Abstract 2384: High-complexity neutral genomic barcoding technology reveals extensive clonal dynamics in multiple human cancer model systems
Bibliographic record
Abstract
Abstract Increasing evidence of extensive intratumoral heterogeneity, along with advances in high-throughput in vivo functional genetic screening technologies, have together highlighted the need to observe growth in cancer models at the clonal level. To address this, we have designed, constructed and validated multiple high-diversity lentivirally delivered barcode libraries, which utilize next-gen sequencing technology to read out millions of clones in heterogeneous cancer populations. These libraries can be used to address a multitude of biological questions in many cancer model systems. To date, we have completed, sequenced and analyzed three different types of barcoding applications to explore clonal dynamics in a variety of human cancer models. First, we tested the serial limiting dilution analysis (LDA) assay for tumor initiating cells (TICs) in patient-derived colon tumor models. In the LDA assay, a range of cell dilutions, down to a single cell, are transplanted into immunocompromised mice. The TIC frequency is calculated using a single-hit model from the proportion of tumors established at each dose. However, quantifying minimum cell numbers required for tumor formation does not reveal the actual diversity of clonal contribution during tumour engraftment and progression. In fact, recent research suggests that the single-hit model, which assumes a static hierarchy of intrinsically determined initiating cells, may not be biologically relevant as some clones may have the potential to form or contribute to a tumor only in specific environment, or in cooperation with another clone. Our barcoded, serial LDAs show interesting and informative patterns of clonal dynamics. Second, we have performed clonal lineage tracing on established human cancer cell lines in vitro compared to in vivo. Our findings demonstrate the presence of TICs in standard cell lines and serve as precursors to future in vivo genetic screens by defining upper limits of library size. Finally, we used a patient-derived xenograft model of brain tumor metastasis to investigate the presence of pre-existing metastatic clones with site-specific homing abilities. In summary, given the extensive heterogeneity present in cancer models, we propose the use of high-complexity barcoding technology to validate novel cancer targets in xenograft models, including LDAs and metastasis models. Furthermore, we suggest the use of barcodes for careful optimization of the system before genetic screens in animal models. Citation Format: Allison ML Nixon, Kevin R. Brown, Jennifer Haynes, Laura K. Donovan, Michael D. Taylor, Catherine W. O’Brien, Jason Moffat. High-complexity neutral genomic barcoding technology reveals extensive clonal dynamics in multiple human cancer model systems. [abstract]. In: Proceedings of the 107th Annual Meeting of the American Association for Cancer Research; 2016 Apr 16-20; New Orleans, LA. Philadelphia (PA): AACR; Cancer Res 2016;76(14 Suppl):Abstract nr 2384.
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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 | 0.001 | 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".