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Myeloma immunoglobulin rearrangement and translocation detection through targeted capture sequencing

View ORCID ProfileSigny Chow, Olena Kis, View ORCID ProfileDavid T Mulder, Arnavaz Danesh, Jeff Bruce, Ting Ting Wang, Donna Reece, Nizar Bhalis, Paola Neri, View ORCID ProfilePeter JB Sabatini, View ORCID ProfileJonathan Keats, Suzanne Trudel  Correspondence email, View ORCID ProfileTrevor J Pugh  Correspondence email
Signy Chow
1University Health Network, Toronto, Canada
2Sunnybrook Health Sciences Centre, Toronto, Canada
3University of Toronto, Toronto, Canada
Roles: Conceptualization, Data curation, Formal analysis, Validation, Investigation, Methodology
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Olena Kis
1University Health Network, Toronto, Canada
Roles: Conceptualization, Data curation, Formal analysis, Investigation, Methodology
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David T Mulder
1University Health Network, Toronto, Canada
Roles: Conceptualization, Data curation, Investigation, Methodology
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Arnavaz Danesh
1University Health Network, Toronto, Canada
Roles: Formal analysis, Investigation
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Jeff Bruce
1University Health Network, Toronto, Canada
Roles: Formal analysis, Investigation
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Ting Ting Wang
1University Health Network, Toronto, Canada
3University of Toronto, Toronto, Canada
Roles: Data curation, Methodology
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Donna Reece
1University Health Network, Toronto, Canada
3University of Toronto, Toronto, Canada
Roles: Resources, Funding acquisition
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Nizar Bhalis
4University of Calgary, Calgary, Canada
Roles: Resources, Investigation
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Paola Neri
4University of Calgary, Calgary, Canada
Roles: Resources, Investigation
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Peter JB Sabatini
1University Health Network, Toronto, Canada
3University of Toronto, Toronto, Canada
Roles: Resources, Investigation, Methodology
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  • ORCID record for Peter JB Sabatini
Jonathan Keats
5Translational Genomics Research Institute, City of Hope, AZ, USA
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Suzanne Trudel
1University Health Network, Toronto, Canada
3University of Toronto, Toronto, Canada
Roles: Conceptualization, Data curation, Supervision, Investigation, Methodology
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  • For correspondence: trevor.pugh@utoronto.ca suzanne.trudel@uhn.ca
Trevor J Pugh
1University Health Network, Toronto, Canada
3University of Toronto, Toronto, Canada
6Ontario Institute for Cancer Research, Toronto, Canada
Roles: Conceptualization, Data curation, Formal analysis, Supervision, Funding acquisition, Investigation, Methodology, Writing—review and editing
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  • For correspondence: trevor.pugh@utoronto.ca suzanne.trudel@uhn.ca
Published 3 November 2022. DOI: 10.26508/lsa.202201543
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  • Figure 1.
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    Figure 1. Experimental Workflow.

    Development and validation of target capture sequencing platform. Genomic DNA from myeloma cell lines (left) and normal donor (middle) was sheered to 300 bp for both cell line and limit-of-dilution experiments. For myeloma cell lines, conventional DNA library prep was used to create DNA libraries. To improve sensitivity for limit-of-dilution series (middle) and patient samples (right), barcoded DNA library prep was used. Targeted capture experiments were performed by pooling targeted capture probe sets either before DNA capture or during sequencing with similar results (not shown). VJC = combination of V-gene, J-gene, and C-gene probes, IgHotspots = known translocation breakpoints within the IGH, IGK, and IGL loci. 38 gene = probes from 38 genes of interest in myeloma. CNV = copy-number probes for chromosomes 1 and 17. * Some bone marrow samples underwent conventional DNA library preparation. Bone marrow samples for minimal residual disease analysis underwent barcoded DNA library preparation.

  • Figure S1.
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    Figure S1. Bioinformatics Analysis Pipeline.

    CluMP, clustering of mate pairs. ^Table S7.

  • Figure 2.
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    Figure 2. Target Probe Design.

    (A) Target probes were designed in five different pools that can be combined together in a single capture, or used in any combination. The probe locations relative to the human genome are shown in a screenshot in the Integrative Genome Viewer2. IgHotspots1 pool was a part of a pre-existing probe set designed to target IGH translocation breakpoints and the c-MYC gene. IgHotspots2 included additional IGH translocation breakpoints and annotated hotspots close to or within IGL and IGK genes. Together, they are referred to as IgHotspots. (B) Immunoglobulin V-region genes were designed to probe the 3′ ends of the V gene, and J-region probes were designed to probe the 5′ ends of J genes in order to increase the likelihood of selecting fragments that cross the V(D)J junctions. Many alleles may overlap in the same genomic locations. (C) Captured fragment of rearranged V(D)J with specific probes. D regions are not baited because of their small size.

