Therapeutic Monitoring of Circulating DNA Mutations in Metastatic Cancer with Personalized Digital PCR

Journal Pre-proof Therapeutic Monitoring of Circulating DNA Mutations in Metastatic Cancer with Personalized Digital PCR Christina M. Wood-Bouwens, Derrick Haslem, Bryce Moulton, Alison F. Almeda, Hojoon Lee, Gregory M. Heestand, Lincoln D. Nadauld, Hanlee P. Ji PII:

S1525-1578(19)30433-7

DOI:

https://doi.org/10.1016/j.jmoldx.2019.10.008

Reference:

JMDI 854

To appear in:

The Journal of Molecular Diagnostics

Received Date: 13 April 2019 Revised Date:

9 September 2019

Accepted Date: 17 October 2019

Please cite this article as: Wood-Bouwens CM, Haslem D, Moulton B, Almeda AF, Lee H, Heestand GM, Nadauld LD, Ji HP, Therapeutic Monitoring of Circulating DNA Mutations in Metastatic Cancer with Personalized Digital PCR, The Journal of Molecular Diagnostics (2020), doi: https://doi.org/10.1016/ j.jmoldx.2019.10.008. This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. Copyright © 2019 Published by Elsevier Inc. on behalf of the American Society for Investigative Pathology and the Association for Molecular Pathology.

Therapeutic Monitoring of Circulating DNA Mutations in Metastatic Cancer with Personalized Digital PCR

Christina M. Wood-Bouwens,* Derrick Haslem,† Bryce Moulton,† Alison F. Almeda,* Hojoon Lee,* Gregory M. Heestand,* Lincoln D. Nadauld,† and Hanlee P. Ji*‡

From the Division of Oncology,* Department of Medicine, Stanford University School of Medicine, Stanford, California; Intermountain Healthcare,† St. George,Utah; and the Stanford Genome Technology Center,‡ Stanford University, Palo Alto, California

Short running head: Custom dPCR monitoring of metastatic cancer

Financial Disclosure and Conflicts of Interest. Stanford University has a patent related to this work in which H.J. is listed as a co-inventor.

To whom correspondences should be addressed. Hanlee P. Ji Division of Oncology, Department of Medicine – Stanford University School of Medicine CCSR 1115, 269 Campus Drive Stanford, CA 94305-5151 Email: [email protected]

Lincoln D. Nadauld Email: [email protected]

FUNDING This work was supported a grants from National Institutes of Health and Intermountain Healthcare. This includes the following NIH grants: NHGRI P01HG000205 to C.W.B., and H.P.J., NHGRI R01HG006137 to H.P.J., 5K08CA166512 to L.D.N. The American Cancer Society provided additional support to H.P.J. [Research Scholar Grant, RSG-13-297-01-TBG]. In addition, H.P.J. and C.W.B. received support from the Clayville Foundation. LDN received additional support from the Conquer Cancer Foundation (Young Investigator Award) and the Carl Kawaja Foundation.

ABSTRACT As a high-performance solution for longitudinal monitoring of patients being treated for metastatic cancer, we developed and a single-color digital PCR (dPCR) assay that detects and quantifies specific cancer mutations present in circulating tumor DNA (ctDNA). This customizable assay has a high sensitivity of detection. One can detect a mutation allelic fraction of 0.1%, equivalent to three mutation-bearing DNA molecules among 3,000 genome equivalents. The objective of this study was to validate the use of personalized dPCR mutation assays to monitor patients with metastatic cancer. We compared our digital PCR results to serum biomarkers indicating disease progression or response. Patients had metastatic colorectal, biliary, breast, lung and melanoma cancers. Mutations occurred in essential cancer drivers such as BRAF, KRAS and PIK3CA. We monitored patients over multiple cycles of treatment up to a year. All patients had detectable ctDNA mutations. Our results correlated with serum markers of metastatic cancer burden including CEA, CA-19-9, and CA-15-3, and qualitatively corresponding to imaging studies. We observed corresponding trends among these patients receiving active treatment with chemotherapy or targeted agents. For example, in one patient under active treatment, we detected increasing quantities of ctDNA molecules over time, indicating recurrence of tumor. Our study demonstrates that personalized digital PCR enables longitudinal monitoring of patients with metastatic cancer and maybe a useful indicator for treatment response.

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INTRODUCTION Circulating tumor DNA (ctDNA) is released into the bloodstream by apoptotic or necrotic tumor cells. Some of the ctDNA molecules have cancer mutations which are not present in the circulating DNA released by normal cells.1, 2,3-5 Multiple studies have demonstrated that individuals with metastatic cancer have elevated levels of ctDNA which is an indicator of high tumor burden and survival.4, 6-11 For example, Scholer et.al. analyzed ctDNA in colorectal cancer patients and demonstrated a concordance between the characteristics ctDNA and the primary tumor.12 Elevations in ctDNA may precede other clinically established methods as a way of detecting recurrence such as elevated serum biomarkers or radiological evidence of disease progression.13

Distinguishing the ctDNA from normal DNA in the blood is a challenge. Specifically, the amount of ctDNA is minute compared to normal DNA. Numerous methods are used to detect presence of ctDNA molecules in the blood of cancer patients.14-19 Next generation sequencing (NGS) with targeted gene assays provides the deep sequencing coverage necessary for discovering ctDNA mutations. Digital PCR (dPCR) is a highly sensitive genotyping method and requires selecting a cancer mutation first. Afterwards, one develops an assay for detecting the presence of a specific ctDNA mutation. Digital PCR relies on amplicons that amplify the tumor DNA with a cancer mutation. Mutation genotyping requires a third fluorescent probe oligonucleotide or mutation-specific primers with fluorescent intercalator dyes that bind to the resulting mutationspecific amplicon.

For longitudinal monitoring of metastatic cancer, NGS and digital PCR have both distinct advantages and limitations. NGS enables one to discover mutations without the requirement of primary tumor sequencing. However, its sensitivity of detection varies, its cost is high and it

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requires extensive computational analysis of sequence reads. In comparison, digital PCR requires a priori information for mutation assay design and is limited to genotyping small number mutations. However, digital PCR is low cost, provides rapid turnaround of results in a matter of hours and has high sensitivity of mutations at near single molecule resolution.5, 17, 20, 21 These features make this single color digital PCR approach ideal for routine clinical monitoring of cancer progression, determining response to treatment or evaluating minimal residual disease.22

Serum protein biomarkers are available for only a handful of solid tumors. For example, the current standard-of-care blood test for monitoring colon cancer is referred to as carcinoembryonic antigen (CEA) where an increase in the levels of this protein are a general indicator of colon cancer growth. This test is widely used, albeit not consistently, for monitoring patients with metastatic colon cancer patients who are receiving therapy. Only 50% of patients with metastatic colorectal cancer have an elevated CEA level which is required for monitoring23. The remainder of patients have normal CEA levels and thus have no options for blood-based testing. Therefore, computed tomography (CT) imaging is the current standard for monitoring response to therapy in advanced cancer. Routine imaging requires long periods of treatment before an assessment can be made, which can expose patients to treatment side-effects associated with ineffective therapies. More rapid and customizable assessment strategies are required to provide short-interval feedback to clinicians about the effectiveness of the chosen therapy. In this regard, we developed and tested personalized dPCR assays for longitudinal monitoring of patients with advanced cancer while they received treatment.

