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Next-generation clinical trials: Novel strategies to address the challenge of tumor molecular heterogeneity.

Catenacci DV - Mol Oncol (2014)

Bottom Line: The promise of 'personalized cancer care' with therapies toward specific molecular aberrations has potential to improve outcomes.These issues have become hurdles to advancing cancer treatment outcomes with novel molecularly targeted agents.Finally intra-patient heterogeneity through time may be addressed by serial biomarker assessment at the time of tumor progression.

View Article: PubMed Central - PubMed

Affiliation: University of Chicago Medical Center, Department of Medicine, Section of Hematology & Oncology, 5841 S. Maryland Avenue, MC2115, Chicago, IL 60637, USA. Electronic address: dcatenac@medicine.bsd.uchicago.edu.

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The ‘Biomarker and Treatment Assignment Algorithm’ of PANGEA. The biomarker and treatment assignment algorithm is premised on optimizing the inhibition of ‘driver-biology’. This 9-point algorithm serves to prioritize treatment assignment should multiple aberrations (genomic and proteomic) be observed in an individual sample. Should multiple aberrations be present, priority could be given to higher allele frequency (for mutations) or higher gene copy/expression. The algorithm acts as a filter to create 5 distinct biomarker categories (with 9 tiers) that will receive 5 specific and most-appropriately matched targeted therapies. Approximate hazard ratios (HR) anticipated for each categorized tier, as well as the aggregate HR (the primary endpoint of PANGEA), are indicated. This first iteration of the ‘PANGEA’ strategy is a compromise within the spectrum between the two extremes of ‘one-size-fits-all’ and completely individualized therapy or ‘N-of-1’ (bottom panel). Rather than being a ‘tailored suit’, PANGEA can be considered fitting to ‘X-large, large, medium, small and X-small’. Future iterations could include more biomarker categories and treatment arms, consequently moving closer towards the ‘N-of-1’ limit.
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Figure 7: The ‘Biomarker and Treatment Assignment Algorithm’ of PANGEA. The biomarker and treatment assignment algorithm is premised on optimizing the inhibition of ‘driver-biology’. This 9-point algorithm serves to prioritize treatment assignment should multiple aberrations (genomic and proteomic) be observed in an individual sample. Should multiple aberrations be present, priority could be given to higher allele frequency (for mutations) or higher gene copy/expression. The algorithm acts as a filter to create 5 distinct biomarker categories (with 9 tiers) that will receive 5 specific and most-appropriately matched targeted therapies. Approximate hazard ratios (HR) anticipated for each categorized tier, as well as the aggregate HR (the primary endpoint of PANGEA), are indicated. This first iteration of the ‘PANGEA’ strategy is a compromise within the spectrum between the two extremes of ‘one-size-fits-all’ and completely individualized therapy or ‘N-of-1’ (bottom panel). Rather than being a ‘tailored suit’, PANGEA can be considered fitting to ‘X-large, large, medium, small and X-small’. Future iterations could include more biomarker categories and treatment arms, consequently moving closer towards the ‘N-of-1’ limit.

Mentions: The Expansion Platform Type IIA design, on the other hand, is a trial design that is not global, nor compartmentalized, and can be considered a ‘Grass-Roots’ or investigator initiated approach. In fact, it can be performed in single institutions as a pilot trial (phase IIa non-randomized) or within smaller collaborative groups as randomized phase IIb trials (Figure 5D). The ‘PANGEA’ concept (Personalized Antibodies for Gastro-Esophageal Adenocarcinoma) is the first example of the Type II holistic design. (Catenacci et al., 2014a) As in the Type IA Expansion Platform, the Type IIA ‘PANGEA’ design identifies various integral molecular subsets within GEC that are tiered by level of priority and degree of anticipated benefit from targeted inhibition (Figure 7) prior to trial initiation. Therapy is assigned specifically only to those within that biological subset. This treatment assignment is based on current understanding of ‘driver’ biology of that tumor type at the time of trial initiation, matching available targeted therapies thought to best suit each molecular subset. It also relies on a pre-specified treatment prioritization algorithm to address multiple ‘drivers’ and inter-patient heterogeneity (Figure 7, Table 3), (which could be considered arbitrary, particularly if anticipated hazard ratios are similar between different potential biomarker-drug pairings). Should multiple aberrations be present, priority in ‘PANGEA’ will be given to higher allele frequency (for mutations) or higher gene copy/expression, (Gomez-Martin et al., 2013) for example. However, the Type IIA design is executed as one uniform (holistic) umbrella trial, with one primary statistical endpoint testing the hypothesis that personalized therapy is better than the current standard therapy for that tumor type – it is ultimately testing the treatment strategy comprised of numerous companion diagnostics and their respective matched targeted therapies. All patients screened are eligible irrespective of their molecular profiling result, due to relegation tier(s) within the treatment algorithm (Figure 7). The design can be applied at any line of therapy to evaluate a ‘personalized strategy’ compared to the standard treatment for that scenario. It can be performed as a phase IIa pilot compared to historical outcomes, as a randomized placebo-controlled phase IIb, or conceivably even as a large registration phase III trial (for the pre-specified treatment strategy), if warranted, based on promising phase IIb trial results (Table 4, Figure 8).


