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DETECTION OF PIK3CA MUTATIONS IN PLASMA
TUMOR DNA CIRCULATING IN PERIPHERAL
BLOOD OF BREAST CANCER PATIENTS
by
Patricia Lourdes Valda Toro
A thesis submitted to Johns Hopkins University in conformity with the
requirements for the degree of Master of Science in Molecular and Cellular
Biology
Baltimore, Maryland
April, 2014
ii
Abstract
Tumor-specific mutations are used as genetic biomarkers for breast cancer
diagnosis and prognosis. The detection and quantification of mutations in tumor DNA
circulating in peripheral blood offers a non-invasive approach for measuring the presence
of cancer in patients and for evaluating individual responses to targeted therapies. We
studied the feasibility of detecting two common mutations in the phosphatidylinositol-
4,5-bisphosphate 3-kinase, catalytic subunit alpha (PIK3CA) gene, in circulating plasma
tumor DNA (ptDNA) of early stage breast cancer patients. We used two Polymerase
Chain Reaction platforms, BEAMing and droplet digital PCR (ddPCR), and showed that
both platforms detect PIK3CA mutations in ptDNA with high specificity and differential
sensitivity (30% for BEAMing, and 93.3% for ddPCR). Additionally, we showed that the
sensitivity of ddPCR for ptDNA detection decreased in blood stored at room temperature
for one week in tubes that do not prevent blood lysis. Our results provide a novel and
non-invasive alternative for clinically detecting and quantifying breast cancer biomarkers
in blood, and suggest the use of blood collection tubes that prevent lymphocyte lysis for
blood storage and transportation. The method presented herein, can allow physicians to
measure tumor burden in breast cancer patients in response to targeted therapies, and to
make more informed decisions regarding the administration of toxic systemic therapies.
Ben Ho Park, M.D. Ph.D. Robert Horner, Ph.D.
iii
Acknowledgments
I want to thank Dr. Ben Ho Park, my principal investigator and advisor. He has been a
positive influence in my academic, personal and professional development. This project
would not have been possible without his mentorship, constant support, and the
opportunity to join his laboratory. I also want to thank Dr. Julia Beaver for conception of
the project and mentorship. I want to thank the Park Lab members for constant support
and guidance, particularly, David Chu and Hong Yuen Wong for taking the patience and
time to train and assist me in the design of the experimental approach.
I want to express my sincere gratefulness to the Biology Department at The Johns
Hopkins University. I thank Dr. Robert Horner and Dr. Kathryn Tifft for their mentorship
and support.
Finally, I want to thank my family who make every accomplishment possible.
Author Contributions
Conception and design: Julia A. Beaver, MD; Danijela Jelovac, MD; Vered Stearns,
MD; Ben Ho Park, MD, PhD.
Development of methodology: Julia A. Beaver, MD; Danijela Jelovac, MD; Sasidharan
Balukrishna, MD; Rory Cochran; Sarah Croessmann; Daniel J. Zabransky; Hong Yuen
Yong; Paula J. Hurley; Michael L. Samules, PhD; Dianna Maar, PhD; Ben Ho Park, MD,
PhD.
Acquisition and analysis of data: Julia A. Beaver, MD; Danijela Jelovac, MD;
Sasidharan Balukrishna, MD; Rory Cochran; Sarah Croessmann; Daniel J. Zabransky;
Hong Yuen Yong; Justin Cidado; Brian G. Blair, PhD; David Chu; Timothy Burns, MD;
Michaela J. Higgins, MB, BCh, MD; Vered Stearns, MD; Lisa Jacobs MD; Mehran
Habibi MD; Julie Lange MD; Josh Lauring MD, PhD; Dustin VanDenBerg; Jill Kessler;
Stacie Jeter; Michael L. Samules, PhD; Dianna Maar, PhD; Leslies Cope PhD; Ashley
Cimino-Mathews MD; Pedram Argani MD; Ben Ho Park, MD, PhD.
iv
TABLE OF CONTENTS
Abstract .............................................................................................................................. ii
Acknowledgements and Author Contributions ................................................................. iii
Table of Contents ............................................................................................................... iv
List of Abbreviations ...........................................................................................................v
List of Figures and Tables....................................................................................................v
List of Supplementary Material ...........................................................................................v
1. Introduction .....................................................................................................................1
2. Experimental Methodology .............................................................................................5
2.1 Detection of PIK3CA Mutations in Plasma Tumor DNA .........................................5
2.1.1 Blood and Tissue Collection ...........................................................................6
2.1.2 DNA Processing From Blood Plasma and Tissue ..........................................6
2.1.3 Detection of PIK3CA Mutations .....................................................................7
2.1.3.1 Sanger Sequencing ...........................................................................7
2.1.3.2 BEAMing .........................................................................................7
2.1.3.3 Droplet Digital PCR .........................................................................7
2.2 Blood Collection Tube Study ....................................................................................8
2.2.1 Blood Collection .............................................................................................8
2.2.2 DNA Processing..............................................................................................8
2.2.3 Droplet Digital PCR .......................................................................................8
3. Results .............................................................................................................................9
3.1 Detection of PIK3CA Mutations Using BEAMing ...................................................9
3.2 Detection of PIK3CA Mutations Using ddPCR ......................................................10
3.2.1 Pre-Surgery Plasma .......................................................................................10
3.2.2 Post-Surgery Plasma .....................................................................................10
3.3 Detection of Mutations in BCT Streck and PAXgene Tubes ..................................11
4. Discussion ......................................................................................................................12
5. Figures............................................................................................................................17
6. Supplementary Material .................................................................................................24
Bibliography ......................................................................................................................29
Curriculum Vita ........................................................................................................................... 31
v
List of Abbreviations
Abbreviation Meaning
BCT Cell-Free DNATM
BCT collection tube from Streck company
BEAMing Beads, Emulsions, Amplification and Magnetic flow for identification
and quantification of mutations
cfDNA Cell-free DNA
