Publications

Publications by LIAAD

2020

Tumors induce de novo steroid biosynthesis in T cells to evade immunity

Authors
Mahata, B; Pramanik, J; van der Weyden, L; Polanski, K; Kar, G; Riedel, A; Chen, X; Fonseca, NA; Kundu, K; Campos, LS; Ryder, E; Duddy, G; Walczak, I; Okkenhaug, K; Adams, DJ; Shields, JD; Teichmann, SA;

Publication
Nature Communications

Abstract

2020

A user guide for the online exploration and visualization of PCAWG data

Authors
Goldman, MJ; Zhang, J; Fonseca, NA; Cortés-Ciriano, I; Xiang, Q; Craft, B; Piñeiro-Yáñez, E; O’Connor, BD; Bazant, W; Barrera, E; Muñoz-Pomer, A; Petryszak, R; Füllgrabe, A; Al-Shahrour, F; Keays, M; Haussler, D; Weinstein, JN; Huber, W; Valencia, A; Park, PJ; Papatheodorou, I; Zhu, J; Ferretti, V; Vazquez, M;

Publication
Nature Communications

Abstract

2020

An RNA-seq Based Machine Learning Approach Identifies Latent Tuberculosis Patients With an Active Tuberculosis Profile

Authors
Estévez, O; Anibarro, L; Garet, E; Pallares, Á; Barcia, L; Calviño, L; Maueia, C; Mussá, T; Fdez Riverola, F; Glez Peña, D; Reboiro Jato, M; López Fernández, H; Fonseca, NA; Reljic, R; González Fernández, Á;

Publication
Frontiers in Immunology

Abstract
A better understanding of the response against Tuberculosis (TB) infection is required to accurately identify the individuals with an active or a latent TB infection (LTBI) and also those LTBI patients at higher risk of developing active TB. In this work, we have used the information obtained from studying the gene expression profile of active TB patients and their infected –LTBI- or uninfected –NoTBI- contacts, recruited in Spain and Mozambique, to build a class-prediction model that identifies individuals with a TB infection profile. Following this approach, we have identified several genes and metabolic pathways that provide important information of the immune mechanisms triggered against TB infection. As a novelty of our work, a combination of this class-prediction model and the direct measurement of different immunological parameters, was used to identify a subset of LTBI contacts (called TB-like) whose transcriptional and immunological profiles are suggestive of infection with a higher probability of developing active TB. Validation of this novel approach to identifying LTBI individuals with the highest risk of active TB disease merits further longitudinal studies on larger cohorts in TB endemic areas. © Copyright © 2020 Estévez, Anibarro, Garet, Pallares, Barcia, Calviño, Maueia, Mussá, Fdez-Riverola, Glez-Peña, Reboiro-Jato, López-Fernández, Fonseca, Reljic and González-Fernández.

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2020

Butler enables rapid cloud-based analysis of thousands of human genomes

Authors
Yakneen, S; Waszak, SM; Gertz, M; Korbel, JO; Aminou, B; Bartolome, J; Boroevich, KA; Boyce, R; Brooks, AN; Buchanan, A; Buchhalter, I; Butler, AP; Byrne, NJ; Cafferkey, A; Campbell, PJ; Chen, ZH; Cho, S; Choi, W; Clapham, P; Davis Dusenbery, BN; De La Vega, FM; Demeulemeester, J; Dow, MT; Dursi, LJ; Eils, J; Eils, R; Ellrott, K; Farcas, C; Favero, F; Fayzullaev, N; Ferretti, V; Flicek, P; Fonseca, NA; Gelpi, JL; Getz, G; Gibson, B; Grossman, RL; Harismendy, O; Heath, AP; Heinold, MC; Hess, JM; Hofmann, O; Hong, JH; Hudson, TJ; Hutter, B; Hutter, CM; Hubschmann, D; Imoto, S; Ivkovic, S; Jeon, SH; Jiao, W; Jung, J; Kabbe, R; Kahles, A; Kerssemakers, JNA; Kim, HL; Kim, H; Kim, J; Kim, Y; Kleinheinz, K; Koscher, M; Koures, A; Kovacevic, M; Lawerenz, C; Leshchiner, I; Liu, J; Livitz, D; Mihaiescu, GL; Mijalkovic, S; Lazic, AM; Miyano, S; Miyoshi, N; Nahal Bose, HK; Nakagawa, H; Nastic, M; Newhouse, SJ; Nicholson, J; O'Connor, BD; Ocana, D; Ohi, K; Ohno Machado, L; Omberg, L; Ouellette, BFF; Paramasivam, N; Perry, MD; Pihl, TD; Prinz, M; Puiggros, M; Radovic, P; Raine, KM; Rheinbay, E; Rosenberg, M; Royo, R; Ratsch, G; Saksena, G; Schlesner, M; Shorser, SI; Short, C; Sofia, HJ; Spring, J; Stein, LD; Struck, AJ; Tiao, G; Tijanic, N; Torrents, D; Van Loo, P; Vazquez, M; Vicente, D; Wala, JA; Wang, ZN; Weischenfeldt, J; Werner, J; Williams, A; Woo, Y; Wright, AJ; Xiang, Q; Yang, LM; Yuen, D; Yung, CK; Zhang, JJ;

