H03

Analysis and Characteriza;on of Hematologic Cancers using a Comprehensive
NGS Panel Comprised of DNA and RNA Baits Targe;ng 704 Genes
Andrew R. Carson1, Bradley A. Patay1, Suzanne M. Graham1, Valerie McClain1, Zhiyi Xie1, Timothy Stenzel1,2,3, and Jeﬀrey E. Miller1,2,3
1Invivoscribe, Inc., 2LabPMM, LLC, San Diego, CA, and 3LabPMM, GmbH, MarOnsried, Germany

Abstract

MyHEME DNA Results: Sensi;vity and Speciﬁcity

IntroducAon: As next-genera;on sequencing (NGS) methodologies improve, so does the ability to characterize hematopoie;c andlymphoid neoplasm genomes. This promises to revolu;onize oncology, allowing more accurate and precise classiﬁca;on of pa;entsand poten;ally leading to novel targeted and combina;on therapies with improved outcomes.
Methods: The MyHEME™ targeted-sequencing panel is comprised of two independent bait sets that target a combined 704 genes
known or predicted to contribute to hematologic cancers (DNA baits for 571 genes and RNA baits for 361 genes; 228 genes are foundin common between the two bait sets). Libraries were constructed using 1 μg of DNA or 0.1 μg of RNA and sequenced on an Illumina®
plagorm. Sequenced reads are analyzed using proprietary MyInforma;cs™ sohware to iden;fy single-nucleo;de variants (SNVs),indels, and structural variants (SVs). Both the MyHEME panel and MyInforma;cs sohware were created under ISO 13485-design
control. To characterize the performance metrics of the MyHEME panel, we used the NIST human reference sample NA12878 alongwith combina;ons of hematologic cancer derived cell lines with known pathogenic variants at various allelic frequencies.

To evaluate MyHEMEs DNA variant detec;on sensi;vity and speciﬁcity, the NIST human reference sample
NA12878 was sequenced. The GIAB consor;um sequenced this “Genome in a Bonle” mul;ple ;mes onmul;ple plagorms to generate an integrated “gold-standard” dataset containing:• A set of 3,641,994 high-conﬁdence variants. Of these variants, there are:• 656 high-conﬁdence coding variants (640 SNVs and 16 indels) within MyHEME targets• 2,171 high-conﬁdence non-coding variants (1,948 SNVs and 223 indels) within MyHEME targets• High-conﬁdent variants were used as gold-standard true posiAves for sensi;vity analyses• High-conﬁdence regions containing 2,565,300,578 bp with highly accurate genotype calls:• 1,594,796 bp and 2,202,265 bp overlap MyHEME coding and non-coding targets (respec;vely)• Non-variant sites were used as gold-standard true negaAves for speciﬁcity analyses
Table 1: Sensi;vity and Speciﬁcity of a) coding and b) non-coding variants in 8 NA12878 MyHEME runs at VAF
cutoﬀs of 2.5% and 5.0%SNVs
IndelsResults: Analy;cal valida;on of the MyHEME panel established an average read depth of 1,175x (with a median read depth of 1,088x)
for the DNA targets and an average transcripts per million (TPM) of 2,256 (with a median TPM of 743) for the RNA targets. For theDNA targets, we establish sensi;vity >95% (99.8% for SNVs at a 2.5% limit of detec;on (LoD); 100% for coding indels at a 5.0% LoD)
and speciﬁcity >95% (95.5% for SNVs at a 2.5% LoD; 97.7% for coding indels at a 5.0% LoD). We also show the ability to cross-conﬁrm
results between the 228 genes common to both the DNA and RNA targets. Importantly, novel gene fusions, which are generally
diﬃcult to detect and validate, were cross-conﬁrmed when observed in both the DNA and RNA targets. For example, we iden;ﬁed anovel t(9;22) transloca;on causing a NUP214-XKR3 gene fusion using both the DNA and RNA targets. Addi;onally, while RNA data
provides the fused exons of the transcripts, DNA data gives the precise genomic breakpoint coordinates.
Conclusion: MyHEME LDT assay service is an extensive panel for sensi;vely and speciﬁcally iden;fying SNV, indel, and SV muta;ons in
704 target genes. This panel can comprehensively characterize muta;ons in mul;ple, diverse hematologic cancer samples, including
AML, ALL, Non-Hodgkin Lymphoma and Mul;ple Myeloma. By u;lizing a high depth of coverage, MyHEME can accurately detectclones present down to 5% of a pa;ent’s sample. In addi;on, by targe;ng both DNA and RNA, MyHEME contains a built in valida;on
method to cross-conﬁrm novel variants of interest.