ddPCR Droplet Digital Polymerase Chain Reaction
EDTA Ethylenediamine tetracetic acid. Used to refer to collection tubes with
this composition
E545K Amino acid substitution at position 545 in PIK3CA, from a glutamic
acid (E) to a lysine (K). Used to refer to the mutation in exon 9 of the
PIK3CA gene
FFPE Formalin fixed paraffin embedded
H1047R Amino acid substitution at position 1047 in PIK3CA, from a histidine
(H) to an arginine (R). Used to refer to the mutation in exon 20 of the
PIK3CA gene
gDNA Genomic DNA
PAXgene PAXgene Blood DNA collection tube
PI3K Phosphatidylinositol 3-kinase
PIK3CA Phosphatidylinositol-4,5-bisphosphate 3-kinase, catalytic subunit alpha
gene
ptDNA Plasma tumor DNA
WT Wild type
List of Figures
Figure 1 Schematic of the experimental approach Page 17
Figure 2 Sanger sequencing of primary breast tissue Page 18
Figure 3 Compared sensitivities of BEAMing, ddPCR and Sanger Sequencing Page 20
Figure 4 Quantification of DNA in blood collection tubes by ddPCR Page 22
Figure 5 Detection of PIK3CA mutations in different collection tubes by ddPCR Page 23
Table 1 Detection of PIK3CA mutations in primary tumors and blood Page 19
List of Supplementary Material
Supp 1 Thermocycling conditions for amplification of PIK3CA loci Page 24
Supp 2 PCR amplification and nested sequencing primers for sequencing Page 24
Supp 3 Primers and Taqman probes for droplet digital PCR Page 24
Supp 4 Thermocycling conditions for droplet digital PCR (Taqman probes) Page 25
Supp 5 Primers and labeled-oligonucleotide probes for ddPCR Page 25
Supp 6 Thermocycling conditions for droplet digital PCR (L-Oligo probes) Page 25
Supp 7 Mutant fractional abundances in different collection tubes Page 26
1
1. Introduction
Breast cancer is the most common cancer and the second leading cause of cancer-
related deaths among women in the United States (1). Prospective studies showed that
60-70% of early stage breast cancer patients are cured with local therapies, such as
surgical removal of the primary tumor lesion (2). However, because imaging and
currently available molecular techniques cannot reliably detect microscopic residual
disease post primary treatment, the current paradigm is to treat patients with adjuvant
systemic therapies, which cause systemic cellular toxicity. Large randomized prospective
trials support the use of systemic therapies to increase the chance of long-term survival
by preventing recurrence (2). However, studies have shown that adjuvant therapies
improve disease free survival and cure rates by only ~ 10% to 20% (3).
A third of breast cancers are caused by somatic mutations in the PIK3CA gene, the
gene encoding the p110α catalytic subunit of the phosphatidylinositol 3-kinase (PI3K)
(4). Somatic mutations in the PIK3CA gene are associated with the disruption of the
normal regulation of cell growth, cell migration and maintenance of tissue morphology
by PI3K (5, 6). E545K within exon 9 of the PIK3CA gene, and H1047R within exon 20
of the PIK3CA gene, are reported as two hotspot mutations in breast cancer (6). The
E545K mutation is associated with the disruption of inhibitory interactions between
p110α catalytic subunit and a PI3K regulatory subunit, while the H1047R mutation
increases the binding affinity of PI3K for its substrate (7). Both mutations lead to PI3K
gain of function, which disrupts the regulation of normal cellular processes and can
consequently cause cancer.
2
PIK3CA mutations are biomarkers for breast cancer prognosis and diagnosis (8). The
traditional approach for identifying these biomarkers is by removing tumor tissue via
invasive biopsies and/or surgical procedures, followed by Sanger sequencing DNA
extracted from the removed tissue specimens. Biopsies for the removal of tissue are
invasive. Additionally, the DNA obtained from biopsy tissue, which is stored in formalin
paraffin embedded (FFPE) slides is fragmented by formalin (9). The low quality of DNA,
and the difficulty of extracting exclusively tumor DNA in a slide containing both,
cancerous and non-cancerous cells closely situated to each other, increases the chances of
getting false negatives during the detection of biomarkers by Sanger sequencing. In the
present study we present a non-invasive alternative for detecting PIK3CA biomarkers.
Cell-free DNA (cfDNA) circulates in peripheral blood as a result of apoptotic and
necrotic processes in which cells burst and shed DNA into the blood (10-12). It is
hypothesized that cancerous cells shed their DNA into the blood by similar apoptotic
processes, providing circulating cell-free tumor DNA (13-17). Tumor-derived DNA is
currently referred to as plasma tumor DNA (ptDNA) (18-20). Higgins et al showed that
PIK3CA mutations, more specifically, E545K and H1047R mutations, are identified in
ptDNA of patients with metastatic breast cancer using a technique called BEAMing (8).
BEAMing allows the detection of low levels of ptDNA in a large pool of wild type DNA
by compartmentalizing DNA molecules before amplification by Polymerase Chain
Reaction (PCR) (21,22). Compartmentalization of DNA in BEAMing is attained by
attaching individual DNA molecules to magnetic beads in water in oil emulsions. The
molecules are then PCR amplified and the mutational status is determined by hybridizing
3
the DNA with fluorescent allele-specific probes for mutant and wild-type PIK3CA.
Finally, mutations are quantified by flow cytometry (21,22).
We hypothesized that BEAMing is sensitive enough to detect E545K and H1047R
mutations in plasma tumor DNA circulating in the peripheral blood of early stage breast
cancer patients. Cancer patients in early stages have much lower tumor burden compared
to patients in metastasis (24). Consequently, the ratio of tumor to normal DNA in blood is
expected to be low and difficult to detect. Compartmentalization previous to
amplification in BEAMing allows for the detection of a single mutant sequence in 10,000
wild type sequences (8, 23). Thus, we hypothesized that BEAMing would still detect
E545K and H1047R mutations with high specificity in early stage breast cancer patients
despite the patients’ much lower levels of ptDNA in circulation compared to patients in
metastatic stages. We further hypothesized that a similar novel method called droplet
digital Polymerase Chain Reaction (ddPCR) would detect E545K and H1047R mutations
in ptDNA of patients with early stage breast cancer with high sensitivity and specificity.
Droplet digital PCR similarly detects amplified fluorescently labeled DNA molecules
after compartmentalization in oil emulsions (3). We speculated that the digital
quantification of DNA molecules and superior partition in oil emulsions during ddPCR,
can allow a more precise measurement of ptDNA at a fraction of the time and cost
compared to BEAMing.
Our ultimate goal is to use peripheral blood of early stage breast cancer patients as a
non-invasive “liquid biopsy” to detect PIK3CA biomarkers. The absolute quantification
of PIK3CA mutations in ptDNA by either BEAMing or ddPCR can serve as a marker for
microscopic residual disease in early stage breast cancer patients, providing physicians
4
with a novel and non-invasive technique to make informed decisions about the necessity
of administering adjuvant systemic therapies. Furthermore, the specificity of fluorescent
probes in BEAMing and ddPCR can reduce false negatives associated with the traditional
approach of Sanger sequencing biopsy tissue specimens.
