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Record W3083341879 · doi:10.1158/1538-7445.am2020-6316

Abstract 6316: High throughput small molecule screening with synthetic 3D lung-mimetic hydrogels in the rare lung cancer lymphangioleiomyomatosis

2020· article· en· W3083341879 on OpenAlexaffabout
Adam Pietrobon, Julien Yockell‐Lelièvre, Carole Doré, Roger Y. Tam, Sean P. Delaney, Molly S. Shoichet, William L. Stanford

Bibliographic record

VenueCancer Research · 2020
Typearticle
Languageen
FieldMedicine
TopicTuberous Sclerosis Complex Research
Canadian institutionsUniversity of TorontoHealth CanadaOttawa Hospital
Fundersnot available
KeywordsLymphangioleiomyomatosisCancer researchmTORC1Viability assayCytotoxicityCell cultureLung cancerCancer cellCellBiologyCancerCell biologyPathologyMedicineBiochemistryTuberous sclerosisInternal medicineProtein kinase BSignal transductionIn vitro

Abstract

fetched live from OpenAlex

Abstract Lymphangioleiomyomatosis (LAM) is a rare lung cancer characterized by immature smooth muscle-like cell invasion of the lung parenchyma, leading to cystonodular destruction and respiratory decline. Interestingly, LAM is a monogetic disease caused by loss of TSC2, conferring constitutive mTORC1 activation. Currently, there are no approved therapeutics which exhibit LAM cell-specific cytotoxicity. Here, we describe a novel 3D drug screening platform which couples a rationally designed, synthetic, lung-mimetic hydrogel to high content imaging. We have designed and synthesized a hyaluronic acid-based hydrogel which mimics endogenous lung tissue. Human LAM cell models maintain mTORC1 activation in the hydrogel compared to matched controls, reflective of human LAM lesions. We observe LAM cells to exhibit both protease-dependent and independent modes of invasion: the former imparted by gel crosslinking with an MMP-cleavable peptide, the latter enabled by methylcellulose conjugation. Proliferation is inhibited as LAM cells actively invade through the gel. We coupled our culture system to high content confocal microscopy, permitting measurements of cell viability and invasion depth in response to therapeutic screening. We subsequently tested an 800-drug library of Health Canada-approved small molecule cancer therapeutics on LAM cells and matched controls. Surprisingly, LAM cells exhibited pan-therapeutic resistance as measured by both invasion and viability metrics. Enrichment analysis revealed categories of therapeutics which demonstrated LAM-selective cytotoxicity and/or anti-invasiveness. We performed a refinement screen on select therapeutics and identified AURA inhibition as an effective LAM cell-specific therapeutic avenue. Work is ongoing to establish the most efficacious small molecule inhibitor. In conclusion, we describe a novel drug screening platform which enables high content measurements of viability and invasion in response to drug screening in a lung-mimetic system. We have identified AURA inhibition as a potential therapeutic avenue for LAM patients using this screening platform. Future work will involve target validation, elucidation of the mechanism of action, and testing in a pre-clinical mouse model. As these small molecules are Health Canada-approved, we anticipate rapid clinical translation of our preferred candidate. Citation Format: Adam Pietrobon, Julien Yockell-Lelievre, Carole Doré, Roger Y. Tam, Sean P. Delaney, Molly Shoichet, William L. Stanford. High throughput small molecule screening with synthetic 3D lung-mimetic hydrogels in the rare lung cancer lymphangioleiomyomatosis [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 6316.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.154
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.003
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.117
GPT teacher head0.384
Teacher spread0.266 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2020
Admission routes2
Has abstractyes

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