RNAi Therapeutics and Whole-Genome Sequencing

It is my expectation that in 15-20 years, every newborn and newly diagnosed cancer patient with good healthcare coverage will have their genomes sequenced. Personally, I cannot wait for consumer genomics companies like 23andMe to provide services that would allow me, a non-bioinformaticist, to readily analyze the sequences of friends and family. All this is made possible by the technical advances in bringing down the cost of sequencing so that for the price of a semi-luxurious holiday (~$4000) you can get your genome sequenced these days. Roche’s bid for the 800 pound gorilla of DNA sequencing, Illumina, illustrates that with its other molecular Dx costing not that much less, next-gen sequencing, especially that of whole genomes, has reached a stage where it has become relevant for diagnostic applications.

15-20 years is also about the time-frame when we should see a healthy flow of RNAi Therapeutics getting approved, many of those as personalized medicines, e.g. for the treatment of dominant-negative diseases such as TTR amyloidosis, Huntington’s Disease, or, of course, cancer. With many people knowing their genome sequence from birth, it will be possible to monitor and then start treating many of these gene carriers before they manifest symptoms and progress to more debilitating states. Others will benefit from an improved ability to correctly diagnose a disease in genetic terms, thus allowing the physician to prescribe the appropriate medicine. As a genetic medicine (aka gene therapy), RNAi Therapeutics is ideally suited to address many of these diseases. Sequencing technologies will also help unearth which gene targets such RNAi Therapeutics ought to go after and thus reduce target risk.

There is considerable discussion in biotech what would be the best business model for sequencing companies. The buzz around PacBio has fizzled a bit as there is a sense that their machines are too expensive and not best suited for the mainstream sequencing applications. Illumina meanwhile has a dual model: It caters to those that don’t want to mess with the sequencing themselves but would rather hand over the job to somebody with experience; it also does well in selling sequencing machines to those that prefer doing it themselves. This model, however, is agnostic to the type of sequence (human or not; whole genome or exome).

Complete Genomics, on the other hand, has specialized on just human whole genome sequencing. I like this model a lot from a commercial perspective as this is the area where the future diagnostic sequencing volume will be, and specialization not only allows the company to optimize its technology for this single application but also to put relatively more resources into developing the requisite, value-adding bioinformatic analysis tools. The company is struggling with convincing the market that the currently more widely used/pushed exome sequencing is not more than a blip in the history of sequencing, a history which will inevitably culminate in routine whole genome sequencing. Claiming that exomes, which cost almost as much as whole genomes to sequence, have staying power feels kind of like arguing that Alnylam has 'proprietary' SNALP technology, just because Alnylam has a larger advertising budget. Maybe not the best analogy, I admit, but if you are still reading this, then chances are that you don't mind and even agree with me.

Ultimately, what I find exciting and scary at the same time about this business model is that at one point a company like Complete Genomics will have assembled a treasure trove of sequence information. It will e.g. have a very good idea where to find the families with severe hypercholesterolemia, information that I imagine a Genzyme or healthcare insurance providers would love to know about.

Vertex Pharmaceuticals today received approval for a genetically personalized Cystic Fibrosis drug, Kalydeco (probably named by the same consultants that gave Kynamro its name), for which it has plans to charge around…three hundred thousand US dollars per annum (note: an earlier version mistakenly referred to Genzyme as the CF drug maker). It’s difficult for me to understand what a 10% improvement in lung function means to a CF patient (probably a lot), but even if it’s a great relieve who does not wonder whether such costs are sustainable: have 10-15 adults work for nothing else but to pay for the treatment of an unfortunate CF patient. I’m sure we’ll witness similar debates raging once the first RNAi Therapeutics get approved. Hopefully, a winning combination like RNAi and whole genome sequencing can contribute to keeping the financial cost of personalized medicines low by increasing drug development success rates through better targets and RNAi platform efficiencies and keeping waste to a minimum by only treating people genetically predicted to respond to the therapeutic.

Disclosure: I do not hold GNOM (Complete Genomics), and unless it drops by 30% for no good reason, do not have plans to buy over the next week.

Speaking of the Devil: Genentech Receives Full Takeover Offer from Roche; Stands to Become Alnylam’s Partner

Just as I was speculating that Genentech may become Alnylam’s next major RNAi Therapeutics partner, Roche makes an offer to buy the rest of Genentech’s shares that it does not own already. I’m sure the pundits will be all over what this event will mean for drug development, so just a few notes on its relevance for RNAi Therapeutics.