  • Figure S2.
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    Figure S2. Determination of Filtering Thresholds of Called V(D)J Rearrangements from MiXCR to Facilitate Manual Review for Dominant Clone Discovery.

    Purple lines intersecting the x-axis indicate the thresholds chosen. (A) Clone counts for all clones plotted by clonal fraction. Blue indicates rearrangements manually verified to be true. Yellow indicates rearrangements manually verified to be false. Gray indicates rearrangements that were not examined manually. At a clonal fraction threshold above 0.1, all of the true rearrangements are captured while minimizing the number of false rearrangements that need to be manually verified. Plots (B, C) include only clones with a clonal fraction of >0.10. (B) Number of sequences plotted by the number of unique counts for each sequence (clone) in a cohort of 20 cell lines. All true rearranged clones had five or fewer instances of the clonal sequence present in the cohort of called rearrangements in 20 cell lines. (C) Number of clones plotted by absolute clone count of each clone. After filtering for clonal fraction >0.10, an absolute clone count captures all true rearrangements in this cohort without any false rearrangements.

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    Figure S3. Targeted Capture Successfully Identifies Clonal V(D)J Rearrangements Confirmed by Whole-Genome Sequencing (WGS) (A) IGK

    Rearrangement in KMS11 Myeloma Cell Line and (B) IGH Rearrangement in MM1S. (A) Reads support a rearrangement between the two distinct loci (IGKJ5*01 and IGKV3D-15*01). Stacked, rainbow-colored reads indicate portions that map to the genomic locus in the corresponding split screen. The sequence (colored by nucleotide) of the multicolored reads in WGS data (top) matches that of the targeted sequencing data (bottom). The top track (KMS11 WGS) is publically available whole-genome data (Yang et al, 2014). The lower track (KMS11 targeted) is generated from the targeted sequencing probe set. Schematic shows V gene (green) and J gene (purple) with complementary sequences across multiple read pairs. (B) Reads support a rearrangement between the two distinct loci (IGHJ6*01 and IGHV3-30*01). Stacked, multicolored reads indicate portions of the read that do not map to reference genome; they comprise unique V-J junction sequences and continue on the corresponding split screen. The top track (MM1S_WGS) is publically available whole-genome data;3 the remaining tracks are WGS or targeted sequencing (targeted) data as indicated. MM1S and MM1R are steroid-sensitive and steroid-resistant derivatives of the same parent human myeloma cell line and bear the same IGHV-IGHJ rearrangement shown. Bioinformatics pipelines reliably call IGHV-IGHJ rearrangements in targeted data but not WGS data (see also Table S1). Manual verification demonstrates the presence of reads with identical sequences in WGS data as in targeted sequencing data.

  • Figure S4.
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    Figure S4. Translocation Breakpoints of FGFR3-MMSET, MAF, MAFBand CCND1 Identified with Immunoglobulin Partners in Myeloma Cell Lines.

    ,Breakpoints are indicated on relevant genes on chromosomes 4, 11, 16, and 20 with the IGH partner breakpoints in chromosome 14 indicated in corresponding colors. Gene locations are as indicated in the reference track. Genomic probe locations in IGH are as indicated. No probes were located on partner chromosomes.

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    Figure S5. Limit of Detection for Chromosome 4 Breakpoint in t(4;14) of KMS11, Limited by Depth of Sampling and Depth of Sequencing (12,000X).

