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

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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 to patients who did not receive any treatment (Supplementary Fig. S3E).

Of note, although the number of patients in this subgroup analysis is small, we observed that alterations in the MAPK, PI3K, receptor tyrosine kinases or signal transducers most likely occurred in patients who received systemic treatment before

BRAF-targeted therapy, compared to treatment-naïve patients (Supplementary Fig.

S3F).

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At disease progression on BRAF-targeted therapy, detectable levels of circulating

BRAFV600E at PD were associated the presence of liver metastasis (Supplementary

Fig. S8A) and the number of metastatic sites (Supplementary Fig. S8B, P = 0.032).

However, the number of previous treatment lines (Supplementary Fig. S8C) did not have an impact on the detection of BRAF mutations in plasma at PD.

Concurrent mutations at baseline - cBioPortal

We compared the frequency of concurrent mutations found in BRAFV600E-mutant patients at baseline, with that reported in 16 studies in the cBioportal (n= 29 patients,

Supplementary Fig. S5). We identified a similar percentage of mutated samples for alterations in CDKN2A, IDH1, PIK3CA TP53 and U2AF1. However, concurrent alterations in AKT1, CTNNB1, ERBB2 and FGFR2 were only detected in our cohort.

Interestingly, our cohort presented 13% of STK11 mutant samples, whereas the samples reported in cBioportal did not harbor STK11 mutations. In the cBioportal, other oncogenic or likely oncogenic alterations (Chakravarty et al. 2017) were reported in genes that are not part of the InVisionFirst®-Lung assay: PBRM1 (7%) and SETD2

(38%). Mutations in TP63, TET1, CREBBP, SMAD4, ATM, KMT2C, ARID1A, RARA,

RBM10, KLF5, KMT2D and MGA, were detected in a one case-basis.

Longitudinal dynamics of BRAF –mutant ctDNA under BRAF-targeted therapy

Patient 33 (Supplementary Fig. S6) presented partial response on vemurafenib and presented disease progression in a lung lesion after 3.5 years under vemurafenib.

Subsequent treatment with chemotherapy showed a rapid tumor response at the first- scanographic evaluation. In this patient, longitudinal serial plasma genotyping revealed that circulating levels of BRAFV600E reflected the tumor responses during treatment as

8 Ortiz-Cuaran, Mezquita et al. they were undetectable during partial response, followed by a subsequent rise coincident with disease progression under vemurafenib.

Genomic ctDNA profiling at disease progression

Genomic ctDNA sequencing was performed on 46 plasma samples, collected from 35 patients, at PD on BRAF inhibitor (24 samples) or on the combination of dabrafenib and trametinib (22 samples). Plasma samples were collected at resistance to different lines of BRAF-targeted therapies in patients 55 and 57 and repeated plasma samples were collected at progression on dabrafenib and trametinib for patients 40, 58, 64, 65 and 69. Repeated plasma sampling for the latter patients was performed beyond disease progression, under combination treatment, with the aim of assessing the presence of genomic alterations associated with resistance to the treatment and evidence a potential further selection of resistant clones under treatment.

Further evidence of the clinical value of ctDNA was demonstrated in three patients during longitudinal follow-up under treatment, for whom we identified heterogeneous potential mechanisms of resistance to BRAF-targeted therapies (Supplementary Fig.

S10).

ctDNA analyses in non-V600E BRAF-mutant NSCLC patients

Concurrent alterations at baseline

In two non-V600E BRAF-mutant cases we evidenced the presence of concurrent

NRASQ61K (P38, BRAFG596R) and KRASA146V (P34, BRAFG466V) alterations (Fig. 1A).

The NRASQ61K mutation is reportedly oncogenic (Supplementary Table S3). The

KRASA146V alteration was also detected in the tumor biopsy (P34) and is reported to be

9 Ortiz-Cuaran, Mezquita et al. likely oncogenic, as described in the OncoKB database 6 (Supplementary Table S3).

In silico analyses revealed that this mutation potentially leads to a steric effect by blocking the GTP binding (Supplementary Table S3, Fig. S4E, Supplementary data).

