Abstract B006: Development of a NF1-MPNST-PDX liquid biopsy model using whole-genome sequencing and quantitative PCR of mouse-derived cell-free DNA
Bibliographic record
Abstract
Abstract Background: Malignant Peripheral Nerve Sheath Tumors (MPNST) are aggressive, NF1-associated soft tissue sarcomas with a dismal 40% 5-year survival rate, high rates of disease relapse and metastasis, and limited treatment options. Our lab has generated a collection of patient-derived xenograft (PDX) models, which recapitulate the variety of genetic changes that occur in human NF1-MPNST. Longitudinal analysis of PDX tumor burden in preclinical studies is currently limited to imprecise tumor measurement or expensive and time-consuming imaging modalities such as MRI. These methods of analysis fail to allow for the early detection of tumor formation, the assessment of treatment-response dynamics, the formation of resistant subclones, or the detection of molecular residual disease. Liquid biopsies using plasma-derived cell-free DNA (cfDNA) have been successful in addressing these challenges in the context of multiple solid tumor types including MPNST by our group previously (Szymanski et al. 2021). However, liquid biopsy of cfDNA in the context murine model systems is quite limited, primarily due the challenge of collecting sufficient plasma for analysis. We hypothesize that we can detect and longitudinally track tumor burden in response to treatment using human-tumor-specific quantitative PCR (qPCR) and whole-genome sequencing of sequentially collected PDX-bearing murine plasma to develop a NF1-MPNST-PDX liquid biopsy model. Methods: To address this challenge, we sequentially collected ≈50 µL of murine plasma weekly from two independent groups of NF1-MPNST-PDX-bearing mice and corresponding PDX-free controls for up to six weeks. The first group consisted of six PDX-bearing mice who underwent whole-genome sequence of murine-derived plasma cfDNA and paired PDX tissue. The second group consisted of fifteen NF1-MPNST-PDX-bearing mice and five PDX-free controls; plasma cfDNA samples collected from this group underwent human-specific LINE-1 qPCR, which should only detect human DNA derived from implanted PDX and allow for longitudinal tracking of tumor burden. Results: We serially collected ≈50 µL of plasma weekly from NF1-MPNST-PDX mice with no obvious impact on mouse survival or weight. Mean total cfDNA yield from the terminally collected samples was 1.12 ± 1.02 ng per uL plasma (n=21) for PDX-bearing animals with cfDNA fragments canonically appearing between 70 and 450 bps as determined by electropherogram. Whole-genome sequencing of murine plasma cfDNA revealed broad genomic aneuploidy detectable in NF1-MPNST-PDX plasma which corresponded to alterations present in PDX tissue, including MPNST-specific chromosome 8q gain, corresponding with our earlier findings (Dehner et al. 2021). Human-specific LINE-1 qPCR showed specific enrichment of tumor-derived LINE-1 in NF1-MPNST-PDX-bearing mice compared to PDX-free mice which showed no human LINE-1 enrichment. Conclusion: Our data suggest that PDX plasma cfDNA may be an informative biomarker that can be non-invasively collected and used to track tumor burden in NF1-MPNST-PDX models. Citation Format: Paul A. Jones, Wenjia Feng, Xiaochun Zhang, Peter K. Harris, Taylor Sundby, Jeffrey J. Szymanski, Divya Srihari, Faridi Qaium, Jack F. Shern, Aadel A. Chaudhuri, Angela C. Hirbe. Development of a NF1-MPNST-PDX liquid biopsy model using whole-genome sequencing and quantitative PCR of mouse-derived cell-free DNA [abstract]. In: Proceedings of the AACR Special Conference: Sarcomas; 2022 May 9-12; Montreal, QC, Canada. Philadelphia (PA): AACR; Clin Cancer Res 2022;28(18_Suppl):Abstract nr B006.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".