Bayesian personalized treatment selection strategies that integrate predictive with prognostic determinants

The evolution of “informatics” technologies has the potential to generate massive databases, but the extent to which personalized medicine may be effectuated depends on the extent to which these rich databases may be utilized to advance understanding of the disease molecular profiles and ultimately integrated for treatment selection, necessitating robust methodology for dimension reduction. Yet, statistical methods proposed to address challenges arising with the high‐dimensionality of omics‐type data predominately rely on linear models and emphasize associations deriving from prognostic biomarkers. Existing methods are often limited for discovering predictive biomarkers that interact with treatment and fail to elucidate the predictive power of their resultant selection rules. In this article, we present a Bayesian predictive method for personalized treatment selection that is devised to integrate both the treatment predictive and disease prognostic characteristics of a particular patient's disease. The method appropriately characterizes the structural constraints inherent to prognostic and predictive biomarkers, and hence properly utilizes these complementary sources of information for treatment selection. The methodology is illustrated through a case study of lower grade glioma. Theoretical considerations are explored to demonstrate the manner in which treatment selection is impacted by prognostic features. Additionally, simulations based on an actual leukemia study are provided to ascertain the method's performance with respect to selection rules derived from competing methods.     

Reconstructing the Lineage Histories and Differentiation Trajectories of Individual Cancer Cells in Myeloproliferative Neoplasms

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Primary pneumonic plague in the African Green monkey as a model for treatment efficacy evaluation

Background Primary pneumonic plague is rare among humans, but treatment efficacy may be tested in appropriate animal models under the FDA ‘Animal Rule’. Methods Ten African Green monkeys (AGMs) inhaled 44–255 LD₅₀ doses of aerosolized Yersinia pestis strain CO92. Continuous telemetry, arterial blood gases, chest radiography, blood culture, and clinical pathology monitored disease progression. Results Onset of fever, >39°C detected by continuous telemetry, 52–80 hours post‐exposure was the first sign of systemic disease and provides a distinct signal for treatment initiation. Secondary endpoints of disease severity include tachypnea measured by telemetry, bacteremia, extent of pneumonia imaged by chest x‐ray, and serum lactate dehydrogenase enzyme levels. Conclusions Inhaled Y. pestis in the AGM results in a rapidly progressive and uniformly fatal disease with fever and multifocal pneumonia, serving as a rigorous test model for antibiotic efficacy studies.     

Modeling Effects of Forest Cover Reduction on Larval Walleye Survival in Lake Erie Tributary Spawning Basins

An integrated modeling approach is used to link land use to river discharge, and then to survival of larval walleye that hatch in northern Ohio streams draining into Lake Erie (USA). First, to link land use and river discharge, the parameters of a simple hydrologic model are statistically related to watershed landscape attributes, including forest cover. One such relationship allows estimation of the change in daily river discharge that could result from a reduction in basin-scale forest cover. Second, to represent the river discharge-larval survival link, we reexamine a dataset from Mion and others to propose a relationship between daily flow velocity, water temperature, and walleye larval survival. Together, these linked models provide estimates of the reduction in larval survival due to reduction in forest cover, along with the uncertainty of those estimates. For the Grand River watershed, decreasing forest cover from 45.2 to 30% is projected to reduce average larval survival by about 45%. In the adjacent Chagrin River, dropping cover from 62.5 to 30% reduces survival by almost 60%. The greater rate of reduction of survival in the Chagrin River as forest levels fall is explained by a relatively greater increase in storm flows for the Chagrin, due to more frequently saturated soils. Therefore, forest preservation in the Chagrin River watershed is projected to be more effective in preserving walleye larval tributary habitat.