Of note, the N581S, G466V and G596R mutations belong to the recently described

Class 3 of BRAF mutations (Yao et al. 2017). These mutants are sensitive to ERK- mediated feedback and their activation of signaling is RAS-dependent. Furthermore, dysregulation of signaling by these mutants in tumors requires coexistent mechanisms for maintaining RAS activation despite ERK-dependent feedback (Yao et al. 2017). We show that ctDNA profiling evidenced that two of the non-V600E patients presented concurrent mutations in KRAS (P34) and NRAS (P38) (Supplementary Table S8), similar to what was reported in melanoma (Yao et al. 2017). No mutations in receptor tyrosine kinases were detected in ctDNA for these patients. Although preliminary, these observations suggest that genomic analysis of liquid biopsies in non-V600E

NSCLC patients might be a complementary tool to tumor biopsy analyses to determine the effectors of RAS activation in these patients.

Concurrent alterations at disease progression

Genomic analysis of a plasma samples collected from a BRAF G469A mutant patient at PD after 1.9 months of vemurafenib treatment revealed the presence of MET amplification and MYC P246R and U2AF1 S34 alterations beyond the BRAFG469A mutation (Fig. 3A). Amplification of MET and U2AF1 S34 alteration are reported to be oncogenic and likely oncogenic, respectively (OncoKB 6. The molecular impact of the

MYC P246R alteration is currently unknown and in silico structural modeling was not performed since the 3D structure was unavailable (Supplementary Table S3)

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In silico structural modeling

Alterations detected at baseline

FGFR2 A553D (Fig. 1D): This mutation is predicted to have influence in protein stability since it is present in a -sheet in the N-lobe of the protein kinase fold. The side chain of the alanine (non-polar amino-acid) point in non-polar groove form with valine, leucine, methionine and tryptophane residues. The mutation of alanine into aspartate (negative charged amino-acid) is predicted to destabilized the 3D structure of the N-lobe of FGFR2.

PTEN R14K (Fig. 1E): This mutation is predicted to have no influence on the protein stability since this charged side chain is exposed to the solvent. Since this region/position is implicated in the de-ubiquitination of PTEN 7, this mutation potentially up-regulates the function of PTEN.

IDH1 I130T (Supplementary Fig. S4A): This mutation is predicted to have a small influence in protein stability since the environment of the side chain is constituted of non-polar and polar amino-acids (valine, isoleucine, tryptophane, cysteine and glutamine). This residue is located in an allosteric pocket, has a consequence this potentially results in up- or down-regulated the activity of IDH1.

NTRK3 P612T (Supplementary Fig. S4B): This mutation is predicted to have no influence in protein stability since it is present in a loop rich in glycine (flexible residue) in the N-lobe of the protein kinase fold.

Alterations detected at disease progression

PPP2R1A D265G (Fig. 3C, left): This mutation is predicted to have a major effect in protein stability. The aspartate 265 is located onto a -helix and it is implicated in two

11 Ortiz-Cuaran, Mezquita et al. salt bridges with lysines 265 and 304. The mutation of aspartate (negative charged amino-acid) to glycine potentially destabilizes the fold of PPP2R1A and down regulated it is activity.

U2AF1 R156H (Fig. 3C, right): This mutation is predicted to have a strong influence in protein stability since it is present onto a -helix implicated in the zinc finger fold.

The arginine156 is implicated in a salt bridge with glutamate159, which stabilizes the

-helix and the zinc finger. The mutation to a histidine residue (polar amino-acid), potentially destabilizes the structure in this region. As a consequence, the binding of

RNA to U2AF1 is disrupted.

GNA11 R214S (Supplementary Fig. S10A): This mutation is predicted to have a minor effect on protein stability. The ariginine214 is implicated in a salt bridge with a glutamate81 of RGS8 protein (Uniprot P57771), which stabilizes this complex. The mutation of arginine in serine (small polar amino-acid) disrupts this salt bridge, which potentially results in the interference of the interaction with RGS8.

NFE2L2 31-32: GV/X (Supplementary Fig. S10B): This mutation potentially has a minor effect in protein stability. The glycine/valine mutation is located before a -helix implicated in the interaction with Keap1 protein (Uniprot Q9Z2X8). The mutation most likely leads to a disrupted the interaction with Keap1 and promotes NFE2L2 accumulation in the nucleus.

