Abstract 3184: Whole genome sequencing of low-input fresh frozen prostate cancer biopsies
Bibliographic record
Abstract
Abstract Prostate cancer is the most commonly diagnosed malignancy among men in the United States. Due to an aging population, prostate cancer incidence has been increasing, with an estimated 200,000 men being diagnosed in 2010 and more than 32,000 deaths resulting from this disease. Better predictors of patient prognosis and treatment outcome are required to individualize prostate cancer treatment. High-throughput genomic sequence-based approaches offer a unique opportunity to identify biomarkers of disease-progression, thereby enabling more individualized therapy. The Canadian Prostate Cancer Genome Network (CPC-GENE) is an outcomes-based initiative that will sequence 500 specimens from 350 prostate cancer patients over a 5-year time span. Previously, whole genome sequencing efforts from biopsy specimens have been hindered by insufficient quantities of extracted DNA required as input for sequencing library construction. As a proof of concept to demonstrate the ability to sequence low input amounts of DNA from prostate biopsies, whole genome sequencing has been initiated for 50 prostate tumor biopsy samples along with their matched blood-derived reference sample. An on-bead sample preparation protocol was optimized using decreasing quantities of input DNA and used to construct sequencing libraries from as low as 100ng of DNA derived from macrodissected fresh frozen prostate biopsies (>70% cellularity). Sequencing is performed on the Illumina HiSeq 2000 platform to generate coverage depths of 50x for tumor samples and 30x for reference samples. Following alignment using NovoAlign and variant-calling using GATK, we compared our results to genotyping-array results generated using the Affymetrix OncoScan platform. Single-nucleotide variants detected using arrays were validated >99% of the time by sequence data, confirming that the use of a low-input library did not hinder mutation detection. Sequencing does not exhibit significant genome-wide coverage biases, and CNV calls were compared between the genotyping arrays and the next-generation sequencing data. Outcomes from the sequencing and analysis of the initial 50 sample sets will similarly be applied over a 5-year period to characterize an additional 450 prostate specimens. The ability to whole genome sequence specimens where minimal amounts of extracted DNA exist presents new opportunities to sequence many samples previously deemed unusable, while also providing encouraging prospects for whole genome sequencing applications for future studies using biopsy specimens. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 103rd Annual Meeting of the American Association for Cancer Research; 2012 Mar 31-Apr 4; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2012;72(8 Suppl):Abstract nr 3184. doi:1538-7445.AM2012-3184
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".