1. Volume 32, Issue 2, Pages 155-168.e6 (August 2017)
Conditional Selection of Genomic Alterations Dictates Cancer Evolution and Oncogenic Dependencies 
Marco Mina, Franck Raynaud, Daniele Tavernari, Elena Battistello, Stephanie Sungalee, Sadegh Saghafinia, Titouan Laessle, Francisco Sanchez-Vega, Nikolaus Schultz, Elisa Oricchio, Giovanni Ciriello 
Cancer Cell 
Volume 32, Issue 2, Pages e6 (August 2017)
DOI: /j.ccell
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2. Cancer Cell 2017 32, 155-168.e6DOI: (10.1016/j.ccell.2017.06.010)
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3. Figure 1 Selected Functional Events across 23 Human Tumor Types
(A) Our pan-cancer dataset includes 6,456 human samples from 23 tumor types profiled by TCGA. Distinctive molecular subtypes are highlighted.
(B) Distribution of selected somatic mutations and copy-number alterations (CNA).
(C) The number of alterations per sample is fit by a log-normal distribution with parameters μ = 1.5 and σ = 0.8, mean = 6.16, and SD = 6.2. y Axis is on log scale.
(D) The ten most frequent SFEs within each alteration type. Each bar plot corresponds to the average alteration frequency; error bars represent 1 SD.
(E) Information entropy of each SFE versus pan-cancer frequency of alteration. Information entropy is defined based on the frequency of the SFE in each tumor type. Entropy values of SFEs in our dataset (colored dots) are compared with the expected entropy values (gray dots). y Axis is on log scale.
(F) Average number of tumor types where an alteration occurs for a given entropy value.
GBM, glioblastoma multiforme; LGG, low-grade glioma; HNSC, head and neck squamous cell carcinoma; LUAD, lung adenocarcinoma; LUSC, lung squamous cell carcinoma; SKCM, skin cutaneous melanoma; AML, acute myeloid leukemia; BLCA, bladder carcinoma; KICH, kidney chromophobe carcinoma; KIRC, kidney renal clear cell carcinoma; KIRP, kidney renal papillary cell carcinoma; BRCA, breast carcinoma; ACC, adrenocortical carcinoma; PRAD, prostate adenocarcinoma; THCA, thyroid papillary carcinoma; CRC, colon and rectum carcinoma; ESCA, esophageal carcinoma; LIHC, liver hepatocellular carcinoma; STAD, stomach adenocarcinoma; CESC, cervix squamous cell carcinoma; OV, ovarian carcinoma; UCS, uterine carcinosarcoma; UCEC, uterine corpus endometrial carcinoma. See also Table S1.
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4. Figure 2
Identification of Pairwise Evolutionary Dependencies: Associations with Pathways and Tumor Lineage
(A) Schematic of the SELECT algorithm.
(B) Ratio of the tail distributions (1 − Cumulative Distribution Function) of the scores of motifs between alterations in the same pathway (within motifs) and between alterations in different pathways (between motifs). This ratio is shown for all motifs (black line), and separately for mutual exclusivity (purple line) and co-occurrence (green line) motifs.
(C) Number of tumor types (y axis) where motifs are testable (gray bars) or detected (colored). Top: co-occurrence motifs. Bottom: mutual exclusivity motifs.
(D) Top: deviation from expected overlap are reported for each motif and each tumor type. Only motifs with >1 SD from the expected overlap in at least two tumor types are shown. Bottom: sum of deviations of each motif toward either co-occurrence (CO, green bars) or mutual exclusivity (ME, purple bars).
See also Figures S1 and S2; Table S2.
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5. Figure 3 Pairwise Evolutionary Dependencies between Selected Events
(A) Overview of all the SFEs involved in at least one significant motif (left) and those involved in more than six motifs (right). SFEs are sorted first by number of motifs and then by the sum of their motif scores. Lineage-specific SFEs involved in few but high-scoring motifs are labeled.
(B–D) Motifs and scores for mutations affecting KRAS (B), TP53 (C), and RNF43 (D). Tested motifs are sorted by score and shown separately for mutual exclusivity or co-occurrence. Colored dots indicate motifs above our significance threshold (Ssig = 0.3); gray dots are below the threshold. Significant motifs include both previously reported (black font) and novel associations (blue font).
(E) Mutation occurrences for RNF43 and ARID1A across all tumor types in our dataset.
(F) Tumor subtype distribution of samples with both ARID1A and RNF43 mutations.
(G) Alteration frequency of ARID1A mutations and events affecting key regulators of Wnt signaling in STAD and CRC microsatellite instability (MSI) cases.
(H) Comparison of β-catenin signaling and cell proliferation signature scores between STAD samples harboring only ARID1A mutation, only RNF43 mutation, or both events. The thick central line of each box plot represents the median number of significant motifs, the bounding box corresponds to the 25th–75th percentiles, and the whiskers extend up to 1.5 times the interquartile range.
See also Figure S3 and Table S3.
SFE are identified as follows: mutations are identified by the gene symbol of the mutated gene, copy number amplifications and deletions by the corresponding cytoband followed by A or D, respectively. Putative targets of copy number alterations, if any, are reported in brackets.