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SUPPLEMENTARY TABLES

Supplementary Table S1: Patients characteristics

Table 1. Patient characteristics

Total number of patients included 78 Age at diagnosis 66 (35-83) n % Sex Male 42 53,8 Female 36 46,2 Smoking status Never 34 43,6 Former 35 44,9 Current 8 10,3 Unknown 1 1,3 Histology Adenocarcinoma 74 94,9 Squamous 1 1,3 NSCLC (subtype not specified) 1 1,3 Other 2 2,6 Metastatic sites at diagnosis Malignant effusion 32 41,0 Brain metastases 8 10,3 Unknown 2 2,6 BRAF mutation in tissue V600E 72 92,3 Non-V600E (G469A, G466V, 6 7,7 N581S, G596R, D594M, K601E)

Stage NA for 30 patients (AcSé)

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Supplementary Table S2: Treatment characteristics.

Table 2. Treatment characteristics

n % Treatment overview Vemurafenib 36 46,2 Dabrafenib 1 1,3 Dabrafenib + Trametinib 26 33,3 BRAFi monotherapy, then Dabrafenib + Trametinib 5 6,4 Other regimens 4 5,1 Patients not treated with BRAF-TT : 5, 22, 34, 37, 38, 43 Not treated with BRAF-TT 6 7,7 BRAF targeted therapy - treatment line (n=72) * First line 17 23,6 Second line 45 62,5 Further lines 10 13,9 * BRAF-targeted therapy received for the first time BRAFi: BRAF inhibitor BRAF-TT: BRAF-targeted therapy

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Supplementary Table S3: Concurrent alterations found at baseline. ND: not detected; GOF: gain-of-function; SOF: switch-of- function.

BRAF BRAF Patient Related Protein AF OncoKB Predicted structural impact (in silico) mutation mutation Gene ID pathway change (%) (August 2019) in tissue in plasma Location Effect 79 ND MAPK KRAS G12C 3.01 Oncogenic, GOF . . 57 + PIK3CA H1047R 12.97 Oncogenic, GOF . . 3 + PIK3CA E545K 0.65 Oncogenic, GOF . . PI3K 48 + AKT1 E17K 1.18 Oncogenic, GOF . . 79 ND PTEN R14K 0.40 Unknown De-ubiquitination region Up-regulation 18 + NTRK3 P612T 1.32 Unknown Glycine-rich loop No effect V600E Protein 47 + FGFR2 A554D 0.41 Unknown N-lobe of the KD 3D destabilization kinases 45 ND ERBB2 amplification Oncogenic, GOF . . 68 + IDH1 R132C 0.26 Oncogenic, SOF . . 2 + Signal IDH1 I130T 0.26 Unknown Allosteric pocket Up or downregulation 12 + transduction CTNNB1 G34V 0.10 Likely oncogenic, GOF . . 64 + U2AF1 Q157P 1.50 Likely oncogenic, SOF . . 38 + NRAS Q61K 19.13 Oncogenic, GOF . . Non V600E MAPK 34 + KRAS A146V 1.37 Likely oncogenic, GOF . .

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Supplementary Table S4: Concurrent alterations found at disease progression on BRAF-targeted therapies. V: vemurafenib; D: dabrafenib; T: trametinib; ND: not detected; GOF: gain-of-function.

BRAF BRAF Predicted structural impact mutation in Patient mutation in Related Protein OncoKB Therapy Gene AF (%) (in silico) tissue at ID plasma at pathway change (August 2019) baseline PD Location Effect 17 V ND KRAS G12C 0.33 Oncogenic, GOF . 68 D + T + KRAS Q61R 1.41 Oncogenic, GOF . 68 D + T + KRAS G12V 0.16 Oncogenic, GOF . 57 D + T + KRAS G12V 0.06 Oncogenic, GOF . 24 V + NRAS Q61K 5.11 Oncogenic, GOF . 57 D + T + MAPK NRAS Q61R 0.49 Oncogenic, GOF . 35 V + MAP2K1 C121S 3.18 Likely Oncogenic, GOF . 24 V + GNAS R201C 0.65 Oncogenic, GOF . 52 D ND GNAS R201H 4.12 Oncogenic, GOF . G-protein alpha Interaction with 26 V ND GNA11 R214S 1.12 Unknown subunit RGS8 1 V + PIK3CA H1047R 0.76 Oncogenic, GOF . 3 V + PIK3CA E545K 10.04 Oncogenic, GOF . V600E 57 V + PI3KCA H1047R 1.93 Oncogenic, GOF . PI3K 57 D + T + PI3KCA H1047R 2.81 Oncogenic, GOF . Protein down- 55 D + T + PPP2R1A D265G 3.00 Unknown Antigen binding regulation Disrupted Keap1 binding 35 V + NFE2L2 31-32:GV/X 0.08 Unknown interaction with site Keap1 Binding of RNA to 68 D + T + U2AF1 R156H 1.46 Unknown Zinc finger 2 Signal U2AF1 disrupted 64 D + T + transduction U2AF1 Q157P 1.66 Likely Oncogenic, SOF . 27 V ND IDH1 R132H 0.22 Oncogenic, SOF . 68 D + T + IDH1 R132C 0.30 Oncogenic, SOF . 59 D + CTNNB1 S37C 0.14 Likely Oncogenic, GOF . 58 D + T + CTNNB1 S45P 0.08 Likely Oncogenic, GOF . 30 V + Protein kinases MET amplification Oncogenic, GOF . Non V600E 30 V + Signal MYC P246R 0.25 Unknown No 3D structure available 30 V + transduction U2AF1 S34F 14.98 Likely Oncogenic, GOF .