Publication
NATURE BIOTECHNOLOGY

Abstract
Efficient, large-scale genomic analysis is facilitated on the cloud by a computational tool with error-diagnosing and self-healing capabilities. We present Butler, a computational tool that facilitates large-scale genomic analyses on public and academic clouds. Butler includes innovative anomaly detection and self-healing functions that improve the efficiency of data processing and analysis by 43% compared with current approaches. Butler enabled processing of a 725-terabyte cancer genome dataset from the Pan-Cancer Analysis of Whole Genomes (PCAWG) project in a time-efficient and uniform manner.

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2020

Cancer LncRNA Census reveals evidence for deep functional conservation of long noncoding RNAs in tumorigenesis

Authors
Carlevaro Fita, J; Lanzós, A; Feuerbach, L; Hong, C; Mas Ponte, D; Pedersen, JS; Abascal, F; Amin, SB; Bader, GD; Barenboim, J; Beroukhim, R; Bertl, J; Boroevich, KA; Brunak, S; Campbell, PJ; Carlevaro Fita, J; Chakravarty, D; Chan, CWY; Chen, K; Choi, JK; Deu Pons, J; Dhingra, P; Diamanti, K; Feuerbach, L; Fink, JL; Fonseca, NA; Frigola, J; Gambacorti Passerini, C; Garsed, DW; Gerstein, M; Getz, G; Gonzalez Perez, A; Guo, Q; Gut, IG; Haan, D; Hamilton, MP; Haradhvala, NJ; Harmanci, AO; Helmy, M; Herrmann, C; Hess, JM; Hobolth, A; Hodzic, E; Hong, C; Hornshøj, H; Isaev, K; Izarzugaza, JMG; Johnson, R; Johnson, TA; Juul, M; Juul, RI; Kahles, A; Kahraman, A; Kellis, M; Khurana, E; Kim, J; Kim, JK; Kim, Y; Komorowski, J; Korbel, JO; Kumar, S; Lanzós, A; Larsson, E; Lawrence, MS; Lee, D; Lehmann, KV; Li, S; Li, X; Lin, Z; Liu, EM; Lochovsky, L; Lou, S; Madsen, T; Marchal, K; Martincorena, I; Martinez Fundichely, A; Maruvka, YE; McGillivray, PD; Meyerson, W; Muiños, F; Mularoni, L; Nakagawa, H; Nielsen, MM; Paczkowska, M; Park, K; Park, K; Pedersen, JS; Pich, O; Pons, T; Pulido Tamayo, S; Raphael, BJ; Reimand, J; Reyes Salazar, I; Reyna, MA; Rheinbay, E; Rubin, MA; Rubio Perez, C; Sabarinathan, R; Sahinalp, SC; Saksena, G; Salichos, L; Sander, C; Schumacher, SE; Shackleton, M; Shapira, O; Shen, C; Shrestha, R; Shuai, S; Sidiropoulos, N; Sieverling, L; Sinnott Armstrong, N; Stein, LD; Stuart, JM; Tamborero, D; Tiao, G; Tsunoda, T; Umer, HM; Uusküla Reimand, L; Valencia, A; Vazquez, M; Verbeke, LPC; Wadelius, C; Wadi, L; Wang, J; Warrell, J; Waszak, SM; Weischenfeldt, J; Wheeler, DA; Wu, G; Yu, J; Zhang, J; Zhang, X; Zhang, Y; Zhao, Z; Zou, L; von Mering, C; Johnson, R;

