This document describes the development of gene expression signatures that can predict sensitivity to various chemotherapeutic drugs using microarray data from cancer cell lines.
1) Signatures were developed for several drugs including docetaxel, topotecan, adriamycin, etoposide, 5-fluorouracil, paclitaxel, and cyclophosphamide that could accurately predict drug sensitivity in independent cancer cell line datasets.
2) These signatures were also shown to predict clinical response to the drugs in human patients, including predicting response to docetaxel in breast cancer and ovarian cancer with over 85% accuracy.
3) The signatures were specific to each individual drug and could predict response to multid
Stratification of TCGA melanoma patients according to Tumor Infiltrative CD8...Antonio Ahn
The tumour microenvironment, namely the interaction between immune cells and tumour cells plays a crucial role in the treatment outcome of immunotherapy.
In order to predict patient responses to immunotherapy a tumour stratification framework has been proposed based on PD-L1 expression and presence of CD8 Tumour Infiltrative Lymphocytes (TIL).
Advances in genomic technologies and computational tools now allow to determine compositions of different immune cell infiltrates in bulk tumors with increasing accuracy and resolution. Our aim here was to use RNA-seq and methylation 450k data to stratify 469 melanoma patients in TCGA dataset according to the presence of CD8 Tumour Infiltrative Lymphocytes (TIL) and PD-L1 mRNA expression.
Selection of genes to include in genomic studies of disease
remains a difficult task. Current methods rely on expert opinion
or manual search engine use. With these methods, the
process and result are neither repeatable nor scalable. To
remedy this situation, we created the Informative Genetic
Content (IGC) system, which enables the algorithmic selection
of genes for inclusion in such studies, given one or more
diseases to target.
The IGC system stands on three components: a database
associating diseases with genes and other diseases, an
algorithm to rank the genes under consideration for inclusion in
a panel, and a module that clusters genes by families of
diseases. The first component, the database, maps diseases
to associated genes and scores each of these mappings
according to the strength of the relationship. The database also
maps diseases to other diseases, such that groups of diseases
or hierarchical relationships between diseases can be
identified. The second component enables the ranking of
candidate genes when multiple diseases are of interest. The
algorithm accounts for the common situation where two or
more diseases are associated with the same gene with varying
strengths of association, weighting and combining the scores
across the diseases associated with each gene. The final
component, the gene clustering module, groups genes by
pathogenic pathways, should the user want to consider
targeting a broader family of diseases affected by a closely
related set of genes.
We validated the IGC system through comparisons of our
automated gene selections with expertly curated gene panel
designs. We found a high degree of overlap between the IGC’s
gene selection and the gene lists chosen by experts,
supporting the viability of our system.
Together with the scalability and repeatability enabled by its
automation, the IGC system greatly improves the gene panel
selection process and therefore advances targeted genomic
studies.
Contribution of genome-wide association studies to scientific research: a pra...Mutiple Sclerosis
Vito A. G. Ricigliano, Renato Umeton, Lorenzo Germinario, Eleonora Alma, Martina Briani, Noemi Di Segni, Dalma Montesanti, Giorgia Pierelli, Fabiana Cancrini, Cristiano Lomonaco, Francesca Grassi, Gabriella Palmieri, and Marco Salvetti,
Struan Frederick Airth Grant, Editor
The factual value of genome-wide association studies (GWAS) for the understanding of multifactorial diseases is a matter of intense debate. Practical consequences for the development of more effective therapies do not seem to be around the corner. Here we propose a pragmatic and objective evaluation of how much new biology is arising from these studies, with particular attention to the information that can help prioritize therapeutic targets. We chose multiple sclerosis (MS) as a paradigm disease and assumed that, in pre-GWAS candidate-gene studies, the knowledge behind the choice of each gene reflected the understanding of the disease prior to the advent of GWAS. Importantly, this knowledge was based mainly on non-genetic, phenotypic grounds. We performed single-gene and pathway-oriented comparisons of old and new knowledge in MS by confronting an unbiased list of candidate genes in pre-GWAS association studies with those genes exceeding the genome-wide significance threshold in GWAS published from 2007 on. At the single gene level, the majority (94 out of 125) of GWAS-discovered variants had never been contemplated as plausible candidates in pre-GWAS association studies. The 31 genes that were present in both pre- and post-GWAS lists may be of particular interest in that they represent disease-associated variants whose pathogenetic relevance is supported at the phenotypic level (i.e. the phenotypic information that steered their selection as candidate genes in pre-GWAS association studies). As such they represent attractive therapeutic targets. Interestingly, our analysis shows that some of these variants are targets of pharmacologically active compounds, including drugs that are already registered for human use. Compared with the above single-gene analysis, at the pathway level GWAS results appear more coherent with previous knowledge, reinforcing some of the current views on MS pathogenesis and related therapeutic research. This study presents a pragmatic approach that helps interpret and exploit GWAS knowledge.
