from patients in whom no driver was identified (log-rank p = 0.559) (Figure 5B). In the multivariable analysis, two variables were found to be associated with OS differences. Patients receiving targeted therapy had increased OS compared with those who were not amenable Cancers 2020, 12, 1124 8 of 14 (hazard ratio (HR) 0.40, 95% CI 0.21–0.71, p = 0.002) and individuals with higher ECOG grades showed decreased OS values (HR 2.47, 95% CI 1.98–3.09, p < 0.001) (Table 3). Table 3. Multivariable Cox proportional hazard model for survival, accounting for confounding variables in patients with full genotyping and diagnosed with metastatic disease through April 2019 (n = 156). Multivariable Variable HR 95% CI p Age 1.00 0.98–1.02 0.851 Gender 0.98 0.61–1.60 0.951 ECOG 2.47 1.98–3.09 <0.001 Targeted Therapy 0.40 0.21–0.71 0.002 ECOG (Eastern Cooperative Oncology Group): performance status; HR: hazard ratio; CI: confidence interval. 3. Discussion Our study demonstrates the feasibility of routinely obtaining multiplex genomic and transcriptomic information useful to select therapies from patients with advanced non-squamous NSCLC. The successful implementation of molecular profiling of patients with lung cancer has already been reported (Table S1). However, outside the United States, genetic testing is variable and very little data on prospective genomic surveys in Europe and Spain have been reported. Our study is, to our knowledge, the first presentation of a full prospective real-world data genomic testing with related outcomes in a public healthcare hospital in Spain, and the first worldwide to integrate two parallel, multiplexed approaches based on DNA sequencing and direct digital detection of RNA with nCounter® technology. Using these two independent multiplexed DNA- and RNA-based platforms, we were able to screen for a total of 27 genes including tumour suppressor genes (TSG). This allowed us to provide a more precise characterization of the genetic alterations underlying non-squamous NSCLC, as compared with other studies in which data w only provided for ten [4] or six [13] of the most common or actionable driver genes (Table S1). In our study, we were able to identify oncogenic drivers in 68% of tumours, a percentage similar to the 64% recorded by The Lung Cancer Mutation Consortium (LCMC) in the USA [12], and higher than the 50% reported by the French Cooperative Thoracic Intergroup (IFCT) [13]. However, in our series, total gene alterations, comprising drivers and non-drivers, were identified in the vast majority of patients (90%). The broader molecular profiling gave us more precise information on the incidence of ‘real’ full-WT tumours, which comprised only 10% of the samples analysed, compared with the 15% previously reported [13]. Our screening failure rates were lower than previously reported, 7.6% in our series, compared with 26% and 35% in biopsies or cytologies for the LCMC [12], reinforcing their practicability in order to support individualised decisions in routine patient care. Although both techniques (NGS and nCounter) require minimum amounts of genetic material, our dropout percentage increased to 15% when considering those samples for which DNA or RNA testing could not be performed. Some groups, which also included combined DNA and RNA in their sequencing workflows, have reported lower invalid test rates of approximately 2%–3% [18,24]. However, these results do not correspond to the total screening failure rates, which should include samples with insufficient material, the major reason for failure in our series. In our study, the prevalence of oncogenic and TSG (TP53, STK11, or CTNNB1) was aligned with the frequencies reported in other Caucasian cohorts (Table S2). Similarly, the highest incidence of drivers was found in never (90%) and former smokers (69%), endorsing the distinctive molecular signature of NSCLC according to smoking history. Differences were observed for EGFR mutations, with an incidence (19%) significantly higher than the frequencies reported for other European and Spanish cohorts (11%–13%), but close to the series Cancers 2020, 12, 1124 9 of 14 collected in the USA (23%) (Table S2). However, variations in mutation prevalence may exist depending on the variant filtering and annotation pipeline performed, and they are thus not entirely valid for comparisons. Whereas in some series, adenocarcinoma roughly accounted for 70% of the cases [13,17], in our cohort, we only included