Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2010 Aug 10;5(8):e11965.
doi: 10.1371/journal.pone.0011965.

Targeted Therapy Database (TTD): a model to match patient's molecular profile with current knowledge on cancer biology

Affiliations

Targeted Therapy Database (TTD): a model to match patient's molecular profile with current knowledge on cancer biology

Simone Mocellin et al. PLoS One. .

Abstract

Background: The efficacy of current anticancer treatments is far from satisfactory and many patients still die of their disease. A general agreement exists on the urgency of developing molecularly targeted therapies, although their implementation in the clinical setting is in its infancy. In fact, despite the wealth of preclinical studies addressing these issues, the difficulty of testing each targeted therapy hypothesis in the clinical arena represents an intrinsic obstacle. As a consequence, we are witnessing a paradoxical situation where most hypotheses about the molecular and cellular biology of cancer remain clinically untested and therefore do not translate into a therapeutic benefit for patients.

Objective: To present a computational method aimed to comprehensively exploit the scientific knowledge in order to foster the development of personalized cancer treatment by matching the patient's molecular profile with the available evidence on targeted therapy.

Methods: To this aim we focused on melanoma, an increasingly diagnosed malignancy for which the need for novel therapeutic approaches is paradigmatic since no effective treatment is available in the advanced setting. Relevant data were manually extracted from peer-reviewed full-text original articles describing any type of anti-melanoma targeted therapy tested in any type of experimental or clinical model. To this purpose, Medline, Embase, Cancerlit and the Cochrane databases were searched.

Results and conclusions: We created a manually annotated database (Targeted Therapy Database, TTD) where the relevant data are gathered in a formal representation that can be computationally analyzed. Dedicated algorithms were set up for the identification of the prevalent therapeutic hypotheses based on the available evidence and for ranking treatments based on the molecular profile of individual patients. In this essay we describe the principles and computational algorithms of an original method developed to fully exploit the available knowledge on cancer biology with the ultimate goal of fruitfully driving both preclinical and clinical research on anticancer targeted therapy. In the light of its theoretical nature, the prediction performance of this model must be validated before it can be implemented in the clinical setting.

PubMed Disclaimer

Conflict of interest statement

Competing Interests: The authors have declared that no competing interests exist.

Figures

Figure 1
Figure 1. Example of evidence synopsis regarding the targeted therapy of melanoma, as obtained by searching the Targeted Therapy Database (TTD).
The available evidence on the relationship between a molecule state (BRAF mutation V600E) and its effects on different therapeutic agents is shown. Due to space considerations, neither all columns nor all rows are displayed.
Figure 2
Figure 2. Example of evidence synopsis regarding the targeted therapy of melanoma, as obtained by searching the Targeted Therapy Database (TTD).
The available evidence on the relationship between a drug (temozolomide) and the molecular determinants of its therapeutic effect is shown. Due to space considerations, neither all columns nor all rows are displayed.
Figure 3
Figure 3. A scheme of the evidence score method to synthesize the literature evidence and identify prevalent hypotheses regarding the relationship of sensitivity/resistance between a given molecule (in a specific state) and a given drug.
Each study is assigned an evidence score based on the experimental model used to generate the findings reported in each article In this example, 70% of the total score (that is, 70% of the published evidence rated according to the experimental model used to generate the findings reported in each article) supports the hypothesis that molecule-X (in a particular state, here not specified for the sake of simplicity) is associated with responsiveness to drug-Y. To be defined as “prevalent”, the hypothesis must be characterized by the fact that the lower bound of the 95% confidence interval of its score percentage does not cross the decision rule value (50%). The same method can be used to identify prevalent hypotheses regarding the relationship of toxicity and synergism (see text for more details).
Figure 4
Figure 4. A scheme of the drug ranking system to match the patient's molecular profile with the available scientific evidence regarding the relationship of sensitivity/resistance between a set of molecules (each in a specific state) and a given drug.
After identifying the prevalent hypothesis (along with its score percentage) for each molecule according to the evidence score method (see text and Figure 3 for more details), the same molecules (and their state) are tested in the tumor of a patient. Each molecule is said to be concordant (positive sign) or discordant (negative sign) according to whether the molecule state found in the patient's tumor is identical or opposite to the state reported in the literature, respectively. Then, a weighted mean of the score percentages is calculated to obtain the overall score for the patient. In this example, the overall score indicates that on average 60% of the available evidence (that is, 60% of the published evidence rated according to the experimental model used to generate the findings reported in each article) is in favor of the hypothesis that the patient's molecular profile is associated with responsiveness to drug-Y. To be defined as “sensitive” (or “resistant”), a molecular profile must be characterized by an overall score with a lower bound of its 95% confidence interval that does not cross the decision rule value (+50% or −50%, respectively). The same method can be used to assess whether the available evidence supports the hypothesis that a molecular profile is associated with higher/lower toxicity for a given drug-Y (see text for more details).

References

    1. World Health Organization. 2004. Mortality and health status: causes of death database.
    1. Jemal A, Siegel R, Ward E, Hao Y, Xu J, et al. Cancer statistics, 2009. CA Cancer J Clin. 2009;59:225–249. - PubMed
    1. Holmes MV, Shah T, Vickery C, Smeeth L, Hingorani AD, et al. Fulfilling the promise of personalized medicine? Systematic review and field synopsis of pharmacogenetic studies. PLoS One. 2009;4:e7960. - PMC - PubMed
    1. Riley LB, Desai DC. The molecular basis of cancer and the development of targeted therapy. Surg Clin North Am. 2009;89:1–15, vii. - PubMed
    1. Allgayer H, Fulda S. An introduction to molecular targeted therapy of cancer. Adv Med Sci. 2008;53:130–138. - PubMed