Optimizing doxorubicin-G-CSF chemotherapy regimens for the treatment of triple-negative breast cancer

La chimiothérapie cytotoxique reste une option de traitement de première intention pour la majorité des cancers. Un effet secondaire majeur dans les schémas chimio-thérapeutiques est la neutropénie. La thérapie prophylactique avec le facteur de stimulation des colonies de granulocytes (G-CSF), une cytokine endogène responsable de la régulation de la production de neutrophiles, est administrée en concomitance. Le moment et la dose exacts pour administrer la chimiothérapie et le G-CSF représentent des éléments cruciaux pour obtenir les résultats souhaités du traitement. En nous appuyant sur des travaux antérieurs qui optimisaient les schémas thérapeutiques du G-CSF, nous sommes basés sur une approche de pharmacologie quantitative des systèmes (QSP) pour étudier la fréquence et l’intensité de la dose dans le but de maximiser les effets anti-tumoraux de la chimiothérapie tout en minimisant la neutropénie. Dans ce travail, nous avons effectué une optimisation sur une large gamme de longueurs de cycle et de valeurs des doses de chimiothérapie afin d’identifier les meilleurs schémas en combinaison avec le G-CSF. Nos résultats suggèrent que la doxorubicine 45mg/BSA tous les 14 jours a un impact positif sur le contrôle de la croissance tumorale, et qu’il est préfèrable de retarder l’administration du G-CSF au septième jour après la chimiothérapie et de donner moins de doses pour minimiser le risque de neutropénie et le fardeau de ce médicament. Cette étude suggère des pistes possibles pour des schémas optimaux de chimiothérapie, avec le soutien prophylactique du G-CSF spécifiquement dans le contexte du cancer du sein triple négatif.Cytotoxic chemotherapy continues to be a first-line treatment option for the majority of cancers. A major side effect in chemotherapy regimens is neutropenia. Prophylactic therapy with granulocyte colony stimulating factor (G-CSF), an endogenous cytokine responsible for regulating neutrophil production, is administered concomitantly; the exact timing of the combination chemotherapy and G-CSF is crucial for achieving treatment results. Leveraging on previous work that optimized treatment regimens based on G-CSF timing, we developed a quantitative systems pharmacology (QSP) framework to study dose frequency and intensity of chemotherapy in order to maximize anti-tumor effects while minimizing neutropenia. In this work, we performed an optimization across a wide range of cycle lengths and dose sizes to identify the best cytotoxic chemotherapy regimens with G-CSF support. Our results suggest that doxorubicin 45mg/BSA every 14 days, has a positive impact on tumour growth control, and that to minimize the risk of neutropenia and the burden to patients it is best to delay the administration of G-CSF to day seven after chemotherapy and give fewer doses . This study suggests possible avenues for optimal chemotherapy regimens with prophylactic support of G-CSF in the context of Triple Negative Breast Cancer

Sparse integrative clustering of multiple omics data sets

High resolution microarrays and second-generation sequencing platforms are powerful tools to investigate genome-wide alterations in DNA copy number, methylation and gene expression associated with a disease. An integrated genomic profiling approach measures multiple omics data types simultaneously in the same set of biological samples. Such approach renders an integrated data resolution that would not be available with any single data type. In this study, we use penalized latent variable regression methods for joint modeling of multiple omics data types to identify common latent variables that can be used to cluster patient samples into biologically and clinically relevant disease subtypes. We consider lasso [J. Roy. Statist. Soc. Ser. B 58 (1996) 267-288], elastic net [J. R. Stat. Soc. Ser. B Stat. Methodol. 67 (2005) 301-320] and fused lasso [J. R. Stat. Soc. Ser. B Stat. Methodol. 67 (2005) 91-108] methods to induce sparsity in the coefficient vectors, revealing important genomic features that have significant contributions to the latent variables. An iterative ridge regression is used to compute the sparse coefficient vectors. In model selection, a uniform design [Monographs on Statistics and Applied Probability (1994) Chapman & Hall] is used to seek "experimental" points that scattered uniformly across the search domain for efficient sampling of tuning parameter combinations. We compared our method to sparse singular value decomposition (SVD) and penalized Gaussian mixture model (GMM) using both real and simulated data sets. The proposed method is applied to integrate genomic, epigenomic and transcriptomic data for subtype analysis in breast and lung cancer data sets.Comment: Published in at http://dx.doi.org/10.1214/12-AOAS578 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org

