Marcia Alves de Inda

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.

Abstract 3938: Predicting first line tamoxifen response of recurrent ER+ breast cancer patients based on transcriptional activity of signaling pathways

Proceedings: AACR 107th Annual Meeting 2016; April 16-20, 2016; New Orleans, LA Introduction Generally, estrogen receptor positive (ER+) breast cancer patients are eligible for hormonal treatment, but roughly half of them respond, and identifying those that do remains a challenge. Predicting hormonal therapy response with higher specificity (without losing sensitivity) is of clinical value, to avoid overtreatment and enable consideration of alternative, more effective (targeted) drugs. We developed Bayesian computational model-based mRNA tests [1] to analyze oncogenic signal transduction pathway activity in cancer tissue samples, for selection of personalized therapy. Here, we show that such a test for the ER pathway can predict hormonal therapy response independent of traditional predictive factors. Methods Gene expression levels (Affymetrix HG-U133+PM microarrays) were used to determine transcriptional activity of the ER, AR, PI3K, Wnt, Hedgehog (HH), TGFβ and NFκB pathways, as described earlier [1], in 132 ER+ primary breast tumor samples of patients who did not receive adjuvant hormonal therapy and subsequently relapsed and were treated with first line tamoxifen treatment. Univariate and multivariate Cox proportional hazards regression was used to associate the pathways’ activities with progression free survival (PFS) on tamoxifen treatment, together with traditional response prediction factors. Results In 102 (77%) of the 132 samples, at least one of the seven pathways was found active. ER pathway activity was observed in 36 samples (27%). Strikingly, 76 samples (58%) had an active NFκB pathway. In 37 samples (28%), two or more pathways were active, predominantly ER with NFκB. The probability of ER pathway activity was associated with a longer PFS, with a hazard ratio of 0.41 in a univariate analysis (p = 0.0039), while activity of the TGFβ pathway was associated with a shorter PFS (HR = 2.2, p = 0.044). In a multivariate analysis using the seven pathways, ER pathway activity remained significant (HR = 0.44, p = 0.011), while TGFβ did not (HR = 1.67, p = 0.24). A Kaplan-Meier analysis as a function of ER activity gave a log-rank p-value of 0.002. A multivariate analysis for PFS including ER pathway activity and the traditional predictive factors disease free interval ( 2yr), site of relapse, age, menopausal status, ESR1 and PGR mRNA expression and Her2 status, showed that ER activity was independently associated with a favorable PFS (HR = 0.43, p = 0.047). Conclusion In ER+ breast cancer patients who did not receive adjuvant hormonal therapy, transcriptional ER pathway activity as measured by our computational ER pathway mRNA test was associated with a favorable outcome upon first line tamoxifen treatment. The ER pathway test can hence be used as an independent predictor of hormonal therapy response in ER+ breast cancer patients with recurrent disease. [1] Verhaegh W et al. Cancer Res. 2014;74:2936-45. Citation Format: Anieta M. Sieuwerts, Marcel Smid, Marcia A. Inda, John A. Foekens, Anja van de Stolpe, John W.M. Martens, Wim F.J. Verhaegh. Predicting first line tamoxifen response of recurrent ER+ breast cancer patients based on transcriptional activity of signaling pathways. [abstract]. In: Proceedings of the 107th Annual Meeting of the American Association for Cancer Research; 2016 Apr 16-20; New Orleans, LA. Philadelphia (PA): AACR; Cancer Res 2016;76(14 Suppl):Abstract nr 3938.

Abstract 5291: Prognosis within different breast cancer subtypes using functional activity of signaling pathways