Talk delivered at Warwick Biomedical Engineering Seminar series 27 November 2014. Develops a theme emerging from a review in 2010:
J Watkins, A Marsh, P C Taylor, D R J Singer
Therapeutic Delivery, 2010, 1, 651-665
"Continued adherence to a single-drug single-target paradigm will limit the ability of chemists to contribute to advances in personalized medicine, whether they be in discovery or delivery"
Stratification of TCGA melanoma patients according to Tumor Infiltrative CD8...Antonio Ahn
The tumour microenvironment, namely the interaction between immune cells and tumour cells plays a crucial role in the treatment outcome of immunotherapy.
In order to predict patient responses to immunotherapy a tumour stratification framework has been proposed based on PD-L1 expression and presence of CD8 Tumour Infiltrative Lymphocytes (TIL).
Advances in genomic technologies and computational tools now allow to determine compositions of different immune cell infiltrates in bulk tumors with increasing accuracy and resolution. Our aim here was to use RNA-seq and methylation 450k data to stratify 469 melanoma patients in TCGA dataset according to the presence of CD8 Tumour Infiltrative Lymphocytes (TIL) and PD-L1 mRNA expression.
Selection of genes to include in genomic studies of disease
remains a difficult task. Current methods rely on expert opinion
or manual search engine use. With these methods, the
process and result are neither repeatable nor scalable. To
remedy this situation, we created the Informative Genetic
Content (IGC) system, which enables the algorithmic selection
of genes for inclusion in such studies, given one or more
diseases to target.
The IGC system stands on three components: a database
associating diseases with genes and other diseases, an
algorithm to rank the genes under consideration for inclusion in
a panel, and a module that clusters genes by families of
diseases. The first component, the database, maps diseases
to associated genes and scores each of these mappings
according to the strength of the relationship. The database also
maps diseases to other diseases, such that groups of diseases
or hierarchical relationships between diseases can be
identified. The second component enables the ranking of
candidate genes when multiple diseases are of interest. The
algorithm accounts for the common situation where two or
more diseases are associated with the same gene with varying
strengths of association, weighting and combining the scores
across the diseases associated with each gene. The final
component, the gene clustering module, groups genes by
pathogenic pathways, should the user want to consider
targeting a broader family of diseases affected by a closely
related set of genes.
We validated the IGC system through comparisons of our
automated gene selections with expertly curated gene panel
designs. We found a high degree of overlap between the IGC’s
gene selection and the gene lists chosen by experts,
supporting the viability of our system.
Together with the scalability and repeatability enabled by its
automation, the IGC system greatly improves the gene panel
selection process and therefore advances targeted genomic
studies.
Contribution of genome-wide association studies to scientific research: a pra...Mutiple Sclerosis
Vito A. G. Ricigliano, Renato Umeton, Lorenzo Germinario, Eleonora Alma, Martina Briani, Noemi Di Segni, Dalma Montesanti, Giorgia Pierelli, Fabiana Cancrini, Cristiano Lomonaco, Francesca Grassi, Gabriella Palmieri, and Marco Salvetti,
Struan Frederick Airth Grant, Editor
The factual value of genome-wide association studies (GWAS) for the understanding of multifactorial diseases is a matter of intense debate. Practical consequences for the development of more effective therapies do not seem to be around the corner. Here we propose a pragmatic and objective evaluation of how much new biology is arising from these studies, with particular attention to the information that can help prioritize therapeutic targets. We chose multiple sclerosis (MS) as a paradigm disease and assumed that, in pre-GWAS candidate-gene studies, the knowledge behind the choice of each gene reflected the understanding of the disease prior to the advent of GWAS. Importantly, this knowledge was based mainly on non-genetic, phenotypic grounds. We performed single-gene and pathway-oriented comparisons of old and new knowledge in MS by confronting an unbiased list of candidate genes in pre-GWAS association studies with those genes exceeding the genome-wide significance threshold in GWAS published from 2007 on. At the single gene level, the majority (94 out of 125) of GWAS-discovered variants had never been contemplated as plausible candidates in pre-GWAS association studies. The 31 genes that were present in both pre- and post-GWAS lists may be of particular interest in that they represent disease-associated variants whose pathogenetic relevance is supported at the phenotypic level (i.e. the phenotypic information that steered their selection as candidate genes in pre-GWAS association studies). As such they represent attractive therapeutic targets. Interestingly, our analysis shows that some of these variants are targets of pharmacologically active compounds, including drugs that are already registered for human use. Compared with the above single-gene analysis, at the pathway level GWAS results appear more coherent with previous knowledge, reinforcing some of the current views on MS pathogenesis and related therapeutic research. This study presents a pragmatic approach that helps interpret and exploit GWAS knowledge.
