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Ludmil B. Alexandrov

Bio: Ludmil B. Alexandrov is an academic researcher from University of California, San Diego. The author has contributed to research in topics: Medicine & Biology. The author has an hindex of 60, co-authored 117 publications receiving 28167 citations. Previous affiliations of Ludmil B. Alexandrov include Beth Israel Deaconess Medical Center & University of California, Berkeley.
Topics: Medicine, Biology, Cancer, Germline mutation, Genome


Papers
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Journal ArticleDOI
Ludmil B. Alexandrov1, Serena Nik-Zainal2, Serena Nik-Zainal3, David C. Wedge1, Samuel Aparicio4, Sam Behjati5, Sam Behjati1, Andrew V. Biankin, Graham R. Bignell1, Niccolo Bolli1, Niccolo Bolli5, Åke Borg3, Anne Lise Børresen-Dale6, Anne Lise Børresen-Dale7, Sandrine Boyault8, Birgit Burkhardt8, Adam Butler1, Carlos Caldas9, Helen Davies1, Christine Desmedt, Roland Eils5, Jorunn E. Eyfjord10, John A. Foekens11, Mel Greaves12, Fumie Hosoda13, Barbara Hutter5, Tomislav Ilicic1, Sandrine Imbeaud14, Sandrine Imbeaud15, Marcin Imielinsk14, Natalie Jäger5, David T. W. Jones16, David T. Jones1, Stian Knappskog17, Stian Knappskog11, Marcel Kool11, Sunil R. Lakhani18, Carlos López-Otín18, Sancha Martin1, Nikhil C. Munshi19, Nikhil C. Munshi20, Hiromi Nakamura13, Paul A. Northcott16, Marina Pajic21, Elli Papaemmanuil1, Angelo Paradiso22, John V. Pearson23, Xose S. Puente18, Keiran Raine1, Manasa Ramakrishna1, Andrea L. Richardson20, Andrea L. Richardson22, Julia Richter22, Philip Rosenstiel22, Matthias Schlesner5, Ton N. Schumacher24, Paul N. Span25, Jon W. Teague1, Yasushi Totoki13, Andrew Tutt24, Rafael Valdés-Mas18, Marit M. van Buuren25, Laura van ’t Veer26, Anne Vincent-Salomon27, Nicola Waddell23, Lucy R. Yates1, Icgc PedBrain24, Jessica Zucman-Rossi15, Jessica Zucman-Rossi14, P. Andrew Futreal1, Ultan McDermott1, Peter Lichter24, Matthew Meyerson14, Matthew Meyerson20, Sean M. Grimmond23, Reiner Siebert22, Elias Campo28, Tatsuhiro Shibata13, Stefan M. Pfister16, Stefan M. Pfister11, Peter J. Campbell29, Peter J. Campbell30, Peter J. Campbell2, Michael R. Stratton2, Michael R. Stratton31 
22 Aug 2013-Nature
TL;DR: It is shown that hypermutation localized to small genomic regions, ‘kataegis’, is found in many cancer types, and this results reveal the diversity of mutational processes underlying the development of cancer.
Abstract: All cancers are caused by somatic mutations; however, understanding of the biological processes generating these mutations is limited. The catalogue of somatic mutations from a cancer genome bears the signatures of the mutational processes that have been operative. Here we analysed 4,938,362 mutations from 7,042 cancers and extracted more than 20 distinct mutational signatures. Some are present in many cancer types, notably a signature attributed to the APOBEC family of cytidine deaminases, whereas others are confined to a single cancer class. Certain signatures are associated with age of the patient at cancer diagnosis, known mutagenic exposures or defects in DNA maintenance, but many are of cryptic origin. In addition to these genome-wide mutational signatures, hypermutation localized to small genomic regions, 'kataegis', is found in many cancer types. The results reveal the diversity of mutational processes underlying the development of cancer, with potential implications for understanding of cancer aetiology, prevention and therapy.

7,904 citations

Journal ArticleDOI
25 May 2012-Cell
TL;DR: This work generated catalogs of somatic mutation from 21 breast cancers and applied mathematical methods to extract mutational signatures of the underlying processes, finding a remarkable phenomenon of localized hypermutation, termed “kataegis,” was observed.