    Chr4 partner of t(4;14) translocation is displayed (chr14 not shown) for all six samples of the KMS11 dilution series: 1/10 dilution in the top panel down to 1/106 in the lowest panel. Orange reads have mates that map to chromosome 14. Solid reads (orange or gray) map correctly to hg19 reference genome. Multicolored reads do not match reference genome at chr4 and map to the region on chr14. Mismatched reads are colored by base (A, green; T, red; C, blue; and G, orange). 1/10 and 1/102 dilutions are shown in compressed configuration, whereas more dilution samples are shown in expanded configuration for ease of manual review with fewer available reads. Three clear breakpoints are available to follow through the dilution series, “Br1,” “Br2,” and “Br3.” chr4 breakpoints are not baited in the targeted sequencing panel; reads mapping to chr4 displayed here are pulled down by probes targeting the chr14 breakpoint. Breakpoint 1 “Br1” has the greatest number of supporting reads to track through dilutions. The number of soft-clipped reads and mate pairs that support a translocation decreases as the sample becomes more dilute. Reads supporting the translocation at “Br1” are seen in the 1/10, 1/102, and 1/103 dilutions but not in the 1/104 dilution. Reads supporting the translocation at “Br3” are seen in the 1/10 and 1/102 dilutions, a single read supporting the translocation at “Br1” is seen in the 1/105 dilution, and a single read supporting the translocation at “Br3” is seen at 1/104 (circled in blue). Bases in this single read match those in the 1/10, 1/102, and 1/103 dilutions, and the mate pair is found on chr14 at the correct corresponding breakpoint. Single reads illustrated at “Br3” in 1/104 and “Br1” in 1/105 dilution, respectively, are not definitive to identify a translocation in an isolated sample; however, the exact sequence and breakpoint location are highly specific to the cell line sample and can be used if there is prior knowledge (such as shown in less dilute samples).

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    Figure S6. Sheered Genomic DNA Underwent End Repair and A-tailing, Producing 5′ Phosphorylated, 3′ d, A-tailed dsDNA Fragments.

    In conventional library preparation, sample-specific indices were ligated to these dsDNA fragments directly, and minimal PCR amplification was performed. In barcoded library preparation, the end-repaired-A-tailed product underwent ligation to duplex barcodes overnight, which attaches unique tags to each molecule. Sample-specific indices were then added to each sample at the PCR step with a universal primer. After sequencing, samples can be identified and demultiplexed based on sample-specific index primers. Duplex barcodes can be disregarded if not proceeding with this type of analysis, and data are analyzed identically to non-barcoded libraries.

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    Figure S7. Serial Translocation t(11;14) from a Myeloma Patient at Diagnosis and Two Follow-up Timepoints.

    Samples are CD38+138+ flow cytometry-sorted serial bone marrow at diagnosis (pre-induction), 100 d after autologous stem cell transplant, and 6 mo after autologous stem cell transplant. Sequencing depth is as indicated for each sample. Only reads mapping to chromosome 11 are shown. Probes are targeted to the paired-end read on chromosome 14 (not shown). Allelic fraction measured at the corresponding location on chromosome 14 is as indicated. Multiparameter flow cytometry of at least 1 × 106 nucleated cells (unsorted) from whole bone marrow performed in the clinical laboratory had a detection sensitivity for abnormal plasma cells (minimal residual disease) of 1/104 cellular events.

Tables

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    Table 1.

    Targeted Capture Sequencing (CapIG-seq) compared to standard PCR-based method (LymphoTrack) for identifying CDR3 of IGH rearrangements in myeloma cell lines.

