RNA sequencing

to detect gene fusions in

acute leukaemia:

A proof of principle study

Melissa ConnollyBirmingham Women’s and Children’s

NHS trust

Acute Leukaemia

• Acquired mutations in haematopoetic stem cell

- Chromosome aberrations

- Gene fusions

- CNVs

- Single base pair mutations

• Results of mutations

- Increased/Decreased expression

- Novel proteins

• Disrupts cell cycle

Gene fusions

Image credit: NIH National Cancer Institute Image credit: Wikipedia

Image credit: Lawce et al, 2017

Acute Lymphoblastic Leukaemia Diagnosis

• 3-7+ FISH tests per ALL

- Expensive and time consuming

- Targeted tests

• Additional may be required depending on response

- E.g.: ABL-class fusion

Solution: RNA sequencing?

• RNA Sequencing

- Not sequence intron- Increasing sequencing capacity

- Reverse transcribe mRNA

- Capture cDNA of interest

- NGS

- Bioinformatics tools

• RNA-seq

- Gene fusions including novel isoforms

- Altered expression levels

- Single base pair changes

Project aims

Proof of principle study

Could RNA-seq be used to detect gene

fusions?

What else can it and can’t it detect?

Archer®

• Made a grant application to Archer for assays

- RNA: gene fusion and mutation detection

• Bespoke bioinformatics solution

- Archer® analysis

• Database of fusions for Archer ® users

- Quiver

• Grant awarded for 48 assays

- Gene Fusion Detection

• Selected PanHeme panel

PanHeme assay

Specification Archer® FusionPlex® PanHeme

Genes 199

Targets 1,054 gene specific primers (GSP)

Design focus Regions with only clinically relevant fusions and

mutations

Method of creating

Stranded cDNA

Ligation of unique molecular barcodes each strand

Target enrichment Anchored multiplex PCR

Read length 2 x 151bp paired end reads

Reads per sample 4.5 million

Bioinformatics Archer® analysis Bespoke pipeline

PanHeme assay

Anchored multiplex PCR

Fusion detection

1. Read cleaning, adapter trimming, molecular barcode binning

2. De-novo alignment

3. Align against Quiver, RefSeq and/or genome (hg19/GCRh37)

4. Determine isoform type (fusion, exon skipping etc)

5. Classify fusions as strong versus weak on evidence

Patient selection

AML B-ALL T-ALL

t(15;17)(q24;q21), FLT3 ITD del(12)(p13p13),t(12;21)(p13;q22) der(3)t(3;9)(p13;p22), t(5;7)(q35.1;q21)

Inv(16)(p13.1q22) KIT D816V Hyperdiploidy t(7;22;12)(p15;q12.1;p13)

t(6;11)(q27;q23) FLT3 ITD der(1)(1;14)(p1;q1?), t(1;19)(q23;p13) t(1;7)(p32;q34)

Complex including t(16;17)(q2;q1) Hyperdiploidy, inv(14)(q11q32.3),

i(17)(q10)

Inv(3)(q21q26) t(11;19)(q23;p13.3)

46,XY t(9;22)(q34;q11)

Doubled up Hypodiploidy

?t(X;14)(p22;q32)

?del(22)(q11q11)

t(4;22)(q2;q11), t(8;22)(q24;q11),

t(14;18)(q32;q21)

t(4;11)(q21;q23)

dic(9;12)(p13;p13)

del(9)(p1p2), idic(21)(p1)

Selected 21 patients with a variety of cytogenetic and molecular genetic findings

Procedure

• RNA quality

- Qubit

- RNA tapestation

• Library enrichment

- Lyophilized pellets and coloured tubes- Very easy assay

- Completed in 3 days

- Library QC: Q-PCR

• Sequencing

- 3 samples v2 300 cycle MiSeq

- Remaining samples HiSeq- Higher throughput and lower cost

Results: Sequencing QC

• RNA- All sample met quality requirements (200ng)

• Mean % reads Q30: 87.1%

• Mean number of total reads: 5.6 million- 1 million unique reads per sample

- Higher number due to HiSeq

- Sequence DNA as well

- Large amount of duplicates

• % Reads on target: 93.36%

• Coverage/Depth- Mean unique cDNA molecules in the starting

reaction: 477- Estimated by the unique molecular barcodes

- Expression dependent

Results: Fusions detected

Gene fusions Detected Gene

rearrangements

Detected Copy number

aberrations

Detected Don’t know Detected

t(15;17)(q24;q21)

PML-RARA

Yes Inv(3)(q21q26)

MECOM

Rearrangement

No Hyperdiploidy No Complex

including

t(16;17)(q2;q1)

No

Inv(16)(p13.1q22)

CBFB-MYH11

Yes Der(1)(1;14)(p1;q1?)

IGH rearrangement

No Hyperdiploidy No t(4;22)(q2;q11) No

t(6;11)(q27;q23)

KMT2A- MLLT4

Yes inv(14)(q11q32.3)

IGH rearrangement

No i(17)(q10) No der(3)t(3;9)

(p13;p22)*

No

t(12;21)(p13;q22)

ETV6-RUNX1

Yes t(X;14)(p22;q32)

IGH rearrangement

No Doubled up

Hypodiploidy

No t(1;7)(p32;q34) No

t(1;19)(q23;p13)

TCF3-PBX1

Yes t(8;22)(q24;q11)

IGL rearrangement

No del(9)(p1p2) No

t(11;19)(q23;p13.3)

KMT2A-MLLT4

Yes t(14;18)(q32;q21)

IGH rearrangement

No idic(21)(p1) No

t(4;11)(q21;q23)

KMT2A-AFF1

Yes t(5;7)(q35.1;q21)

TLX3 rearrangement

No*

dic(9;12)(p13;p13)

PAX5-ETV6

Yes

t(7;22;12)

(p15;q12.1;p13)

ETV6-MN1

Yes*

t(9;22)(q34;q11)

BCR-ABL1

Yes

Example fusion

t(6;11)(q27;q23) KMT2A-MLLT4 fusion

Summary

table

# Reads/% of total reads Can visualise and blast

fusion # of Unique start sites

Location of GSP Fusion breakpoint

Define partner gene

Karyotype: t(7;22;12)(p15;q12.1;p13)

FISH: ETV6 gene rearrangement, MN1 gene rearrangement

ETV6-HOXA11-AS

ETV6-MN1

Previously undetected gene fusion

Whole karyotype: der(3)t(3;9)(p13;p22), t(5;7)(q35.1;q21)

FISH: partial loss of ABL1

NUP214-ABL1

Mutations of interest

FLT3 ITD

KIT D816V

Artefacts/Variants

Artefacts

Read through transcription events

Advantages

• Advantages:

- Can detect gene fusions and mutations of interest- Detect multiple fusions at once

- Don’t need to know specific fusion partner

- Potential to replace multiple assays- FISH, RT-PCR, fragment analysis

- Kit and software user friendly

• Disadvantages

- Cannot detect chromosome rearrangements that do not produce a fusion transcript

- Cannot detect CNV

- Part of testing pathway

Further work

• Validate some of these findings

• Cost benefit analysis

• Collate list of diseases/genes (batching)

- Numbers received and TAT

- Design bespoke panel to fit needs

• Prospective study

- Run assay alongside diagnostic workflow

- Assess TAT

Acknowledgments

• WMRGL

- Sara Dyer

- Sally Jeffries

- Joanne Mason

- Anna Yeung

• Archer

- Laura Griffin

- Chris Celone

• CEHRB Biobank

Any questions???