Personalization of prostate cancer therapy through phosphoproteomics

Comparative Analysis of Proteomes and Phosphoproteomes in Patients with Prostate Cancer Using Different Surgical Conditions

Purpose

To establish the standard of procedure in preparing benign and cancerous prostate tissues and evaluate the quality of proteomics and phosphoproteomics during transurethral resection of the prostate (TUR-P) with different surgical conditions.

Materials and Methods

TUR-P tissue samples from three patients, two diagnosed with prostate cancer and one with benign prostatic hyperplasia, were each analyzed under three different conditions, based on differences in energy values, tissue locations, and surgical techniques. Global- and phosphorylated proteomic profiles of prostate tissues were analyzed by liquid chromatography-tandem mass spectrometry.

Results

A total of 6,019 global proteins and 4,280 phosphorylated peptides were identified in the nine tissues. The quantitative distributions of proteins and phosphorylation in tissues from the same patient were not affected by changes in the surgical conditions, but indirect relative comparisons differed among patients. Phosphorylation levels, especially of proteins involved in the androgen receptor pathway, important in prostate cancer, were preserved in each patient.

Conclusions

Proteomic profiles of prostate tissue collected by TUR-P were not significantly affected by energy levels, tissue location, or surgical technique. In addition, since protein denaturation of samples through TUR-P is rarely confirmed in this study, we think that it will be an important guide for tissue samples in castration resistant prostate cancer patients, where it is difficult to obtain tissue. This result is the first report about proteomic and phosphoproteomic results with TUR-P samples in prostate cancer and will be theoretical basis in protein analysis research with prostate cancer tissues.

From Omics to Multi-Omics Approaches for In-Depth Analysis of the Molecular Mechanisms of Prostate Cancer

Cancer arises following alterations at different cellular levels, including genetic and epigenetic modifications, transcription and translation dysregulation, as well as metabolic variations. High-throughput omics technologies that allow one to identify and quantify processes involved in these changes are now available and have been instrumental in generating a wealth of steadily increasing data from patient tumors, liquid biopsies, and from tumor models. Extensive investigation and integration of these data have led to new biological insights into the origin and development of multiple cancer types and helped to unravel the molecular networks underlying this complex pathology. The comprehensive and quantitative analysis of a molecule class in a biological sample is named omics and large-scale omics studies addressing different prostate cancer stages have been performed in recent years. Prostate tumors represent the second leading cancer type and a prevalent cause of cancer death in men worldwide. It is a very heterogenous disease so that evaluating inter- and intra-tumor differences will be essential for a precise insight into disease development and plasticity, but also for the development of personalized therapies. There is ample evidence for the key role of the androgen receptor, a steroid hormone-activated transcription factor, in driving early and late stages of the disease, and this led to the development and approval of drugs addressing diverse targets along this pathway. Early genomic and transcriptomic studies have allowed one to determine the genes involved in prostate cancer and regulated by androgen signaling or other tumor-relevant signaling pathways. More recently, they have been supplemented by epigenomic, cistromic, proteomic and metabolomic analyses, thus, increasing our knowledge on the intricate mechanisms involved, the various levels of regulation and their interplay. The comprehensive investigation of these omics approaches and their integration into multi-omics analyses have led to a much deeper understanding of the molecular pathways involved in prostate cancer progression, and in response and resistance to therapies. This brings the hope that novel vulnerabilities will be identified, that existing therapies will be more beneficial by targeting the patient population likely to respond best, and that bespoke treatments with increased efficacy will be available soon.

Mapping Tyrosine Kinases Based on a TK Activity-Representing Peptide Library Reveals a Role for SRC in H1975 Drug Resistance

Tyrosine kinases (TKs) are prominent targets in cancer therapies, and more than 30 TK inhibitors have been approved for treatments in tumors with abnormal TK. Disappointingly, an incomplete response can occur with the long-term use of TK inhibitors, known as cancer drug resistance, which can be caused by kinome reprogramming. Hence, monitoring the status of TKs is crucial for revealing the underlying drug resistance mechanism. Here, we describe a TK activity-representing peptide library-based multiple reaction monitoring (TARPL-MRM) strategy for directly inferring TK activities. The strategy facilitated the assay of 87 human TKs through target quantification of 301 phosphorylation sites. Using this strategy, we demonstrated the heterogeneity of TK activity in different non-small cell lung cancer (NSCLC) cell lines and assessed the response of TK activities to the EGFR inhibitor AZD9291 in NSCLC cells. We found that the acquired resistance of H1975 cells to AZD9291 requires SRC activity, and inhibition of SRC plays potential roles in overcoming this resistance. In summary, our work reveals that this strategy has the potential to become a powerful tool for TK studies, clinical diagnostics, and the discovery of new therapeutic targets.

