Abstract C008: Patient-derived organoids and precision medicine: Insights from the PASS-01 clinical trial in PDAC
Bibliographic record
Abstract
Abstract Introduction: Pancreatic ductal adenocarcinoma (PDAC) is challenging as most patients are diagnosed at an advanced stage with limited treatment options. Tailoring therapies for individual patients is paramount due to the aggressive nature of the disease. Our work explores patient- derived organoids (PDOs) as in vitro tumor models to dissect molecular signatures and conduct pharmacotyping, aiming to enhance precision medicine. Methods: In the PASS-01 stage IV PDAC clinical trial, biopsies were collected for molecular correlatives, including establishment of PDO models. Tissue samples were collected from patients at six institutes across the United States and Canada, highlighting our ability to integrate PDO models into a robust, multi- institutional clinical framework. Most biopsies were collected from liver metastases, followed by primary pancreas tumors, peritoneal, omental, lymph node, lung, and brain metastases. KRAS mutation status by ddPCR was used to validate neoplastic cells in the organoid cultures and identify pseudonormal outgrowth. Established PDO lines were subjected to high throughput drug screening for 120+ compounds, comprising both standard-of-care (as monotherapy and in combination therapies) and experimental agents. Subsequently, validated PDO lines were expanded, biobanked and harvested for RNA and DNA sequencing. Results: During the trial, 186 biopsies from 183 enrolled patients were shipped to CSHL for PDO establishment. The overall malignant PDO establishment rate was 50% across all biopsy sites and patients. Interestingly, PDOs could be generated more often from patients that rapidly progressed on therapy (p = 0.03). Furthermore, by comparing the characteristics of the primary biopsies to the established PDO, PDOs were more frequently generated from Moffitt subtype classical (64% establishment) compared to basal (35%), and from those with a KRASminor imbalance (67% establishment) compared with KRAS wild-type (36%), balanced (58%), and KRASmajor imbalance (52%). The average time from tissue receipt to first drug screen data was 65 days, with six PDOs screened in under 30 days. This turnaround time enabled PDO therapeutic data to be presented at monthly molecular tumor boards. Consequently, these data were considered alongside other clinical trial correlates to aid in selection of second-line therapies when patients progressed. Conclusions: The PASS-01 trial facilitated the real-time generation of PDO models and reporting of therapeutic results. We identified correlations between PDO establishment and patient characteristics, and improved the methods to detect pseudonormal outgrowth. PDO pharmacoptyping identified sensitivity that correlated with patient outcomes on GnP, while also highlighting challenges of using empiric drug testing for chemotherapy sensitivity. Moving forward we aim to continue incorporation of PDOs and tumor molecular profiling to aid in patient therapy selection. We anticipate this work to be crucial as targeted therapies, including KRAS inhibitors, become mainstream in PDAC care. Citation Format: Amber N. Habowski, Dennis Plenker, Hardik Patel, Caitlin Tsang, Luce St. Surin, Fatim Kouassi, Deepthi Budagavi, Grainne M O'Kane, Stephanie Ramotar, Kenneth H Yu, Faiyaz Notta, Andrew Aguirre, Brian Wolpin, Dan Laheru, Daniel A King, Elizabeth M Jaffee, Jennifer J Knox, David A Tuveson. Patient-derived organoids and precision medicine: Insights from the PASS-01 clinical trial in PDAC [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Advances in Pancreatic Cancer Research; 2024 Sep 15-18; Boston, MA. Philadelphia (PA): AACR; Cancer Res 2024;84(17 Suppl_2):Abstract nr C008.
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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 | 0.006 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".