From the press release and based on historical comments against the notion of mega-mergers with fellow Big Pharma and against entering generics, it is clear that Roche is very keen on leading the development of innovative medicines, particularly personalized medicine. What better candidate than Genentech with all its know-how in this arena as well as size to sufficiently impact Roche's top and bottom lines? Just to demonstrate that personalized medicine is more and more becoming a reality, Roche appropriately also announced today European approval for an interferon-ribavirin treatment regimen for hepatitis C based on individual viral titers and rapid viral response. Interestingly, Alnylam disclosed really for the first time in the Pharmaceutical Executive report its interest in hepatitis C which might be best addressed by SNALP delivery. And how much more personalized can hepatitis C treatment get than to sequence the virus in a given patient and then deliver the appropriate siRNA combination?

Reassuringly, the press release prominently mentions RNAi Therapeutics as a central component of the innovation engine of the combined company:

“The structure of the combined company will allow for a diversity of approaches in research and early development, while also strengthening cross fertilization between the companies, leading to enhanced overall innovation within the Group. Roche's recently adopted Disease Biology Area approach, which allows five diverse groups to manage their innovative portfolios, will be maintained and strengthened. This, together with recent moves into RNAi (Ribonucleic Acid interference) and delivery technologies, as well as licensing activities, continues to provide a stimulating
environment for the creation of medically differentiated medicines.”

And on the “enhanced ability to innovate”, the press release goes:

“The transaction will over time significantly enhance cooperation and cross fertilization among all research hubs inside and outside of the combined company. Sharing of technologies (e.g. RNAi, novel protein architectures), assets (e.g. chemical libraries), intellectual property(e.g. antibody production), unique capabilities (e.g. exploratory development, modeling and simulation) and know-how of the combined research organization will strengthen the Group's ability to innovate.”

I wonder how much Genentech will be surprised by the offer. In this regard, it is also interesting to speculate whether Alnylam had been in negotiations with Genentech or not. If so, would an unsolicited takeover of Genentech by Roche mean that Alnylam has to forego platform license fees that it would have otherwise received if Genentech remained independent? On the other hand, could it be that Genentech’s moves into RNAi Therapeutics had long been co-ordinated with Roche’s efforts, relying on being able to access RNAi know-how and IP through Roche? I guess the answer will come whether the $89 a share offer will be recommended by Genentech management to shareholders or not. Given the close ties between the companies a Ventana-like takeover battle is difficult to imagine. The weak dollar may also aid Roche in increasing the price a little bit to satisfy all Genentech shareholders.

Today’s development promises to be a defining moment in the history of drug development. With the recent acquisitions of diagnostics company Ventana and now Genentech, Roche has now fully committed to embracing the future of personalized medicine. Based on comments by the new CEO Severin Schwan who has now more than amply demonstrated that he really means what he says, RNAi Therapeutics is to play a key part in this.

RNAi Therapeutics: Qiagen High-Throughput RNAi User Meeting San Francisco (Part 1 of 2)

An army of RNAi enthusiasts descended today onto the UCSF Mission Bay Campus in San Francisco, just across the street from Sirna Therapeutics/Merck, to discuss the latest in the use of RNAi for high-throughput screening. Illustrating the ever increasing popularity of RNAi, 175 scientists registered for this year’s one-day meeting, some from as far as Japan, compared to 75 when it was held for the first time two years ago in Boston. It was therefore fitting that a number of presenters noted as an aside that the discovery of RNAi and microRNAs not only transformed molecular biology, but also a number of lab personnel from bench-weary scientists to those that live and breathe RNAi every day. In the next two posts, I will try and highlight a few points from the meeting seen through my RNAi Therapeutics filter.

Carl Novina (Harvard and scientific advisor to mdRNA/Nastech) gave the keynote address with an introduction to microRNAs and their molecular mechanism of action. In a paper earlier this year, his lab had developed a biochemical model system to study microRNA repression that led them to conclude that microRNAs repress translation through the inhibition of the joining of the large 60S ribosomal subunit to the mRNA-bound 40S subunit of the protein translation apparatus. In today’s presentation, he expanded on that work by describing an RNAi-based screen for the discovery of genes involved in microRNA function. For this, his lab created luciferase cell lines in which one of the reporters contained a target site for an endogenously expressed microRNA and was therefore repressed. Knocking down a gene contributing to microRNA-mediated repression should therefore lead to an increase in the signal intensity from that reporter gene.

