Posts Tagged ‘gene fusions’

Complex rearrangements and oncogene amplification revealed by long-read DNA and RNA sequencing of a breast cancer cell line, Volume 2 (Volume Two: Latest in Genomics Methodologies for Therapeutics: Gene Editing, NGS and BioInformatics, Simulations and the Genome Ontology), Part 1: Next Generation Sequencing (NGS)

Complex rearrangements and oncogene amplification revealed by long-read DNA and RNA sequencing of a breast cancer cell line

Reporter: Stephen J. Williams, PhD

In a Genome Research report by Marie Nattestad et al. [1], the SK-BR-3 breast cancer cell line was sequenced using a long read single molecule sequencing protocol in order to develop one of the most detailed maps of structural variations in a cancer genome to date.  The authors detected over 20,000 variants with this new sequencing modality, whereas most of these variants would have been missed by short read sequencing.  In addition, a complex sequence of nested duplications and translocations occurred surrounding the ERBB2 (HER2) while full-length transcriptomic analysis revealed novel gene fusions within the nested genomic variants.  The authors suggest that combining this long-read genome and transcriptome sequencing results in a more comprehensive coverage of tumor gene variants and “sheds new light on the complex mechanisms involved in cancer genome evolution.”

Genomic instability is a hallmark of cancer [2], which lead to numerous genetic variations such as:

  • Copy number variations
  • Chromosomal alterations
  • Gene fusions
  • Deletions
  • Gene duplications
  • Insertions
  • Translocations

Efforts such as the Cancer Genome Atlas [3], and the International Genome Consortium (2010) use short-read sequencing technology to detect and analyze thousands of commonly occurring mutations however short-read technology has a high false positive and negative rate for detecting less common genetic structural variations {as high as 50% [4]}. In addition, short reads cannot detect variations in close proximity to each other or on the same molecule, therefore underestimating the variation number.

Methods:  The authors used a long-read sequencing technology from Pacific Biosciences (SMRT) to analyze the mutational and structural variation in the SK-BR-3 breast cancer cell line.  A split read and within-read mapping approach was used to detect variants of different types and sizes.  In general, long-reads have better alignment qualities than short reads, resulting in higher quality mapping. Transcriptomic analysis was performed using Iso-Seq.

Results: Using the SMRT long-read sequencing technology from Pacific Biosciences, the authors were able to obtain 71.9% sequencing coverage with average read length of 9.8 kb for the SK-BR-3 genome.

A few notes:

  1. Most amplified regions (33.6 copies) around the locus spanning the ERBB2 oncogene and around MYC locus (38 copies), EGFR locus (7 copies) and BCAS1 (16.8 copies)
  2. The locus 8q24.12 had the most amplifications (this locus contains the SNTB1 gene) at 69.2 copies
  3. Long-read sequencing showed more insertions than deletions and suggests an underestimate of the lengths of low complexity regions in the human reference genome
  4. Found 1,493 long read variants, 603 of which were between different chromosomes
  5. Using Iso-Seq in conjunction with the long-read platform, they detected 1,692,379 isoforms (93%) mapping to the reference genome and 53 putative gene fusions (39 of which they found genomic evidence)

A table modified from the paper on the gene fusions is given below:

Table 1. Gene fusions with RNA evidence from Iso-Seq and DNA evidence from SMRT DNA sequencing where the genomic path is found using SplitThreader from Sniffles variant calls. Note link in table is  GeneCard for each gene.