The objective of our pilot study was to determine the feasibility of using single color digital PCR to monitor ctDNA trends and its correlation with clinical course. First, we used the results from diagnostic cancer sequencing to identify cancer mutations in known cancer drivers and with the highest allelic fractions. Second, we developed personalized dPCR assays for quantitative 5

genotyping of these mutations. Third, we used these custom assays to monitor the changes in ctDNA mutation levels – for some patients this went as long as a year for the duration of their therapy. All of the patients had metastatic cancer and were monitored during the course of multiple cycles of treatment. During the routine clinical visits for chemotherapy follow up, we collected a large number of samples with as many as 20 timepoints per patient. During the study we also obtained CT images and findings from these radiographic imaging studies as well as the levels of the serum protein biomarkers CEA, CA-19-9, CA-27-29 and CA-15-3 when available for a given patient. These data served as orthogonal metrics of disease response or progression for which we compared our ctDNA mutation results. To establish the potential utility of dPCR for ctDNA longitudinal monitoring, we determined the correlation between levels of ctDNA mutations and levels of serum biomarkers. We also compared our dPCR findings to findings from radiographic imaging.

MATERIALS AND METHODS Clinical Samples and Circulating DNA Preparation The Institutional Review Board (IRB) at Stanford University and Intermountain Healthcare approved the study. All patients underwent informed consent. We enrolled patients with metastatic cancers of various types receiving treatment.

We collected 10ml of whole blood in STRECK cell free DNA BCT (STRECK, La Vista, NE) or standard EDTA tubes (Becton Dickinson and Company, Franklin Lakes, NJ). Blood samples were collected during routine clinical follow up during therapy and not on a fixed schedule. We selected nine patients for this study; the cancer type and number of time points collected for each patient are listed in Table 1. In addition to blood, we obtained the CT images and

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radiology reports as well as the results of longitudinal serum protein test when available for each patient. Replicate measures were not available for serum protein levels.

We collected one 10mL vial of whole blood per patient at various timepoints throughout treatment. For all blood samples, two spin steps were performed to isolate plasma containing cell-free DNA. First, the blood was spun at 2,000Xg for 10 minutes and the plasma fraction transferred to a fresh Eppendorf LoBind Microtube (Hamburg, Germany) after which another spin was performed at 2,000Xg for 10 minutes. The final plasma fraction was transferred to a fresh cryovial and flash frozen in liquid nitrogen before being stored at -80ºC until cell-free DNA extraction. We used a Promega (Madison, WI) Maxwell® 16 circulating DNA Plasma Kit to extract cell-free DNA from 0.5 - 1.4mL aliquots of the spun plasma. Circulating DNA was quantified with the Qubit® dsDNA BR Assay Kit (Invitrogen, Carlsbad CA) and a custom simplex single color dPCR assay targeting the human RPP30 gene (F: 5’TGCCCTCAATCAGCCCCTGG-3’ R: 5’-TTGCCAAGGAAAATCTAAAGG-3’).

Developing Digital PCR Assays for Cancer Mutations As part of their clinical evaluation for metastatic cancer, these patients’ tumors were tested with targeted sequencing using either the Intermountain ICG-100 test (100 gene panel) or the Stanford STAMP test (200 gene panel) (Table 1). The reports listed cancer mutations with a 10% allele fraction or higher. We designed single color dPCR assays for 1-3 mutations per each patient. The mutation(s) selected for each patient are listed Table 1.

For each assay, the primer specific for the mutation allele was selected with the mutation specific base located on the last 3’ position of the primer and a paired genomic reference primer was selected between 50 and 150bp up or downstream of the mutation primer. The genomic primer selected was within 3ºC variance in melting temperature (Tm) from the mutation specific 7

primer with high uniqueness in the genome. We synthesized all primers though Integrated DNA Technologies (IDT, Coralville, IA) and the Stanford Protein and Nucleic Acid Facility (Stanford, CA). We optimized each dPCR assay for these mutations using a known mixture of normal reference cell line NA18507 DNA and DNA with the mutation of interest noted in Table 2. For the BRAF V600E (c.1799T>A), KRAS G12D (c.35 G>A) and KRAS G12V (c.35 G>T) assays, we multiplexed the mutation and corresponding wild-type allele assays together. Multiplexing of these dual allele assays relied on non-complementary amplicon tails that distinguish the mutation versus wildtype amplicon. For all other assays we only tested for the presence of the mutation allele per mutation detection assay. The final primer sequences and digital PCR assay conditions for each customized assay are listed in Table 3.

We used the Bio-Rad EvaGreen QX200 dPCR system and protocol (Bio-Rad Laboratories, Hercules, CA) for all digital PCR reactions. Each 22µL dPCR reaction used 1X EvaGreen dPCR super-mix and a final concentration of 50-100nM of each genotyping primer set per reaction (Table 3). The PCR master mix was partitioned and plated per Bio-Rad’s QX200 droplet generation protocol. We used a standard thermal cycler with the lid temperature set to 105ºC: one cycle of enzyme activation at 95ºC for 5 minutes; denaturation at 95ºC for 30 seconds immediately followed by annealing/extension between 54-68ºC for between 1-2 minutes depending on the assay target for a total of 40 cycles; a final droplet stabilization step at 4ºC for 5 minutes; 90ºC for 5 minutes; extended 4ºC hold. The corresponding annealing and extension temperature and time are noted for each mutation detection assay in Table 3. After thermal cycling, we transferred the plate to the Bio-Rad QX200 Droplet reader, and generated amplitude files using the QuantaSoft droplet reader software (version 1.7.4.0917).

When testing patient cell free DNA, we included three to four replicates for mutation detection assays. The overall number of replicates per sample per assay were dependent on the amount 8

of sample available for a given assay. As a reference, we used the RPP30 dPCR assay as a quantitative measure of DNA. Subsequently, we calculated the mean and standard deviation. As a positive control, we included at least three replicates using cancer cell line DNA with the mutation of interest. As a negative control, we also include a sample with only reference wild type NA18507 DNA, and a no template control with water.