Introduction Gene signatures to assess prognosis of breast cancer patients are generally restricted to ER-positive/luminal breast cancers, and provide limited insight into the reason of a good or poor prognosis. Based on our computational models that determine functional activity of oncogenic signaling pathways [1], we are able to assess prognosis across all breast cancer subtypes, and explain the results in terms of tumor driving pathway. Methods Previously, we developed models to determine functional activity [1] of the ER, Wnt, Hedgehog (HH), AR, PI3K and TGFβ pathways, based on gene expression levels of the respective pathway9s target genes. Here, we report results of applying these models on 1294 breast cancer samples from public data sets GSE6532, GSE9195, GSE20685, GSE21653 and E-MTAB-365 (1169 with survival data), in relation to breast cancer subtype. The pathways’ individual risk association was assessed by univariate Cox proportional hazards regression. Next, a multi-pathway score (MPS) was derived using Cox regression coefficients on 164 training samples, and its prognostic value was tested on the remaining 1005 samples. Hazard ratios on different test subsets were calculated after scaling MPS to yield a unit length 95%-confidence interval, to enable fair comparison. Results In 749 (58%) of the 1294 breast cancer samples, at least one of the six pathways was found active, and in 1026 (79%) at least one moderately active. Of the 749, 167 showed two or more active pathways. Different distributions of active pathways were observed across different subtypes. For instance, ER was active in most luminal A samples, less often in luminal B samples, and not in HER2 and basal samples. Conversely, Wnt, HH and TGFβ were more often active in HER2 and basal samples. PI3K was mostly active in luminal B and HER2 samples. ER pathway activity was associated with a better prognosis (HR = 0.42, p = 9.8e-7), while Wnt, HH, PI3K and TGFβ were associated with worse prognosis (HR = 1.46 - 3.56, p = 4e-10 - 7.1e-2); AR was not associated with prognosis and left out of the MPS. On the 1005 test samples, MPS was highly associated with prognosis (HR = 4.90, p = 7.3e-15). Not only can it distinguish good from poor prognosis cases among luminal cancers (HR = 4.11, p = 2.1e-7), but also within the luminal A and B groups (HR = 5.15 & 2.43, p = 4.7e-5 & 1.3e-2, respectively). Furthermore, MPS can identify HER2 cases with a very poor prognosis (HR = 4.81, p = 3.2e-5), and basal cases with a fairly good prognosis (HR = 3.40, p = 3.7e-3). Conclusion We have demonstrated that we can assess prognosis of breast cancer patients based on functional activity of oncogenic signaling pathways. A combined multi-pathway score (MPS) clearly distinguishes good from poor prognosis cases, even within each breast cancer subtype. Furthermore, the underlying pathway activities can give relevant information for therapy selection. [1] Verhaegh W et al. Cancer Res. 2014;74:2936-45. Citation Format: Henk van Ooijen, Marcia A. Inda, Ralf Hoffmann, Paul van de Wiel, Anja van de Stolpe, Wim Verhaegh. Prognosis within different breast cancer subtypes using functional activity of signaling pathways. [abstract]. In: Proceedings of the 106th Annual Meeting of the American Association for Cancer Research; 2015 Apr 18-22; Philadelphia, PA. Philadelphia (PA): AACR; Cancer Res 2015;75(15 Suppl):Abstract nr 5291. doi:10.1158/1538-7445.AM2015-5291

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

Proceedings: AACR 104th Annual Meeting 2013; Apr 6-10, 2013; Washington, DC 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][1]). 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][2]. Next, we tested the second Wnt model on other cancer types. On 25 breast cancer cell lines from [GSE12777][3] with known Wnt status, the model correctly identified the two samples with an active pathway. On two patient data sets ([GSE12276][4], n=204; [GSE21653][5], 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][6], [GSE6764][7]) and medulloblastoma sets ([GSE10327][8]) 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][9]). Applied on breast cancer cell line data from [GSE21618][10], increased incidence of ER pathway activity was found in tamoxifen-sensitive cell lines compared to resistant ones. On breast cancer patient data ([GSE12276][4], [GSE9195][11], [GSE6532][12]) 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][13], [GSE36133][14]), as did the Hedgehog model on medulloblastoma samples ([GSE10327][8]). 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 [1]: /lookup/external-ref?link_type=NCBIGEO&access_num=GSE8671&atom=%2Fcanres%2F73%2F8_Supplement%2F59.atom [2]: /lookup/external-ref?link_type=NCBIGEO&access_num=GSE20916&atom=%2Fcanres%2F73%2F8_Supplement%2F59.atom [3]: /lookup/external-ref?link_type=NCBIGEO&access_num=GSE12777&atom=%2Fcanres%2F73%2F8_Supplement%2F59.atom [4]: /lookup/external-ref?link_type=NCBIGEO&access_num=GSE12276&atom=%2Fcanres%2F73%2F8_Supplement%2F59.atom [5]: /lookup/external-ref?link_type=NCBIGEO&access_num=GSE21653&atom=%2Fcanres%2F73%2F8_Supplement%2F59.atom [6]: /lookup/external-ref?link_type=NCBIGEO&access_num=GSE9843&atom=%2Fcanres%2F73%2F8_Supplement%2F59.atom [7]: /lookup/external-ref?link_type=NCBIGEO&access_num=GSE6764&atom=%2Fcanres%2F73%2F8_Supplement%2F59.atom [8]: /lookup/external-ref?link_type=NCBIGEO&access_num=GSE10327&atom=%2Fcanres%2F73%2F8_Supplement%2F59.atom [9]: /lookup/external-ref?link_type=NCBIGEO&access_num=GSE8697&atom=%2Fcanres%2F73%2F8_Supplement%2F59.atom [10]: /lookup/external-ref?link_type=NCBIGEO&access_num=GSE21618&atom=%2Fcanres%2F73%2F8_Supplement%2F59.atom [11]: /lookup/external-ref?link_type=NCBIGEO&access_num=GSE9195&atom=%2Fcanres%2F73%2F8_Supplement%2F59.atom [12]: /lookup/external-ref?link_type=NCBIGEO&access_num=GSE6532&atom=%2Fcanres%2F73%2F8_Supplement%2F59.atom [13]: /lookup/external-ref?link_type=NCBIGEO&access_num=GSE34211&atom=%2Fcanres%2F73%2F8_Supplement%2F59.atom [14]: /lookup/external-ref?link_type=NCBIGEO&access_num=GSE36133&atom=%2Fcanres%2F73%2F8_Supplement%2F59.atom