Abstract B036: Integrating gene expression evaluation in molecular diagnostics for pediatric AML molecular classification
Bibliographic record
Abstract
Abstract Next generation sequencing (NGS) based molecular profiling has been proven to be robust and reliable in detecting most biological attributes in acute myeloid leukemia (AML). Targeted NGS testing on genomic DNA for SNVs/Indels and on RNA for oncogenic gene fusions are commonly performed in molecular diagnostics laboratories. With the initial clinical molecular diagnostic framework established in our institution, we used whole genome and whole transcriptome sequencing (WGS and WTS) to evaluate SNVs/Indels, structural variations (SVs) and copy number variations (CNVs) in pediatric and adolescent AMLs. In addition, gene expression was assessed using WTS data to evaluate the possible pathogenicity of variants identified through DNA sequencing. In the 154 AML cases analyzed, WGS revealed recurrent AML oncogenic fusions in 88, all of which were confirmed to be in-frame fusion transcripts by WTS. AML-defining SNVs/Indels and internal tandem duplications were detected by WGS in 45 cases with supporting evidence from WTS. WGS revealed potential enhancer hijacking fusions in 10 cases (MECOM-r in 6, HOXA-r in 2 and BCL11B::TLX3 in 2). Among them, increased expression of the oncogene of interest was verified by WTS in 8 cases (6 MECOM-r and 2 HOXA-r). In the remaining two cases (FAB classification of AML M0 and M1, respectively) for which WGS suggested an SV of BCL11B::TLX3, expression of TLX3 was barely detectable and expression of BCL11B was not increased. Furthermore, global gene expression profiling did not cluster these cases within any known AML molecular categories. Together, the gene expression data of the two cases was not consistent with an SV leading to aberrant TLX3 or BCL11B activation, resulting in the final classification of AML, NOS. Among the 7 cases that initially could not be molecularly classified based on sequence variants only, gene expression assessment helped elucidate the AML class-defining genetic driver in one case. While no apparently known genetic driver was detected by WGS and WTS analyses, an acquired heterozygous frameshift variant in the N-terminal transcription activation domain of CEBPA (35% variant allele frequency, VAF) was found. Notably, the same variant in CEBPA was observed in WTS at 97% VAF, indicating the exclusive expression of the mutant allele in the tumor. Furthermore, global gene expression profiling demonstrated the same characteristic expression profile as seen in CEBPA double mutants (CEBPA-dm). Together, the gene expression assessment obtained from WTS provided an essential tool to classify this case to the molecular subgroup of CEBPA-dm. In this study, gene expression obtained from WTS was used to complement WGS, providing gene/allele-specific expression and global gene expression profiling, to verify the possible pathogenicity of sequence variants identified through DNA sequencing. We demonstrated that molecular classification of AML can be further improved using a diagnostic framework of WGS and WTS as well as integrating gene expression evaluation to complement sequence variants analysis. Citation Format: Lu Wang, Rebecca Voss, Victor Pastor Loyola, Maria F. Cardenas, Jing Ma, Priya Kumar, Mark R. Wilkinson, David A. Wheeler, Jeffery M. Klco. Integrating gene expression evaluation in molecular diagnostics for pediatric AML molecular classification [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Advances in Pediatric Cancer Research; 2024 Sep 5-8; Toronto, Ontario, Canada. Philadelphia (PA): AACR; Cancer Res 2024;84(17 Suppl):Abstract nr B036.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".