Patient-specific Boolean models of signalling networks guide personalised treatments

Prostate cancer is the second most occurring cancer in men worldwide. To better understand the mechanisms of tumorigenesis and possible treatment responses, we developed a mathematical model of prostate cancer which considers the major signalling pathways known to be deregulated. We personalised this Boolean model to molecular data to reflect the heterogeneity and specific response to perturbations of cancer patients. A total of 488 prostate samples were used to build patient-specific models and compared to available clinical data. Additionally, eight prostate cell line-specific models were built to validate our approach with dose-response data of several drugs. The effects of single and combined drugs were tested in these models under different growth conditions. We identified 15 actionable points of interventions in one cell line-specific model whose inactivation hinders tumorigenesis. To validate these results, we tested nine small molecule inhibitors of five of those putative targets and found a dose-dependent effect on four of them, notably those targeting HSP90 and PI3K. These results highlight the predictive power of our personalised Boolean models and illustrate how they can be used for precision oncology.

Proteomic discovery of non-invasive biomarkers of localized prostate cancer using mass spectrometry

Prostate cancer is the second most frequently diagnosed non-skin cancer in men worldwide. Patient outcomes are remarkably heterogeneous and the best existing clinical prognostic tools such as International Society of Urological Pathology Grade Group, pretreatment serum PSA concentration and T-category, do not accurately predict disease outcome for individual patients. Thus, patients newly diagnosed with prostate cancer are often overtreated or undertreated, reducing quality of life and increasing disease-specific mortality. Biomarkers that can improve the risk stratification of these patients are, therefore, urgently needed. The ideal biomarker in this setting will be non-invasive and affordable, enabling longitudinal evaluation of disease status. Prostatic secretions, urine and blood can be sources of biomarker discovery, validation and clinical implementation, and mass spectrometry can be used to detect and quantify proteins in these fluids. Protein biomarkers currently in use for diagnosis, prognosis and relapse-monitoring of localized prostate cancer in fluids remain centred around PSA and its variants, and opportunities exist for clinically validating novel and complimentary candidate protein biomarkers and deploying them into the clinic.

Activation of RSK2 upregulates SOX8 to promote methotrexate resistance in gestational trophoblastic neoplasia

Resistance to chemotherapy is frequently driven by aberrantly activated kinases in cancer. Herein, we characterized the global phosphoproteomic alterations associated with methotrexate (MTX) resistance in gestational trophoblastic neoplastic (GTN) cells. A total of 1111 phosphosites on 713 proteins were significantly changed, with highly elevated Ribosomal S6 Kinase 2 (RSK2) phosphorylation (pS227) observed in MTX-resistant GTN cells. Activation of RSK2 promoted cell proliferation and survival after MTX treatment in GTN cell models. Interestingly, RSK2 might play an important role in the regulation of reactive oxygen species (ROS) homeostasis, as manipulation of RSK2 activation affected ROS accumulation and SOX8 expression in GTN cells. In addition, overexpression of SOX8 partly rescued cell proliferation and survival in RSK2-depleted MTX-resistant GTN cells, suggesting that SOX8 might serve as a downstream effector of RSK2 to promote MTX resistance in GTN cells. Highly activated RSK2/SOX8 signaling was observed in MTX-resistant GTN specimens. Further, the RSK2 inhibitor BIX02565 effectively reduced SOX8 expression, induced ROS accumulation, and enhanced MTX-induced cytotoxicity in vitro and in vivo. Collectively, our findings suggested that RSK2 activation could promote MTX resistance via upregulating SOX8 and attenuating MTX-induced ROS in GTN cells, which may help to develop experimental therapeutics to treat MTX-resistant GTN.

