ReviewJournal of thoracic disease2018
Mechanisms of tumor immunotherapy, with a focus on thoracic cancers.
Review in Journal of thoracic disease, 2018. 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, 12 citations in OpenAlex.
- Pulmonary Squamous Cell Carcinoma with Repeated Increases and Decreases in Size.Surgical case reports · 2025Article
- Review
- Article
- Engineering strategies to enhance oncolytic viruses in cancer immunotherapy.Signal transduction and targeted therapy · 2022Review
- TNFα and Immune Checkpoint Inhibition: Friend or Foe for Lung Cancer?International journal of molecular sciences · 2021Review
- Immune cell engineering: opportunities in lung cancer therapeutics.Drug delivery and translational research · 2020Review
- Spontaneous regression of ALK fusion protein-positive non-small cell lung carcinoma: a case report and review of the literature.BMC pulmonary medicine · 2020Review
- Updates to the antitumor mechanism of oncolytic virus.Thoracic cancer · 2019Review
- Human Biofield Therapy Modulates Tumor Microenvironment and Cancer Stemness in Mouse Lung Carcinoma.Integrative cancer therapiesArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
If immunotherapy is currently considered as a valid treatment strategy in oncology, the concept that cancer could be cured by the mere manipulation of the immune system was almost inconceivable until few years ago, particularly in lung cancer. The use of immune checkpoint inhibitors has instead demonstrated to mediate significant long-term disease control so to rapidly enter clinical practice and represent the basis for most of the combination approaches under development. In light of the revolutionary results achieved through the pivotal clinical trials and the large expectations about the possibility to further improve clinical benefit and discover novel therapeutic targets, it is becoming nowadays mandatory to increase our knowledge on the basics of immunology in lung cancer. Defining the pathways that rule the interactions between tumor and immune cells and the requirements to achieve full-fledged immune responses able to mediate meaningful antitumor activity are present goals of the research ongoing worldwide. This knowledge would not only foster a more scientifically-based clinical development of novel drugs and combinations, but also provide valid biomarkers for patient selection and monitoring. In the present review we will address the available information about the immunological features of lung cancer, the backgrounds to the use of immunotherapeutics, the possible mechanisms underlying resistance and the strategies to improve immune-mediated tumor control. In doing this, we will be following the path traced in melanoma, the tumor histotype that taught us most of what we know about cancer immunotherapy.
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.