The facts

„Colorectal carcinoma“ means several malignant tumor types in the colon or rectum. Malignant tumors rarely occur in other intestinal areas like small intestine or caecum. ^[1]

Colorectal cancer (CRC) had a low incidence rate in 1950. However, it became a predominant cancer type and now accounts for about 10% of cancer-related mortality in western countries.^[1,2] CRC is the second most common cancer type in men and women in Germany. It ranks third behind lung and breast cancer worldwide. In Germany, about 26.000 women and 32.300 men developed CRC in 2016, while 11.400 women and 13.400 men died from it. More than half of the patients developing CRC had an age of 70 years and above. Only 10 % of the new cases occur before the age of 55. ^[2] The lifetime risk of developing CRC is about 6 %, with chronic inflammatory bowel diseases and other important factors like unhealthy diet or lack of exercise raising the cancer risk. Inheritable genetic mutations cause 3-5 % of all CRC cases, especially colon cancer. Affected people have a very high risk of developing cancer in the early age of 20-40 years. ^[1]

In the year 2018, the World Health Organization counted about 1,8 Mio new CRC cases and 880.000 deaths. ^[3,4] More than 50 % of the new cases were registered in Asia (see Fig. 1). The global incidence rate is estimated to increase up to 3 Mio until 2040. ^[3]

As described above, men are stronger affected by CRCs than women. ^[2,3,6] Additionally, cancer cases strongly increased in the developed countries due to the ageing population in the recent years (see Fig. 2). ^[3]

Sex differences

Although women have a lower incidence of CRC than men, right-sided CRCs occur more frequently in women compared to men, whereas left-sided CRCs have an equal frequency between both sexes. Several evidences demonstrate that the rate of right-sided CRC cases is strikingly higher in women than in men (61.7% vs 38.3%), while only slightly more left-sided CRC cases are observed in women than men (52.1% vs 47.8%). The reason for this sex difference is still not proven and of concern for women, due to the association of right-sided CRCs with the poorest clinical outcomes among all CRC patients. ^[5]

Studies revealed that metabolite rates in the glycolysis pathway, pentose phosphate pathway, carnitine shuttle metabolism, asparagine synthesis, methionine metabolism and the polyamine synthesis pathway showed sex-related differences when measured in tumor samples compared to normal samples. Tumors from women and men also used different metabolic intermediates between the sexes for energy production to support cell growth. ^[5] Thus, modern CRC therapy approaches should ideally consider not only common molecular information, but also details of sex-specific mechanisms.

Further studies also indicate that the gut microbiota is partly correlated with the tumor development as well as emerging drug resistance. ^[4,6]

The upgrade of personalized colorectal cancer treatment

Personalizing cancer treatment is still challenging. No expert believes in finding a magic cure against CRC nowadays. However, therapeutic approaches based on patient’s genetic variations provide the most promising chances for patients. Numerous genetic mutations are already associated with risk of CRC. ^[7] But the manual search for gene variants and appropriate drugs in cancer therapy is very labour intensive and time consuming, while the number of new medical insights gained from scientific research continuously increases. Further, economic acting must be brought into accordance with patient welfare.

Therefore, BioVariance has now developed the web-based platform OncoVariant to identify the most suitable medication for patients suffering from colorectal, breast or prostate cancer. State-of-the-art automatization and parallelization techniques are combined for comparing patient’s gene variants with profound knowledge from worldwide databases regarding treatment options. The final report contains information about patient’s variants and appropriate clinical evidence as well as information on the proposed drugs and their interactions. The examination of numerous high-quality databases ensures the high value of the proposed therapy options. Thus, OncoVariant enables personalized cancer therapy while minimizing time and effort on behalf of attending physicians. ^[8] Finding the perfect treatment for each patient is now as easy as never before.

Here you can find more information about the web-based platform OncoVariant.

Contact Person:

Kerstin Hammer

Sources

Der Beitrag Colorectal cancer – The evil within erschien zuerst auf BioVariance - data-driven diagnostics.

Technological progress

The rapid advances in cutting-edge technologies and informatics tools utilized in biomedical science to generate and process high-throughput biological datasets have finally spread into various economic sectors. Omic technologies adopt a holistic view of the molecular characteristics and actions that make up a cell, tissue or organism. “Omics” are novel comprehensive approaches for the analysis of complete genetic or molecular profiles of organisms by using integrative pipelines, showing how complex interactions between genes and molecules influence the phenotype, e.g. the disease symptoms in a patient. Great improvements in next-generation sequencing, microarray, mass spectrometry and nuclear magnetic resonance technologies have transformed biological and biomedical research over the past years. The analysis of omic data is a rapidly expanding field, with the constant development of new statistical methods and the creation of new perspectives not only for medical research and diagnosis. ^[1,2,3,4,5]

Examples of different types of omics: ^[2,3,4,5]

• Genomics: entire set of genes, “the genetic landscape” of an organism
• Proteomics: entire set of proteins produced by an organism
• Transcriptomics: entire set of RNA molecules
• Pharmacogenomics: the effect of variations within the human genome on drug response

