Fragment Analysis: Versatility that Fuels Cancer Research

Despite more than half a century of intensive research, cancer remains an unconquered disease.  However, great strides have been made in understanding the link between genomic alterations and tumorigenesis.  Tools that can analyze the genome are therefore instrumental in understanding the mechanisms that lead to cancer, detecting cancerous cells, and providing avenues to research cancer treatments. Applied Biosystems has been at the forefront of development of these tools and has a deep portfolio of solutions that are used in cancer research.

Beyond sequencing

Analysis of fragment length by Capillary Electrophoresis (CE)

Fragment analysis is a highly flexible method that involves the separation of different-sized and differentially labeled DNA fragments by CE. One of the most common methods of generating fragments for analysis is polymerase chain reaction (PCR); because of the flexibility afforded with the choice of PCR primers, a specifically sized fragment corresponding to a PCR target sequence is straightforward to generate.  Along with the ability to label fragments with up to four different fluorophores, researchers have a large degree of flexibility in experimental design.  Cancer researchers rely on fragment analysis on CE genetic analyzers as an important tool for understanding genetic anomalies in many different kinds of cancers.

Detecting microsatellite instability

A hallmark of some tumor types, including colorectal, gastric, endometrial and other tumors, is dysregulation of DNA mismatch repair (MMR) enzymes, resulting in genomic instability (1).  One way to detect genomic instability is by analyzing length changes in short, repetitive sequences that are scattered throughout the genome.  This is known as microsatellite instability (MSI analysis) and is typically performed by analyzing fragment length changes in these microsatellite sequences.

MSI analysis has been performed by designing primer sets against desired microsatellite loci (for a recent example, see 2) or by using a kit optimized for MSI analysis (3,4).  Although NGS can be used for MSI analysis, reading through and aligning the highly repetitive sequences can be challenging, and recent results suggest that aligning to standard reference genomes can introduce undesirable bias (5).  Because fragment analysis data results do not have to be aligned to a reference, it is a comparatively unambiguous method for analyzing microsatellite instability.

Analyzing changes in repeat length in specific genes

Loci other than microsatellite repeats can demonstrate changes in repeat length, and some of these have been tied to tumor formation.  One of the most studied of these is the FMS-like tyrosine kinase 3 gene (FLT3), involved in crucial steps of hematopoiesis. The most common form of FLT3 mutation in acute myeloid leukemia (AML) is an internal tandem duplication (ITD) that is detected by fragment analysis (6-9).  Although it could not pick up certain types of mutations, such as SNPs, fragment analysis of ITD was shown to be an important method for FLT3 mutational analysis (10).

One other example of the use of fragment analysis to analyze repeat lengths came from a study of CAG repeat lengths in the androgen receptor (AR) gene.  Typically, expansion of the CAG repeats is associated with the neurodegenerative disease spinobulbar muscular atrophy (SBMA, also known as Kennedy’s disease).  Because changes in CAG repeat length can alter androgen receptor expression levels, Grassetti et al (11) used fragment analysis to correlate repeat lengths differences with different pathologies in testicular tumor samples.

Detecting pathogenic indel mutations in protooncogenes

Other types of mutations commonly seen in tumors are small indels – insertions or deletions of small numbers of nucleotides within a coding sequence.  Because this can change the sequence length at a locus, these types of mutations are also amenable to fragment analysis.  For example, deletions in Exon 19 of the EGFR gene in various tumor types have been commonly screened by fragment analysis (12-13).  Similarly, indels in the calreticulin (CALR) gene have been screened using fragment analysis methods (14-16).  The flexibility of this approach has also been illustrated by detecting indels in BRCA1 (17-18), NPM1 (19), GATA1 (20), CCR4 (21), and NOTCH1 (22).  Finally, Calvacante et al. (23) analyzed indels in 12 different genes involved in gastric and colorectal cancer using fragment analysis.  Fragment analysis can therefore be a valuable tool for screening for pathogenic indels.

Analysis of rearrangements in the genome

By carefully choosing primers for amplification, fragment analysis can also be used to detect large rearrangements in oncology samples.  For example, T-cell clonality in Korean T-acute lymphoblastic leukemia samples was analyzed by fragment analysis (24).  And Shetty et al. (25) designed a fragment analysis assay to detect translocations in synovial sarcomas.  In their study, they found that fragment analysis was more sensitive than RT-PCR for detecting fusion transcripts. 

Novel methods and analysis for fragment analysis

The flexibility of fragment analysis continues to be exploited by researchers developing extensions and new uses for this method.  The complementarity of FA analysis with NGS techniques was illustrated by Friedrich et al. (26), where they developed an assay for detecting multiple deletions in the ASXL-1 gene in hematological neoplasms that might be missed by NGS.  Fragment analysis can be used to analyze the DNA damage induced by chemotherapeutic agents (27-28).  Fragment analysis was used to analyze DNA methylation in the BRCA1 and RAD51C genes in pancreatic cancers (29).  Finally, a novel multiplexed method was developed to classify acute lymphoblastic leukemia subtypes based on gene expression patterns (30-31).

Fragment analysis: versatility that fuels cancer research

One of the key advantages of fragment analysis is its flexibility.  Judicious choice of PCR primers and PCR conditions can be used to query almost any kind of genomic anomaly.  We have provided some examples of how fragment analysis can be useful for understanding the function and dysfunction of genes in tumor cells.  However, this is not a complete or comprehensive list.  The flexibility of fragment analysis assays and their applications to research depends only on the investigators’ imagination and experimental creativity.

