Abstract B32: Fidelity of genomic and proteomic features of patient-derived xenografts of lung cancers
Bibliographic record
Abstract
Abstract The establishment of valid lung carcinoma preclinical models for testing new cancer therapies is necessary, as existing cell lines and mouse models may not recapitulate the full spectrum of heterogeneity of patient tumors. Studies suggest lung cancer patient-derived xenograft (PDX) models recapitulate well gene copy number variation, gene expression profiles, and metabolic states of corresponding patient tumors. However, an understanding of mechanisms linking cancer-associated genome, transcriptome, and proteome alterations with driver mutations and dysregulated signal transduction networks in primary and PDX models is lacking. We report a large (>150) resource of lung cancer PDX models, derived from surgically resected tumors, and endobronchial ultrasound-guided (EBUS) and CT-guided biopsies. Tumor specimens were grown and serially passaged in the subcutaneous pocket at the flanks of NSG mice (NOD SCID gamma) at initial implant, and following passages in NOD SCID (non-obese diabetic severe combined immunodeficiency) mice. Among 127 established PDX models from 441 surgically derived tumor specimens, all major histological subtypes were included: 52 adenocarcinomas, 62 squamous cell carcinomas, 1 adeno-squamous cell carcinomas, 5 sarcomatoid carcinomas, 5 large cell neuroendocrine carcinomas, and 2 small cell lung cancers. Over 100 PDX models have been profiled by next-generation exome sequencing (SureSelect Human 50Mbp kit), and array-based assays for copy number variant (HumanOmni 2.5-Quad BeadChip), DNA methylation (Infinium HumanMethylation450 BeadChip) and mRNA (DASL HumanHT-12 v4 BeadChip) profiles. Smaller subsets of PDXs have been characterized by mass spectrometry (MS)-based comprehensive proteome and protein-phosphotyrosine characterization. Genome/transcriptome/proteome profiles of 36 non-small cell lung carcinoma (NSCLC) PDX models correlated with patient primary tumors but to a much lesser extent with established NSCLC cell lines. A number of PDX models have genetic abnormalities linked to targeted therapies including mutations in EGFR (6), PIK3CA (13), and KRAS (21), and amplifications in FGFR1 (7) and CDK4 (6). This study provides the most solid evidence as yet that PDXs established from lung cancers mimic closely the genomic and proteomic characteristics of patient primary tumors and retain driver genetic abnormalities. Citation Format: Nhu-An Pham, Dennis Wang, Jiefei Tong, Chang-Qi Zhu, Lei Li, Wen Zhang, Ruoshi Shi, Shingo Sakashita, Melania Pintilie, Michael F. Moran, Geoffrey Liu, Ming-Sound Tsao. Fidelity of genomic and proteomic features of patient-derived xenografts of lung cancers. [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 B32.
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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) | 0.000 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".