Predictive Therapeutics Protocol

The general schema for a predictive therapeutics protocol is outlined in Figure 2. The primary objective of the protocol is to evaluate the merits of the various predictive methodologies outlined above while simultaneously providing information back to treating physicians in a real-time fashion for consideration in the design of a treatment plan. While a full description of the protocol is beyond the scope of this chapter, we have enrolled 50 patients in the first phase that was focused on the development of the critical infrastructure and logistics. From each patient, highly qualified tumor tissue (or isolated tumor cells) is processed using standard operating procedures to create a gene expression profile. The signature is used to compare to other well- annotated samples

Figure 2 High-level review of our IRB-approved predictive therapeutics protocol in which patient tumors are processed using Affymetrix GeneChip technology after the required consenting and pathology clearance, to generate a gene expression signature that is reflective of the underlying biological context. These samples are processed using standardized procedures, to minimize confounding variables that can significantly influence the interpretation of the results. Molecular data are analyzed statistically relative to a wide variety of well-annotated samples within the database, and these intermediate results are applied further to the integrated knowledge base that includes systems biology tools. For example, enriched networks are identified and further refined to categorize significant convergence and/or divergence hubs that represent drugable targets with existing agents with known molecular mechanisms of action. Irrespective of the predictive method employed, each drug is associated with a normalized score for predicted efficacy. A report with these standardized predictions (indicated by the arrow) is provided back to the medical oncologist, who determines a treatment selection using all information available to him or her, which may include the molecular evidence. The patient's administered treatment is captured and their tumor response is assessed using standard clinical criteria. In this fashion, the association between the drug score predicted and the tumor response can be determined. In addition, a section of the patient's tumor is implanted directly into immune-compromised mice to establish a series of tumor grafts, which naturally more closely resemble the human disease at the molecular and histological level relative to established-cell-line xenograft models. These tumors are expanded in additional mouse cohorts and alternative predictive methods are tested to prioritize those with the most promise. Over time it is hoped that this approach of predictive modeling from standardized data, experimental testing, and model refinement may provide a means to identify optimal therapeutics with a high degree of confidence in a systematic fashion.

Figure 2

within a large database, and deregulated patterns of gene expression used in conjunction with a knowledge base of known drug-target interactions to infer treatment strategies. We also attempt to establish a xenograft after implantation of a section of the fresh tumor into immune-compromised mice. These tumor grafts are expanded through two generations to create a large colony of mice harboring the patient's tumor, and these are then used to statistically evaluate the different predictive methodologies and their corresponding treatment recommendations. While the preclinical component of the protocol does not typically provide useful information to the treating physician, it represents an excellent resource for prioritizing predictive methodologies and for developing a biomarker strategy for novel therapeutics.

At the onset, it is apparent that this multidisciplinary protocol requires several infrastructural components as well as integrated logistics. These include the development of centralized informatics capabilities that permit full integration of clinical and molecular data, drug-biomarker knowledge, predictive modeling, and reporting. Standardized tissue procurement and pathological characterization with attention to quality control are essential to ensure consistency in the raw molecular data that are used to derive treatment predictions. Consistent feedback from the clinical and preclinical treatment outcomes is critical to assess the validity of the predictive methods. Each component of a therapeutic regimen is scored objectively based on the predictive methodology, and the ultimate success of the method is determined by comparing this standardized score with tumor response.

It is important to state that at this time, the results obtained from the clinical arm of the protocol remain anecdotal, due to the underpowered nature of the initial proof-of-feasibility experimental design. However, despite representing a nonvalidated method for drug prioritization, any molecular information that can be provided to the treating physician is deemed valuable, especially for late-stage metastatic or refractory patients who have exhausted their standard of care options. In this sense, this protocol serves as a rudimentary clearinghouse where patients are placed onto experimental protocols, including off-l abel protocols based on the molecular profile of their disease. With the multitude of predictive models now available to suggest optimal combinational strategies based on a standardized gene expression signature from an individual tumor, the preclinical tumor grafts provide an invaluable resource for triaging ineffective methodologies. At this time, we are exploring a range of methods that range from rudimentary target expression to the more sophisticated signature-based methods and network inference. In general, the molecular similarities between the human tumor and the derived tumor grafts are excellent and represent a significant improvement from the classical cell line xenograft models (Figure 3) . This implies that the molecular network within the tumor system as a whole is generally maintained in the human and mouse hosts, although some clear exceptions are noted; for example, the expected reduction in markers of human vasculature and inflammatory cells are evident and expected. Although it is too early to claim direct equivalence

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Project Management Made Easy

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