The use of whole genome amplification to study chromosomal changes in prostate cancer: insights into genome-wide signature of preneoplasia associated with cancer progression
Bibliographic record
Abstract
BACKGROUND: Prostate cancer (CaP) is a disease with multifactorial etiology that includes both genetic and environmental components. The knowledge of the genetic basis of CaP has increased over the past years, mainly in the pathways that underlie tumourigenesis, progression and drug resistance. The vast majority of cases of CaP are adenocarcinomas that likely develop through a pre-malignant lesion and high-grade prostatic intraepithelial neoplasia (HPIN). Histologically, CaP is a heterogeneous disease consisting of multiple, discrete foci of invasive carcinoma and HPIN that are commonly interspersed with benign glands and stroma. This admixture with benign tissue can complicate genomic analyses in CaP. Specifically, when DNA is bulk-extracted the genetic information obtained represents an average for all of the cells within the sample. RESULTS: To minimize this problem, we obtained DNA from individual foci of HPIN and CaP by laser capture microdissection (LCM). The small quantities of DNA thus obtained were then amplified by means of multiple-displacement amplification (MDA), for use in genomic DNA array comparative genomic hybridisation (gaCGH). Recurrent chromosome copy number abnormalities (CNAs) were observed in both HPIN and CaP. In HPIN, chromosomal imbalances involving chromosome 8 where common, whilst in CaP additional chromosomal changes involving chromosomes 6, 10, 13 and 16 where also frequently observed. CONCLUSION: An overall increase in chromosomal changes was seen in CaP compared to HPIN, suggesting a universal breakdown in chromosomal stability. The accumulation of CNAs, which occurs during this process is non-random and may indicate chromosomal regions important in tumourigenesis. It is therefore likely that the alterations in copy number are part of a programmed cycle of events that promote tumour development, progression and survival. The combination of LCM, MDA and gaCGH is ideally suited for the identification of CNAs from small cell clusters and may assist in the discovery of potential genomic markers for early diagnosis, or identify the location of tumour suppressor genes (TSG) or oncogenes previously unreported in HPIN and CaP.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".