Tag Archives: clinical research

Cell Lines: multi-omics network of associations to model precision treatment

Modern advances in personalized medicine have used technology to characterize a patient’s fundamental biology, in terms of DNA, RNA, and protein. This can be used to classify a disease (such as breast cancer subtype) or to characterize important details of the patient’s disease(such as genes related to drug response for a particular treatment). These techniques can also be used in research for diseases such as cancer and genetic diseases.

 

The high variety of cancer mutations for each individual patient means that effective diagnosis and treatment of cancer must take into account a high degree of complexity. By sequencing individual cancer genomes, researchers and physicians may develop more targeted medical solutions. Cancer is the second major cause of mortality in the United States and targeted cancer therapies are a growing treatment type for many cancers as it brings an exponential increase in effectiveness over traditional cancer therapies.

 

Breast cancer can arise from many different types of mutations. As a result of these mutations, it  can be subdivided into a number of subtypes. Six major subtypes, previously identified and documented, are considered particularly useful for prognosis and treatment strategy. These subtypes respond differently to chemotherapy and hormone treatments. Currently, doctors only test for a handful of molecular signatures and over 40% of those patients’ cancers do not fit into one subtype. Cell lines are often used first in research for pre-clinical models, as they mirror many of the molecular characteristics of tumors, and are a less complicated model than a human.Cell lines are used to study cancer in a lab without human or animal subject involvement and are utilized to  model interactions between cell types and various drugs and therapeutics.

 

This project was inspired by Daemon et al., 2013, “Modeling precision treatment of breast cancer”, which focuses on over 70 different Breast cancer cell lines and over 90 different therapeutic agents. The project included SNP array (a type of microarray that discovers variations in the genome), RNA-seq (which looks at the whole transcriptome), exome-seq (exome capture, which looks at all of the expressed genes at a given point in time), genome-wide methylation (study of epigenetic alterations), and as well as integrating a number of algorithmic methods to identify molecular features,using advanced machine learning algorithms.

 

The TBioInfo platform has a number of advanced machine learning algorithms including,  Biassociation algorithm, which was used to integrate a number of different omics data types.This includes RNA expression, cell mutations, and drug effectiveness to find relationships and better understand how medications affect the breast cancer cells.This work was able to develop predictive drug response signatures and this research can be built upon with future clinical models. One issue with this study is a cell panel does not capture features such as tumor microenvironment, which is critical to understanding tumors.

Introducing Omics Logic

The Human Genome Project showed that while mankind’s genetic makeup is 99.1% identical, and only 0.9% of genetic variability creates vast variability that exists within the human species (Novelli 2010). Personalized medicine is the effort to prescribe the most appropriate drug for each individual patient based on their specific biology. Genetics explain some of the variations in responses seen during clinical trials. The variety of cancer mutations means that effective diagnosis and treatment of cancer must take into account a high degree of complexity. By sequencing individual cancer genomes, researchers and physicians may develop more targeted medical solutions. By using interpreted data in routine patient exams, clinicians can analyze how the symptoms of a disease in a patient correlate with their specific biology, resulting in a more effective treatment. Cancer is the second major cause of mortality in the United States, but targeted cancer therapies are bringing about an exponential increase in effectiveness over traditional cancer therapies.The potential exists to identify early indicators of disease, including cancer, in the form of biomarkers for early detection of a disease.

 

According to the Global Oncology Trend Report, global spending on cancer medications rose 10.3 percent in 2014, bringing the total to $100 billion, up from $75 billion in 2010. The rising cost of cancer treatment is linked to the emergence of precision therapeutics, which are costly to develop and often fail before they reach the market. While more effective, they target a smaller population which is hard to identify. The pharmaceutical industry recently turned to theoretical and computational modelling to improve the drug discovery process, lowering the cost of care in the process.

 

The cost of next-generation sequencing and other techniques that provide comprehensive whole-patient data is decreasing rapidly, making personalized multi-omics analysis increasingly cost effective and accessible. There is a long and costly effort to introduce precision treatment based on molecular data into both health delivery and pharma. A massive amount  of data is available, yet few know how to use the data insights effectively. To be truly effective, the data has to be analyzed effortlessly with an integrative approach. Using multi-omics data in developing an informed, personalized approach to treatment, access to effective clinical trials, and preventive strategies can provide major cost savings in terms of avoiding ineffective treatments, expensive diagnostic regimens, etc.

 

With affordable whole-patient scale data just around the corner, the challenge has now moved into the realm of extraction of meaningful insights from the data. To get the most value from multi-omics data analysis in clinical applications, Pine Biotech is developing an omics-first machine learning platform, OmicsLogic. The platform goes beyond analytics, integrating clinical knowledge with multi-omics raw data analysis for biomarker discovery and personalized molecular studies. As the field evolves and data continues to become available, algorithmic innovation is poised to be a driving force in solving healthcare ecosystem challenges. The wealth of data that is generated should be exploited – ultimately to improve care, and benefit consumers.

 

 

 

Novelli, G. Personalized genomic medicine. Int Emerg Med. 2010;5(Suppl 1):S81-90. doi:10.1007/s11739-010-0455-9