Increasing knowledge about signal transduction pathways as drivers of cancer growth has elicited the development of “targeted drugs,” which inhibit aberrant signaling pathways. They require a companion diagnostic test that identifies the tumor-driving pathway; however, currently available tests like estrogen receptor (ER) protein expression for hormonal treatment of breast cancer do not reliably predict therapy response, at least in part because they do not adequately assess functional pathway activity. We describe a novel approach to predict signaling pathway activity based on knowledge-based Bayesian computational models, which interpret quantitative transcriptome data as the functional output of an active signaling pathway, by using expression levels of transcriptional target genes. Following calibration on only a small number of cell lines or cohorts of patient data, they provide a reliable assessment of signaling pathway activity in tumors of different tissue origin. As proof of principle, models for the canonical Wnt and ER pathways are presented, including initial clinical validation on independent datasets from various cancer types. Cancer Res; 74(11); 2936–45. ©2014 AACR.

Signal transduction pathways are important in physiology and pathophysiology. Targeted drugs aim at modifying pathogenic pathway activity, e.g., in cancer. Optimal treatment choice requires assays to measure pathway activity in individual patient tissue or cell samples. We developed a method enabling quantitative measurement of functional pathway activity based on Bayesian computational model inference of pathway activity from measurements of mRNA levels of target genes of the pathway-associated transcription factor. Oestrogen receptor, Wnt, and PI3K-FOXO pathway assays have been described previously. Here, we report model development for androgen receptor, Hedgehog, TGFβ, and NFκB pathway assays, biological validation on multiple cell types, and analysis of data from published clinical studies (multiple sclerosis, amyotrophic lateral sclerosis, contact dermatitis, Ewing sarcoma, lymphoma, medulloblastoma, ependymoma, skin and prostate cancer). Multiple pathway analysis of clinical prostate cancer (PCa) studies showed increased AR activity in hyperplasia and primary PCa but variable AR activity in castrate resistant (CR) PCa, loss of TGFβ activity in PCa, increased Wnt activity in TMPRSS2:ERG fusion protein-positive PCa, active PI3K pathway in advanced PCa, and active PI3K and NFκB as potential hormonal resistance pathways. Potential value for future clinical practice includes disease subtyping and prediction and targeted therapy response prediction and monitoring.

The phosphatidylinositol 3-kinase (PI3K) pathway is commonly activated in cancer. Tumors are potentially sensitive to PI3K pathway inhibitors, but reliable diagnostic tests that assess functional PI3K activity are lacking. Because PI3K pathway activity negatively regulates forkhead box-O (FOXO) transcription factor activity, FOXO target gene expression is inversely correlated with PI3K activity. A knowledge-based Bayesian computational model was developed to infer PI3K activity in cancer tissue samples from FOXO target gene mRNA levels and validated in cancer cell lines treated with PI3K inhibitors. However, applied to patient tissue samples, FOXO was often active in cancer types with expected active PI3K. SOD2 was differentially expressed between FOXO-active healthy and cancer tissue samples, indicating that cancer-associated cellular oxidative stress alternatively activated FOXO. To enable correct interpretation of active FOXO in cancer tissue, threshold levels for normal SOD2 expression in healthy tissue were defined above which FOXO activity is oxidative stress induced and below which PI3K regulated. In slow-growing luminal A breast cancer and low Gleason score prostate cancer, FOXO was active in a PI3K-regulated manner, indicating inactive PI3K. In aggressive luminal B, HER2, and basal breast cancer, FOXO was increasingly inactive or actively induced by oxidative stress, indicating PI3K activity. We provide a decision tree that facilitates functional PI3K pathway activity assessment in tissue samples from patients with cancer for therapy response prediction and prognosis.

Background: The Notch signal transduction pathway is pivotal for various physiological processes, including immune responses, and has been implicated in the pathogenesis of many diseases. The effectiveness of various targeted Notch pathway inhibitors may vary due to variabilities in Notch pathway activity among individual patients. The quantitative measurement of Notch pathway activity is therefore essential to identify patients who could benefit from targeted treatment. Methods: We here describe a new assay that infers a quantitative Notch pathway activity score from the mRNA levels of generally conserved direct NOTCH target genes. Following the calibration and biological validation of our Notch pathway activity model over a wide spectrum of human cancer types, we assessed Notch pathway activity in a cohort of T-ALL patient samples and related it to biological and clinical parameters, including outcome. Results: We developed an assay using 18 select direct target genes and high-grade serous ovarian cancer for calibration. For validation, seven independent human datasets (mostly cancer series) were used to quantify Notch activity in agreement with expectations. For T-ALL, the median Notch pathway activity was highest for samples with strong NOTCH1-activating mutations, and T-ALL patients of the TLX subtype generally had the highest levels of Notch pathway activity. We observed a significant relationship between ICN1 levels and the absence/presence of NOTCH1-activating mutations with Notch pathway activity scores. Patients with the lowest Notch activity scores had the shortest event-free survival compared to other patients. Conclusions: High Notch pathway activity was not limited to T-ALL samples harboring strong NOTCH1 mutations, including juxtamembrane domain mutations or hetero-dimerization combined with PEST-domain or FBXW7 mutations, indicating that additional mechanisms may activate Notch signaling. The measured Notch pathway activity was related to intracellular NOTCH levels, indicating that the pathway activity score more accurately reflects Notch pathway activity than when it is predicted on the basis of NOTCH1 mutations. Importantly, patients with low Notch pathway activity had a significantly shorter event-free survival compared to patients showing higher activity.