Encouragingly and attesting to the high specificity of RNAi, the screen uncovered many of the known microRNA pathway components such as Drosha, Dicer, and Argonautes 1 and 2. In addition, the screen making use of an siRNA library targeting the “druggable genome”, that is the ~7000 genes in our genome encoding for kinases, cell surface receptors etc. that are amenable to small molecule and/or monoclonal antibody inhibition, uncovered a number of proteasomal components. This raises the intriguing question whether an old hypothesis, namely that microRNAs may induce co-translational protein degradation may indeed be part of the complicated reality of the molecular microRNA repression process.

As an aside, Carl Novina noted that one problem with working on RNAi-related aspects in Boston is the considerable demand for talent there by the many RNAi companies located there. A nice problem to have for the many hungry post-docs at a time when public funding for basic research has stalled to pay for apparently more important projects overseas.

One recurrent theme of the conference was the role of small RNAs in the new era of personalized medicine, particularly for cancer, which not coincidentally also was the loud message heard from last week’s ASCO mega meeting. The case was made by Lynne Bemis (University of Colorado) as she compared the costs of today’s monoclonal antibody cancer therapeutics that run at about $30k per annum with clinical responses in only a subset of patients. To avoid patients wasting precious time on ineffective therapies and also to reduce healthcare costs, it is therefore important that these treatments are accompanied by diagnostics that can predict the response of a patient to the therapy, by the way an area first pioneered by the local Genentech.

The striking conclusion from her research was that microRNAs were by far emerged the best biomarkers for this purpose, far better than single protein or mRNA diagnostics. When compared to proteins this is likely due to the fact that microRNAs function as master regulators, often with direct and critical functions in cancer itself, while a single protein may only reflect a much smaller aspect of a much larger picture. When compared to mRNAs, it is the relatively high stability in most tissue preservation methods and their compatibility with formalin fixation that adds to the competitive advantage of microRNA diagnostics.

As an example, EGFR tyrosine kinase inhibitors (TKI) are now widely used for cancer therapy, yet only 10% respond to treatment. Lynne’s team therefore looked at whether EGFR protein levels, EGFR mutations, or genomic amplification levels of the EGFR gene would be good predictors of drug response. Unfortunately, all of them either failed to do so (EGFR levels) or were not practical (EGFR mutations). Running out of options, they then turned to microRNAs. First they made use of microRNA target prediction software to discover two microRNAs that might target the EGF receptor mRNA. They then hypothesized that in cancers where EGFR had been aberrantly activated one of these microRNAs was lost. As an interesting technical aspect, unlike most other microRNA diagnostics currently being developed by Asuragen, Rosetta Genomics, Exiqon and others, they did not make use of qRT-PCR or microRNA microarray for the detection of microRNA expression levels, but the even simpler pPCR from DNA isolated from tumor tissue microdissected from formalin-fixed samples.

Amazingly, from a sample of 60 tumors of the lung, the miR-128b gene dosage was able to nicely segregate those patients that responded to TKI treatment versus those that did not. None of the other methods tested came anywhere close. As, if not even more spectacularly, it turns out that miR-128b is located on a region of the short arm of chromosome 3 that had been previously implicated and was frequently deleted in lung cancer (around 90% of late-stage lung cancers), but of which the identity of the underlying gene had long been a mystery.

In another elegant study, her group employed mRNA microarray analysis to look for genes either over- or under-expressed in a panel of cancer cell lines. Once identified, microRNA target prediction was used to come up with potential clusters of genes being targeted by the same microRNA. Demonstrating yet another promising method of discovering microRNA diagnostic candidates, 5 out of the top 7 overexpressed genes shared a common microRNA signature, and sure enough, this microRNA alone was more predictive of drug responsiveness than any of the proteins alone.

Eric Lader from the host Qiagen rounded up the session on microRNAs with an overview of products for the quantitation and inhibition of microRNAs sold by Qiagen. Qiagen’s qRT-PCR quantitation method allows for the one-step conversion of all microRNAs into cDNA so that many individual microRNAs can be measured from this one cDNA sample thereby reducing the use of precious clinical material. As to the microRNA inhibition technology, a number of nucleotide modifications were tested in the antagomirs and it was interesting that while a number of them were able to inhibit microRNA function, depending on the modification, they probably did so at various steps of microRNA biogenesis and function and included both microRNA degradation and competitive binding. LNAs and other chemistries with high melting temperature generally worked best while RNase H-dependent antisense did not appear to be an effective microRNA inhibition technology.

In my next post, I will summarize the rest of the meeting including more RNAi screens, cancer, and systems biology. Stay tuned…