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Genomic characterization of metastatic breast cancers


Navin, N. et al. Evolution of the tumor deduced from unicellular sequencing. Nature 472, 90-94 (2011).


Wang, Y. et al. Clonal evolution in breast most cancers revealed by single nucleotide genome sequencing. Nature 512, 155-160 (2014).


Gerlinger, M. et al. Intratumoral heterogeneity and branched evolution revealed by multiregion sequencing. N. Engl. J. Med. 366, 883-892 (2012).


Yates, L.R. et al. Genomic evolution of metastases and relapses of breast most cancers. Most cancers Cell 32, 169-184 (2017).


Ng, C.Okay. Y. et al. Genetic heterogeneity in naïve major synchronous breast most cancers remedy and metastasis. Clin. Most cancers Res. 23, 4402-4415 (2017).


Yu, H.A. et al. Evaluation of tumor samples on the time of acquired resistance to EGFR-TKI remedy in 155 sufferers with EGFR mutant lung most cancers. Clin. Most cancers Res. 19, 2240-2247 (2013).


Gramza, A.W., Corless, C.L. and Heinrich, M.C. Resistance to tyrosine kinase inhibitors in gastrointestinal stromal tumors. Clin. Most cancers Res. 15, 7510-7518 (2009).


Robinson, D.R. et al. Activation of ESR1 mutations in hormone-resistant metastatic breast most cancers. Nat. Broom. 45, 1446-1451 (2013).


Fumagalli, D. et al. Somatic mutation, copy quantity, and transcriptomic profiles of breast cancers with major and corresponding metastatic estrogen receptors. Ann. Oncol. 27, 1860-1866 (2016).


Lefebvre, C. et al. Mutational profile of metastatic breast most cancers: a retrospective evaluation. PLoS Med. 13, e1002201 (2016).


Brastianos, P.Okay. et al. The genomic characterization of mind metastases reveals a branched evolution and potential therapeutic targets. Most cancers Discov. 5, 1164-1177 (2015).


Brown, D. et al. Phylogenetic evaluation of metastatic development of breast most cancers utilizing somatic mutations and replica quantity aberrations. Nat. Frequent. eight, 14944 (2017).


Savas, P. et al. The subclonal structure of metastatic breast most cancers: findings of a doable fast community-based post-mortem program "CASCADE". PLoS Med. 13, e1002204 (2016).


Murtaza, M. et al. Multifocal clonal evolution characterised by way of circulating tumor DNA in a case of metastatic breast most cancers. Nat. Frequent. 6, 8760 (2015).


Schrijver, W.A.M.E. et al. Mutation profile of key most cancers genes in major breast cancers and their distant metastases. Most cancers Res. 78, 3112-3121 (2018).


De Mattos-Arruda, L. et al. Genetic heterogeneity and actionable mutations in major HER2-positive breast cancers and their mind metastases. Oncotarget 9, 20617-20630 (2018).


Razavi, P. et al. The genomic panorama of superior breast cancers proof against the endocrine system. Most cancers Cell 34, 427-438 (2018).


Nayar, U. et al. HER2 mutations acquired in ER + metastatic breast most cancers confer resistance to estrogen receptor-directed therapies. Nat. Broom. 51, 207-216 (2019).


Li, Z. et al. The lack of tumor suppressor FAT1 promotes resistance to CDK4 / 6 inhibitors through the hippopotamus pathway. Most cancers Cell 34, 893-905 (2018).


Knudsen, E.S. & Wang, J. Y. J. Concentrating on the RB pathway within the remedy of most cancers. Clin. Most cancers Res. 16, 1094-1099 (2010).


Lock, R. et al. The MNK and MEK cotargeting kinases induce the regression of mutant cancers of NF1. J. Clin. Make investments. 126, 2181-2190 (2016).


Gala, Okay. et al. KMT2C attenuates estrogen dependence of breast most cancers by regulating the ERα enhancement operate. Oncogene 37, 4692-470 (2018).


Chakravarty, D. et al. OncoKB: a information base in precision oncology. JCO Summary. Oncol. (2017).


Regulation, E.Okay. et al. The DNA cytotoxin deaminase APOBEC3B promotes tamoxifen resistance in ER-positive breast most cancers. Sci. Adv. 2, e1601737 (2016).


Popova, T. et al. Ploidy and large-scale genomic instability constantly establish basal-type breast carcinomas with inactivation of BRCA1 / 2. Most cancers Res. 72, 5454-5462 (2012).


Riaz, N. et al. Pan-cancer evaluation of bi-allelic alterations in homologous recombination DNA restore genes. Nat. Frequent. eight,857 (2017).


Polak, P. et al. A mutation signature reveals alterations underlying homologous recombination restore poor in breast most cancers. Nat. Broom. 49, 1476-1486 (2017).


Edwards, S. L. et al. Resistance to intragenic deletion remedy in BRCA2. Nature 451, 1111-1115 (2008).


Sakai, W. et al. Secondary mutations as a mechanism of cisplatin resistance in BRCA2 mutated cancers. Nature 451, 1116-1120 (2008).


Lee, J. Y. et al. In situ lobular carcinomas present genetic heterogeneity by intralesion and clonal development in development to invasive lobular carcinoma. Clin. Most cancers Res. 25, 674-686 (2019).


