Next-generation sequencing refers to non-Sanger-based high-throughput DNA sequencing technologies. Up to billions of DNA strands can be sequenced in parallel, yielding substantially more throughput concepts as well as minimizing the need for the fragment-cloning methods that are often used in Sanger sequencing of genomes. Next-generation sequencing has mostly superseded conventional Sanger sequencing. There are numerous opportunities to use NGS in clinical practice to improve patient care.
There are a number of different NGS platforms using different sequencing technologies. However, all NGS platforms perform sequencing of millions of small fragments of DNA in parallel. Bioinformatics analyses are used to piece together these fragments by mapping the individual reads to the human reference genome. NGS can be used to sequence entire genomes of 22,000 coding genes (a whole exome) or also can be constrained to specific areas of interest.
In 3DMed, we use ILLUMINA Nextseq500 to perform whole exome sequencing. Each of the bases in the human genome is sequenced more than 500 times to deliver accurate data and an insight into unexpected DNA variation.
3DMed has built up the world's largest collection of Patient Derived cancer Cell lines (PDCs), based on the abundant Chinese oncology samples from the liver, lung, esophagus, colon, rectum, gallbladder, head and neck.
These PDCs derived from Chinese cancer patients harbor the original genetic mutations and reflect the tumor heterogeneity. This PDC platform is greatly valuable in several ways for biomarker-driven drug discovery:
1) Population drug responsiveness for a unique indication can be estimated at as early as pre-clinical stage, by high throughput cell based assays on those PDCs that represent a large cohort of individuals.
2) Biomarkers may be extracted from subgroups of differentiated response by further comprehensive researches, such as genetic alterations, transcriptomics, proteomics and therefore algorithm.
3）The estimated population response data and biomarkers may guide the subsequent clinical trials, which might effectively save the cost and increase the success rate
In 3DMed, our 3in1 businesses model: precision cancer prevention, precision cancer diagnosis, and new drug development, is leading us to being a big data-driven company.
It's the knowledge learnt from the data that makes the data truly valuable. Big data involves a set of computational technologies to collect data, discover knowledge and develop applications. In 3DMed, we use distributed data mining and deep learning technologies to analyze data and develop an Oncology Data Service Platform (OncoDSP).
OncoDSP integrates genome profiles, clinical records and pharmacology data.The cancer genomic data is the key aspect in the three major businesses of 3DMed. With genomic profiles from healthy people and cancer patients, 3DMed can serve healthy patients to individualize their cancer prevention program. At the same time, data about cancer genomic profile in cancer patients combined with clinical records can help tailor precise medical treatment. Big data can also predict the drug efficacy and help clinical trial enrollment and increase the success rate of new drug development. In fact, acquiring and application of cancer big data results in the success of our three businesses—precision cancer prevention, precision cancer diagnosis, and new drug development.