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Supplementary Table S5: Uniprot and PDB codes used to analyze the impact of mutation at the protein level.

PDB code to Protein Uniprot code PDB code generate model abbreviation (species) PTEN P60484 1D5R / NTRK3 Q16288 4YMJ / FGFR2 P21802 2PVF / IDH1 O75874 3INM / CTNNB1 P35222 1P22 / U2AF1 Q01081 / 4YH8 (yeast) KRAS P01116 4LPK / MAP2K1 Q02750 3EQD / GNA11 P29992 5DO9 / PPP2R1A P30153 1B3U / NFE2L2 Q16236 / 3WN7 (mouse)

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Supplementary Table S6. Genomic alterations reported in the tumor sample at diagnosis for patients presenting concurrent mutations in plasma at baseline. For concordance in tissue: detected (green), not detected (red), not tested (blue). Time lag represents the number of weeks between the diagnostic tumor sample and the plasma sample at baseline. Systemic progression: progression in more than one metastatic site.

Genomic alterations in plasma Other Case ID Metastatic genomic (Facchinetti Time Patient pattern at alterations et al. Eur J lag BRAF KRAS PIK3CA IDH1 PTEN CCTNB1 FGFR2 ERBB2 AKT1 U2AF1 TP53 CDKN2A STK11 found in

ID plasma Lung Bone Brain Liver Others Pleural Cancer. (weeks) Adrenal tumor tissue

collection Nodal (M1) 2020) 2 13 Systemic 1 1 1 0 0 0 0 2 p.V600E p.I130T p.L194R None 3 48 Systemic 1 1 0 1 0 1 1 0 p.V600E p.E545K p.I251N None 12 38 Localized 0 1 0 0 0 0 0 0 p.V600E p.G34V None 18 29 Systemic 0 1 1 1 0 0 1 2 p.V600E p.A554D None 47 37 Localized 0 0 0 1 0 0 0 0 p.V600E amp p.W91* None 48 111 Systemic 0 0 1 0 0 1 0 0 p.V600E p.E17K p.S241F;p.V143M p.E88Q None 57 16 Localized 0 0 1 0 0 0 0 0 p.V600E p.H1047R p.E286K FLT3 mut 64 34 Localized 0 0 0 1 0 0 0 0 p.V600E p.Q157P p.Q192* None 68 MR372 0 Localized 0 0 0 1 0 0 0 0 Not found p.R132C TP53 p.H168N 68 16 Localized 0 0 0 1 0 0 0 0 p.V600E p.R132C p.R280I TP53 p.H168N 79 19 Systemic 0 0 0 1 1 0 0 0 Not found p.G12C p.R14K p.104:R/X* None

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Supplementary Table S7. Genomic alterations reported in the tumor sample at disease progression. For concordance in tissue: detected (green), not detected (red). Time lag represents the number of weeks between the diagnostic tumor sample and the plasma sample at baseline. Systemic progression: progression in more than one metastatic site. Genomic alterations in tumor biopsies were assessed by targeted NGS and whole-exome sequencing.

