ArticleCancer cell2019
Overcoming Treatment Toxicity through Sequential Therapy.
Article in Cancer cell, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
2 citing papers in PubMed.
- Key Proteins of Replication Stress Response and Cell Cycle Control as Cancer Therapy Targets.International journal of molecular sciences · 2024Review
- Harnessing DNA Double-Strand Break Repair for Cancer Treatment.Frontiers in oncology · 2019Review
Corrections and comments
- Comment on
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Combined inhibitions of PARP and DNA damage checkpoint have the potential for high anti-cancer efficacy, but concurrent inhibitions have been hampered by intolerable side effects. In this issue of Cancer Cell, Fang and colleagues (Fang et al., 2019) propose that sequential inhibitions of PARP and DNA damage checkpoint considerably widen the therapeutic window.
Indexed as
Identifiers
31185206What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.