ReviewBritish journal of clinical pharmacology2013
Implications of genome-wide association studies in cancer therapeutics.
Review in British journal of clinical pharmacology, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers, 1 of them a synthesis that pooled it.
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
9 citing papers in PubMed, 1 synthesis or guideline pooled it.
- A systematic review and meta-analysis of somatic and germline DNA sequence biomarkers of esophageal cancer survival, therapy response and stage.Annals of oncology : official journal of the European Society for Medical Oncology · 2015Pooled it
- The vitamin D receptor gene as a determinant of survival in pancreatic cancer patients: Genomic analysis and experimental validation.PloS one · 2018Trial
- Pharmacogenomics in epithelial ovarian cancer first-line treatment outcome: validation of GWAS-associated NRG3 rs1649942 and BRE rs7572644 variants in an independent cohort.The pharmacogenomics journal · 2019Article
- Bringing a genomic perspective to the safety of drug treatment in oncology.F1000Research · 2017Review
- Association between GWAS-identified genetic variations and disease prognosis for patients with colorectal cancer.PloS one · 2015Article
- LightSNiP assay is a good strategy for pharmacogenetics test.Frontiers in pharmacology · 2015Article
- Personalised medicine, disease prevention, and the inverse care law: more harm than benefit?European journal of epidemiology · 2014Article
- European Medicines Agency initiatives and perspectives on pharmacogenomics.British journal of clinical pharmacology · 2014Review
- Cancer therapeutics revisited; novel drugs targeting cell signalling pathways, genome wide association studies and other trials and tribulations.British journal of clinical pharmacology · 2013Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
Abstract
Genome wide association studies (GWAS) provide an agnostic approach to identifying potential genetic variants associated with disease susceptibility, prognosis of survival and/or predictive of drug response. Although these techniques are costly and interpretation of study results is challenging, they do allow for a more unbiased interrogation of the entire genome, resulting in the discovery of novel genes and understanding of novel biological associations. This review will focus on the implications of GWAS in cancer therapy, in particular germ-line mutations, including findings from major GWAS which have identified predictive genetic loci for clinical outcome and/or toxicity. Lessons and challenges in cancer GWAS are also discussed, including the need for functional analysis and replication, as well as future perspectives for biological and clinical utility. Given the large heterogeneity in response to cancer therapeutics, novel methods of identifying mechanisms and biology of variable drug response and ultimately treatment individualization will be indispensable.
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What 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.