A comprehensive clinically informed map of dependencies in cancer cells and framework for target prioritization.

Genetic screens in cancer cell lines inform gene function and drug discovery. More comprehensive screen datasets with multi-omics data are needed to enhance opportunities to functionally map genetic vulnerabilities. Here, we construct a second-generation map of cancer dependencies by annotating 930 cancer cell lines with multi-omic data and analyze relationships between molecular markers and cancer dependencies derived from CRISPR-Cas9 screens. We identify dependency-associated gene expression markers beyond driver genes, and observe many gene addiction relationships driven by gain of function rather than synthetic lethal effects. By combining clinically informed dependency-marker associations with protein-protein interaction networks, we identify 370 anti-cancer priority targets for 27 cancer types, many of which have network-based evidence of a functional link with a marker in a cancer type. Mapping these targets to sequenced tumor cohorts identifies tractable targets in different cancer types. This target prioritization map enhances understanding of gene dependencies and identifies candidate anti-cancer targets for drug development.

TP53 mutations emerge with HDM2 inhibitor SAR405838 treatment in de-differentiated liposarcoma.

In tumours that harbour wild-type p53, p53 protein function is frequently disabled by the mouse double minute 2 protein (MDM2, or HDM2 in humans). Multiple HDM2 antagonists are currently in clinical development. Preclinical data indicate that TP53 mutations are a possible mechanism of acquired resistance to HDM2 inhibition; however, this resistance mechanism has not been reported in patients. Utilizing liquid biopsies, here we demonstrate that TP53 mutations appear in circulating cell-free DNA obtained from patients with de-differentiated liposarcoma being treated with an inhibitor of the HDM2-p53 interaction (SAR405838). TP53 mutation burden increases over time and correlates with change in tumour size, likely representing selection of TP53 mutant clones resistant to HDM2 inhibition. These results provide the first clinical demonstration of the emergence of TP53 mutations in response to an HDM2 antagonist and have significant implications for the clinical development of this class of molecules.

Relationship between quantitative GRB7 RNA expression and recurrence after adjuvant anthracycline chemotherapy in triple-negative breast cancer.

PURPOSE: To conduct an exploratory analysis of the relationship between gene expression and recurrence in patients with operable triple-negative breast cancer (TNBC) treated with adjuvant doxorubicin-containing chemotherapy. EXPERIMENTAL DESIGN: RNA was extracted from archived tumor samples derived from 246 patients with stage I-III TNBC treated with adjuvant doxorubicin-containing chemotherapy, and was analyzed by quantitative reverse transcriptase PCR for a panel of 374 genes. The relationship between gene expression and recurrence was evaluated using weighted Cox proportional hazards model score tests. RESULTS: Growth factor receptor bound protein 7 (GRB7) was the only gene for which higher expression was significantly associated with increased recurrence in TNBC (Korn's adjusted P value = 0.04). In a Cox proportional hazards model adjusted for clinicopathologic features, higher GRB7 expression was associated with an increased recurrence risk (HR = 2.31; P = 0.04 using the median as the split). The 5-year recurrence rates were 10.5% [95% confidence intervals (CI), 7.8-14.1] in the low and 20.4% (95% CI, 16.5-25.0) in the high GRB7 groups. External validation in other datasets indicated that GRB7 expression was not prognostic in two adjuvant trials including variable systemic therapy, but in two other trials showed that high GBR7 expression was associated with resistance to neoadjuvant doxorubicin and taxane therapy. CONCLUSIONS: GRB7 was associated with an increased risk of recurrence in TNBC, suggesting that GRB7 or GRB7-dependent pathways may serve as potential biomarkers for therapeutic targets. Therapeutic targeting of one or more factors identified which function as interaction nodes or effectors should also be considered.

Relationship between Topoisomerase 2A RNA Expression and Recurrence after Adjuvant Chemotherapy for Breast Cancer.

PURPOSE: To perform an exploratory analysis of the relationship between gene expression and recurrence in operable hormone receptor (HR)-positive, human epidermal growth factor receptor 2 (HER2)-normal breast cancer patients treated with adjuvant doxorubicin-containing chemotherapy. EXPERIMENTAL DESIGN: RNA was extracted from archived tumor samples derived from 378 patients with stage I to III HR-positive, HER2-normal breast cancer and analyzed by reverse transcription-PCR for a panel of 374 genes, including the 21-gene recurrence score (RS). Patients were randomized to receive adjuvant doxorubicin plus cyclophosphamide or docetaxel in trial E2197, with no difference in recurrence seen in the treatment arms. All available recurrent cases were selected plus a nonrecurrent cohort. Cox proportional hazard models were used to identify relationships between gene expression and recurrence. RESULTS: TOP2A expression exhibited the strongest association with increased recurrence risk (P = 0.01), and was significantly associated with recurrence (P = 0.008) in a multivariate analysis adjusted for clinicopathologic features. Elevated TOP2A expression above the median was associated with a 2.6-fold increase (95% confidence interval, 1.3-5.2; P = 0.008) in risk of recurrence if the RS was <18, and a 2.0-fold increase (95% confidence interval, 1.2-3.2, P = 0.003) if there was an intermediate RS of 18 to 30. CONCLUSIONS: In patients with HR-positive, HER2-normal breast cancer, a population known to have a low incidence of TOP2A gene alterations thought to be predictive of anthracycline benefit, there is a range of TOP2A RNA expression that is strongly associated with recurrence after adjuvant anthracyclines, which provides information complementary to RS, indicating that it merits further evaluation as a prognostic and predictive marker. (Clin Cancer Res 2009;15(24):7693-700).

