Pancreatic Cancer - Early Detection, Prognostic Factors, and Treatment

Research output: ThesisDoctoral Thesis (compilation)

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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

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,
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).
Original languageEnglish
Awarding Institution
  • Surgery (Lund)
  • Andersson, Roland, Supervisor
  • Andersson, Bodil, Supervisor
  • Marko-Varga, György, Supervisor
Award date2014 Sep 26
ISBN (Print)9789176190326
Publication statusPublished - 2014

Bibliographical note

Defence details

Date: 2014-09-26
Time: 13:00
Place: Lecture Room 4, Main building, Skåne University Hospital, Lund

External reviewer(s)

Name: Friess, Helmut
Title: Professor
Affiliation: Department of General Surgery, The University Hospital Rechts der Isar, Technical University Munich, Munich, Germany


I. Ansari D, Andersson R, Bauden MP, Andersson B, Connolly J, Welinder C,
Sasor A, Marko-Varga G. Protein deep sequencing applied to biobank samples
from patients with pancreatic cancer. Journal of Cancer Research and Clinical
Oncology 2014; In press.

II. Ansari D, Nilsson J, Andersson R, Regnér S, Tingstedt B, Andersson B.
Artificial neural networks predict survival from pancreatic cancer after radical
surgery. American Journal of Surgery 2013;205:1-7.

III. Ansari D, Williamsson C, Tingstedt B, Andersson B, Lindell G, Andersson R.
Pancreaticoduodenectomy - the transition from a low- to a high-volume center.
Scandinavian Journal of Gastroenterology 2014;49:481-4.

IV. Ansari D, Urey C, Gundewar C, Bauden MP, Andersson R. Comparison of
MUC4 expression in primary pancreatic cancer and paired lymph node metastases.
Scandinavian Journal of Gastroenterology 2013;48:1183-7.

V. Ansari D, Bauden MP, Sasor A, Gundewar C, Andersson R. Analysis of MUC4
expression in human pancreatic cancer xenografts in immunodeficient mice.
Anticancer Research 2014;34:3905-10.

VI. Ansari D, Urey C, Said Hilmersson K, Bauden MP, Ek F, Olsson R,
Andersson R. Apicidin sensitizes pancreatic cancer cells to gemcitabine by
epigenetically regulating MUC4 expression. Anticancer Research 2014; In press.

Subject classification (UKÄ)

  • Surgery


  • artificial neural networks
  • apicidin
  • centralization
  • early detection
  • epigenetics
  • high definition mass spectrometry
  • MUC4
  • pancreatic cancer
  • pancreaticoduodenectomy
  • prognostic factors
  • xenograft model
  • treatment


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