Breast Cancer Subtype Classification Using 4-Plex Droplet Digital PCR

Abstract BACKGROUND Infiltrating ductal carcinoma (IDCA) is the most common form of invasive breast cancer. Immunohistochemistry (IHC) is widely used to analyze estrogen receptor 1 (ER), progesterone receptor (PR), and human epidermal growth factor receptor 2 (HER2) that can help classify the tumor to guide the medical treatment. IHC examinations require experienced pathologists to provide interpretations that are subjective, thereby lowering the reproducibility of IHC-based diagnosis. In this study, we developed a 4-plex droplet digital PCR (ddPCR) for the simultaneous and quantitative analyses of estrogen receptor 1 (ESR1), progesterone receptor (PGR), erb-b2 receptor tyrosine kinase 2 (ERBB2), and pumilio RNA binding family member 1 (PUM1) expression levels in formalin-fixed paraffin-embedded (FFPE) samples. METHODS We evaluated the sensitivity, reproducibility, and linear dynamic range of 4-plex ddPCR. We applied this method to analyze 95 FFPE samples from patients with breast IDCA and assessed the agreement rates between ddPCR and IHC to evaluate its potential in classifying breast cancer subtypes. RESULTS The limits of quantification (LOQ) were 25, 50, 50, and 50 copies per reaction for ERBB2, ESR1, PGR, and PUM1, respectively. The dynamic ranges of ESR1, PGR, and PUM1 extended over 50–1600 copies per reaction and those of ERBB2 from 25 to 1600 copies per reaction. The concordance correlation coefficients between 4-plex ddPCR and IHC were 96.8%, 91.5%, and 85.1% for ERBB2, ESR1, and PGR, respectively. Receiver operating characteristic curve area under the curve values of 0.991, 0.977, and 0.920 were generated for ERBB2, ESR1, and PGR, respectively. CONCLUSIONS Evaluation of breast cancer biomarker status by 4-plex ddPCR was highly concordant with IHC in this study.

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A single droplet digital PCR for ESR1 activating mutations detection in plasma

10.1101/507608 ◽

2019 ◽

Author(s):

Emmanuelle Jeannot ◽

Lauren Darrigues ◽

Marc Michel ◽

Marc-Henri Stern ◽

Jean-Yves Pierga ◽

...

Keyword(s):

Breast Cancer ◽

Estrogen Receptor ◽

Metastatic Breast Cancer ◽

Limit Of Detection ◽

Growth Factor Receptor ◽

Metastatic Breast ◽

Digital Pcr ◽

Droplet Digital Pcr ◽

Plasma Samples ◽

Activating Mutations

AbstractBackgroundActivating mutations in the estrogen receptor 1 (ESR1) gene are recurrent mechanisms of acquired resistance to aromatase inhibitors (AI), and may be the target of other selective estrogen receptor down-regulators. To assess the clinical utility of monitoring ESR1 resistant mutations, a droplet digital PCR (ddPCR)-based assay compatible with body fluids is ideal due to its cost-effectiveness and quick turnaround.MethodsWe designed a multiplex ddPCR, which combines a drop-off assay, targeting the clustered hotspot mutations found in exon 8, with another pair of probes interrogating the E380Q mutation in exon 5. We assessed its sensitivity in vitro using synthetic oligonucleotides, harboring E380Q, L536R, Y537C, Y537N, Y537S or D538G mutations. Validation of the assay was performed on plasma samples from a prospective study and compared to next generation sequencing (NGS) data.ResultsThe multiplex ESR1-ddPCR showed a high sensitivity with a limit of detection ranging from 0.07 to 0.19% in mutant allele frequency depending on the mutation tested. The screening of plasma samples from patients with AI-resistant metastatic breast cancer identified ESR1 mutations in 29% of them with perfect concordance (and higher sensitivity) to NGS data obtained in parallel. Additionally, this test identifies patients harboring polyclonal alterations. Furthermore, the monitoring of ctDNA using this technique during treatment follow-up predicts the radiological response to palbociclib-fulvestrant.ConclusionThe multiplex ESR1-ddPCR detects, in a single reaction, the most frequent ESR1 activating mutations and is compatible with plasma samples. This method is thus suitable for real-time ESR1 mutation monitoring in large cohorts of patients.Statement of translational relevanceExons 5 and 8 mutations in ESR1 are recurrent mechanisms of resistance to aromatase inhibitors (AI) in estrogen receptor (ER)-positive metastatic breast cancer and may be targeted by selective ER down-regulators. We implemented a novel droplet digital PCR, which allows for the detection of the most frequent ESR1 mutations in circulating cell-free DNA. In prospectively collected plasma samples, ESR1 mutations were found in 29% of AI-resistant patients, with excellent concordance and higher sensitivity to next generation sequencing. Moreover, circulating ESR1 mutations appear to be reliable markers for ctDNA monitoring in order to predict treatment response. Ultimately, the short turnaround time, high sensitivity and limited cost of the ESR1-ddPCR are compatible with repeated samplings to detect the onset of resistance to AI before the radiological progression. This opens a window of opportunity to develop new clinical strategies for breast cancer hormone therapy, as tested in an ongoing phase 3 trial.List of abbreviationsAIAromatase InhibitorcfDNACell-free DNActDNACirculating tumor DNAddPCRDroplet digital PCRER+ HER2-MBCER+ HER2-negative Metastatic Breast CancerEREstrogen ReceptorER+Estrogen Receptor positiveLOBLimit of blankLODLimit of detectionMAFMutant Allele FrequencyPBMCPeripheral blood mononuclear cellsPDProgressive diseaseSDStandard deviationToPTime of progressionWTWild typeHuman genesESR1: Estrogen Receptor 1HER2: Human Epidermal Growth Factor Receptor 2EGFR: Epithelial Growth Factor ReceptorKRAS: KRAS proto-oncogene, GTPaseBRAF: B-Raf Proto-Oncogene, Serine/Threonine kinase