Next-generation clinical trials: Novel strategies to address the challenge of tumor molecular heterogeneity.

Catenacci DV - Mol Oncol (2014)

The ‘Biomarker and Treatment Assignment Algorithm’ of PANGEA. The biomarker and treatment assignment algorithm is premised on optimizing the inhibition of ‘driver-biology’. This 9-point algorithm serves to prioritize treatment assignment should multiple aberrations (genomic and proteomic) be observed in an individual sample. Should multiple aberrations be present, priority could be given to higher allele frequency (for mutations) or higher gene copy/expression. The algorithm acts as a filter to create 5 distinct biomarker categories (with 9 tiers) that will receive 5 specific and most-appropriately matched targeted therapies. Approximate hazard ratios (HR) anticipated for each categorized tier, as well as the aggregate HR (the primary endpoint of PANGEA), are indicated. This first iteration of the ‘PANGEA’ strategy is a compromise within the spectrum between the two extremes of ‘one-size-fits-all’ and completely individualized therapy or ‘N-of-1’ (bottom panel). Rather than being a ‘tailored suit’, PANGEA can be considered fitting to ‘X-large, large, medium, small and X-small’. Future iterations could include more biomarker categories and treatment arms, consequently moving closer towards the ‘N-of-1’ limit.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4402102&req=5

Figure 7: The ‘Biomarker and Treatment Assignment Algorithm’ of PANGEA. The biomarker and treatment assignment algorithm is premised on optimizing the inhibition of ‘driver-biology’. This 9-point algorithm serves to prioritize treatment assignment should multiple aberrations (genomic and proteomic) be observed in an individual sample. Should multiple aberrations be present, priority could be given to higher allele frequency (for mutations) or higher gene copy/expression. The algorithm acts as a filter to create 5 distinct biomarker categories (with 9 tiers) that will receive 5 specific and most-appropriately matched targeted therapies. Approximate hazard ratios (HR) anticipated for each categorized tier, as well as the aggregate HR (the primary endpoint of PANGEA), are indicated. This first iteration of the ‘PANGEA’ strategy is a compromise within the spectrum between the two extremes of ‘one-size-fits-all’ and completely individualized therapy or ‘N-of-1’ (bottom panel). Rather than being a ‘tailored suit’, PANGEA can be considered fitting to ‘X-large, large, medium, small and X-small’. Future iterations could include more biomarker categories and treatment arms, consequently moving closer towards the ‘N-of-1’ limit.
Mentions: The Expansion Platform Type IIA design, on the other hand, is a trial design that is not global, nor compartmentalized, and can be considered a ‘Grass-Roots’ or investigator initiated approach. In fact, it can be performed in single institutions as a pilot trial (phase IIa non-randomized) or within smaller collaborative groups as randomized phase IIb trials (Figure 5D). The ‘PANGEA’ concept (Personalized Antibodies for Gastro-Esophageal Adenocarcinoma) is the first example of the Type II holistic design. (Catenacci et al., 2014a) As in the Type IA Expansion Platform, the Type IIA ‘PANGEA’ design identifies various integral molecular subsets within GEC that are tiered by level of priority and degree of anticipated benefit from targeted inhibition (Figure 7) prior to trial initiation. Therapy is assigned specifically only to those within that biological subset. This treatment assignment is based on current understanding of ‘driver’ biology of that tumor type at the time of trial initiation, matching available targeted therapies thought to best suit each molecular subset. It also relies on a pre-specified treatment prioritization algorithm to address multiple ‘drivers’ and inter-patient heterogeneity (Figure 7, Table 3), (which could be considered arbitrary, particularly if anticipated hazard ratios are similar between different potential biomarker-drug pairings). Should multiple aberrations be present, priority in ‘PANGEA’ will be given to higher allele frequency (for mutations) or higher gene copy/expression, (Gomez-Martin et al., 2013) for example. However, the Type IIA design is executed as one uniform (holistic) umbrella trial, with one primary statistical endpoint testing the hypothesis that personalized therapy is better than the current standard therapy for that tumor type – it is ultimately testing the treatment strategy comprised of numerous companion diagnostics and their respective matched targeted therapies. All patients screened are eligible irrespective of their molecular profiling result, due to relegation tier(s) within the treatment algorithm (Figure 7). The design can be applied at any line of therapy to evaluate a ‘personalized strategy’ compared to the standard treatment for that scenario. It can be performed as a phase IIa pilot compared to historical outcomes, as a randomized placebo-controlled phase IIb, or conceivably even as a large registration phase III trial (for the pre-specified treatment strategy), if warranted, based on promising phase IIb trial results (Table 4, Figure 8).

Bottom Line: The promise of 'personalized cancer care' with therapies toward specific molecular aberrations has potential to improve outcomes.These issues have become hurdles to advancing cancer treatment outcomes with novel molecularly targeted agents.Finally intra-patient heterogeneity through time may be addressed by serial biomarker assessment at the time of tumor progression.

View Article: PubMed Central - PubMed

Affiliation: University of Chicago Medical Center, Department of Medicine, Section of Hematology & Oncology, 5841 S. Maryland Avenue, MC2115, Chicago, IL 60637, USA. Electronic address: dcatenac@medicine.bsd.uchicago.edu.

Show MeSH
Related in: MedlinePlus