1,699 citations

Journal ArticleDOI
Serena Nik-Zainal1, Serena Nik-Zainal2, Helen Davies2, Johan Staaf3, Manasa Ramakrishna2, Dominik Glodzik2, Xueqing Zou2, Inigo Martincorena2, Ludmil B. Alexandrov2, Sancha Martin2, David C. Wedge2, Peter Van Loo2, Young Seok Ju2, Michiel M. Smid4, Arie B. Brinkman5, Sandro Morganella6, Miriam Ragle Aure7, Ole Christian Lingjærde7, Anita Langerød8, Markus Ringnér3, Sung-Min Ahn9, Sandrine Boyault, Jane E. Brock, Annegien Broeks10, Adam Butler2, Christine Desmedt11, Luc Dirix12, Serge Dronov2, Aquila Fatima13, John A. Foekens4, Moritz Gerstung2, Gerrit Gk Hooijer14, Se Jin Jang15, David Jones2, Hyung-Yong Kim16, Tari Ta King17, Savitri Krishnamurthy18, Hee Jin Lee15, Jeong-Yeon Lee16, Yang Li2, Stuart McLaren2, Andrew Menzies2, Ville Mustonen2, Sarah O’Meara2, Iris Pauporté, Xavier Pivot19, Colin Ca Purdie20, Keiran Raine2, Kamna Ramakrishnan2, Germán Fg Rodríguez-González4, Gilles Romieu21, Anieta M. Sieuwerts4, Peter Pt Simpson22, Rebecca Shepherd2, Lucy Stebbings2, Olafur Oa Stefansson23, Jon W. Teague2, Stefania Tommasi, Isabelle Treilleux, Gert Van den Eynden12, Peter B. Vermeulen12, Anne Vincent-Salomon24, Lucy R. Yates2, Carlos Caldas25, Laura Van't Veer10, Andrew Tutt26, Andrew Tutt27, Stian Knappskog28, Benita Kiat Tee Bk Tan29, Jos Jonkers10, Åke Borg3, Naoto T. Ueno18, Christos Sotiriou11, Alain Viari, P. Andrew Futreal2, Peter J. Campbell2, Paul N. Span5, Steven Van Laere12, Sunil R. Lakhani22, Jorunn E. Eyfjord23, Alastair M Thompson, Ewan Birney6, Hendrik G. Stunnenberg5, Marc J. van de Vijver14, John W.M. Martens4, Anne Lise Børresen-Dale8, Andrea L. Richardson13, Gu Kong16, Gilles Thomas, Michael R. Stratton2 
02 Jun 2016-Nature
TL;DR: This analysis of all classes of somatic mutation across exons, introns and intergenic regions highlights the repertoire of cancer genes and mutational processes operative, and progresses towards a comprehensive account of the somatic genetic basis of breast cancer.
Abstract: We analysed whole-genome sequences of 560 breast cancers to advance understanding of the driver mutations conferring clonal advantage and the mutational processes generating somatic mutations. We found that 93 protein-coding cancer genes carried probable driver mutations. Some non-coding regions exhibited high mutation frequencies, but most have distinctive structural features probably causing elevated mutation rates and do not contain driver mutations. Mutational signature analysis was extended to genome rearrangements and revealed twelve base substitution and six rearrangement signatures. Three rearrangement signatures, characterized by tandem duplications or deletions, appear associated with defective homologous-recombination-based DNA repair: one with deficient BRCA1 function, another with deficient BRCA1 or BRCA2 function, the cause of the third is unknown. This analysis of all classes of somatic mutation across exons, introns and intergenic regions highlights the repertoire of cancer genes and mutational processes operating, and progresses towards a comprehensive account of the somatic genetic basis of breast cancer.

