There are more than 100 types of cancer in humans, and even patients with the same type usually need different types of treatment due to their genetic backgrounds, lifestyles and other factors. One size doesn’t fit all. 

Tailoring drugs to tumors can thus lead to personalized treatment and new therapies. Choosing the right drug for each cancer patient is the key to successful treatment, but until now, oncologists who treat cancer patients have had few reliable pointers to guide them in designing treatment protocols. 

Fortunately, researchers at the Weizmann Institute of Science in Rehovot and the Broad Institute of the Massachusetts Institute of Technology and Harvard University have now developed a new method for selecting the best drug therapy for a given tumor based on assigning scores to the cells’ internal messaging activities. The Broad Institute was launched in Cambridge, Massachusetts in 2004 to improve human health by using genomics to advance our understanding of the biology and treatment of human disease and to help lay the groundwork for a new generation of therapies.

Besides helping physicians choose from a list of existing treatments, the method can help identify new molecular targets for the development of additional cancer drugs in the future. In fact, the researchers have already used it to single out a gene that can be targeted for effectively treating breast cancers with a BRCA mutation. The study was recently published in the prestigious journal Nature Communications.

The most common molecular technique to match drugs to a tumor is to look for particular mutations in the tumor’s cells. Unfortunately, the presence of such mutations is no guarantee a drug will work; in any case, many drugs are not aimed at mutations. There have also been attempts to predict a drug’s effectiveness by analyzing the expression of certain genes in a tumor, but the expression level of very few genes was shown to be helpful in guiding physicians in making treatment decisions.

In the new Israeli-American study, two labs – one headed by Dr. Ravid Straussman of Weizmann’s molecular cell biology department and the other by Prof. Gad Getz of the Broad Institute – joined forces to develop a more effective approach, basing their study on the enormous datasets on cancer that have become available in the past few years. 

Instead of relying on mutations or individual genes, it is based on signaling pathways – chains of biochemical signals that convey crucial cellular messages, such as whether a cell should divide or grow or in what way its metabolism should be altered. Many genes are expressed in the cell to transmit the message in each pathway, so sophisticated methods are needed to uncover the activity in these chains.

Postdoctoral fellow Dr. Rotem Ben-Hamo analyzed a large number of international datasets containing information on the expression of all genes in some 460 cancer cell lines – that is, different models of cancer – from ten different cancer types. Using an advanced bioinformatics tool PathOlogist, which was developed by Prof. Sol Efroni of Bar-Ilan University, the researchers assigned to each pathway an activity score, which takes into account not only gene expression levels but also prior knowledge about the structure of each pathway, the interactions of the genes within it and whether a given gene blocks or stimulates the pathway’s message. 

The scientists then correlated these scores with datasets containing information on the sensitivity of different cancer cells to nearly 500 different anti-cancer drugs. They found that the activity scores of some of the pathways enabled them to predict whether a specific cancer would be sensitive to a particular drug. In other words, the researchers created a profile for the cancerous tissue that could direct clinicians to the best drugs for wiping out the tumor. 

Overall, they were able to make such predictions for more than 30 existing drugs. For example, when certain lung cancer cells had a high score for a pathway triggering apoptosis – a form of cell suicide – these cells were likely to be killed by a class of drugs known as microtubule inhibitors. 

Next, the scientists showed that they could use the pathway knowledge not only to predict but to alter the cells’ response to a drug. They took from lung cancer tissue one patient that, according to their analysis, the apoptosis-triggering pathway was not particularly active. In test tube experiments, this lung cancer tissue – as expected –was resistant to microtubule inhibitor drugs. But when, in addition to microtubule inhibitors, the researchers simultaneously applied a substance that increased the activity in the apoptosis pathway, these drugs effectively killed the cancer cells.

In further analysis of the datasets, the researchers correlated the activity scores of signaling pathways with yet another type of information – which genes play such an essential role in various tumors that silencing, or blocking these genes can kill the tumor. They found that here too, the pathway scores helped them identify such “sensitive” genes in a variety of tumors. For example, they found that breast tumors with specific activity in the BRCA pathway – which correlated with the presence of a BRCA mutation – were extremely dependent on the activity of a gene called MAD2L1.

The bioinformatics analysis predicted that silencing this gene can result in the death of tumor cells in patients with a BRCA mutation. This prediction can serve as a starting point in the search for new or existing drugs to treat this devastating breast cancer.

The new research findings suggest that signaling pathways can serve as predictive biological markers in personalized medicine of the future, helping physicians tell in advance which patients will best respond to which drug. In addition, the pathways can help researchers identify the weak point of various tumors to which drug development can be directed.