Three oncolytic virotherapies approved for cancer patient treatment

Oncolytic virotherapy (drugs based on viruses which trigger cancer cell death and an immune response) have been approved for doctors to prescribe to patients for longer than most realise.

Back in 2003 Gendicine was approved by the State Food and Drug Administration of China (SFDA) for head and neck squamous cell carcinoma (HNSCC) [1].  It is a replication-incompetent, recombinant, serotype 5 human adenovirus (Ad5) engineered to contain the human wild-type p53 tumor-suppressor gene. It could be argued that Gendicine is not in fact a “true” oncolytic virus as it is replication incompetent, however I include it here because in recent years it has been recognised that oncolytic virotherapies generate an immune reaction against the tumour. This can lead to tumour lysis indirectly and may be more important than direct viral lysis. Indeed Gendicine leads to tumour cell lysis.

In 2005 the SFDA of China approved a classic (replication competent) oncolytic virotherapy called Oncorine for HNSCC [2]. Oncorine is an Ad5 with an E1B and E3 gene deletion. The development in the USA of a very similar virus called Onyx-15 was halted at the outset of a phase III trial due to a funding crisis. This funding crisis has resulted in a ten year lag behind China in the approval of an oncolytic virus.

In October 2015, the US food and drug administration (FDA) approved Imlygic, for the treatment of melanoma in patients with inoperable tumors [3]. In Jan 2016 it was approved in Europe for some inoperable melanoma [4]. Imlygic is a herpes simplex virus 1 (HSV-1) based oncolytic vector delivered via injection. It was generated from a fresh isolation of HSV-1 virus (JS1) and has a GM-CSF replacement of the two copies of the ICP34.5 gene which normally reverses the interferon induced phosphorylation of the α subunit of the eukaryotic initiation factor 2 (EIF2S1) [5]. The interferon pathway is usually disrupted in cancer thus lending the vector specificity to cancer cells.

Although the first FDA approval has been a long time coming there are many oncolytic viruses now in the clinical pipeline with more approvals likely.

 

  1. Pearson, Sue, Hepeng Jia, and Keiko Kandachi. ‘China Approves First Gene Therapy’. Nature Biotechnology 22, no. 1 (January 2004): 3–4. doi:10.1038/nbt0104-3.
  2. Garber, Ken. ‘China Approves World’s First Oncolytic Virus Therapy For Cancer Treatment’. Journal of the National Cancer Institute 98, no. 5 (3 January 2006): 298–300. doi:10.1093/jnci/djj111.
  3. ‘FDA Approves Amgen’s Injected Immunotherapy for Melanoma’. Reuters, 27 October 2015. http://www.reuters.com/article/us-amgen-fda-idUSKCN0SL2YH20151027.
  4. Semedo, Daniela, and PhD. ‘Metastatic Melanoma Therapy, Imlygic, Now Available in EU’. Immuno-Oncology News, 7 January 2016. http://immuno-oncologynews.com/2016/01/07/metastatic-melanoma-therapy-imlygic-now-available-eu/.
  5. Liu BL, Robinson M, Han Z-Q, Branston RH, English C, Reay P, et al. ICP34.5 deleted herpes simplex virus with enhanced oncolytic, immune stimulating, and anti-tumour properties. Gene Ther 2003; 10:292–303. [PMID: 12595888]
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New candidate biomarkers for early detection of pancreatic cancer

One of the reasons the prognosis of pancreatic cancer patients is so poor is due to its late diagnosis as symptoms do not present until an advanced stage. Until recently there have not been effective candidate markers for screening of early stage pancreatic cancer.

Recently the situation has improved. There are now candidates for blood and urine tests.

The protein Glypican-1 shed into blood from tumours as exosomes (lipid droplets) can be  detected in the blood of pancreatic cancer patients but not healthy volunteers [1].

A three protein urine test has been developed which can detect early-stage pancreatic cancer [2].

With further development these markers could form the basis of tests that make screening for pancreatic cancer a routine procedure. If the majority of pancreatic cancer cases could be diagnosed early this would dramatically improve the prognosis of patients.

