Pancreatic Cancer - Early Detection, Prognostic Factors, and Treatment
Sammanfattning: Background: Pancreatic cancer is the fourth leading cause of cancer-related death. Only about 6% of patients are alive 5 years after diagnosis. One reason for this low survival rate is that most patients are diagnosed at a late stage, when the tumor has spread to surrounding tissues or distant organs. Less than 20% of cases are diagnosed at an early stage that allows them to undergo potentially curative surgery. However, even for patients with a tumor that has been surgically removed, local and systemic recurrence is common and the median survival is only 17-23 months. This underscores the importance to identify factors that can predict postresection survival. With technical advances and centralization of care, pancreatic surgery has become a safe procedure. The future optimal treatment for pancreatic cancer is dependent on increased understanding of tumor biology and development of individualized and systemic treatment. Previous experimental studies have reported that mucins, especially the MUC4 mucin, may confer resistance to the chemotherapeutic agent gemcitabine and may serve as targets for the development of novel types of intervention. Aim: The aim of the thesis was to investigate strategies to improve management of pancreatic cancer, with special reference to early detection, prognostic factors, and treatment. Methods: In paper I, 27 prospectively collected serum samples from resectable pancreatic cancer (n=9), benign pancreatic disease (n=9), and healthy controls (n=9) were analyzed by high definition mass spectrometry (HDMSE). In paper II, an artificial neural network (ANN) model was constructed on 84 pancreatic cancer patients undergoing surgical resection. In paper III, we investigated the effects of transition from a low- to a high volume-center for pancreaticoduodenectomy in 221 patients. In paper IV, the grade of concordance in terms of MUC4 expression was examined in 17 tissue sections from primary pancreatic cancer and matched lymph node metastases. In paper V, pancreatic xenograft tumors were generated in 15 immunodeficient mice by subcutaneous injection of MUC4+ human pancreatic cancer cell lines; Capan-1, HPAF-II, or CD18/HPAF. In paper VI, a 76-member combined epigenetics and phosphatase small-molecule inhibitor library was screened against Capan-1 (MUC4+) and Panc-1 (MUC4-) cells, followed by high content screening of protein expression. Results/Conclusion: 134 differentially expressed serum proteins were identified, of which 40 proteins showed a significant up-regulation in the pancreatic cancer group. Pancreatic disease link associations could be made for BAZ2A, CDK13, DAPK1, DST, EXOSC3, INHBE, KAT2B, KIF20B, SMC1B, and SPAG5, by pathway network linkages to p53, the most frequently altered tumor suppressor in pancreatic cancer (I). An ANN survival model was developed, identifying 7 risk factors. The C-index for the model was 0.79, and it performed significantly better than the Cox regression (II). We experienced improved surgical results for pancreaticoduodenectomy after the transition to a high-volume center (≥25 procedures/year), including decreased operative duration, blood loss, hemorrhagic complications, reoperations, and hospital stay. There was also a tendency toward reduced operative mortality, from 4% to 0% (III). MUC4 positivity was detected in most primary pancreatic cancer tissues, as well as in matched metastatic lymph nodes (15/17 vs. 14/17), with a high concordance level (82%) (IV). The tumor incidence was 100% in the xenograft model. The median MUC4 count was found to be highest in Capan-1 tumors. α-SMA and collagen extent were also highest in Capan-1 tumors (V). Apicidin (a histone deacetylase inhibitor) had potent antiproliferative activity against Capan-1 cells and significantly reduced the expression of MUC4 and its transcription factor HNF4α. The combined treatment of apicidin and gemcitabine synergistically inhibited growth of Capan-1 cells (VI).
KLICKA HÄR FÖR ATT SE AVHANDLINGEN I FULLTEXT. (PDF-format)