Development of dual-ligand titanium (IV) hydrophilic network sorbent for highly selective enrichment of phosphopeptides

A hydrophilic metal-organic network based on Ti⁴⁺ and dual natural ligand, tannic acid (TA) and phytic acid (PA), has been developed to enrich phosphopeptides from complex bio-samples prior to liquid chromatography-mass spectrometric analysis. Due to the strong chelation ability of TA and PA, abundant Ti⁴⁺ can be immobilized in the material, forming hydrophilic network by one-step coordination-driven self-assembly approach. The sorbent, TA-Ti-PA@Fe₃O₄, exhibited satisfactory selectivity for the phosphopeptides in the tryptic digest of β-casein, and can eliminate the interference components in 1000-fold excess of bovine serum albumin. The adsorption capacity of the sorbents for phosphopeptides was up to 35.2 mg g^-1 and the adsorbing equilibrium can be reached in 5 min. The adsorbing mechanism has been investigated and the results indicated that the Ti⁴⁺ in forms of [Ti(f-TA)(H₂O)₄]²⁺, [Ti(f-PA)(H₂O)₄]²⁺ and Ti(f-PA)₂(H₂O)₂ may play an important role in the adsorption process. The sorbent of the TA-Ti-PA@Fe₃O₄ has been applied to enrichment of the phosphopeptides in tryptic digest of rat liver lysate, and 3408 phosphopeptides have been identified, while the numbers of the identified phosphopeptides were 2730 and 1217 when the sample was enriched by the commercial TiO₂ and Fe³⁺-IMAC kit, respectively. This work provides a strategy to enrich phosphopeptides from complex samples and shows great potential application in phosphoproteome research.

Proteomic Landscape of Prostate Cancer: The View Provided by Quantitative Proteomics, Integrative Analyses, and Protein Interactomes

Prostate cancer is the second most frequent cancer of men worldwide. While the genetic landscapes and heterogeneity of prostate cancer are relatively well-known already, methodological developments now allow for studying basic and dynamic proteomes on a large scale and in a quantitative fashion. This aids in revealing the functional output of cancer genomes. It has become evident that not all aberrations at the genetic and transcriptional level are translated to the proteome. In addition, the proteomic level contains heterogeneity, which increases as the cancer progresses from primary prostate cancer (PCa) to metastatic and castration-resistant prostate cancer (CRPC). While multiple aspects of prostate adenocarcinoma proteomes have been studied, less is known about proteomes of neuroendocrine prostate cancer (NEPC). In this review, we summarize recent developments in prostate cancer proteomics, concentrating on the proteomic landscapes of clinical prostate cancer, cell line and mouse model proteomes interrogating prostate cancer-relevant signaling and alterations, and key prostate cancer regulator interactomes, such as those of the androgen receptor (AR). Compared to genomic and transcriptomic analyses, the view provided by proteomics brings forward changes in prostate cancer metabolism, post-transcriptional RNA regulation, and post-translational protein regulatory pathways, requiring the full attention of studies in the future.

Proteomic profiling of bladder cancer for precision medicine in the clinical setting: A review for the busy urologist

At present, proteomic methods have successfully identified potential biomarkers of urological malignancies, such as prostate cancer (PC), bladder cancer (BC), and renal cell carcinoma (RCC), reflecting different numbers of key cellular processes, including extracellular environment modification, invasion and metastasis, chemotaxis, differentiation, metabolite transport, and apoptosis. The potential application of proteomics in the detection of clinical markers of urological malignancies can help improve patient assessment through early cancer detection, prognosis, and treatment response prediction. A variety of proteomic studies have already been carried out to find prognostic BC biomarkers, and a large number of potential biomarkers have been reported. It is worth noting that proteomics research has not been applied to the study of predictive markers; this may be due to the incompatibility between the number of measured variables and the available sample size, which has become particularly evident in the study of therapeutic response. On the contrary, prognostic correlation is more common, which is also reflected in existing research. We are now entering an era of clinical proteomics. Driven by proteomic-based workflows, computing tools, and the applicability of cross-correlation of proteomic data, it is now feasible to use proteomic analysis to support personalized medicine. In this paper, we will summarize the current emerging technologies for advanced discovery, targeted proteomics, and proteomic applications in BC, particularly in discovery of human-based biomarkers.

