Impact of classical and basal-like molecular subtypes on overall survival in resected pancreatic cancer in the SPACIOUS-2 multicentre study.

BACKGROUND: The recently identified classical and basal-like molecular subtypes of pancreatic cancer impact on overall survival (OS). However, the added value of routine subtyping in both clinical practice and randomized trials is still unclear, as most studies do not consider clinicopathological parameters. This study examined the clinical prognostic value of molecular subtyping in patients with resected pancreatic cancer. METHODS: Subtypes were determined on fresh-frozen resected pancreatic cancer samples from three Dutch centres using the Purity Independent Subtyping of Tumours classification. Patient, treatment, and histopathological variables were compared between subtypes. The prognostic value of subtyping in (simulated) pre- and postoperative settings was assessed using Kaplan-Meier and Cox regression analyses. RESULTS: Of 199 patients with resected pancreatic cancer, 164 (82.4 per cent) were classified as the classical and 35 (17.6 per cent) as the basal-like subtype. Patients with a basal-like subtype had worse OS (11 versus 16 months (HR 1.49, 95 per cent c.i. 1.03 to 2.15; P = 0.035)) than patients with a classical subtype. In multivariable Cox regression analysis, including only clinical variables, the basal-like subtype was a statistically significant predictor for poor OS (HR 1.61, 95 per cent c.i. 1.11 to 2.34; P = 0.013). When histopathological variables were added to this model, the prognostic value of subtyping decreased (HR 1.49, 95 per cent c.i. 1.01 to 2.19; P = 0.045). CONCLUSION: The basal-like subtype was associated with worse OS in patients with resected pancreatic cancer. Adding molecular classification to inform on tumor biology may be used in patient stratification.

Digoxin treatment reactivates in vivo radioactive iodide uptake and correlates with favorable clinical outcome in non-medullary thyroid cancer.

PURPOSE: Non-medullary thyroid cancer (NMTC) treatment is based on the ability of thyroid follicular cells to accumulate radioactive iodide (RAI). However, in a subset of NMTC patients tumor dedifferentiation occurs, leading to RAI resistance. Digoxin has been demonstrated to restore iodide uptake capacity in vitro in poorly differentiated and anaplastic NMTC cells, termed redifferentiation. The aim of the present study was to investigate the in vivo effects of digoxin in TPO-Cre/LSL-BrafV600E mice and digoxin-treated NMTC patients. METHODS: Mice with thyroid cancer were subjected to 3D ultrasound for monitoring tumor growth and 124I PET/CT for measurement of intratumoral iodide uptake. Post-mortem analyses on tumor tissues comprised gene expression profiling and measurement of intratumoral autophagy activity. Through PALGA (Dutch Pathology Registry), archived tumor material was obtained from 11 non-anaplastic NMTC patients who were using digoxin. Clinical characteristics and tumor material of these patients were compared to 11 matched control NMTC patients never treated with digoxin. RESULTS: We found that in mice, tumor growth was inhibited and 124I accumulation was sustainably increased after short-course digoxin treatment. Post-mortem analyses revealed that digoxin treatment increased autophagy activity and enhanced expression of thyroid-specific genes in mouse tumors compared to vehicle-treated mice. Digoxin-treated NMTC patients exhibited significantly higher autophagy activity and a higher differentiation status as compared to matched control NMTC patients, and were associated with favourable clinical outcome. CONCLUSIONS: These in vivo data support the hypothesis that digoxin may represent a repositioned adjunctive treatment modality that suppresses tumor growth and improves RAI sensitivity in patients with RAI-refractory NMTC.

Selection of optimal molecular targets for tumor-specific imaging in pancreatic ductal adenocarcinoma.