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6. Figure 4
Systematic Analysis of Drug Sensitivity Associated to Co-occurrence Motifs in Human Cancer Cell Lines
(A) Left: schematic of the comparison between double- and single-mutant cell lines and between double-mutant cell lines and cell lines derived from the same tumor type in terms of drug response similarity. Center: distribution of p values for the 72 tested motifs in the two comparisons. Right: overlap between the motifs significant in the two comparisons.
(B) Distribution of drug response similarity among double-mutant cell lines for the top five significant motifs compared with the background distribution of all the intra-tumor-type concordance scores (gray box plot). Each dot represents a pair of double-mutant cell lines derived from either different tumor types (green dots) or the same tumor type (orange dots).
(C) Differential response between double-mutant cell lines and single-mutant cell lines to single drug compounds. y Axis: p values from the ANOVA analysis (−log10(p)). Results are shown separately for changes associated to increased sensitivity (upper quadrant) and increased resistance (lower quadrant).
(D) Distribution of 72 co-occurrence motifs based on the number of drugs (hits) having significantly different response between double-mutant and single-mutant cell lines.
(E) Response to HDAC-inhibitor AR-42 (measured by IC50 levels) in cell lines harboring either RPL22, CCND1, or both RPL22 and CCND1 mutations.
(F) Response to Bcl-inhibitor Navitoclax in cell lines harboring either TET2, DNMT3A, or both TET2 and DNMT3A mutations.
(G) Response to Aurora Kinases (AURK) inhibitor VX-680 in cell lines harboring either RNF43, ARID1A, or both RNF43 and ARID1A mutations.
The thick central line of each box plot in all panels represents the median number of significant motifs, the bounding box corresponds to the 25th–75th percentiles, and the whiskers extend up to 1.5 times the interquartile range. See also Figure S4 and Table S4.
SFE are identified as follows: mutations are identified by the gene symbol of the mutated gene, copy number amplifications and deletions by the corresponding cytoband followed by A or D, respectively. Putative targets of copy number alterations, if any, are reported in brackets.
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7. Figure 5
Tissue-Independent Modules of Alteration Motifs in Human Cancers
(A) A significant module found by SELECT aggregates multiple pairwise interdependencies between 11 SFEs. Pairwise interdependencies are shown by the upper triangular matrix reporting in each cell the type of motif observed between the alterations in the corresponding row and column. High-scoring: score >0.3.
(B) Alterations in the module affect genes in the MAPK/ERK and Wnt pathways and the receptor tyrosine kinase (RTK) EGFR.
(C) Proliferation scores of tumors with co-occurrent alterations in MAPK/ERK- and Wnt-related genes (green), tumors with alterations in genes related to only one of the two pathways (dark gray), and tumors with no alterations in the module genes (light gray).
(D) Differential response to multiple drug compounds between cell lines with co-occurrent APC and KRAS mutations and cell lines with only APC or only KRAS mutation. y Axis: p values from the ANOVA analysis (−log10(p)). Results are shown separately for changes associated to increased sensitivity (upper quadrant) and increased resistance (lower quadrant).
(E) Differential response to multiple drug compounds between colorectal cancer cell lines with co-occurrent APC and KRAS mutations and cell lines with only APC or only KRAS mutation. RTK inhibitors (green) and bromodomain inhibitors (blue) are highlighted.
(F) A second significant module found by SELECT aggregates multiple pairwise interdependencies between nine SFEs.
(G) Alterations in the module affect genes in the cell-cycle and apoptosis pathways.
(H) Mean increase of proliferation scores of tumors with co-occurrent alterations in cell-cycle- and apoptosis-related genes (green bars) and tumors with alterations in genes related to only one of these pathways (dark gray) compared with tumors with no alterations in the module genes (baseline). SDs are reported for each mean value as error bars.
KIPAN, union of KICH, KIRC, and KIRP. See also Table S5.
SFE are identified as follows: mutations are identified by the gene symbol of the mutated gene, copy number amplifications and deletions by the corresponding cytoband followed by A or D, respectively. Putative targets of copy number alterations, if any, are reported in brackets.
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8. Figure 6
Emergence of Motifs through Event Conditional Selection in Cancer Evolution
(A–D) Schematic of motif comparisons between 1,000 synthetic tumors generated using a numerical model of cancer evolution assuming independently selected events (ISE, disconnected black dots) (A) and the real breast cancer dataset (n = 965) (B), 1,000 synthetic tumors generated with sparse conditionally selected events (sCSE) (C), and 1,000 synthetic tumors generated with dense conditionally selected events (dCSE) (D). In CSE models, the selection of one alteration can either favor (green edges) or hinder (purple edges) the selection of another.
(E–G) Comparisons by tail distribution ratio of the motif scores emerging from the ISE model versus the breast cancer cohort (E), sCSE cohort (F), and dCSE cohort (G).
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9. Figure 7 Cancer Evolution through Event Conditional Selection
Cancer evolution proceeds through emergence and selection of functional alterations. Our results indicate that the selection process is dependent on one side on tissue of origin and on the other on functional interactions between proteins, which in turn establish evolutionary dependencies giving rise to alteration motifs across tumor cohorts.
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