Using proteomic and transcriptomic data to assess activation of intracellular molecular pathways

Analysis of molecular pathway activation is the recent instrument that helps to quantize activities of various intracellular signaling, structural, DNA synthesis and repair, and biochemical processes. This may have a deep impact in fundamental research, bioindustry, and medicine. Unlike gene ontology analyses and numerous qualitative methods that can establish whether a pathway is affected in principle, the quantitative approach has the advantage of exactly measuring the extent of a pathway up/downregulation. This results in emergence of a new generation of molecular biomarkers-pathway activation levels, which reflect concentration changes of all measurable pathway components. The input data can be the high-throughput proteomic or transcriptomic profiles, and the output numbers take both positive and negative values and positively reflect overall pathway activation. Due to their nature, the pathway activation levels are more robust biomarkers compared to the individual gene products/protein levels. Here, we review the current knowledge of the quantitative gene expression interrogation methods and their applications for the molecular pathway quantization. We consider enclosed bioinformatic algorithms and their applications for solving real-world problems. Besides a plethora of applications in basic life sciences, the quantitative pathway analysis can improve molecular design and clinical investigations in pharmaceutical industry, can help finding new active biotechnological components and can significantly contribute to the progressive evolution of personalized medicine. In addition to the theoretical principles and concepts, we also propose publicly available software for the use of large-scale protein/RNA expression data to assess the human pathway activation levels.

THE ROLE OF PHOSPHORYLATION IN ATRIAL FIBRILLATION: A FOCUS ON MASS SPECTROMETRY APPROACHES

Atrial fibrillation (AF) is the most common arrhythmia worldwide. It is associated with significant increases in morbidity in the form of stroke and heart failure, and a doubling in all-cause mortality. The pathophysiology of AF is incompletely understood, and this has contributed to a lack of effective treatments and disease-modifying therapies. An important cellular process that may explain how risk factors give rise to AF includes post-translational modification (PTM) of proteins. As the most commonly occurring PTM, protein phosphorylation is especially relevant. Although many methods exist for studying protein phosphorylation, a common and highly resolute technique is mass spectrometry (MS). This review will discuss recent evidence surrounding the role of protein phosphorylation in the pathogenesis of AF. MS-based technology to study phosphorylation and uses of MS in other areas of medicine such as oncology will also be presented. Based on these data, future goals and experiments will be outlined that utilize MS technology to better understand the role of phosphorylation in AF and elucidate its role in AF pathophysiology. This may ultimately allow for the development of more effective AF therapies.

Post-Translational Modifications That Drive Prostate Cancer Progression

While a protein primary structure is determined by genetic code, its specific functional form is mostly achieved in a dynamic interplay that includes actions of many enzymes involved in post-translational modifications. This versatile repertoire is widely used by cells to direct their response to external stimuli, regulate transcription and protein localization and to keep proteostasis. Herein, post-translational modifications with evident potency to drive prostate cancer are explored. A comprehensive list of proteome-wide and single protein post-translational modifications and their involvement in phenotypic outcomes is presented. Specifically, the data on phosphorylation, glycosylation, ubiquitination, SUMOylation, acetylation, and lipidation in prostate cancer and the enzymes involved are collected. This type of knowledge is especially valuable in cases when cancer cells do not differ in the expression or mutational status of a protein, but its differential activity is regulated on the level of post-translational modifications. Since their driving roles in prostate cancer, post-translational modifications are widely studied in attempts to advance prostate cancer treatment. Current strategies that exploit the potential of post-translational modifications in prostate cancer therapy are presented.

Clinical proteomics for prostate cancer: understanding prostate cancer pathology and protein biomarkers for improved disease management

Following the introduction of routine Prostate Specific Antigen (PSA) screening in the early 1990′s, Prostate Cancer (PCa) is often detected at an early stage. There are also a growing number of treatment options available and so the associated mortality rate is generally low. However, PCa is an extremely complex and heterogenous disease and many patients suffer disease recurrence following initial therapy. Disease recurrence commonly results in metastasis and metastatic PCa has an average survival rate of just 3–5 years. A significant problem in the clinical management of PCa is being able to differentiate between patients who will respond to standard therapies and those who may benefit from more aggressive intervention at an earlier stage. It is also acknowledged that for many men the disease is not life threatenting. Hence, there is a growing desire to identify patients who can be spared the significant side effects associated with PCa treatment until such time (if ever) their disease progresses to the point where treatment is required. To these important clinical needs, current biomarkers and clinical methods for patient stratification and personlised treatment are insufficient. This review provides a comprehensive overview of the complexities of PCa pathology and disease management. In this context it is possible to review current biomarkers and proteomic technologies that will support development of biomarker-driven decision tools to meet current important clinical needs. With such an in-depth understanding of disease pathology, the development of novel clinical biomarkers can proceed in an efficient and effective manner, such that they have a better chance of improving patient outcomes.