Prognostic utility of the 21-gene assay in hormone receptor-positive operable breast cancer compared with classical clinicopathologic features.

PURPOSE: Adjuvant! is a standardized validated decision aid that projects outcomes in operable breast cancer based on classical clinicopathologic features and therapy. Genomic classifiers offer the potential to more accurately identify individuals who benefit from chemotherapy than clinicopathologic features. PATIENTS AND METHODS: A sample of 465 patients with hormone receptor (HR) -positive breast cancer with zero to three positive axillary nodes who did (n = 99) or did not have recurrence after chemohormonal therapy had tumor tissue evaluated using a 21-gene assay. Histologic grade and HR expression were evaluated locally and in a central laboratory. RESULTS: Recurrence Score (RS) was a highly significant predictor of recurrence, including node-negative and node-positive disease (P < .001 for both) and when adjusted for other clinical variables. RS also predicted recurrence more accurately than clinical variables when integrated by an algorithm modeled after Adjuvant! that was adjusted to 5-year outcomes. The 5-year recurrence rate was only 5% or less for the estimated 46% of patients who have a low RS (< 18). CONCLUSION: The 21-gene assay was a more accurate predictor of relapse than standard clinical features for individual patients with HR-positive operable breast cancer treated with chemohormonal therapy and provides information that is complementary to features typically used in anatomic staging, such as tumor size and lymph node involvement. The 21-gene assay may be used to select low-risk patients for abbreviated chemotherapy regimens similar to those used in our study or high-risk patients for more aggressive regimens or clinical trials evaluating novel treatments.

Estrogen- and progesterone-receptor status in ECOG 2197: comparison of immunohistochemistry by local and central laboratories and quantitative reverse transcription polymerase chain reaction by central laboratory.

PURPOSE: Central and local laboratory concordance for hormone receptor measurement is therapeutically important. This study compares estrogen receptor (ER) and progesterone receptor (PR) measured by local laboratory immunohistochemistry (IHC), central IHC, and central reverse-transcriptase polymerase chain reaction (RT-PCR) using a proprietary 21-gene assay. PATIENTS AND METHODS: A case-control sample of 776 breast cancer patients from Eastern Cooperative Oncology Group (ECOG) study E2197 was evaluated. Central IHC Allred score for ER and PR was obtained using tissue microarrays and 1D5 ER antibody and 636 PR antibody. Quantitative RT-PCR for ER and PR in whole sections was performed using the 21-gene assay. RESULTS: For ER, the concordance between local and central IHC was 90% (95% CI, 88% to 92%), between local IHC and central RT-PCR was 91% (95% CI, 89% to 93%), and between central IHC and central RT-PCR was 93% (95% CI, 91% to 95%). For PR, the concordance between local IHC and central IHC was 84% (95% CI, 82% to 87%), between local IHC and central RT-PCR was 88% (95% CI, 85% to 90%), and between central IHC and central RT-PCR was 90% (95% CI, 88% to 92%). Although concordance was high, IHC ER-negative cases that were RT-PCR positive were more common than IHC ER-positive cases that were RT-PCR negative. In ER-positive patients, ER expression by central IHC Allred score was marginally associated with recurrence (P = .091), and ER expression by central RT-PCR was significantly associated with recurrence (P = .014). However, recurrence score, which incorporates additional genes/pathways, was a highly significant predictor of recurrence (P < .0001). CONCLUSION: There is a high degree of concordance among local IHC, central IHC, and central RT-PCR by the proprietary gene assay for ER and PR status. Although ER expression is marginally associated with relapse in ER-positive patients treated with chemohormonal therapy, recurrence score is a highly significant predictor of recurrence.

Analyzing protein lists with large networks: edge-count probabilities in random graphs with given expected degrees.

We present an analytical framework to analyze lists of proteins with large undirected graphs representing their known functional relationships. We consider edge-count variables such as the number of interactions between a protein and a list, the size of a subgraph induced by a list, and the number of interactions bridging two lists. We derive approximate analytical expressions for the probability distributions of these variables in a model of a random graph with given expected degrees. Probabilities obtained with the analytical expressions are used to mine a protein interaction network for functional modules, characterize the connectedness of protein functional categories, and measure the strength of relations between modules.

Detection of activity centers in cellular pathways using transcript profiling.

We present a new computational method for identifying regulated pathway components in transcript profiling (TP) experiments by evaluating transcriptional activity in the context of known biological pathways. We construct a graph representing thousands of protein functional relationships by integrating knowledge from public databases and review articles. We use the notion of distance in a graph to define pathway neighborhoods. The pathways perturbed in an experiment are then identified as the subgraph induced by the genes, referred to as activity centers, having significant density of transcriptional activity in their functional neighborhoods. We illustrate the predictive power of this approach by performing and analyzing an experiment of TP53 overexpression in NCI-H125 cells. The detected activity centers are in agreement with the known TP53 activation effects and our independent experimental results. We also apply the method to a serum starvation experiment using HEY cells and investigate the predicted activity of the transcription factor MYC. Finally, we discuss interesting properties of the activity center approach and its possible applications beyond the comparison of two experiments.