Abstract P5-02-08: Androgen receptor pathway activity and the ratio between androgen and estrogen receptor pathway activity in breast cancer subtypes

Background: Androgen receptor (AR) immunohistochemistry staining in breast cancer has revealed frequent AR expression in all breast cancer subtypes. Activity of the AR signaling pathway can be either tumor suppressive or tumor promoting, depending on tumor context (Pharmacol Ther., In press, https://doi.org/10.1016/j.pharmthera.2019.05.005). For this reason, in an individual patient, AR phenotypic activity needs to be defined in order to choose the proper AR targeted therapy. The AR/ER protein ratio has been suggested as a biomarker to define this AR function and enable the appropriate therapy choice, i.e. androgen therapy in case of a low AR/ER protein expression ratio, and anti-androgen therapy in case of a high ratio. However, AR and ER expression are not necessarily associated with an active signaling pathway. Therefore we investigated the ratio between functional AR and ER pathway activities as this may have an advantage in guiding therapy choice. Methods: AR and ER pathway activity and AR/ER pathway activity ratio were determined on public Affymetrix HG-U133 Plus 2.0 gene expression microarray data of clinical breast cancer studies (available in the GEO database: GSE12276, GSE21653, GSE20685, GSE42568, GSE6532, GSE9195, GSE45827, GSE7307, GSE10780, GSE17907, GSE21422, GSE5764, GSE31192, GSE54002 and Array Express: E-MTAB-365; total n=2090). For this, we used previously described tests that enable quantification of functional pathway activity, based on Bayesian computational model inference of pathway activity from measurements of mRNA levels of target genes of the ER and AR transcription factor (Cancer Res., 2014, vol. 74, no. 11, pp. 2936-2945; Sci Rep., 2019, vol. 9, no. 1, p. 1603). Intrinsic subtyping of all samples has been done using the same methodology as described elsewhere (Am J Pathol, 2018, vol. 188, no. 9, pp 1956-1972).Results: AR pathway activity was increased in all breast cancer subtypes compared to normal breast tissue (p<0.0001); highest AR activity scores were observed in the HER2-enriched subtype. As shown before, ER activity scores were highest in luminal A and B subtypes. The AR/ER pathway activity ratio was low in luminal A and B, and comparable with normal tissue, but high in HER2 and basal breast cancer subtypes (p<0.0001). Discussion: Results are compatible with currently available evidence and show that the AR pathway can be activated in all breast cancer subtypes. We hypothesize that its function depends on the level of co-existent ER pathway activity, i.e. a tumor suppressive function in ER pathway active breast cancer, and a tumor promoting function in ER pathway inactive breast cancer. Upon future clinical conformation, the AR/ER pathway activity ratio as determined on an individual patient tissue sample may be an improved biomarker to guide the choice of (complementary) AR pathway targeted therapy in breast cancer, i.e. androgen receptor pathway inhibitors in case of a high ratio and potential use of androgen receptor modulators in case of a low ratio.

Citation Format: Anja van de Stolpe, Yvonne Wesseling-Rozendaal, Marcia Alves de Inda, Henk van Ooijen, Wim Verhaegh. Androgen receptor pathway activity and the ratio between androgen and estrogen receptor pathway activity in breast cancer subtypes [abstract]. In: Proceedings of the 2019 San Antonio Breast Cancer Symposium; 2019 Dec 10-14; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2020;80(4 Suppl):Abstract nr P5-02-08.

Abstract A131: Fulvestrant resistance in an MCF-7 model for breast cancer is associated with complete loss of ER pathway activity and gain of MAPK-AP1 pathway activity

