{'fraction_non_alphanumeric': 0.030011254220332625, 'fraction_numerical': 0.008878329373515069, 'mean_word_length': 5.022590361445783, 'pattern_counts': {'":': 0, '<': 1, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 2, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Analysis of the Coding Genome of Diffuse Large B-Cell Lymphoma

Diffuse large B-cell lymphoma (DLBCL) is the most common form of human lymphoma. While a number of structural alterations have been associated with the pathogenesis of this malignancy, the full spectrum of genetic lesions that are present in the DLBCL genome, and therefore the identity of dysregulated cellular pathways, remains unknown. By combining next-generation sequencing and copy number analysis, we show that the DLBCL coding genome contains on average more than 30 clonally represented gene alterations/case. This analysis also revealed mutations in genes not previously implicated in DLBCL pathogenesis, including those regulating chromatin methylation (MLL2, 24% of cases) and immune recognition by T cells. These results provide initial data on the complexity of the DLBCL coding genome and identify novel dysregulated pathways underlying its pathogenesis.

of aberration, we then curated the list of consensus regions by visual inspection using the Integrative Genomics Viewer (IGV) software (http://www.broadinstitute.org/igv) to retain only the core consensus regions. In particular, we focused on focal MCRs of gain or loss, which were defined according to the following criteria: i) the consensus interval across all affected tumors is smaller that 1Mb and/or encompasses 1-3 genes; and ii) the segment of abnormal gene dosage encompasses 1-3 genes in at least one of the affected tumors. These criteria exclude MCRs that contain ≤ 3 genes but derive from the overlap between large segments, as well as regions devoid of coding genes, which were not considered in the present study.

Genomic Identification of Significant Targets in Cancer (GISTIC) analysis. In order to
identify relevant regions of copy number alteration, we applied the GISTIC method described by Beroukhim R. et al. (2007) 3 to the curated segmentation results. GISTIC identifies significantly amplified or deleted regions of the genome across a set of samples.
Each aberration is assigned a G-score that considers the amplitude of the aberration as well as the frequency of its occurrence across samples. GISTIC then assesses the statistical significance of each aberration by comparing the observed G-scores to the results that would be expected by chance, using a permutation test that is based on the overall pattern of aberrations seen across the genome. The method accounts for multiple-hypothesis testing using the false-discovery rate (FDR) framework to assign a q value to each aberration. We used the default q-value cutoff of 0.25 here, as shown by the green vertical line in Figure S2. Significant regions are listed in Tables S7 and S8. For each significant region, a peak region was identified, which is the part of the aberrant region with greatest amplitude and frequency of alteration and with minimal q-value. In addition, a wide peak was determined using a leave-one-out algorithm to allow for errors in the boundaries in a single sample. The wide peak boundaries are more robust for identifying the most likely gene targets in the region.

Algorithm for detection of Common Focal genomic alterations (ComFocal).
As an additional tool to rank genes found affected by copy number aberrations, we developed an algorithm that assigns a score to individual genes in the genome, based on the size of the aberration encompassing that gene, the amplitude of the aberration (copy number value), and the number of cases involved (V.T. and R.R., manuscript in preparation). Specifically, each aberrant region (in the curated segmentation data) is first assigned a score S A that is equal to the inverse of its size L A (i.e., the number of genes that are included in the region) weighted by the amplitude, defined as the deviation D A from normal (diploid) copy number. The individual scores of each aberration encompassing a particular gene across different samples are then aggregated into a score S G for that given gene: This algorithm was applied to the set of curated segmentation data obtained after CBS as described in the SNP array analysis methods section.
Functional annotation clustering of DLBCL target genes. In order to determine whether genes affected by genetic lesions in DLBCL were enriched in specific functional categories, the genes resulting from WES analysis (n=93) and those included in focal MCRs of aberration encompassing 1-3 genes (n=480) were merged in a unique gene list, and functional annotation clustering analysis was performed using the DAVID software (http://david.abcc.ncifcrf.gov) 4,5 based on Gene Ontology terms. This tool groups terms with similar annotations in clusters and subsequently ranks them by Enrichment Score; clusters were then selected by retaining only the ones including at least one significant (p<0.05) term. The DAVID software was also used to run a Functional Annotation chart analysis based on KEGG, BIOCARTA, PANTHER, REACTOME and BBID pathways.
Moreover, the overlap between the gene list and the canonical pathways of the Molecular Signatures Database (MSigDB, Broad Institute) was calculated by the compute overlap tool of the GSEA software (http://www.broadinstitute.org/gsea/msigdb/index.jsp) 6 .
Statistical analysis. Statistical significance in the frequencies of nucleotide and dinucleotide targeting was assessed by the Poisson distribution [dinucleotide counts that are less or equal than expected (underrepresented) or more or equal than expected (overrepresented)]. After correcting for multiple hypotheses (Sidak), only CpG appears significantly (over) represented (p =0.0025, p(Sidak)=0.038). Similar values were obtained by the Fisher's exact test.

Validation of candidate somatic mutations identified by WES
Overall, 386 variants were predicted by the WES analysis to be specifically associated with the tumor sample in the six patients analyzed. Of these, 96 were confirmed to be somatic in origin by Sanger sequencing of the corresponding paired tumor and normal genomic DNA, while 106 were also found in the matched normal DNA, thus representing non previously annotated germline polymorphisms that escaped detection in the high throughput sequencing analysis, possibly due to the relatively low mean depth of coverage (Supplementary Table 4). The remaining 184 variants were absent in both tumor and normal genomic DNA, when tested by Sanger sequencing (data not shown).

Supplementary Figure 3. Top 50 genes affected by copy number losses (top) and gains (bottom) in
DLBCL, based on the ComFocal algorithm. Genes are ranked from highest to lowest, according to their significance score (see Methods). Light blue denotes common fragile sites. Note that gains/amplifications encompassing only a subset of gene exons were also considered in this analysis, although the functional consequences of these lesions are less clear.

Supplementary Figure 3
Supplementary Figure 4. Genetic lesions of immunomodulatory genes in DLBCL. a, Frequency of genomic aberrations at five genes involved in T cell-B cell recognition. b, Relationship between inactivating mutations of B2M and CD58. Each column in the heatmap represents a patient, and the status of the two genomic loci is indicated by the color. c, Overall proportion of DLBCL patients with biallelic loss of CD58 and B2M due to truncating mutations and/or deletions. d, Graphic display of copy number data from 15 patients harboring amplifications and rearrangements involving the PDL1 and PDL2 loci. Segmentation data were visualized using the IGV tool, where each track represents one sample, white denotes a normal (diploid) copy number, and blue indicates a region of copy number loss. Note that, due to the presence of non-tumor cells infiltrating the biopsy, the inferred copy number, and the corresponding color intensity, may vary across samples. Individual genes in the region are aligned in the bottom panel, and the boxed areas (defined by the red bars at the top) highlights the two PDL genes.