Molecular mechanisms of anticancer effect of rutin

Because of the extensive biological functions of natural substances such as bioflavonoids, and their high safety and low costs, they could have high priority application in the health care system. The antioxidant properties of rutin, a polyphenolic bioflavonoid, have been well documented and demonstrated a wide range of pharmacological applications in cancer research. Since chemotherapeutic drugs have a wide range of side effects and rutin is a safe anticancer agent with minor side effects so recent investigations are performed for study of mechanisms of its anticancer effect. Both in-vivo and in-vitro examinations on anticancer mechanisms of this natural agent have been widely carried out. Regulation of different cellular signaling pathways such as Wnt/β-catenin, p53-independent pathway, PI3K/Akt, JAK/STAT, MAPK, p53, apoptosis as well as NF-ĸB signaling pathways helps to mediate the anticancer impacts of this agent. This study tried to review the molecular mechanisms of rutin anticancer effect on various types of cancer. Deep exploration of these anticancer mechanisms can facilitate the development of this beneficial compound for its application in the treatment of different cancers.

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.

RNA Sequencing in Comparison to Immunohistochemistry for Measuring Cancer Biomarkers in Breast Cancer and Lung Cancer Specimens

RNA sequencing is considered the gold standard for high-throughput profiling of gene expression at the transcriptional level. Its increasing importance in cancer research and molecular diagnostics is reflected in the growing number of its mentions in scientific literature and clinical trial reports. However, the use of different reagents and protocols for RNA sequencing often produces incompatible results. Recently, we published the Oncobox Atlas of RNA sequencing profiles for normal human tissues obtained from healthy donors killed in road accidents. This is a database of molecular profiles obtained using uniform protocol and reagents settings that can be broadly used in biomedicine for data normalization in pathology, including cancer. Here, we publish new original 39 breast cancer (BC) and 19 lung cancer (LC) RNA sequencing profiles obtained for formalin-fixed paraffin-embedded (FFPE) tissue samples, fully compatible with the Oncobox Atlas. We performed the first correlation study of RNA sequencing and immunohistochemistry-measured expression profiles for the clinically actionable biomarker genes in FFPE cancer tissue samples. We demonstrated high (Spearman's rho 0.65-0.798) and statistically significant (p < 0.00004) correlations between the RNA sequencing (Oncobox protocol) and immunohistochemical measurements for HER2/ERBB2, ER/ESR1 and PGR genes in BC, and for PDL1 gene in LC; AUC: 0.963 for HER2, 0.921 for ESR1, 0.912 for PGR, and 0.922 for PDL1. To our knowledge, this is the first validation that total RNA sequencing of archived FFPE materials provides a reliable estimation of marker protein levels. These results show that in the future, RNA sequencing can complement immunohistochemistry for reliable measurements of the expression biomarkers in FFPE cancer samples.

Oncobox Method for Scoring Efficiencies of Anticancer Drugs Based on Gene Expression Data

We describe here the Oncobox method for scoring efficiencies of anticancer target drugs (ATDs) using high throughput gene expression data. The method rationale, design, and validation are given along with the examples of its practical applications in biomedicine. The method is based on the analysis of intracellular molecular pathways activation and measuring expressions of molecular target genes for every ATD under consideration. Using Oncobox method requires collection of normal (control) expression profiles and annotated databases of molecular pathways and drug target genes. Both microarray and RNA sequencing profiles are acceptable, although the latter type of data prevails in the most recent applications of this technique.

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.

High-Throughput Mutation Data Now Complement Transcriptomic Profiling: Advances in Molecular Pathway Activation Analysis Approach in Cancer Biology

We recently reviewed the current progress in the use of high-throughput molecular “omics” data for the quantitative analysis of molecular pathway activation. These quantitative metrics may be used in many ways, and we focused on their application as tumor biomarkers. Here, we provide an update of the most recent conceptual findings related to pathway analysis in tumor biology, which were not included in the previous review. The major novelties include a method enabling calculation of pathway-scale tumor mutation burden termed “Pathway Instability” and its application for scoring of anticancer target drugs. A new technique termed Shambhala emerged that enables accurate common harmonization of any number of gene expression profiles obtained using any number of experimental platforms. This may be helpful for merging various gene expression data sets and for comparing their pathway activation characteristics. Another recent bioinformatics method, termed FLOating-Window Projective Separator (FloWPS), has the potential to significantly enhance the value of pathway activation profiles as biomarkers of cancer response to treatments. It reduces the minimum required number of training samples needed to construct a machine-learning-based classifier. Finally, several documented clinical cases have been recently published, in which gene-expression-based pathway analysis was successfully used for personalized off-label prescription of target drugs to metastatic cancer patients.

Pathway Based Analysis of Mutation Data Is Efficient for Scoring Target Cancer Drugs

Despite the significant achievements in chemotherapy, cancer remains one of the leading causes of death. Target therapy revolutionized this field, but efficiencies of target drugs show dramatic variation among individual patients. Personalization of target therapies remains, therefore, a challenge in oncology. Here, we proposed molecular pathway-based algorithm for scoring of target drugs using high throughput mutation data to personalize their clinical efficacies. This algorithm was validated on 3,800 exome mutation profiles from The Cancer Genome Atlas (TCGA) project for 128 target drugs. The output values termed Mutational Drug Scores (MDS) showed positive correlation with the published drug efficiencies in clinical trials. We also used MDS approach to simulate all known protein coding genes as the putative drug targets. The model used was built on the basis of 18,273 mutation profiles from COSMIC database for eight cancer types. We found that the MDS algorithm-predicted hits frequently coincide with those already used as targets of the existing cancer drugs, but several novel candidates can be considered promising for further developments. Our results evidence that the MDS is applicable to ranking of anticancer drugs and can be applied for the identification of novel molecular targets.

Personalized prescription of tyrosine kinase inhibitors in unresectable metastatic cholangiocarcinoma

Background

Cholangiocarcinoma is an aggressive tumor with poor prognosis. Most of the cases are not available for surgery at the stage of the diagnosis and the best clinical practice chemotherapy results in about 12-month median survival. Several tyrosine kinase inhibitors (TKIs) are currently under investigation as an alternative treatment option for cholangiocarcinoma. Thus, the report of personalized selection of effective inhibitor and case outcome are of clinical interest.

Case presentation

Here we report a case of aggressive metastatic cholangiocarcinoma (MCC) in 72-year-old man, sequentially treated with two targeted chemotherapies. Initially disease quickly progressed during best clinical practice care (gemcitabine in combination with cisplatin or capecitabine), which was accompanied by significant decrease of life quality. Monotherapy with TKI sorafenib was prescribed to the patient, which resulted in stabilization of tumor growth and elimination of pain. The choice of the inhibitor was made based on high-throughput screening of gene expression in the patient’s tumor biopsy, utilized by Oncobox platform to build a personalized rating of potentially effective target therapies. However, time to progression after start of sorafenib administration did not exceed 6 months and the regimen was changed to monotherapy with Pazopanib, another TKI predicted to be effective for this patient according to the same molecular test. It resulted in disease progression according to RECIST with simultaneous elimination of sorafenib side effects such as rash and hand-foot syndrome. After 2 years from the diagnosis of MCC the patient was alive and physically active, which is substantially longer than median survival for standard therapy.

Conclusion

This case evidences that sequential personalized prescription of different TKIs may show promising efficacy in terms of survival and quality of life in MCC.

Electronic supplementary material

The online version of this article (10.1186/s40164-018-0113-x) contains supplementary material, which is available to authorized users.