Background: 10-15 signal transduction pathways govern major cellular processes, e.g. cell division, differentiation and migration, both in physiology and pathophysiology. They are frequently abnormally active in cancer and can drive cancer growth and metastasis. Resistance to targeted drugs directed towards specific signaling pathways like the ER pathway, can be mediated by induction of activity of other signaling pathways. The past decade we developed a new method to quantitatively measure functional activity status of signal transduction pathways in individual cell and tissue samples, based on Bayesian computational model inference of pathway activity from measurements of mRNA levels of target genes of the pathway-associated transcription factor1,2,3. Assays have been developed for androgen (AR) and estrogen receptor (ER), Hedgehog (HH), Wnt, TGFb, Notch, NFkB, PI3K, JAK-STAT 1/2 and 3, and MAPK-AP1 pathways. They can be performed simultaneously on a single cell or tissue sample using Affymetrix HG-U133 Plus 2.0 microarray or qPCR, and provide quantitative pathway activity scores expressed on a log2odds scale. Method. Pathway analysis using these assays was performed on public Affymetrix expression microarray data (GSE74391,4) from ER-positive MCF-7 cells that were either sensitive or resistant to fulvestrant (n=26). Results. Resistance to fulvestrant was associated with complete loss of functional ER pathway activity (difference 16 log2odds score, p=0.00029) and gain of MAPK-AP1 pathway activity (difference 5.8 log2odds, p=0.00029) in ER positive MCF-7 cells. In the resistant MCF-7 cells, PI3K and MAPK-AP1 pathway activity were negatively correlated (Pearson 0.652, p=0.002), suggesting mutually exclusive pathway activation. Conclusion. Our pathway assays enabled identification of the previously unidentified mechanism of resistance to fulvestrant in a cell culture model system for breast cancer, demonstrating that: (1) ER positivity is a prerequisite, but not sufficient, for ER pathway activity, in agreement with our earlier clinical studies1,5,6;; (2) MAPK pathway activity may confer resistance to hormonal therapy; (3) in the absence of MAPK-AP1 pathway activity, the PI3K pathway may be an alternative resistance pathway; (3) signalling pathway assays are well suited to (quantitatively) investigate activity of signal transduction pathways which may confer resistance to a (targeted) drug treatment. References 1. Verhaegh, W. et al. SCancer Res. 74, 2936–2945 (2014). 2. van Ooijen, H. et al. Am. J. Pathol. 188, 1956–1972 (2018). 3. Stolpe, A. van de, et al. Sci Rep 9, 1603 (2019). 4. Alves, C. L. et al. Clin. Cancer Res. 22, 5514–5526 (2016). 5. Blok, E. J. et al. Cancer Res 76, P3-07-65-P3-07–65 (2016). 6. Yang, S.-R. et al. Cancer Res 79, P5-11-06-P5-11–06 (2019).

Citation Format: Yvonne Wesseling-Rozendaal, Anja van de Stolpe, Laurent Holtzer, Marcia Alves de Inda, Paul van de Wiel. Fulvestrant resistance in an MCF-7 model for breast cancer is associated with complete loss of ER pathway activity and gain of MAPK-AP1 pathway activity [abstract]. In: Proceedings of the AACR-NCI-EORTC International Conference on Molecular Targets and Cancer Therapeutics; 2019 Oct 26-30; Boston, MA. Philadelphia (PA): AACR; Mol Cancer Ther 2019;18(12 Suppl):Abstract nr A131. doi:10.1158/1535-7163.TARG-19-A131

Abstract 2645: Quantitative measurement of multiple signal transduction pathway activities in cell and tissue culture, including cancer, fibroblast, and immune cell types

To improve pathophysiology research, biomarker discovery and drug development, cell culture models should adequately mimic human (patho)physiology and provide reproducible results. This requires comparison between cultured cells/tissue and actual histopathology in the patient, as well as standardization of culture experiments to ensure experimental reproducibility, preferably in a quantitative manner1. 10-15 signal transduction pathways govern major cellular processes, e.g. cell division, differentiation and migration. The past decade we developed tests to quantitatively measure functional activity of signal transduction pathways in individual cell/tissue samples, based on Bayesian computational model inference of pathway activity from measurements of mRNA levels of target genes of the transcription factor associated with the respective signalling pathway. Tests provide quantitative pathway activity scores and are intended to be used for diagnostics and life sciences research2-4.

