Abstract PO-077: Establishment of a novel living biobank of patient-derived pancreatic cancer organoids with genomic and drug response characterization
Bibliographic record
Abstract
Abstract Advanced pancreatic cancer has a dismal prognosis and current treatment options (FOLFIRINOX, Gemcitabine/nab-paclitaxel [GnP]) are associated with toxicity. Although some patients achieve partial responses, most progress rapidly and become chemorefractory. While RNA subtypes, genomic alterations, and protein biomarkers have prognostic value, predictive biomarkers to guide therapy are needed. Patient-derived organoids (PDOs) are an increasingly popular model for predicting patient responses to standard-of-care therapy and investigating personalized therapy options. We present a novel biobank of 42 PDOs and drug profiling data with 5 standard of care agents and 3 kinase inhibitors. Tissue was processed from n=103 biopsies from 97 patients with a confirmed pathologic diagnosis of advanced (Stage III-IV) pancreatic ductal adenocarcinoma who presented to a single Canadian tertiary care centre between 2017-2020. Matched WGS was available in all cases. Our PDO generation success was 42/103 (41%). We observed a trend towards decreased establishment in tumors that were KRAS WT, TP53 WT, or had higher HRDetect scores. Conversely, polyploidy, SMAD4 WT, and major imbalances in mutant KRAS were associated with successful PDO establishment. These associations were not statistically significant after multiple comparisons correction, but suggest selection for success with more aggressive tumors. Drug profiling was performed on all 42 PDOs with the individual agents of FOLFIRINOX (5-FU, irinotecan, oxaliplatin), GnP (gemcitabine, paclitaxel), and three targeted agents (afatinib, trametinib, and talazoparib). Combination testing was also performed for gemcitabine + paclitaxel. Drug responses were measured through both viability and growth rate (GRMetrics). We found that GRMetrics minimized effects from different PDO growth rates. Matched clinical data were available for 23 patients who received FOLFIRINOX, 11 patients who received GnP, and one patient who received gemcitabine monotherapy. Similar to previous studies, we found that in vitro PDO responses to 5-FU, irinotecan, and GnP were correlated with patient responses based on RECIST criteria. Interestingly, and similar to previous reports in colorectal cancer PDOs, we found that oxaliplatin responses were not predictive of RECIST response. As expected, PDOs were resistant to afatinib (EGFRi), which reflects negative clinical trials, and may also be masked by use of EGF in growth media. A range of responses to trametinib (MEKi) were seen but were not correlated with KRAS allelic dosage. A similar range of response was seen to talazoparib (PARPi), but did not correlate with oxaliplatin response or HRDetect scores. In summary, we have established a novel biobank of PDOs from advanced pancreatic cancer patients. Notably, PDOs were less likely to establish from tumors that were KRAS WT or HR-deficient, even though these patients are likely to benefit from targeted approaches. Further investigation is required to develop PDO use in clinical drug prediction and drug discovery. Citation Format: Irene Y. Xie, Laura Tamblyn, Karen Ng, Eugenia Flores-Figueroa, Julie M. Wilson, Gun Ho Jang, Amy X. Zhang, Stephanie Ramotar, Anna Dodd, Nikolina Radulovich, Jennifer J. Knox, Grainne M. O'Kane, Steven Gallinger, Faiyaz Notta. Establishment of a novel living biobank of patient-derived pancreatic cancer organoids with genomic and drug response characterization [abstract]. In: Proceedings of the AACR Virtual Special Conference on Pancreatic Cancer; 2021 Sep 29-30. Philadelphia (PA): AACR; Cancer Res 2021;81(22 Suppl):Abstract nr PO-077.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".