Additionally, we sought to evaluate pre-analytic sources of error that might
compromise the accuracy of ptDNA measurements. After phlebotomy, lymphocytes in
plasma lyse and release their genomic DNA. Genomic DNA increases the background
noise and decreases the chances of getting positive signal from low levels of ptDNA (25,
29). Recently, the company Streck released a blood collection tube called Cell-Free
DNATM
BCT (BCT), which contains a chemical cocktail that stabilizes lymphocytes for
14 days at room temperature after phlebotomy (25, 30). However, due to its price, many
clinical practices use PAXgene Blood DNA collection tube and K3EDTA collection tube
instead.
Previous studies showed that PAXgene tubes are better at maintaining stable levels of
nucleic acid in blood after phlebotomy compared to EDTA tubes (26). The
manufacturers claim that chemicals in PAXgene tubes, in addition to the anticoagulant,
ethylenediamine tetraacetic acid, stabilize nucleic acids in blood left at room temperature
for 14 days after phlebotomy (26 - 28).
We hypothesized that cfDNA levels are more stable in plasma stored in BCT tubes at
room temperature for one week than in plasma stored in PAXgene tubes under the same
conditions. We speculated that the lysis of unstable lymphocytes in PAXgene tubes
5
decrease the sensitivity of detecting PIK3CA mutations by ddPCR by increasing DNA
background noise.
Our results showed that BEAMing and ddPCR detected E545K and H1047R
mutations in ptDNA of early stage breast cancer patients with 100% specificity.
BEAMing detected PIK3CA mutations in ptDNA of early stage breast cancer with 30%
sensitivity while ddPCR detected identical mutations with 93.3% sensitivity.
Furthermore, we found that increases in genomic DNA in plasma stored in PAXgene
tubes at room temperature for seven days decreased the sensitivity of detecting mutations
in ptDNA using ddPCR. Together, our results propose a novel approach to revolutionize
adjuvant system therapies in early stage breast cancer patients and recommend the use of
BCT tubes for blood storage and transport.
2. Experimental Methodology
2.1. Detection of PIK3CA Mutations in Plasma Tumor DNA
Primary tumor tissue from patients with early stage breast cancer was obtained via
surgery and was Sanger sequenced to determine the presence of PIK3CA mutations. The
following mutations were queried: Exon 9 1633G>A E545K and Exon 20 3140A>G
H1047R. The specificity and sensitivity of BEAMing was analyzed by querying E545K
and H1047R mutations in cell-free DNA in blood plasma obtained before surgery, and by
comparing the total number mutations identified by BEAMing to those identified by
Sanger sequencing DNA from tumor tissue. For the following assay, ddPCR was used to
query E545K and H1047R mutations in both, DNA extracted from pre-surgery plasma
and DNA extracted from FFPE tumor tissue. The specificity and sensitivity of ddPCR
6
was calculated by comparing the total number of mutations identified in ptDNA to those
identified in FFPE DNA by ddPCR. Additionally, BEAMing and ddPCR was used to
query E545K and H1047R mutations in DNA from plasma obtained after surgery. The
total number of PIK3CA mutations found in post-surgery plasma was compared to those
identified in pre-surgery plasma and FFPE tissue in order to assess if BEAMing and
ddPCR can detect ptDNA in blood after removal of the primary tumor lesion (Figure 1).
2.1.1. Blood and Tissue Collection: Patients recently diagnosed with early stage (I-III)
breast cancer (n=29) enrolled in an IRB approved prospective repository study at The
Johns Hopkins Sidney Kimmel Comprehensive Cancer Center. Primary tissue was
collected in unstained sectioned formalin-fixed paraffin embedded (FFPE) tissue slides.
Blood was collected via phlebotomy in EDTA tubes; pre-surgery blood was collected for
all patients, and post-surgery blood was collected for 17 patients. Plasma was isolated
within two hours after phlebotomy to prevent DNA degradation.
2.1.2. DNA Processing From Blood Plasma and FFPE Tissue: DNA from tumor cells
in FFPE breast tissue was extracted using Zymo pen and Pinpoint solutions (Zymo
Research), and consequently isolated using QIAamp DNA FFPE tissue kit (Qiagen) per
the manufacturer’s protocol. DNA from non-cancerous cells was identically obtained,
and used as a negative control. Cell-free DNA was extracted from plasma samples using
QIAamp circulating nucleic acid (CNA) kit (Qiagen) per the manufacturer’s protocol*.
Quanti-iT Picogreen assay form Life Technologies was used to measure DNA
concentrations*.
* This step was performed by Dr. Julia Beaver, a former member of our lab and other collaborators.
7
2.1.3. Detection of PIK3CA Mutations in Blood Plasma and Tissue
2.1.3.1. Sanger Sequencing: A specific DNA region spanning E545K in exon 9 and
H1047R in exon 20 of the PIK3CA gene were PCR amplified with touchdown thermo-
cycling conditions (Supplement 1) using specific primers (Supplement 2). The purified
DNA product was Sanger sequenced by Macrogen.
2.1.3.2. BEAMing: Exon 9 1633G>A E545K and Exon 20 3140 A>G H1047R
mutations in ptDNA were queried using BEAMing (21)*.
2.1.3.3. Droplet Digital PCR: Mutant and wild type PIK3CA sequences were separately
identified and quantified according to different fluorescent signals using ddPCR. First,
pre-amplified DNA was mixed with amplification primers for exon 9 or exon 20
(Supplement 3), fluorescent probes for DNA query and ddPCRTM
Supermix (Bio-Rad).
Then, the mixture was compartmentalized in oil droplets by a droplet generator and PCR
amplified by thermal cycling (Parameters in supplement 4). After amplification, a
Droplet Reader (QX200 Droplet Digital PCR System Bio-Rad) digitally enumerated
mutant PIK3CA sequences, and their corresponding wild type sequences, according to the
fluorescent signal detected for each probe. For this study, Taqman probes with VIC
fluorophores were designed for wild type sequences, and probes with 6-FAM
fluorophores were designed for mutant sequences (Supplement 3). This allowed
simultaneous quantification of each PIK3CA allele (Figure 6). In order to quantify the
percent of ptDNA containing mutant PIK3CA in plasma samples, fractional abundance
was calculated using the QuantaSoft program (Bio-Rad Technologies), which uses the
* This step was performed by Dr. Julia Beaver, a former member of our lab and other collaborators.
