Pancreatic cancer acquires resistance to MAPK pathway inhibition by clonal expansion and adaptive DNA hypermethylation.

BACKGROUND: Pancreatic ductal adenocarcinoma (PDAC) is an aggressive cancer with poor prognosis. It is marked by extraordinary resistance to conventional therapies including chemotherapy and radiation, as well as to essentially all targeted therapies evaluated so far. More than 90% of PDAC cases harbor an activating KRAS mutation. As the most common KRAS variants in PDAC remain undruggable so far, it seemed promising to inhibit a downstream target in the MAPK pathway such as MEK1/2, but up to now preclinical and clinical evaluation of MEK inhibitors (MEKi) failed due to inherent and acquired resistance mechanisms. To gain insights into molecular changes during the formation of resistance to oncogenic MAPK pathway inhibition, we utilized short-term passaged primary tumor cells from ten PDACs of genetically engineered mice. We followed gain and loss of resistance upon MEKi exposure and withdrawal by longitudinal integrative analysis of whole genome sequencing, whole genome bisulfite sequencing, RNA-sequencing and mass spectrometry data. RESULTS: We found that resistant cell populations under increasing MEKi treatment evolved by the expansion of a single clone but were not a direct consequence of known resistance-conferring mutations. Rather, resistant cells showed adaptive DNA hypermethylation of 209 and hypomethylation of 8 genomic sites, most of which overlap with regulatory elements known to be active in murine PDAC cells. Both DNA methylation changes and MEKi resistance were transient and reversible upon drug withdrawal. Furthermore, MEKi resistance could be reversed by DNA methyltransferase inhibition with remarkable sensitivity exclusively in the resistant cells. CONCLUSION: Overall, the concept of acquired therapy resistance as a result of the expansion of a single cell clone with epigenetic plasticity sheds light on genetic, epigenetic and phenotypic patterns during evolvement of treatment resistance in a tumor with high adaptive capabilities and provides potential for reversion through epigenetic targeting.

Comparative proteome and serum analysis identified FSCN1 as a marker of abiraterone resistance in castration-resistant prostate cancer.

BACKGROUND: Abiraterone (Abi) is an androgen receptor signaling inhibitor that significantly improves patients' life expectancy in metastatic prostate cancer (PCa). Despite its beneficial effects, many patients have baseline or acquired resistance against Abi. The aim of this study was to identify predictive serum biomarkers for Abi treatment. METHODS: We performed a comparative proteome analysis on three Abi sensitive (LNCaPabl, LAPC4, DuCaP) and resistant (LNCaPabl-Abi, LAPC4-Abi, DuCaP-Abi) PCa cell lines using liquid chromatography tandem mass spectrometry (LC-MS/MS) technique. Two bioinformatic selection workflows were applied to select the most promising candidate serum markers. Serum levels of selected proteins were assessed in samples of 100 Abi-treated patients with metastatic castration-resistant disease (mCRPC) using ELISA. Moreover, FSCN1 serum concentrations were measured in samples of 69 Docetaxel (Doc) treated mCRPC patients. RESULTS: Our proteome analysis identified 68 significantly, at least two-fold upregulated proteins in Abi resistant cells. Using two filtering workflows four proteins (AMACR, KLK2, FSCN1 and CTAG1A) were selected for ELISA analyses. We found high baseline FSCN1 serum levels to be significantly associated with poor survival in Abi-treated mCRPC patients. Moreover, the multivariable analysis revealed that higher ECOG status (>1) and high baseline FSCN1 serum levels (>10.22 ng/ml by ROC cut-off) were independently associated with worse survival in Abi-treated patients (p < 0.001 and p = 0.021, respectively). In contrast, no association was found between serum FSCN1 concentrations and overall survival in Doc-treated patients. CONCLUSIONS: Our analysis identified baseline FSCN1 serum levels to be independently associated with poor survival of Abi-treated, but not Doc-treated mCRPC patients, suggesting a therapy specific prognostic value for FSCN1.

Plasma Proteomics Enable Differentiation of Lung Adenocarcinoma from Chronic Obstructive Pulmonary Disease (COPD).

