Minimal Residual Disease Monitoring:

NGS as a promising tool for MRD detection for several hematological diseases.

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Author:

Dr. Avrajit Chakraborty

Image courtesy:  UPMC Health Beat

Cancer treatment and recovery may have different stages depending on various clinical or physiological aspects. After completion of treatment, a patient can achieve “complete remission” where there is no presence of pathological or clinical signs of cancer. However, this is not the case when we consider blood cancer like leukemia, lymphoma or multiple myeloma.

Minimal Residual Disease or Measurable Residual Disease (MRD) refers to a clinical stage after or during treatment when there is a small number of cancer cells present and cannot be detected by conventional serological or morphological methods1.  For better progression-free-survival (PFS) and overall survival (OS) it is important to utilize new methods of MRD detection which show high sensitivity and specificity. The International Myeloma Working Group (IMW) – under International Myeloma Foundation, developed new methods and criteria to distinguish MRD positive individuals from MRD negative ones. One such criterion requires identification of 1 malignant cell per 1000 normal cells2. Now a days, MRD detection techniques are broadly classified into two groups viz. immunophenotyping methods (Multiparametric Flow-cytometry or MFC) and molecular techniques (NGS, quantitative PCR, etc.) Associating data from different imaging tools may also provide some useful additional information.

Identification through immunophenotyping methods needs high quality of samples and human expertise for precise identification of pathological cell types. On other hand, quantitative PCR uses allele specific primers to target junction regions of immunoglobulin genes, which is a gold standard for MRD detection3.  However, this is a labour intensive method and requires construction of standard curve for every single patient. In contrast, NGS based techniques can be introduced in a clinical setup with rapid adaptation and implementation. These techniques offer identification of multiple target genes/alleles in a single run which can also be scaled up using different multiplexing options. However, implementation of NGS techniques for MRD, needs various considerations during experimental setup, data generation and data analysis stages.

The biggest challenge for any such NGS based clinical detection system is to determine a broadly applicable targeted gene set for MRD patients of different segments.  One of the common PCR based MRD testing termed PML-RARA for acute promyelocytic leukemia (APL)-a type of myeloid leukemia, is now available , it utilizes multiplexing RNASeq method4,. Table1. shows some of the NGS based MRD detection techniques and related Variant Allele Frequency (VAF).

VAF is the one of key terms of NGS based MRD identification which represents fraction of reads containing a mutation, divided by the total number of reads identified at that locus. This number provides mutational abundance. For e.g. if mutation p.S34F identified by 105 reads with total reads of 230 at the Locus in U2AF1 gene then the VAF of this mutation is 45.6%. Several such VAF from different gene mutations are used for reconstruction of clonal architecture of leukemia6,7. The clone with the highest VAF score is known as “founding clone” where low scored VAF from same mutation are known as sub-clones of that founding clones. In case of NGS based assay, VAF also stands for sensitivity of NGS testing method. For e.g. 1% maximum selectivity(VAF) of a NGS based test means 1 mutation in 50 normal cells. If VAF is 0.1% then it can detect 1 mutation in 500 normal cells.

However, the problem is, Standard NGS based clinical pipelines can detect 2-5% VAF where the desired VAF is 0.1%-0.5%. This happens due to error rate in NGS based sequencing methods. One of the approaches to overcome such errors is Unique Molecular Identity (UMI) based error correction and mutation identification.  In this method, reads carrying same UMI are referred as “read family”. Error correction method and mutation detection method identify the mutations which are only present in all the reads in a “read family”. This approach shows drastic decrease in false positive identification.

In the present scenario, NGS based MRD detection system may have good potential and NGS based MRD can be used in multiple ways.  Since, NGS based MRD testing can work with blood samples without bone marrow biopsy; it can be used for frequent monitoring of a patient under MRD treatment or for detecting early stages of MRD. It can also be a great tool to understand drug effects for MRD as well as detecting the underlying mutational dynamics of MRD. It is also possible in near future the NGS based MRD detection tools may take the lead role for clinical identification with more sensitivity, accuracy and be cost effective.

Reference:

  1. Medina, A., Puig, N., Flores-Montero, J. et al. Comparison of next-generation sequencing (NGS) and next-generation flow (NGF) for minimal residual disease (MRD) assessment in multiple myeloma. Blood Cancer J. 10, 108 (2020). https://doi.org/10.1038/s41408-020-00377-0.
  2. Kumar, S. et al. International Myeloma Working Group consensus criteria for response and minimal residual disease assessment in multiple myeloma. Lancet Oncol 17, e328–e346 (2016)
  3. Sarasquete, M. E. et al. Minimal residual disease monitoring in multiple myeloma: a comparison between allelic-specific oligonucleotide real-time quantitative polymerase chain reaction and flow cytometry. Haematologica. 90,1365–1372 (2005).
  4. Dillon, L. W., Hayati, S., Roloff, G. W., Tunc, I., Pirooznia, M., Mitrofanova, A.,et al. (2019). Targeted RNA-sequencing for the quantification of measurable residual disease in acute myeloid leukemia. Haematologica 104, 297–304. doi:10.3324/haematol.2018.203133.
  5. Yoest Jennifer M., Shirai Cara Lunn, Duncavage Eric J.,Sequencing-Based Measurable Residual Disease Testing in Acute Myeloid Leukemia;Frontiers in Cell and Developmental Biology:8,2020.10.3389/fcell.00249, URL=https://www.frontiersin.org/article/10.3389/fcell.2020.00249(2020).
  6. Ding, L., Ley, T. J., Larson, D. E., Miller, C. A., Koboldt, D. C., Welch, J. S., et al. (2012). Clonal evolution in relapsed acute myeloid leukaemia revealed by whole-genome sequencing. Nature 481, 506–510. doi: 10.1038/nature10738
  7. Dohner, H., Estey, E., Grimwade, D., Amadori, S., Appelbaum, F. R., Buchner, T., et al. (2017). Diagnosis and management of AML in adults: 2017 ELN recommendations from an international expert panel. Blood 129, 424–447. doi: 10.1182/blood-2016-08-733196
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