non-squamous NSCLC histologies, with a vast majority (90%) of adenocarcinomas. Moreover, some groups considered all tested patients for rating [13], including those partially genotyped, whereas we only considered those patients with a complete informative result. Finally, some studies only consider the most common EGFR sensitizing mutations (E19del and L858R), while in our cohort, we included all EGFR mutations in exons 18–21. Indeed, when considering only E19del and L858R EGFR-sensitizing mutations, our rates of EGFR positivity decrease to 16% (Figure 2). Regarding the MET gene, we were unexpectedly able to identify 5% of patients carrying MET∆ex14 skipping mutations, which represented the third most common driver alteration in our cohort. This percentage is over the 2%–3% usually reported [14,16,18,25,26] (Table S2), and endorses the advantage of using an RNA-based detection technique for MET∆ex14 transcript identification [26]. Actually, commercially available DNA NGS panels have been reported to detect only 63% of literature-described MET∆ex14 mutations [27]. Interestingly, contrary to other reports [3], our cohort of MET∆ex14 patients was more frequently related to never-smokers (15%) and women (7%), highlighting the need for more data to properly characterize the phenotype related to this recently documented oncogenic driver. In our cohort of genotyped patients, the median OS of 0.93 years was inferior to OS reported in previous studies [4]. However, our results reflect more accurately the real-world setting, and include a significant number of patients with ECOG 3–4 (24%) and not eligible for any treatment (16%). The final goal of molecular testing is to personalise therapies and improve outcomes. Similar to previous reports [4,14], our molecular findings affected the treatment decision for 24% of the genotyped patients and 35% of patients with oncogenic drivers. Significant differences were observed when comparing median OS according to the presence or absence of a driver alteration (median OS 1.40 years vs. 0.61 years, respectively, log-rank p = 0.008). However, this survival advantage was not conditioned by the presence of a driver, but by the ability to receive a matched targeted therapy. Indeed, a significant increase in survival was obtained in patients with a driver who received a targeted therapy (1.90 years), while similar outcomes were observed in the cohort of oncogenic-driven patients not receiving any targeted therapy and those without drivers (median OS 0.59 years vs. 0.61 years, respectively) (Figure 5B). In the multivariable analysis, both treatment with targeted therapy and ECOG were independent variables of survival. Remarkably, the majority (78%) of patients with the MET∆ex14 received a targeted therapy with an MET inhibitor, achieving an outstanding median OS of 1.77 years and 12-month OS rate of 80%. These data compare favourably with the poor outcomes of this group of NSCLC patients [28,29], and emphasize the potential for targetability of this not-yet approved biomarker. Recently, the Food and Drug Administration (FDA) has granted a breakthrough therapy designation to capmatinib (INC280) as a first-line treatment for patients with MET∆ex14 mutated NSCLC [30], based on the primary findings of the phase II GEOMETRY mono-1 study (NCT02414139) [31]. The final outcomes of this study are eagerly awaited, and we anticipate MET∆ex14 to be soon added to the long list of actionable must-be-tested oncogenes in NSCLC. The median OS in our KRAS-mutated cohort was dismal (0.55 years) and might be attributed to the lack of effective targeted therapies. Although this outcome clearly differs from the 2.41 years previously reported in the LCMC cohort [4], it resembles the OS recently reported in a retrospective analysis of chemotherapy-treated patients with advanced NSCLC and KRAS activating mutations (8.8 months vs. 13.5 months, p = 0.038, in KRAS-mutated and –WT, respectively) [32]. Although oncogenic KRAS could serve as an excellent drug target opportunity owing to its high incidence in lung cancer, its direct inhibition has proven to be challenging owing to a lack of traditional small molecule binding pockets on the protein [33]. However, emerging direct inhibitors of the most prevalent G12C variant—overall, the second most frequent driver-subtype alteration in our cohort accounting for 12% Cancers 2020, 12, 1124 10 of 14 of all genotyped patients—have recently shown encouraging results [34], and further highlight the need for comprehensive panels to characterize lung cancer beyond the current biomarker recommendations. Our single-institution prospective report may be subject to several inherent limitations. Because it was conducted with a rather small sample size, the findings provided on outcomes should be considered cautiously. Moreover, treatment was not randomly assigned, but decided by the physician, and a selection bias cannot be excluded. Finally, we did not adjust for several known confounding variables that might impact the outcomes, such as smoking status, stage at diagnosis, or prior therapies. 