Abstract 3389: Determining optimal conditions for collection and processing of metastatic liver biopsies collected for a multicenter, prospective study to identify biomarkers of clinical resistance to first-line therapy in metastatic colorectal cancer
Bibliographic record
Abstract
Abstract Introduction: The biomarker discovery process requires patient tissue samples from which histology is verified and high-quality genomic material is isolated. Several methods have been developed to either preserve tissue morphology or extract sufficient quality and quantity of RNA and DNA for downstream discovery efforts. As clinical trials incorporate patient biopsies for biomarker discovery or validation, it is becoming increasingly important to ensure quality material and identify methods that allow for preservation of morphology and stabilization of molecular content concurrently. We assessed, in liver needle-core biopsies, different sampling, fixation, and genomic isolation methods to maintain morphology and obtain high-quality genomic material for a multi-center prospective study to identify biomarkers of clinical resistance to first-line therapy in metastatic colorectal cancer. Four sampling methods (snap freezing, RNAlater, frozen RNAlater, formalin), two different fixation protocols for histological studies (10% formalin, RNAlater followed by OCT embedding and freezing) and two RNA isolation procedures (Triazol and AllprepDNA/RNA isolation) were evaluated. Results: Keeping in mind site feasibility, we report that the ideal condition to both preserve morphology and obtain high-quality genomic material of patient liver biopsy samples is to collect biopsies in RNAlater for shipping to a Central Pathology core, followed by washing with cold PBS (on dry ice) to permit proper RNA preservation during OCT embedding and cryostat sectioning for histological verification. Simultaneous extraction of DNA and RNA from the same biopsy core yields nucleic acids of optimal concentration and quality for downstream genomic applications. Conclusion: The collection of biospecimens using pre-determined protocol-specific standard operating procedures (SOPs) is essential to control for pre-analytical variability inherent to multicenter trials. Furthermore, histological control of percent tumor cells in each biopsy is absolutely necessary to ensure optimal representation of tumor (>70%) in the specimen. The above conditions were used in the multicenter Q-CROC-01 study (NCT00984048), where three needle core biopsies are collected from liver metastases of patients with colorectal cancer. One biopsy is collected in formalin and is set aside for downstream immunohistochemistry experiments. Two biopsies are collected in RNAlater and verified for histology. If they pass quality control, both DNA and RNA are isolated concurrently and sent to discovery platforms (DNA: array comparative genomic hybridization (aCGH), methylation profiles, RNA: gene expression profiles, RT-PCR, microRNA profiles, alternative splicing profiles). 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 3389. doi:1538-7445.AM2012-3389
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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 | 0.003 | 0.003 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".