Compared to single omics interrogations, multi-omics can provide researchers with a greater understanding of the flow of information, from the original cause of disease (genetic, environmental or developmental) to the functional consequences or relevant interactions. ^[2,3]

Computational Biology

Bioinformatics uses advanced computational tools for the management and analysis of biological data that are mined from large databases. The combination of bioinformatics tools with adequate databases allows the generation of maps of cellular and physiological pathways. This integrative approach is called computational biology. Bioinformatics techniques enable a complete representation of the cell and the organism as well as the prediction of highly complex systems like cellular interaction networks or the phenotypes of organisms (Fig. 1). ^[6]

Merging omics and bioinformatics provides the basis of systems biology, a study field used in modeling organisms to enhance the understanding of the complex biological interactions occurring within cells and tissues at the gene, protein and metabolite level. ^[2,5] But with decreasing time and cost effort to generate these datasets, omics data integration has created both exciting opportunities and immense challenges for biologists, computational biologists, biostatisticians and biomathematicians.

Challenges

Nowadays, there are plenty of web-based solutions for data storage, sharing and analysis. Examples of common portals to access and download omic data are Gene Expression Omnibus, ArrayExpress, Expression Atlas or Ensembl. Other sites provide a framework for analyzing omics data in an interactive and multi-layered fashion, such as NCBI, UCSC or Human Brain Atlas. The rise of a high number of bioinformatics tools has fostered initiatives aimed at generating portals to list them and support their effective use. For example, EBI has a bioinformatics service portal listing a variety of databases and tools tailored for specific quests or topics, while OMICtools is a library of software, databases and platforms for big-data processing and analysis. ^[4,5]

But although there are various systems biology tools and applications available for network analysis, pathway construction, genome alignments, visualization and many more tasks, hardly any of these approaches can integrate three or more omics datasets. Managing and integrating such multi-dimensional data continues to be difficult, partly because each omics analysis can generate tera- to peta-byte sized data files daily. Thus, current biological and technical challenges include differences in data cleaning, storage and processing, biological contextualization, statistical validation, computational power and capacity as well as the lack of robust pipelines to integrate additional types of data. Innovative techniques of omic data integration are urgently needed for a broad range of research areas like food and nutrition science, analysis of microbiomes, genotype–phenotype interactions, systems biology and disease biology. ^[3,4,5]

Importance of omic tools in medicine

Comprehensive profiling has provided deep insight into the origin of diseases like cancer, coronary or infectious diseases, the search for diagnostic markers, potential therapies and the prediction of treatment response. Predictive markers or early diagnostic markers are of great benefit especially in cancer therapy, as various cancer types are detected at an advanced stage and 5‐year survival is poor. ^[1,5]

Multi-omic integrative analyses are meant to provide a comprehensive view of disease mechanisms that disrupt normal cellular functions and lead to disease progression and drug resistance. While computational and statistical analyses of single-omic datasets are well established, approaches for integrating multi-omics data are still far from being standardized. To keep up with the pace of data generation and growth of biological knowledge, existing methods should be extended or generalized, and new computational tools need to cope with the complexity and multi-level structure of the available information. ^[7]

Importance of omic tools in other sectors

Not only the medical sector benefits from specialized and integrated bioinformatics tools, but also other important fields like agriculture, plant science and animal science. As an example, honey bees are essential pollinators and crucial to the world’s agriculture. The health of honey bees has been declining over the past decade, with beekeepers losing more than a quarter of their colonies each winter since 2007. The causes of bee declines are complex, variable over space and time, and often difficult to identify.

The Canadian project BeeCSI, which was started in 2018, uses genomic tools to develop a new health assessment and diagnosis platform powered by stressor-specific markers. The project leaders working at York University and University of British Columbia join a cross-nation team of researchers, informaticians, beekeepers and diagnostic labs to develop a new tool which will be used to assess and diagnose honey bee health. The aim of BeeCSI is to bring industrial modernization in the form of this innovative tool which will give a quick diagnosis of bee health within living colonies, thus allowing beekeepers to immediately take appropriate measures to reduce honey bee loss. The 10 Mio $ project is funded by Genome Canada and Ontario Genomics. ^[8]

Future aspects

Studies in genomics, transcriptomics and proteomics have shaped our understanding of cellular complexity and heterogeneity. In the case of medicine, as the costs of omic analyses continually decrease, more types of high throughput data can be integrated into the clinic for individualized treatment regimens. Multi-scale omic data generation, development of analytical methodology, adaptation of those methods to specific disease, repeating this process for multiple diseases and integrating between them are the fundamental tasks of omic research, that can’t be conducted by only one research group. To ensure the proactive flow of data across and between different fields of expertise, these undertakings necessitate coordinated efforts of many skilled groups, leading to a standardization of data formats and pipelines for integrated multi-omic analyses. ^[1,2,4,5]

Contact person:

Kerstin Hammer

Sources

Der Beitrag Omic Tools erschien zuerst auf BioVariance - data-driven diagnostics.