Abstract 3404: Identification of potential oncogenes in osteosarcoma pathogenesis by high-resolution array comparative genomic hybridization
Bibliographic record
Abstract
Abstract An unstable karyotype and global gene copy number changes typify osteosarcoma tumors, which are the most common primary malignant tumors of bone. Tumors often exhibit aberrant broadly disregulated gene expression and a high number of genomic aberrations and biological heterogeneity that is thought to confer adaptability to aggressive treatment regimens. The molecular basis for the pathogenesis and aggressive nature of osteosarcoma is clinically relevant, but has yet to be well-elucidated. In this study, we utilized high-resolution array comparative genomic hybridization (Human Genome CGH Microarray Kit 244A) to identify and characterize recurrent signatures of genomic imbalances using 15 osteosarcoma tumors. The tumors were then clustered using hierarchical techniques according to chromosomal location, size and type (gain or loss) of their genomic aberrations. We observed an approximate correlation of the clusters with percent tumor necrosis in response to chemotherapy; however, of even greater interest were some apparent relationships between tumor response to treatment and copy number changes in genes with potential for oncogenic activity. In tumors with <90% necrosis in response to chemotherapy relative to tumors with good response, there was overall gain of BMP2, TERT, and SPP1; universal gain and amplification of RUNX2; and loss of P53 and CDKN1A/p21. Investigation of the interactions among these proteins yielded insights into mechanisms of continued cell survival and cell proliferation. Similarly gained in nearly 80% of the tumors with poor chemotherapy response was the BRMS1 gene, whose protein product reduces metastases but not tumorigenicity in multiple cancers and interacts with pRB, which is frequently inactivated in osteosarcomas. We detected that the majority of the 15 tumors had lost at least one copy of the well-characterized tumor suppressor genes PTEN (12 of 15 tumors), RB1 (11 of 15 tumors), P53 (14 of 15 tumors), CDKN2A/p16 (12 of 15 tumors), and CDKN2B/p15 (12 of 15 tumors); all of which encode proteins that may function in stabilizing the genome and checking the growth of osteosarcoma cells. The CDKN1A/p21 gene, on the other hand, was gained in 11 of 15 tumors, particularly in all of those with >90% necrosis in response to chemotherapy (6 of 6 tumors). Many of the genes with aberrant copy number detected by our analyses have been characterized in cell lines and in vivo studies representing other types of cancer. Ideally, our results will prompt functional investigations of overexpression or, alternatively, inactivation of the genes identified in this study. Our future objectives are to determine the effects in osteosarcoma xenografts of modulating expression of these genes. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 101st Annual Meeting of the American Association for Cancer Research; 2010 Apr 17-21; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2010;70(8 Suppl):Abstract nr 3404.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".