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Record W2937737908 · doi:10.1158/0008-5472.can-18-3631

Exploiting DNA Replication Stress for Cancer Treatment

2019· review· en· W2937737908 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueCancer Research · 2019
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicDNA Repair Mechanisms
Canadian institutionsCanada Research ChairsUniversity of Toronto
FundersCanadian Institutes of Health Research
KeywordsReplication (statistics)DNA replicationBiologyComputational biologyDNAGeneticsVirology

Abstract

fetched live from OpenAlex

Complete and accurate DNA replication is fundamental to cellular proliferation and genome stability. Obstacles that delay, prevent, or terminate DNA replication cause the phenomena termed DNA replication stress. Cancer cells exhibit chronic replication stress due to the loss of proteins that protect or repair stressed replication forks and due to the continuous proliferative signaling, providing an exploitable therapeutic vulnerability in tumors. Here, we outline current and pending therapeutic approaches leveraging tumor-specific replication stress as a target, in addition to the challenges associated with such therapies. We discuss how replication stress modulates the cell-intrinsic innate immune response and highlight the integration of replication stress with immunotherapies. Together, exploiting replication stress for cancer treatment seems to be a promising strategy as it provides a selective means of eliminating tumors, and with continuous advances in our knowledge of the replication stress response and lessons learned from current therapies in use, we are moving toward honing the potential of targeting replication stress in the clinic.

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.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.982
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

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

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 head0.269
GPT teacher head0.511
Teacher spread0.242 · 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