Measuring Levels of ctDNA Mutations We calculated the mass of one haploid human genome (m) from a single normal cell in grams using the genome size in base pairs (n). One haploid genome is approximately 3.3pg.25 We counted the number of haploid genome-equivalents (GE) present for all assays with the following equation. The GE value is reflective of the number of mutation-containing DNA molecules present in the analyte.

m  n 1.096 10 g/bp)

For clustering the data from single color dPCR, we used a clustering program written in R.24 These controls were used as input into this program; the software is openly available at the following URL (https://github.com/billytcl/calico). A sample was considered to be positive if on average across replicates at least one positive droplet was identified. All mutation detection experiments were analyzed with multiple replicates which had at least 15,000 droplets per assay. Afterwards, we calculated the total number of haploid GE per microliter detected in each dPCR reaction well using the formula noted below. This equation uses Poisson modeling to determine the total number of positive droplets:

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target negative * total droplets 0.001μL droplet volume, 22 μL ddPCR reaction7  6 6 GE/μL   template DNA input volume μL)  6  6 – ln



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Using the formula listed below, we calculated the number of haploid GE per mL of extracted plasma for a given dPCR reaction. The total number of positive droplets in a digital droplet PCR (ddPCR) reaction follows a Poisson distribution. All ddPCR reactions were performed at a final volume of 22 microliters and the droplet volume used was 1 nanoliter.

target negative total droplets * 0.001μL droplet volume, 22 μL ddPCR reaction7  6  6 total circulating DNA elution volume μL) total circulating DNA input volume μL)  6  6 – ln

GE/mL  

5 volume of plasma used for extraction mL)

We determined the GE of total circulating DNA using the RPP30 single color dPCR assay. To account for volume differences among samples, we multiplied the total GE/mL from these reactions by the following ratio:

volume DNA used for mutation detection μL) volume DNA used for :;;30 ddPCR reaction μL)

We included positive and negative mutation controls using cancer cell line DNA and normal cell DNA. Using the droplet values, we calculated the average number of genome equivalents detected across replicates. In addition, for any given assay we determined the PCR limit-ofdetection (PCR-LOD); this involved calculating the average number of false positive droplets present in the reference negative controls and no template controls for each assay plate run 10



(Supplemental Table S1). The total circulating DNA elution volume was 53µL for all clinical samples and the input volume for each mutation detection assay was between 2 and 8µL per replicate. We used the following equation to adjust the PCR-LOD; this equations accounts for total circulating DNA, includes the total DNA elution volume and inputs the sample volume used for a given dPCR assay.

assay specific PCR LOD GE)

volume extracted total circulating DNA μL) volume total circulating DNA input μL) volume adjusted LOD GE/mL)  Volume plasma used for extraction mL)

Statistical Analysis We conducted a Pearson correlation (two-tailed p-values) analysis between the average number of the mutation-specific ctDNA (or total cell free DNA) versus the serum biomarker levels. In addition to individual patient correlation analyses, we aggregated patient cell free DNA data with across the same serum biomarker. To perform this analysis, we used the repeat measures correlation (rmcorr, version 0.3.0), a statistical script (R package). We determined the repeat measure correlation coefficient rrm, which takes into account variability associated with aggregating data sets26.

To describe the change in mean ctDNA quantity between sequential timepoints, we calculated the relative fold change in mutation positive genome equivalents per mL of plasma (GE/mL). This value was calculated using the following formula where A was equal to the mean GE/mL of ctDNA at the timepoint of interest and B was equal to the mean GE/mL ctDNA at a different timepoint.

A − C) C

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To determine the statistical significance of longitudinal changes in ctDNA levels, we sequentially compared the mean ctDNA GE detected for each timepoint to the previous timepoint, using a two-sided two-sample t-test assuming unequal variance. We considered a P value of 0.05 or lower to be significant. Individual patient correlation metrics and all plots were generated using the GraphPad (La Jolla, CA) statistical analysis software Prism 8 version 8.0.2. R scripts were used to generate plots of repeat measures correlation metrics.

RESULTS We enrolled nine patients with metastatic cancer undergoing active treatment. Figure 1 shows our study workflow. Prior to enrollment in the study, these patients had undergone diagnostic tumor sequencing with either a 100 or 200 gene panel (Methods). We developed customized dPCR assays specific for one to three cancer mutations per an individual’s tumor. When available, we used their tumor DNA to confirm the presence of the mutation with our dPCR assays. Five patients had tumor DNA available and all were positive for the mutation using the dPCR assay (Table 1).

Study Overview Samples from these patients were collected throughout the duration of treatment with a range between 2.5 to 12 months (Table 1). From nine patients, we collected and analyzed 99 samples representing different timepoints (Methods). For mutation detection, all samples were analyzed with between three to four replicates. To quantify the total cell free DNA (both normal and tumor), all samples were analyzed with between two and three replicates depending on the volume of sample available.

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Digital PCR provides an absolute measurement of DNA molecules in a given sample, unlike conventional quantitative PCR. Thus, our results are a direct measurement of the number of ctDNA molecules with a specific mutation that are present in the total DNA extracted from a given volume of plasma sample. Our results are reported as average genome equivalents per milliliter of plasma (GE/mL) detected per timepoint. We identified changes in the ctDNA mutation levels over the extended longitudinal monitoring among five of the nine patients (55%). Among the four patients which showed no significant differences in ctDNA mutation levels, three patients showed radiographic evidence of stable disease. We compared our the ctDNA mutation levels, measured as genome equivalent per volume of sample (GE/mL) to the serum biomarker levels measure on the same day. We also compared the total cell free DNA (GE/mL) to the same biomarker levels.

Correlations Between Personalized ctDNA Mutation and Serum Biomarkers Seven of the nine patents had longitudinal serum biomarker levels, including carcinoembryonic antigen (CEA) CA-19-9, CA-15-3, and CA-27-29 (Table 1). These markers came from blood samples obtained on the same day as those used for ctDNA analysis. When these levels were available across multiple patients, we aggregated the data points from a given day where we had the serum protein biomarker levels as well as ctDNA mutation levels (SupplementalTable S2). For each individual patient we calculated the Pearson correlation coefficient between the mean of the ctDNA mutation level and the serum biomarker levels obtained on the same day. In addition, we determined if there was a correlation between the average total circulating DNA (both normal and tumor) and the corresponding serum biomarker levels both for aggregate data sets (Supplemental Table S3) and individual patient data sets.

CEA and ctDNA Mutation Levels

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CEA levels (ng/mL) were available for four patients; two with colorectal cancer, one with cholangiocarcinoma and one with lung cancer (Table1). Among all patients, we aggregated the 55 data points with overlap (samples drawn on the same day) between CEA and ctDNA mutation levels for BRAF V600E (c.1799T>A), KRAS G13D (c.38 G>A), KRAS G12D (c.35 G>A), PIK3CA H1047R (c.3140 A>G) and TP53 E285K (c.853G>A) (Supplemental Table S2). The range of CEA values was between 2.2 and 48 ng/mL and the range of the mutation ctDNA quantities was between 0 and 328.7 GE/mL.

When comparing all of the data from the four patients, we calculated the repeat measure correlation coefficient rrm, which takes into account variability associated with aggregating data sets26. We identified a positive and statistically significant correlation (rrm=0.56, p<0.0001) between the ctDNA mutation levels (GE/mL) detected and the overall level of CEA (ng/mL) (Supplemental Figure S1A). In contrast, when we aggregated the 44 data points with overlap between CEA and total circulating DNA levels (Supplemental Table S3), we found no correlation between them (Supplemental Figure S1A). Therefore, measuring ctDNA mutation levels, specific to the tumor DNA released into the blood, was better correlated with CEA than total circulating DNA.

