Opinion

Cancerous tissue is highly heterogeneous and cancer biomarkers vary across types ofdisease and stages of disease`s progression, this complicates cancer detection andidentification at early stages [1]. The cancer identification of mutations present in anindividual tumor often rely on analysis of biopsy or cytology samples, where only asmall fraction of the tumor is analyzed, and may not provide a complete representation oftumor heterogeneity, these has significant disadvantages, because low abundancemutations relative to wild type DNA [2]. In addition, the sub clonal populations of cellswithin a tumor may contain a mutation that differs from the primary mutation, and thesub clonal mutation could be correlated to a prognosis and/or a response to personalizedtreatments [1]. Sequencing is still the gold standard for mutations identification, its costis steadily decreasing as much effort is made to reduce the cost and also to improve thedata interpretation in downstream analysis of next-generation sequencing [3], despitethis, its use remains limited for the diagnostic in a routine laboratory. The detection ofmutations is performed mainly by real-time quantitative PCR (qPCR) [4], but thistechnique presents limitations including preferential amplification of small fragments,production of chimeric sequences, the amplify all alleles with approximately equalefficiency comparable to their initial concentrations, and difficulty in detecting lowabundance or poorly represented sequences [5].

In the last years, a technology calleddigital PCR (dPCR) has become commercialized. As in qPCR, fluorescent dyes areincluded in the DNA amplification reaction. However, unlike qPCR the amplificationreaction in dPCR is divided into thousands of individual reactions prior to amplification.These partitioning can be achieved by using microwell plates, capillaries, oil emulsions,or arrays. Ideally, partitioning occurs such that each individual reaction mixturecontains either a single target molecule or none at all[4]. The acquisition of data atreaction end point, and the number of positive (fluorescent) and negative partitions iscounted, the target copy number in the sample is calculated based on the number ofpositive and negative partitions [6]. The Poisson’s Law is used to accurately calculate the number of DNA targets per partition and the copy number in the original sample.These offers the advantage of quantify directly the absolute concentration of targetspresent in a DNA sample without the need for external calibrators [7], it is lesssusceptible to PCR inhibition and high background DNA levels in samples such asDNA isolated from FFPE biopsies [8]. The sensitivity is significantly higher thanqPCR, the accuracy and precision of the assay improves by counting larger numbers ofmolecules individually.

These detection limits facilitate the detection of minor alleles,such as in circulating tumor DNA, with a relatively simple and non-invasive approachto monitoring disease recurrence, which requires a high sensitivity of mutation detectionto provide effective therapies at the earliest stage of progression. Cases such as thedetection of the BRAFV600E mutation as well as for followup monitoring todetermine the treatment response in patients with malignant melanomas [9,10], thedetection of mutated genes in liquid biopsies for metastatic colorectal cancer [11,12],the detection mutant KRAS and TP53 in circulating exosomes for pancreatic cancer[13],the detection of minimal residual disease with BCR-ABLtranslocationsforlymphoproliferative disorders [14], all of them reflect the need tools to mutationdetection highly sensitives, the which is relevant to for determine the treatmentresponse. The dPCR technology, being promissory for the detection of mutations in therange of 0.001% of occurrence and maximized to enable transformational advances incancer research, could be this such seeked tool. However, the implementation of dPCRassays should be undertaken after some consideration, although dPCR has severalspecific advantages over qPCR, dPCR is not likely to replace all qPCR assays in theclinical laboratory. Also requires a precise standardization that varies from one platformto another.

References

1. Milbury C, Zhong Q, Lin J, Williams M, Olson J, et al. (2014) Determining lower limits of detection of digital PCR assays for cancer-related gene mutations. Biomolecular Detection and Quantification 1: 8-22.
2. Jackson J, Choi D, Luketich J, Pennathur A, Ståhlberg A, et al. (2016) Multiplex Preamplification of Serum DNA to Facilitate Reliable Detection of Extremely Rare Cancer Mutations in Circulating DNA by Digital PCR. Journal Molecular Diagnostics 18(2): 235-243.
3. Song Ch, Castellanos Rizaldos E, Bejar R, Ebert B, Makrigiorgos GM (2015) DMSO increases mutation-scanning detection sensitivity in clinical samples using high resolution meeting. Clin Chem 61(11): 1354-1362.
4. Kuypers J, Jerome K (2017) Applications of Digital PCR for Clinical Microbiology. J Clin Microbiol 55(6): 1621-1628.
5. Hudecova I (2015) Digital PCR analysis of circulating nucleic acids. Clinical Biochemistry 48: 948-956.
6. Basu A (2017) Digital Assays Part I: Partitioning Statistics and Digital PCR. SLAS Technology 22(4): 369-386.
7. Taylor S, Laperriere G, Germain H (2017) Droplet Digital PCR versus qPCR for gene expression analysis with low abundant targets: from variable nonsense to publication quality data. Scientific Reports 7: 2409.
8. Nadauld L, Regan J, Miotke L, Pai R, Longacre T, et al. (2012) Quantitative and Sensitive Detection of Cancer Genome Amplifications from Formalin Fixed Paraffin Embedded Tumors   with   Droplet   Digital      Transl Med (Sunnyvale) 2(2): 87.         
9. Burjanivova T, Malicherova B, Grendar M, Minarikova E, Dusenka R, et al. (2019) Detection of BRAFV600E Mutation in Melanoma Patients by Digital PCR of Circulating    GEN   TESTING   AND MOLECULAR BIOMARKERS.
10. Dong L, Wang X, Wang Sh, Duc M, Niu Ch, et al. (2020) Interlaboratory assessment of droplet digital PCR for quantification of BRAF V600E mutation using a novel DNA reference material. Talanta 207: 120293.
11. Furuki H, Yamada T, Takahashi G, Iwai T, Koizumi M, et al (2018) Evaluation of liquid biopsies for detection of emerging mutated genes in metastatic colorectal cancer. European Journal of Surgical Oncology 44(7): 975-982.
12. Liebs S, Keilholz U, Kehler I, Schweiger C, Haybäck J, et al. (2019) Detection of mutations in circulating cell free DNA in relation to disease stage in colorectal cancer. Cancer Medicine 8(8): 3761-3769.
13. Yang S, Che S, Kurywchak P, Tavormina J, Gansmo L, et al. (2017) Detection of Mutant KRAS and TP53 DNA in Circulating Exosomes from Healthy Individuals and Patients with Pancreatic Cancer. Cancer Biol Ther 18(3): 158-165.
14. Drandi D, Ferrero S, Ladetto M (2018). Droplet Digital PCR for Minimal Residual Disease Detection in Mature Lymphoproliferative Disorders. Digital PCR: Methods and Protocols, Methods in Molecular Biology 1768.

Abstract

Cancerous tissue is highly heterogeneous and cancer biomarkers vary across types ofdisease and stages of disease`s progression, this complicates cancer detection andidentification at early stages [1]. The cancer identification of mutations present in anindividual tumor often rely on analysis of biopsy or cytology samples, where only asmall fraction of the tumor is analyzed, and may not provide a complete representation oftumor heterogeneity, these has significant disadvantages, because low abundancemutations relative to wild type DNA [2]. In addition, the sub clonal populations of cellswithin a tumor may contain a mutation that differs from the primary mutation, and thesub clonal mutation could be correlated to a prognosis and/or a response to personalizedtreatments [1].