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Record W3214724472 · doi:10.1182/blood-2021-154005

High-Throughput Characterization of Cytogenomic Heterogeneity of MDS Using High-Resolution Optical Genome Mapping

2021· article· en· W3214724472 on OpenAlexaboutno aff
Hui Yang, Guillermo Garcia‐Manero, Guillermo Montalban‐Bravo, Kelly S. Chien, Awdesh Kalia, Zhenya Tang, Yue Wei, Manjunath Nimmakayalu, Diana Rush, Saradhi Mallampati, Hagop M. Kantarjian, Rajyalakshmi Luthra, L. Jeffrey Medeiros, Rashmi Kanagal‐Shamanna

Bibliographic record

VenueBlood · 2021
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCancer Genomics and Diagnostics
Canadian institutionsnot available
Fundersnot available
KeywordsCopy number analysisBiologyComputational biologyDNA sequencingGeneticsGenomeMolecular Inversion ProbeCopy-number variationGeneSingle-nucleotide polymorphismGenotype

Abstract

fetched live from OpenAlex

Abstract Introduction Introduction of next-generation sequencing has defined the somatic mutational landscape in MDS. Comprehensive high-throughput structural variant profiling (SVP) is as important as mutation profiling in characterizing MDS clonal architecture since these large genomic aberrations have already shown to be critical for diagnosis and risk-stratification of MDS. A subset (MECOM, KMT2A rearrangements) are therapeutic targets in clinical trials. At this time, technical advances in SVP for copy number alterations (CNAs) and fusions have not been congruent with mutation profiling due to the inability of short-read (150bp) NGS to detect SVs. Currently available long-read (10-20Kbp) and whole genome sequencing cannot detect all SVs due to the presence of repeat sequences. Hence, conventional karyotyping (CK) remains the gold standard. Optical genome mapping (OGM) is a novel single-platform technique that measures ultra-long-range sequence patterns (>300Kbp), thereby unaffected by repeat sequences, enabling unbiased evaluation of all types of SVs at a high resolution. Here, we performed comprehensive SVP and mutation profiling in a large well-characterized cohort of MDS. Methods We selected samples with available fresh/frozen BM cells from consecutive treatment-naïve MDS pts who also underwent standard-of-care tests (CK, FISH, targeted 81-gene NGS for mutations). For OGM, ultra-high-molecular-weight-DNA was extracted, followed by labeling, linearization and imaging of DNA (Saphyr, Bionano) [median coverage:>300X]. The results were analyzed using de novo (>500 bp), rare variant (>5000 bp) and copy number (>500,000 bp) pipelines. The data was compared against 200 healthy controls to exclude common germline SVs. Clinical significance of the SVs was determined based on the location/overlap with the coding region of myeloid malignancy associated genes. The detection sensitivity was 10%. Results There were 76 treatment naïve MDS patients. Baseline characteristics, comprehensive cytogenetic scoring system (CCSS) and R-IPPS risk categories and somatic mutations are in Fig 1. OGM identified all clonal abnormalities detected by CK [CNAs, inversions, inter/intra-chromosomal translocations, dicentric, complex derivative chromosomes]. Precise mapping of SVs by OGM at gene-level allowed determining the status of clinically informative biomarkers such as TET2, MECOM, TP53 and KMT2A, without the need for confirmatory assays. Detailed gene-level characterization of different SVs included KMT2A-ELL [t(11;19)] in MDS with WT1 mut, t(9;11) with SYTL2 fusion (and not KMT2A), der(1;7) leading to del(7q) in MDS with GATA2 mut/IDH2 mutand t(1;3)(p36;q21) rearrangements with potential PRDM16 disruption in SF3B1 mut/RUNX1 mutMDS, among others. Using OGM, we mapped the sequence patterns in both samples with IM with high level of confidence. Additionally, OGM identified 23 cryptic, clinically significant SVs in 14 (18%) of 76 pts. These included deletions of TET2, KMT2A, and del(5q), KMT2A amplification in MDS with FLT3-ITD/DNMT3A mut/RAS mut, NUP98-PRRX2, MECOM rearrangement in TET mut mutated NK-MDS. In addition, there were SVs of uncertain significance: duplications of chr1 (PDE41P), deletions of chr21 (involving RUNX1), chr2 (DNMT3A, ASXL2), chr12 (ETV6) and chr22 (EP300) and der(16)t(12;16)(q21.1;q12.1). These cryptic SVs were noted across all R-IPSS risk categories (highest yield in very-low and low R-IPSS) and across all cytogenetic risk-groups (very-good to very-poor). In complex karyotype setting, OGM could resolve the markers and additional genetic material, and in most cases, showed a much higher the degree of complexity within the genome than was apparent by CK. Four pts showed SV patterns typical of chromothripsis/chromoplexy. The median number of mutations per pt was 1 (0-6). When compared to mutation subsets, cryptic SVs were only identified in pts with ≤3 mutations. Majority represented either MDS with TP53 mut (6, 29%) or SF3B1 mut/TET mut (deletions of TET2, KMT2A, NOTCH1 and EP300 genes). Conclusions Unbiased, high-throughput whole genome SVP revealed cryptic, clinically significant SVs in ~18% of MDS pts. OGM is a single-platform cytogenomic tool that can facilitate SVP at a gene-level resolution. This study provides strong support for further validation in expanded cohorts to guide clinical implementation and integration of SVP for routine work-up. Figure 1 Figure 1. Disclosures Wei: Daiichi Sanko: Research Funding. Kantarjian: Ipsen Pharmaceuticals: Honoraria; Amgen: Honoraria, Research Funding; Astellas Health: Honoraria; Astra Zeneca: Honoraria; AbbVie: Honoraria, Research Funding; KAHR Medical Ltd: Honoraria; NOVA Research: Honoraria; Ascentage: Research Funding; Aptitude Health: Honoraria; Novartis: Honoraria, Research Funding; Pfizer: Honoraria, Research Funding; Jazz: Research Funding; Immunogen: Research Funding; Daiichi-Sankyo: Research Funding; BMS: Research Funding; Precision Biosciences: Honoraria; Taiho Pharmaceutical Canada: Honoraria.

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)0.000
metaresearch head score (Gemma)0.000
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 score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.017
GPT teacher head0.234
Teacher spread0.217 · 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
Published2021
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