ArticleVaccines2015
Anti-Tumor Effects of Peptide Therapeutic and Peptide Vaccine Antibody Co-targeting HER-1 and HER-2 in Esophageal Cancer (EC) and HER-1 and IGF-1R in Triple-Negative Breast Cancer (TNBC).
Article in Vaccines, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 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
9 citing papers in PubMed, 19 citations in OpenAlex.
- HLA class I-specific nucleolin peptides induce therapeutic T cells in triple-negative breast cancer patients.International journal of biological sciences · 2026Article
- Recent advances in targeted strategies for triple-negative breast cancer.Journal of hematology & oncology · 2023Review
- Muramyl dipeptide-based analogs as potential anticancer compounds: Strategies to improve selectivity, biocompatibility, and efficiency.Frontiers in oncology · 2022Review
- Strategies for Using Muramyl Peptides - Modulators of Innate Immunity of Bacterial Origin - in Medicine.Frontiers in immunology · 2021Review
- Insulin-Like Growth Factor (IGF) Pathway Targeting in Cancer: Role of the IGF Axis and Opportunities for Future Combination Studies.Targeted oncology · 2017Review
- Tumor-targeting templated silica nanoparticles as a dual-drug delivery system for anti-angiogenic ovarian cancer therapy.Experimental and therapeutic medicine · 2017Article
- ERBB3: A potential serum biomarker for early detection and therapeutic target for devil facial tumour 1 (DFT1).PloS one · 2017Article
- Peptide-based synthetic vaccines.Chemical science · 2016Review
- Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors at 3 institutions in 1 country.
Funding
Abstract
Despite the promise of targeted therapies, there remains an urgent need for effective treatment for esophageal cancer (EC) and triple-negative breast cancer (TNBC). Current FDA-approved drugs have significant problems of toxicity, safety, selectivity, efficacy and development of resistance. In this manuscript, we demonstrate that rationally designed peptide vaccines/mimics are a viable therapeutic strategy for blocking aberrant molecular signaling pathways with high affinity, specificity, potency and safety. Specifically, we postulate that novel combination treatments targeting members of the EGFR family and IGF-1R will yield significant anti-tumor effects in in vitro models of EC and TNBC possibly overcoming mechanisms of resistance. We show that the combination of HER-1 and HER-2 or HER-1 and IGF-1R peptide mimics/vaccine antibodies exhibited enhanced antitumor properties with significant inhibition of tumorigenesis in OE19 EC and MDA-MB-231 TNBC cell lines. Our work elucidates the mechanisms of HER-1/IGF-1R and HER-1/HER-2 signaling in these cancer cell lines, and the promising results support the rationale for dual targeting with HER-1 and HER-2 or IGF-1R as an improved treatment regimen for advanced therapy tailored to difference types of cancer.
Indexed as
Identifiers
What OpenQuestion holds
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.