Chronic obstructive pulmonary disease (COPD) is a major risk factor for the development of lung adenocarcinoma (AC). AC often develops on underlying COPD; thus, the differentiation of both entities by biomarker is challenging. Although survival of AC patients strongly depends on early diagnosis, a biomarker panel for AC detection and differentiation from COPD is still missing. Plasma samples from 176 patients with AC with or without underlying COPD, COPD patients, and hospital controls were analyzed using mass-spectrometry-based proteomics. We performed univariate statistics and additionally evaluated machine learning algorithms regarding the differentiation of AC vs. COPD and AC with COPD vs. COPD. Univariate statistics revealed significantly regulated proteins that were significantly regulated between the patient groups. Furthermore, random forest classification yielded the best performance for differentiation of AC vs. COPD (area under the curve (AUC) 0.935) and AC with COPD vs. COPD (AUC 0.916). The most influential proteins were identified by permutation feature importance and compared to those identified by univariate testing. We demonstrate the great potential of machine learning for differentiation of highly similar disease entities and present a panel of biomarker candidates that should be considered for the development of a future biomarker panel.

Proteome profiling of enzalutamide-resistant cell lines and serum analysis identified ALCAM as marker of resistance in castration-resistant prostate cancer.

Enzalutamide (ENZA) is a frequently used therapy in metastatic castration-resistant prostate cancer (mCRPC). Baseline or acquired resistance to ENZA have been observed, but the molecular mechanisms of resistance are poorly understood. We aimed to identify proteins involved in ENZA resistance and to find therapy-predictive serum markers. We performed comparative proteome analyses on ENZA-sensitive parental (LAPC4, DuCaP) and -resistant prostate cancer cell lines (LAPC4-ENZA, DuCaP-ENZA) using liquid chromatography tandem mass spectrometry (LC-MS/MS). The top four most promising candidate markers were selected using bioinformatic approaches. Serum concentrations of selected markers (ALCAM, AGR2, NDRG1, IDH1) were measured in pretreatment samples of 72 ENZA-treated mCRPC patients using ELISA. In addition, ALCAM serum levels were measured in 101 Abiraterone (ABI) and 100 Docetaxel (DOC)-treated mCRPC patients' baseline samples. Results were correlated with clinical and follow-up data. The functional role of ALCAM in ENZA resistance was assessed in vitro using siRNA. Our proteome analyses revealed 731 significantly differentially abundant proteins between ENZA-sensitive and -resistant cells and our filtering methods identified four biomarker candidates. Serum analyses of these proteins revealed only ALCAM to be associated with poor patient survival. Furthermore, higher baseline ALCAM levels were associated with poor survival in ABI- but not in DOC-treated patients. In LAPC4-ENZA resistant cells, ALCAM silencing by siRNA knockdown resulted in significantly enhanced ENZA sensitivity. Our analyses revealed that ALCAM serum levels may help to identify ENZA- and ABI-resistant patients and may thereby help to optimize future clinical decision-making. Our functional analyses suggest the possible involvement of ALCAM in ENZA resistance.

Comparative proteome analysis identified CD44 as a possible serum marker for docetaxel resistance in castration-resistant prostate cancer.

Baseline or acquired resistance to docetaxel (DOC) represents a significant risk for patients with metastatic prostate cancer (PC). In the last years, novel therapy regimens have been approved providing reasonable alternatives for DOC-resistant patients making prediction of DOC resistance of great clinical importance. We aimed to identify serum biomarkers, which are able to select patients who will not benefit from DOC treatment. DOC-resistant PC3-DR and DU145-DR sublines and their sensitive parental cell lines (DU145, PC3) were comparatively analyzed using liquid chromatography-coupled tandem mass spectrometry (LC-MS/MS). Results were filtered using bioinformatics approaches to identify promising serum biomarkers. Serum levels of five proteins were determined in serum samples of 66 DOC-treated metastatic castration-resistant PC patients (mCRPC) using ELISA. Results were correlated with clinicopathological and survival data. CD44 was subjected to further functional cell culture analyses. We found at least 177 two-fold significantly overexpressed proteins in DOC-resistant cell lines. Our bioinformatics method suggested 11/177 proteins to be secreted into the serum. We determined serum levels of five (CD44, MET, GSN, IL13RA2 and LNPEP) proteins in serum samples of DOC-treated patients and found high CD44 serum levels to be independently associated with poor overall survival (p = 0.001). In accordance, silencing of CD44 in DU145-DR cells resulted in re-sensitization to DOC. In conclusion, high serum CD44 levels may help identify DOC-resistant patients and may thereby help optimize clinical decision-making regarding type and timing of therapy for mCRPC patients. In addition, our in vitro results imply the possible functional involvement of CD44 in DOC resistance.

