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Abstract IA06: Leveraging genome-wide CRISPR screens and synthetic lethal interactions for novel cancer therapeutics

2017· article· en· W2767673199 on OpenAlexaff
Stéphane Angers

Bibliographic record

VenueMolecular Cancer Therapeutics · 2017
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCRISPR and Genetic Engineering
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsCRISPRFrizzledBiologyWnt signaling pathwayCancerGenome editingComputational biologyCas9GenomeContext (archaeology)Cancer researchGeneticsGene

Abstract

fetched live from OpenAlex

Abstract Forward genetic screens with CRISPR-Cas9 genome editing enable high-resolution detection of genetic vulnerabilities in cancer cells. We conducted genome-wide CRISPR-Cas9 screens in RNF43 mutant pancreatic ductal adenocarcinoma (PDAC) cells, which rely on Wnt signaling for proliferation, and discovered a unique requirement for a Wnt signaling circuit engaging FZD5, one of the ten Frizzled receptors encoded in the human genome. Our results uncover an underappreciated level of context dependent specificity at the Wnt receptor level. We further derived a panel of recombinant antibodies that reports the expression of nine FZD proteins and confirm that FZD5 functional specificity cannot be explained by protein expression patterns. Antibodies that specifically bind FZD5 and FZD8 also robustly inhibited the growth of RNF43 mutant PDAC cells grown in vitro and as xenografts in vivo, providing strong orthogonal support for the functional specificity observed genetically. Proliferation of a patient-derived PDAC cell line harboring a RNF43 variant previously associated with PDAC was also selectively inhibited by the FZD5 antibodies, further demonstrating their use as a potential targeted therapy. Highlighting the potential generalizability of these findings, beyond PDAC, tumor organoid cultures from colorectal carcinoma patients that carried RNF43 mutations were also sensitive to the FZD5 antibodies. Our results provide the first example of how CRIPSR-based genetic screens can be leveraged to identify and validate cell surface targets for antibody development and cancer therapy. Citation Format: Stephane Angers. Leveraging genome-wide CRISPR screens and synthetic lethal interactions for novel cancer therapeutics [abstract]. In: Proceedings of the AACR Precision Medicine Series: Opportunities and Challenges of Exploiting Synthetic Lethality in Cancer; Jan 4-7, 2017; San Diego, CA. Philadelphia (PA): AACR; Mol Cancer Ther 2017;16(10 Suppl):Abstract nr IA06.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.008
Threshold uncertainty score0.026

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0020.000
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0080.003

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 head0.041
GPT teacher head0.356
Teacher spread0.316 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
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
Published2017
Admission routes1
Has abstractyes

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