André, F. et al. Comparative desk of genomic hybridization and DNA sequencing for the direct remedy of metastatic breast most cancers: potential multicenter trial (SAFIR01 / UNICANCER). Lancet Oncol. 15, 267-274 (2014).


Massard, C. et al. Excessive throughput genomics and scientific outcomes in superior cancers tough to deal with: outcomes of the MOSCATO 01 check. Most cancers Discov. 7, 586-595 (2017).


The Tourneau, C. et al. Focused molecular remedy primarily based on the molecular profile of the tumor in comparison with standard superior most cancers remedy (SHIVA): multicentre, open-label, concept-validation, randomized and managed part 2 trial. Lancet Oncol. 16, 1324-1334 (2015).


Hortobagyi, G. N. et al. Ribociclib as a first-line remedy for superior breast most cancers with constructive HR. N. Engl. J. Med. 375, 1738-1748 (2016).


Tripathy, D. et al. Ribociclib plus endocrine remedy in premenopausal girls with hormone receptor-positive superior breast most cancers (MONALEESA-7): randomized part III trial. Lancet Oncol. 19, 904-915 (2018).


Slamon, D.J. et al. Randomized part III examine of ribociclib and fulvestrant in human hormone receptor-positive superior human epidermal progress issue receptor 2 breast most cancers: MONALEESA-Three. J. Clin. Oncol. 36, 2465-2472 (2018).


Li, H. & Durbin, R. Quick and correct quick studying alignment with the Burrows – Wheeler transformation. Bioinformatics 25, 1754-1760 (2009).


McKenna, A. et al. The Toolkit for Genome Evaluation: A MapReduce Framework for Analyzing Subsequent-Era DNA Sequencing Knowledge. Genome Res. 20, 1297-1303 (2010).


Cibulskis, Okay. et al. Delicate detection of somatic level mutations in impure and heterogeneous most cancers samples. Nat. Biotechnol. 31, 213-219 (2013).


Saunders, C. T. et al. Strelka: exact somatic name with small variants from pairs of regular tumor-sequenced samples. Bioinformatics 28, 1811-1817 (2012).


Chang, M.T. et al. Accelerated discovery of practical mutant alleles in most cancers. Most cancers Discov. eight, 174-183 (2018).


Gao, J. et al. 3D clusters of somatic mutations in most cancers reveal many uncommon mutations as practical targets. Genome Med. 9, four (2017).


Costello, M. et al. Discovery and characterization of synthetic mutations in deep-targeted seize sequencing knowledge, as a consequence of oxidative DNA harm throughout pattern preparation. Nucleic Acids Res. 41, e67 (2013).


Shen, R. & Seshan, V. E. FACETS: Clone-specific copy and clonal heterogeneity evaluation software for alleles for prime throughput DNA sequencing. Nucleic Acids Res. 44, e131 (2016).


Riester, M. et al. PureCN: name copy quantity and SNV classification utilizing focused quick studying sequencing. Supply code Biol. Med. 11, 13 (2016).


Ye, Okay., Schulz, MH, Lengthy, Q., Apweiler, R. & Ning, Z. Pindel: A Mannequin Progress Strategy to Detecting Breakpoints of Giant Medium-sized Deletions and Insertions from Reads paired quick. Bioinformatics 25, 2865-2871 (2009).


Schröder, J. et al. Socrates: identification of genomic rearrangements in tumor genomes by realigning clipped readings. Bioinformatics 30, 1064-1072 (2014).


Newman, A.M. et al. Built-in digital error suppression for higher detection of circulating tumor DNA. Nat. Biotechnol. 34, 547-555 (2016).


Atlas community of the most cancers genome. Full molecular portraits of human mammary tumors. Nature 490, 61-70 (2012).


Lawrence, M.S. et al. Mutational heterogeneity in most cancers and seek for new genes related to most cancers. Nature 499, 214-218 (2013).


Rosenthal, R., N. McGranahan, J. Herrero, BS and Swanton, C. DeconstructSigs: The delineation of mutational processes in easy tumors distinguishes deficiencies in DNA restore and the evolution of the illness. 39, evolution of carcinoma. Genome Biol. 17, 31 (2016).


Alexandrov, L.B. et al. Signatures of mutational processes in human most cancers. Nature 500, 415-421 (2013).


Roth, A. et al. PyClone: ​​statistical inference of the construction of the clonal inhabitants in most cancers. Nat. Strategies 11, 396-398 (2014).


Hundal, J. et al. pVAC-Seq: a genome-guided in silico method for the identification of neo-tumor antigens. Genome Med. eight, 11 (2016).


McGranahan, N. et al. Clonal neoantigens induce T cell immunoreactivity and immune blocking sensitivity. Science 351, 1463-1469 (2016).


Piscuoglio, S. et al. The genomic panorama of breast most cancers in people. Clin. Most cancers Res. 22, 4045-4056 (2016).


Leiserson, M.D., Wu, H.T., Vandin, F. and Raphael, B. J. CoMEt: a statistical method to figuring out mixtures of mutually unique modifications in most cancers. Genome Biol. 16, 160 (2015).

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