Pancreatic Cancer Survival: Proteomics Reveals Prognostic Factors and Therapeutic Targets

Recently, Chen et al. (2015) applied label-free proteomics to cancerous tissues for the purpose of characterizing the relationship between pancreatic cancer survival and the proteome.¹ Their study highlights potential prognostic factors and therapeutic targets (as summarized in Table 1) for improving survival outcomes.

Table 1. Prognostic factors and therapeutic targets

Prognostic factors	prolargin (PRELP) stromal galectin-1 (LGALS1) ribosomal protein S8 (RPS8)
Therapeutic targets	LGALS1 RPS8

Pancreatic ductal adenocarcinoma (PDAC) carries a poor prognosis and high lethality. Most patients present with unresectable disease, which usually results in death within six months. Without surgical resection, only 6% of those diagnosed become five-year survivors, although this rate increases to 20% with surgical resection. Patients achieving 10 years of survival or better are rare. 

Using samples drawn from very-long-term survival (VLTS, ≥10 years) and short-term survival (STS, <14 months) patients, Chen et al. extracted and prepped proteins from clinical PDAC specimens for analysis using an LTQ Orbitrap hybrid ion trap-Orbitrap mass spectrometer (Thermo Scientific). Overall, the team identified 1,050 unique proteins. Of these, they found differential expression of 332 proteins, including 139 upregulated (≥ two-fold) in the VLTS group and 193 upregulated (≥ two-fold) in the STS group; researchers have previously reported many of these proteins as overexpressed in pancreatic tumors, in comparison with normal pancreatic tissue.

The scientists ran these proteins through the Database for Annotation, Visualization, and Integrated Discovery (DAVID) in order to annotate functional proteins and pathways. They discovered the notable enriched clusters listed in Table 2.

Table 2. Types and numbers of STS- and VLTS-associated proteins found after DAVID search 

STS-associated proteins	cytoskeleton: 30 proteins ribonucleoprotein complex, protein biosynthesis, and RNA processing: 31 proteins generation of precursor metabolites and energy: 14 proteins
VLTS-associated proteins	copine: 8 proteins mitochondrion: 19 proteins generation of precursor metabolites and energy: 10 proteins

Pathway analysis highlighted major upstream regulators for both STS- and VLTS-associated proteins. The top three upstream regulators linked to STS (see Table 3), together with their targets, comprised a network mainly concerned with regulating proliferation and apoptosis, which are necessary for cancer cell progression.

Table 3. Top three upstream regulators linked to STS

STS-associated upstream regulators

MYC insulin-like growth factor 1 receptor (IGF1R) tumor-suppressor gene p53 (TP53)

The top three upstream regulators associated with VLTS (see Table 4) also formed a network, connecting 35 proteins.

Table 4. Top three upstream regulators linked to VLTS

VLTS-associated upstream regulators

vascular endothelial growth factor A (VEGFA) apolipoprotein E (APOE) TGF-β1

Chen et al. applied immunohistochemistry (IHC) analysis to a separate collection of 145 PDAC samples for validation. They confirmed upregulated prolargin (PRELP) and ribosomal protein S8 (RPS8) in VLTS and STS samples, respectively.

The team’s previous study (2012) had identified PRELP as a VLTS-associated differential protein,² and the current proteomic data revealed a 3.3-fold increase in abundance among VLTS tissues. IHC and Kaplan-Meier survival analysis revealed that specimens with higher-intensity PRELP staining correlated with a mean survival time of 40.9 months, in comparison to 27.0 months for lower-intensity staining. Univariate Cox regression analysis confirmed the association between high-abundance PRELP and increased survival.

RPS8, on the other hand, indicated a 7.7-fold increase in abundance in STS tumors in the proteomic data. Kaplan-Meier survival analysis of IHC staining revealed that samples with high-intensity RPS8 staining had a mean survival time of 23.8 months, compared with 39.3 months for low-intensity RPS8. Univariate Cox regression agreed with the association between high-abundance RPS8 and poor survival outcomes.

Taken together, Kaplan-Meier survival analysis associated the combination “High-RPS8 and Low-PRELP” variable with significantly shorter overall mean survival: 16.9 months, in comparison to 38.0 months for other staining status. Similarly, “Low-RPS8 and High-PRELP” correlated with longer overall mean survival: 43.1 months when compared with other staining status (mean survival of 27.1 months). Multivariate Cox regression analysis indicated a statistically significant association between both combinations and survival time. The “High-RPS8 and Low-PRELP” pairing emerged as showing enhanced prognostic value over any single marker or the Low-RPS8/High-PRELP combination.

Finally, Chen et al. knocked down galectin-1, a protein previously linked (adversely) with pancreatic cancer survival and that showed a 7-fold increase in abundance in STS samples in the proteomic data. Knockdown of this protein in pancreatic fibroblasts resulted in a significant decrease in cell migration and invasion, highlighting its likely role in pancreatic cancer progression and metastasis. Galectin-1 inhibition has already been identified as a potential therapeutic target for other cancers; the current findings suggest its inclusion as a novel target in pancreatic cancer treatment.

Overall, the team offers both potential prognostic factors and therapeutic targets that may improve pancreatic cancer survival. They call for further investigation into the mechanisms underlying clinical survival rates.

References

1. Chen, R., et al. (2015) “Proteins associated with pancreatic cancer survival in patients with resectable pancreatic ductal adenocarcinoma,” Laboratory Investigation, 95 (pp. 43–55), doi: 10.1038/labinvest.2014.128.

2. Chen, R., et al. (2012) “Stromal galectin-1 expression is associated with long-term survival in resectable pancreatic ductal adenocarcinoma,” Cancer Biology & Therapy, 13 (pp. 899–907).

Post Author: Melissa J. Mayer. Melissa is a freelance writer who specializes in science journalism. She possesses passion for and experience in the fields of proteomics, cellular/molecular biology, microbiology, biochemistry, and immunology. Melissa is also bilingual (Spanish) and holds a teaching certificate with a biology endorsement.