Heterogeneity in Signaling Pathway Activity within Primary and between Primary and Metastatic Breast Cancer.

Targeted therapy aims to block tumor-driving signaling pathways and is generally based on analysis of one primary tumor (PT) biopsy. Tumor heterogeneity within PT and between PT and metastatic breast lesions may, however, impact the effect of a chosen therapy. Whereas studies are available that investigate genetic heterogeneity, we present results on phenotypic heterogeneity by analyzing the variation in the functional activity of signal transduction pathways, using an earlier developed platform to measure such activity from mRNA measurements of pathways' direct target genes. Statistical analysis comparing macro-scale variation in pathway activity on up to five spatially distributed PT tissue blocks (n = 35), to micro-scale variation in activity on four adjacent samples of a single PT tissue block (n = 17), showed that macro-scale variation was not larger than micro-scale variation, except possibly for the PI3K pathway. Simulations using a "checkerboard clone-size" model showed that multiple small clones could explain the higher micro-scale variation in activity found for the TGFß and Hedgehog pathways, and that intermediate/large clones could explain the possibly higher macro-scale variation of the PI3K pathway. While within PT, pathway activities presented a highly positive correlation, correlations weakened between PT and lymph node metastases (n = 9), becoming even worse for PT and distant metastases (n = 9), including a negative correlation for the ER pathway. While analysis of multiple sub-samples of a single biopsy may be sufficient to predict PT response to targeted therapies, metastatic breast cancer treatment prediction requires analysis of metastatic biopsies. Our findings on phenotypic intra-tumor heterogeneity are compatible with emerging ideas on a Big Bang type of cancer evolution in which macro-scale heterogeneity appears not dominant.

Prediction of clinical benefit from androgen deprivation therapy in salivary duct carcinoma patients.

Androgen deprivation therapy (ADT) is first-line palliative treatment in androgen receptor-positive (AR+) salivary duct carcinoma (SDC), and response rates are 17.6-50.0%. We investigated potential primary ADT resistance mechanisms for their predictive value of clinical benefit from ADT in a cohort of recurrent/metastatic SDC patients receiving palliative ADT (n = 30). We examined mRNA expression of androgen receptor (AR), AR splice variant-7, intratumoral androgen synthesis enzyme-encoding genes AKR1C3, CYP17A1, SRD5A1 and SRD5A2, AR protein expression, ERBB2 (HER2) gene amplification and DNA mutations in driver genes. Furthermore, functional AR pathway activity was determined using a previously reported Bayesian model which infers pathway activity from AR target gene expression levels. SRD5A1 expression levels and AR pathway activity scores were significantly higher in patients with clinical benefit from ADT compared to those without benefit. Survival analysis showed a trend toward a longer median progression-free survival for patients with high SRD5A1 expression levels and high AR pathway activity scores. The AR pathway activity analysis, and not SRD5A1 expression, also showed a trend toward better disease-free survival in an independent cohort of locally advanced SDC patients receiving adjuvant ADT (n = 14) after surgical tumor resection, and in most cases a neck dissection (13/14 patients) and postoperative radiotherapy (13/14 patients). In conclusion, we are the first to describe that AR pathway activity may predict clinical benefit from ADT in SDC patients, but validation in a prospective study is needed.

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.