  1. Melo, Sonia A., Linda B. Luecke, Christoph Kahlert, Agustin F. Fernandez, Seth T. Gammon, Judith Kaye, Valerie S. LeBleu, et al. ‘Glypican-1 Identifies Cancer Exosomes and Detects Early Pancreatic Cancer’. Nature 523, no. 7559 (9 July 2015): 177–82. doi:10.1038/nature14581.
  2. Radon, Tomasz P., Nathalie J. Massat, Richard Jones, Wasfi Alrawashdeh, Laurent Dumartin, Darren Ennis, Stephen W. Duffy, et al. ‘Identification of a Three-Biomarker Panel in Urine for Early Detection of Pancreatic Adenocarcinoma’. Clinical Cancer Research 21, no. 15 (8 January 2015): 3512–21. doi:10.1158/1078-0432.CCR-14-2467.

Some interesting facts about the phylogeny of CUB domains

The Complement subcomponents C1r/ C1s, sea urchin epidermal growth factor (Uegf), Bone morphogenetic protein 1 (Bmp1) domain (CUB domain) is a structural fold named after the first proteins in which it was identified [1].

The CUB domain is predominantly found in multicellular eukaryotes excluding fungi. However they are found in some unicellular plants and protozoa. The genomes of single celled alga and plankton as well as multicellular moss and poplar tree contain CUB proteins. There are few known CUB proteins from protozoa, however the parabasalid human parasite Trichomonas vaginalis expresses three proteins that contain a CUB domain and the slime mold Polysphondylium pallidum expresses a CUB protein. CUB domains appear to have been present in some of the earliest unicellular marine eukaryotes such as alga and plankton and have become established in multicellular eukaroytes.

There are examples of CUB domains in bacteria such as Clostridium perfringens [2] and archaea. The Clostridium perfringens and archaea CUB domain gene was probably obtained by horizontal gene transfer from a eukaryote [2].

Interestingly CUB domains have structural similarity to a number of viral capsid proteins including the small protein subunit of the bean-pod mottle virus (BPMV) capsid [3,4]. CUB domains and these capsid proteins may have evolved similar structures through convergent evolution [4].

  1. Bork, P. and G. Beckmann, The CUB Domain : A Widespread Module in Developmentally Regulated Proteins. Journal of Molecular Biology, 1993. 231(2): p. 539-545.

  2. Briggs, D.C. and A.J. Day, A bug in CUB’s clothing: similarity between clostridial CBMs and complement CUBs. Trends in Microbiology, 2008. 16(9): p. 407-408.
  3. Varela, P.F., et al., The 2.4 Å resolution crystal structure of boar seminal plasma PSP-I/PSP-II: a zona pellucida-binding glycoprotein heterodimer of the spermadhesin family built by a CUB domain architecture. Journal of Molecular Biology, 1997. 274(4): p. 635-649.
  4. Romero, A., et al., The crystal structures of two spermadhesins reveal the CUB domain fold. Nat Struct Biol, 1997. 4(10): p. 783-8.

R script, microarray data, and the interesting outliers

I previously mentioned that outliers in microarray data are what you should be interested in. But what did I mean by that?

Let’s take a look. The following is an excerpt of simple R script which can be used to examine microarray data outputted from the CARMAweb service:

> gene.normal <- normal.df[“LAMP3”, ]

> t.gene.normal <- as.data.frame(t(gene.normal))

> gene.cancer <- cancer.df[“LAMP3”, ]

> t.gene.cancer <- as.data.frame(t(gene.cancer))

> boxplot(t.gene.normal$LAMP3, t.gene.cancer$LAMP3, names = c(“Normal”, “Cancer”), ylab = “RMA”, main = “LAMP3”)

> mean.normal <- mean(t.gene.normal$LAMP3)

> mean.cancer <- mean(t.gene.cancer$LAMP3)

> fold.change <- mean.cancer/ mean.normal

> mean.normal

[1] 5.377307

> mean.cancer

[1] 5.634959

> fold.change

[1] 1.047915

In this example LAMP3 is being examined. Judging from the average fold change there is no difference between normal tissue and cancer. However if you look at the box plot:
LAMP3

You see that there are three tumours in which the robust multiarray average (RMA) is much higher than in normal tissue. These could represent a small subpopulation of tumours. Although it is difficult to tell from this dataset alone which contains only 45 tumours.

These tumours are potentially important and this would be missed by only looking at the fold change. You could imagine a drug that targets LAMP3 being effective only in this subset. This is much better than no drug being developed because on average there appears to be no fold change.

