Hormones and Cancer

, Volume 7, Issue 1, pp 3–8 | Cite as

5th International ACC Symposium: Classification of Adrenocortical Cancers from Pathology to Integrated Genomics: Real Advances or Lost in Translation?

  • Ronald E. de Krijger
  • Jérôme Bertherat


For the clinician, despite its rarity, adrenocortical cancer is a heterogeneous tumor both in term of steroid excess and tumor evolution. For patient management, it is crucial to have an accurate vision of this heterogeneity, in order to use a correct tumor classification. Pathology is the best way to classify operated adrenocortical tumors: to recognize their adrenocortical nature and to differentiate benign from malignant tumors. Among malignant tumors pathology also aims at prognosis assessment. Although progress has being made for prognosis assessment, there is still a need for improvement. Recent studies have established the value of Ki67 for adrenocortical cancer (ACC) prognostication, aiming also at standardization to reduce variability. The use of genomics to study adrenocortical tumors gives a very new insight in their pathogenesis and molecular classification. Genomics studies of ACC give now a clear description of the mRNA (transcriptome) and miRNA expression profile, as well as chromosomal and methylation alterations. Exome sequencing also established firmly the list of the main ACC driver genes. Interestingly, genomics study of ACC also revealed subtypes of malignant tumors with different pattern of molecular alterations, associated with different outcome. This leads to a new vision of adrenocortical tumors classification based on molecular analysis. Interestingly, these molecular classifications meet also the results of pathological analysis. This opens new perspectives on the development and use of various molecular tools to classify, along with pathological analysis, ACC, and guides patient management at the area of precision medicine.


Adenoma Methylation Level Adrenocortical Carcinoma Driver Gene Adrenocortical Tumor 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Compliance with Ethical Standards

Disclosure Summary

The authors have nothing to declare.


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Copyright information

© Springer Science+Business Media New York 2015

Authors and Affiliations

  1. 1.Department of PathologyErasmus MC University Medical CenterRotterdamThe Netherlands
  2. 2.Department of PathologyReinier de Graaf HospitalDelftThe Netherlands
  3. 3.INSERM U1016, CNRS UM8104Institut CochinParisFrance
  4. 4.Université Paris DescartesSorbonne Paris CitéParisFrance
  5. 5.Center for Rare Adrenal Diseases, Endocrinology Department, Hôpital CochinAssistance Publique Hôpitaux de ParisParisFrance

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