Absolute lymphocyte count is associated with survival in ovarian cancer independent of tumor-infiltrating lymphocytes

Abstract Background The immune system strongly influences outcome in patients with ovarian cancer. In particular, the absolute lymphocyte count in peripheral blood (ALC) and the presence of tumor-infiltrating lymphocytes (TIL) have each been associated with favourable prognosis. However, the mechanistic relationships between ALC, TIL and prognosis are poorly understood. We hypothesized that high ALC values might be associated with stronger tumor immunity as manifested by increased TIL, decreased tumor burden and longer survival. Methods ALC values were collected from patient records ≥ 2 years before, during or after primary treatment for high-grade serous ovarian cancer (HGSC). Lymphocyte subsets were assessed in peripheral blood by flow cytometry. CD8+ and CD20+ TIL were assessed by immunohistochemistry. Results Overall, patients had normal ALC values two or more years prior to diagnosis of HGSC. These values were not predictive of disease severity or survival upon subsequent development of HGSC. Rather, ALC declined upon development of HGSC in proportion to disease burden. This decline involved all lymphocyte subsets. ALC increased following surgery, remained stable during chemotherapy, but rarely recovered to pre-diagnostic levels. ALC values recorded at diagnosis did not correlate with CD8+ or CD20+ TIL but were associated with progression-free survival. Conclusions Patients with high intrinsic ALC values show no clinical or survival advantage upon subsequent development of HGSC. ALC values at diagnosis are prognostic due to an association with disease burden rather than TIL. Therapeutic enhancement of ALC may be necessary but not sufficient to improve survival in HGSC

HER2 overexpression and amplification is present in a subset of ovarian mucinous carcinomas and can be targeted with trastuzumab therapy

<p>Abstract</p> <p>Background</p> <p>The response rate of ovarian mucinous carcinomas to paclitaxel/carboplatin is low, prompting interest in targeted molecular therapies. We investigated HER2 expression and amplification, and the potential for trastuzumab therapy in this histologic subtype of ovarian cancer.</p> <p>Methods</p> <p>HER2 status was tested in 33 mucinous carcinomas and 16 mucinous borderline ovarian tumors (BOT)). Five cases with documented recurrence and with tissue from the recurrence available for testing were analyzed to determine whether HER2 amplification status changed over time. Three prospectively identified recurrent mucinous ovarian carcinomas were assessed for HER2 amplification and patients received trastuzumab therapy with conventional chemotherapy.</p> <p>Results</p> <p>Amplification of HER2 was observed in 6/33 (18.2%) mucinous carcinomas and 3/16 (18.8%) BOT. HER2 amplification in primary mucinous carcinomas was not associated with an increased likelihood of recurrence. The prospectively identified recurrent mucinous carcinomas showed overexpression and amplification of HER2; one patient's tumor responded dramatically to trastuzumab in combination with conventional chemotherapy, while another patient experienced an isolated central nervous system recurrence after trastuzumab therapy.</p> <p>Conclusion</p> <p>HER2 amplification is relatively common in ovarian mucinous carcinomas (6/33, 18.2%), although not of prognostic significance. Trastuzumab therapy is a treatment option for patients with mucinous carcinoma when the tumor has HER2 amplification and overexpression.</p

Regulation of pH by Carbonic Anhydrase 9 Mediates Survival of Pancreatic Cancer Cells With Activated KRAS in Response to Hypoxia.

Background & Aims Most pancreatic ductal adenocarcinomas (PDACs) express an activated form of KRAS, become hypoxic and dysplastic, and are refractory to chemo and radiation therapies. To survive in the hypoxic environment, PDAC cells upregulate enzymes and transporters involved in pH regulation, including the extracellular facing carbonic anhydrase 9 (CA9). We evaluated the effect of blocking CA9, in combination with administration of gemcitabine, in mouse models of pancreatic cancer. Methods We knocked down expression of KRAS in human (PK-8 and PK-1) PDAC cells with small hairpin RNAs. Human and mouse (KrasG12D/Pdx1-Cre/Tp53/RosaYFP) PDAC cells were incubated with inhibitors of MEK (trametinib) or extracellular signal-regulated kinase (ERK), and some cells were cultured under hypoxic conditions. We measured levels and stability of the hypoxia-inducible factor 1 subunit alpha (HIF1A), endothelial PAS domain 1 protein (EPAS1, also called HIF2A), CA9, solute carrier family 16 member 4 (SLC16A4, also called MCT4), and SLC2A1 (also called GLUT1) by immunoblot analyses. We analyzed intracellular pH (pHi) and extracellular metabolic flux. We knocked down expression of CA9 in PDAC cells, or inhibited CA9 with SLC-0111, incubated them with gemcitabine, and assessed pHi, metabolic flux, and cytotoxicity under normoxic and hypoxic conditions. Cells were also injected into either immune-compromised or immune-competent mice and growth of xenograft tumors was assessed. Tumor fragments derived from patients with PDAC were surgically ligated to the pancreas of mice and the growth of tumors was assessed. We performed tissue microarray analyses of 205 human PDAC samples to measure levels of CA9 and associated expression of genes that regulate hypoxia with outcomes of patients using the Cancer Genome Atlas database. Results Under hypoxic conditions, PDAC cells had increased levels of HIF1A and HIF2A, upregulated expression of CA9, and activated glycolysis. Knockdown of KRAS in PDAC cells, or incubation with trametinib, reduced the posttranscriptional stabilization of HIF1A and HIF2A, upregulation of CA9, pHi, and glycolysis in response to hypoxia. CA9 was expressed by 66% of PDAC samples analyzed; high expression of genes associated with metabolic adaptation to hypoxia, including CA9, correlated with significantly reduced survival times of patients. Knockdown or pharmacologic inhibition of CA9 in PDAC cells significantly reduced pHi in cells under hypoxic conditions, decreased gemcitabine-induced glycolysis, and increased their sensitivity to gemcitabine. PDAC cells with knockdown of CA9 formed smaller xenograft tumors in mice, and injection of gemcitabine inhibited tumor growth and significantly increased survival times of mice. In mice with xenograft tumors grown from human PDAC cells, oral administration of SLC-0111 and injection of gemcitabine increased intratumor acidosis and increased cell death. These tumors, and tumors grown from PDAC patient-derived tumor fragments, grew more slowly than xenograft tumors in mice given control agents, resulting in longer survival times. In KrasG12D/Pdx1-Cre/Tp53/RosaYFP genetically modified mice, oral administration of SLC-0111 and injection of gemcitabine reduced numbers of B cells in tumors. Conclusions In response to hypoxia, PDAC cells that express activated KRAS increase expression of CA9, via stabilization of HIF1A and HIF2A, to regulate pH and glycolysis. Disruption of this pathway slows growth of PDAC xenograft tumors in mice and might be developed for treatment of pancreatic cancer

