Monthly Archives: February 2014

SugarCone Biotech consolidates all its online activities to one new website

On February 10 SugarCone Biotech will officially launch a new website, bringing together the SugarCone Biotech blog, our Twitter feed, a new blog roll, guest posts and descriptions of our broad suite of Consulting services. Our new site, at www.sugarconebiotech.com, will be updated regularly with news, events and special offers for the biopharma industry.

SugarCone Biotech R D Screenshot

SugarCone Biotech will be covering the American Association for Cancer Research (AACR) Annual Meeting

AM2014_150x357In addition to our usual in-depth analysis of critical news at the meeting, we will be offering customized AACR coverage packages for clients and investors. These packages feature real-time alerts on breaking news, deep dives into competitive landscapes and evolving clinical care, and unflinching looks at novel therapeutics moving through the pipeline. If you need actionable data from AACR this year, contact us now. We offer highly competitive rates and absolute satisfaction. Click to see our ASH 2013 coverage for examples of the work we can do.

SugarCone Biotech founder joins MassConnect

MassCONNECT

SugarCone Biotech founder Paul Rennert has joined MassConnect as a project team mentor. MassConnect is the MassBio group dedicated to helping new entrepreneurs succeed in developing innovative technologies. MassConnect’s mission is to “link new entrepreneurs and founders with seasoned biotechnology professionals to provide industry expertise, evaluation, and guidance as a means to help commercialize new ideas.” See more information about MassConnect at their website http://www.massbio.org/innovation/massconnect.

CRISPR Technology and Therapeutic Gene Editing – TJ Cradick

A Guest Post from Thomas (TJ) Cradick, Director of the Protein Engineering Facility, Georgia Institute of Technology; @NucleaseLab

Genome editing has remained a therapeutic goal since before specific, disease-causing mutations were discovered. Introducing mutations into cell lines and model organisms have also created very useful research reagents. The rate of both processes is greatly enhanced by creating nearby DNA breaks. These effects were first shown with meganucleases, which are very specific but have proved very difficult to convert to targeting novel sequences. The first readily engineered nuclease group was ZFNs (zinc-finger nucleases), followed by TALENs (transcription activator-like effector nucleases), and most recently CRISPRs (clustered regularly interspersed short palindromic repeats). The DNA breaks caused by these nucleases are repaired by the cellular DNA repair machinery and can lead to precise modification. Genome editing is no longer science fiction, though issues remain on delivery and specificity.

TALENs are easier to design than Zinc Finger Nucleases due to straightforward rules linking DNA binding repeats to a target sequence [1,2]. These rules don’t help pick the highest activity sites, though a new program, SAPTA, helps pick sites that can be targeted with high activity and specificity [3]. Several groups have developed high-throughput cloning methods to assemble the DNA binding repeats in TALENs, though new proteins must be assembled for each target [4,5].

For each target, CRISPR systems have the advantage of using identical proteins identified as a means for bacteria to fend off pathogens. These gene-editing systems are called clustered regularly interspaced short palindromic repeats (CRISPR) and pronounced “crisper”. Cas (CRISPR-associated) proteins clone a piece of the foreign DNA into the CRISPR genomic locus between the repeats. Many of these foreign DNAs are saved in the daughter cells. One bacterial protein called Cas9 and a guide strand RNA expressed from these saved DNA pieces allow targeting complementary sequences if the foreign DNA is encountered again. The key is that CRISPR works by cutting DNA complementary to the “guide strand” RNA. Directing cleavage to a new target site only requires cloning a pair of annealed oligos into the guide strand expression cassette [6,7]. This saves the very difficult step of designing and cloning new DNA binding proteins, as are required for ZFNs or TALENs.

In the beginning of 2013 papers began describing genome editing in mammalian cells [7,8]. A number of labs made their plasmids available on Addgene and several created websites and online forums to spread the word. A new company, Editas Medicine, founded by five world leaders in genome editing was founded to use CRISPR and TALENS as treatments for genetic diseases.

One of the big concerns with each type of nuclease is “off-target mutation” in different region of the genome. Several programs help verify and optimize specificity by listing putative off-target cleavage sites, including PROGNOS for ZFNs and TALENs [9]. Others have also found ZFN and TALEN off-target sites, primarily through experimentally guided off-target searches. Currently, there are limited data indicating that TALENs have improved specificity over ZFNs and lower cytotoxicity [10-12]. A number of publications have described the high level of off-target cleavage possible using CRISPR [13,14] and chromosomal deletions or re-arrangements [15]. Although ZFNs and TALENs have off-target cleavage as well, the high levels seen with current CRISPR methods has many groups scurrying to develop newer, safer methods. Use of pairs of Cas proteins that each cut only one strand holds promise, though has yet to be optimized for gene repair [16,17]. It is also likely that improved CRISPR systems will be developed that provide more specificity, though they may have decreased targeting efficiency. For many applications, the ease and speed of the current generation of CRISPR systems will provide a valuable research tool while the work on CRISPR 2.0 continues.