CA-19-9 and ctDNA Mutation Levels We obtained measurements of the protein biomarker CA-19-9 from three patients diagnosed with cholangiocarcinoma and one patient with lung cancer (Table 1). We aggregated data from the 23 timepoints with biomarker overlap between the quantity of ctDNA mutations (GE/mL) and the CA-19-9 levels (Supplemental Table S2). We then calculated the repeat measures correlation coefficient, and found no correlation between them (rrm = -0.26, N= 23, p =0.26) for these four patients (Supplemental Figure S1B). We additionally compared the overall quantity

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of total circulating DNA to the CA-19-9 levels, and found a significant inverse correlation between the two measures (rrm= -0.62, N = 23, p<0.0001) (Supplemental Figure S1B).

Association of ctDNA Mutation Levels with Clinical Course To validate our personalized dPCR markers, we compared the ctDNA mutation levels with specific clinical events indicating progression on treatment. Our study included imaging reports from CT scans and serum protein biomarkers. All of these patients received first or second line combinations of chemotherapy, biotherapy such as vascular endothelia growth (VEGF) antibody like bevacizumab or targeted inhibitors. We had three general categories of clinical course during treatment. The first category included patients (1, 2 and 3) with disease progression on treatment. The second category included patients (4, 5, and 6) with variable response to treatment at different timepoints in the study. The third category included patients (7, 8 and 9) that showed evidence of stable disease with no signs of progression.

ctDNA Monitoring Reveals Disease Progression on Treatment Patient 1 had metastatic breast adenocarcinoma and received serial treatment for the duration of the study (Figure 2A). Three different therapies were used sequentially included capecitabine which is an oral fluoropyrimidine chemotherapy, pazopanib which is a multitargeted kinase inhibitor and nab-paclitaxel which is a tubulin-targeting agent. By Day 79, she had progression based on CT imaging (Figure 2A). She had undergone longitudinal testing with both protein biomarkers CA-27-29 (U/mL) and CA-15-3 (U/mL) which are used in monitoring breast cancer for a subset of patients. Both CA-27-29 and CA-15-3 had level well above the normal baseline 38U/mL and 30U/mL respectively (Figure 2C). We measured the quantities of the PIK3CA H1047R (c.3140 A>G) mutation over duration of treatment.

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We compared the levels of each biomarker to the levels of ctDNA GE/mL of the PIK3CA H1047R (c.3140 A>G) mutation and identified a correlation between CA-27-29 levels and the quantity ctDNA (r = 0.99, p=0.004, N = 4). We observed a correlation between CA-15-3 and ctDNA quantity quantities without statistical significance (r = 0.70, N=4, p= 0.15). Similarly, we identified a correlation between CA-27-29 and CA-15-3 individually to the overall quantity of circulating DNA with correlations of R=1.0 (N=4, p=0.001) and R=0.72 (N=4, p=0.14), although the latter did not reach statistical significance.

Figure 2 illustrates the disease course with panels showing treatment duration, imaging study timepoints, levels of PIK3CA mutation and levels of serum biomarkers. For all timepoints, the levels of PIK3CA mutation were well above the PCR-limit of detection of 2.0 GE and volume adjusted limit of detection of 21.2 GE/mL. We observed increases in PIK3CA H1047R levels that were statistically significant (Figure 2B). For example, there was a 1.4-fold increase from 1,726.1 GE/mL (±299.7) to 4,133.1 GE (±282.3) (p<0.001) as observed between Days 65 and 79 of the study. This rise in her PIK3CA mutation was consistent with increases seen in the serum CA-27-29 levels on Day 79, elevation above healthy range of CA-15-3 and CT imaging results of disease progression.

Patient 2 had metastatic colon cancer. Initially, the patient received treatment with 5-fluoruracil, irinotecan (FOLFIRI) and bevacizumab. Given issues with 5-fluorouracil toxicity, an adjustment was made to the treatment regimen. (Figure 3A). CT imaging and serum CEA levels indicated that the patient had a partial response to initial treatment but had disease progression after Day 100 (Figure 3A and 3C). From the ctDNA, we monitored the levels of two driver mutations KRAS G13D (c.38 G>A)and PIK3CA H1047R (c.3140 A>G). For the first 63 days of the study, both ctDNA mutation levels remained at or below the LOD for each assay. Following a similar trend, the serum CEA level remained below the normal baseline range of 5ng/mL. This was 16

consistent with the CT findings at earlier in the study which identified a partial response to treatment (Figure 3A). Subsequently at Day 77, we observed that the PIK3CA mutation had a 4.8-fold increase to 23.0 GE/mL (±8.2) that was statistically significant (p<0.05) compared to Day 63 where the level was 4.1 GE/mL (±7.1) (Figure 3B). For the same time interval, we identified a similar trend for the KRAS mutation which showed a 3.3-fold increase to 17.3 GE/mL (±5.9) although the differences were slightly above the 0.05 significance threshold (p=0.07). At Day 112, CT imaging showed evidence of progression. Likewise, the levels of the KRAS mutation showed an increased above the LOD at 40.2 GE/mL (±23.6).

For the final timepoints, both the PIK3CA and KRAS G13D mutations demonstrated a statistically significant rise above the previous time point (p<0.05 for both). We compared the seven timepoints (Figure3A) which had CEA levels and ctDNA KRAS mutation and PIK3CA mutation levels. We identified a significant correlation between the markers for both mutations independently (KRAS p.G13D: r=0.996, p<0.0001 and PIK3CA p.H1047R: r=0.995, p<0.0001). There was no correlation between the total cell free DNA levels measured by dPCR (Materials and Methods) and CEA levels (Supplemental Table S3).

Patient 3 had metastatic colorectal cancer and was treated with capecitabine and bevacizumab throughout the monitoring period. This patient showed evidence of progression through both CT imaging and serum biomarkers (Figure 4A and 4C). We measured the ctDNA quantity of KRAS G12D (c.35 G>A) and PIK3CA H1047R (c.3140 A>G) (Figure 4B). At Day 112, the KRAS mutation level was 29.2 GE/mL (±18.4) and the PIK3CA mutation level was at 25.3 GE/mL (±12.1). By Day 154, the KRAS mutation level had increased to 44.1 GE/mL (±18). The PIK3CA mutation had a statistically significant increase (p<0.02) at Day 196 compared to Day 154. The rise in both corresponded with the CEA levels which showed a systematic rise at Day 196 and evidence of disease progression while on treatment. Given all timepoints had both CEA 17

and ctDNA mutation marker values, we calculated the correlation between the measures. We found a significant correlation between CEA and KRAS mutation ctDNA molecules (r=0.99, p<0.0001, N=5). A correlation was also observed statistical between CEA and PIK3CA mutation level but was did not reach the threshold of statistical significance (r=0.73, p=0.08, N=5). There was no correlation between the CEA and total circulating DNA (Supplemental Table S3).