Talk delivered at Warwick Biomedical Engineering Seminar series 27 November 2014. Develops a theme emerging from a review in 2010:
J Watkins, A Marsh, P C Taylor, D R J Singer
Therapeutic Delivery, 2010, 1, 651-665
"Continued adherence to a single-drug single-target paradigm will limit the ability of chemists to contribute to advances in personalized medicine, whether they be in discovery or delivery"
Autologous Bone Marrow Cell Therapy for Autism: An Open Label Uncontrolled C...remedypublications2
The aim of this study is to assess the safety and effectiveness of autologous bone marrow
mononuclear stem cell (BMMNC) transplantation in patients with autism.
Multiscale integrative data analytics in pharmacogenomicsDr. Gerry Higgins
This strategy for mapping drug networks provides insight into the mechanistic on- and off-target effects, laying a foundation for subsequent preclinical studies.
A slide series to learn and appreciate the importance and the potential of Personalized/Individualized Genomic Medicine. It briefly goes through the idea of biotechnology and the advancements we have made in biology and technology. A series of applications for genomic medicine is then explored, not failing to mention the challenges we have to overcome as well, for the next medical revolution.
A case for personalized medicine is presented.
SciTech Development LLC - Intelligent Technology to Solve Unmet Clinical Needs - Often the difference between success and failure is the dedication and persistence of the executive team. SciTech Development’s principal asset, fenretinide, is guided by a deeply experienced team in the broad portfolio of pharmaceutical development, clinical strategy, and scientific formulation.
E1512 Trial Spotlight for May 2013 ECOG-ACRIN NewsletterSara Bucknam
E1512 Trial spotlight for ECOG-ACRIN Newsletter. E1512 is a randomized, three‐arm phase II trial investigating the clinical efficacy and safety of erlotinib alone, cabozantinib alone, or erlotinib plus cabozantinib as second‐ or third‐line therapy in patients with EGFR wild‐type NSCLC.
Autologous Bone Marrow Cell Therapy for Autism: An Open Label Uncontrolled C...remedypublications2
The aim of this study is to assess the safety and effectiveness of autologous bone marrow
mononuclear stem cell (BMMNC) transplantation in patients with autism.
Multiscale integrative data analytics in pharmacogenomicsDr. Gerry Higgins
This strategy for mapping drug networks provides insight into the mechanistic on- and off-target effects, laying a foundation for subsequent preclinical studies.
A slide series to learn and appreciate the importance and the potential of Personalized/Individualized Genomic Medicine. It briefly goes through the idea of biotechnology and the advancements we have made in biology and technology. A series of applications for genomic medicine is then explored, not failing to mention the challenges we have to overcome as well, for the next medical revolution.
A case for personalized medicine is presented.
SciTech Development LLC - Intelligent Technology to Solve Unmet Clinical Needs - Often the difference between success and failure is the dedication and persistence of the executive team. SciTech Development’s principal asset, fenretinide, is guided by a deeply experienced team in the broad portfolio of pharmaceutical development, clinical strategy, and scientific formulation.
E1512 Trial Spotlight for May 2013 ECOG-ACRIN NewsletterSara Bucknam
E1512 Trial spotlight for ECOG-ACRIN Newsletter. E1512 is a randomized, three‐arm phase II trial investigating the clinical efficacy and safety of erlotinib alone, cabozantinib alone, or erlotinib plus cabozantinib as second‐ or third‐line therapy in patients with EGFR wild‐type NSCLC.
Assessing the clinical utility of cancer genomic and proteomic data across tu...Gul Muneer
Molecular profiling of tumors promises to advance the clinical
management of cancer, but the benefits of integrating
molecular data with traditional clinical variables have not been
systematically studied. Here we retrospectively predict patient
survival using diverse molecular data (somatic copy-number
alteration, DNA methylation and mRNA, microRNA and protein
expression) from 953 samples of four cancer types from The
Cancer Genome Atlas project. We find that incorporating
molecular data with clinical variables yields statistically
significantly improved predictions (FDR < 0.05) for three
cancers but those quantitative gains were limited (2.2–23.9%).