Method: Tests have been developed for androgen (AR) and estrogen receptor (ER), Hedgehog (HH), Wnt, TGFβ, Notch, NFκB, PI3K, JAK-STAT 1/2 and 3, and MAPK pathways. After calibration and freezing of the models, extensive biological test validation was performed on healthy/diseased cell and tissue types, including multiple cancer, fibroblast and immune cell types. Using Affymetrix expression microarray data (GEO database) >900 cell lines of most cell and cancer types were analyzed, as well as primary cultures of most immune cell types. LnCaP (prostate), MCF7, BT474 (breast), HCC827 (lung) and A2780 (ovarian) cancer cell lines were compared across laboratories.

Results: Typical expected single or combined pathway activities were confirmed, e.g. ER activity in breast, AR activity in prostate, and Wnt activity in colon cancer; HH activity in soft tissue tumor, NFκB activity in lymphoma, frequently combined with PI3K and/or MAPK and/or JAK-STAT growth factor pathways. Quantitative pathway activities were reproducible within studies, but highly variable between labs, and dependent on culture conditions.

Conclusion: Our pathway tests measure signaling pathway activity in many cell and tissue types, and can be used as quantitative readout for cell/tissue culture. Applications are: standardization of cell/tissue culture to ensure reproducibility; comparison between culture-based disease model and patient histopathology; quantitative assessment of drug efficacy on disease models; assessment of toxicity on healthy cell/tissue models. A number of tests have been adapted to qPCR, enabling use on FFPE tissue and small samples. 1 Ben-David U, et al. Nature, 2018;560(7718):325; 2 Verhaegh W, et al. Cancer Res 2014;74(11):2936-45; 3 Verhaegh W, Stolpe A van de. Oncotarget, 2014:5(14):5196-7; 4 Ooijen H. van, et al. Am J Pathol 2018;188(9):1956-1972

Citation Format: Anja Van De Stolpe, Marcia Alves de Inda, Eveline den Biezen-Timmermans, Laurent Holtzer, Henk van Ooijen, Wim Verhaegh. Quantitative measurement of multiple signal transduction pathway activities in cell and tissue culture, including cancer, fibroblast, and immune cell types [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 2645.

Heterogeneity in signaling pathway activity within primary breast cancer and between primary and metastases

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Background: Treatment with targeted drugs aims to block tumor driving signaling pathway(s). Drug choice is often based on a single preoperative primary breast cancer biopsy. It is important that biopsied cancer tissue is representative for the primary tumor (PT) or metastases to treat. Little is known about pathway heterogeneity within the PT, and between PT and metastatic tumors. A novel analysis method was developed to identify and quantify activity of signal transduction pathways in cancer tissue, based on Bayesian models that infer a pathway activity score from transcription factor target gene mRNA levels (Cancer Res 2014;74:2936-45). Methods: Pathway analysis, originally developed for AffymetrixU133Plus2.0, was adapted to RT-qPCR for use on formalin fixed paraffin embedded tissue. Estrogen (ER) and androgen (AR) receptor, PI3K, Hedgehog (HH), TGFβ and Wnt pathway activities were analyzed. Samples were from multiple locations (“quadrants”) in 15 luminal A, 9 luminal B, and 8 ER-negative primary breast cancers; from subdivided quadrant samples (4 “subquadrants”) of respectively 9, 4, and 4 PTs; and from 13 distant and 24 lymph node (LN) metastases of respectively 9 and 7 matched luminal PTs. Analysis of pathway activity score (PAS) variance was performed with linear mixed models with subgroup-dependent standard deviations. Results: In primary breast cancer intra-tumor PAS variance was not larger at macroscale (“quadrant”) than at microscale (“subquadrant”). For ER, AR, HH, and Wnt pathways, PAS variation was higher between distant metastases and PT than within the PT (p < 0.0002). For HH and Wnt pathways, PAS variation was higher between LN metastases than within the PT (p < 0.002). Correlation between primary and metastatic pathway activities ranged from -0.34 for ER to 0.47/0.50 for TGFβ/HH pathways. Conclusions: A single location tissue sample was representative for the whole primary tumor with respect to signaling pathway activity, suggesting one biopsy as generally sufficient for (neo)adjuvant therapy choice. Pathway activities varied between primary cancer and metastases, indicating the necessity of metastatic sample analysis (biopsies or liquid biopsy) to improve therapy choice.