Discrimination of pancreatic ductal adenocarcinoma (PDAC) from chronic pancreatitis (CP) or peritumoral inflammation is challenging, both at preoperative imaging and during surgery, but it is crucial for proper therapy selection. Tumor-specific molecular imaging aims to enhance this discrimination and to help select and stratify patients for resection. We evaluated various biomarkers for the specific identification of PDAC and associated lymph node metastases. Using immunohistochemistry (IHC), expression levels and patterns were investigated of integrin αvß6, carcinoembryonic antigen-related cell adhesion molecule 5 (CEACAM5), Cathepsin E (Cath E), epidermal growth factor receptor (EGFR), hepatocyte growth factor receptor (c-MET), thymocyte differentiation antigen 1 (Thy1), and urokinase-type plasminogen activator receptor (uPAR). In a first cohort, multiple types of pancreatic tissue were evaluated (n=62); normal pancreatic tissue (n=8), CP (n=7), PDAC (n=9), tumor associated lymph nodes (n=32), and PDAC after neoadjuvant radiochemotherapy (n=6). In a second cohort, tissues were investigated (n=55) with IHC and immunofluorescence (IF) for concordance of biomarker expression in all tissue types, obtained from an individual patient. Integrin αvß6 and CEACAM5 showed significantly higher expression levels in PDAC versus normal pancreatic tissue (P=0.001 and P<0.001, respectively) and CP (P=0.003 and P<0.001, respectively). Avß6 and CEACAM5 expression identified tumor-positive lymph nodes correctly in 84% and 68%, respectively, and in 100% of tumor-negative nodes for both biomarkers. In conclusion, αvß6 and CEACAM5 are excellent biomarkers to differentiate PDAC from surrounding tissue and to identify lymph node metastases. Individually or combined, these biomarkers are promising targets for tumor-specific molecular imaging of PDAC.

Expression of HIF-1α in medullary thyroid cancer identifies a subgroup with poor prognosis.

BACKGROUND: Medullary thyroid cancer (MTC) comprises only 4% of all thyroid cancers and originates from the parafollicular C-cells. HIF-1α expression has been implied as an indicator of worse prognosis in various solid tumors. However, whether expression of HIF-1α is a prognosticator in MTC remained unclear. Our aim was to evaluate the prognostic value of HIF-1α in patients with MTC. METHODS: All patients with MTC who were operated on between 1988 and 2014 in five tertiary referral centers in The Netherlands were included. A tissue microarray was constructed in which 111 primary tumors could be analyzed for expression of HIF-1α, CAIX, Glut-1, VEGF and CD31 and correlated with clinicopathologic variables and survival. RESULTS: The mean age of patients was 46.3 years (SD 15.6), 59 (53.2%) were male. Of the 111 primary tumors, 49 (44.1%) were HIF-1α negative and 62 (55.9%) were HIF-1α positive. Positive HIF-1α expression was an independent negative indicator for progression free survival (PFS) in multivariate cox regression analysis (HR 3.1; 95% CI 1.3 - 7.3). Five-years survival decreased from 94.0% to 65.9% for the HIF-1α positive group (p=0.007). Even within the group of patients with TNM-stage IV disease, HIF-1α positivity was associated with a worse prognosis, shown by a decrease in 5-years survival of 88.0% to 49.3% (p=0.020). CONCLUSION: Expression of HIF-1α is strongly correlated with adverse prognosis of MTC. This could open up new ways for targeted systemic therapy of MTC.

De novo discovery of phenotypic intratumour heterogeneity using imaging mass spectrometry.

An essential and so far unresolved factor influencing the evolution of cancer and the clinical management of patients is intratumour clonal and phenotypic heterogeneity. However, the de novo identification of tumour subpopulations is so far both a challenging and an unresolved task. Here we present the first systematic approach for the de novo discovery of clinically detrimental molecular tumour subpopulations. In this proof-of-principle study, spatially resolved, tumour-specific mass spectra were acquired, using matrix-assisted laser desorption/ionization (MALDI) imaging mass spectrometry, from tissues of 63 gastric carcinoma and 32 breast carcinoma patients. The mass spectra, representing the proteomic heterogeneity within tumour areas, were grouped by a corroborated statistical clustering algorithm in order to obtain segmentation maps of molecularly distinct regions. These regions were presumed to represent different phenotypic tumour subpopulations. This was confirmed by linking the presence of these tumour subpopulations to the patients' clinical data. This revealed several of the detected tumour subpopulations to be associated with a different overall survival of the gastric cancer patients (p = 0.025) and the presence of locoregional metastases in patients with breast cancer (p = 0.036). The procedure presented is generic and opens novel options in cancer research, as it reveals microscopically indistinct tumour subpopulations that have an adverse impact on clinical outcome. This enables their further molecular characterization for deeper insights into the biological processes of cancer, which may finally lead to new targeted therapies.