Monitoring Therapeutic Response Patient 4 had metastatic cholangiocarcinoma. The initial treatment involved a combination of cisplatin and gemcitabine, both of which are chemotherapies. The patient was noted to have progression on chemotherapy by Day 228 as determined by CT scan. The patient was switched to a targeted combination therapy, specifically vemurafenib, a BRAF inhibitor and cobimetinib, a MEK inhibitor. We measured the BRAF mutation levels and made comparisons to the CA-19-9 levels and CT imaging (Figure 5C and 5A). There were four timepoints where we had both CA-19-9 levels and ctDNA BRAF mutation levels (Figure 5A) - we did not identify a correlation (Supplemental Table S2). Interestingly, we observed a non-significant inverse correlation between total circulating free DNA and CA-19-9 (Supplemental Table S3). At Day 77, we observed a significant increase in BRAF mutation levels to 27.6 GE/mL (±7.7, p=0.03) compared to the previous timepoint (Figure 5B). This result preceded the CT results on Day 90 which showed evidence of progression. The BRAF ctDNA level was above the limits-ofdetection for the three subsequent timepoints with a significant increase to 568.8 GE/mL (±142.6, p=0.02) at Day 299. Likewise, the CT results showed cancer progression. After the patient started treatment on vemurafenib, the BRAF mutation dropped below the LOD (p=0.02) at Day 357. The BRAF mutation levels corresponded to CT scan which showed response after Day 350.

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Patient 5 had metastatic cholangiocarcinoma. This patient underwent palliative radiation to a right chest wall metastasis followed by chemotherapy with combined gemcitabine and cisplatin chemotherapy (Figure 6A). After this patient completed radiation treatment we observed a statistically significant decrease in the TP53 E285K levels between day 7 (82.7 GE/mL ±16.7) and 28 where these level of this mutation were no longer detected (Figure 6B). In addition, we observed a statistically significant 6.7-fold increase in ctDNA levels from the on-treatment Day 56 at 7.7 GE/mL (±15.4) compared to off-treatment Day 131 at 59.3 GE/mL (± 32.3).

Notably, in the latter end of the study, we observed high levels of ctDNA when Patient 5 was not receiving chemotherapy (Figure 6A and 6B). This observation was consistent with the CT results from Days 124 at and 182 which showed tumor progression. Finally, we compared the seven timepoints with both CEA levels and TP53 mutation levels from the same days. Likewise, we had eight timepoints with both CA-19-9 and TP53 mutation levels (Figure 6A). We did not identify a correlation between CEA and TP53 mutation ctDNA. Interestingly, we identified an inverse correlation between CA-19-9 and TP53 mutation ctDNA (r= -0.70, p=0.04) (Supplemental Table S2). The total cell free DNA levels (both normal and ctDNA) did not correlate with either serum biomarker (Supplemental Table S3).

Patient 6 had metastatic melanoma and lacked any serum biomarkers for monitoring of disease. Different cancer mutations had been identified from two biopsies take on different dates (Table 1). The NRAS Q61R (C.182 A>G) and ERBB4 T716I (Gr37 Chr:2 212488702 G>A) mutations were identified at the first biopsy, 30 days before the start of the study and the ALK G977R (c.2929G>A) mutation was identified at the second biopsy collected on Day 190. CT imaging reports were available for four timepoints over the course of treatment with the patient showing evidence of progression at Day 76 and Day 266 (Figure 7A). On Day 76, we detected the NRAS and ERBB4 mutation at 28.6 GE/mL (±18.7) and 12.9 GE/mL (±13.2) respectively 19

(Figure 7B). These levels were above the LOD and corresponded to progression as noted by imaging studies. During the following timepoints, the mutation levels of NRAS and ERBB4 remained below the LOD until day 337 when we detected the NRAS and ERBB4 mutation above the LOD. In contrast, ALK mutation levels remained below LOD for the duration of the study, being a potential indicator of this tumor site being a distinct subclone of the primary and being adequately controlled on therapy (Figure 7A).

Patient 7 had metastatic colorectal cancer. We monitored the BRAF V600E (c.1799T>A) levels over a year, and overall observed a stable trend (Supplemental Figure S2). On Day 327, the BRAF mutation level was elevated (16.2 GE/mL ±14.0) which coincided with a CT scan at the same time that demonstrated signs of metastatic progression as well as an elevated CEA level (Supplemental Figure S2B). The CEA levels were elevated through the course of the study, but after an initial spike at 26.4ng/mL on day 21, the levels fluctuated between 3.5ng/mL at the lowest and 10.8 ng/mL at the highest (Supplemental Figure S2C). Among the 15 timepoints with biomarker overlap (Supplemental Figure S2A), we did not observe a correlation between the CEA levels and either BRAF V600E ctDNA molecules or the total cell free DNA molecule quantities.

Patient 8 was diagnosed with metastatic cholangiocarcinoma. We measured the quantity of TP53 R175H (c.524 G>A) over 336 days. This patient had stable disease as per CT scans and serum CA-19-9 levels. Six CT scans were obtained during this period. The CA-19-9 was monitored for the first 111 days of the study. The TP53 mutation levels were just above the limit of detection at 20.9 GE/mL (± 19.3) at Day 0 of the study and this remained the case for the subsequent 10 timepoints (Supplemental Figure S3B). The ctDNA results matched what was noted on the CA-19-9 levels that remained at normal levels for the first 111 days

20

(Supplemental Figure S3C). Likewise, CT scans results indicated stable disease (Supplemental Figure S3A).

Patient 9 had metastatic lung cancer and stable disease for the 383-day period of the study as noted by CT imaging (Supplemental Figure S4A). The serum protein markers CA-19-9 and CEA were available for this patient, although none of the measurements were elevated over the course of monitoring (Supplemental Figure S4C). We detected BRAF V600E (c.1799T>A) mutation levels just above the limit of detection at Day 0 with 13.3 GE/mL (± 11.2) and 341 with 5.7 GE/mL (± 0.5) of the study. Overall, there were no statistically significant increases or decreases in the number of BRAF V600E ctDNA levels over the course of the study, with a majority of the timepoint levels at or below the LOD of one ctDNA molecule (Supplemental Figure S4B). Our results were consistent with CT studies that indicated the patient’s disease was under control throughout the duration of therapy.

DISCUSSION In this study, our results demonstrated the feasibility of using personalized single color dPCR assays to provide rapid and long-term longitudinal monitoring of ctDNA. We selected mutations specific to each patient’s primary tumor, monitored their quantity and change in abundance overtime, and corroborated evidence identified by serum biomarker levels, radiograph imaging and overall clinical findings. Our findings confirm the positive quantitative correlation between ctDNA and disease response measurements through serum biomarkers and general agreement through radiograph image results. This study provides early evidence that personalized single color dPCR assays are likely to provide a rapid, real-time approach for assessing response to interventions in patients with advanced cancer.

21

The detection of any ctDNA mutation level above the LOD provides useful clinical information. In contrast to elevations or decreases in serum protein markers which are frequently nonspecific, ctDNA mutations only originate from the tumor and their levels correlate tumor size and metastatic spread4, 6-11. In this study, we observed changes in the levels of ctDNA mutations that closely paralleled clinical findings of response and progression. For Patients 1, 2 and 3 who showed gradual disease progression, mutation ctDNA levels increased over time, closely paralleling what was noted clinically. For Patients 7 and 9 with a durable response to therapy, mutation specific ctDNA remained stable at or below the limit of detection. The serum protein biomarkers followed a similar trend, remaining in the normal range during the study interval days. Finally, we observed that the quantity of mutation specific ctDNA was generally correlated with serum protein than the quantity of total circulating DNA (both tumor and normal). For all CT scans whose findings pointed to progression, we identified detectable levels above the ctDNA mutation LOD. Overall, ctDNA mutation levels were generally concordant between orthogonally collected CT scan results and serum protein levels.