An overview of advances in multi-omics analysis in prostate cancer

Prostate cancer (PCa) is a deadly disease for men, and studies of all types of omics data are necessary to promote precision medicine. The maturity of sequencing technology, the improvements of computer processing power, and the progress achieved in omics analysis methods have improved research efficiency and saved research costs. The occurrence and development of PCa is due to multisystem and multilevel pathological changes. Although omics research at a single level is important, this approach often has limitations. In contrast, the combined analysis of multiple types of omics data can better analyze PCa changes as a whole, thus ensuring the validity of research results to the greatest extent. This paper introduces the applications of single omics in PCa and then summarizes research progress in the combined analysis of two or more types of omics data, so as to systematically and comprehensively analyze the necessity of combined analysis of multiple omics data in PCa.

Exploring protein phosphorylation by combining computational approaches and biochemical methods

Post-translational modifications of proteins expand their functional diversity, regulating the response of cells to a variety of stimuli. Among these modifications, phosphorylation is the most ubiquitous and plays a prominent role in cell signaling. The addition of a phosphate often affects the function of a protein by altering its structure and dynamics. However, these alterations are often difficult to study and the functional and structural implications remain unresolved. New approaches are emerging to overcome common obstacles related to the production and manipulation of these samples. Here, we summarize the available methods for phosphoprotein purification and phosphomimetic engineering, highlighting the advantages and disadvantages of each. We propose a general workflow for protein phosphorylation analysis combining computational and biochemical approaches, building on recent advances that enable user-friendly and easy-to-access Molecular Dynamics simulations. We hope this innovative workflow will inform the best experimental approach to explore such post-translational modifications. We have applied this workflow to two different human protein models: the hemeprotein cytochrome c and the RNA binding protein HuR. Our results illustrate the usefulness of Molecular Dynamics as a decision-making tool to design the most appropriate phosphomimetic variant.

Amyotrophy Induced by a High-Fat Diet Is Closely Related to Inflammation and Protein Degradation Determined by Quantitative Phosphoproteomic Analysis in Skeletal Muscle of C57BL/6 J Mice

BACKGROUND

Ectopic fat accumulation in skeletal muscle results in dysfunction and atrophy, but the underlying molecular mechanisms remain unclear.

OBJECTIVE

The aim of this study was to investigate the effects of a high-fat diet (HFD) in modulating the structure and energy metabolism of skeletal muscle and the underlying mechanisms in mice.

METHODS

Four-week-old male C57BL/6 J mice (n = 30) were allowed 1 wk for acclimatization. After 6 mice with low body weight were removed from the study, the remaining 24 mice were fed with a normal-fat diet (NFD; 10% energy from fat, n = 12) or an HFD (60% energy from fat, n = 12) for 24 wk. At the end of the experiment, serum glucose and lipid concentrations were measured, and skeletal muscle was collected for atrophy analysis, inflammation measurements, and phosphoproteomic analysis.

RESULTS

Compared with the NFD, the HFD increased (P < 0.05) body weight (35.8%), serum glucose (64.5%), and lipid (27.3%) concentrations, along with elevated (P < 0.05) expressions of the atrophy-related proteins muscle ring finger 1 (MURF1; 27.6%) and muscle atrophy F-box (MAFBX; 44.5%) in skeletal muscle. Phosphoproteomic analysis illustrated 64 proteins with differential degrees of phosphorylation between the HFD and NFD groups. These proteins were mainly involved in modulating cytoskeleton [adenylyl cyclase-associated protein 2 (CAP2) and actin-α skeletal muscle (ACTA1)], inflammation [NF-κB-activating protein (NKAP) and serine/threonine-protein kinase RIO3 (RIOK3)], glucose metabolism [Cdc42-interacting protein 4 (TRIP10); protein kinase C, and casein kinase II substrate protein 3 (PACSIN3)], and protein degradation [heat shock protein 90 kDa (HSP90AA1)]. The HFD-induced inhibitions of the insulin signaling pathway and activations of inflammation in skeletal muscle were verified by Western blot analysis.

