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A novel tool for robust detection of clinically relevant translocations in FFPE tumor biopsies

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3 min read

Genomic rearrangements are known to drive various types of cancer.1,2 Indeed, a variety of hematolymphoid malignancies and solid tumors often exhibit rearrangements, such as translocations and inversions. These distinctive features can serve, in turn, as diagnostic targets for physicians and help guide treatment options. However, medical doctors standardly resort to obsolete techniques such as FISH, which fails to provide sequence details on the rearrangements. Moreover, FISH analysis is often labor-intensive, error prone, expensive and its inability to multiplex renders it a rather cumbersome and unattractive approach for such purpose.3,4 Despite a strong inclination - from pathologists - to transition towards NGS-based solutions, current options are unfortunately still suboptimal for genomic rearrangement.

TLA fits routine diagnostic pathology procedures like a glove

Diagnostic pathology practices are reliant on formalin-fixed, paraffin-embedding (FFPE) procedures for long-term preservation and morphological assessment.5 Interestingly, these inherent preparative steps overlap perfectly with the initial steps of TLA’s protocol (namely, the crosslinking and DNA fragmentation steps). By using these as a starting point, we can subsequently re-ligate DNA fragments that are most proximal within the cell nucleus. The resulting approach dubbed as FFPE-TLC (Targeted Locus Capture-based sequencing) therefore shows great promise for the detection of genomic rearrangements in FFPE biopsies.6,7



Fig. 1 Schematic overview of the FFPE-TLC workflow.


FFPE-TLC, coupled with PLIER, markedly outperforms FISH and conventional NGS-capture approaches

In a recent and collaborative study published in Nature Communications, we demonstrate the robustness of our approach, by applying FFPE-TLC to 149 (anonymized) lymphoma and control FFPE samples.8

By designing probes that are complementary to the genomic sequence of interest and by leveraging the principle of physical proximity, our FFPE-TLC method can generate extremely broad NGS sequencing coverage spanning both the locus of interest and > 100 kb of neighboring DNA sequences. Next, by evaluating the density of proximity-ligation DNA products across the genome, our computational and statistical framework - labelled as PLIER – will automatically detect rearrangement partners of target genes.6,7

Of note, the samples were selected for the presence or absence of rearrangements involving genes relevant for lymphoma diagnostics, as originally detected by FISH. Among those, we find: MYC. PLIER identified 56 MYC rearrangements in 49 lymphoma samples, among which 2 cases that had been missed by FISH. To verify the “trueness” of the identified rearrangements, butterfly plots were employed. Ultimately, those butterfly plots found MYC in 41 balanced translocations and 15 more complex rearrangements. In this manner, our PLIER algorithm was able to accurately detect MYC rearrangements as well as its partners. Finally, PLIER also required only 1M reads to accurately detect rearrangements that were present in only 5% of the FFPE samples.8


ID structural variants with PLEIR_LI

Fig. 2. Identifying genomic rearrangements with PLIER.


A new revolutionary tool for cancer diagnostics

To date, the robust detection of all genetic mutations, including structural variants in and around genes of interest remains a challenge.6 This especially holds true for FFPE specimens.

In this study, we successfully underscored the advantages of FFPE-TLC over conventional NGS approaches in:

  1. Uncovering rearrangements in areas that are difficult to target or sequence
  2. Its ability to discriminate between relevant and non-relevant rearrangements
  3. Revealing both known and previously uncharacterized complex rearrangements
  4. Sidestepping the need for manual curation

In conclusion, our FFPE-TLC positions itself as a novel and revolutionary tool for cancer diagnostics, by enabling accurate and automatic detection of clinically relevant translocations in FFPE tumor biopsies. Lastly, it is worth noting that our FFPE-TLC method is also compatible - for integration – with any standard targeted NGS workflows.

For more information, we invite you to watch the jointly hosted webinar, previously organized by GenomeWeb, where both Prof. dr. Daphne de Jong and Prof. dr. Bauke Ylstra - from Amsterdam University Medical Center - highlight the promise of our TLA-based panel assay for the robust detection of structural variants (e.g., translocation analysis) in lymphoma diagnostics and other types of cancer.

Robust Detection of Translocations in FFPE Biopsies: Application in Lymphoma Diagnostics

Recorded 20 May 2020

Watch webinar


[1] Li, Y. et al. (2020). Patterns of somatic structural variation in human cancer genomes. Nature 578, 112–121

[2] Macintyre, G., Ylstra, B. & Brenton, J. D. (2016). Sequencing structural variants in cancer for precision therapeutics. Trends Genet. 32, 530–542

[3] Muñoz-Mármol, A. M. et al. (2013). MYC status determination in aggressive B-cell lymphoma: the impact of FISH probe selection. Histopathology 63, 418–424

[4] Scott, D. W. et al. (2018). High-grade B-cell lymphoma with MYC and BCL2 and/or BCL6 rearrangements with diffuse large B-cell lymphoma morphology. Blood 131, 2060–2064

[5] Blow, N. Tissue issues. (2007). Nature 448, 959–960

[6] de Vree, P., de Wit, E., Yilmaz, M. et al. (2014). Targeted sequencing by proximity ligation for comprehensive variant detection and local haplotyping. Nat Biotechnol 32, 1019–1025. https://doi.org/10.1038/nbt.2959

[7] Cergentis’ application note: Robust detection of structural variants and single nucleotide variants in FFPE samples using TLA-based NGS

[8] Allahyar, A., Pieterse, M., Swennenhuis, J. et al. (2021). Robust detection of translocations in lymphoma FFPE samples using targeted locus capture-based sequencing. Nat Commun 12, 3361. https://doi.org/10.1038/s41467-021-23695-8

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