Literature DB >> 33309276

The role of molecular profiling in the diagnosis and management of metastatic undifferentiated cancer of unknown primary: Molecular profiling of metastatic cancer of unknown primary.

Josephine K Dermawan1, Brian P Rubin2.   

Abstract

Cancer of unknown primary (CUP) refers to metastatic tumors for which the primary tumor of origin cannot be determined at the time of diagnosis, despite extensive clinicopathologic investigations. Molecular profiling is increasingly able to predict a probable primary tumor type for CUP when clinicopathologic workup is inconclusive. Numerous studies have explored the use of various molecular profiling techniques for identification of site/tissue of origin of CUP. These techniques include gene expression profiling utilizing microarray, reverse transcriptase polymerase chain reaction, RNA-sequencing, somatic gene mutation profiling with next-generation DNA sequencing, and epigenomics including DNA methylation profiling. Despite the generally poor prognosis of CUP, a minority of patients can expect to benefit from targeted therapy despite being agnostic to the tissue of origin. Studies have explored the use of various molecular profiling techniques to predict prognostic and therapeutic biomarkers, with the goal of improving outcome for patients with CUP. However, discordant results between non-randomized and randomized clinical trials in evaluating tumor-type specific therapies raise uncertainties of the benefits of molecularly-predicted tissue of origin-based treatment in routine clinical use. Nevertheless, the current overall trend is in favor of using molecular tools to refine the diagnosis and clinical management of patients with CUP. More large-cohort, randomized prospective studies are needed to assess and validate the utility and feasibility of molecular profiling to uncover potentially targetable genetic alterations. These efforts will also yield further biological insights into the biology and pathogenesis of CUP (Graphical Abstract).
Copyright © 2020 Elsevier Inc. All rights reserved.

Entities:  

Keywords:  Cancer of unknown primary; Clinical trial; Molecular profiling; Next generation sequencing; Targeted therapy

Year:  2020        PMID: 33309276     DOI: 10.1053/j.semdp.2020.12.001

Source DB:  PubMed          Journal:  Semin Diagn Pathol        ISSN: 0740-2570            Impact factor:   3.464


  1 in total

1.  A deep learning model to classify neoplastic state and tissue origin from transcriptomic data.

Authors:  James Hong; Laureen D Hachem; Michael G Fehlings
Journal:  Sci Rep       Date:  2022-06-11       Impact factor: 4.996

  1 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.