CONCLUSIONS

Quantitative phosphoproteomic analysis in C57BL/6 J mice fed an NFD or HFD for 24 wk revealed that the phosphorylation of inflammatory proteins and proteins associated with glucose metabolism at specific serine residues may play critical roles in the regulation of skeletal muscle atrophy induced by an HFD. This work provides information regarding underlying molecular mechanisms for inflammation-induced dysfunction and atrophy in skeletal muscle.

Comprehensive characterization of the phosphoproteome of gastric cancer from endoscopic biopsy specimens

Rationale: Cancer phosphoproteomics can provide insights regarding kinases that can be targeted for therapeutic applications. Monitoring the phosphoproteomics in cancer is expected to play a key role in optimizing treatments with kinase inhibitors. Clinical phosphoproteomics in surgical tissues and patient-derived models has been studied intensively. However, the reported data may not accurately reflect the phosphosignaling status in patients due to the effect of ischemia occurring during surgery or changes in the characteristics of cancer cells when establishing the models. In contrast, endoscopic biopsies have an advantage for clinical phosphoproteomics because they can be rapidly cryo-preserved. We aimed to develop a highly sensitive method for phosphoproteomics in endoscopic biopsies of gastric cancer.

Methods: Three tumor biopsies and three normal gastric biopsies were obtained by endoscopy at one time, and subjected to our optimized phosphoproteomics. Phosphopeptides were enriched with an immobilized metal affinity chromatography, and labeled with Tandem Mass Tag reagent. Quantified phosphosites were compared between the pairs of tumor/normal biopsies within same patient. Cancer-specific activated pathways and kinases were identified by pathway enrichment analysis and kinase-substrate enrichment analysis.

Results: Our protocol enabled the identification of more than 10,000 class 1 phosphosites from endoscopic biopsies. A comparison between samples from cancer tissue and normal mucosa demonstrated differences in the phosphosignaling, including biomarkers of response to DNA damage. Finally, cancer-specific activation of DNA damage response signaling was validated by additional phosphoproteomics of other patients and western blotting of gastric cancer/normal cells.

Conclusion: In summary, our pioneering approach will facilitate more accurate clinical phosphoproteomics in endoscopic biopsies, which can be applied to monitor the activities of therapeutic kinases and, ultimately, can be a useful tool to precision medicine.

Machine learning empowers phosphoproteome prediction in cancers

MOTIVATION

Reversible protein phosphorylation is an essential post-translational modification regulating protein functions and signaling pathways in many cellular processes. Aberrant activation of signaling pathways often contributes to cancer development and progression. The mass spectrometry-based phosphoproteomics technique is a powerful tool to investigate the site-level phosphorylation of the proteome in a global fashion, paving the way for understanding the regulatory mechanisms underlying cancers. However, this approach is time-consuming and requires expensive instruments, specialized expertise and a large amount of starting material. An alternative in silico approach is predicting the phosphoproteomic profiles of cancer patients from the available proteomic, transcriptomic and genomic data.

RESULTS

Here, we present a winning algorithm in the 2017 NCI-CPTAC DREAM Proteogenomics Challenge for predicting phosphorylation levels of the proteome across cancer patients. We integrate four components into our algorithm, including (i) baseline correlations between protein and phosphoprotein abundances, (ii) universal protein-protein interactions, (iii) shareable regulatory information across cancer tissues and (iv) associations among multi-phosphorylation sites of the same protein. When tested on a large held-out testing dataset of 108 breast and 62 ovarian cancer samples, our method ranked first in both cancer tissues, demonstrating its robustness and generalization ability.

