Publication

Whole-genome analysis informs breast cancer response to aromatase inhibition.

Decoding the DNA of patients with advanced breast cancer has allowed scientists to identify distinct cancer "signatures" that could help predict which women are most likely to benefit from estrogen-lowering therapy.

Nature. 2012 Jun 10;486(7403):353-60. doi: 10.1038/nature11143.

Abstract

To correlate the variable clinical features of oestrogen-receptor-positive breast cancer with somatic alterations, we studied pretreatment tumour biopsies accrued from patients in two studies of neoadjuvant aromatase inhibitor therapy by massively parallel sequencing and analysis. Eighteen significantly mutated genes were identified, including five genes (RUNX1, CBFB, MYH9, MLL3 and SF3B1) previously linked to haematopoietic disorders. Mutant MAP3K1 was associated with luminal A status, low-grade histology and low proliferation rates, whereas mutant TP53 was associated with the opposite pattern. Moreover, mutant GATA3 correlated with suppression of proliferation upon aromatase inhibitor treatment. Pathway analysis demonstrated that mutations in MAP2K4, a MAP3K1 substrate, produced similar perturbations as MAP3K1 loss. Distinct phenotypes in oestrogen-receptor-positive breast cancer are associated with specific patterns of somatic mutations that map into cellular pathways linked to tumour biology, but most recurrent mutations are relatively infrequent. Prospective clinical trials based on these findings will require comprehensive genome sequencing.

Following are supplementary tables related to the manuscript titled "Whole-genome analysis informs breast cancer response to aromatase inhibition." 10 June 2012, Nature.

The documents are available in Excel format.
Last update: 24 July 2012

Supplementary_Table_1.xls Supplementary_Table_10.xls
Supplementary_Table_2.xls Supplementary_Table_11.xls
Supplementary_Table_3.xls Supplementary_Table_12.xls
Supplementary_Table_4.xls Supplementary_Table_13.xls
Supplementary_Table_5a.xls Supplementary_Table_14.xls
Supplementary_Table_5b.xls Supplementary_Table_15.xls
Supplementary_Table_5c.xls Supplementary_Table_16.xls
Supplementary_Table_5d.xls Supplementary_Table_17.xls
Supplementary_Table_6.xls Supplementary_Table_18.xls
Supplementary_Table_7.xls Supplementary_Table_19.xls
Supplementary_Table_8.xls Supplementary_Table_20.xls
Supplementary_Table_9.xls

 

Authors

Ellis MJ, Ding L, Shen D, Luo J, Suman VJ, Wallis JW, Van Tine BA, Hoog J, Goiffon RJ, Goldstein TC, Ng S, Lin L, Crowder R, Snider J, Ballman K, Weber J, Chen K, Koboldt DC, Kandoth C, Schierding WS, McMichael JF, Miller CA, Lu C, Harris CC, McLellan MD, Wendl MC, DeSchryver K, Allred DC, Esserman L, Unzeitig G, Margenthaler J, Babiera GV, Marcom PK, Guenther JM, Leitch M, Hunt K, Olson J, Tao Y, Maher CA, Fulton LL, Fulton RS, Harrison M, Oberkfell B, Du F, Demeter R, Vickery TL, Elhammali A, Piwnica-Worms H, McDonald S, Watson M, Dooling DJ, Ota D, Chang LW, Bose R, Ley TJ, Piwnica-Worms D, Stuart JM, Wilson RK, Mardis ER.