Additional analyses revealed little predictive power across
tumor types except for one case. In clinically relevant genes,
we identified 10,281 somatic alterations across 12 cancer types
in 2,928 of 3,277 patients (89.4%), many of which would
not be revealed in single-tumor analyses. Our study provides
a starting point and resources, including an open-access
model evaluation platform, for building reliable prognostic and
therapeutic strategies that incorporate molecular data
Drug-induced liver injury (DILI) is one of the most important reasons for drug development failure at both pre-approval and post-approval stages. There has been increased interest in developing predictive in vivo, in vitro and in silico models to identify compounds that cause idiosyncratic hepatotoxicity. In the current study we applied machine learning, Bayesian modeling method with extended connectivity fingerprints and other interpretable descriptors. The model that was developed and internally validated (using a training set of 295 compounds) was then applied to a large test set relative to the training set (237 compounds) for external validation. The resulting concordance of 60%, sensitivity of 56%, and specificity of 67% were comparable to internal validation. The Bayesian model with ECFC_6 fingerprint and interpretable descriptors suggested several substructures that are chemically reactive and may also be important for DILI-causing compounds, e.g. ketones, diols and -methyl styrene type structures. Using SMARTS filters published by several pharmaceutical companies we evaluated whether such reactive substructures could be readily detected by any of the published filters. It was apparent that the most stringent filters used in this study, like the Abbott alerts which captures thiol traps and other compounds, may be of utility in identifying DILI-causing compounds (sensitivity 67%). A significant outcome of the present study is that we provide predictions for many compounds that cause DILI by using the knowledge we have available from previous studies for computational approaches. These computational models may represent a cost effective selection criteria prior to costly in vitro or in vivo experimental studies.
Part of the "2016 Annual Conference: Big Data, Health Law, and Bioethics" held at Harvard Law School on May 6, 2016.
This conference aimed to: (1) identify the various ways in which law and ethics intersect with the use of big data in health care and health research, particularly in the United States; (2) understand the way U.S. law (and potentially other legal systems) currently promotes or stands as an obstacle to these potential uses; (3) determine what might be learned from the legal and ethical treatment of uses of big data in other sectors and countries; and (4) examine potential solutions (industry best practices, common law, legislative, executive, domestic and international) for better use of big data in health care and health research in the U.S.
The Petrie-Flom Center for Health Law Policy, Biotechnology, and Bioethics at Harvard Law School 2016 annual conference was organized in collaboration with the Berkman Center for Internet & Society at Harvard University and the Health Ethics and Policy Lab, University of Zurich.
Learn more at http://petrieflom.law.harvard.edu/events/details/2016-annual-conference.
CXCL1, CCL20, STAT1 was Identified and Validated as a Key Biomarker Related t...semualkaira
Growing evidence suggests a correlation between ulcerative colitis (UC) and immune markers. Pathogenesis of UC was not yet been clearly elucidated, and few researches on immune-related biomarkers published.
CXCL1, CCL20, STAT1 was Identified and Validated as a Key Biomarker Related t...semualkaira
Growing evidence suggests a correlation between ulcerative colitis (UC) and immune markers. Pathogenesis
of UC was not yet been clearly elucidated, and few researches on
immune-related biomarkers published.
MEDICAL IMAGING MUTIFRACTAL ANALYSIS IN PREDICTION OF EFFICIENCY OF CANCER TH...cscpconf
Based on pressing need for predictive performance improvement, we explored the value of pretherapy
tumour histology image analysis to predict chemotherapy response. It was shown that
multifractal analysis of breast tumour tissue prior to chemotherapy indeed has the capacity to
distinguish between histological images of the different chemotherapy responder groups with
accuracies of 91.4% for pPR, 82.9% for pCR and 82.1% for PD/SD.
MEDICAL IMAGING MUTIFRACTAL ANALYSIS IN PREDICTION OF EFFICIENCY OF CANCER TH...csandit
Based on pressing need for predictive performance improvement, we explored the value of pretherapy
tumour histology image analysis to predict chemotherapy response. It was shown that
multifractal analysis of breast tumour tissue prior to chemotherapy indeed has the capacity to
distinguish between histological images of the different chemotherapy responder groups with
accuracies of 91.4% for pPR, 82.9% for pCR and 82.1% for PD/SD.