Evidence map›Paper›PMID 22764768›Full record

ReviewFuture oncology (London, England)2012

Signatures of tumor-immune interactions as biomarkers for breast cancer prognosis.

Masoud H Manjili, Kayvan Najarian, Xiang-Yang Wang

Abstract readReview
In one paragraph

Review in Future oncology (London, England), 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.

0numbers the graph read from it
0cells of the map it votes in
14citing 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

14 citing papers in PubMed.

  1. Article
  2. Review
  3. Article
  4. Article
  5. Article
  6. Article
  7. Article
  8. Article
  9. Review
  10. BTG1 underexpression is an independent prognostic marker in esophageal squamous cell carcinoma.Tumour biology : the journal of the International Society for Oncodevelopmental Biology and Medicine · 2014
    Article
  11. Article
  12. Article
  13. BTG1 expression correlates with the pathogenesis and progression of breast carcinomas.Tumour biology : the journal of the International Society for Oncodevelopmental Biology and Medicine · 2014
    Article
  14. Review
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

3 authors.

Masoud H ManjiliDepartment of Microbiology & Immunology, Massey Cancer Center, Richmond, VA 23298, USA. mmanjili@vcu.edu
Kayvan Najarian
Xiang-Yang Wang

Funding

Large Stress Proteins in Biology and MedicineR01CA154708 · NCI · VIRGINIA COMMONWEALTH UNIVERSITY · PI WANG, XIANG-YANG SHAWN · 2011 to 2015
$1.5M
NCI NIH HHS R01 CA154708
6 · The paper itself

Abstract

Breast cancer mortality is usually due to distant recurrence of cancer at an advanced stage of the disease rather than from primary cancer. Therefore, prediction of breast cancer recurrence at the time of diagnosis could lead to advances in personalized treatment of cancer patients in order to prevent risk of recurrence. Two prognostic biomarkers that are currently being used in clinical practice are a 70-gene MammaPrint signature and a 21-gene Oncotype DX panel. These assays generate relative risks of recurrence, but they do not provide a 'yes' or 'no' answer about recurrence in a given patient. These tests include genes that are involved in the cell cycle, invasion, metastasis and angiogenesis related to breast cancer. Emerging evidence suggests that a signature of genes involved in tumor-immune interactions may provide a more accurate prognostic tool. This paper reviews recent advances in the discovery of prognostic biomarkers for breast cancer patients.

Indexed as

Biomarkers, TumorBreast NeoplasmsFemaleGene Expression ProfilingHumansLymphocytes, Tumor-InfiltratingNeoplasm GradingPrognosisRecurrenceStromal CellsBiomarkers, Tumor

Identifiers

PMID22764768
PMCPMC4634870

What OpenQuestion holds

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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.