For two patients, we observed conflicting results specifically between the serum marker CA-199 and mutation specific ctDNA levels. Both Patients 4 and 5 were diagnosed with cholangiocarcinoma and we observed varying degrees of response or progression. The CA-199 protein is expressed in non-tumor cells and can be elevated in benign conditions as well as in patients diagnosed with cancers of the upper gastrointestinal track27, 28. We hypothesize that the discordance may be related to low specificity for tumor specific events and our result suggest that fluctuations in the serum protein levels may be influenced by other clinical factors.

Clinical monitoring of an individual’s response to cancer treatment remains a challenge. As new therapies undergo clinical trials, determining effective methods of monitoring disease response takes extended periods of time given the need for imaging.29 Furthermore, many cancer types 22

do not have clinically established protein biomarkers. Even among those that do, these serum biomarkers are elevated as a result of other ongoing physiologic processes not related to cancer – for example, CEA elevation is frequently seen in patients with a history of tobacco use. A notable example of this assay’s applicability to monitoring is demonstrated by Patient 6 who had no serum marker levels available. We observed signal above the limit of detection from two of the three tumor specific mutations at various points in the study, one specifically coinciding with evidence of progression. Our findings overall support the utility and feasibility of long-term ctDNA monitoring as a minimally invasive adjunct technology for direct tissue biopsies and imaging studies or a surrogate marker when serum biomarkers are otherwise unavailable.

We encountered specific limitations which will be addressed in future studies. As circulating DNA has a short half-life, it is important for comparative purposes for these days to overlap such that the ctDNA “snapshot” of disease is as accurate as possible. While we had a number of samples which fulfilled the serum/cell free DNA overlap requirement, the study would benefit from additional imaging study overlaps as this is a “gold standard” metric for progression. Additionally, as with many cell free DNA monitoring studies, DNA sample input is an inherent limitation. Given that cell free DNA concentration varies across degrees of tumor burden, cancer types, and even between patients, collecting increase volume from blood samples may improve the limit of detection for many of our assays by simply increasing the number of DNA for a given assay. Finally, as a study intended to monitor know mutations, it is inherently limited to detect those mutations identified through initial sequencing of one to two biopsies and would not be useful for novel mutation discovery.

We are conducting an expanded clinical study that will enroll additional patients, improving the speed of assay result turnaround and increasing the throughput of new assays. This includes developing new processes which increase the number of mutation specific primers available. 23

As we develop more assays, this enables expansion of single color dPCR monitoring to test a broader number of patients and increase the utility of this approach.

ACKNOWLEDGEMENT We thank Li Xia for statistical consultation.

24

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FIGURE LEGENDS Figure 1: Workflow of generating customized single-color digital PCR assays for routine and extended longitudinal monitoring of circulating tumor DNA throughout treatment

Figure 2: Summary of Patient 1’s (A) clinical information including treatment type and dates as well as radiograph images and summarized results (B) circulating tumor DNA quantities expressed in genome equivalents detected per mL of plasma; Replicates were used and plotted to quantify the average PIK3CA H1047R levels per timepoint (N=3), the error bars represent the standard deviation (SD) across replicates; no bars indicate that the SD was too low to be visualized on the scale used. The volume adjusted limit of detection (LOD) was calculated to be 21.2 GE/mL for all timepoints. A two-tailed t-test was used to compare the ctDNA quantities between sequential timepoints (* p ≤ 0.05; ** p ≤ 0.01; *** p ≤ 0.001). (C) Serum protein marker levels followed over treatment, data from multiple replicates were not provided.

Figure 3: Summary of Patient 2’s (A) clinical information including treatment type and dates as well as radiograph images and summarized results (B) circulating tumor DNA levels expressed in genome equivalents detected per mL of plasma. N=3 replicates each were analyzed for each timepoint to quantify the KRAS G13D and PIK3CA H1047R ctDNA levels timepoint, except for the KRAS G13D assay for days 98, 112, 126, and 168 where n=2 due to low droplet counts in the third replicate. Error bars represent the standard deviation (SD) across replicates; no bars indicate that the SD was too low to be visualized on the scale used. The volume adjusted limit of detection (LOD) was calculated for each ctDNA timepoint for each assay and is denoted by a red dashed line. A two-tailed t-test was used to compare the ctDNA mutation levels between sequential timepoints (* p ≤ 0.05). (C) Quantities of carcinoembryonic antigen (CEA) followed over treatment; data from multiple replicates were not provided.

30

Figure 4: Summary of Patient 3’s (A) clinical information including treatment type and dates as well as summarized radiograph image results (B) circulating tumor DNA quantities expressed in genome equivalents (GE) detected per mL of plasma, N=3 replicates each were analyzed to quantify of KRAS G12D and PIK3CA H1047R molecules per timepoint, and standard deviation (SD) is represented by error bars; no bars indicate that the SD was too low to be visualized on the scale used. A two-tailed t-test was used to compare the ctDNA levels between sequential timepoints (* = p ≤ 0.05). The volume adjusted limit of detection (LOD) was calculated for each ctDNA timepoint for each assay and is denoted by a red dashed line. (C) Quantities of carcinoembryonic antigen (CEA) followed over treatment.

Figure 5: Summary of Patient 4’s (A) clinical information including treatment type and dates as well as summarized results of radiograph images(B) circulating tumor DNA quantities expressed in genome equivalents detected (GE) per mL of plasma; N=3 replicates were analyzed to quantity ctDNA BRAF V600E levels per timepoint and standard deviation (SD) is represented by error bars; no bars indicate that the SD was too low to be visualized on the scale used. A twotailed t-test was used to compare the ctDNA quantities between sequential timepoints (* p ≤ 0.05). The volume adjusted limit of detection (LOD) was calculated for each ctDNA timepoint for each assay and is denoted by a red dashed line. (C) Quantities of CA 19-9 followed over treatment; data from multiple replicates were not provided.

Figure 6: Summary of Patient 5’s (A) clinical information including treatment type and dates as well as radiograph images and summarized results (B) circulating tumor DNA quantities expressed in genome equivalents (GE) per mL of plasma. N=4 replicates were analyzed quantity of TP53 E285K ctDNA levels timepoint, and the standard deviation (SD) is represented by error bars for each point; no bars indicate that the SD was too low to be visualized on the 31

scale used. A two-tailed t-test was used to compare the ctDNA quantities between sequential timepoints. The total number of tests made were 12 (* p ≤ 0.05; ** p ≤ 0.01). The volume adjusted limit of detection (LOD) was calculated for each ctDNA timepoint for each assay and is denoted by a red dashed line. (C) Quantities of CA 19-9 and carcinoembryonic antigen (CEA) followed over treatment; data from multiple replicates were not provided.

