ReviewEndocrine pathology2014
Therapeutic biomarkers in lung neuroendocrine neoplasia.
Review in Endocrine pathology, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
What it found
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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
8 citing papers in PubMed.
- Rachel score: a nomogram model for predicting the prognosis of lung neuroendocrine tumors.Journal of endocrinological investigation · 2024Article
- Article
- Systemic treatment for lung carcinoids: from bench to bedside.Clinical and translational medicine · 2019Review
- Cyto-histology in NET: what is necessary today and what is the future?Reviews in endocrine & metabolic disorders · 2017Review
- Ki-67 labeling index of neuroendocrine tumors of the lung has a high level of correspondence between biopsy samples and surgical specimens when strict counting guidelines are applied.Virchows Archiv : an international journal of pathology · 2017Observational
- Bi-directional roles of IRF-1 on autophagy diminish its prognostic value as compared with Ki67 in liver transplantation for hepatocellular carcinoma.Oncotarget · 2016Article
- The expanding role of somatostatin analogs in gastroenteropancreatic and lung neuroendocrine tumors.Drugs · 2015Review
- Insights into Novel Prognostic and Possible Predictive Biomarkers of Lung Neuroendocrine Tumors.Cancer genomics & proteomicsReview
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The well-known classification of neuroendocrine neoplasms of the lung into four major subtypes (including typical and atypical carcinoids and small- and large-cell neuroendocrine carcinomas) has a proven prognostic validity but only partially helps to predict the response to specific therapies. Therapeutic biomarkers are incompletely known and include morphological, immunophenotypic, and molecular markers. Morphology alone has no specific predictive role, nor has any immunophenotypic marker been proven to bear predictive implications. Ki67 is a relevant prognostic marker and can indirectly predict response to chemotherapy, when levels are extremely high in high-grade neuroendocrine (NE) carcinomas. The expression of somatostatin receptors, especially of the type 2A, has been shown to predict response to somatostatin analog treatments, paralleling the information derived from octreotide scintigraphy. mTOR pathway is targeted by specific inhibitors, but the exact cellular molecules predicting response are still to be defined. It seems that high levels of phosphorylated forms of mTOR and of its downstream factor S6K are associated to a better response to rapalogs in experimental models. Data from gene expression profiling and mutational analyses are currently emerging, providing a more detailed map of different molecular activation pathways, potentially leading to a more accurate molecular classification of lung NE tumors as well as to the discovery of new therapeutic targets. The combination of mutational profiles with those of upregulated or downregulated genes also by gene gains or losses may ultimately provide a better characterization of NE tumor histological types in terms of response to specific chemotherapy or biotherapy.
Indexed as
Identifiers
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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.