8
total number of droplets, with and without DNA, to calculate the number of DNA
molecules as copies/μl. The number of mutant DNA molecules was divided by the
number of total DNA molecules, and multiplied by 100 to yield a percentage, taking into
account a Poisson distribution of occupied to unoccupied droplets (25). Additionally, this
program was used to sum droplets in multiple replicates to create a single meta-well for
each sample.
2.2. Blood Collection Tube Study
2.2.1. Blood Collection: Patients diagnosed with metastatic breast cancer (n=10) were
enrolled in an IRB approved repository study at The Johns Hopkins Sidney Kimmel
Comprehensive Cancer Center. All patients were diagnosed with HER2-positive breast
cancer and had unknown PIK3CA mutational status. Five plasma samples were obtained
per patient: three plasma samples processed within 24 hours of phlebotomy, one collected
in EDTA tubes (as basal control), one in PAXgene tubes, and one in BCT tubes; and two
plasma samples processed seven days after phlebotomy, one collected in PAXgene tube
and one in BCT tubes.
2.2.2. DNA Processing: Cell-free DNA was extracted from each five samples per patient
and purified using QIAamp CNA kit, per manufacturer’s protocol.
2.2.3. Droplet Digital PCR: Mutations were queried using the methodology explained in
2.1.3.3. However, for this study, 6-FAM labeled-oligonucleotides (Integrated DNA
Technologies) were used as fluorescent probes to query Exon 9 1633G>A E545K and
Exon 20 3140A>G H1047R; HEX labeled-oligonucleotides were used as probes to query
the corresponding wild type sequence in exon 9 and exon 20 (Supplement 6). Labeled-
9
oligonucleotides were used in this assay because they contain an additional quencher that
enhances the binding specificity of the florescent probe to the target DNA, according to
the manufacturer. In order to assess if DNA concentrations increased in either PAXgene
or BCT tubes after storage at room temperature for a week, a ratio of total wild type DNA
molecules (in ul of DNA/droplet generated) in plasma stored in for one week after
phlebotomy versus total wild type DNA molecules in plasma processed the same day of
phlebotomy was calculated for either PAXgene or BCT tubes. In addition, to analyze if
increases in DNA concentration in each tube skew the detection of mutations, fraction
abundance of E545K or H1047R mutations was calculated as explained in 2.1.3.3.
Fractional abundances were only calculated in samples with more than 1000 wild type
DNA molecules, which were quantified by the QX200 Droplet Digital PCR software as
HEX-labeled positive droplets. Results were recorded as the summation of eight
replicates, creating a single meta-well for each sample.
3. Results
3.1. Detection of PIK3CA Mutations Using BEAMing: We studied the detection of
PIK3CA mutations in ptDNA using BEAMing. Sanger sequencing of FFPE sample
controls identified 10/29 patients with PIK3CA mutations: seven patients with H1047R
mutations and three patients with E545K mutations. The mutations identified in tumor
tissue were wild type in adjacent non-cancerous cells used as a negative control (Figure
2). No other mutations were identified within the amplified loci. BEAMing showed a
sensitivity of 30% for detecting PIK3CA mutations in ptDNA: of the ten PIK3CA
mutations found in FFPE controls, three were identified by BEAMing in pre-surgery
10
plasma (one H1047R mutation and two E545K mutations) (Figure 3 and Table 1).
BEAMing detected no mutations in post-surgery plasma.
3.2. Detection of PIK3CA Mutations Using ddPCR: We then analyzed the detection of
PIK3CA mutations in ptDNA using ddPCR. For this assay, we used ddPCR instead of
Sanger sequencing to identify PIK3CA mutations in FFPE tissue samples, which were
used as positive controls. The same mutations identified by Sanger sequencing were
found by ddPCR; however, ddPCR identified five additional mutations: two patients with
H1047R, one patient with E545K, and one patient with both H1047R and E545K (Table
1). In summary, ddPCR identified a total of 15 PIK3CA mutations while Sanger
Sequencing identified a total 10 PIK3CA mutations (Figure 3 and Table 1). The fraction
abundance of PIK3CA mutations in FFPE samples according to ddPCR ranged from
13.8% to 55.6% (Table 1).
3.2.1. Pre-Surgery Plasma: Droplet digital PCR identified 14 PIK3CA mutations in
ptDNA extracted from pre-surgery plasma (Figure 3 and Table 1). Given that ddPCR
identified a total of 15 PIK3CA mutations in gDNA of FFPE controls, we calculated the
sensitivity of ddPCR for detecting ptDNA mutations in pre-surgery plasma as 93.3%
(95% confidence interval 75.5% - 93.3%) and the specificity as 100% (95% confidence
interval 78.9% - 96.7%). Four patients presented E545K mutations with fractional
abundances ranging from 0.01% to 0.07% and ten patients presented with H1047R
mutations with fractional abundances ranging from 0.01% to 2.99% (Table 1).
3.2.2. Post-Surgery Plasma: Droplet digital PCR identified five patients with H1047R
mutations in ptDNA extracted from post-surgery plasma (Figure 3 and Table 1). Five
patients who had a PIK3CA mutation in their pre-surgery plasma had wild type loci in
11
their post-surgery plasma. Patient four, who presented both E545K and H1047R
mutations in pre-surgery plasma, showed mutant H1047R, but wild type E545K in post-
op plasma (Table 1).
3.3. Detection of PIK3CA Mutations in BCT and PAXgene Tubes: We analyzed if
BCT and PAXgene tubes prevent the lysis of lymphocytes, and if the release of genomic
DNA into peripheral blood by lysed lymphocytes hampers ptDNA detection by ddPCR.
We found that, on average, the total wild type DNA molecules in plasma stored one week
at room temperature in PAXgene tubes increased by a factor of 42.03 17.3 (95%
confidence interval 2.94, 81.12). This was not true for plasma collected in BCT tubes, for
which the ratio of DNA in plasma stored for one week versus DNA in plasma processed
the same day of phlebotomy was 1.107 0.19 (95% confidence interval 0.67, 1.54)
(Figure 4). We determined the basal level of DNA in each tube by quantifying DNA
molecules in plasma collected in EDTA tubes and processed the same day of phlebotomy
(Figure 4).