References
1. Boch J, Scholze H, Schornack S, Landgraf A, Hahn S, et al. (2009) Breaking the code of DNA binding specificity of TAL-type III effectors. Science 326: 1509-1512.
2. Moscou MJ, Bogdanove AJ (2009) A simple cipher governs DNA recognition by TAL effectors. Science 326: 1501.
3. Lin Y, Fine EJ, Zheng Z, Antico CJ, Voit RA, et al. (2014) SAPTA: a new design tool for improving TALE nuclease activity. Nucleic Acids Research: gkt1363.
4. Reyon D, Tsai SQ, Khayter C, Foden JA, Sander JD, et al. (2012) FLASH assembly of TALENs for high-throughput genome editing. Nat Biotechnol 30: 460-465.
5. Schmid-Burgk JL, Schmidt T, Kaiser V, Höning K, Hornung V (2013) A ligation-independent cloning technique for high-throughput assembly of transcription activator–like effector genes. Nat Biotechnol 31: 76-81.
6. Jinek M, Chylinski K, Fonfara I, Hauer M, Doudna JA, et al. (2012) A programmable dual-RNA-guided DNA endonuclease in adaptive bacterial immunity. Science 337: 816-821.
7. Cong L, Ran FA, Cox D, Lin S, Barretto R, et al. (2013) Multiplex genome engineering using CRISPR/Cas systems. Science 339: 819-823.
8. Mali P, Yang L, Esvelt KM, Aach J, Guell M, et al. (2013) RNA-guided human genome engineering via Cas9. Science 339: 823-826.
9. Fine EJ, Cradick TJ, Zhao CL, Lin Y, Bao G (2013) An online bioinformatics tool predicts zinc finger and TALE nuclease off-target cleavage. Nucleic acids research: gkt1326.
10. Tesson L, Usal C, Ménoret S, Leung E, Niles BJ, et al. (2011) Knockout rats generated by embryo microinjection of TALENs. Nature Biotechnology 29: 695-696.
11. Hockemeyer D, Wang H, Kiani S, Lai CS, Gao Q, et al. (2011) Genetic engineering of human pluripotent cells using TALE nucleases. Nature Biotechnology 29: 731-734.
12. Mussolino C, Morbitzer R, Lutge F, Dannemann N, Lahaye T, et al. (2011) A novel TALE nuclease scaffold enables high genome editing activity in combination with low toxicity. Nucleic Acids Res 39: 9283-9293.
13. Fu Y, Foden JA, Khayter C, Maeder ML, Reyon D, et al. (2013) High-frequency off-target mutagenesis induced by CRISPR-Cas nucleases in human cells. Nat Biotechnol.
14. Hsu PD, Scott DA, Weinstein JA, Ran FA, Konermann S, et al. (2013) DNA targeting specificity of RNA-guided Cas9 nucleases. Nat Biotechnol.
15. Cradick TJ, Fine EJ, Antico CJ, Bao G (2013) CRISPR/Cas9 systems targeting β-globin and CCR5 genes have substantial off-target activity. Nucleic Acids Research 41: 9584-9592.
16. Mali P, Aach J, Stranges PB, Esvelt KM, Moosburner M, et al. (2013) CAS9 transcriptional activators for target specificity screening and paired nickases for cooperative genome engineering. Nat Biotechnol.
17. Ran FA, Hsu PD, Lin CY, Gootenberg JS, Konermann S, et al. (2013) Double nicking by RNA-guided CRISPR Cas9 for enhanced genome editing specificity. Cell 154: 1380-1389.

The Cancer Genomic Ecosystem

There have been several important recent advances in our understanding of tumor genomic ecosystems, and these advances have interesting implications for drug discovery in oncology.

The Journal Nature recently published a large data set on gene mutations in 21 distinct tumor types (http://www.nature.com/nature/journal/v505/n7484/full/nature12912.html). Much of the data came from the The Cancer Genome Atlas (TCGA) database, with additional data generated by the study authors. This study is sufficiently powered to uncover significance in several different ways. There is a cluster of mutations that are significant only in the combined tumor analyses, that is, when lumping different tumor types together. Conversely there a large cluster of mutations that are significant only in the analysis of individual tumor types, that is, the significance is lost if you look too broadly. Therefore these are genes that are important for specific tumor types. Finally there is a large cluster of gene mutations that are significant in both the combined analyses and in individual tumor analyses. This complexity of analysis is nicely shown in Figure 3 (http://www.nature.com/nature/journal/v505/n7484/full/nature12912.html#f3).

