ReviewGenome medicine2012
Getting personalized cancer genome analysis into the clinic: the challenges in bioinformatics.
Review in Genome medicine, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 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
19 citing papers in PubMed.
- Worldwide analysis of actionable genomic alterations in lung cancer and targeted pharmacogenomic strategies.Heliyon · 2024Article
- Gastric cancer actionable genomic alterations across diverse populations worldwide and pharmacogenomics strategies based on precision oncology.Frontiers in pharmacology · 2024Article
- INSL4 as prognostic marker for proliferation and invasiveness in Non-Small-Cell Lung Cancer.Journal of Cancer · 2021Article
- A New Insight for the Identification of Oncogenic Variants in Breast and Prostate Cancers in Diverse Human Populations, With a Focus on Latinos.Frontiers in pharmacology · 2021Article
- Spontaneous and Induced Animal Models for Cancer Research.Diagnostics (Basel, Switzerland) · 2020Review
- Computational algorithms for in silico profiling of activating mutations in cancer.Cellular and molecular life sciences : CMLS · 2019Review
- A Model Information Management Plan for Molecular Pathology Sequence Data Using Standards: From Sequencer to Electronic Health Record.The Journal of molecular diagnostics : JMD · 2019Article
- Integrating cancer genomic data into electronic health records.Genome medicine · 2016Review
- Early biomarkers of joint damage in rheumatoid and psoriatic arthritis.Arthritis research & therapy · 2015Review
- Who qualifies to be a bioinformatician?Frontiers in genetics · 2015Article
- Cancer3D: understanding cancer mutations through protein structures.Nucleic acids research · 2015Article
- Patient-derived xenograft models: an emerging platform for translational cancer research.Cancer discovery · 2014Review
- Integrated next-generation sequencing and avatar mouse models for personalized cancer treatment.Clinical cancer research : an official journal of the American Association for Cancer Research · 2014Article
- Transcriptional dissection of pancreatic tumors engrafted in mice.Genome medicine · 2014Article
- wKinMut: an integrated tool for the analysis and interpretation of mutations in human protein kinases.BMC bioinformatics · 2013Article
- DNA sequencing methods in human genetics and disease research.F1000prime reports · 2013Review
- Computational approaches to identify functional genetic variants in cancer genomes.Nature methods · 2013Article
- How bioinformatics influences health informatics: usage of biomolecular sequences, expression profiles and automated microscopic image analyses for clinical needs and public health.Health information science and systems · 2013Review
- Chapter 14: Cancer genome analysis.PLoS computational biology · 2012Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Progress in genomics has raised expectations in many fields, and particularly in personalized cancer research. The new technologies available make it possible to combine information about potential disease markers, altered function and accessible drug targets, which, coupled with pathological and medical information, will help produce more appropriate clinical decisions. The accessibility of such experimental techniques makes it all the more necessary to improve and adapt computational strategies to the new challenges. This review focuses on the critical issues associated with the standard pipeline, which includes: DNA sequencing analysis; analysis of mutations in coding regions; the study of genome rearrangements; extrapolating information on mutations to the functional and signaling level; and predicting the effects of therapies using mouse tumor models. We describe the possibilities, limitations and future challenges of current bioinformatics strategies for each of these issues. Furthermore, we emphasize the need for the collaboration between the bioinformaticians who implement the software and use the data resources, the computational biologists who develop the analytical methods, and the clinicians, the systems' end users and those ultimately responsible for taking medical decisions. Finally, the different steps in cancer genome analysis are illustrated through examples of applications in cancer genome analysis.
Identifiers
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.