ArticleOncotarget2018
Patient derived xenografts (PDX) predict an effective heparanase-based therapy for lung cancer.
Article in Oncotarget, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 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
7 citing papers in PubMed, 11 citations in OpenAlex.
- Lung cancer: Animal model of lung cancer, molecular carcinogenesis of lung cancer, and antitumor effect ofOpen veterinary journal · 2025Review
- A New Synthesized Dicarboxylated Oxy-Heparin Efficiently Attenuates Tumor Growth and Metastasis.Cells · 2024Article
- Application of Animal Models in Cancer Research: Recent Progress and Future Prospects.Cancer management and research · 2021Review
- Elucidating the Consequences of Heparan Sulfate Binding by Heparanase 2.Frontiers in oncology · 2020Article
- Targeting Heparanase in Cancer: Inhibition by Synthetic, Chemically Modified, and Natural Compounds.iScience · 2019Review
- Cancer Metastasis: The Role of the Extracellular Matrix and the Heparan Sulfate Proteoglycan Perlecan.Frontiers in oncology · 2019Review
- Heparan Sulfate Mimetics in Cancer Therapy: The Challenge to Define Structural Determinants and the Relevance of Targets for Optimal Activity.Molecules (Basel, Switzerland) · 2018Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Heparanase, the sole heparan sulfate (HS) degrading endoglycosidase, regulates multiple biological activities that enhance tumor growth, metastasis, angiogenesis, and inflammation. Heparanase accomplishes this by degrading HS and thereby facilitating cell invasion and regulating the bioavailability of heparin-binding proteins. HS mimicking compounds that inhibit heparanase enzymatic activity were examined in numerous preclinical cancer models. While these studies utilized established tumor cell lines, the current study utilized, for the first time, patient-derived xenografts (PDX) which better resemble the behavior and drug responsiveness of a given cancer patient. We have previously shown that heparanase levels are substantially elevated in lung cancer, correlating with reduced patients survival. Applying patient-derived lung cancer xenografts and a potent inhibitor of heparanase enzymatic activity (PG545) we investigated the significance of heparanase in the pathogenesis of lung cancer. PG545 was highly effective in lung cancer PDX, inhibiting tumor growth in >85% of the cases. Importantly, we show that PG545 was highly effective in PDX that did not respond to conventional chemotherapy (cisplatin) and vice versa. Moreover, we show that spontaneous metastasis to lymph nodes is markedly inhibited by PG545 but not by cisplatin. These results reflect the variability among patients and strongly imply that PG545 can be applied for lung cancer therapy in a personalized manner where conventional chemotherapy fails, thus highlighting the potential benefits of developing anti-heparanase treatment modalities for oncology.
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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.