Genomic alterations in plasma Case ID Metastatic (Facchinetti Tissue Other genomic Patient Tissue PD date Time lag pattern at et al. Eur J available Therapy BRAF KRAS NRAS MAP2K1 GNAS PPP2R1A U2AF1 IDH1 CCTNB1 ERBB2 AKT1 TP53 alterations found

ID date (plasma) (weeks) plasma Lung Bone Brain Liver Others Pleural

Cancer. at PD Adrenal in tumor tissue

collection Nodal (M1)

2020) Pericardium 53 NA Yes 21/12/2017 01/02/2018 6 Localiized 0 0 0 0 0 0 0 0 1 D + T Not found None BRAF V600E MEK1 K57N 55 MR113 Yes 06/09/2016 28/06/2016 -10 Systemic 1 1 0 1 1 0 0 0 0 D + T Not found Not found FANCD2 Q706* RARA T285I 58 MR326 Yes 31/08/2018 30/05/2018 -13 Systemic 0 0 0 0 1 0 0 1 0 D + T p.V600E p.S45P None 59 MR187 Yes 12/04/2017 12/04/2017 0 Systemic 1 1 1 0 0 0 0 0 1 D p.V600E p.S37C None 64 MR320 Yes 10/04/2018 10/04/2018 0 Systemic 0 0 0 1 1 1 0 0 2 D + T p.V600E p.Q157P p.Q192* None

BRAF V600E AKT1 E17K 65 MR279 Yes 17/01/2018 24/01/2018 1 Localiized 1 0 0 0 0 0 0 0 0 D + T Not found Not found Not found Not found ERBB4 S303Y SETD2 G1081Vfs. NRAS Q61R

BRAF V600E KRAS Q61R TP53 R280I p.Q61R KMT2E L1610Ffs 68 MR372 Yes 05/11/2018 31/10/2018 -1 Systemic 0 0 0 0 1 0 1 0 0 D + T p.V600E p.R132C p.R156H p.R280I p.G12V MPL Q247Sfs ZFHX3 S2515* MED12 Q2160* ARID1A F1809fs

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Supplementary Table S8: Concurrent ctDNA mutations found in BRAF non- V600E mutant NSCLC patients. *: Mutations in the D594 codon have been listed as Class 3 (Yao et al. 2017). However, the D594M specifically was not listed in this report.

BRAF Plasma sample Patient mutation in Class (Yao ID tissue at et al. 2017) diagnosis Timepoint BRAF KRAS NRAS MET MYC U2AF1 TP53 STK11

22 N581S 3 Baseline Not found p.323:L/X

34 G466V 3 Baseline p.G466V p.A146V p.K292* p.E165*

37 K601E 2 Baseline p.K601E p.R248W p.M237I ; 38 G596R 3 Baseline p.G596R p.Q61K p.R213R 43 D594M 3* Baseline Not found p.R283R p.G163V

30 G469A 2 PD p.G469A MET amp p.P246R p.S34F p.L194R

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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. Alterations at baseline (black), at PD on BRAF-inhibitor monotherapy (blue), at PD on combined BRAF/MEK therapy (orange) and at PD on BRAF/MEK/EGFR combination therapy (purple). Only the genes covered by the InVisionFirst panel were included. (*): role in resistance to BRAF-TT functionally validated.

Cancer Subject of BRAF REF Timepoint MET MYC

type study mutant IDH1 AKT1 PTEN GNAS KRAS NRAS U2AF1 ERBB2 FGFR2 GNA11 NTRK3 PIK3CA NFE2L2 MAP2K1 CTNNB1 PPP2R1A I130T H1047R Baseline E17K G34V amp A554D G12C P612T R14K Q157P R132C E545K V600E G12V H1047R Ortiz- S45P R201H R132C 31- Q157P PD R214S Q61R C121S Q61K H1047R D265G Cuaran Patients S37C R201C R132H 32:GV/X R156H G12C E545K et al. G466V Baseline A146V G596R Baseline Q61K LUNG G469A PD amp P246R S34F Baseline E17K 25 Patients V600E PD E17K(*) G12A 26 Patient V600E PD G12D 27 Patient V600E PD G12V E203K Q61K 33 Patients V600E PD Q79K K57E (*) G13R 34 Q61K(*) 41 Patient V600E PD C121S (*) Q61K/R/L D350G (*) E17K(*) G12C C121S 28 Patients V600E PD G13R E545G (*) Del (*) Q79K(*) Q61H K57N G12D E545K (*) V60E(*)

