Ortiz-Cuaran, Mezquita et al. SUPPLEMENTARY ONLINE CONTENT METHODS 3 Sample collection 3 Assessment of response to therapy 3 ctDNA sequencing 3 In silico structural modeling 4 RESULTS 6 Patient’s characteristics 6 Patient samples 6 Modifiers of ctDNA detection 7 Concurrent mutations at baseline - cBioPortal 8 Genomic ctDNA profiling at disease progression 9 ctDNA analyses in non-V600E BRAF-mutant NSCLC patients 9 In silico structural modeling 11 SUPPLEMENTARY TABLES 13 Supplementary Table S1: Patients characteristics 13 Supplementary Table S2: Treatment characteristics. 14 Supplementary Table S3: Concurrent alterations found at baseline. 15 Supplementary Table S4: Concurrent alterations found at disease progression on BRAF- targeted therapies. 16 Supplementary Table S5: Uniprot and PDB codes used to analyze the impact of mutation at the protein level. 17 Supplementary Table S6. Genomic alterations reported in the tumor sample at diagnosis for patients presenting concurrent mutations in plasma at baseline. 18 Supplementary Table S7. Genomic alterations reported in the tumor sample at disease progression. 19 Supplementary Table S8: Concurrent ctDNA mutations found in BRAF non-V600E mutant NSCLC patients. 20 Supplementary Table S9: Genomic alterations found at baseline or at disease progression on BRAF-targeted therapies in this study and in other reports in BRAF-mutant melanoma, lung and colorectal cancer. 21 SUPPLEMENTARY FIGURES 23 Supplementary Figure S1: The InVisionFirst®-Lung assay. 23 Supplementary Figure S2: Study flowchart 24 Supplementary Figure S3: Modifiers of ctDNA and BRAF-mutant ctDNA detection in targeted therapy naïve BRAF-driven NSCLC patients. 25 1 Ortiz-Cuaran, Mezquita et al. Supplementary Figure S4: In silico modeling of the concurrent mutations found at baseline. 26 Supplementary Figure S5: Concurrent mutations in BRAF-targeted therapy-naïve, BRAFV600E-mutant NSCLC patients.. 27 Supplementary Figure S6: Longitudinal dynamics of BRAF-mutant ctDNA (%AF) in patient 33 28 Supplementary Figure S7: Impact of BRAF-mutant ctDNA detection at the first-radiological evaluation under BRAF-targeted therapy (n=18). A. P 29 Supplementary Figure S8: Metastatic pattern and BRAF-mutant ctDNA detection at disease progression on targeted therapy in BRAF-driven NSCLC patients. 30 Supplementary Figure S9: Impact of total mutant ctDNA detection at disease progression (PD, n=33) on overall survival (OS). 31 Supplementary Figure S10: Heterogeneous mechanisms of resistance to BRAF-targeted therapies. 32 2 Ortiz-Cuaran, Mezquita et al. METHODS Sample collection Samples collected at different time points were classified in three groups for analysis: 1) at time of diagnosis before the start of BRAF-targeted therapy, 2) at time of PD to therapy, with concurrent radiological confirmation of PD, and 3) under treatment response (radiological confirmation of objective response or stable disease). For the analysis of the sensitivity of the BRAF mutation detection in plasma at diagnosis, we included only the samples collected at time of diagnosis. For the analysis of the resistance mutations, we considered only the samples collected at confirmed progression to BRAF-targeted therapy, concurrent with radiological examination. Assessment of response to therapy Systemic response and central nervous system (CNS) response were assessed by body CT scan and by brain CT scan or MRI, respectively, every 2 to 3 months, according to the clinical practice of each center. Systemic and CNS objective response rate (ORR: complete or partial response) and disease control rate (DCR: complete response, partial response or stable disease) were evaluated by RECIST criteria v1.1 1 or per investigator assessment. ctDNA sequencing Blood (20 to 30mL) was collected in Ethylenediaminetetraacetic-acid (EDTA) or Cell- Free DNA BCT® Streck tubes. Blood in Cell-Free DNA BCT® Streck tubes was accepted for processing up to 7 days following collection based on in-house stability 3 Ortiz-Cuaran, Mezquita et al. data. Plasma was isolated using a Standard Operating Procedure and ctDNA quantified by digital PCR (BioRad QX200) targeting a 108bp region of the ribonuclease P/MRP subunit p30 (RPP30) gene. ctDNA analysis was centralized (Inivata, Cambridge, UK and Research Triangle Park, US) using InVisionFirst®-Lung, a tagged amplicon-based NGS CGP assay which identifies single nucleotide variants, insertions and deletions, copy number variations and fusions, with whole gene and gene hotspots across a 36-gene panel (Supplementary Figure S1). Briefly, NGS libraries were prepared from 2,000-16,000 amplifiable copies of DNA in a two-step PCR-amplification process, targeting the regions of interest followed by the incorporation of replicate and patient-specific barcodes and