Materials & Methods

Coding (n=5,120)

Non-Coding (n=128)

Coding (n=15,584)

Non-Coding (n=1,784)

2.5%

5.0%

2.5%

5.0%

2.5%

5.0%

2.5%

5.0%

Sensi;vity

99.8%

99.8%

99.8%

99.8%

100%

100%

95.6%

95.6%

Speciﬁcity

94.9%

98.3%

95.7%

98.6%

87.1%

97.7%

83.1%

84.7%

Sensi;vity is calculated as Detected True Posi;ves / Gold-Standard True Posi;ves (n in above table)
Speciﬁcity is calculated as Detected True Posi;ves / All Detected Variants

704 genes are targeted by MyHEME baits• Genes were chosen due to published associa;ons with hematologic cancers• DNA baits: Targets the coding sequences of 571 genes• RNA baits: Targets the transcripts of 371 genes• 228 genes are targeted by both DNA and RNA baits, allowing cross-valida;on of observed variants
Analysis method• 1 μg of DNA or 0.1 μg of RNA is used as input before hybridizing to the MyHEME baits• Captured targets are then sequenced on the Illumina® plagorm• Customized bioinforma;cs pipeline iden;ﬁes and characterizes SNVs, indels, and SVs
Samples used to evaluate quality metrics:• NIST human reference sample NA12878 (aka “Genome in a Bonle”)• High-conﬁdence variants are used to conﬁrm true posi;ves (TP) and iden;fy false nega;ves (FN)• Sanger-sequenced regions are used to iden;fy false posi;ves (FP) and conﬁrm true nega;ves (TN)• Contrived samples containing dilu;ons of 6 cell lines at diﬀerent allelic frequencies• Used to analyze LoD, reproducibility and linearity of variant detec;on• 6 cell lines with known gene fusions used to evaluate the ability to detect fusions in RNAseq data

• Using a SNV cutoﬀ of 2.5% and an indel cutoﬀ of 5.0%, the following was observed:• >95% sensi;vity for both coding and non-coding SNVs and indels• 95% speciﬁcity for SNVs and >80% speciﬁcity for indels

MyHEME DNA & RNA Results: Transloca;ons and Gene Fusions
MyHEME DNA baits contain targets to detect structural variants that occur within the breakpoint hotspot inKMT2A and in small introns adjacent to targeted exons.
• The 6 cell lines in the contrived dilu;on samples contain the following detectable transloca;ons:• t(9;22)(BCR;ABL1) – b3a2 (e14a2)• t(9;22)(NUP214;XKR3)• These cell lines were sequenced 8 ;mes at: 2.5%, 3.75%, 5%, and 10% allelic frequencies
Table 2: DNA detec;on of transloca;ons

TranslocaAont(9;22)t(9;22)MyHEME DNA Results: Coverage and Sequencing Depth
We evaluated DNA target coverage (Figure 1) and sequencing depth (Figure 2) across the coding sequencesfrom 571 genes. These analyses incorporate data from 16 samples, including 8 runs of NA12878 and 2 runs of 4
contrived samples from cell-line dilu;ons.
Figure 1: MyHEME DNA Target Coverage
Figure 2: MyHEME DNA Sequencing Depth