Claudin 4 Is Differentially Expressed between Ovarian Cancer Subtypes and Plays a Role in Spheroid Formation

Claudin 4 is a cellular adhesion molecule that is frequently overexpressed in ovarian cancer and other epithelial cancers. In this study, we sought to determine whether the expression of claudin 4 is associated with outcome in ovarian cancer patients and may be involved in tumor progression. We examined claudin 4 expression in ovarian cancer tissues and cell lines, as well as by immunohistochemical staining of tissue microarrays (TMAs; n = 500), spheroids present in patients’ ascites, and spheroids formed in vitro. Claudin 4 was expressed in nearly 70% of the ovarian cancer tissues examined and was differentially expressed across ovarian cancer subtypes, with the lowest expression in clear cell subtype. No association was found between claudin 4 expression and disease-specific survival in any subtype. Claudin 4 expression was also observed in multicellular spheroids obtained from patients’ ascites. Using an in vitro spheroid formation assay, we found that NIH:OVCAR5 cells treated with shRNA against claudin 4 required a longer time to form compact spheroids compared to control NIH:OVCAR5 cells that expressed high levels of claudin 4. The inability of the NIH:OVCAR5 cells treated with claudin 4 shRNA to form compact spheroids was verified by FITC-dextran exclusion. These results demonstrate a role for claudin 4 and tight junctions in spheroid formation and integrity

Classification of Pancreatic Ductal Adenocarcinoma Using MALDI Mass Spectrometry Imaging Combined with Neural Networks

Despite numerous diagnostic and therapeutic advances, pancreatic ductal adenocarcinoma (PDAC) has a high mortality rate, and is the fourth leading cause of cancer death in developing countries. Besides its increasing prevalence, pancreatic malignancies are characterized by poor prognosis. Omics technologies have potential relevance for PDAC assessment but are time-intensive and relatively cost-intensive and limited by tissue heterogeneity. Matrix-assisted laser desorption/ionization mass spectrometry imaging (MALDI-MSI) can obtain spatially distinct peptide-signatures and enables tumor classification within a feasible time with relatively low cost. While MALDI-MSI data sets are inherently large, machine learning methods have the potential to greatly decrease processing time. We present a pilot study investigating the potential of MALDI-MSI in combination with neural networks, for classification of pancreatic ductal adenocarcinoma. Neural-network models were trained to distinguish between pancreatic ductal adenocarcinoma and other pancreatic cancer types. The proposed methods are able to correctly classify the PDAC types with an accuracy of up to 86% and a sensitivity of 82%. This study demonstrates that machine learning tools are able to identify different pancreatic carcinoma from complex MALDI data, enabling fast prediction of large data sets. Our results encourage a more frequent use of MALDI-MSI and machine learning in histopathological studies in the future.Medicine, Faculty ofNon UBCPathology and Laboratory Medicine, Department ofReviewedFacult

Reproducibility of tumor budding assessment in pancreatic cancer based on a multicenter interobserver study.

Tumor budding has been reported to be an independent prognostic factor in pancreatic ductal adenocarcinoma (PDAC). Its use in daily diagnostics would improve the prognostic stratification of patients. We performed a multicenter interobserver study to test various budding assessment methods for their reproducibility. Two serial sections of 50 resected, treatment-naïve PDACs were stained for Hematoxylin and Eosin (H&E) and pancytokeratin. Tumor budding was scored by independent observers at five participating centers in Switzerland, Germany, and Canada. Pathologists assessed tumor budding on a digital platform comparing H&E with pancytokeratin staining in 10 high-power fields (10HPF) and one HPF hotspot (1HPF). Additionally, tumor budding was assessed in one H&E hotspot at × 20 magnification, as suggested by the International Tumor Budding Consensus Conference (ITBCC). Correlation coefficients for bud counts between centers ranged from r = 0.58648 to r = 0.78641 for H&E and from r = 0.69288 to r = 0.81764 for pancytokeratin. The highest interobserver agreement across all centers was observed for pancytokeratin 10HPFs (ICC = 0.6). ICC values were 0.49, 0.48, 0.41, and 0.4 for H&E in 1HPF hotspot, H&E in 10HPFs, pancytokeratin in 1HPF, and H&E in one hotspot at ×20, respectively (ITBCC method). This interobserver study reveals a range between moderately poor to moderate agreement levels between pathologists for the different tumor budding assessment methods in PDAC. Acceptable levels of agreement were reached with the pancytokeratin 10HPF method, which can thus be recommended for the assessment of tumor budding in PDAC resection specimens. To improve the levels of interobserver agreement, the implementation of machine learning applications should be considered