Although it is increasingly evident that cancer is influenced by signals emanating from tumor stroma, little is known regarding how changes in stromal gene expression affect epithelial tumor progression. We used laser capture microdissection to compare gene expression profiles of tumor stroma from 53 primary breast tumors and derived signatures strongly associated with clinical outcome. We present a new stroma-derived prognostic predictor (SDPP) that stratifies disease outcome independently of standard clinical prognostic factors and published expression-based predictors. The SDPP predicts outcome in several published whole tumor-derived expression data sets, identifies poor-outcome individuals from multiple clinical subtypes, including lymph node-negative tumors, and shows increased accuracy with respect to previously published predictors, especially for HER2-positive tumors. Prognostic power increases substantially when the predictor is combined with existing outcome predictors. Genes represented in the SDPP reveal the strong prognostic capacity of differential immune responses as well as angiogenic and hypoxic responses, highlighting the importance of stromal biology in tumor progression.

SUMMARY The tumor stroma is believed to contribute to some of the most malignant characteristics of epithelial tumors. However, signaling between stromal and tumor cells is complex and remains poorly understood. Here we show that the genetic inactivation of Pten in stromal fibroblasts of mouse mammary glands accelerated the initiation, progression and malignant transformation of mammary epithelial tumors. This was associated with the massive remodeling of the extra-cellular matrix (ECM), innate immune cell infiltration and increased angiogenesis. Loss of Pten in stromal fibroblasts led to increased expression, phosphorylation (T72) and recruitment of Ets2 to target promoters known to be involved in these processes. Remarkably, Ets2 inactivation in Pten stroma-deleted tumors ameliorated disruption of the tumor microenvironment and was sufficient to decrease tumor growth and progression. Global gene expression profiling of mammary stromal cells identified a Pten-specific signature that was highly represented in the tumor stroma of breast cancer patients. These findings identify the Pten-Ets2 axis as a critical stroma-specific signaling pathway that suppresses mammary epithelial tumors.

SUMMARY Background Diffuse large-B-cell lymphoma (DLBCL) is curable but when treatment fails, outcome is poor. Imaging scans help identify patients at risk of treatment failure but are often imprecise, and the radiation exposure is a potential health risk. Specific, sensitive and readily available biomarkers of treatment failure are needed. Methods We retrospectively analyzed cell-free circulating tumor DNA (ctDNA) in patients treated on one of 3 treatment protocols using quantitative next-generation DNA sequencing. Eligible patients had DLBCL, no evidence of indolent lymphoma and were previously untreated. Serial serum samples and concurrent computed tomography scans were obtained at specified times during most treatment cycles and 5-years of follow-up. VDJ gene segments of the rearranged immunoglobulin receptor genes were amplified and sequenced from pre-treatment specimens and serum ctDNA encoding the VDJ rearrangements was quantitated. Findings Tumor clonotype(s) were identified in pretreatment specimens from 126 patients who were followed for a median (interquartile range) of 11 (6.8 to 14.2) years. Interim ctDNA monitoring at the end of 2 treatment cycles in 108 patients showed a time to progression (TTP) of 41.7% (95% Confidence Interval (CI): 22.2% to 60.1%) and 80.2% (95% CI: 69.6% to 87.3%), at 5-years (p<0.0001) in patients with and without detectable ctDNA, respectively, and a positive and negative predicative value (PPV and NPV) of 63% and 80%, respectively. Surveillance ctDNA monitoring was performed in 107 patients who achieved complete remission. A Cox proportional hazards model showed patients who developed detectable ctDNA during surveillance had a hazard ratio 228 times that of patients with undetectable ctDNA for clinical disease progression (95% CI: 51 to 1022) (p<0.0001). Surveillance ctDNA had a PPV and NPV of 88% and 98%, respectively, and identified recurrence a median (range) of 3.5 months (0 to 200) before evidence of clinical disease. Interpretation Surveillance ctDNA identifies patients at risk of recurrence before clinical evidence of disease in most patients and results in lower disease burden at relapse. Interim ctDNA is a promising biomarker to identify patients at high risk of treatment failure.

BackgroundFirst-generation molecular profiles for human breast cancers have enabled the identification of features that can predict therapeutic response; however, little is known about how the various data types can best be combined to yield optimal predictors. Collections of breast cancer cell lines mirror many aspects of breast cancer molecular pathobiology, and measurements of their omic and biological therapeutic responses are well-suited for development of strategies to identify the most predictive molecular feature sets.ResultsWe used least squares-support vector machines and random forest algorithms to identify molecular features associated with responses of a collection of 70 breast cancer cell lines to 90 experimental or approved therapeutic agents. The datasets analyzed included measurements of copy number aberrations, mutations, gene and isoform expression, promoter methylation and protein expression. Transcriptional subtype contributed strongly to response predictors for 25% of compounds, and adding other molecular data types improved prediction for 65%. No single molecular dataset consistently out-performed the others, suggesting that therapeutic response is mediated at multiple levels in the genome. Response predictors were developed and applied to TCGA data, and were found to be present in subsets of those patient samples.ConclusionsThese results suggest that matching patients to treatments based on transcriptional subtype will improve response rates, and inclusion of additional features from other profiling data types may provide additional benefit. Further, we suggest a systems biology strategy for guiding clinical trials so that patient cohorts most likely to respond to new therapies may be more efficiently identified.