Recurrent DNA Mutations In Non-Hodgkin Lymphomas Reveal Candidate Therapeutic Targets
Bibliographic record
Abstract
Abstract Abstract 632 Introduction: Non-Hodgkin lymphomas (NHL) are the most common type of lymphoma and can be broadly classified as indolent (slow-growing) diseases, progressing over many years; and aggressive (fast-growing) diseases, which progress rapidly. The latter class includes diffuse large B-cell lymphoma (DLBCL), which accounts for approximately 30% of all NHL diagnoses. Three DLBCL subtypes have been identified based on gene expression profiling, namely: germinal center B-cell (GCB), activated B-cell (ABC) and primary mediastinal B-cell lymphoma (PMBCL). These subtypes show substantial differences in response to treatment and ultimate disease outcome, suggesting that molecular subtyping is an important prognostic indicator and that each subtype may benefit from a distinct treatment regimen. Despite recent advances in cancer genomics revealing molecular and mutational differences between these subtypes, further studies focused on the common NHL subtypes are required to identify critical players in the pathogenesis of DLBCL that may be targeted by pharmacological intervention to improve patient outcome. Methods: Using ultra-high throughput whole genome shotgun sequencing (WGSS) and whole transcriptome shotgun sequencing (WTSS/RNA-seq) we have discovered protein-coding mutations in NHL genomes. With a focus on recurrent and likely gain-of-function mutations we have established procedures to model the three-dimensional structures of mutant proteins and using a computational “molecular docking” pipeline have identified candidate molecules with specificity for the mutant protein. These small molecule compounds are acquired and tested in cell proliferation assays against a suite of DLBCL cell lines characterized for target mutations. Results: Mutations affecting a single key tyrosine in the catalytic site of enhancer of zeste, homolog 2 (EZH2), a member of the Polycomb-group family involved in transcriptional repression were identified (Morin, R. et al. 2010 Nature Genetics 42(2):181-5). This mutation, in a gene previously unknown to be mutated in cancer, is restricted to the GCB subtype of lymphomas and is highly prevalent in patient samples and DLBCL cell lines. Mutations have also been observed in other proteins involved in epigenetic regulation and thus afford potentially novel therapeutic targets. In proof-of-principle experiments small molecule inhibitors were identified using molecular docking approaches to target the effect of EZH2 mutations in both mutant and wild-type DLBCL cell lines. We identified and imported 96 compounds from the Developmental Therapeutic Program NCI/NIH repository. These compounds were tested in alamarBlue cell proliferation assays revealing three with activity at 10uM concentration in EZH2 mutant but not wild-type cells. Computational optimization of these compounds is underway to identify related compounds with improved activities at reduced concentrations. Conclusions: High-throughput sequencing platforms have enabled the identification of recurrent, non-synonymous protein mutations in tumor genomes and transcriptomes. Such a catalogue of mutations provides new avenues of exploration for targeted therapy including small molecule inhibitors. Despite intensive efforts launched in recent years to determine the crystal structure for every human protein, many (including EZH2) do not currently have three dimensional structures. This poses a challenge to novel drug discovery but can be overcome using homology modeling and/or targeting other members of a pathway. Our observations also demonstrate the importance of epigenetic regulation in NHL tumorigenesis and thus provide potential new therapeutic targets. 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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.
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".