SplitThreader path


# Genes Distance
of variants
in path
Previously observed in references
1 KLHDC2 SNTB1 9837 3 14|17|8 Asmann et al. (2011) as only a 2-hop fusion
2 CYTH1 EIF3H 8654 2 17|8 Edgren et al. (2011); Kim and Salzberg
(2011); RNA only, not observed as 2-hop
3 CPNE1 PREX1 1777 2 20 Found and validated as 2-hop by Chen et al. 2013
4 GSDMB TATDN1 0 1 17|8 Edgren et al. (2011); Kim and Salzberg
(2011); Chen et al. (2013); validated by
Edgren et al. (2011)
5 LINC00536 PVT1 0 1 8 No
6 MTBP SAMD12 0 1 8 Validated by Edgren et al. (2011)
7 LRRFIP2 SUMF1 0 1 3 Edgren et al. (2011); Kim and Salzberg
(2011); Chen et al. (2013); validated by
Edgren et al. (2011)
8 FBXL7 TRIO 0 1 5 No
9 ATAD5 TLK2 0 1 17 No
10 DHX35 ITCH 0 1 20 Validated by Edgren et al. (2011)
11 LMCD1-AS1 MECOM 0 1 3 No
12 PHF20 RP4-723E3.1 0 1 20 No
13 RAD51B SEMA6D 0 1 14|15 No
14 STAU1 TOX2 0 1 20 No
15 TBC1D31 ZNF704 0 1 8 Edgren et al. (2011); Kim and Salzberg
(2011); Chen et al. (2013); validated by
Edgren et al. (2011); Chen et al. (2013)


SplitThreader found two different paths for the RAD51B-SEMA6D gene fusion and for the LINC00536-PVT1 gene fusion. Number of Iso-Seq reads refers to full-length HQ-filtered reads. Alignments of SMRT DNA sequence reads supporting each of these gene fusions are shown in Supplemental Note S2.




  1. Nattestad M, Goodwin S, Ng K, Baslan T, Sedlazeck FJ, Rescheneder P, Garvin T, Fang H, Gurtowski J, Hutton E et al: Complex rearrangements and oncogene amplifications revealed by long-read DNA and RNA sequencing of a breast cancer cell line. Genome research 2018, 28(8):1126-1135.
  2. Hanahan D, Weinberg RA: The hallmarks of cancer. Cell 2000, 100(1):57-70.
  3. Kandoth C, McLellan MD, Vandin F, Ye K, Niu B, Lu C, Xie M, Zhang Q, McMichael JF, Wyczalkowski MA et al: Mutational landscape and significance across 12 major cancer types. Nature 2013, 502(7471):333-339.
  4. Sudmant PH, Rausch T, Gardner EJ, Handsaker RE, Abyzov A, Huddleston J, Zhang Y, Ye K, Jun G, Fritz MH et al: An integrated map of structural variation in 2,504 human genomes. Nature 2015, 526(7571):75-81.


Other articles on Cancer Genome Sequencing in this Open Access Journal Include:


International Cancer Genome Consortium Website has 71 Committed Cancer Genome Projects Ongoing

Loss of Gene Islands May Promote a Cancer Genome’s Evolution: A new Hypothesis on Oncogenesis

Identifying Aggressive Breast Cancers by Interpreting the Mathematical Patterns in the Cancer Genome

CancerBase.org – The Global HUB for Diagnoses, Genomes, Pathology Images: A Real-time Diagnosis and Therapy Mapping Service for Cancer Patients – Anonymized Medical Records accessible to


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Validation of FoundationOne Heme in New Study: Integrated genomic DNA/RNA profiling of hematologic malignancies in the clinical setting

Reporter: Aviva Lev-Ari, PhD, RN


Integrated genomic DNA/RNA profiling of hematologic malignancies in the clinical setting

