Evidence map›Paper›PMID 30796200›Full record

ArticleOncogenesis2019

Study of Cathepsin B inhibition in VEGFR TKI treated human renal cell carcinoma xenografts.

Chun-Hau Chen, Swati Bhasin, Prateek Khanna, Mukta Joshi, Patrick Mn Joslin, Ruchi Saxena, Seema Amin, Suhu Liu, Shreya Sindhu, Sarah R Walker and 5 more

Open access · goldAbstract read
In one paragraph

Article in Oncogenesis, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
15citing papers in PubMed, 1 pooled it
0.9field-weighted citation impact, top 31% 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

15 citing papers in PubMed, 1 synthesis or guideline pooled it, 23 citations in OpenAlex.

  1. Pooled it
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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

15 authors at 4 institutions in 1 country.

Chun-Hau ChenDepartment of Medicine, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, 02215, USA.
Swati BhasinDepartment of Medicine, Division of Interdisciplinary Medicine and Biotechnology, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, 02115, USA.
Prateek KhannaDepartment of Medicine, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, 02215, USA.
Mukta JoshiBIDMC Genomics, Proteomics, Bioinformatics and Systems Biology Center, Beth Israel Deaconess Medical Center, Boston, MA, 02115, USA.
Patrick Mn JoslinDepartment of Medicine, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, 02215, USA.
Ruchi SaxenaDepartment of Medicine, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, 02215, USA.
Seema AminDepartment of Medicine, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, 02215, USA.
Suhu LiuDepartment of Medical Oncology, Dana-Farber Cancer Institute, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, 02215, USA.
Shreya SindhuDepartment of Medicine, Division of Interdisciplinary Medicine and Biotechnology, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, 02115, USA.
Sarah R WalkerDepartment of Medical Oncology, Dana-Farber Cancer Institute, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, 02215, USA.
Paul CatalanoDepartment of Data Sciences, Dana-Farber Cancer Institute, Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA.
David A FrankDepartment of Medical Oncology, Dana-Farber Cancer Institute, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, 02215, USA.
Seth L AlperDepartment of Medicine, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, 02215, USA.
Manoj BhasinDepartment of Medicine, Division of Interdisciplinary Medicine and Biotechnology, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, 02115, USA. mbhasin@bidmc.harvard.edu.
Rupal S BhattDepartment of Medicine, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, 02215, USA. rbhatt@bidmc.harvard.edu.
Beth Israel Deaconess Medical Center · USHarvard University · USBrigham and Women's Hospital · USDana-Farber Cancer Institute · US

Funding

Treating the P13-Kinase/AKTP50CA101942 · NCI · BETH ISRAEL DEACONESS MEDICAL CENTER · PI David McDermott · 2003 to 2026
$53.4M
Combating Resistance to Antiangiogenic Therapy in Renal Cell CarcinomaR01CA196996 · NCI · BETH ISRAEL DEACONESS MEDICAL CENTER · PI BHATT, RUPAL S · 2015 to 2019
$2.0M
Dana-Farber Cancer Institute (DFCI) CA101942NCI NIH HHS P50 CA101942NCI NIH HHS R01 CA196996U.S. Department of Health & Human Services | NIH | NIH Clinical Center (Clinical Center) CA181364-01A1
6 · The paper itself

Abstract

Several therapeutic options are available for metastatic RCC, but responses are almost never complete, and resistance to therapy develops in the vast majority of patients. Consequently, novel treatments are needed to combat resistance to current therapies and to improve patient outcomes. We have applied integrated transcriptome and proteome analyses to identify cathepsin B (CTSB), a cysteine proteinase of the papain family, as one of the most highly upregulated gene products in established human RCC xenograft models of resistance to vascular endothelial growth factor receptor (VEGFR) tyrosine kinase inhibitors (TKI). We used established RCC models to test the significance of CTSB in the progression of renal cancer. Our evaluation of CTSB showed that stable CTSB knockdown suppressed RCC growth in vitro and in vivo. Stable over-overexpression of wild-type CTSB (CTSB

Identifiers

PMID30796200
PMCPMC6386754
OpenAlexW2915729824

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.