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Record W2519777424 · doi:10.1182/blood.v126.23.368.368

Evaluation of Circulating Tumour DNA for the Mutational Characterisation of Multiple Myeloma

2015· article· en· W2519777424 on OpenAlexaff
Andrew Spencer, Sridurga Mithraprabhu, Malarmathy Ramachandran, Daniela Klarica, Jane S. Hocking, Laura Mai, Stephanie J. Walsh, David Broemeling, Andre Marziali, Anna Kalff, Matthew Wiggin, Brian G.M. Durie, Tiffany Khong

Bibliographic record

VenueBlood · 2015
Typearticle
Languageen
FieldMedicine
TopicMultiple Myeloma Research and Treatments
Canadian institutionsCollège Boréal
Fundersnot available
KeywordsNeuroblastoma RAS viral oncogene homologDigital polymerase chain reactionKRASExomeExome sequencingDNACell-free fetal DNAMolecular biologyLiquid biopsyMultiple myelomaCancer researchBiologyMutationPolymerase chain reactionCancerMedicineGeneticsGeneImmunology

Abstract

fetched live from OpenAlex

Abstract Background: Whole exome sequencing (WES) of bone marrow (BM) has demonstrated recurring single nucleotide variations in multiple myeloma (MM) with activating mutations (MTS) of the RAS-MAPK pathway identified in 50% of patients. These same data have also demonstrated significant intra-clonal heterogeneity. Based on the technical limitations of BM biopsy in MM we have evaluated the utility of circulating free (tumour derived) DNA (ctDNA) in the plasma of MM patients as a potential target for the mutational characterisation of MM. Methods: Blood (30ml) from MM patients (pts) and normal volunteers (NV) was collected into Streck Cell-Free DNA BCT tubes, centrifuged immediately and DNA extracted using the QIAamp circulating nucleic acid kit (Qiagen). Plasma ctDNA was quantified with a QUBIT Fluorometer and high sensitivity DNA detection kits (Life Technologies). BM aspirates from MM patients were CD138 enriched using the MACS Bead System and DNA extracted (Qiagen). Paired BM CD138 DNA and ctDNA mutational profiles were characterised with the OnTarget™ Mutation Detection (OMD) platform (Boreal Genomics) that includes 42 unique MTS potentially relevant to MM (KRAS n=18, NRAS n=10, TP53 n=8, BRAF n=6). OMD findings were subsequently validated with ddPCR (Biorad QX200 droplet digital PCR system). For WES of ctDNA, library prep and exome capture were undertaken with the NEBNext Ultra Library prep kit (Genesearch) and SureSelect XT2 human exome V5.0 kit (Agilent), respectively. Sequencing was then undertaken on an Illumina HiSeq 2500 and processed via the APF human exome pipeline. Results: Higher quantities of circulating free DNA were obtained from MM pts (n=37) than NV (n=21) (median 23ng/ml [range 5-195ng/ml] versus 15ng/ml [range 6-32ng/ml], respectively, p = 0.009). Twenty-eight MM pts (10 newly diagnosed [ND] and 18 relapsed/refractory [RR]) had contemporaneous CD138 enriched MM tumour cell populations collected and all 28 paired BM MM DNA and ctDNA specimens along with 3 wild-type (WT) DNA controls underwent OMD. A total of 72 MTS (KRAS n=36 [50%], NRAS n=19 [26%], BRAF n=4 [6%], TP53 n=13 [18%]) were detected in the MM pts (BM and/or ctDNA) with none detected in WT controls. More MTS were present in RR pts compared with ND pts - median 2.5 (range, 0-11) versus 1 (range, 0-3), respectively, p=0.03 and all 13 TP53 MTS were found exclusively in RR patients. Twenty-four randomly selected OMD low-level positive or negative samples (but with an identified MTS in matched BM or ctDNA samples) were studied with mutation-specific ddPCR. Of 11 OMD positive samples 10 (91%) were positive with ddPCR while 3 negative with OMD tested positive with ddPCR. Thirty MTS were found with OMD both in BM and ctDNA, 23 only in BM and 19 only in ctDNA. Moreover, in 5 of 30 MTS found in BM and ctDNA the mutational load (mutational allele frequency versus WT allele frequency) was proportionately greater within the ctDNA, thus a total of 24 MTS (33%) were detected with OMD, exclusively or predominantly, distant to the BM biopsy site. Activating MTS of the RAS-MAPK pathway (KRAS/NRAS/BRAF) were detected (BM and/or ctDNA) in 22 of 28 pts (79%) comprising 90% of ND pts (median MTS 1, range 0-3) and 72% of RR pts (median MTS 1, range 0-11), moreover, 8 of 18 (44%) RR pts harboured ≥2 activating MTS (2, 2, 3, 4, 4, 8, 8, 11 each). Exploratory WES was undertaken on 4 ctDNA samples and demonstrated predominantly exonic variants of 108, 152, 101 and 98 distinct genes with median read depths of 115, 79, 78 and 65, respectively. Variants were enriched for C>T transitions (51%, 45%, 51% and 44% of all variants, respectively) reflecting spontaneous deamination of methylated cytosine to thymine as has been described with WES of MM BM. Conclusions: Our data confirm the utility of ctDNA evaluation as an adjunct to the mutational characterization of MM. Furthermore, using highly sensitive targeted approaches we have demonstrated a more complex mutational landscape in MM than previously shown with BM WES. In our cohort, activating MTS of the RAS-MAPK pathway were highly prevalent with our findings suggesting a striking subclonal convergence on this pathway. We conclude that high-sensitivity approaches incorporating plasma ctDNA evaluation aimed at identifying actionable MTS may represent a significant advance in attempts to personalize future MM treatment strategies and that future studies incorporating RAS-MAPK pathway targeted approaches for MM are essential. Disclosures Mai: Boreal Genomics: Employment. Walsh:Boreal Genomics: Employment. Broemeling:Boreal Genomics: Employment. Marziali:Boreal Genomics: Employment. Wiggin:Boreal Genomics: Employment.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0030.001

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.127
GPT teacher head0.362
Teacher spread0.235 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations1
Published2015
Admission routes1
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