We analyzed the fraction abundance of E545K mutations in two patients whose
plasma samples had enough DNA in order to accurately detect mutations by ddPCR
without DNA pre-amplification. Patient four presented 7/1016 E545K mutant droplets
(0.69%) in plasma collected in EDTA tubes. We observed 7/911 (0.77%) mutant droplets
in plasma collected in PAXgene tubes and processed the same day of phlebotomy. The
mutant fraction abundance decreased to 2/1483 (0.13%) in PAXgene tubes stored for one
week at RT. On the other hand, we identified 1/1197 (0.08%) mutant droplets in plasma
collected in BCT tubes and processed the same of phlebotomy and 5/1026 (0.49%) in
plasma stored in BCT for one week at room temperature. The comparisons of mutant
12
fractional abundances across the five tubes were informative given that the total number
of droplets was consistent for all five samples, with an order of magnitude of 90,000
(Supplement 7).
Patient six presented 15/2368 E545K mutant droplets (0.63%) in plasma collected
in EDTA tubes, 4/1616 (0.24%) in plasma collected in PAXgene tubes, and 1/2488 in
plasma collected in BCT tubes (0.04%). The fraction abundance decreased to 2/3546
(0.04%) in plasma stored at room temperature for one week in PAXgene tubes and
increased to 4 /2484 (0.63%) in plasma stored at room temperature for one week in BCT
tubes. The total droplets for each tube were in the same order of magnitude (10,000);
however, the droplets for BCT samples were slightly lower than for PAXgene samples
(Supplement 8).
The rest of the eight patients showed an average mutant fractional abundance of
1/300; therefore, we were not able to conclude with precision if mutant droplets were true
PIK3CA mutations or artifacts. For precise mutation detection, these samples would
undergo pre-amplification of plasma DNA to yield a wild type background of at least
10,000 molecules, followed by repeated testing and analysis for PIK3CA mutations by
ddPCR.
4. Discussions
We showed that droplet digital PCR detects PIK3CA mutations in ptDNA of patients
with early stage breast cancer with high sensitivity and specificity. Higgins et al showed
that BEAMing of ptDNA correlates 100% with mutational status in patients with
metastatic breast cancer (7). BEAMing was not as sensitive with ptDNA from early stage
13
breast cancer patients, which is likely due to the low tumor burden in these patients. We
hypothesize that ddPCR was more sensitive than BEAMing because it has fewer
technical steps, reducing the chances of losing sample and the ability to analyze more
genome equivalents at a fraction of the time and cost compared to BEAMing.
The high sensitivity and specificity of ddPCR for detecting PIK3CA mutations in
ptDNA presents the opportunity of using blood as a “liquid biopsy” in patients with early
stage breast cancer despite their low levels of tumor burden. The use of blood for
biomarker detection can eliminate the necessity of accessing tumor tissue via invasive
biopsies in order to detect mutations by the traditional approach of Sanger sequencing
DNA from FFPE slides. This novel approach has several clinical implications. First,
blood can be drawn at different time points to monitor the response to therapies based on
varying levels of ptDNA. Persistent ptDNA levels can indicate negative response to
directed therapies and encourage physicians to change the current treatment. As a result,
physicians can make more informed decisions regarding changes in treatment and
consequently design more individualized therapies. Second, the fact that ddPCR detected
PIK3CA mutations in ptDNA circulating in plasma that was collected after surgery,
suggests that ddPCR can identify residual micrometastatic disease. The presence of
ptDNA in blood after local therapies, such as surgical removal of tumor tissue, can
indicate that cancer cells have not been eradicated from the body, and consequently
suggest that the patient would benefit from adjuvant systemic therapies. On the contrary,
the absence of ptDNA after local therapies can prevent the delivery of unnecessary
systemic therapies and the toxicity associated with them.
14
Furthermore, our results show that due to its high specificity, ddPCR can still detect
mutations in DNA extracted from FFPE samples that would otherwise show as wild type
by Sanger sequencing. It is possible that the additional mutations we found by ddPCR
were false positives. However, this is unlikely given that identical mutations were found
in ptDNA. The higher specificity of ddPCR could become a more precise molecular test
for identifying patients that are candidates for targeted therapies.
Additionally, ddPCR circumvents the hurdles of traditional sequencing of FFPE in
the following aspects. First, phlebotomy to obtain plasma samples is much less invasive
that tissue dissection. Second, the specificity of fluorescent probes can prevent false
negatives by sequencing tumor DNA from FFPE slides that may be contaminated with
normal DNA from adjacent cells. Third, the DNA assayed is not chemically degraded by
formalin, as it is the case for DNA extracted from FFPE slides.
In order to make this method universally accessible, we studied blood collection tubes
in which plasma can be stored and shipped to facilities with access to ddPCR. Our results
showed significant increases in DNA in plasma collected in PAXgene tubes that have
been left at room temperature for one week, which suggests that these tubes do not
prevent lymphocyte lysis under these conditions. We showed that for two patients, the
fraction abundance of mutant DNA detected by ddPCR decreased in plasma stored in
PAXgene tubes for one week at room temperature. This suggests that the significant
increase in background DNA decreases ptDNA signal and hampers the detection of
PIK3CA mutations.
15
The mutant fraction abundance in plasma collected in BCT tubes was much lower
than that in plasma collected in either EDTA or PAXgene tubes. This decrease cannot be
reliably explained by varying total number of droplets across reactions given that BCT
reactions did not show significant lower droplets compared to EDTA and PAXgene
reactions. Nonetheless, the fraction abundance in BCT after one week at RT increased to
levels comparable to our basal control. This suggests that the use of BCT tubes may be
the most optimal means of storing blood and not losing ptDNA signal.
This section of the study was not a test of sensitivity but rather an analysis of two
tube technologies; thus, we did not push the limits of sensitivity for detecting PIK3CA
mutations by pre-amplifying plasma DNA. Previous studies have suggested that
confident conclusions about the mutational status of a patient require an order of
magnitude of 10,000 wild type molecules as background (25), which was not obtained in
the meta-wells for any of the ten patients assayed in the blood collection tube study. Our
results are consistent with previous data showing that very low levels of cell free DNA
circulate in blood plasma and that pre-amplification may be necessary to confidently
conclude the mutational status of a patient. Future studies with pre-amplification of
ptDNA should be carried out to conclude if contamination of cell free DNA with
genomic DNA from bursting lymphocytes in PAXgene tubes affect mutational analysis
by ddPCR.
We propose that ddPCR is a reliable clinical tool for the detection of cancer
biomarkers. This method can replace the traditional approach of detecting biomarkers in
tissue, reducing the invasive nature of biopsies and/or surgeries to obtain tissue, and
providing more sensitive results that Sanger sequencing of DNA extracted from tissue.