I spent a fair amount of time staring at this figure and going through the supplemental data (posted online and see also http://www.tumorportal.org/) and there are some results that I found interesting. First, the study confirmed many known cancer-related genes. The study also identified a fair number of new cancer-related genes mutated across or within tumor types, although these were found at the lower levels of significance. This is because they are mutated at a low rate, or the sample size for a particular tumor type was small, or both. The authors are transparent about this, and call for larger studies to increase sample size. This does beg the question as to the rate of gene mutation below which the knowledge is no longer actionable (because there will be so few patients), regardless the data will be critical to understanding tumor pathway biologies. Another interesting question is the extent to which new patterns of gene-mutation will emerge across tumor types, allowing binning (across tumor types) to complement subsetting (within a tumor type). Finally, the data might allow a different type of query, which is to ask which combinations of mutations are found within specific tumor types.

I want mention a few of the more common mutations, because these data held some surprises for me (although some readers know all this already, I’m sure). First, the best known cancer-related gene mutations cluster at the very highest levels of significance both across the 21 tumor types and within specific tumors. This makes sense, as these genes include those that contribute obligate cancer mutations: TP53; PTEN, PIK3CA and PIK3R1; KRAS, BRAF and NRAS; APC; EGFR, etc. There were a few genes in this category that surprised me, not so much because they made the list but because these at first glance appear more common than I had thought. GATA3 is a good example. Mutations in this gene are most commonly see in breast cancer but there are enough mutations in other tumor types to drive significance in the pooled tumor analysis, even though no tumor type other than breast is significantly associated with GATA3 mutations. Examination of the FTL3 data reveal a very similar pattern: mutations are significantly associated with acute myeloid leukemia (AML), as is well known, but also present in other tumor types, notably endometrial tumors and lung adenocarcinomas. When the mutational data across tumor type is pooled, significance is achieved. What are we do with such data? I think the answer perhaps is to simply know that these mutations can occur, and to look for them when typical mutations are missing in a given patient’s tumor. Such cataloging is of course the goal of personalized medicine. The other use of such data is to raise awareness of rare drug resistance mutations that may arise when targeting the major tumor oncogenic pathway in a particular tumor type. Many examples of this phenomena have been described (more on this below).

A different pattern emerges when we look at some other genes that are commonly mutated across tumor types but whose significant in these analyses is lower, due to a lower mutational rate. IDH1 is a good example here, having significant association with AML and glioblastoma multiforma, as is well known, but also with multiple myeloma (MM) and perhaps chronic lymphocytic leukemia (CLL). IDH2 is also most commonly associated with AML, but is present in colorectal cancer at “near significance” (love that fuzzy language). Notably, no other tumor metabolism genes appear in the analysis.

There are same gaps too I think. Looking at those genes that are significantly mutated only in a specific tumor type or types, we find some interesting genes. TGFBR2 has been described as a mutational driver in colorectal cancer, along with SMAD4. In the present analysis SMAD4 and SMAD2 are found to be significantly mutated in colorectal cancer, but the TGFBR2 mutation rate only reaches significance in Head and Neck cancer, although a few mutations do appear in the colorectal cancer data set used. Either the original studies are incorrect, which does not make sense biologically (TGFBR2 protein signals through the SMAD pathway), or this is an example of sampling error. Again, bigger data sets may be needed. Other tumor-type restricted patterns of gene mutation are very well known, such as EZH2 and CARD11 mutations in diffuse large B cell lymphoma (DLBCL). The CARD11 observation is interesting, as these mutations are associated with activation of MYD88, a gene known to be mutated in DLBCL and CLL.

There are lots of examples like these, and the data are easy to see and analyze: this is fun data to play with so have at it (see http://www.tumorportal.org/).

There is much discussion in the paper on new genes identified, and we’ll have to see how much of it is actionable at the drug development level.

That brings us to a different data set. If you go to the tumor portal you can sort by tumor type. Choosing melanoma, a highly mutated cancer, brings forth a whole spectrum of genes. Here’s a screengrab right from the tumor portal site (http://cancergenome.broadinstitute.org/index.php?ttype=MEL)

Screen Shot 2014-02-02 at 11.51.43 AM
In the table above, blue refers to known cancer-related genes, red indicates genes whose function is relevant to cancer biology, and black are novel genes. As many readers know, BRAF mutations are the canonical melanoma oncogenic driver, signaling through the MEK/ERK pathway to drive melanoma cell proliferation, migration and metastasis. Antagonists of the BRAF and MEK proteins have emerged as the best line of defense against melanoma, but its a complicated fight. BRAF inhibitors were developed several years ago, starting with vemurafenib (Roche). Although BRAF inhibition induced responses in many melanoma patients, BRAF resistance mutants and MEK1 escape mutations evolve quickly and patients relapse. Common BRAF resistance mutations include V600E and V600K mutations that confer protection against the first generation drugs. Second generation inhibitors that target the resistance mutations were developed, such as dabrafenib (GSK). In addition, the MEK inhibitor trametinib (GSK, Japan Tobacco) was approved last year for use in treating melanoma. Several weeks ago the combination of these two drugs was granted accelerated approval for the treatment of advanced (metastatic) or unresectable melanoma that is positive for either mutation (V600E or K). This is a great example of cancer genetics=driven drug development in action.