Knowledge driven decomposition of tumor expression profiles

<p>Abstract</p> <p>Background</p> <p>Tumors have been hypothesized to be the result of a mixture of oncogenic events, some of which will be reflected in the gene expression of the tumor. Based on this hypothesis a variety of data-driven methods have been employed to decompose tumor expression profiles into component profiles, hypothetically linked to these events. Interpretation of the resulting data-driven components is often done by post-hoc comparison to, for instance, functional groupings of genes into gene sets. None of the data-driven methods allow the incorporation of that type of knowledge directly into the decomposition.</p> <p>Results</p> <p>We present a linear model which uses knowledge driven, pre-defined components to perform the decomposition. We solve this decomposition model in a constrained linear least squares fashion. From a variety of options, a lasso-based solution to the model performs best in linking single gene perturbation data to mouse data. Moreover, we show the decomposition of expression profiles from human breast cancer samples into single gene perturbation profiles and gene sets that are linked to the hallmarks of cancer. For these breast cancer samples we were able to discern several links between clinical parameters, and the decomposition weights, providing new insights into the biology of these tumors. Lastly, we show that the order in which the Lasso regularization shrinks the weights, unveils consensus patterns within clinical subgroups of the breast cancer samples.</p> <p>Conclusion</p> <p>The proposed lasso-based constrained least squares decomposition provides a stable and relevant relation between samples and knowledge-based components, and is thus a viable alternative to data-driven methods. In addition, the consensus order of component importance within clinical subgroups provides a better molecular characterization of the subtypes.</p

Unconventional machine learning of genome-wide human cancer data

Recent advances in high-throughput genomic technologies coupled with exponential increases in computer processing and memory have allowed us to interrogate the complex aberrant molecular underpinnings of human disease from a genome-wide perspective. While the deluge of genomic information is expected to increase, a bottleneck in conventional high-performance computing is rapidly approaching. Inspired in part by recent advances in physical quantum processors, we evaluated several unconventional machine learning (ML) strategies on actual human tumor data. Here we show for the first time the efficacy of multiple annealing-based ML algorithms for classification of high-dimensional, multi-omics human cancer data from the Cancer Genome Atlas. To assess algorithm performance, we compared these classifiers to a variety of standard ML methods. Our results indicate the feasibility of using annealing-based ML to provide competitive classification of human cancer types and associated molecular subtypes and superior performance with smaller training datasets, thus providing compelling empirical evidence for the potential future application of unconventional computing architectures in the biomedical sciences

Emergence of Anti-Cancer Drug Resistance: Exploring the Importance of the Microenvironmental Niche via a Spatial Model

Practically, all chemotherapeutic agents lead to drug resistance. Clinically, it is a challenge to determine whether resistance arises prior to, or as a result of, cancer therapy. Further, a number of different intracellular and microenvironmental factors have been correlated with the emergence of drug resistance. With the goal of better understanding drug resistance and its connection with the tumor microenvironment, we have developed a hybrid discrete-continuous mathematical model. In this model, cancer cells described through a particle-spring approach respond to dynamically changing oxygen and DNA damaging drug concentrations described through partial differential equations. We thoroughly explored the behavior of our self-calibrated model under the following common conditions: a fixed layout of the vasculature, an identical initial configuration of cancer cells, the same mechanism of drug action, and one mechanism of cellular response to the drug. We considered one set of simulations in which drug resistance existed prior to the start of treatment, and another set in which drug resistance is acquired in response to treatment. This allows us to compare how both kinds of resistance influence the spatial and temporal dynamics of the developing tumor, and its clonal diversity. We show that both pre-existing and acquired resistance can give rise to three biologically distinct parameter regimes: successful tumor eradication, reduced effectiveness of drug during the course of treatment (resistance), and complete treatment failure

BAYESIAN INTEGRATIVE ANALYSIS OF OMICS DATA

Technological innovations have produced large multi-modal datasets that range in multiplatform genomic data, pathway data, proteomic data, imaging data and clinical data. Integrative analysis of such data sets have potentiality in revealing important biological and clinical insights into complex diseases like cancer. This dissertation focuses on Bayesian methodology establishment in integrative analysis of radiogenomics and pathway driver detection applied in cancer applications. We initially present Radio-iBAG that utilizes Bayesian approaches in analyzing radiological imaging and multi-platform genomic data, which we establish a multi-scale Bayesian hierarchical model that simultaneously identifies genomic and radiomic, i.e., radiology-based imaging markers, along with the latent associations between these two modalities, and to detect the overall prognostic relevance of the combined markers. Our method is motivated by and applied to The Cancer Genome Atlas glioblastoma multiforme data set, wherein it identifies important magnetic resonance imaging features and the associated genomic platforms that are also significantly related with patient survival times. For another aspect of integrative analysis, we then present pathDrive that aims to detect key genetic and epigenetic upstream drivers that influence pathway activity. The method is applied into colorectal cancer incorporated with its four molecular subtypes. For each of the pathways that significantly differentiates subgroups, we detect important genomic drivers that can be viewed as “switches” for the pathway activity. To extend the analysis, finally, we develop proteomic based pathway driver analysis for multiple cancer types wherein we simultaneously detect genomic upstream factors that influence a specific pathway for each cancer type within the cancer group. With Bayesian hierarchical model, we detect signals borrowing strength from common cancer type to rare cancer type, and simultaneously estimate their selection similarity. Through simulation study, our method is demonstrated in providing many advantages, including increased power and lower false discovery rates. We then apply the method into the analysis of multiple cancer groups, wherein we detect key genomic upstream drivers with proper biological interpretation. The overall framework and methodologies established in this dissertation illustrate further investigation in the field of integrative analysis of omics data, provide more comprehensive insight into biological mechanisms and processes, cancer development and progression