Personalized Cancer Treatment and Patient Stratification Using Massive Parallel Sequencing (MPS) and Other OMICs Data

Abstract

Cancer research is moving at a startling pace, most particularly with the identification and characterization of molecular signatures and biomarkers that will be used for prevention, early detection, diagnosis, treatment, prediction, and prognostication. The goal of cancer therapy is to match the right treatment with the right patient. To this end, several clinical trials are now employing patient stratification using “Massive Parallel Sequencing” or informally called “Next -Generation Sequencing” (NGS) to identify clinically actionable targets in real time.

The omics revolution is yielding important new insights into the causes and mechanisms of diseases and drug responses and in understanding the effects of genes and environment in disease predisposition and acquired resistance. It is paving the way for precision medicine (PM) a.k.a. personalized medicine, which focuses its attention on factors specific to an individual patient to provide individualized care; information about a patient’s genes, proteins, and environment is used to prevent, diagnose, and tailor medical care to that of the individual. The use of NGS and omics data is currently revolutionizing how cancer patients are treated. Targeted therapies that take advantage of the knowledge about an individual’s specific cancer cells are currently being applied to treat many different types of cancer.

PM also requires that we address the multidimensionality of cancer biomarkers. PM will require a systems biology approach integrating omics platform data to develop novel probabilistic models that can be applied to early detection, prognosis, prediction, and prevention. Ultimately, PM will be based on real-time profiling of an individual’s tumor, which translates into optimized treatment that will extend both overall survival and quality of life.

Appendix 10.1: Next-Generation Sequencing (NGS)

“Massive Parallel Sequencing” or informally called next-generation sequencing (NGS) has greatly increased the speed of DNA sequencing, taking it from 84 kilobase (kb) per run in 1998 to greater than 1 gigabase (Gb) per run in 2005 to multiple Gb per run today. The ability to perform NGS, also known as massive parallel sequencing, has revolutionized throughput, heralding genomic science’s “next generation.”

Sequencing the human genome involves sequencing 3.2 billion bases at 30× coverage (on average each base in the genome is sequenced 30 times). In 2005, capacity was limited to 1.3 human genomes sequenced annually. This has risen exponentially to the point where as of 2014, approximately 18,000 genomes per year can be sequenced, which has come with a tremendous reduction in cost (approximately $1,000 per genome).

Since the introduction of NGS, major advances have focused on further increasing speed and accuracy, which has greatly reduced manpower and cost. The current bottleneck is storage, processing, and analysis of the voluminous amount of sequencing data generated.

The Nobel Prize in 1980 was awarded to Wally Gilbert and Fred Sanger for developing the first methods for DNA sequencing. Sanger sequencing became the gold standard in molecular diagnostics, but it has finally given way to NGS. While NGS is based on Sanger sequencing, which involves the incorporation of fluorescently labeled deoxyribonucleotide triphosphates (dNTPs) into a DNA template strand during sequential cycles of DNA synthesis that are identified using fluorophore excitation, the major difference is that in NGS, millions of fragments are being sequenced simultaneously. It is this massively parallel process that has brought sequencing into the twenty-first century.

There are several companies (e.g., Life Technologies and Applied Biosystems (Thermo Fisher Scientific), Illumina, Roche, and Pacific Biosciences) that have developed NGS systems, and while there are differences, four fundamental steps are shared: (1) DNA preparation of the sequencing library, (2) amplification, (3) sequencing, and (4) data analysis (see Fig. 10.7). Each of these is dealt with in turn:

• 1. DNA Preparation of the Sequencing Library

  Crucial to this step is the preparation of random DNA fragments, and size is dependent on the particular sequencing platform and application: whole-genome versus whole-exome sequencing (only exons of genes are sequenced or ~1% of the genome). The DNA sample is prepared using a process that involves either sonication or enzymes to generate random fragments. Adapters are then added to both ends of the fragments and this constitutes the sequencing library. This library can now be anchored and immobilized to a solid support on which the sequencing reaction will take place. Different types of adapters and support systems can be used.

• 2. Amplification

  In this next step, amplification of fragments takes place either in an emulsion or in solution. On the Illumina platform, for example, fragments are captured on a surface of bound oligos complementary to the library adapters. This allows each fragment to be amplified into distinct, clonal clusters through what is termed bridge amplification.

• 3. Sequencing

  Sequencing can be accomplished using different methodologies depending on the platform. In general, fluidic systems running on a microliter scale are involved in the sequencing reaction. The immobilized DNA reacts with the regulated flow of reagents. Life Technologies and Roche sequencing systems involve the addition of a single nucleotide, which, if complementary to the sequence, is incorporated. Any nucleotides that are not incorporated are washed away, and the DNA is mixed with another nucleotide-containing solution. If this additional nucleotide is incorporated, then the system registers the event. Detection can be based on light emission (GS FLX system, Roche) or emission of hydrogen ions released during the polymerization reaction (Ion Torrent, Life Technologies). On the Illumina platform, their proprietary sequencing by synthesis (SBS) system is used, in which all four reversible terminator-bound dNTPs are present in each sequencing cycle, resulting in natural competition that effectively minimizes incorporation bias and reduces raw error rates.

• 4. Data Analysis

  Data analysis systems are critical to the effective interpretation of the vast amounts of sequencing data generated and represent a potential bottleneck for going from raw output to aligned sequences. The “draft” sequencing data must first be aligned to a reference genome. Once processed, various analyses can be performed, including but not limited to identifying single nucleotide polymorphisms (SNPs), insertion-deletions (indels), performing read counting for RNA methods, as well as phylogenetic or metagenomic analysis.

  Increasing the speed of sequence data analysis and developing the necessary data storage capacity are important considerations moving forward. By some estimates, up to one billion people may have their genomes sequenced by 2025, producing an inordinate amount of data within the next decade. How this will be handled is a top priority as we continue to embrace PM.

Fig. 10.7

(a–d) Next-generation sequencing steps. (Courtesy of Illumina, Inc.)