16
We have demonstrated that ddPCR can reliably detect mutations in plasma stored in BCT
blood collection tubes for one week at room temperature. This suggests that blood from
patients at any location could be sent in BCT tubes to facilities with access to ddPCR,
making this novel method universally accessible. We hope that the use of ddPCR will
take clinical oncology a step further towards personalized medicine by providing
physicians with an accurate method of monitoring tumor DNA levels in each patient and
tailoring directed therapies accordingly.
17
5. Figures
Figure 1: Schematic of the experimental approach. Primary tissue was collected for 29
patients via surgery in the form of FFPE slides. Tumor and non-cancerous genomic DNA
was extracted from FFPE slides and Sanger sequenced to determine the mutational status
of each patient. BEAMing was used to query PIK3CA mutations in cell-free DNA in
plasma obtained before and after surgery. Alternatively, droplet digital Polymerase Chain
Reaction (PCR) was used to query identical PIK3CA mutations in pre-surgery plasma,
post-surgery plasma and FFPE genomic DNA.
18
Figure 2: Sanger sequencing of formalin-fixed paraffin embedded primary breast
tissue. Tumor and normal DNA were extracted from primary tissue. DNA regions
spanning E545 and H1047 of the PIK3CA gene was amplified and sequenced. Sanger
sequencing of tissue DNA from one patient with E545K mutation. A) Normal tissue
showing wild type E545 depicted by guanidine at position 1633 in exon 9 of the PIK3CA
gene (control). B) Tumor tissue denoting E545K 1633G>A mutation in exon 9 of the
PIK3CA gene.
19
Table 1: Detection of PIK3CA mutations in primary tumors and blood plasma.
Summary of E545K or H1047R mutations identified in early stage breast cancer patients
(n=29) by Sanger sequence, BEAMing and ddPCR. Percents represent the mutant
fractional abundance determined by ddPCR. WT denotes wild type loci for both E545K
and H1047R.
20
Figure 3: PIK3CA mutations found in FFPE and plasma of early stage breast cancer
patients. ddPCR detected more PIK3CA mutations than BEAMing or Sanger sequencing
A) BEAMing on pre-surgery plasma identified only one of the seven H1047R mutations
identified in tissue by sequencing and two of three E545K mutations found in FFPE
controls. B) ddPCR identified in plasma all ten H1047R identified by Sanger sequencing
of FFPE DNA but only four E545K mutations of five identified in FFPE DNA by
sequencing. C) ddPCR identified a total of fifteen PIK3CA mutations in FFPE DNA
while Sanger sequencing identified a total of ten PIK3CA mutations FFPE DNA.
21
22
Figure 4: Quantification of wild type DNA molecules in plasma collected in
PAXgene and BCT Streck tubes by ddPCR for a single patient. Wild Type DNA
molecules were quantified by the ddPCR software according to the detection of
fluorescent signal from HEX labeled-oligonucleotides querying wild type sequence in
exon 9. Equivalent amounts of DNA were observed in plasma collected in BCT Streck
tubes processed the same day of phlebotomy versus seven days after. In contrast, a
sixteen-fold increase in DNA concentration was observed in blood collected in PAXgene
and left unprocessed at room temperature for seven days. Blood collected in EDTA tubes
and processed the same day of phlebotomy was used as a reference for DNA basal levels.
Water was used as a negative control and the cell line MCF10A was used as a positive
control for DNA detection.
23
Figure 5: Detection of PIK3CA mutations in ptDNA of plasma collected in EDTA,
BCT Streck and PAXgene tubes. ddPCR 1D amplitude plots for exon 9 loci in patient
nine in metastatic cohort. A) Fluorescent droplets for 6-FAM probe for mutant E545K
DNA sequences. B) Fluorescent droplets for HEX probe for wild type exon 9 sequences.
MCF7 cell line was used as a positive control and MCF10A as a negative control for
E545K sequences. Water was used as a negative control.
24
6. Supplementary Material.
Supplement 1: Thermocycling conditions for amplification of PIK3CA loci
Step Temperature (ºC) Time (sec) Cycles
1 98 10 4
2 64 15 4
3 61 15 4
4 58 15 4
5 72 90 4
6 98 10 30
7 55 15 30
8 72 90 30
9 4 hold 1
Supplement 2: PCR amplification and nested sequencing primers for FFPE sequencing
PIK3CA
locus
Exon Size (bp) Forward Primer
(5’ -3’)
Reverse Primer
(5’ -3’)
Sequencing Primer
(5’ 3’)
E545K 9 132 ttacagagtaacagactagc cttacctgtgactccatagaa gctagagacaatgaattaaggg
H1047R 20 132 gatgacattgcatacattcg gtggaagatccaatccattt cgaaagaccctagccttag
Supplement 3: Primers and Taqman probes used for ddPCR
A. FFPE
PIK3CA
locus
Size
(bp)
ddPCR Forward
Primer 5’-3’
ddPCR Reverse
Primer 5’-3’
Wild Type Probe Sequencing Primer
(5’ 3’)
E545K 91 tcaaagcaatttctacac
gagatcct
ctccattttagcactta
cctgtgac
VIC-
ctctctgaaatcactgag
cag-MGB-3’
6FAM-
ctctgaaatcactaagcag-
MGB-3’
H1047R 98 gcaagaggctttggagt
atttcatg
gctgtttaattgtgtgg
aagatccaa
VIC-
ccaccatgatgtgcatc-
MGB-3’
6FAM-
caccatgacgtgcatc-
MGB-3’
B. ptDNA
PIK3CA
locus
Size
(bp)
ddPCR Forward
Primer 5’-3’
ddPCR Reverse
Primer 5’-3’
Wild Type Probe Sequencing Primer
(5’ 3’)
E545K 97 aaaatgacaaagaaca
gctcaaag
acttacctgtgactccata
gaaaatc
VIC-
tctgaaatcactgagcagg-
MGB3’
6FAM-
ctgaaatcactaagcagg-
MGB-3’
H1047R 80 gagcaagaggctttgg
agtattt
atccaatccatttttgttgtc
c
VIC-ccaccatgatgtgca-
MGB-3’
6FAM-
ccaccatgacgtgca-
MGB-3’
25
Supplement 4: Thermocycling conditions for ddPCR using Taqman probes
Step Temperature (ºC) Time Cycles
1 95 10 min 1
2 94 30 sec 40
3 58 1 min 40
4 98 10 min 1
5 4 hold 1
Supplement 5: Primers and labeled-oligonucleotide probes used for ddPCR on ptDNA
samples of metastatic patient cohort
PIK3CA
locus
Size
(bp)
ddPCR Forward
Primer 5’-3’
ddPCR Reverse
Primer 5’-3’
Wild Type Probe Sequencing Primer
(5’ 3’)
E545K 91 caaagcaatttctacacg
agatcct
ctccattttagcacttacct
gtgact
HEX-
ctctgaaatcactgagcag
gagaaagatt-Iowa
Black(w/Zen)
6’-FAM-
ctctgaaatcactaagcaggag
aaagattt-Iowa
Black(w/Zen)
H1047R
98
ctgagcaagaggctttg
gag
gtggaatccagagtgag
ctt
HEX-
tgaatgatgcacatcatgg
tggct-Iowa
Black(w/Zen)
6-FAM-
tgaatgatgcacgtcatggtgg
ct-Iowa Black(w/Zen)
Supplement 6: Thermocycling conditions for ddPCR using labeled-oligonucleotide
probes
Step Temperature (ºC) Time Cycles
1 95 10 min 1
2 94 30 sec 40
3 64 1 min 40
4 98 10 min 1
5 4 hold 1
26
Supplement 7: Mutant droplets detected by ddPCR in 10 patients with metastatic breast
cancer compared to total droplets. Each section represents the summation of eight
multiple replicates for a single meta-well. Due to technical issues a meta-well of four
replicates was done for patient two. Patient four and six were used for assessing the
detection of PIK3CA mutations.