1,696 citations

Journal ArticleDOI
05 Feb 2020-Nature
TL;DR: The characterization of 4,645 whole-genome and 19,184 exome sequences, covering most types of cancer, identifies 81 single-base substitution, doublet- base substitution and small-insertion-and-deletion mutational signatures, providing a systematic overview of the mutational processes that contribute to cancer development.
Abstract: Somatic mutations in cancer genomes are caused by multiple mutational processes, each of which generates a characteristic mutational signature1. Here, as part of the Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium2 of the International Cancer Genome Consortium (ICGC) and The Cancer Genome Atlas (TCGA), we characterized mutational signatures using 84,729,690 somatic mutations from 4,645 whole-genome and 19,184 exome sequences that encompass most types of cancer. We identified 49 single-base-substitution, 11 doublet-base-substitution, 4 clustered-base-substitution and 17 small insertion-and-deletion signatures. The substantial size of our dataset, compared with previous analyses3–15, enabled the discovery of new signatures, the separation of overlapping signatures and the decomposition of signatures into components that may represent associated—but distinct—DNA damage, repair and/or replication mechanisms. By estimating the contribution of each signature to the mutational catalogues of individual cancer genomes, we revealed associations of signatures to exogenous or endogenous exposures, as well as to defective DNA-maintenance processes. However, many signatures are of unknown cause. This analysis provides a systematic perspective on the repertoire of mutational processes that contribute to the development of human cancer. The characterization of 4,645 whole-genome and 19,184 exome sequences, covering most types of cancer, identifies 81 single-base substitution, doublet-base substitution and small-insertion-and-deletion mutational signatures, providing a systematic overview of the mutational processes that contribute to cancer development.

1,521 citations

Journal ArticleDOI
22 May 2015-Science
TL;DR: Across 234 biopsies of sun-exposed eyelid epidermis from four individuals, the burden of somatic mutations averaged two to six mutations per megabase per cell, similar to that seen in many cancers, and exhibited characteristic signatures of exposure to ultraviolet light.
Abstract: How somatic mutations accumulate in normal cells is central to understanding cancer development but is poorly understood. We performed ultradeep sequencing of 74 cancer genes in small (0.8 to 4.7 square millimeters) biopsies of normal skin. Across 234 biopsies of sun-exposed eyelid epidermis from four individuals, the burden of somatic mutations averaged two to six mutations per megabase per cell, similar to that seen in many cancers, and exhibited characteristic signatures of exposure to ultraviolet light. Remarkably, multiple cancer genes are under strong positive selection even in physiologically normal skin, including most of the key drivers of cutaneous squamous cell carcinomas. Positively selected mutations were found in 18 to 32% of normal skin cells at a density of ~140 driver mutations per square centimeter. We observed variability in the driver landscape among individuals and variability in the sizes of clonal expansions across genes. Thus, aged sun-exposed skin is a patchwork of thousands of evolving clones with over a quarter of cells carrying cancer-causing mutations while maintaining the physiological functions of epidermis.

1,312 citations


Cited by
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Christopher M. Bishop1
01 Jan 2006
TL;DR: Probability distributions of linear models for regression and classification are given in this article, along with a discussion of combining models and combining models in the context of machine learning and classification.
Abstract: Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

