ANATOMIC PATHOLOGY. UVEAL MELANOMA.

AutoDiscovery was of great help in choosing the best markers to focus on in our confirmatory phase ...

Dr. Santiago Ramón y Cajal, MD PhD, Head of Translational Molecular Pathology  (Vall d'Hebrón Research Institute)

What Is This Work About?

 

Dr. Santiago Ramón y Cajal Agüeras is Head of Translational Molecular Pathology at Vall d'Hebrón Research Institute (Barcelona, Spain). He is a descendant from the namesake Nobel Prize winner. 

 

The fundamental objectives of the group are the study of molecular tumour pathology related to the identification of new diagnostic, prognostic and therapeutic targets.

 

One of the goals of this study in particular was to assess how the immunohistochemical expression of the markers that are part of the signaling act as independent predictors of the risk of metastasis or variables of global survival.

Dr. Santiago Ramón y Cajal

Patients at stage T3cN0M0 and high levels of expression of p4E-BP1 show a higher index of Ki67 than those with low/mid levels of expression. This relationship supports the idea that mTOR signalling way has a relevant role in the tumoral growth.

What Problem Did We Face?

 

We combined up to 48 factors related to demographics, prognosis, biomarkers and clinical follow-up from 101 patients and provided that information to an external biostatistician.

 

Given the exhaustive exploratory approach of this study, we were forced to invest a significant amount of time and money in technical meetings with the biostatistician in order to discuss each step forward in the discovery process.

Which Was The Contribution Of AutoDiscovery To This Work?

 

One of the most interesting relationship identified with AutoDiscovery was that patients at stage T3cN0M0 and high levels of expression of p4E-BP1 show a higher index of Ki67 than those with low/mid levels of expression. This relationship supports the idea that mTOR signalling way has a relevant role in the tumoral growth.

 

In this work we combined classical statistical tools (hypothesis testing) with the automated exploratory analysis of AutoDiscovery.
The key features of AutoDiscovery applied to this work were:

 

  • The depth of analysis, which allowed us to identify relevant markers that were potentially associated to particular subsets of our patients (patients at different stages, tumour samples with different levels of regulation of protein synthesis, etc.).

 

  • The Hypo Booster tool to easily focus on the most relevant factors potentially involved to be tested in a further confirmatory study, and thus minimizing the number of meetings with the biostatistician.

 

  • The traceability of the results, which enabled an effective integration with classical statistical tools (such as SPSS).

Would you like to contact Santiago?

Address:

Vall d’Hebron Institut de Recerca 

Passeig Vall d’Hebron, 119-129
08035 Barcelona (Spain)

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