- Università di Milano Biccocca
martedì 8 maggio 2018
Sala verde. Inizio seminario 17:00.
Cancer is a disease characterized by the accumulation of somatic
alterations to the genome, which selectively make cancer cells fitter
to survive. The understanding of progression models for cancer, i.e.,
the identification of sequences of mutations that leads to the
emergence of the disease, is still unclear. The problem of
reconstructing such progression models is not new; in fact, several
methods to extract progression models from cross-sectional samples
have been developed since the late 90s.
Recently, we have proposed a number of algorithms to reconstruct
cancer progression models both from aggregate, population level data
-- i.e., collections of patients' data, as many TCGA datasets -- and
from individual level data -- i.e., multiple biopsies of single tumor,
or even single cell data. We perform our reconstruction using a
variety of algorithms based on a "probability raising" score that
guarantees statistical dependencies on the inferred precedence
relations. Our methods are complementary to traditional phylogeny
In this setting, we have proven the correctness of our algorithms and
characterized their performance. Our algorithms are collected in a R
BioConductor package "TRanslational ONCOlogy" (TRONCO) that we have
successfully used as part of our "Pipeline for Cancer Inference"
(PiCnIc) to analyse Colorectal Cancer (CRC) data from TCGA, which
highlighted possibly biologically significant patterns in the
progressions inferred. Part of TRONCO is also the "Temporal oRder of
Individual Tumors" (TRaIT) a new collection of algorithms that can be
used for multi-region and single-cell progression analisys of cancers.
Contact person: Tiziano Villa
- Data pubblicazione
25 febbraio 2018