However, other mechanisms of resistance are independent of BRAF mutational status because of additional MEK resistance mutations. These additional mutational strategies were discussed in a series of papers published online on November 21, 2013, in Cancer Discovery. These studies used tumor samples from patients that had relapsed after either BRAF inhibitor of dual BRAF/MEK inhibitor therapy. Mutations were found in the MEK1, MEK2, ERK1, ERK2 pathway and the PI3K, AKT1, PTEN pathway (PTEN is a negative regulator of PI3K signaling to mTOR and AKT1). The papers were reviewed in the January 9th issue of SciBx.

What does this single example tell us about the mutational landscape and drug discovery. First let’s note that some of the resistance mechanisms for melanoma do not show up in the proposed melanoma mutational landscape chart above, that is, these did not appear in the tumor ecosystem until that ecosystem came under selective pressure via drug treatment. This has 2 implications: the first is that the TCGA type overview of tumor mutations is just one source of data and following patients longitudinally as they experience therapy is another source of data. The other implication is that the mutational landscape contains putative additional mechanisms of escape at least in some patients. So using our melanoma example, we see in the table evidence of other potential escape pathways (NRAS, several checkpoint genes, and KIT stand out to me). So how many drugs will any individual patient need to keep a rapidly evolving melanoma under control?

The good news is that drug developers have taken notice and ERK1/2. AKT and PI3K inhibitors of various specificity are under development. The bad news I guess is that this is just one example of how complicated cancer therapy is likely to become. One good question not addressed here is how the immune checkpoint drugs will overlay with targeted therapies, for melanoma and many other tumors. Thats a question for another day.

stay tuned.

Creating a New Therapeutic Focus

In 2012 we were engaged by a large local biotech company to evaluate a new therapeutic area. This effort was driven by the desire of the client to move aggressively into a new suite of diseases. We began by doing a deep dive into the client’s existing portfolio in order to identify assets already in development that could be directed to novel diseases. Concurrently we began a comprehensive review of preclinical and clinical stage assets available for partnering or in-licensing. Finally we engaged in pathological pathway analysis to identify novel targets for discovery programs. This effort, initiated and completed within two quarters, led to the eventual acquisition of a private company and its Phase 2 clinical stage assets, for nearly 100MM $USD. Based on our analyses the client also started several new discovery and preclinical development programs to complement the clinical stage acquisition.

Launching an Immunotherapeutic Antibody Company

In 2012 and 2013 we worked with a local venture capital firm to evaluate all of the newly emerging immunotherapy checkpoint pathways. We selected the best possible pathways for prosecution in a carefully defined subset of cancers. Drug therapeutic profiles and pathway-specific preclinical development plans were then developed. This effort led to the establishment of the new company’s portfolio and evaluation of the clinical landscape into which their drugs would ultimately emerge – critical information that drove the  preclinical development strategy, specifically, to identify novel combination therapies that would be attractive in treating specific cancers. The firm then successfully used this innovative strategic positioning in its pitch for syndication. The firm was able to attract not only additional venture capital investment also investment from a top-10 pharmaceutical company.

Building a New Asset-Centric Small Molecule Development Company

In 2013 we embedded with a local private company that had developed best-in-class small molecule screening capabilities for biotech and pharma clients. Their work validating the technology had created a unique collection of proprietary assets. We evaluated 50 distinct assets and their target pathways and defined a portfolio that could be spun out into a new asset-centric drug development company. Each asset was evaluated in the context of the competitive landscape, the attractiveness of the therapeutic hypothesis and disease area, and the ability to develop tractable medchem and preclinical programs. The screening company then used this information to raise an 11MM USD seed round from private investors. We served in the role executive consultant and acting CSO in order to launch and enable all company operations, leading to four programs launched and prosecuted for preclinical development.

Crossing the Biotech Valley of Death

In 2012 we partnered with a small privately held virtual biotech company that was seeking means of funding while it developed a data package suitable for promoting a Series A round. We took a critical look at the funding options and defined a foundation and government granting strategy based on the attractiveness of the company’s innovative technology and target therapeutic area. We then worked closely with the company to write and submit diverse grants, targeting disease specific foundation grants and small company innovation grants, including the scientific and technical rationale, workplans, costs, timelines, and milestones.