MELANOMA R151H C121S(*) Q61R 42 Patients V600E PD R453W Del (*) P124S(*) T58I W66* G128V(*) C121S(*) 43 Patients V600E PD G128V(*)

21 Ortiz-Cuaran, Mezquita et al.

Cancer Subject of BRAF REF Timepoint MET MYC

type study mutant IDH1 AKT1 PTEN GNAS KRAS NRAS U2AF1 ERBB2 FGFR2 GNA11 NTRK3 PIK3CA NFE2L2 MAP2K1 CTNNB1 PPP2R1A Cell lines and 47 V600E PD Del (*) patients Q61K(*) 35 Cell lines V600E Resistance K59del(*) A146T(*) V600E Baseline S45del 36 Patients V600E PD Q61K Del H1047R 37 Patient V600E PD Q61K/R (*) Q61K 38 Patients V600E/K PD E17K R132C G128D P169S G12D R201H, MELANOMA L159R(#) R132C Q56P 40 PDX V600E PD S45F D243N(#) amp amp Q61K H1047Y Del E120K(#) A51V(#) K57E S139S(#) Q61K (*) F271V 29 Patients V600E PD Q61H F129L G12R/C Del (*) Baseline YES YES YES YES 48 Patients V600E PD YES YES 51 Cell lines V600E NA Exp (*) G12D/C/ 31 Patients V600E PD V/R Q61L G13D Cell lines V600E PD G13D (*) 32 Patients V600E PD F53L (*)

COLORECTAL G12D/C K57N 30 Patients V600E PD Q61K Q61H/L K57E

22 Ortiz-Cuaran, Mezquita et al.

SUPPLEMENTARY FIGURES

Supplementary Figure S1: The InVisionFirst®-Lung assay. The BRAF regions covered are: R462-K475 (exon 11) and F594-S605 (exon 15).

Figure'1S!

23 Ortiz-Cuaran, Mezquita et al.

Supplementary Figure S2: Study flowchart

78 BRAF - mutant advanced NSCLC patients N = 30, AcSé vemurafenib N = 15, Centre Léon Bérard and LIBIL study centers N = 26, Gustave Roussy N = 7, Toulouse University Hospital

N= 232 samples collected prospectively

Therapy naïve, N = 15 Before BRAF-targeted therapy, N = 47 BRAF-targeted therapy naïve, N = 32

At first radiographic evaluation, N = 19

During treatment follow-up, N = 115

Vemurafenib, N = 22 At disease Dabrafenib N = 2 progression, N = 46 Dabrafenib + Trametinib N = 22

24 Ortiz-Cuaran, Mezquita et al.

Supplementary Figure S3: Modifiers of ctDNA and BRAF-mutant ctDNA detection in targeted therapy naïve BRAF-driven NSCLC patients. Detection of ctDNA, as defined as the total median allelic fraction in plasma (A), and BRAF mutation in ctDNA (B), according to the metastatic sites. Percentage of patients with undetectable (UND) or positive levels (+) of BRAF-mutant ctDNA according to the number of metastatic sites (C), the metastatic pattern (D) and prior lines of therapy (E). (F) Frequency of concurrent alterations in ctDNA in patients at baseline. Treatment naïve (blue, n=15); Pre-treated with systemic therapy (red, n=32)

A B

- + - + 1 2

C Metastatic pattern D No. Metastatic sites E Previous treatment 140 LOCALIZED 140 120 ≤ 2 > 2 Naïve Treated SYSTEMIC P value 0.0061 120 120 P value 0.8790 P value summary ** * 100 ** P value summary ns 100 100 One- or two-sided Two-sided 44 80 80 33 80 P value 0.0138 57 P60 value summary69* 68 60 54 60 % of patients % of patients 40 40 63 % of patients 40 66 45 20 20 43 20 31 32 0 0 0 UND + 20200507_Muts fccieUND Baseline+ Chemo vs NaiveUND +

F 0.8 0.7 Treatment naïve 0.6 0.5 Pre-treated 0.4 0.2

0.1

Fraction of positive samples 0.0 IDH1 TP53 AKT1 PTEN KRAS NRAS STK11 U2AF1 FGFR2 NTRK3 ERBB2 PIK3CA CCTNB1 CDKN2A MAPK PI3K RTKs Signal pathway pathway transducers

