Cells from gastric organoids were collected from the air liquid interface collagen gel by disaggregation with collagenase IV. For transplantation, 400,000 cells per mouse flank were mixed with matrigel and injected into NOD. Cg Prkdcscid Ilr2rgtm1Sug/JicTac mice. Mice were sacrificed after day 50, after which tumors were dissected and exam ined by H E staining. P values were determined using a two tailed Students t test assuming unequal variances. A P value of 0. 05 was considered significant. Data availability The data from this study have been submitted to the NCBI Sequence Read Archive under the accession num ber SRP044347. Background Breast cancer is a clinically and genomically heteroge neous disease.

Six subtypes were defined approximately a decade ago based on transcriptional characteristics and were designated luminal A, luminal B, ERBB2 enriched, basal like, claudin low and normal like. New cancers can be assigned to these subtypes using a 50 gene tran scriptional signature designated the PAM50. However, the number of distinct subtypes is increasing steadily as multiple data types are integrated. Integration of genome Entinostat copy number and transcriptional profiles defines 10 subtypes, and adding mutation status, methylation pattern, pattern of splice variants, protein and phosphoprotein expression and microRNA expression and pathway activity may define still more subtypes. The Cancer Genome Atlas project and other international genomics efforts were founded to improve our understanding of the molecular landscapes of most major tumor types with the ultimate goal of increasing the precision with which individual cancers are man aged.

One application of these data is to identify mo lecular signatures that can be used to assign specific treatment to individual patients. However, strategies to develop optimal predictive marker sets are still being explored. Indeed, it is not yet clear which molecular data types will be most useful as response predictors. In breast cancer, cell lines mirror many of the molecular characteristics of the tumors from which they were derived, and are therefore a useful preclinical model in which to ex plore strategies for predictive marker development. To this end, we have analyzed the responses of 70 well charac terized breast cancer cell lines to 90 compounds and used two independent machine learning approaches to identify pretreatment molecular features that are strongly associated with responses within the cell line panel. For most com pounds tested, in vitro cell line systems provide the only experimental data that can be used to identify predictive response signatures, as most of the compounds have not been tested in clinical trials.