Mutant Fraction Abundance Determined by Droplet Digital PCR
Patient Mutation Tube Mutant
Droplets
Total Positive
Droplets
Total
Droplets
1
E545K
EDTA 0 533 100720
PAXgene 2 238 87915
BCT 1 174 143160
PAXgene 1 week 2 1621 100274
BCT 1 week 0 558 74947
H1047R
EDTA 0 221 104013
PAXgene 0 169 109608
BCT 1 65 61546
PAXgene 1 week 1 10149 98831
BCT 1 week 0 184 98557
2
E545K
EDTA 5 35 56724
PAXgene 8 44 47750
BCT 2 23 44112
PAXgene 1 week 3 26 49122
BCT 1 week 5 43 50228
H1047R
EDTA 0 12 27913
PAXgene 0 18 43076
BCT 0 26 42117
PAXgene 1 week 0 467 43582
BCT 1 week 1 22 37189
3
E545K
EDTA 6 200 89971
PAXgene 2 250 92095
BCT 2 318 87745
PAXgene 1 week 0 2466 97247
BCT 1 week 2 303 91954
H1047R
EDTA 0 93 77984
PAXgene 0 103 83888
BCT 0 124 85529
PAXgene 1 week 0 1406 91141
BCT 1 week 6 129 85201
27
Mutant Fraction Abundance Determined by Droplet Digital PCR
Patient Mutation Tube Mutant
Droplets
Total Positive
Droplets
Total
Droplets
4
E545K
EDTA 7 1016 90202
PAXgene 7 911 92602
BCT 1 1197 91060
PAXgene 1 week 2 1483 92770
BCT 1 week 5 1026
H1047R
EDTA 0 398 90534
PAXgene 0 562 95899
BCT 1 592 95650
PAXgene 1 week 1 1073 94173
BCT 1 week 0 634 87902
5
E545K
EDTA 0 398 91347
PAXgene 0 346 98908
BCT 1 390 92916
PAXgene 1 week 0 5721 90210
BCT 1 week 2 289 89833
H1047R
EDTA 2 194 97026
PAXgene 3 194 109901
BCT 4 189 86453
PAXgene 1 week 4 4293 99527
BCT 1 week 3 159 95527
6
E545K
EDTA 15 2368 97625
PAXgene 4 1616 107229
BCT 1 2488 99036
PAXgene 1 week 2 3546 100068
BCT 1 week 4 2484 94916
H1047R
EDTA 2 956 103064
PAXgene 6 530 1008294
BCT 0 720 100717
PAXgene 1 week 0 1016 100659
BCT 1 week 1 545 102741
7
E545K
EDTA 0 201 108778
PAXgene 1 162 109292
BCT 0 326 96233
PAXgene 1 week 0 12976 89797
BCT 1 week 0 123 91227
H1047R
EDTA 2 70 96675
PAXgene 2 59 76701
BCT 0 67 103580
PAXgene 1 week 1 6696 82242
BCT 1 week 0 60 94210
28
Mutant Fraction Abundance Determined by Droplet Digital PCR
Patient Mutation Tube Mutant
Droplets
Total Positive
Droplets
Total
Droplets
8
E545K
EDTA 0 59 105821
PAXgene 3 84 106403
BCT 0 76 99105
PAXgene 1 week 2 1988 101854
BCT 1 week 0 35 92371
H1047R
EDTA 2 15 103629
PAXgene 2 37 103074
BCT 1 39 106096
PAXgene 1 week 2 1361 109908
BCT 1 week 0 17 96087
9
E545K
EDTA 0 307 95852
PAXgene 0 301 96658
BCT 1 244 97329
PAXgene 1 week 1 2709 96831
BCT 1 week 3 297 96149
H1047R
EDTA 1 118 103275
PAXgene 3 113 93519
BCT 0 100 106539
PAXgene 1 week 0 2572 98869
BCT 1 week 2 96 99176
10
E545K
EDTA 0 377 104257
PAXgene 1 154 106526
BCT 0 218 102827
PAXgene 1 week 2 24085 107471
BCT 1 week 2 297 105545
H1047R
EDTA 2 184 103852
PAXgene 1 122 110279
BCT 0 107 95464
PAXgene 1 week 1 16059 108099
BCT 1 week 0 140 104550
29
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31
PATRICIA L. VALDA TORO 3900 N. Charles St. Apt 916. Baltimore, MD 21218
Cell phone: 410-336-3292
EDUCATION
Johns Hopkins University Baltimore, MD
Master of Science in Molecular and Cellular Biology Sept 2013 - May 2014
Thesis: Detection of PIK3CA Mutations in Plasma Tumor DNA
Circulating in Peripheral Blood of Breast Cancer Patients
Bachelor of Science in Molecular and Cellular Biology Sept 2009 - May 2013
Science GPA: 3.88, Cumulative GPA: 3.86
HONORS - Graduated with General Honors and Departmental Honors
- Dean’s list: Fall (2009) - Spring (2013)
- Commemorated in JHMI Milestone Celebration for being on JHU Dean’s List (2012).
- TriBeta National Biological Honor Society: membership (2012 - Present).