25 Ortiz-Cuaran, Mezquita et al.

Supplementary Figure S4: In silico modeling of the concurrent mutations found at baseline. A. Schematic protein domains of IDH1 showing location of I130T substitution in the catalytic domain. Representation of the catalytic domain in 3D (gray cartoon), NADPH (yellow), I130T mutation (blue) and amino-acid side-chain surrounding the mutation (gray) with a zoom at the mutation site. B. Schematic protein domains of NTRK3 showing location of P612T substitution in the kinase domain. Representation of the kinase domain in 3D (gray cartoon), P612T mutation (blue) and amino-acid side- chain surrounding the mutation (gray) with a zoom at the mutation site.

A I130T

IDH1 Catalytic unit 1 414

IDH1 WT IDH1 I130T

B P612T L Ig- NTRK3 R Ig-like like Kinase R 1 839 NTRK3 WT NTRK3 P612T

26 Ortiz-Cuaran, Mezquita et al.

Supplementary Figure S5: Concurrent mutations in BRAF-targeted therapy- naïve, BRAFV600E-mutant NSCLC patients. Mutation frequency of selected genes in patients from our cohort (BRAF ctDNA positive, n=31, red) and the cBioportal database (n= 29, blue).

20200510_Muts fccie Baseline vs cBioportal 16 studies

60 50 cBioportal 40 Our cohort 30 20

15

10

5

Fraction of positive samples 0 IDH1 TP53 AKT1 PTEN STK11 U2AF1 FGFR2 NTRK1 ERBB2 PIK3CA CTNNB1 MAP2K1 CDKN2A

27 Ortiz-Cuaran, Mezquita et al.

Supplementary Figure S6: Longitudinal dynamics of BRAF-mutant ctDNA (%AF) in patient 33 during therapy with vemurafenib and subsequent chemotherapy. PR: partial response; PD: progressive disease.

28 Ortiz-Cuaran, Mezquita et al.

Supplementary Figure S7: Impact of BRAF-mutant ctDNA detection at the first- radiological evaluation under BRAF-targeted therapy (n=18). A. Progression-free survival (PFS). B. Overall survival (OS). Disease progression was assessed by RECIST (n=14) or by investigator’s criteria (n=4).

A CLEARENCE BRAF ctDNA PFS

1.0 Median (95%CI) 5.7 (3.6-8.7) 0.9 10.2 (3.4-NE) 0.8 Logrank P-value: 0.141 2.31 [ 95% CI 0.73-7.29] 0.7 0.6 0.5 0.4 0.3 BRAF ctDNA undetectable 0.2 0.1 BRAF ctDNA positive Progression Survival probability 0 0 3 6 9 12 15 18 21 24 Months from treatment initiation POS 6 3 0 UND 12 7 5 2 0

B CLEARENCE BRAF ctDNA OS

1.0 Median (95%CI) 0.9 11.0 (0.7-19.0) NE (12.1-NE) 0.8 Logrank P-value: 0.021 4.37 [ 95% CI 1.12-17.00] 0.7

0.6 BRAF ctDNA undetectable 0.5 0.4 0.3

Survival probability 0.2

0.1 BRAF ctDNA positive 0 0 3 6 9 12 15 18 21 24 27 30 33 36 Months from treatment initiation POS 6 4 2 1 0 UND 12 11 10 7 2 0

29 Ortiz-Cuaran, Mezquita et al.

Supplementary Figure S8: Metastatic pattern and BRAF-mutant ctDNA detection at disease progression on targeted therapy in BRAF-driven NSCLC patients. Detection of BRAF mutation in ctDNA (A) according to the metastatic sites. BRAF allelic fraction (%AF) according to the number of metastatic sites (B) and the number of prior lines of therapy (C). * : P < 0.05.

A 2.0

17 1.5 18 Pleural 26 Lung 57 13 Nodal Mann Whitney test 1.0 20 Brain P value 0.0323 Exact or approximate P value? Exact 18 Bone P value summary * 36 Liver Significantly different (P < 0.05)? Yes 29 0.5 Adrenal 18 20 21

Fraction of metastatic sites metastatic of Fraction 13 0.0 4 UND POS 20190715_No. Metastatic sites at PD (including chemo and IO) B C 30 *

20

10 BRAF V600E %AF 0 ≤ 2 > 2 Number of metastatic sites

30 Ortiz-Cuaran, Mezquita et al.

Supplementary Figure S9: Impact of total mutant ctDNA detection at disease progression (PD, n=33) on overall survival (OS).

