Evidence map›Paper›PMID 23556079›Full record

ArticleTherapeutic advances in hematology2011

Rapid Identification of Therapeutic Targets in Hematologic Malignancies via Functional Genomics.

Jeffrey W Tyner

Abstract read
In one paragraph

Article in Therapeutic advances in hematology, 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from 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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

1 author.

Jeffrey W Tyner

Funding

OREGON CLINICAL AND TRANSLATIONAL SCIENCE INSTITUTEUL1RR024140 · NCRR · OREGON HEALTH & SCIENCE UNIVERSITY · PI ORWOLL, ERIC S. · 2006 to 2011
$61.1M
ROR1 as a Therapeutic Target in Acute Lymphoblastic LeukemiaK99CA151457 · NCI · OREGON HEALTH & SCIENCE UNIVERSITY · PI TYNER, JEFFREY WALLACE · 2010 to 2011
$256k
NCI NIH HHS K99 CA151457NCRR NIH HHS UL1 RR024140
6 · The paper itself

Abstract

The clinical application of gene-targeted drugs has transformed cancer therapy. The hallmark example of this strategy is use of the ABL kinase inhibitor imatinib for treatment of patients with chronic myeloid leukemia (CML). This remarkable clinical success has also stimulated an expansive search for personalized gene targets in all patients to facilitate broad application of targeted therapy for cancer. However, achievement of this objective will require simultaneous work towards several complementary goals. The first step towards broad application of gene-targeted therapy must entail a rapid means to identify target oncogenes in individual patients. Next, we must identify well-tolerated, gene-specific drugs that are collectively effective against a wide diversity of gene targets. Finally, we must develop protocols by which individual patients are matched with appropriate, gene-targeted drugs in a clinically relevant time frame. While these may seem like difficult tasks, we are fortunate to have a wide variety of new and rapidly evolving research tools at our disposal. These include next-generation sequencing of the genome and transcriptome, single nucleotide polymorphism (SNP)/copy number variations (CNV) and gene expression microarrays, and RNAi libraries for the application of functional screens. In this review we discuss the advantages and disadvantages of each of these techniques with the goal of demonstrating that no single technique will be sufficient as a standalone technology, but rather it will be the integration of all techniques that will enable broad application of gene-targeted cancer therapies.

Indexed as

deep sequencinggene chipneoplasiapersonalized medicineshRNA screensiRNA screen

Identifiers

PMID23556079
PMCPMC3573394

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

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.