Validation of Cyclic Adenosine Monophosphate Phosphodiesterase-4D7 for its Independent Contribution to Risk Stratification in a Prostate Cancer Patient Cohort with Longitudinal Biological Outcomes

BACKGROUND

The clinical metrics used to date to assess the progression risk of newly diagnosed prostate cancer patients only partly represent the true biological aggressiveness of the underlying disease.

OBJECTIVE

Validation of the prognostic biomarker phosphodiesterase-4D7 (PDE4D7) in predicting longitudinal biological outcomes in a historical surgery cohort to improve postsurgical risk stratification.

DESIGN, PATIENTS, AND METHODS

RNA was extracted from biopsy punches of resected tumors from 550 patients. PDE4D7 was quantified using one-step quantitative reverse transcription-polymerase chain reaction. PDE4D7 scores were calculated by normalization of PDE4D7 to reference genes. Multivariate analyses were adjusted for clinical prognostic variables. Outcomes tested were: prostate-specific antigen relapse, start of salvage treatment, progression to metastases, overall mortality, and prostate cancer-specific mortality. The PDE4D7 score was combined with the clinical risk model Cancer of the Prostate Risk Assessment Postsurgical Score (CAPRA-S) using multivariate regression modeling; the combined score was tested in post-treatment progression free survival prediction.

OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS

Correlations with outcomes were analyzed using multivariate Cox regression and logistic regression statistics.

RESULTS AND LIMITATIONS

The PDE4D7 score was significantly associated with time-to-prostate specific antigen failure after prostatectomy (hazard ratio [HR]: 0.53, 95% confidence interval [CI]: 0.41-0.67 for each unit increase, p<0.0001). After adjustment for postsurgical prognostic variables the HR was 0.56 (95% CI: 0.43-0.73, p<0.0001). The PDE4D7 score remained significant after adjusting the multi-variate analysis for the CAPRA-S model categories (HR=0.54, 95% CI=0.42-0.69, p<0.0001). Combination of the PDE4D7 score with the CAPRA-S demonstrated a significant incremental value of 4-6% in 2-yr (p=0.004) or 5-yr (p=0.003) prediction of progression free survival after surgery. The combined model of PDE4D7 and CAPRA-S improves patient selection with very high risk of fast disease relapse after primary intervention.

CONCLUSIONS

The PDE4D7 score has the potential to provide independent risk information and to restratify patients with clinical intermediate- to high-risk characteristics to a very low-risk profile.

PATIENT SUMMARY

In this report, we studied the potential of a novel biomarker to predict outcomes of a cohort of prostate cancer patients who underwent surgery more than 10 yr ago. We found that a gene called phosphodiesterase-4D7 added extra information to the available clinical data. We conclude that the measurement of this gene in tumor tissue may contribute to more effective treatment decisions.

Validation of cAMP phosphodiesterase-4D7 (PDE4D7) for its independent contribution to risk stratification in a prostate cancer patient cohort with longitudinal biological outcomes

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Background: In this study we present the retrospective validation of the prognostic prostate cancer biomarker PDE4D7 in predicting longitudinal biological outcomes in a historical cohort of radical prostatectomy patients. Methods: Biopsy punches from 550 patients were collected from a representative tumor area of FFPE surgical resections. RNA was extracted and PDE4D7 quantified by one-step RT-qPCR. PDE4D7 scores were calculated by normalization of PDE4D7 to the averaged expression of four reference genes. The independent prognostic value of the PDE4D7 scores were evaluated using uni- and multivariate Cox proportional hazard regression. Multivariate analyses were adjusted for clinical prognostic variables. Post-surgical outcomes tested were: PSA relapse, start of salvage treatment, progression to metastases, overall and prostate cancer specific mortality. Logistic regression was used to create a combined prognostic model of PDE4D7 with clinical risk and tested in outcome prediction. Results: The PDE4D7 score was significantly associated with time to PSA failure after prostatectomy (HR 0.53; 95% CI 0.41-0.67 for each unit increase; p < 1.0E-04). After adjustment for pathology Gleason, pT stage, surgical margin status, and seminal vesicle invasion the HR was 0.55 (95% CI 0.43-0.72; p < 1.0E-04). Patients with a high PDE4D7 score that were clinically classified as intermediate to high risk of progression were re-classified into a group with an average progression risk less than the average cohort risk of clinically very low risk patients. The maximum benefit, compared to Gleason score, was observed in the clinically intermediate favorable risk group. Combining clinical risk with PDE4D7 scores improved the overall risk stratification. Conclusions: The PDE4D7 score has potential to provide independent risk information and, in particular, to re-stratify patients with clinical intermediate to high risk characteristics to a very low risk profile.