AVAILABILITY AND IMPLEMENTATION

Our code and reproducible results are freely available on GitHub: https://github.com/GuanLab/phosphoproteome_prediction.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

RNA sequencing for research and diagnostics in clinical oncology

Molecular diagnostics is becoming one of the major drivers of personalized oncology. With hundreds of different approved anticancer drugs and regimens of their administration, selecting the proper treatment for a patient is at least nontrivial task. This is especially sound for the cases of recurrent and metastatic cancers where the standard lines of therapy failed. Recent trials demonstrated that mutation assays have a strong limitation in personalized selection of therapeutics, consequently, most of the drugs cannot be ranked and only a small percentage of patients can benefit from the screening. Other approaches are, therefore, needed to address a problem of finding proper targeted therapies. The analysis of RNA expression (transcriptomic) profiles presents a reasonable solution because transcriptomics stands a few steps closer to tumor phenotype than the genome analysis. Several recent studies pioneered using transcriptomics for practical oncology and showed truly encouraging clinical results. The possibility of directly measuring of expression levels of molecular drugs' targets and profiling activation of the relevant molecular pathways enables personalized prioritizing for all types of molecular-targeted therapies. RNA sequencing is the most robust tool for the high throughput quantitative transcriptomics. Its use, potentials, and limitations for the clinical oncology will be reviewed here along with the technical aspects such as optimal types of biosamples, RNA sequencing profile normalization, quality controls and several levels of data analysis.

Cellular and Molecular Mechanisms Underlying Prostate Cancer Development: Therapeutic Implications

Prostate cancer is the most frequent nonskin cancer and second most common cause of cancer-related deaths in man. Prostate cancer is a clinically heterogeneous disease with many patients exhibiting an aggressive disease with progression, metastasis, and other patients showing an indolent disease with low tendency to progression. Three stages of development of human prostate tumors have been identified: intraepithelial neoplasia, adenocarcinoma androgen-dependent, and adenocarcinoma androgen-independent or castration-resistant. Advances in molecular technologies have provided a very rapid progress in our understanding of the genomic events responsible for the initial development and progression of prostate cancer. These studies have shown that prostate cancer genome displays a relatively low mutation rate compared with other cancers and few chromosomal loss or gains. The ensemble of these molecular studies has led to suggest the existence of two main molecular groups of prostate cancers: one characterized by the presence of ERG rearrangements (~50% of prostate cancers harbor recurrent gene fusions involving ETS transcription factors, fusing the 5' untranslated region of the androgen-regulated gene TMPRSS2 to nearly the coding sequence of the ETS family transcription factor ERG) and features of chemoplexy (complex gene rearrangements developing from a coordinated and simultaneous molecular event), and a second one characterized by the absence of ERG rearrangements and by the frequent mutations in the E3 ubiquitin ligase adapter SPOP and/or deletion of CDH1, a chromatin remodeling factor, and interchromosomal rearrangements and SPOP mutations are early events during prostate cancer development. During disease progression, genomic and epigenomic abnormalities accrued and converged on prostate cancer pathways, leading to a highly heterogeneous transcriptomic landscape, characterized by a hyperactive androgen receptor signaling axis.

Towards precision medicine: the role and potential of protein and peptide microarrays

Techniques to comprehensively analyze protein signatures are pivotal to unravel disease mechanisms, develop novel biomarkers and targeted therapies. In this frame, protein and peptide microarrays can play a major role in fuelling precision medicine.

Therapeutic Targeting of mTOR in T-Cell Acute Lymphoblastic Leukemia: An Update

T-cell acute lymphoblastic leukemia (T-ALL) is an aggressive blood malignancy that arises from the clonal expansion of transformed T-cell precursors. Although T-ALL prognosis has significantly improved due to the development of intensive chemotherapeutic protocols, primary drug-resistant and relapsed patients still display a dismal outcome. In addition, lifelong irreversible late effects from conventional therapy are a growing problem for leukemia survivors. Therefore, novel targeted therapies are required to improve the prognosis of high-risk patients. The mechanistic target of rapamycin (mTOR) is the kinase subunit of two structurally and functionally distinct multiprotein complexes, which are referred to as mTOR complex 1 (mTORC1) and mTORC2. These two complexes regulate a variety of physiological cellular processes including protein, lipid, and nucleotide synthesis, as well as autophagy in response to external cues. However, mTOR activity is frequently deregulated in cancer, where it plays a key oncogenetic role driving tumor cell proliferation, survival, metabolic transformation, and metastatic potential. Promising preclinical studies using mTOR inhibitors have demonstrated efficacy in many human cancer types, including T-ALL. Here, we highlight our current knowledge of mTOR signaling and inhibitors in T-ALL, with an emphasis on emerging evidence of the superior efficacy of combinations consisting of mTOR inhibitors and either traditional or targeted therapeutics.