Koninklijke Philips Corporation N.v.

University of Glasgow

Innosign B.v.

Ralf Dieter Hoffmann

Wilhelmus Franciscus Johannes Verhaegh

Anja Van De Stolpe

Joannes Baptist Adrianus Dionisius Van Zon

Dimitrios Mavroeidis

Ron Martinus Laurentius Van Lieshout

Jacek Lukasz Kustra

Hendrik Jan Van Ooijen

Sergio Consoli

Marcia Alves De Inda

Monique Hendriks

Anne Godefrida Catharina Van Brussel

Pieter Christiaan Vos

Dianne Arnoldina Margaretha Wilhelmina Van Strijp

Janneke Wrobel

Joukje Garrelina Orsel

Eveline Den Biezen-Timmermans

George Baillie

Kalyana Chakravarthi Dulla

Miles Douglas Houslay

David James Peter Henderson

Maud De Klerk - Starmans

Eveline Den Biezen - Timmermans

David J P Henderson

Miles D Houslay

The invention relates to a method of pre-surgical risk stratification of a prostate cancer subject, comprising determining a gene expression profile for phosphodiesterase 4D variant 7 (PDE4D7) in a biological sample obtained from the subject, determining a gene expression profile for DExH-box helicase 9 (DHX9) in the same or another biological sample obtained from the subject, and determining a pre-surgical prognostic risk score for the subject based on the gene expression profile for PDE4D7 and the gene expression profile for DHX9. This may allow for an improved stratification of the subject in a pre-surgical setting that may result in better primary treatment decisions. For instance, the pre-surgical prognostic risk score may allow to make better recommendations on whether to select active surveillance vs. active intervention, e.g., radical prostatectomy, for certain sub-populations of prostate cancer patients.

Pre-surgical risk stratification based on PDE4D7 and DHX9 expression

A method of clustering or grouping subjects that are similar to one another. A dataset contains, for each subject, a set of quantitative values which each represent a respective clinical or pathological feature of that subject. A principal component analysis, PCA, is performed on the dataset. Loadings of one of the first two principal components identified by the PCA are used to generate a respective dataset of weighting values. These weighting values are used to weigh or modify each set of quantitative values in the dataset. A clustering algorithm is performed on the weighted sets of subject data. The process may be iterated until user-defined stopping conditions are satisfied.

Subject clustering method and apparatus

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Eindhoven, Netherlands

Ralf Dieter Hoffmann