    Cell lineCapIG-seqLymphoTrack
    IGHVIGHDIGHJClonal fractionIGHVIGHJFraction total readsCDR3 (common)
    ALMC1IGHV3-21IGHD2-21IGHJ40.2857IGHV3-21IGHJ40.5090GTGAGAGCGTGGGGTGGGGAACTGTGGTGGTTACCAGGCTAC
    IGHV3-29IGHD3-3IGHJ60.2704Not foundNot foundACATAAGGTTCCAAGTGAGGAAACATCGGTGTGAGTCCAGACACAAAATTTCCTGCAAAAAGAAGAAAGGAGTCa
    EJMIGHV2-5IGHD3-22IGHJ40.7765IGHV2-5IGHJ40.7065GCACACTTCCCCTCGCCTACCTCTGATAATAATGGTTATTACTTTGACTAC
    FR4IGHV3-7IGHD4-11IGHJ40.6575IGHV3-7IGHJ40.6618GCACGAGAGCAACTCAAAGGTACTGTAGTGGCTGCCCGGATGAC
    H1112IGHV3-9IGHD2-8IGHJ30.4744IGHV3-9IGHJ30.5887GCAAGAGATAGCTCTATGGGGGGCGGAGACGACAATGGTCATCTTTTTGACATG
    JJN3IGHV4-59IGHD1-26IGHJ40.9726IGHV4-59IGHJ40.6988GCGAAACCGTATAGTGGGAGCTACCCCGACGGTCACTTTGGGCTAC
    JMW1IGHV4-39IGHD3-10IGHJ50.0227IGHV4-39IGHJ50.2841GCGAGACACGTAAGGCAGGTCGGGGCCGACTGCTTCGACCCCb
    IGHV4-39IGHD3-10IGHJ50.7914IGHV4-39IGHJ50.2590GCGAGACATTTGAGGCAGGTCGGGGCCGACTGGTTCGACCCC
    Karpas25IGHV4-4IGHD3-3IGHJ40.6384IGHV4-4IGHJ40.7129GCGAGAGAGACTGGGGGCGATTTCGATCGTTGGAGTGGCCAGCACTACTACTTTGACTCC
    KP6IGHV3-33IGHD1-7IGHJ20.4906IGHV3-33IGHJ20.7018GCGAGAGAGTGGGAACTACGCTCGGGCTGGCACTTCGATCTC
    LP1IGHV3-30IGHD2-8IGHJ60.6876IGHV3-30IGHJ60.6735GCGAAGACATTATTACAGATGGGGACAAGGGGCCACTACTACGGTTTGGACGTC
    MM1SIGHV3-30IGHD2-2IGHJ60.8802IGHV3-30IGHJ60.6864GCGAGAGATTTGAGAGGTTAGGGTGAAAGGTTCCTTGTTTGTAGTAGTACCAGCTGCTACGAGGACTCCTACTACTACGATATGGACGTC
    OCI_MY5IGHV4-4IGHD1-26IGHJ40.5823IGHV4-4IGHJ40.7158GCGAGTGAGGGACAGGTGGGAAGTCAGGACTAC
    SKMM1IGHV4-39IGHD3-22IGHJ40.4260IGHV4-39IGHJ40.6792GCGGGCATGGGAGTGGCGAGGCATAACTATGATCATTGTGCTTCTTACTGGGTGGCCAC
    XG2IGHV4-4IGHD6-19IGHJ60.6423IGHV4-61IGHJ60.6460GCGAGAATAGCCGTGGCTGGTAGTAGGGACTTTTACAACTACAACCACGATATGGACGTC
    • ↵a CapIG-seq subsequently filtered out this sequence for lack of specificity.

    • ↵b CapIG-seq did not include this sequence in the final output because of low clonal fraction.

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    Table 2.

    Targeted Capture Sequencing (CapIG-seq) identification of recurrent translocations in myeloma cell lines.

    Family/GeneTranslocationCell lines carrying translocationAlgorithm calledManual reviewSensitivity
    c-MAFt(14;16)ANBL6, ARD, ARP1, CAG, JJN3, KMS11, KMS26, MM1R, MM1S, OCI-MY5, PCM610/1110/110.91 (0.74–1.08)
    t(16;22)XG6, Colo77, RPMI-82263/33/31.00 (1.00–1.00)
    MAFBt(14;20)ALMC1, ALMC2, EJM, SKMM13/43/40.75 (0.35–1.15)
    t(8;20)H9291/11/11.00 (1.00–1.00)
    t(20;22)L3630/10/10.00 (0.00–0.00)
    FGFR/MMSETt(4;14)JIM1, JIM3, JMW1, KAS-6/1, KHM1, KMS11, KMS11adh, KMS11sus, KMS18, KMS26, KMS28BM, KMS28PE, KMS34, LP1, NCI-H929, OPM1, OPM2, PE2, UTMC2, XG718/2020/201.00 (1.00–1.00)
    CCND1t(11;14)FLAM76, H1112, INA6, Karpas620, KMS12BM, KMS12PE, KMS21BM, KMS27, MOLP8, OCI-MY7, PE1, SKMM2, U266, XG17/1410/140.71 (0.47–0.95)
    CCND2t(12;14)AMO1, XG21/21/20.50 (−0.19–1.19)
    CCND3t(6;14)SKMM1, FR4, KMM12/32/30.67 (0.14–1.2)
    t(6;22)OCI-MY11/11/11.00 (1.00–1.00)
    Novel translocations
    t(14;15)KMS20YY
    t(14;17)KMS21BMYY
    t(14;18)ARP, ARP1, CAGYY
    *not c-MAFt(14;16)EJMYY
    *not-CCND1t(11;14)L363YY
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    Table 3.

    Limit of detection of translocations, V(D)J rearrangements and mutations in myeloma cell lines diluted into peripheral blood mononuclear cells.

