Evidence map›Paper›PMID 32832592›Full record

ArticleAdvanced biosystems2018

CRISPR Technology for Breast Cancer: Diagnostics, Modeling, and Therapy.

Rachel L Mintz, Madeleine A Gao, Kahmun Lo, Yeh-Hsing Lao, Mingqiang Li, Kam W Leong

Open access · greenAbstract read
In one paragraph

Article in Advanced biosystems, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.

0numbers the graph read from it
0cells of the map it votes in
13citing papers in PubMed
0.4field-weighted citation impact, top 39% of its field
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

13 citing papers in PubMed, 25 citations in OpenAlex.

  1. Review
  2. Review
  3. Review
  4. Review
  5. Article
  6. Review
  7. Article
  8. Review
  9. Matrine Targets BTF3 to Inhibit the Growth of Canine Mammary Tumor Cells.International journal of molecular sciences · 2023
    Article
  10. Article
  11. Review
  12. Recent advances in liposome formulations for breast cancer therapeutics.Cellular and molecular life sciences : CMLS · 2021
    Review
  13. 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

6 authors at 3 institutions in 2 countries.

Rachel L MintzDepartment of Biomedical Engineering, Columbia University, New York, NY 10027, USA.
Madeleine A GaoDepartment of Biomedical Engineering, Columbia University, New York, NY 10027, USA.
Kahmun LoDepartment of Biomedical Engineering, Columbia University, New York, NY 10027, USA.
Yeh-Hsing LaoDepartment of Biomedical Engineering, Columbia University, New York, NY 10027, USA.
Mingqiang LiGuangdong Provincial Key Laboratory of Liver Disease The Third Affiliated Hospital of Sun Yat-Sen University Guangzhou, Guangdong 510630, China.
Kam W LeongDepartment of Systems Biology, Columbia University Medical Center, New York, NY 10032, USA.
Columbia University · USColumbia University Irving Medical Center · USSun Yat-sen University · CN

Funding

Integrated Microphysiological System of Cerebral Organoid and Blood Vessel for Disease Modeling and Neuropsychiatric Drug screeningUH3TR002151 · NCATS · COLUMBIA UNIVERSITY HEALTH SCIENCES · PI LEONG, KAM W · 2020 to 2022
$3.6M
Integrated Microphysiological System of Cerebral Organoid and Blood Vessel for Disease Modeling and Neuropsychiatric Drug screeningUG3TR002151 · NCATS · COLUMBIA UNIVERSITY HEALTH SCIENCES · PI LEONG, KAM W · 2018 to 2019
$2.6M
Systemic Inflammation in Microphysiological Models of Muscle and Vascular DiseaseUG3TR002142 · NCATS · DUKE UNIVERSITY · PI TRUSKEY, GEORGE A · 2017 to 2018
$2.1M
Overcoming Immunosenescence by Nanoparticle-Mediated ActivationR01AI096305 · NIAID · DUKE UNIVERSITY · PI ABRAHAM, SOMAN N, LEONG, KAM W · 2012 to 2016
$1.8M
A synthetic biology approach to analyze evolution of programmed bacterial deathR01GM110494 · NIGMS · DUKE UNIVERSITY · PI YOU, LINGCHONG · 2014 to 2017
$1.2M
Modeling Marfan Syndrome with iSMC-based Tissue-engineered Blood VesselR21HL140275 · NHLBI · COLUMBIA UNIVERSITY HEALTH SCIENCES · PI LEONG, KAM W · 2017 to 2018
$240k
NCATS NIH HHS UG3 TR002142NCATS NIH HHS UG3 TR002151NCATS NIH HHS UH3 TR002151NHLBI NIH HHS R21 HL140275NIAID NIH HHS R01 AI096305NIGMS NIH HHS R01 GM110494
6 · The paper itself

Abstract

Molecularly, breast cancer represents a highly heterogenous family of neoplastic disorders, with substantial interpatient variations regarding genetic mutations, cell composition, transcriptional profiles, and treatment response. Consequently, there is an increasing demand for alternative diagnostic approaches aimed at the molecular annotation of the disease on a patient-by-patient basis and the design of more personalized treatments. The clustered regularly interspaced short palindromic repeats (CRISPR)/CRISPR-associated (Cas) technology enables the development of such novel approaches. For instance, in diagnostics, the use of the RNA-specific C2c2 system allows ultrasensitive nucleic acid detection and could be used to characterize the mutational repertoire and transcriptional breast cancer signatures. In disease modeling, CRISPR/Cas9 technology can be applied to selectively engineer oncogenes and tumor-suppressor genes involved in disease pathogenesis. In treatment, CRISPR/Cas9 can be used to develop gene-therapy, while its catalytically-dead variant (dCas9) can be applied to reprogram the epigenetic landscape of malignant cells. As immunotherapy becomes increasingly prominent in cancer treatment, CRISPR/Cas9 can engineer the immune cells to redirect them against cancer cells and potentiate antitumor immune responses. In this review, CRISPR strategies for the advancement of breast cancer diagnostics, modeling, and treatment are highlighted, culminating in a perspective on developing a precision medicine-based approach against breast cancer.

Indexed as

breast cancerCRISPRprecision medicinetargeted therapeutics

Identifiers

PMID32832592
PMCPMC7437870
OpenAlexW2886292081

What OpenQuestion holds

Textmetadata
LicenceTDM
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.