4. Materials and Methods 4.1. Patients and Samples Between June 2017 and August 2019, all of the patients at our institution with newly diagnosed advanced non-squamous NSCLC, as well as those for whom molecular profiling had not been previously completed, were prospectively included in our study with prior full informed patient consent and approval from the Local Ethical Committee (Comité Ético de Investigación Clínica del Hospital Clínic de Barcelona, V2-05/11/2016). The study was conducted in accordance with the principles of the Declaration of Helsinki. The decision to recommend a targeted therapy to a patient harbouring an actionable driver gene was left to the treating physician, who used the genomic data to select an ad-hoc therapy following clinical guidelines or via inclusion in a clinical trial. Information on therapy and outcomes was collected regardless of whether the patient received targeted therapy. The data obtained were age; gender; histology; ECOG performance status; smoking history (never, former, or current smoker); TNM stage at diagnose, as defined by the seventh edition of the American Joint Committee on Cancer [35]; treatment setting at molecular assessment; the effect of the molecular results on the treatment decision with targeted therapy; and outcomes. 4.2. Molecular Analyses All molecular analyses were performed on a routine basis at our institution using OncomineTM Solid Tumour DNA Kit and an in-house nCounter-based assay [23]. FFPE sample slides (4 µm) were stained with haematoxylin and eosin and the percentage of tumour cell content was estimated by a pathologist. Samples with tumour cell content higher than 20% were considered assessable for molecular analysis. Macrodissection was performed in those samples with a tumour content superior to 50%. In the case where only one technique could be performed, NGS was prioritized to nCounter. Purified DNA (10 ng) was used as a template to generate genomic libraries using the Ion Torrent platform. The OncomineTM Solid Tumour DNA Kit (ThermoFisher Scientific, Life Technologies, CA, USA) interrogates somatic mutations on 22 genes, including EGFR, ALK, ERBB2, ERBB4, FGFR1, FGFR2, FGFR3, MET, DDR2, KRAS, PIK3CA, BRAF, AKT1, PTEN, NRAS, MAP2K1, STK11, NOTCH1, CTNNB1, SMAD4, FBXW7, and TP53. Libraries were sequenced using the Ion Personal Genome Machine platform (ThermoFisher Scientific) and analysed by the Ion Reporter Software (ThermoFisher Scientific) according to the manufacturer’s instructions. For the nCounter assay (Nanostring Technologies, WA, USA), total RNA (25–200 ng) was directly hybridized with a customized codeset designed to detect specific fusion transcripts (7 ALK, 10 ROS1, 6 RET, and 2 NTRK1) and MET∆ex14 mutations. All processes of hybridization, capture, clean-up, and digital data acquisition were performed as described in previous literature [23]. Regarding MET∆ex14 testing, log ratios were obtained dividing the normalized counts of the MET∆ex14 probe by the normalized counts for the MET WT probe. The cut-off for MET∆ex14 positivity was established as the average log ratio of the sample cohort plus two standard deviations. Those samples without enough tumour content for a complete nCounter analysis were tested for ALK, ROS1, and RET fusions using alternative recommended tests. ALK status was checked by IHC (anti-ALK rabbit monoclonal antibody D5F3, Ventana, Tuzson, AZ) and FISH (ZytoLight® SPEC ALK Cancers 2020, 12, 1124 11 of 14 Dual Color Break Apart Probe). ROS1 and RET were characterized by FISH (ZytoLight® SPEC ROS1 Dual Color Break Apart Probe and ZytoLight® SPEC RET Dual Color Break Apart Probe, respectively). 