Download the Fragment Analysis User’s Guide here

Fragment analysis has been applied in a number of fields, including oncology, infectious disease, rare and under-diagnosed genetic diseases, and neuroscience. While techniques such as Sanger sequencing and next-generation sequencing (NGS) are also used, fragment analysis offers the accuracy of capillary electrophoresis combined with faster, simplified workflows and multiplexing capabilities.

In this guide we provide thorough coverage of the principles of fragment analysis technologies, the basics of multi-color capillary electrophoresis, the steps in the various workflows, required materials, experimental design strategies, and pre-developed solutions for some commonly-used applications.

References

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8. Kim Y et al. Quantitative fragment analysis of FLT3-ITD efficiently identifying poor prognostic group with high mutant allele burden or long ITD length.  Blood Cancer Journal 5:e336 (2015) https:// doi.org/10.1038/bcj.2015.61
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11. Grassetti D. et al. Androgen receptor polymorphisms and testicular cancer risk.  Andrology 3:27-33 (2015).  https:// doi.org/10.1111/j.2047-2927.2014.00252.x
12. Rennert et al. Long term follow-up of EGFR mutated NSCLC clones.  Translational Oncology 14:100934 (2021). https://doi.org/10.1016/j.tranon.2020.100934
13. Forsythe M. et al. Molecular profiling of non-small cell lung cancer.  PLoS One (2020) https://doi.org/10.1371/journal.pone.0236580
14. Grinsztejn E. et al. The prevalence of CALR mutations in a cohort of patients with myeloproliferative neoplasms.  Int J Lab Hematol 38:102-106 (2015) https://doi.org/10.1111/ijlh12447
15. Gardner J-A. et al. Detection of CALR mutation in clonal and non-clonal hematologic diseases using fragment analysis and next generation sequencing.  Am J Clin Pathol 146:448-455 (2016) https://doi.org/10.1093/AJCP/AQW129
16. Chi J et al. Calreticulin mutations in myeloproliferative neoplasms and a new methodology for their detection and monitoring.  Ann Hematol 94:399-408 (2015)  https://doi.org/10.1007/s00277-014-2232-8
17. Yang C. et al. Characterization of a novel BRCA1 splice variant, c5332+4delA. Breast Cancer Res Treat 168(2) 543-550 (2018) https://doi.org/10.1007/s10549-017-4595-8
18. Zidekova D. et al. Rapid screening test of the most frequent BRCA1/BRCA2 pathogenic variants in the NGS era.  Neoplasma (2018) https://doi.org/10.4149/neo_2018_170507N328
19. Hindley A. et al. Significance of NPM1 gene mutations in AML.  Int J Mol Sci 2021(22) 40 (2021) https://doi.org/10.3390/ijms221810040
20. Panferova A. et al. GATA1 mutational analysis and molecular landscape characterization in acute myeloid leukemia with trisomy 21 in pediatric patients.  Int J Lab Hematol 43:713-723 (2021).  https://doi.org/10.1111/ijlh.13451
21. Mizuta S. et al. cDNA-based mutation screening using a combination of high resolution melting curve and fragment analysis facilitates efficient CCR4 mutation analysis in adult T-cell leukemia/lymphoma.  Am J Clin Pathol 154:336-351 (2020)  https://doi.org/10.1093/AJCP/AQAA037
22. Vavrova E. et al. Fragment analysis represents a suitable approach for the detection of hotspot c.7541_7542delCT NOTCH1 mutation in chronic lymphocytic leukemia.  Leukemia Research 60:145-150 (2017).  https://doi.org/10.1016/j.leukres.2017.08.001
23. Cavalcante GC et al. Analysis of 12 variants in the development of gastric and colorectal cancers.  World J Gastroenterol 23(48):8533-8543 (2017) https://doi.org/10.3748/wjg.v23.i48.8533
24. Kim H et al. T-cell receptor rearrangements determined using fragment analysis in patients with T-acute lymphoblastic leukemia.  Ann Lab Med 39:125-132 (2019) htttps://doi.org/10.3343/alm.2019.32.2.125
25. Shetty O. et al. Comparison between fluorescence in-situ hybridization (FISH), reverse transcriptase PCR (RT-PCR), and fragment analysis, for detection of t(X;18)(p11;q11) translocation in synovial sarcomas.  Indian J Pathol Microbiol 63:64-72 (2020)  https://doi.org/10.4103/IJPM.IJPM_851_18
26. Friedrich C. et al. PCR-Fluo-ASXL1-FA:  A fast, sensitive and inexpensive complimentary method to detect ASXL1 mutations in haematological malignancies.  Int J Lab Hematol 44:928-933 (2022) https://doi.org/10.1111/ijlh.13931
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29. Abdella R. et al. BRCA1 and RAD51C promoter methylation in human resectable pancreatic adenocarcinoma.  Clinics Research Hepatology Gastroenterology 46:101880 (2022) https://doi.org/10.1016/j.clinre.2022/101880
30. Sun Y. et al. An improved advanced fragment analysis-based classification and risk stratification of pediatric acute lymphoblastic leukemia.  Cancer Cell Int 19:110 (2019) https://doi.org/10.1186/s12395-019-0825-y
31. Zhang H. et al. An advanced fragment analysis-based individualized subtype classification of pediatric acute lymphoblastic leukemia.  Sci Rep 5:12435 (2015) https://doi.org/10.1038/srep12435