Estrogen receptor positive (ER+) breast cancer patients are eligible for hormonal treatment, but only around half respond. A test with higher specificity for prediction of endocrine therapy response is needed to avoid hormonal overtreatment and to enable selection of alternative treatments. A novel testing method was reported before that enables measurement of functional signal transduction pathway activity in individual cancer tissue samples, using mRNA levels of target genes of the respective pathway-specific transcription factor. Using this method, 130 primary breast cancer samples were analyzed from non-metastatic ER+ patients, treated with surgery without adjuvant hormonal therapy, who subsequently developed metastatic disease that was treated with first-line tamoxifen. Quantitative activity levels were measured of androgen and estrogen receptor (AR and ER), PI3K-FOXO, Hedgehog (HH), NFκB, TGFβ, and Wnt pathways. Based on samples with known pathway activity, thresholds were set to distinguish low from high activity. Subsequently, pathway activity levels were correlated with the tamoxifen treatment response and progression-free survival. High ER pathway activity was measured in 41% of the primary tumors and was associated with longer time to progression (PFS) of metastases during first-line tamoxifen treatment. In contrast, high PI3K, HH, and androgen receptor pathway activity was associated with shorter PFS, and high PI3K and TGFβ pathway activity with worse treatment response. Potential clinical utility of assessment of ER pathway activity lies in predicting response to hormonal therapy, while activity of PI3K, HH, TGFβ, and AR pathways may indicate failure to respond, but also opens new avenues for alternative or complementary targeted treatments.

Background Immunohistochemical (IHC) loss of expression of the estrogen (ER) and progesterone receptor (PR) from endometrial carcinomas is associated with high tumor grade, and with recurrent disease. However, expression of these receptors is not always indicative of functional pathway activity. A novel approach to predict signaling pathway activity, based on Knowledge-based Bayesian computational models interprets quantitative transcriptome data as the functional output of an active signaling pathway by using expression levels of transcriptional target genes. The objectives of this study were to compare ER pathway activity to ER and PR IHC results with respect to endometrial carcinoma grade and disease recurrences. Materials and Methods Originally for Affymetrix, the pathway analysis was adapted for RT-qPCR enabling use on FFPE tissue. Calibration and threshold definition was performed using tissue with known pathway activity. Pathway analysis was performed on: (1) A public dataset (GSE56026) containing Affymetrix expression microarray data from 62 patients with grade 1 (n=23),2 (n=17) and 3 (n=11) endometrioid and serous (n=12) carcinomas, and (2) formalin fixed, paraffin embedded (FFPE) tissue from endometrioid (n=70) and serous carcinomas (n=15), treated at the Radboud university medical center, which were immunohistochemically stained for ER and PR, and analyzed for pathway activity using qPCR. Results (1) Analysis of the public dataset showed an inverse relation between ER pathway activity score and cancer grade, with the lowest ER activity in serous carcinomas. The Mann-Whitney U test showed significant differences between grade 1 and 2 (p=0.015), grade 1 and 3 (p=0.004), and grade 1 and serous carcinomas (p<0.0001). (2) These results were confirmed in the FFPE tissue. The ER pathway was active in 30% (n=18), and inactive in 70% (n=41) of the samples. In advanced stage endometrioid and all serous carcinomas, ER pathway was active in 8% (n=2) and inactive in 92% (n=24) of the samples (Fisher p=0.03). Endometrial carcinoma related death (ECD) rate was 20% (n=17). While individually, ER pathway activity, ER and PR IHC were all significantly correlated with ECD, (logistic regression odds ratio, OR = 0.53, 0.12, 0.15, and p=0.0004, 0.0005, 0.002, respectively). In a multivariate analysis only ER pathway activity and disease stage remained significantly correlated with ECD (OR=0.52, p=0.03). Conclusion In two independent patient cohorts, the ER pathway model analysis, performed on data from Affymetrix microarrays and qPCR measurements showed an inverse relationship between the pathway activity and increasing tumor grade. While ER activity and ER/PR staining were related, only ER activity and cancer stage remained significant in a multivariate analysis with respect to immunohistochemistry alone when trying to predict recurrent disease. Citation Format: Louis J. van der Putten, Anne van Brussel, Willem Jan van Weelden, Márcia A. de Inda, Leon F. Massuger, Henk van Ooijen, Anja van de Stolpe, Johanna M. Pijnenborg. Estrogen receptor pathway activity in endometrial carcinomas and its relation to tumor grade and recurrence [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 2656.