10,141 citations

Journal ArticleDOI
Ludmil B. Alexandrov1, Serena Nik-Zainal2, Serena Nik-Zainal3, David C. Wedge1, Samuel Aparicio4, Sam Behjati5, Sam Behjati1, Andrew V. Biankin, Graham R. Bignell1, Niccolo Bolli1, Niccolo Bolli5, Åke Borg3, Anne Lise Børresen-Dale6, Anne Lise Børresen-Dale7, Sandrine Boyault8, Birgit Burkhardt8, Adam Butler1, Carlos Caldas9, Helen Davies1, Christine Desmedt, Roland Eils5, Jorunn E. Eyfjord10, John A. Foekens11, Mel Greaves12, Fumie Hosoda13, Barbara Hutter5, Tomislav Ilicic1, Sandrine Imbeaud14, Sandrine Imbeaud15, Marcin Imielinsk15, Natalie Jäger5, David T. W. Jones16, David T. Jones1, Stian Knappskog11, Stian Knappskog17, Marcel Kool11, Sunil R. Lakhani18, Carlos López-Otín18, Sancha Martin1, Nikhil C. Munshi19, Nikhil C. Munshi20, Hiromi Nakamura13, Paul A. Northcott16, Marina Pajic21, Elli Papaemmanuil1, Angelo Paradiso22, John V. Pearson23, Xose S. Puente18, Keiran Raine1, Manasa Ramakrishna1, Andrea L. Richardson22, Andrea L. Richardson20, Julia Richter22, Philip Rosenstiel22, Matthias Schlesner5, Ton N. Schumacher24, Paul N. Span25, Jon W. Teague1, Yasushi Totoki13, Andrew Tutt24, Rafael Valdés-Mas18, Marit M. van Buuren25, Laura van ’t Veer26, Anne Vincent-Salomon27, Nicola Waddell23, Lucy R. Yates1, Icgc PedBrain24, Jessica Zucman-Rossi14, Jessica Zucman-Rossi15, P. Andrew Futreal1, Ultan McDermott1, Peter Lichter24, Matthew Meyerson15, Matthew Meyerson20, Sean M. Grimmond23, Reiner Siebert22, Elias Campo28, Tatsuhiro Shibata13, Stefan M. Pfister16, Stefan M. Pfister11, Peter J. Campbell29, Peter J. Campbell2, Peter J. Campbell30, Michael R. Stratton2, Michael R. Stratton31 
22 Aug 2013-Nature
TL;DR: It is shown that hypermutation localized to small genomic regions, ‘kataegis’, is found in many cancer types, and this results reveal the diversity of mutational processes underlying the development of cancer.
Abstract: All cancers are caused by somatic mutations; however, understanding of the biological processes generating these mutations is limited. The catalogue of somatic mutations from a cancer genome bears the signatures of the mutational processes that have been operative. Here we analysed 4,938,362 mutations from 7,042 cancers and extracted more than 20 distinct mutational signatures. Some are present in many cancer types, notably a signature attributed to the APOBEC family of cytidine deaminases, whereas others are confined to a single cancer class. Certain signatures are associated with age of the patient at cancer diagnosis, known mutagenic exposures or defects in DNA maintenance, but many are of cryptic origin. In addition to these genome-wide mutational signatures, hypermutation localized to small genomic regions, 'kataegis', is found in many cancer types. The results reveal the diversity of mutational processes underlying the development of cancer, with potential implications for understanding of cancer aetiology, prevention and therapy.

7,904 citations

Journal ArticleDOI
TL;DR: The following Clinical Practice Guidelines will give up-to-date advice for the clinical management of patients with hepatocellular carcinoma, as well as providing an in-depth review of all the relevant data leading to the conclusions herein.

7,851 citations

Journal ArticleDOI
29 Mar 2013-Science
TL;DR: This work has revealed the genomic landscapes of common forms of human cancer, which consists of a small number of “mountains” (genes altered in a high percentage of tumors) and a much larger number of "hills" (Genes altered infrequently).
Abstract: Over the past decade, comprehensive sequencing efforts have revealed the genomic landscapes of common forms of human cancer. For most cancer types, this landscape consists of a small number of “mountains” (genes altered in a high percentage of tumors) and a much larger number of “hills” (genes altered infrequently). To date, these studies have revealed ~140 genes that, when altered by intragenic mutations, can promote or “drive” tumorigenesis. A typical tumor contains two to eight of these “driver gene” mutations; the remaining mutations are passengers that confer no selective growth advantage. Driver genes can be classified into 12 signaling pathways that regulate three core cellular processes: cell fate, cell survival, and genome maintenance. A better understanding of these pathways is one of the most pressing needs in basic cancer research. Even now, however, our knowledge of cancer genomes is sufficient to guide the development of more effective approaches for reducing cancer morbidity and mortality.

6,441 citations

Journal ArticleDOI
03 Apr 2015-Science
TL;DR: Treatment efficacy was associated with a higher number of mutations in the tumors, and a tumor-specific T cell response paralleled tumor regression in one patient, suggesting that the genomic landscape of lung cancers shapes response to anti–PD-1 therapy.
Abstract: Immune checkpoint inhibitors, which unleash a patient’s own T cells to kill tumors, are revolutionizing cancer treatment. To unravel the genomic determinants of response to this therapy, we used whole-exome sequencing of non–small cell lung cancers treated with pembrolizumab, an antibody targeting programmed cell death-1 (PD-1). In two independent cohorts, higher nonsynonymous mutation burden in tumors was associated with improved objective response, durable clinical benefit, and progression-free survival. Efficacy also correlated with the molecular smoking signature, higher neoantigen burden, and DNA repair pathway mutations; each factor was also associated with mutation burden. In one responder, neoantigen-specific CD8+ T cell responses paralleled tumor regression, suggesting that anti–PD-1 therapy enhances neoantigen-specific T cell reactivity. Our results suggest that the genomic landscape of lung cancers shapes response to anti–PD-1 therapy.

6,215 citations