Abstract B31: Establishment and molecular characterization of patient-derived tumor xenografts from resected tumors or ascites fluids of patients with pancreatic/ampullary/bile duct carcinomas
Bibliographic record
Abstract
Abstract Pancreatic adenocarcinoma (PDAC) is the 4th most common cause of cancer deaths in North America, for both men and women with a 5-year survival rate of less than 5%. The poor prognosis rate is attributed to late presentation of the disease and the lack of effective treatment options. Large-scale genome sequencing efforts on PDAC tumors show evidence of high mutational burden and revealed a number of mutated genes affecting multiple oncogenic pathways. While there are significant endeavors in developing specific targeted agents against “driver” mutations, tumor diversity within and across patient population remains a key factor affecting therapeutic efficacy. In this context, the availability of large cohorts of genomically characterized patient-derived xenograft (PDX) tumor models may help to accelerate the development of novel therapies against this lethal cancer. PDX models provide a renewable resource to maintain a patient's tumor ex vivo for pre-clinical or co-clinical studies. As part of The International Cancer Genome Consortium (ICGC), our laboratory has established 93 PDX models in non-obese diabetic and severe combined immune-deficient (NOD-SCID) mice from Whipple resection specimens. These tumors represent a heterogeneous group of neoplasms arising from the head, body and tail of pancreas, bile duct and Ampulla of Vater. All implantations including in the subcutaneous pocket at the flank or at the orthotopic pancreas site, were performed using 4-8 weeks old NOD-SCID mice. Successful growth and serial transplant to multiple mouse generations were observed in in 74 PDX models of the 93 implanted PDAC specimens, achieving an 80% engraftment rate, one of the highest reported in any type of cancer. Histology fidelity was preserved in the PDX models compared to corresponding patient tumors. Failed implants were due to specimens characterized by borderline malignancy and absence of tumor cells. Whole exome sequencing and copy number aberration profiling was completed for 61 PDXs and blood from the matched patients. Cancer-specific single nucleotide variation (SNV) load varied widely from 38 to 305 in PDXs. The most recurrent activating mutation was observed in KRAS with 77% of PDX models showing alterations at codon G12 (65%), G13 (8%) and Q61 (4%); in addition, 26% PDXs had a copy number gain in KRAS. Molecular comparisons of the 21 PDX models and their matched patient tumors showed that alternate allele frequency of KRAS mutation from exome sequencing of primary tumor is a strong indicator of the tumor cellularity; a higher tumor cellularity results in a larger overlap of cancer specific alterations between xenografts and corresponding patient tumors. We have demonstrated a successful establishment of PDX models that represent genomic architecture of major subclonal populations of patient PDAC primary tumors. Citation Format: Nikolina Radulovich, Emin Ibrahimov, Carson Holt, Vibha Raghavan, Tracy Zhao, Rob Denroch, Nhu-An Pham, Steve Gallinger, Melania Pintilie, Lincoln Stein, John McPherson, Lakshmi Muthuswamy, Ming Sound Tsao. Establishment and molecular characterization of patient-derived tumor xenografts from resected tumors or ascites fluids of patients with pancreatic/ampullary/bile duct carcinomas. [abstract]. In: Proceedings of the AACR Special Conference: Patient-Derived Cancer Models: Present and Future Applications from Basic Science to the Clinic; Feb 11-14, 2016; New Orleans, LA. Philadelphia (PA): AACR; Clin Cancer Res 2016;22(16_Suppl):Abstract nr B31.
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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 | 0.000 | 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 | 0.001 |
| 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".