Publication
COMMUNICATIONS BIOLOGY

Abstract
Joana Carlevaro-Fita, Andres Lanzos et al. present the Cancer LncRNA Census (CLC), a manually curated dataset of 122 long noncoding RNAs (lncRNAs) with experimentally-validated functions in cancer based on data from the ICGC/TCGA Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium. CLC lncRNAs have unique gene features, and a number display evidence for cancer-driving functions that are conserved from humans to mice. Long non-coding RNAs (lncRNAs) are a growing focus of cancer genomics studies, creating the need for a resource of lncRNAs with validated cancer roles. Furthermore, it remains debated whether mutated lncRNAs can drive tumorigenesis, and whether such functions could be conserved during evolution. Here, as part of the ICGC/TCGA Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium, we introduce the Cancer LncRNA Census (CLC), a compilation of 122 GENCODE lncRNAs with causal roles in cancer phenotypes. In contrast to existing databases, CLC requires strong functional or genetic evidence. CLC genes are enriched amongst driver genes predicted from somatic mutations, and display characteristic genomic features. Strikingly, CLC genes are enriched for driver mutations from unbiased, genome-wide transposon-mutagenesis screens in mice. We identified 10 tumour-causing mutations in orthologues of 8 lncRNAs, including LINC-PINT and NEAT1, but not MALAT1. Thus CLC represents a dataset of high-confidence cancer lncRNAs. Mutagenesis maps are a novel means for identifying deeply-conserved roles of lncRNAs in tumorigenesis.

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2020

Combined burden and functional impact tests for cancer driver discovery using DriverPower

Authors
Shuai, S; Abascal, F; Amin, SB; Bader, GD; Bandopadhayay, P; Barenboim, J; Beroukhim, R; Bertl, J; Boroevich, KA; Brunak, S; Campbell, PJ; Carlevaro Fita, J; Chakravarty, D; Chan, CWY; Chen, K; Choi, JK; Deu Pons, J; Dhingra, P; Diamanti, K; Feuerbach, L; Fink, JL; Fonseca, NA; Frigola, J; Gambacorti Passerini, C; Garsed, DW; Gerstein, M; Getz, G; Guo, Q; Gut, IG; Haan, D; Hamilton, MP; Haradhvala, NJ; Harmanci, AO; Helmy, M; Herrmann, C; Hess, JM; Hobolth, A; Hodzic, E; Hong, C; Hornshøj, H; Isaev, K; Izarzugaza, JMG; Johnson, R; Johnson, TA; Juul, M; Juul, RI; Kahles, A; Kahraman, A; Kellis, M; Khurana, E; Kim, J; Kim, JK; Kim, Y; Komorowski, J; Korbel, JO; Kumar, S; Lanzós, A; Larsson, E; Lawrence, MS; Lee, D; Lehmann, KV; Li, S; Li, X; Lin, Z; Liu, EM; Lochovsky, L; Lou, S; Madsen, T; Marchal, K; Martincorena, I; Martinez Fundichely, A; Maruvka, YE; McGillivray, PD; Meyerson, W; Muiños, F; Mularoni, L; Nakagawa, H; Nielsen, MM; Paczkowska, M; Park, K; Park, K; Pedersen, JS; Pons, T; Pulido Tamayo, S; Raphael, BJ; Reimand, J; Reyes Salazar, I; Reyna, MA; Rheinbay, E; Rubin, MA; Rubio Perez, C; Sahinalp, SC; Saksena, G; Salichos, L; Sander, C; Schumacher, SE; Shackleton, M; Shapira, O; Shen, C; Shrestha, R; Shuai, S; Sidiropoulos, N; Sieverling, L; Sinnott Armstrong, N; Stein, LD; Stuart, JM; Tamborero, D; Tiao, G; Tsunoda, T; Umer, HM; Uusküla Reimand, L; Valencia, A; Vazquez, M; Verbeke, LPC; Wadelius, C; Wadi, L; Wang, J; Warrell, J; Waszak, SM; Weischenfeldt, J; Wheeler, DA; Wu, G; Yu, J; Zhang, J; Zhang, X; Zhang, Y; Zhao, Z; Zou, L; von Mering, C; Gallinger, S; Stein, L;

Publication
Nature Communications

Abstract
The discovery of driver mutations is one of the key motivations for cancer genome sequencing. Here, as part of the ICGC/TCGA Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium, which aggregated whole genome sequencing data from 2658 cancers across 38 tumour types, we describe DriverPower, a software package that uses mutational burden and functional impact evidence to identify driver mutations in coding and non-coding sites within cancer whole genomes. Using a total of 1373 genomic features derived from public sources, DriverPower’s background mutation model explains up to 93% of the regional variance in the mutation rate across multiple tumour types. By incorporating functional impact scores, we are able to further increase the accuracy of driver discovery. Testing across a collection of 2583 cancer genomes from the PCAWG project, DriverPower identifies 217 coding and 95 non-coding driver candidates. Comparing to six published methods used by the PCAWG Drivers and Functional Interpretation Working Group, DriverPower has the highest F1 score for both coding and non-coding driver discovery. This demonstrates that DriverPower is an effective framework for computational driver discovery. © 2020, The Author(s).

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