4.3. Molecular Subgroups Molecular alterations—EGFR, KRAS, NRAS, BRAF, MET∆ex14, ERBB2, ERBB4, PIK3CA—and gene fusions—ALK, ROS1, RET, NTRK1—were referred to as oncogenic drivers [36–38]. Among the aforementioned, mutations at EGFR, BRAF, and MET∆ex14, and gene fusions at ALK, ROS1, RET, and NTRK1 were considered actionable driver genes. Non-driver mutations included those reported in TP53, STK11, CTNNB1, AKT1, SMAD4, DDR2, FGFR2, MAPK, ALK, and METnon∆ex14. Tumours with an unchanged form were referred to as full WT tumours. Patients carrying co-mutations were considered once and categorized by the driver alteration. 4.4. Statistical Methods Descriptive statistics, including the median and range for continuous variables and the percentages and frequencies for categorical variables, were tabulated and presented. OS was defined as the time from date of the metastatic disease diagnosis to the date of last follow-up or death. OS probability curves were calculated for all patients diagnosed with metastatic disease until April 2019 and for groups of interest by the Kaplan–Meier method, and compared with a two-sided log-rank test. For OS analysis, the cohort of patients with no oncogenic driver included those with non-driver mutations and full-WT tumours. A p-value lower than 0.05 was considered for statistical significance. A multivariable Cox proportional hazard model was applied to adjust for potential confounders (clinical or molecular characteristics associated with OS). Adjusted HRs with 95% CI were calculated and the results were interpreted according to the following criteria: lack of association if HR = 1, increased risk of death if HR > 1, and lower risk of death if HR < 1. The SPSS v20.0 program was used for statistical analysis. 5. Conclusions Our study demonstrates that embedding two DNA and RNA molecular genomic platforms early in the workup of patients with advanced NSCLC provides a complete understanding of the molecular NSCLC subsets that can be used to select patients who are candidates for targeted therapeutic intervention, resulting in significantly improved outcomes. The present paper echoes the need for expanding genomic surveys worldwide to integrate precision medicine into health care in order to guarantee the best outcomes for patients with advanced lung cancer. Supplementary Materials: The following are available online at http://www.mdpi.com/2072-6694/12/5/1124/s1, Table S1: Summary of comprehensive genotyping studies in NSCLC; Table S2: Summary of the Incidence of Driver Mutations in NSCLC. Author Contributions: Conceptualization, E.M., C.T., E.C.-R., and N.R.; methodology, E.M., C.T., P.J., and M.Á.M.-V.; software, P.J., C.T., and M.-Á.M.-V.; formal analysis, E.M. and A.P. (acknowledgment); investigation, E.M., C.T., and N.R.; resources, M.S., I.V., M.G., S.A., A.M., P.J., and N.R.; data curation, E.M., C.T., E.C.-R., R.R., A.A., and A.R.; writing—original draft preparation, E.M., C.T., E.R-C., and N.R.; writing—review and editing, E.M., C.T., R.R., N.V., C.C., S.C., S.M., I.G.S., P.J., A.P., M.Á.M.-V., N.R., and K.J. (acknowledgment); visualization, E.M., C.T., E.C.-R., and N.R; supervision, M.Á.M.-V. and N.R.; project administration, E.M., A.A., M.R.-M., and N.R.; funding acquisition, N.R. All authors have read and agreed to the published version of the manuscript. Funding: This research was funded by Pfizer, grant number WI230526; Novartis, grant number CINC280AES01T; and Acción Estratégica en Salud, Instituto de Salud Carlos III, Spain, grant number FIS16/00890, PI N.Reguart. Acknowledgments: We would like to acknowledge Alejandro Pedromingo for his assistance with the statistics used in this report and Kyla M. Juett for improving the use of English in the manuscript. Conflicts of Interest: E.M. reports no conflicts of interest. C.T. reports grants and personal fees from Pfizer and Novartis during the conduct of the study; personal fees from Roche, Pfizer, Takeda, Novartis, Astrazeneca, MSD, and BMS outside the submitted work. A.P. reports grants and personal fees from Novartis, Pfizer, Lilly, Amgen; grants from Roche; personal fees from Nanostring Technologies, Oncolytics Biotech, Daiichi Sankyo, PUMA, BMS, Cancers 2020, 12, 1124 12 of 14 and Daiichi Sankyo, outside the submitted work. N.R. reports grants from Ministry of Health, Instituto de Salud Carlos III, Spain (FIS16/00890) during the conduct of the study, grants and personal fees from Pfizer and Novartis; personal fees from MSD, BMS, Roche, Boehringer Ingelheim, Guardant Health, Abbvie, Ipsen, Astrazeneca, Lilly, and Takeda outside the submitted work. 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