Figure 7: Summary of Patient 6’s (A) clinical information including treatment type and dates as well as summarized results of radiograph images and (B) the quantity of circulating tumor DNA (ctDNA) detected for the NRAS Q61R, ERBB4 T716I and ALK G977R mutations expressed in genome equivalents (GE) per mL of plasma detected. N=3 replicates per timepoint per mutation assay were analyzed to determine the average level, error bars represent the standard deviation (SD); no bars indicate that the SD was too low to be visualized on the scale used. (A two-tailed t-test was used to compare the mutation bearing ctDNA and total circulating DNA quantities between sequential timepoints. The volume adjusted limit of detection (LOD) was calculated for each ctDNA timepoint for each assay and is denoted by a red dashed line.

32

Patient

Cancer type

Total ccfDNA samples

1

Breast

4

2

3

Colon

Colon

12

5

Study Length (Days)

Serum biomarker(s)

Mutation(s) identified by NGS

mutation specific ctDNA detected (≥ 1 timepoint above LOD)

Verified in tumor by dPCR

79

CA-15-3 and CA-27-29

PIK3CA p. H1047R

+

+

KRAS p.G13D

+

+

PIK3CA p. H1047R

+

+

KRAS p. G12D

+

+

PIK3CA p. H1047R

+

+

168

196

CEA

CEA

4

Cholangiocarcinoma

8

357

CA-19-9

BRAF p.V600E

+

Tissue not available

5

Cholangiocarcinoma

10

222

CEA and CA-19-9

TP53 p.E285K

+

Tissue not available

NRAS p.Q61R

+

+

ERBB4 p.T716I

+

+

ALK p.G977R

-

Tissue not available (additional biopsy)

6

Melanoma

12

337

None

7

Colon

20

383

CEA

BRAF p.V600E

+

+

8

Cholangiocarcinoma

11

336

CA-19-9

TP53 p.R175H

+

Tissue not available

9

Lung

17

383

CEA and CA-19-9

BRAF p.V600E

+

Tissue not available

Table 1. Clinical cohort overview. Noted for each patient is the type of cancer, total number of circulating cell free DNA (ccfDNA) samples collected for tumor mutation analysis, number of days the patient was followed in the study, the serum biomarker data collected as part of routine care, the mutations identified through Next Generation Sequencing (NGS) and selected for digital PCR analysis. We note each mutation that was identified at least once in among the blood samples collected over the course of the study as well as whether or not the mutation was confirmed in tumor tissue samples when DNA was available. 33

Sample Name

Source

Sample Type

Mutation of Interest

NA18507

Coriell

DNA/Cell Line

LS411N

ATCC

DNA/Cell Line

none

Variant Allele Fraction 0.00

BRAF V600E 0.68 (c.1799T>A) PIK3CA H1047R (c.3140 RKO

ATCC

DNA/Cell Line

0.49 A>G) TP53 R175H

LS123

ATCC

DNA/Cell Line

0.99 (c.524 G>A) KRAS G12D

GP2D

Sigma Aldrich

DNA/Cell Line

0.43 (c.35 G>A) KRAS G12V

RCM-1

JCRB

DNA/Cell Line

1.00 (c.35 G>T) KRAS G13D

HCT116

ATCC

DNA/Cell Line

0.56 (c.38 G>A) NRAS Q61R

KU1919

DSMZ

DNA/Cell Line

0.46 (C.182 A>G) ERBB4 T716I

P3C4

Stanford

Control DNA

(Gr37 Chr:2 212488702

1.00

G>A) ALK G977R P4M23

Stanford

Control DNA

1.00 (c.2929G>A)

Table 2. DNA samples, sources, type and mutations. Included are the catalogue names and sample names of all DNA used in this study. The variant allele fraction of the mutation of interest for each cell line derived DNA sample was determined using the cBio website (www.cbioportal.org). Cell line DNA was obtained from the following sources: American Type Culture Collection (ATCC, Manassas, VA), the Leibniz Institute DSMZ-German Collection of Microorganisms and Cell Cultures (DSMZ, Braunschweig, Germany), the National Institute of

34

Biomedical Innovation’s Japanese Collection of Research Bioresources Cell Bank (JCRB, Asagi Saito Ibaraki-City, Osaka) and Sigma Aldrich (St. Louis, MO).

35

Assay Target

BRAF V600E (c.1799 T>A)

Primer Name

Primer Sequence (5’

3’) (Tail Sequence) mis-match bases

Amplicon Length

BRAF_600_Forward

5'- (GGGGGG)CATGAAGACCTCACAGTAAA-3’

BRAF_600_WT_39T

5'(AAATAAATAAATAAATAAATAAATAAATAAATAAATAAA)CCACTCCATCGAGATTTC A-3’

104

–

BRAF_600_Mut_6T

5'- (GGGGGG)CCACTCCATCGAGATTTCT-3’

71

KRAS_12__Forward

5'- (GCG)TGTTGGATCATATTCGTCCACAA-3’

–

KRAS_12_WT

5'- (GCG)ACTTGTGGTAGTTGGAGCAGG-3’

88

KRAS_12_Mut_D_12T

5'- (AAATAAATAAAT)ACTTGTGGTAGTTGGAGCAGA-3’

100

KRAS_12_Forward

5'- (GCG)TGTTGGATCATATTCGTCCACAA-3’

–

KRAS_12_WT

5'- (GCG)ACTTGTGGTAGTTGGAGCAGG-3’

88

KRAS_12_Mut_V_100T

5'-25(AAAT)ACTTGTGGTAGTTGGAGCAGT-3’

188

KRAS G13D (c.38 G>A)

KRAS_13_genomic_F

5'-GCTGTATCGTCAAGGCACTC-3’

–

KRAS_G13D_m3G_R

5'-GTGGTAGTTGGAGCTGGGGA-3’

49

PIK3CA H1047R (c.3140 A>G)

PIK3CA_1047_Forward

5'- (CG)CGAAAGACCCTAGCCTTAGA-3’

–

PIK3CA_1047_Mut_R

5'-TTGTCCAGCCACCATGACGC-3’

93

TP53 R175H (c.524 G>A)

TP53_175_Forward

5'-GGGGCCAGACCTAAGAGCAATC-3’

–

TP53_175_Mut_R

5'-ATGACGGAGGTTGTGAGGCA-3’

TP53 E285K (c.853G>A)

TP53_285_Forward

5'-CGCTTAGTGCTCCCTGGGGG-3’-3’

–

TP53_E285K_Mut_R

5'-TGGGAGAGACCGGCCCATAA-3’-3’

84

NRAS_genomic_F

5'-GCCTGTCCTCATGTATTGGT-3’

–

NRAS_Q61R_m3C_R

5'-ATACTGGATACAGCTGGCCG-3’

63

ALK_genomic_977_F

5'-ATTCGTCTACCTCACAGTGA-3’

–

ALK_G977R_m3G_R

5'-ACTAGTGATGGAAGGCCGCA-3’

94

ERBB4_genomic_R

5'-GAGAGGACTGACTATCGGAC-3’

–

ERBB4_T716I_Mut_F

5'-TTTTACCCTCTTCAGCTCAA-3’

