Signal-Targeted Therapies and Resistance Mechanisms in Pancreatic Cancer: Future Developments Reside in Proteomics.
Célia CintasThibault DoucheNicole ThervilleSilvia ArcucciFernanda Ramos-DelgadoCéline BassetBertrand Jean-ClaudeJulie Guillermet-GuibertPublished in: Cancers (2018)
For patients with metastatic pancreatic cancer that are not eligible for surgery, signal-targeted therapies have so far failed to significantly improve survival. These therapeutic options have been tested in phase II/III clinical trials mostly in combination with the reference treatment gemcitabine. Innovative therapies aim to annihilate oncogenic dependency, or to normalize the tumoural stroma to allow immune cells to function and/or re-vascularisation to occur. Large scale transcriptomic and genomic analysis revealed that pancreatic cancers display great heterogeneity but failed to clearly delineate specific oncogene dependency, besides oncogenic Kras. Beyond these approaches, proteomics appears to be an appropriate approach to classify signal dependency and to identify specific alterations at the targetable level. However, due to difficulties in sampling, proteomic data for this pathology are scarce. In this review, we will discuss the current state of clinical trials for targeted therapies against pancreatic cancer. We will then highlight the most recent proteomic data for pancreatic tumours and their metastasis, which could help to identify major oncogenic signalling dependencies, as well as provide future leads to explain why pancreatic tumours are intrinsically resistant to signal-targeted therapies. We will finally discuss how studies on phosphatidylinositol-3-kinase (PI3K) signalling, as the paradigmatic pro-tumoural signal downstream of oncogenic Kras in pancreatic cancer, would benefit from exploratory proteomics to increase the efficiency of targeted therapies.
Keyphrases
- clinical trial
- phase ii
- label free
- mass spectrometry
- transcription factor
- single cell
- open label
- electronic health record
- minimally invasive
- big data
- squamous cell carcinoma
- rna seq
- phase iii
- coronary artery disease
- radiation therapy
- acute coronary syndrome
- double blind
- tyrosine kinase
- atrial fibrillation
- machine learning
- data analysis
- free survival