OS - TOTAL ctDNA at PD

1.0 BRAF ctDNA undetectable 0.9 0.8 0.7 Median (95%CI) NE (16.8-NE) 0.6 19.0 (10.2-64.7) 0.5 Logrank P-value: 0.023 0.14 [ 95% CI 0.02-1.03] 0.4 0.3

Survival probability 0.2 0.1 BRAF ctDNA positive 0

31 Ortiz-Cuaran, Mezquita et al.

Supplementary Figure S10: Heterogeneous mechanisms of resistance to BRAF- targeted therapies. A. Frequency of the observed the genomic alterations in ctDNA at disease progression. Longitudinal dynamics of BRAFV600E and potential alterations associated with resistance in ctDNA (%AF) in patient 35 (B) and patient 57 (C). PR: partial response; PD: progressive disease.

A BRAF inhibitor BRAF/MEK inhibitor monotherapy Combination therapy

KRAS NRAS KRAS 4% 4% MAP2K1 11% 4% NRAS GNAS 6% 7%

PIK3CA GNA11 6% 4% Undetected Undetected 41% 50% PPP2R1A 6% PIK3CA 11%

NFE2L2 U2AF1 4% 12% U2AF1 CTNNB1 IDH1 4% CTNNB1 IDH1 4% 4% 12% 6% B C

32 Ortiz-Cuaran, Mezquita et al.

Supplementary Figure S11: Concurrent alterations found at disease progression. A. Longitudinal dynamics of BRAFV600E, PPP2R1AD265G and TP53 splice-site mutation in ctDNA (%AF) in patient 55 during therapy with dabrafenib + trametinib. PD: progressive disease; NT: not treated. Graphic gene-level representations and structure-function models of novel mutants in NFE2L231-32:GV (B) and GNA11R214S (C), showing locations of these mutations at the protein level. B. Representation of the NFE2L2 domain in 3D (gray cartoon) in interaction Keap1 in 3D (pink), GV31-32/X mutation (blue) with a zoom at the mutation site. C. Representation of the GTPase domain in 3D (gray cartoon) in interaction with RGS8 protein in 3D (pink), R214S mutation (blue) with a zoom at the mutation site.

A

C p.31-32:GV R214S

bZIP NFE2L2 MAF D GNA11 H GTPase 1 605 1 359

GNA11 WT GNA11 R214S NFE2L2 31-32GV/X

33 Ortiz-Cuaran, Mezquita et al.

REFERENCES TO SUPPLEMENTARY DATA

1. Schwartz LH, Litière S, De Vries E, et al. RECIST 1.1 - Update and clarification: From the RECIST committee. Eur J Cancer. 2016;62:132-137. doi:10.1016/j.ejca.2016.03.081 2. Pons J-L, Labesse G. @TOME-2: a new pipeline for comparative modeling of protein-ligand complexes. Nucleic Acids Res. 2009;37(Web Server issue):W485-91. doi:10.1093/nar/gkp368 3. Webb B, Sali A. Protein Structure Modeling with MODELLER. Methods Mol Biol. 2017;1654:39-54. doi:10.1007/978-1-4939-7231-9_4 4. Pandurangan AP, Ochoa-Montano B, Ascher DB, Blundell TL. SDM: a server for predicting effects of mutations on protein stability. Nucleic Acids Res. 2017;45(W1):W229-W235. doi:10.1093/nar/gkx439 5. Rodrigues CH, Pires DE, Ascher DB. DynaMut: predicting the impact of mutations on protein conformation, flexibility and stability. Nucleic Acids Res. 2018;46(W1):W350-W355. doi:10.1093/nar/gky300 6. Chakravarty D, Gao J, Phillips S, et al. OncoKB: A Precision Oncology Knowledge Base. JCO Precis Oncol. 2017;1(1):1-16. doi:10.1200/po.17.00011 7. Song MS, Salmena L, Carracedo A, et al. The deubiquitinylation and localization of PTEN are regulated by a HAUSP-PML network. Nature. 2008;455(7214):813- 817. doi:10.1038/nature07290

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