About Myself
- Faqiang Wu, PhD
- Staff Scientist in Clinical Diagnostics at NeoGenomics Laboratories
- Job responsibility: NGS assay development for cancer testing
- Personal interest: Clinical data analysis and visualization
About NeoGenomics Laboratories
- NeoGenomics, Inc. specializes in cancer genetics testing and information services.
- Providing one of the most comprehensive oncology-focused testing menus in the world for physicians to help them diagnose and treat cancer.
- Serving pharmaceutical clients in clinical trials and drug development.
- CAP accredited and CLIA certified laboratories for full-service sample processing in FL, CA, NC, TX and the United Kingdom.
- Small, non-processing laboratory locations across the United States for providing analysis services.
AMP Poster
Abstract # 1874602 | Poster # TT004
INTRODUCTION
In this study, we evaluated the performance of the G4 sequencing platform developed by Singular Genomics, using a capture-based gene fusion NGS assay from NeoGenomics Laboratories.
A benchtop sequencing platform delivering accuracy, speed, power, and unprecedented flexibility for a wide range of genomic applications.
| Instrument Specifications | Singular G4 |
|---|---|
| SBS Chemistry | 4 color |
| No. of Flow Cells / Instrument | 4 |
| No. of Lanes / Flow Cell | 4 |
| Bases ≥ Q30 | > 85% |
| Flow Cell Specifications | F2 | F3 |
|---|---|---|
| Paired Reads / Flow Cell | 200 M | 400 M |
| Paired Reads / Run | 800M | 1.6B |
| Run time (300 cycles) | ~19 hrs | ~24 hrs |
A RNA capture-based next-generation sequencing test that detects translocations and fusions with known and novel fusion partners in 250 genes (including EGFRvIII and METex14 variants).
It detects gene fusions across multiple solid tumors, including lung, brain, breast, thyroid, salivary gland, prostate, sarcoma, colorectal, cholangiocarcinoma, and pancreas.
EXPERIMENT DESIGN
- Four clinical samples were selected for evaluation.
- Each sample carries gene fusions previously detected on Illumina NovaSeq 6000 using the Solid Tumor Gene Fusion NGS Assay developed by Genomics Laboratories.
- When converting the libraries for sequencing on G4, additional PCR cycles were applied to replace Illumina’s P5 and P7 after library capturing.
- The converted libraries were sequenced on single lanes of F2 flow cells on the Singular G4 sequencer.
- The sequencing results were run on a modified pipeline for QC and fusion calls.
RESULTS
Quality score of the majority reads are above 30 throughout the cycles, suggesting excellent sequencing quality of Singular G4 sequencer.
- In general, the sequencing quality of G4 is comparable to that of NovaSeq 6000.
- The higher duplicaton in G4 is likely due to the higher initial reads.
- The extra PCR step to replace Illumina flow cell binding sequences does not seem to impact library quality. No obvious differences observed between 5- and 7-cycle PCR.
- All (14/14) fusion genes detected on Novaseq 6000 were detected on G4.
- The additional fusion genes detected on G4 are probably due to the higher sequencing depth on G4 than on NovaSeq 6000.
| Fusion genes | Detected on | Concordance | ||
|---|---|---|---|---|
Illumina |
Singular |
|||
| Patient A | WWTR1:CAMTA1 | ✓ |
✓ |
Yes |
| Patient B | NPTN:HMGN2P46 | ✓ |
✓ |
Yes |
| SLC45A3:ELK4 | ✓ |
✓ |
Yes | |
| TMPRSS2:ERG | ✓ |
✓ |
Yes | |
| YWHAE:HAP1 | ✓ |
✓ |
Yes | |
| Patient C | ETV6:NTRK3 | ✓ |
✓ |
Yes |
| Patient D | APLP2:ST14 | ✓ |
✓ |
Yes |
| ARMC8:MBNL1 | ✓ |
✓ |
Yes | |
| ATE1:FGFR2 | ✓ |
✓ |
Yes | |
| CYSTM1:ANKHD1 | ✓ |
✓ |
Yes | |
| GRTP1:DNAJC14 | ✓ |
No | ||
| PDGFRA:AL139811.2 | ✓ |
No | ||
| PPP2R2A:BNIP3L | ✓ |
✓ |
Yes | |
| PXDC1:KPNA4 | ✓ |
✓ |
Yes | |
| SRP9:DNAH14 | ✓ |
✓ |
Yes | |
CONCLUSION
This evaluation study demonstrates that the G4 sequencing platform’s high-fidelity performance matches that of the Illumina platform when applied to a solid tumor gene fusion NGS assay.
The G4 platform offers a reliable and efficient alternative for genomic research and clinical applications.
ACKNOWLEDGEMENT
NeoGenomics Laboratories:
- Faqiang Wu,
- Long Vu,
- Steven Rivera,
- Segun Jung,
- Yanglong Mou,
- Brad Thomas,
- Cynthie Wong,
- Jiannan Guo
- and more…
Singular Genomics:
- Jordan Williams,
- Jonathan Slasinski,
- Ryan Shultzaberger,
- Laure Moller,
- Eli Glezer,
- Martín Fabani,
- Jeremy Schurman,
- and more…