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Analysis of the copy number profiles of several tumor samples from the same patient reveals the successive steps in tumorigenesis

Eric Letouzé123*, Yves Allory45, Marc A Bollet6, François Radvanyi23 and Frédéric Guyon1

Author Affiliations

1 INSERM, UMR-S 973, MTi, Université Paris Diderot - Paris 7, 35 rue Hélène Brion, 75205 Paris Cedex 13, France

2 Institut Curie, Centre de Recherche, Paris, F-75248 France

3 CNRS, UMR 144, 26 rue d'Ulm, 75248 Paris Cedex 05, France

4 INSERM, Unité 955, Créteil F-94000, France

5 AP-HP, Groupe Hospitalier Albert Chenevier - Henri Mondor, Département de Pathologie, Créteil F-94000, France

6 Département d'Oncologie Radiothérapie, Institut Curie, 26 rue d'Ulm, 75248 Paris Cedex 05, France

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Genome Biology 2010, 11:R76  doi:10.1186/gb-2010-11-7-r76

Published: 22 July 2010

Additional files

Additional file 1:

Consistency of tumor progression trees and the partial identity score for sample clonality. The partial identity score was developed by Bollet et al. [18] to determine whether two samples have a monoclonal origin. We investigated the consistency of our results with this approach by investigating the clonality of our bladder samples with the partial identity score (top), and reconstructing tumor progression trees for the pairs of breast samples characterized as non-clonal in the paper by Bollet et al. (bottom). (a) The partial identity scores were calculated for each pair of bladder tumors analyzed. The distributions of these scores for pairs of samples from different patients were calculated from the reference data sets (left, BAC array data; right, SNP data). The 95% quantile, used by Bollet et al. as the threshold for classifying a pair as monoclonal, is indicated as a red line. The only pairs of samples from the same patient with a partial identity score below the threshold were those involving sample S5C. Detailed numbering of pairs for CGH data: 1, S1A-S1B; 2, S2A-S2B; 3, S3A-S3D; 4, S3B-S3D; 5, S3C-S3D; 6, S3A-S3B; 7, S3A-S3C; 8, S3B-S3C. Detailed numbering of pairs for SNP data: 1, S4A-S4B; 2, S5A-S5B; 3, S5A-S5C; 4, S5B-S5C. (b) The tumor progression tree obtained for the pair of breast tumors P2, classified as non-clonal by Bollet et al. TuMult identified no common events between the samples. (c) Boxplots of the number of aberrations occurring at each step in the tumor progression trees obtained for true recurrences (left) or new primary tumors (right). CP, in the common precursor; PT, between the common precursor and the primary tumor; IR, between the common precursor and the ipsilateral recurrence. Very few events are found in the common precursors of the trees for new primary tumors, consistent with their low partial identity scores.

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Additional file 2:

Tumor progression trees of metastatic prostate cancers with several metastases from the same anatomic site or type of organ. (a) Tumor progression trees of three patients with several metastases from the same anatomic site (liver). Liver samples were always more closely related to each other than to metastases from the other organs. In each tree, the liver samples are derived from a single common precursor, with a substantial number of events not encountered in the other samples. (b) Tumor progression trees of six patients with several metastases from the same type of organ but at different anatomic sites (lymph node and/or bone metastases). The tumors from the same type of organ are associated in P24, P28 and P30, but not in P17, P32 and P33.

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Additional file 3:

Clinical data for the 15 bladder samples constituting the reference data set for bladder SNP data.

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Additional file 4:

Segmentation of the profiles and generation of the amplitude vectors. Generation of the amplitude matrix. (a) Discretized copy number profiles of three tumors for a given chromosome (yellow, 'normal copy number'; green, 'loss'; red, 'gain'). The four breakpoints identified in the samples (dashed lines) divide the chromosome into five 'homogeneous segments'. (b) The profiles are equally well represented in a segment matrix, in which the copy number for each segment and each sample is encoded by an integer (-1, loss; 0, normal; 1, gain), in a breakpoint matrix, in which each value represents the difference in copy number between two adjacent segments, or an amplitude matrix, in which 'up' and 'down' breakpoints are distinguished by their position in the vector. A common breakpoint (gray regions) appears as a number of the same sign at the same position in the breakpoint matrix, or as a non-zero number at the same position in the amplitude matrix.

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Additional file 5:

Two scenarios lead to an unbalanced chromosome in the common precursor. (a) Left: the two tumors independently acquire two different aberrations with a breakpoint in common. Right: the 'up' breakpoint between segments B and C in the common precursor is lost in tumor 2 due to the loss of the neighboring segment C. (b) In both cases, only one breakpoint remains common to both samples, resulting in an unbalanced chromosome for their common precursor.

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Additional file 6:

Correction of an unbalanced chromosome. Detailed procedure of the correction of chromosomes with unbalanced 'up' and 'down' breakpoints.

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