RPP30_Forward

5’-TGCCCTCAATCAGCCCCTGG-3’

RPP30_Reverse_Med

5’-TTGCCAAGGAAAATCTAAAGG-3’

KRAS G12D (c.35 G>A)

KRAS G12V (c.35 G>T)

NRAS Q61R (C.182 A>G) ALK G977R (c.2929G>A) ERBB4 T716I (Gr37 Chr:2 212488702 G>A)

Assay Format

Anneal/Extend Temp & Time Final concentration per primer (ºC – MM:SS)

Dual Allele

58ºC – 01:00 (100nM)

Dual Allele

61ºC – 02:00 (50nM)

Dual Allele

63ºC – 02:00 (50nM)

Single Allele

Single Allele

63.5ºC – 02:00 (50nM)

Single Allele

68ºC – 01:00 (100nM)

Single Allele

64ºC – 1:30 (50nM)

Single Allele

61ºC – 2:00 (50nM)

144

Single Allele

61ºC – 2:00 (50nM)

Single Allele

58ºC – 2:00 (50nM)

140

RRP30

110

36

63ºC – 02:00 (50nM)

Single target

54ºC — 1:00 (100nM)

Table 3. Assay conditions and primer sequences

37

38

Workflow: Customized longitudinal analysis of circulating tumor DNA using Single Color ddPCR Clinical Processes Target Identification Clinical Tumor Sequencing

Personalized Cancer Monitoring Treatment

Primer Selection Forwa rd Prim e r Approx . 2 0 bp Ca nc e r Muta tion T A

Therapy Period

Ge noty ping Re v e rs e Prim e r e i th e r wi l d -ty pe or mu ta tio n

Variant Identification N ucleotide change Mutation (C>T) KRAS p.G 13D (A>G ) PIK3CA p.H1047R (C>T) APC p.Q1429STOP (C>T) HN F1A p.P588L (G >A) JAK3 p.P195L (C>T) FG FR3 p.P404S (C>T) PDG FRA p.T320I (C>T) MET p.R988C

MAF in Tumor 37.57% 23.55% 19.55% 17.06% 15.18% 13.54% 12.64% 8.63%

ddPCR Assay Optimization And Verification Mutation Wild Type

Longitudinal Plasma Analysis of ctDNA Mutation Molecules

Diagnosis

Customized sc-ddPCR assay design and monitoring Single Color ddPCR Assay Development

40 KRAS p.G13D

30 20 10 0

LOD: 1 GE 0

20

40

60

80

100

Days

120

140

160

180

200

Patient 1 Metastatic Breast Cancer

Capecitabine Pazopanib

Paclitaxel protein-bound particles

A

Treatment

Baseline

Progression

Radiograph Imaging Serum/ctDNA Overlap

B

0 5000

PIK3CA p.H1047R

4000

60

80

*** *

**

1000

C

40

Day

Volume Adjusted LOD

PIK3CA 3000 p.H1047R 2000 (GE/mL) 0

20

0

20

40

60

80

Day

10000 CA-27-29

8000 6000

CA-27-29 U/mL 4000 2000 0

Baseline ≤ 38U/mL 0

20

40

60

80

Day

5000 CA-15-3

4000

CA-15-3 3000 U/mL 2000 1000 0

BaselineBaseline ≤ 30U/mL 0

20

40

Day

60

80

Patient 2 Metastatic Colorectal Cancer

Leucovorin Calcium, Flurouracil, Irinotecan Hydrochloride (FOLFIRI) and Bevacizumab Irinotecan and Bevacizumab

A

Treatment

Progression

Radiological Imaging Baseline: Mixed Response

Serum/ctDNA Overlap 400

B

0

40

60

100

120

140

120

140

Day

*

160

180

200

160

180

200

160

180

200

200 100 0 -50

0

20

40

60

200 PIK3CA p.H1047R Volume Adjusted LOD

150

PIK3CA p.H1047R (GE/mL)

80

KRAS p.G13D Volume Adjusted LOD

300

KRAS p.G13D (GE/mL)

20

80

100

*

*

100 50 0 -50

20

40

C

40

60

80

100

120

140

40

60

80

100

120

140

CEA

30

CEA ng/mL

20 10 0

Baseline ≤ 5ng/uL 0

20

Day

160

180

200

Patient 3 Metastatic Colon Cancer Capecitabine Bevacizumab

A

Treatment Treatment

Radiograph Radiograph Imaging Imaging

Baseline Baseline

Serum/ctDNA Overlap

0

20

Stable Stable

40

60

80

150

B KRAS KRAS p.G12D p.G12D (GE/mL)

120

140

160

180

200

120

140

160

180

200

Day

KRAS p.G12D Volume Adjusted LOD

100

100

Progression

50 0 0 100

40

60

80

100

*

PIK3CA p.H1047R Volume Adjusted LOD

75

PIK3CA PIK3CA p.H1047R p.H1047R (GE/mL)

20

50 25 0

C

50

0

20

40

60

80

100

120

140

160

180

200

CEA

40

CEA 30 ng/mL 20 10 0

Baseline ≤ 5ng/uL 5ng/uL 0

20

40

60

80

100

Day

120

140

160

180

200

Patient 4 Cholangiocarcinoma Cisplatin Gemcitabine

A

vemurafenib/ cobimetinib

Treatment response

Radiograph Imaging

progression

response

progression

response

Serum/ctDNA Overlap 0

B

900

BRAF p.V600E

BRAF p.V600E 300 (GE/mL) 100

C

200

Day

300

*

400

*

Volume Adjusted LOD

600

0

100

* 0

4000

100

200

300

400

200

300

400

CA-19-9

3000 CA-19-9 U/mL 2000 1000 0

0

100

Day

Patient 5 Cholangiocarcinoma A

Gemcitabine/Cisplatin Palliative radiation

Treatment

Progression

Progression

Radiograph Imaging

Serum/ctDNA Overlap

0

25

50

75

100

125

150

175

200

125

150

175

200

Day

B

** 150

*

TP53 p.E285K Volume Adjusted LOD

TP53 p. E285K 100 (GE/mL) 50 0 -50

C

0

25

50

75

100

80

CEA ng/mL

60 40 20 Baseline ≤ 5ng/mL

0

0

25

50

75

100

125

150

175

200

1500

CA-19-9 1000 U/mL 500 0

Baseline ≤ 37U/mL

0

25

50

75

100

125

150

175

200

Patient 6 Metastatic Melanoma Pembroluzimab Temozolomide Paclitaxel and Carboplatin

A

Treatment Radiograph Imaging

Baseline

Blood Draws

B

0

Progression

50

100

Stable

150

200

250

300

350

200

250

300

350

200

250

300

350

200

250

300

350

Day

NRAS p.Q61R

60

Progression

Volume Adjusted LOD

40

NRAS p.Q61R (GE/mL)

20 0 0

50

100

150

Day

50

ERBB4 p.T716I Volume Adjusted LOD

ERBB4 p.T716I (GE/mL)

25

0 0 30

100

150

Day

ALK p.G977R Volume Adjusted LOD

20

ALK p.G977R (GE/mL)

50

10 0 0

50

100

150

Day