- The Latino Pre-Health Honor Society of The Johns Hopkins
University: Co Vice President (2012 - Present).
- Golden Key International Honor Society: Diploma and membership: top 15% GPA
range among juniors and seniors at JHU (2011 - Present).
- The National Society of Collegiate Scholars: Academic excellence, diploma and
membership (2010 - present).
Saint Andrew’s School La Paz, Bolivia
High School Diploma Jan 2005 – Nov 2008
Cummulative GPA: 4.0 (4.0 scale).
HONORS - Best GPA in entire highschool (300 students, 4 years)
- Academic Excellence Diploma Saint Andrew’s School (12 years).
- National Asociation of Private Schools: best GPA in Saint Andrew’s School (4 years).
- Municipal Government: academic outstanding among 100 private schools in La Paz (2008).
- The National Society of High School Scholars: certificate academic excellence (2008).
- Global Young Leaders Conference: certificate for acedmic performance and leadership (2008).
PUBLICATIONS AND PRESENTATIONS - Jelovac D, Beaver J.A., Balukrishna S, Wong H.Y., Valda Toro P, et al. A PIK3CA mutation
detected in plasma from a patient with synchronous primary breast and lung cancers. Human
Pathology. 2013; 45: 880–883
- Beaver J.A., Jelovac D, Balukrishna S, Valda Toro P, et al. Detection of of Cancer Specific
Mutations in Plasma of Early Stage Breast Cancer Patients. Clin Can Res. 2013; 20:1709-1718
- Cochran R. Cravero K, Chu D, Valda Toro P., et al. Analysis of BRCA2 loss-of-heterozygosity
in tumor tissue using droplet digital PCR. Human Pathology. 2014. Accepted manuscript.
- Beaver J.A., Valda P, et al. Abstract SY11-01: Plasma tumor DNA: Changing the paradigm for
administering systemic therapies. Cancer Research. 2013; 73: 1538-7445
32
- Beaver J.A., Balukrishna S, Valda Toro P, Sensitivity for Detecting PIK3CA Mutations in Early-
Stage Breast Cancer with Droplet Digital PCR. ASCO. 2013. Abstract Annual Meeting
- Valda Toro P. Plasma tumor DNA identifies cancer specific PIK3CA mutations in early stage
breast cancer through BEAMing. Johns Hopkins University. 2013. Poster presentation in the Tri-
Beta Annual Poster Session
RESEARCH EXPERIENCE Research Assistant, Laboratory of Ben Ho Park, M.D., Ph.D. Baltimore, MD
The Sidney Kimmel Comprehensive Cancer Center at Johns Hopkins June 2012- Present
- Design a novel protocol for selecting genomic rearrangements using DNA pull-down.
- Studied the feasibility and optimization of detecting cancer in the peripheral blood of patients
using digital PCR platforms.
- Assist in the analysis of genetic variances using conventional PCR followed by Sanger
Sequencing and droplet digital PCR.
- Maintain laboratory equipment, monitor inventory supplies and clean working areas.
Research Assistant, Laboratory of Trina Schroer, Ph.D. Baltimore, MD
Biology Department, Johns Hopkins University . July 2011 – May 2012
- Studied the binding orientation between subunits of the motor protein dynein and it adaptor.
- Analyzed protein constructs cloned in bacterial expression vectors.
- Attended weekly lab meetings.
TEACHING EXPERIENCE Teaching Assistant,Biology Department Baltimore, MD
Johns Hopkins University Sept 2013- Present
- Teach hands-on laboratory techniques for biological research to 20 undergraduates weekly.
- Grade assignments, proctor exams and help students understand basic biological concepts.
Tutor, The Learning Den, Baltimore, MD
Johns Hopkins University Jan 2011 – April 2013
- Tutored groups of 6 undergraduates in problem-solving in Organic Chemistry and Spanish.
EXTRACURRICULAR ACTIVITIES Co Vice-President, Lambda Epsilon Mu Baltimore, MD
The Latino Pre-Health Honor Society of Johns Hopkins University April 2013 – Present
- Contact physicians and other healthcare representatives for participation in university events.
- Coordinate visits to medical conferences and alocate resources for transportation.
Community Service Chair, Golden Key International Honor Society Baltimore, MD
Johns Hopkins University Chapter Sept 2012 – May 2013
- Mobilized funds, recruited volunteers, and contacted representatives for service opportunities.
- Organized a fundraiser with Medlife student group for mobile clinics in South America.
Secretary, Advertising Chair, SALUD Baltimore, MD
Johns Hopkins Latino Hispanic Health Initiative Group April 2011 – May 2013
- Managed the email account, outreached to find guest speakers.
- Organized meetings, designed an anual agenda of events, designed advertisements.
- Launched a new tutoring project for children struggling with English as a second language to
accomadate new volunteers.
33
COMMUNITY SERVICE Volunteer, Hospital del Niño La Paz, Bolivia
Public Pediatric and Adolescent Hospital Dec 2013
- Documented medical histories for diagnosis during clinical rounds.
- Took dictations during outpatient consultations in hospital units.
- Admitted patients and healed wounds/burns in the Emergency Room.
Volunteer, Home Care for a Patient with Multiple Sclerosis Baltimore, MD
- Help with paperwork, email account and schedule. Sept 2013 – Present
- Feed the patient and assist her with physical therapy.
Volunteer, Baltimore City Health Department East Carolina Clinic Baltimore, MD
Sexually Transmitted Disease Division March 2010 – April 2011
- Served as a Spanish-English translator during outpatient consultations.
- Assisted patients in interpreting test results, organized medical records.
SKILLS
Laboratory: Droplet Digital PCR, Taqman probe/primer/nested primer design, WGA, Sanger
Sequencing, tissue culture, DNA extraction (plasma, FFPE), protein and DNA Magnetic Bead
Pulldown, PCR optimization, digestions, dsDNA fragmentation, Random Hexamer DNA
elongation, clonning, transformations into E. coli, protein purification by affinity
chormatography, gel filtration and sucrose gradient sedimentation, SDS-PAGE, Western Blots.
Systems: Bio-Rad QX100TM
ddPCRTM
, NCBI Blast, ApE. A Plasmid Editor, CLC Sequence
Viewer 6, Finch TV—DNA sequence chromatogram trace viewer, MS Office (Windows/Mac).
Certifications: HIPAA, Lab Assitant: Lab and Fire Safety, Hazard Communication, Compliance
Awareness (Hopkins Medicine).
Language: Fluent in Spanish-English.