Abstract 59: Identifying tumor driving signaling pathways for companion diagnostics using computational pathway models

Introduction

Targeted drug treatment requires reliable companion diagnostics for therapy selection. Genomic and transcriptomic data can provide input for this, provided tools exist to convert this complex data into meaningful clinical information. We develop computational models of oncogenic pathways, to assess which one drives tumor growth in an individual patient and what is the causing (epi)genetic defect.

Computational pathway models

Based on a selection of experimentally validated direct target genes, we built initial models of the Wnt, ER, AR and Hedgehog pathways, covering their transcriptional program. We have modeled each pathway by a Bayesian network, which interprets the target genes’ mRNA levels (Affymetrix U133Plus2.0), and infers a probability that the respective pathway is active in a certain sample. Model parameters are based on literature insights and experimental data.

Results

A first Wnt model, calibrated on cell line data, validated perfectly on 32 normal colon samples and 32 colon adenomas from patients (GSE8671). A second Wnt model, calibrated on these 64 patient samples, correctly predicted no Wnt activity in all 44 normal colon samples, and Wnt activity in 97 of 101 colon cancer samples from GSE20916.

Next, we tested the second Wnt model on other cancer types. On 25 breast cancer cell lines from GSE12777 with known Wnt status, the model correctly identified the two samples with an active pathway. On two patient data sets (GSE12276, n=204; GSE21653, n=266) Wnt activity was predicted in a higher number of basal samples compared to other subtypes (p=0.021 and p=2.7e-5, respectively), in line with increasing evidence for Wnt activity in this subtype. Finally, tests on liver (GSE9843, GSE6764) and medulloblastoma sets (GSE10327) confirm the power of these models to predict Wnt pathway activity.

A first ER model was calibrated on estrogen-deprived and -stimulated MCF7 cell lines (GSE8697). Applied on breast cancer cell line data from GSE21618, increased incidence of ER pathway activity was found in tamoxifen-sensitive cell lines compared to resistant ones. On breast cancer patient data (GSE12276, GSE9195, GSE6532) the model showed no pathway activity in ER- samples, and an active ER pathway in 26-38% of the ER+ samples. Within the latter group, model-predicted ER activity correlated with improved survival. Clinical utility studies to correlate ER activity to hormone therapy response are in progress.

Finally, the AR model showed promising results on prostate cancer cell lines (GSE34211, GSE36133), as did the Hedgehog model on medulloblastoma samples (GSE10327).

Conclusion

Our computational pathway models predict functional activity of oncogenic pathways for an individual patient based on mRNA data, complementary to existing molecular and histopathology staining tests. Clinical utility for therapy response prediction is currently being validated with clinical partners.

Citation Format: Wim Verhaegh, Henk van Ooijen, Marcia Alves de Inda, Kalyan Dulla, Ralf Hoffmann, Dianne van Strijp, Pantelis Hatzis, Hans Clevers, Anja van de Stolpe. Identifying tumor driving signaling pathways for companion diagnostics using computational pathway models. [abstract]. In: Proceedings of the 104th Annual Meeting of the American Association for Cancer Research; 2013 Apr 6-10; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2013;73(8 Suppl):Abstract nr 59. doi:10.1158/1538-7445.AM2013-59