Splicing Mutations in the Hyaluronan Synthase I Gene of Patients with Monoclonal Gammopathy of Undetermined Significance.
Bibliographic record
Abstract
Abstract Abstract 2841 Poster Board II-817 Introduction: Monoclonal gammopathy of undetermined significance (MGUS) is the most common plasma cell disorder. A small proportion of MGUS transform to multiple myeloma (MM) or other B lineage malignancies. Hyaluronan synthase I (HAS1) is overexpressed in many human cancers, including MM. Here we show that intron 3, a region involved in aberrant splicing of HAS1, is heavily mutated in 34 patients with MGUS. Aberrant splicing of HAS1 correlated with poor outcome in a cohort of MM patients. HAS1 is GC-rich and as such is likely to be subject to mutation events. We recently reported a series of nearly 200 HAS1 mutations in 17 MM and Waldenstrom's macroglobulinemia (WM) patients, that were absent from healthy donors (Adamia et al, Blood, 2008, 112:5111). Forty-nine of these HAS1 mutations were recurrent, defined as being present in more than one patient. None of them were present in sequenced regions of HAS1 from a small series of MGUS patients. Mutated HAS1 DNA directs aberrant splicing of HAS1 and in vitro mutagenesis of HAS1 intron 3 alters HAS1 splicing patterns (see abstract by Kriangkum et al.), mimicking the aberrant splicing seen in MM patients. To sequence HAS1 from a larger cohort, we developed a streamlined sequencing strategy that allowed rapid sequencing of exon 3 and intron 3, regions of the HAS1 gene rich in mutations. Our objective was to identify HAS1 mutations in MGUS as compared to a larger cohort of MM and healthy donors. Many reports have shown familial clusters of MGUS and MM and of other B cell malignancies involving MGUS, supporting the idea that genetic factors, and possibly genetic predispositions, contribute to the development of disease. Patients and Methods: We sequenced HAS1 regions from 34 MGUS patients, 50 MM patients, and 45 healthy donors. In all cases, we sequenced an 850 bp region covering exon 3 and intron 3 of the HAS1 gene. For each patient, we sequenced eight HAS1 region subclones, in both directions. To predict impact of recurrent HAS1 mutations on the splicing process, we performed in silico analysis using online bioinformatic tools. Results: To date we have identified, 117 HAS1 mutations in HAS1 gene in MGUS patients including 5 frequently detected NCBI single nucleotide polymorphisms (SNPs). Although the same NCBI SNPs were also identified in healthy donors, increased homozygosity was detected in MGUS patients compared to healthy donors. Overall, among the novel HAS1 mutations, 32 (27%) were shared between MGUS and MM but absent in healthy donors, defining them as recurrent. Seven of the 32 mutations (23%) shared between MGUS and MM were recurrent in 2-3 individual MGUS patients; three of the recurrent MGUS mutations were missense mutations present in the coding region of exon 3, changing the protein sequence. Our initial bioinformatic analysis showed that the two of the shared recurrent mutations were found in the SR protein binding sites of the pre-mRNA and may contribute to aberrant splicing of the primary HAS1 transcript. We predict that our ongoing analysis HAS1 mutations in MGUS patients will identify a subpopulation(s) at high risk of aberrant splicing and thus at increased risk of transformation to overt MM. Conclusion: HAS1 appears to be hypermutated, perhaps due to its GC-rich nature. HAS1 mutations were identified in patients with a clinical diagnosis of MGUS (range of 1-14 mutations per MGUS). Thirty-two HAS1 mutations were recurrent in MGUS and MM but were absent from healthy donors. Some of these mutations are predicted to have functional significance for protein sequence and pre-mRNA splicing, possibly leading to increased risk for or predisposition to disease. Although genetic background and environmental context are likely to be important factors, our report of novel mutations in HAS1 of MM and WM patients, confirmed now in MGUS, suggests that mutations in HAS1 may play a key role in oncogenesis. Further genetic analysis of patient populations and the biochemical characterization of the mutated proteins will provide considerable insight into the function of HAS1 during transformation from MGUS to overt MM. Disclosures: No relevant conflicts of interest to declare.
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 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
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".