New class of drugs: stapled peptides

Stapled peptides refers to a computational drug design technique that may create a whole new class of drugs by being able to more effectively target substances within cells and increase the number of proteins which can be targeted. Stapled peptides are generated through the synthetic enhancement of a 3-D alpha-helix protein segment with hydrocarbon bonds to make proteins more rigid and able to penetrate cell walls. The more rigid structure also gives stapled peptides longer lives through greater protease degradation resistance.

The two current classes of drugs, small molecules and biologics, are limited in that they can only target 20% of all proteins. Stapled peptides could allow a wider range of proteins to be used in drug-targeting. They are currently in clinical trials for the inhibition of a BCL-2 family protein, oncogene MCL-1, using an exclusive inhibitor, the MCL-1 BH3 helix, which could unblock caspase-dependent apoptosis in cancer cells (paper).

Personal genome: data analysis challenge

Five themes emerged from the material presented at the 3rd annual personal genomes meeting at Cold Spring Harbor Laboratory held September 10-12, 2010.

First was the trend of family sequencing becoming more of a norm; looking at genetic disease as it is represented in trios, quartets or other family groups.

Second was the trend of increasingly common multi-level analysis, investigating traditional genotype data together with structural variation, expression data, pathways, and cell lines.

Third was the trend of greater breadth and sophistication in cancer genome analysis; the fledgling field of a few years ago now including dozens of sequenced cancer tumor genomes, the first cancer methylome, and cancer transcriptome analysis.

Fourth was the trend of the oft-heard challenge of scaling personal genome interpretation, making it automated, affordable, and actionable.

The fifth theme was the continued improvement in genome sequencing technology through new approaches such as quantum dot nanocrystal sequencing and strobe sequencing.

Microbubbles and photoacoustic probes energize cancer researchers

The Canary Foundation’s eighth year of activities was marked with a symposium held at Stanford University May 25-27, 2010. The Canary Foundation focuses on the early detection of cancer, specifically lung, ovarian, pancreatic, and prostate cancer, in a three-step process of blood tests, imaging, and targeted treatment.

Imaging advances: microbubbles and photoacoustic probes
Imaging is an area that continues to make advances. One exciting development is the integration of multiple technologies, for example superimposing molecular information onto traditional CT scans. Contemporary scans may show that certain genes are over-expressed in the heart, for example, but obscure the specific nodule (tumor) location. Using integrins to bind to cancerous areas may allow their specific location to be detected (4 mm nodules now, and perhaps 2-3 mm nodules as scanning technologies continue to improve).

Other examples of integrated imaging technologies include microbubbles, which are gassy and can be detected with an ultrasound probe as they are triggered to vibrate. Similarly, photoacoustic probes use light to perturb cancerous tissue, and then sound detection tools transmit the vibrations. Smart probes are being explored to detect a variety of metaloproteases on the surface of cancer cells, breaking apart and entering cancer cells where they can be detected with an ultrasound probe.

Systems biology approaches to cancer
Similar to aging research, some of the most promising progress points in cancer research are due to a more systemic understanding of disease, and the increasing ability to use tools like gene expression analysis to trace processes across time. One example is being able to identify and model not just one, but whole collections of genes that may be expressed differentially in cancers, seeing that whole pathways are disrupted, and the downstream implications of this.

Cancer causality
Also as in aging research, the 'chicken or the egg' problem arises as multiple things that go wrong are identified, but which happens first, and causality, is still unknown. For example, in ovarian cancer, where there are often mutations in the p53 gene, and gene rearrangements and CNV (copy number variation; different numbers of copies of certain genes), but which occurs first and what causes both is unknown.

Predictive disease modeling
There continues to be a need for models that predict clinical outcome, and serve as accurate representations of disease. DNA and gene expression, integrated with traits and other phenotypic data in global coherent datasets could allow the ability to build probabilistic causal models of disease. It also may be appropriate to shift to physics/accelerator-type models to manage the scale of data now being generated and reviewed in biomedicine.

Status of cancer detection

The Canary Foundation’s annual symposium held May 4-6, 2009 indicated progress in two dimensions of a systemic approach to cancer detection: blood biomarker identification and molecular imaging analysis.

Systems approach to cancer detection
A systems approach is required for effective cancer detection as assays show that many proteins, miRNAs, gene variants and other biomarkers found in cancer are also present in healthy organisms. The two current methods are one, looking comprehensively at the full suite of genes and proteins, checking for over-expression, under-expression, mutation, quantity, proximity and other factors in a tapestry of biological interactions and two, seeking to identify biomarkers that are truly unique to cancer, for example resulting from post-translational modifications like glycosylation and phosphorylation. Establishing mathematical simulation models has also been an important step in identifying baseline normal variation, treatment windows and cost trade-offs.

Blood biomarker analysis
There are several innovative approaches to blood biomarker analysis including blood-based protein-assays (identifying and quantifying novel proteins related to cancer), methylation analysis (looking at abnormal methylation as a cancer biomarker) and miRNA biomarker studies (distinguishing miRNAs which originated from tumors). Creating antibodies and assays for better discovery is also advancing particularly protein detection approaches using zero, one and two antibodies.

Molecular Imaging
The techniques for imaging have been improving to molecular level resolution. It is becoming possible to dial-in to any set of 3D coordinates in the body with high-frequency, increase the temperature and destroy only that area of tissue. Three molecular imaging technologies appear especially promising: targeted microbubble ultrasound imaging (where targeted proteins attach to cancer cells and microbubbles are attached to the proteins which make the cancerous cells visible via ultrasound; a 10-20x cheaper technology than the CT scan alternative), Raman spectroscopy (adding light-based imaging to endoscopes) and a new imaging strategy using photoacoustics (light in/sound out).

Tools: Cancer Genome Atlas and nextgen sequencing
As with other high-growth science and technology areas, tools and research findings evolve in lockstep. The next generation of tools for cancer detection includes a vast cataloging of baseline and abnormal data and a more detailed level of assaying and sequencing. In the U.S., the NIH’s Cancer Genome Atlas is completing a pilot phase and being expanded to include 50 tumor types (vs. the pilot phase’s three types: glioblastoma, ovarian and lung) and abnormalities in 25,000 tumors. The project performs a whole genomic scan of cancer tumors, analyzing mutations, methylation, coordination, pathways, copy number, miRNAs and expression. A key tool is sequencing technology itself which is starting to broaden out from basic genomic scanning to targeted sequencing, whole RNA sequencing, methylome sequencing, histone modification sequencing, DNA methylation by arrays and RNA analysis by arrays. The next level would be including another layer of detail, areas such as acetylation and phosphorylation.

Future paradigm shifts: prevention, omnisequencing, nanoscience and synthetic biology
Only small percentages of annual cancer research budgets are spent on detection vs. treatment, but it is possible that the focus will be further upstreamed to prevention and health maintenance as more is understood about the disease mechanisms of cancer. Life sciences technology is not just moving at Moore’s Law paces but there are probably also some paradigm shifts coming.

The three most suggestive areas for coming life science discontinuities are genomic sequencing, nanoscience and synthetic biology.

Genomic sequencing contemplates the routine scanning of each individual and tumor at multiple levels: genomic, proteomic, methylomic, etc. Nanoscience is the ability to design, construct and render mobile a large variety of molecular [biological] devices. Synthetic biology is designing new or modifying existing biological pathways in order to produce systems with superior or different properties, exercised by both traditional practitioners (recent conferences: Advances in Synthetic Biology, Synthetic Biology 4.0) and diybio’ers.