Moffitt Cancer Center

Today is my first official day on my new position as a research scientist in the Integrated Mathematical Oncology group at Moffitt Cancer Center. I'll be working with Alexander 'Sandy' Anderson, my former PhD-supervisor, who is now heading the IMO.

Apart from new hire orientation and other exciting administrative stuff I'm currently working on a project related to evolution of resistance. The plan is to look at how drug specificity and fitness landscape topography influences the evolution of resistance. Hopefully I'll have more to say in a not too distant future.

Robust science

As a part of my book project on complexity I have been reading William Wimsatt's 'Re-engineering philosophy for limited beings', which in essence is a subset of his papers published in the last 30 years merged into a coherent whole.

The main purpose of the book is to introduce a new philosophy of science, which accounts for our limitations as human beings. Traditionally philosophy of science has assumed that scientists are perfect beings, having infinite computational power and never making any mistakes, and Wimsatt's aim is to replace this view with one in which scientists are fallible and error-prone. Now if this is the case, how do we formulate a scientific method that accounts for and embraces these limitations?

The greater part of the book is devoted to answering this and related questions, and I will here only mention one aspect that I found particularly intriguing.

The traditional account of a scientific theory is a set of assumptions or axioms together with some rules of deduction that dictate how novel and true statements can be produced. Assuming that the axioms are true we can generate a possibly infinite set of true statements all connected somehow by the rules of deduction. The picture is that of a network, where true statements are nodes and deductions form the links.

In reality however scientific statements are rarely held together by truth preserving rules of inference, but rather by experimental data, hand-waving analytical results and results from models. All of these may contain flaws, which run the risk of undermining the theory. If an experimental result turn out to be wrong for some reason, then the corresponding link in the network breaks, and if the link points to a statement with only one link, then that statement has to go.

How then should we deal with this situation? Wimsatt's answer is that we already are dealing with it by making robust inferences. In general we don't trust the results of single model, but instead require some independent verification. And if two different models provide the same answer then it's more likely to be accurate. And the more links that are pointing towards a statement the more likely it is to be true. Even if some of the experimental results or conclusions made from models turn out to be flawed the statement still stands.

I think this network analogy is useful way of illustrating the how scientific knowledge is accumulated and has certainly helped me in thinking about my work.

Comparative drug pair screening across multiple glioblastoma cell lines reveals novel drug-drug interactions

I'm the co-author of a newly published paper on drug-pair screening on glioblastoma (brain tumour) cell lines. The bulk of the work was carried out by Linnéa Schmidt in the Nelander lab at Gothenburg University. Below is the abstract, and the full paper can be found here.

Abstract

Background Glioblastoma multiforme (GBM) is the most aggressive brain tumor in adults, and despite state-of-the-art treatment, survival remains poor and novel therapeutics are sorely needed. The aim of the present study was to identify new synergistic drug pairs for GBM. In addition, we aimed to explore differences in drug-drug interactions across multiple GBM-derived cell cultures and predict such differences by use of transcriptional biomarkers.

Methods We performed a screen in which we quantified drug-drug interactions for 465 drug pairs in each of the 5 GBM cell lines U87MG, U343MG, U373MG, A172, and T98G. Selected interactions were further tested using isobole-based analysis and validated in 5 glioma-initiating cell cultures. Furthermore, drug interactions were predicted using microarray-based transcriptional profiling in combination with statistical modeling.

Results Of the 5 × 465 drug pairs, we could define a subset of drug pairs with strong interaction in both standard cell lines and glioma-initiating cell cultures. In particular, a subset of pairs involving the pharmaceutical compounds rimcazole, sertraline, pterostilbene, and gefitinib showed a strong interaction in a majority of the cell cultures tested. Statistical modeling of microarray and interaction data using sparse canonical correlation analysis revealed several predictive biomarkers, which we propose could be of importance in regulating drug pair responses.

Conclusion We identify novel candidate drug pairs for GBM and suggest possibilities to prospectively use transcriptional biomarkers to predict drug interactions in individual cases.

New perspective on metastatic spread

Recently I have, together with collaborators from Moffitt Cancer Center, uploaded a pre-print on arXiv on the topic of metastatic spread. I've touched on this topic previously and the work is in part inspired by a book by Leonard Weiss that I've previously reviewed here, and also previous work by the people at Moffitt.

In the last couple of decades the focus of research on metastases has been on the effect of genes, that when mutated provide the cancer cells with the properties necessary to form distant metastases. The answer to the riddle of metastases is believed to be written in the genome. Indeed this must partly be the case, since we know that cancer cells are full of genetic alterations, but what we currently don't know is how important genetic effects are compared to purely physiological constraints. In order to appreciate this it's worth mentioning that a late-stage solid tumour releases roughly 100 million cancer cells per day(!) into the blood stream, but out of this astronomic number at most a handful cells form detectable metastases in the lifespan of the patient.

It is well known that primary tumours from different anatomical locations have a propensity to form metastases in certain organs. For example, breast tumours are known to metastasise to the adrenal gland and the bone. This is known as the 'seed-soil hypothesis', and suggests in analogy with seeds from plants, that the cancer cells will only flourish if they find the right soil/organ. In opposition to the seed-soil hypothesis stands the 'mechanistic hypothesis' which proposes that metastatic distribution is largely explained by the blood flow to different organs. In our paper we try to reconcile these two views by disentangling the effects of biology and physiology.

In order to do this we have to consider the fate of cancer cells as they reach the blood circulatory system and become circulatory cancer cells (CTCs). The blood is not the native environment for these cells and many of them quickly perish, but the main obstacle they face is the capillary beds where the vessels narrow down to roughly 10 microns, which is the size of the CTCs themselves. Most cells get stuck, are damaged, and die, and we know from animal models that approximately only 1 in 10 000 CTCs pass through a capillary bed unharmed. This means that if 100 million CTCs leave a breast tumour then only 10 000 make it past the capillary bed of the lung, and these remaining CTCs are then distributed to downstream organs in proportion to the blood flow each organ receives. The adrenal gland e.g. receives 0.3% of the total cardiac output, which means that of the 100 million cells leaving the breast on average 30 CTCs reach the adrenal gland.

This framework can be used in order to disentangle seed-soil effects from physiological constraints by normalising (i.e. dividing) the metastatic involvement of each target organ with the relative blood flow it receives. This is known as the metastatic efficiency index (MEI) and was invented by Leonard Weiss. A high MEI suggests beneficial seed-soil effects, while a negative MEI indicates detrimental effects.

What we have done is to extend the MEI to take into account the effect of capillary beds. This extension also makes it possible to investigate the effects of micrometastatic deposits, since these in effect increase the number of CTCs downstream, or in other words, reduce the filtration that occurs within that organ. By posing different scenarios of micrometastatic disease one can then show that the MEI is strongly affected by micrometastases, which in turn suggests that knowledge of micromets could have a strong impact on disease progression, and hence that it would be an important biomarker.

Enough said! Please read the paper.