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Sensitivity for detecting PIK3CA mutations in early-stage breast cancer with droplet digital PCR.

Journal of Clinical Oncology ◽

10.1200/jco.2013.31.15_suppl.11019 ◽

2013 ◽

Vol 31 (15_suppl) ◽

pp. 11019-11019 ◽

Cited By ~ 1

Author(s):

Julia A. Beaver ◽

Sasidharan Balukrishna ◽

Danijela Jelovac ◽

Michaela Jane Higgins ◽

Stacie Jeter ◽

...

Keyword(s):

Breast Cancer ◽

Digital Pcr ◽

Droplet Digital Pcr ◽

Wild Type ◽

Pik3ca Mutations ◽

Tumor Specimen ◽

Ffpe Samples ◽

Exon 9 ◽

Hotspot Mutations ◽

Exon 20

11019 Background: PIK3CA is mutated in up to 30% of breast cancers. Classically somatic mutations are identified by Sanger sequencing of the primary tumor specimen. However third generation droplet digital PCR technologies offer a novel platform for quantitative mutation detection with improved sensitivity. Methods: Thirty stage I-III breast cancer patients were consented on an IRB-approved prospective repository study at Johns Hopkins for collection of their primary breast tumor specimen. Formalin-fixed paraffin embedded (FFPE) samples were analyzed by standard sequencing for three PIK3CA hotspot mutations. The DNA from these samples was then analyzed using the RainDrop digital PCR platform with TaqMan probes in a triplex format to simultaneously detect and quantitate hotspot mutations and genome equivalents. Results are expressed as a percentage of mutant to wild-type PIK3CA molecules for each sample. Results: Standard sequencing of all tumors (n=30) identified seven PIK3CA Exon 20 mutations (H1047R) and three Exon 9 mutations (E545K). Samples were scored as PIK3CA mutation positive by digital PCR if the tumor DNA contained at least 5% mutant molecules. All ten mutations identified by sequencing were verified by digital PCR with quantities of mutant molecules ranging from 20.3-55.6% in a given sample. Digital PCR identified additional PIK3CA mutations that were wild type by standard sequencing including three mutant Exon 20 samples, two mutant Exon 9 samples and one sample with an Exon 20 and Exon 9 mutation. Quantities of mutant molecules in these additional samples ranged from 5-28.9%. Conclusions: RainDrop digital PCR offers improved sensitivity and quantification for detecting PIK3CA mutations in FFPE samples using nanograms of DNA. Additional mutations identified by digital PCR may reflect genetic heterogeneity or possibly tissue contamination. The clinical utility of identifying a small proportion of mutations is unknown but may impact eligibility for targeted therapies and clinical trials. Ongoing studies will also address whether the identification of solid tumor mutations in circulating cell-free plasma DNA by digital PCR can improve diagnostics and aid in therapeutic decisions.

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