Genomc-Wide Profiling of Gene Expression and DNA Copy Number Alterations in Multiple Myeloma.
Bibliographic record
Abstract
Abstract In an effort to identify novel genetic abnormalities in multiple myeloma (MM) we analyzed an initial set, as part of the Multiple Myeloma Research Consortium (MMRC) Genomics Initiative, of 94 MM patient samples on high-resolution array-based comparative genomic hybridization (aCGH) and gene expression profiling (GEP) technologies. The MMRC Genomics Initiative is a three-year research program spanning a full-spectrum of genomic technologies with the goal of analyzing 250 MM patient samples by 2009. Raw data from the Initiative are released in near real time through the MMRC Multiple Myeloma Genomics Portal. This initial aCGH dataset from the Agilent 244K platform represents the highest resolution gene copy number analysis with corresponding GEP released to date in MM. It has confirmed the cytogenetic definitions of MM with the data set being split between patients with hyperdiploid and non-hyperdiploid MM. Furthermore, the 244K platform has helped to refine minimal regions of change present on 1p, 1q, 6q, 8p, 13q, 14q, and 16q. A comprehensive screen of the dataset for genes residing in regions of homozygous deletion identified 105 genes. The most commonly affected gene was CDKN2C(p18) (8.3%) while 12 other regions were recurrently targeted. Several of these regions were recently implicated in the pathogenesis of MM but the large majority represents novel observations. The aCGH and GEP data were integrated using two different programs developed by the core institutions (The Broad Institute of Harvard & MIT and Translational Genomics Research Institute). First, Genomic Identification of Significant Targets in Cancer (GISTIC), which identifies highly significant regions of change and a limited set of candidate genes within these regions, was used to identify 285 genes associated with DNA content alterations. Second, Breakpoint Expression Correlation (BEC), which identifies dysregulated genes on either side of a copy number alteration, was used to identify 194 genes with expression changes associated with a breakpoint. This latter gene list includes all known IgH translocation target genes identified to date in myeloma. Overlapping the gene lists generated by GISTIC, BEC, and the homozygous deletion screen identified a number of genes previously implicated in MM such as CDKN2C(p18), CYLD, BIRC2/BIRC3, and two novel genes, FAM46C and RNF6. Ongoing data integration with RNAi and high content sequencing projects of the current Initiative holds great promise in furthering our understanding of MM. The MMRC Genomics Initiative and resulting efforts from the myeloma research community will increase our understanding of MM at the molecular level, help to identify new targets, and ultimately lead to the development of better, more effective therapies for myeloma.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".