Genes
ABL1 – BCR
NUP214 – XKR3

Detected
88

False NegaAves
00

MyHEME RNA baits contain targets to detect gene fusions that occur within any of 371 genes.
• We sequenced 6 diﬀerent cell lines containing a known gene fusion:• t(1;19)(TCF3;PBX)• t(9;22)(BCR;ABL1) – b2a2 (e13a2)• t(9;22)(BCR;ABL1) – b3a2 (e14a2)• t(8;21)(RUNX1;RUNX1T1)• t(15;17)(PML;RARA) – “L-form”• inv(16)(CBFB;MYH11)• We use 3 RNA fusion ﬁnding programs for the detec;on of gene fusions• All fusions were detected with their expected fusion types/forms
Table 3: Evalua;on of gene fusion detec;on and sensi;vity using 3 gene fusion detec;on methods

ProgramAB
C
Combined
2 of 3

Total Fusions
1818
12910

Known Fusions
66666

SensiAvity
100%100%
100%100%100%

Figure 4: Venn diagram showing the overlap of gene-fusion detec;on from 3 diﬀerent programs.

A8B

MyHEME DNA Results: Limit of Detec;on and Linearity
To es;mate the LoD and linearity of DNA variant detec;on using MyHEME, contrived samples comprised of 6
AML cell lines were used. Five cell lines were diluted into a 6th cell line (background) at the following dilu;ons:
5%, 7.5%, 10%, and 20%. Note, for heterozygous variants, the allelic frequency is half of the dilu;on, so LoD wastested at levels as low as 2.5%.
Figure 3: Linearity of a) 4 heterozygous SNVs, b) 4 homozygous SNVs and, c) 1 heterozygous and 2 homozygous
indels.

2515

R2 = 0.980
R2 = 0.959

20Observed Frequency

R2 = 0.887

R2 = 0.986

R2 = 0.935

10

R2 = 0.919

R2 = 0.984

30
25

R2 = 0.919

15

R2 = 0.935

1500510
Expected Frequency

15GATA2 p.A164T
MET p.T1010I
NRAS p.Q61L
PTCH1 p.T1195S

500510 15 20
Expected Frequency

Conclusions• Combining 3 gene fusion programs results in a very high sensi;vity with a low false-posi;ve detec;on rate
We demonstrated that MyHEME is a highly sensi;ve, accurate and reproducible assay that comprehensively characterizes muta;onswithin samples from a variety of hematological malignancies.

ITPKB 9bp del
FLT3 30bp ITD
NPM1 4bp ins

50251• Transloca;ons with an LoD as low as 2.5% can be detected

R2 = 0.997

10ATM p.S707P
DNMT3A p.R882C
FLT3 p.D835Y
WT1 p.C350Y0• Structural variants using both DNA and RNA can be detected105890Using sequence data obtained from 1) the NIST reference NA12878, 2) contrived samples containing diluAons of 6 AML cell lines, and3) 6 cell lines with known gene fusions, the following was established:• Variant SensiAvity > 95%• Sensi;vity was highest for SNVs (99.8%)• Variant Speciﬁcity of 95% for SNVs and >80% for indels• Using an LoD of 5%, our coding speciﬁcity for both SNVs and indels is >97%
• LoD of at least 5% allelic frequency for >99% of the coding bases of targeted genes• In addi;on, as much as 98% of the coding bases of the targeted genes should have an LoD of at least 2.5% and poten;ally >70%of the coding bases should have an LoD of 1.0%
• Signiﬁcant linearity for detecAon of SNVs and indels, including pathogenic mutaAons such as FLT3/ITD

R2 = 0.949

201CNote: Of the 4 novel fusions using 2 of 3programs, 3 are reciprocal gene fusions ofone of the known fusions. The other is at(9;22)(NUP214-XKR3) fusion observedwith high conﬁdence by all 3 programs,and conﬁrmed by DNA transloca;onanalysis.

0510 15 20
Expected Frequency

AMP Global, April 3–5 2017, Berlin, Germany
25