Illumina sequencing adapters. Sequencing was performed on the Illumina NextSeq 500 (300 cycle PE) with 5% PhiX. Sequencing files were analyzed using the Inivata Somatic Mutation Analysis (ISoMA) pipeline to identify SNVs, CNVs and indels. Fusions were not analyzed for this cohort of patients. For the ISoMA pipeline a minimum Phred quality score of 30 for each base was required for inclusion in the analytics. In silico structural modeling Aminoacid sequences of wild-type proteins were recovered from UNIPROT (http://www.uniprot.org/uniprot/, Supplementary Table S5). The search for homologous sequences and alignment were performed using @TOME-2 server 2, in cases where the 3D structure of the protein or the domain was not available in the Protein Data Bank (http://www.rcsb.org/). Final models were built using Modeller 7.0 3 and evaluated using the dynamic evolutionary trace as implemented in ViTOusing @TOME-2 comparative option (Supplementary Table S5). The 3D model or the 3D structure was carefully examined in order to determine the position of the mutation and 4 Ortiz-Cuaran, Mezquita et al. whether this mutation is predicted to have an influence on the folding of each protein. The mutation was also inspected to visualize the close contact surrounding in the side chain of the amino-acid. Graphical representations of structure-function models were done with Pymol. Two servers were used to evaluate the protein stability upon the mutation: The first one calculates the stability difference score between the wild-type and mutant protein 4. The server is available at http://structure.bioc.cam.ac.uk/sdm2. The second server, DynaMut, implements two distinct, well established normal mode approaches, which can be used to analyze and visualize protein dynamics by sampling conformations and assess the impact of mutations on protein dynamics and stability resulting from vibrational entropy changes. DynaMut integrates our graph-based signatures along with normal mode dynamics to generate a consensus prediction of the impact of a mutation on protein stability 5 http://biosig.unimelb.edu.au/dynamut/. 5 Ortiz-Cuaran, Mezquita et al. RESULTS Patient’s characteristics Median age at diagnosis was 66 years (range 35-83), gender was balanced, 34% of patients were never-smokers and the majority presented lung adenocarcinoma histology. Thirty-two patients had malignant effusion, and 9 had brain metastases at diagnosis. The median number of systemic therapies was two, with a maximum of eight. BRAF- targeted therapy was mainly received at second line (62.5% of patients). Two patients withdrawn treatment voluntarily and six patients stopped BRAF-targeted therapy due to undesirable side effects leading acute respiratory failure (4 on vemurafenib and 2 on dabrafenib plus trametinib). Thus, the potential impact of the concurrent alterations in the time and response to subsequent treatment could not be evaluated in these patients. Four out of the six non-V600E BRAF-mutant patients were treated with first- line platinum-based chemotherapy with median time of treatment of 2.5 months (range 2.2 – 2.8). Patient samples At baseline, thirty-two (68%) samples were collected at disease progression to first- line chemotherapy, immunotherapy or radiotherapy (one case: oligometastatic recurrence disease under dabrafenib, with a unique metastatic axillary lymph node that was resected, the patient then continued BRAF therapy combining dabrafenib + trametinib). Patients 35, 68 and 72 had plasma samples collected at two different time 6 Ortiz-Cuaran, Mezquita et al. points before targeted therapy, resulting in a total of 47 plasma samples collected at baseline. Modifiers of ctDNA detection We compared the prevalence of metastatic sites from patients who presented positive or undetectable levels of the BRAF mutation in plasma. At baseline, in patients with brain metastases, BRAF ctDNA or total mutant ctDNA were not optimally represented in plasma (Supplementary Fig. S3A). Yet, detection of a BRAF mutation in plasma was associated with the presence of liver metastasis: 7% in positive BRAF ctDNA vs 0% in undetectable BRAF ctDNA (Supplementary Fig. S3A). Similar results were observed when we evaluated the median AF detected in plasma (Supplementary Fig. S3B). Patients with detectable BRAF-mutant ctDNA presented mostly with systemic disease (Supplementary Fig. S3C) and more than two metastatic sites (Supplementary Fig. S3D), compared to patients with undetectable BRAF mutations in plasma. Baseline BRAF-TT samples were collected at disease progression on chemotherapy or immunotherapy (n=32) or before any treatment (n=17). Of note, pretreatment with chemotherapy or immunotherapy in BRAF-TT naïve patients did not have an impact on the detection of BRAF ctDNA, compared