Application of Label-Free Proteomics for Quantitative Analysis of Urothelial Carcinoma and Cystitis Tissue.

A label-free approach based on a highly reproducible and stable workflow allows for quantitative proteome analysis . Due to advantages compared to labeling methods, the label-free approach has the potential to measure unlimited samples from clinical specimen monitoring and comparing thousands of proteins. The presented label-free workflow includes a new sample preparation technique depending on automatic annotation and tissue isolation via FTIR-guided laser microdissection, in-solution digestion, LC-MS/MS analyses, data evaluation by means of Proteome Discoverer and Progenesis software, and verification of differential proteins. We successfully applied this workflow in a proteomics study analyzing human cystitis and high-grade urothelial carcinoma tissue regarding the identification of a diagnostic tissue biomarker. The differential analysis of only 1 mm2 of isolated tissue cells led to 74 significantly differentially abundant proteins.

Integrated Fourier Transform Infrared Imaging and Proteomics for Identification of a Candidate Histochemical Biomarker in Bladder Cancer.

Histopathological differentiation between severe urocystitis with reactive urothelial atypia and carcinoma in situ (CIS) can be difficult, particularly after a treatment that deliberately induces an inflammatory reaction, such as intravesical instillation of Bacillus Calmette-Guèrin. However, precise grading in bladder cancer is critical for therapeutic decision making and thus requires reliable immunohistochemical biomarkers. Herein, an exemplary potential biomarker in bladder cancer was identified by the novel approach of Fourier transform infrared imaging for label-free tissue annotation of tissue thin sections. Identified regions of interest are collected by laser microdissection to provide homogeneous samples for liquid chromatography-tandem mass spectrometry-based proteomic analysis. This approach afforded label-free spatial classification with a high accuracy and without interobserver variability, along with the molecular resolution of the proteomic analysis. Cystitis and invasive high-grade urothelial carcinoma samples were analyzed. Three candidate biomarkers were identified and verified by immunohistochemistry in a small cohort, including low-grade urothelial carcinoma samples. The best-performing candidate AHNAK2 was further evaluated in a much larger independent verification cohort that also included CIS samples. Reactive urothelial atypia and CIS were distinguishable on the basis of the expression of this newly identified and verified immunohistochemical biomarker, with a sensitivity of 97% and a specificity of 69%. AHNAK2 can differentiate between reactive urothelial atypia in the setting of an acute or chronic cystitis and nonmuscle invasive-type CIS.

Quantitative Secretome Analysis of Activated Jurkat Cells Using Click Chemistry-Based Enrichment of Secreted Glycoproteins.

Quantitative secretome analyses are a high-performance tool for the discovery of physiological and pathophysiological changes in cellular processes. However, serum supplements in cell culture media limit secretome analyses, but serum depletion often leads to cell starvation and consequently biased results. To overcome these limiting factors, we investigated a model of T cell activation (Jurkat cells) and performed an approach for the selective enrichment of secreted proteins from conditioned medium utilizing metabolic marking of newly synthesized glycoproteins. Marked glycoproteins were labeled via bioorthogonal click chemistry and isolated by affinity purification. We assessed two labeling compounds conjugated with either biotin or desthiobiotin and the respective secretome fractions. 356 proteins were quantified using the biotin probe and 463 using desthiobiotin. 59 proteins were found differentially abundant (adjusted p-value ≤0.05, absolute fold change ≥1.5) between inactive and activated T cells using the biotin method and 86 using the desthiobiotin approach, with 31 mutual proteins cross-verified by independent experiments. Moreover, we analyzed the cellular proteome of the same model to demonstrate the benefit of secretome analyses and provide comprehensive data sets of both. 336 proteins (61.3%) were quantified exclusively in the secretome. Data are available via ProteomeXchange with identifier PXD004280.