Next-Generation Sequencing of Lymphoid Cancers: From Discovery to Clinical Translation
Bibliographic record
Abstract
Abstract Abstract SCI-12 Since completion of the reference human genome and the introduction of next-generation sequencing (NGS) technologies, a number of key discoveries have significantly added to our understanding of the fundamental biology and mutational landscape of the mature B-cell lymphoid malignancies. By the end of 2012, most mature B-lineage lymphoid cancers will have been sequenced to a reasonable number and depth of coverage to inform on the pattern of recurrent mutations, gene fusions, and other somatic genetic events contributing to the biology. Discerning the important driver mutations from passengers and using filters to decide on what alterations to carry through to functional validation will be critical to informing on the biology in a timely manner. The value of whole genome sequencing with respect to noncoding alterations and discovery has already been realized and will become even more apparent when contrasting these data with the more economical exome-based sequencing strategies. These and related topics will be discussed by Dr. Elaine Mardis in her scientific session presentation. In 2010 we saw these technologies lead to the discovery of recurrent activating (gain-of-function) mutations in EZH2, a histone methyltransferase and member of the polycomb repressor 2 complex that is responsible for laying down repressive chromatin marks by trimethylating H3K27. These mutations were found in follicular lymphoma (FL) and in the germinal center B-cell-like subtype of diffuse large B-cell lymphoma (DLBCL). Subsequent genomic studies uncovered loss-of-function mutations in EZH2 in myeloid tumors, contrasting the very different contributions of one gene in these diverse entities. Studies by Pasqualucci, Morin, and colleagues further expanded the role of histone modification and chromatin remodeling in both FL and DLBCL by implicating recurrent mutations in CREBBP, EP300, MLL2, MEF2B, SETD2, and core histone protein genes, including HIST1H1E, HIST1H1D, HIST1H2AC, and HIST1H2BD. RNA-seq technology was used to uncover novel gene fusions in classical Hodgkin lymphoma and the related entity primary mediastinal large B-cell lymphoma that target CIITA, the master regulator of MHC class II expression. Fusion partners in some of the cases involve the ligands of PD-1 and thus establish immune escape as a key oncogenic driver in some lymphoid malignancies. The application of NGS strategies to chronic lymphocytic leukemia, hairy cell leukemia, mantle cell lymphoma, and lymphoplasmacytic lymphoma has revealed a number of recurrent somatic mutations of key genes, including NOTCH1, MYD88, XPO1, KLHL6, SF3B1, and BRAF (V600E) to name but a few. Validation in extension sets and functional studies will be required to fully understand the contribution of these genetic alterations to lymphoma biology and to know which targets are logically suited to the development of targeted therapies. Last, the deployment of these NGS discoveries into the clinical laboratory may take several forms, including targeted resequencing used for diagnosis and subclassification, assessing clonal dominance for sequencing of therapies in some lymphomas, as well as their use in minimal residual disease detection. Disclosures: No relevant conflicts of interest to declare.
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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 | 0.006 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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".