  1. Jie He1,
  2. Omar Abdel-Wahab2,
  3. Michelle K. Nahas1,
  4. Kai Wang1,
  5. Raajit K. Rampal3,
  6. Andrew M. Intlekofer4,
  7. Jay Patel3,
  8. Andrei Krivstov5,
  9. Garrett M. Frampton1,
  10. Lauren E. Young1,
  11. Shan Zhong1,
  12. Mark Bailey1,
  13. Jared R. White1,
  14. Steven Roels1,
  15. Jason Deffenbaugh1,
  16. Alex Fichtenholtz1,
  17. Timothy Brennan1,
  18. Mark Rosenzweig1,
  19. Kimberly Pelak1,
  20. Kristina M. Knapp5,
  21. Kristina W. Brennan1,
  22. Amy L. Donahue1,
  23. Geneva Young1,
  24. Lazaro Garcia1,
  25. Selmira T. Beckstrom1,
  26. Mandy Zhao1,
  27. Emily White1,
  28. Vera Banning1,
  29. Jamie Buell1,
  30. Kiel Iwanik1,
  31. Jeffrey S. Ross1,
  32. Deborah Morosini1,
  33. Anas Younes4,
  34. Alan M. Hanash6,
  35. Elisabeth Paietta7,
  36. Kathryn Roberts8,
  37. Charles Mullighan8,
  38. Ahmet Dogan9,
  39. Scott A. Armstrong5,
  40. Tariq Mughal1,
  41. Jo-Anne Vergilio1,
  42. Elaine Labrecque1,
  43. Rachel Erlich1,
  44. Christine Vietz1,
  45. Roman Yelensky1,
  46. Philip J. Stephens1,
  47. Vincent A. Miller1,
  48. Marcel R. M. van den Brink10,
  49. Geoff A. Otto1,
  50. Doron Lipson1, and
  51. Ross L. Levine2,*
Author Affiliations
  1. * Corresponding author; email: leviner@mskcc.org

Key Points

  • Novel clinically-available comprehensive genomic profiling of both DNA and RNA in hematologic malignancies.

  • Profiling of 3696 clinical hematologic tumors identified somatic alterations that impact diagnosis, prognosis, and therapeutic selection.


The spectrum of somatic alterations in hematologic malignancies includes substitutions, insertions/deletions (indels), copy number alterations (CNAs) and a wide range of gene fusions; no current clinically available single assay captures the different types of alterations. We developed a novel next-generation sequencing-based assay to identify all classes of genomic alterations using archived formalin-fixed paraffin-embedded (FFPE), blood and bone marrow samples with high accuracy in a clinically relevant timeframe, which is performed in our CLIA-certified CAP-accredited laboratory. Targeted capture of DNA/RNA and next-generation sequencing reliably identifies substitutions, indels, CNAs and gene fusions, with similar accuracy to lower-throughput assays which focus on specific genes and types of genomic alterations. Profiling of 3696 samples identified recurrent somatic alterations that impact diagnosis, prognosis and therapy selection. This comprehensive genomic profiling approach has proved effective in detecting all types of genomic alterations, including fusion transcripts, which increases the ability to identify clinically-relevant genomic alterations with therapeutic relevance.

  • Submitted August 16, 2015.
  • Accepted February 28, 2016.



Foundation Medicine Shares Results From Validation of FoundationOne Heme in New Study

In addition to the concordance analysis, genomic profiling of the 76 test samples using FoundationOne Heme also identified 126 additional somatic alterations including clinically relevant genomic alterations in KRAS, TET2, EZH2, and DNMT3A.

Importantly, the study also showed that the molecular information supplied by the test can help accurately match patients with a particular targeted therapy.

In the study Foundation Medicine shared clinical data from genomic profiling of 3,696 hematologic malignancies submitted to its CLIA-certified, NYS-approved lab.

More than 90 percent of the specimens — 3,433 out of 3696 — were successfully characterized. The test identified at least one driver alteration in 95 percent of the tumor specimens, and results showed that 77 percent of the cases harbored at least one alteration linked to a commercially available targeted therapy or one that is in clinical development, the MSKCC researchers reported.

In addition, 61 percent of the cases harbored at least one alteration with known prognostic relevance in that tumor type.

In discussion of the results, the study authors argued that clinical merit of the test was underscored by the demonstrated ability to identify genetic lesions with prognostic and therapeutic relevance in specific diseases.

For example, the authors wrote, “In the case of B-cell ALL … the challenge has been that the critical genes … can be altered by whole gene/intragenic deletions, DNA base-pair substitutions, and larger indels, as well as chromosomal, intergenic, and cryptic rearrangements, which lead to expression of fusion transcripts.”

“Currently, most centers use an amalgam of DNA, FISH, and gene-specific RNA approaches to identify a subset of the most critical genetic lesions in B-ALL. Our assay provides a single profiling platform that can reliably identify all known actionable disease alleles relevant to B-ALL to improve diagnosis and risk-adapted therapy for B-ALL patients,” they wrote.



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