ArticleExpert review of molecular diagnostics2008
Previstage GCC test for staging patients with colorectal cancer.
Article in Expert review of molecular diagnostics, 2008. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 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
3 citing papers in PubMed, 7 citations in OpenAlex.
- Detection of lymph node metastases in human colorectal cancer by using 5-aminolevulinic acid-induced protoporphyrin IX fluorescence with spectral unmixing.International journal of molecular sciences · 2013Article
- GUCY2C molecular staging personalizes colorectal cancer patient management.Biomarkers in medicine · 2012Review
- Genetic and epigenetic traits as biomarkers in colorectal cancer.International journal of molecular sciences · 2011Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors at 1 institution in 1 country.
Funding
Abstract
The presence of tumor cells in regional lymph nodes is the most important prognostic and predictive marker in staging patients with colorectal cancer. Cancer cells in lymph nodes are associated with a poorer prognosis and an increased risk of recurrent disease. Additionally, nodal metastases identify patients who derive maximum benefit from adjuvant therapy. However, traditional paradigms for staging patients with colorectal cancer underestimate the extent of metastases and patients whose lymph nodes are ostensibly free of tumor cells by histopathology (pN0) have a 25-30% risk of developing recurrent disease, reflected by the presence of occult nodal metastases. These observations underscore the unmet clinical need for molecular approaches to accurately detect metastatic disease and identify patients at risk for disease relapse that could benefit from adjuvant chemotherapy. Detection of disease-specific mRNA targets as prognostic and predictive markers employing quantitative reverse transcription (qRT)-PCR is an emerging technology that has become a benchmark for individualization of patient management. However, to date, applications of qRT-PCR to detecting occult nodal metastases in colorectal cancer have been equivocal, reflecting markers with suboptimal sensitivity and specificity; limitations of utilizing qualitative, rather than quantitative, RT-PCR; and underpowered study designs based on inadequate patient populations. In that context, guanylyl cyclase C (GCC) is the most sensitive and specific biomarker for metastatic colorectal cancer in extra-intestinal tissues. GCC qRT-PCR detects occult metastases in lymph nodes, providing the most powerful independent prognostic information for predicting disease recurrence in pN0 patients in prospective multicenter clinical trials. This technology forms the basis for the Previstagetrade mark GCC Colorectal Cancer Staging Test encompassing a proprietary multiplex qRT-PCR assay compatible with formalin-fixed, paraffin-embedded lymph nodes for detecting occult metastases. Previstage GCC is a new diagnostic tool that may improve the accuracy of staging, prognosis of clinical outcomes and prediction of therapeutic responses to adjuvant therapy, representing a key advance in the management of patients with colorectal cancer.
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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.