Next generation treatments for type I diabetes – Biology vs Engineering

Some time ago I produced an article for the science website Apptheneum on the future of type I diabetes treatments which focused on gene and cell therapy as potential cures:

http://www.apptheneum.com/next-generation-treatments-type-diabetes/

These are purely biological solutions, however the engineers also have their own.

The solution offered by the engineers has one distinct advantage – the immune system is irrelevant. One of the problems of using gene or cell therapy is that type I diabetes is an autoimmune disease. This means that even if you “cure” the disease it is likely to recur unless the underlying immune dysfunction is also dealt with or circumvented. There is a long way to go on this front. The engineers solution is an artificial pancreas which is not exposed to the immune system as it rests outside of the body.

“The artificial pancreas is not a replica organ; it is an automated insulin delivery system designed to mimic a healthy person’s glucose-regulating function”:

http://news.harvard.edu/gazette/story/2016/01/artificial-pancreas-system-aimed-at-type-1-diabetes-mellitus/

You can argue as to whether the name is appropriate as blood glucose regulation is only one aspect of the function of the pancreas but I’m sure someone has already. The NIH launched a $20 million program to fund artificial pancreas clinical trials in 2014:

http://grants.nih.gov/grants/guide/rfa-files/RFA-DK-16-008.html

So it looks like improved insulin delivery devices will become available in the not so distant future. However the problem with non-biologically engineered solutions is that they cannot perfectly regulate blood glucose levels (as cell and gene therapies potentially can) and it is likely that over time people with type I diabetes using artificial pancreases will still develop problems with their feet associated with reduced circulation and nerve damage. Cardiovascular disease, retinopathy (eye damage), general nerve damage, kidney disease, and sexual dysfunction will still be major problems. However artificial pancreases will no doubt be a vast improvement over manual insulin injection.

For this reason I believe that the engineers will win the race but lose the war. It is only a matter of time until the immune system is understood to a level where the underlying autoimmune disease can be dealt with.

The pancreatic cancer database – an excellent resource

The pancreatic cancer database1 is a one-stop shop for finding information derived from the literature on the expression levels of mRNA, miRNA, and protein in pancreatic cancer:

http://pancreaticcancerdatabase.org/index.php

It has been produced by the team behind the 2009 PLOS Medicine paper2 which catalogued a list of potential biomarkers for pancreatic cancer using an algorithm that examined microarray databases and the published literature for overexpressed mRNAs and proteins.

You can search by gene or protein identifiers or browse by gene symbol. All results are hyperlinked to the relevant PubMed entries.

It should be noted that the database is not complete. For example searches for “CDCP1” (there is data in the literature) or “IL24” yield no results.

The database is a useful resource to quickly check the status of a gene of interest. However the results are not fine grained. Microarray data is reported as average fold change. Pancreatic cancer is an extremely heterogeneous disease and it is worth keeping in mind that average fold change can mask important outliers. It is the outliers that are interesting/ important.

It is certainly worth taking a closer look at the underlying microarray data. This will require some processing and analysis. However the R statistical programming language and various web resources such as CARMAweb3 make this relatively straightforward:

https://carmaweb.genome.tugraz.at/carma/

  1. Thomas, Joji Kurian, Min-Sik Kim, Lavanya Balakrishnan, Vishalakshi Nanjappa, Rajesh Raju, Arivusudar Marimuthu, Aneesha Radhakrishnan, et al. ‘Pancreatic Cancer Database: An Integrative Resource for Pancreatic Cancer’. Cancer Biology & Therapy 15, no. 8 (August 2014): 963–67. doi:10.4161/cbt.29188.
  2.  Harsha, H. C., Kumaran Kandasamy, Prathibha Ranganathan, Sandhya Rani, Subhashri Ramabadran, Sashikanth Gollapudi, Lavanya Balakrishnan, et al. ‘A Compendium of Potential Biomarkers of Pancreatic Cancer’. PLoS Medicine 6, no. 4 (7 April 2009): e1000046. doi:10.1371/journal.pmed.1000046.
  3.  Rainer, J., F. Sanchez-Cabo, G. Stocker, A. Sturn, and Z. Trajanoski. ‘CARMAweb: Comprehensive R- and Bioconductor-Based Web Service for Microarray Data Analysis’. Nucleic Acids Research 34, no. Web Server (1 July 2006): W498–503. doi:10.1093/nar/gkl038.