    Cell lineDilutionTranslocationsIG rearrangementsSomatic mutations
    Barcoding analysisaFiltering analysis
    Breakpoint 1Breakpoint 2Breakpoint 3Breakpoint 4V(D)J 1V(D)J 2Mutation 1Mutation 2Mutation 1Mutation 2
    KMS111/10t(4;14)—1t(4;14)—2t(4;14)—3t(14;16)IGKV3-15_J5IGKV1-37_J4FGFR3 p.Y373CNAFGFR3 p.Y373CNA
    1/102t(4;14)—1t(4;14)—2t(4;14)—3t(14;16)IGKV3-15_J5IGKV1-37_J4FGFR3 p.Y373C
    1/103t(4;14)—1t(14;16)IGKV3-15_J5FGFR3 p.Y373C
    1/104t(4;14)—3
    1/105t(4;14)—1
    1/106
    RPMI-82261/10t(16;22)—1t(16;22)—2NANAIGKV2-28_J4IGLV2-14_J3KRAS p.G12ATP53 p.E285KKRAS p.G12ATP53 p.E285K
    1/102t(16;22)—1t(16;22)—2IGKV2-28_J4IGLV2-14_J3KRAS p.G12ATP53 p.E285K
    1/103t(16;22)—1IGKV2-28_J4KRAS p.G12ATP53 p.E285K
    1/104
    1/105
    1/106
    MM1S1/10t(14;16)NANANAIGHV3-30_D2-2_J6IGLV2-14_J3KRAS p.G12ANAKRAS p.G12ANA
    1/102t(14;16)IGHV3-30_D2-2_J6IGLV2-14_J3KRAS p.G12A
    1/103IGHV3-30_D2-2_J6IGLV2-14_J3KRAS p.G12A
    1/104
    1/105
    1/106
    • ↵a All four of these mutations were all detected down to 1/103 with barcoding but filtered out after 1/10 with LOD score calling.

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    Table 4.

    CapIG-Seq detection of multiple genomic alterations in longitudinal/contemporaneous bone marrow and cfDNA in myeloma patients.

    PatientGenomic alteration/markerClinical bone marrow FISHBone marrow sequencingcfDNA contemporaneous samplescfDNA follow-up 1 samplescfDNA follow-up 2 samples
    t(4;14)t(11;14)t(14;16)
    Patient Rt(11;14)NegativeDetectedNegativeDetectedDetected
    IGHV3-66/D3-22/J5Detected (0.69)
    IGLV2-23/J2Detected (0.8)
    NRAS p.Q61KDetected (0.58)Detected (0.016)
    FAM46C p.F274LDetected (0.46)Detected (0.0062)
    LTB p.P75LDetected (0.50)Detected (0.43)
    Patient St(4;14)DetectedNegativeNegativeDetectedDetectedDetected
    IGKV1-37/J3Detected (0.61)Detected (0.61)Detected (0.82)
    IGKV1-5/J4Detected (0.11)Detected (0.11)Detected (0.12)
    KRAS p.A146TDetected (0.19)Detected (0.065)
    FAM46C p.D150YDetected (0.23)
    PRKD2 p.Y566CDetected (0.65)PRKD2 p.Y566C (0.040)Detected (0.11)
    Patient TNo translocations detectedNegativeNo translocations detected
    IGKV3-11/J3Detected (0.62)Detected (0.55)
    IGHV2-5/D3-3/J6Detected (0.21)
    KRAS p.Q61HDetected (0.047)Detected (0.015)
    PRDM1 p.P66SDetected (0.39)Detected (0.46)
    Patient Ut(12;14)Negativet(12;14)t(12;14)t(12;14)
    IGKV4-1/J2Detected (0.59)Detected (0.04)Detected (0.21)
    MAX p.R27WDetected (0.90)Detected (0.54)Detected (0.65)Sample failed for mutation calls
    KRAS p.G13DDetected (0.080)Detected (0.19)Detected (0.091)
    CYLD p.G930GDetected (0.017)
    Patient Vt(12;14)Negativet(12;14)t(12;14)
    IGKV1-39/J1Detected (0.68)Detected (0.70)
    IGKV4-1/J4Detected (0.29)Detected (0.29)
    KRAS p.G12VDetected (0.45)Sample fail for mutations
    BRAF p.D594NDetected (0.45)
    Patient WNo translocations detectedNegativeNo translocations detectedNo translocations detected
    No V(D)J rearrangement found
    IDH1 p.F86IDetected (0.091)Detected (0.085)Detected (0.089)
    ATR p.E650KDetected (0.089)Detected (0.094)Detected (0.089)
    PIK3CA p.H59PDetected (0.41)Detected (0.38)Detected (0.37)
    CYLD p.L135MDetected (0.044)
    CYLD p.G930GDetected (0.045)
    IKZF3 p.S88RDetected (0.039)
    Patient XNo translocations detectedNegativeNo translocations detectedNo translocations detected
    IGLV1-44/J2Detected (0.83)Detected (0.0069)Detected (0.0099)
    NRAS p.G13R (0.35)Detected (0.35)Detected (0.73)
    FAM46C p.G37V (0.25)Detected (0.25)
    MAX p.R51W (0.72)Detected (0.72)
    Patient YNo translocations detectedNegativeNo translocations detectedNo translocations detected
    IGKV3-20/J2Detected (0.55)Detected (0.54)
    EGR1 p.S42TDetected (0.41)Detected (0.34)
    KRAS p.G13DDetected (0.79)Detected (0.48)
    ZFHX4 p.P3167PDetected (0.014)
    • Embedded Image Concordance with bone marrow/clinical data

