  Precision medicine is a paradigm shift in healthcare relying heavily on
genomics data. However, the complexity of biological interactions, the large
number of genes as well as the lack of comparisons on the analysis of data,
remain a tremendous bottleneck regarding clinical adoption. In this paper, we
introduce a novel, automatic and unsupervised framework to discover
low-dimensional gene biomarkers. Our method is based on the LP-Stability
algorithm, a high dimensional center-based unsupervised clustering algorithm,
that offers modularity as concerns metric functions and scalability, while
being able to automatically determine the best number of clusters. Our
evaluation includes both mathematical and biological criteria. The recovered
signature is applied to a variety of biological tasks, including screening of
biological pathways and functions, and characterization relevance on tumor
types and subtypes. Quantitative comparisons among different distance metrics,
commonly used clustering methods and a referential gene signature used in the
literature, confirm state of the art performance of our approach. In
particular, our signature, that is based on 27 genes, reports at least $30$
times better mathematical significance (average Dunn's Index) and 25% better
biological significance (average Enrichment in Protein-Protein Interaction)
than those produced by other referential clustering methods. Finally, our
signature reports promising results on distinguishing immune inflammatory and
immune desert tumors, while reporting a high balanced accuracy of 92% on tumor
types classification and averaged balanced accuracy of 68% on tumor subtypes
classification, which represents, respectively 7% and 9% higher performance
compared to the referential signature.