    • Embedded Image Found in bone marrow but not in cfDNA

    • Embedded Image Found in cfDNA and not in bone marrow

    • Embedded Image Not applicable/no sample.

Supplementary Materials

  • Figures
  • Tables
  • Table S1. Bioinformatic pipeline.[LSA-2022-01543_TableS1.xlsx]

  • Table S2. (A) CapIG-seq V-region probes and sequences. (B) CapIG-seq J-region probes. (C) CapIG-seq C-region probes and sequences. (D) Immunoglobulin translocation hotspot 1 probe panel. (E) Immunoglobulin translocation hotspot 2 probe panel.[LSA-2022-01543_TableS2.xlsx]

  • Table S3. Unfiltered myeloma cell line VDJ calls from MiXCR.[LSA-2022-01543_TableS3.xlsx]

  • Table S4. (A) Myeloma cell line V(D)J calls from CapIG-seq compared to whole genome sequencing. (B) CDR3 sequencings of V(D)J rearrangements found in (A). (C) Myeloma cell line V(D)J calls from CapIG-seq without reference whole genome sequencing.[LSA-2022-01543_TableS4.xlsx]

  • Table S5. Unfiltered IGH V(D()J calls and sequences from PCR-based lymphoTrack assay.[LSA-2022-01543_TableS5.xlsx]

  • Table S6. Unfiltered comparison of IGH V(D)J calls between CapIG-seq and lymphoTrack.[LSA-2022-01543_TableS6.xlsx]

  • Table S7. CCND1 cell line breakpoints in myeloma cell lines.[LSA-2022-01543_TableS7.xlsx]

  • Table S8. FGFR cell line breakpoints in myeloma cell lines.[LSA-2022-01543_TableS8.xlsx]

  • Table S9. MAF cell line breakpoints in myeloma cell lines.[LSA-2022-01543_TableS9.xlsx]

  • Table S10. CapIG-Seq detection of minimal residual disease using V(D)J and recurrent translocations in longitudinal/contemporaneous bone marrow and cfDNA.[LSA-2022-01543_TableS10.xlsx]

  • Table S11. DNA input for patient samples.[LSA-2022-01543_TableS11.xlsx]

  • Table S12. Duplex index sequences for barcoding analysis.[LSA-2022-01543_TableS12.xlsx]

  • Table S13. Repetitive sequences removed from CluMP output.[LSA-2022-01543_TableS13.xlsx]

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Target capture detection of V(D)J and IG translocation
Signy Chow, Olena Kis, David T Mulder, Arnavaz Danesh, Jeff Bruce, Ting Ting Wang, Donna Reece, Nizar Bhalis, Paola Neri, Peter JB Sabatini, Jonathan Keats, Suzanne Trudel, Trevor J Pugh
Life Science Alliance Nov 2022, 6 (1) e202201543; DOI: 10.26508/lsa.202201543

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Target capture detection of V(D)J and IG translocation
Signy Chow, Olena Kis, David T Mulder, Arnavaz Danesh, Jeff Bruce, Ting Ting Wang, Donna Reece, Nizar Bhalis, Paola Neri, Peter JB Sabatini, Jonathan Keats, Suzanne Trudel, Trevor J Pugh
Life Science Alliance Nov 2022, 6 (1) e202201543; DOI: 10.26508/lsa.202201543
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January 2023
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