5 research outputs found
TranscriptAchilles: a genome-wide platform to predict isoform biomarkers of gene essentiality in cancer
Background
Aberrant alternative splicing plays a key role in cancer development. In recent years, alternative splicing has been used as a prognosis biomarker, a therapy response biomarker, and even as a therapeutic target. Next-generation RNA sequencing has an unprecedented potential to measure the transcriptome. However, due to the complexity of dealing with isoforms, the scientific community has not sufficiently exploited this valuable resource in precision medicine.
Findings
We present TranscriptAchilles, the first large-scale tool to predict transcript biomarkers associated with gene essentiality in cancer. This application integrates 412 loss-of-function RNA interference screens of >17,000 genes, together with their corresponding whole-transcriptome expression profiling. Using this tool, we have studied which are the cancer subtypes for which alternative splicing plays a significant role to state gene essentiality. In addition, we include a case study of renal cell carcinoma that shows the biological soundness of the results. The databases, the source code, and a guide to build the platform within a Docker container are available at GitLab. The application is also available online.
Conclusions
TranscriptAchilles provides a user-friendly web interface to identify transcript or gene biomarkers of gene essentiality, which could be used as a starting point for a drug development project. This approach opens a wide range of translational applications in cancer
TranscriptAchilles: a genome-wide platform to predict isoform biomarkers of gene essentiality in cancer
Background
Aberrant alternative splicing plays a key role in cancer development. In recent years, alternative splicing has been used as a prognosis biomarker, a therapy response biomarker, and even as a therapeutic target. Next-generation RNA sequencing has an unprecedented potential to measure the transcriptome. However, due to the complexity of dealing with isoforms, the scientific community has not sufficiently exploited this valuable resource in precision medicine.
Findings
We present TranscriptAchilles, the first large-scale tool to predict transcript biomarkers associated with gene essentiality in cancer. This application integrates 412 loss-of-function RNA interference screens of >17,000 genes, together with their corresponding whole-transcriptome expression profiling. Using this tool, we have studied which are the cancer subtypes for which alternative splicing plays a significant role to state gene essentiality. In addition, we include a case study of renal cell carcinoma that shows the biological soundness of the results. The databases, the source code, and a guide to build the platform within a Docker container are available at GitLab. The application is also available online.
Conclusions
TranscriptAchilles provides a user-friendly web interface to identify transcript or gene biomarkers of gene essentiality, which could be used as a starting point for a drug development project. This approach opens a wide range of translational applications in cancer
BOSO: A novel feature selection algorithm for linear regression with high-dimensional data
With the frenetic growth of high-dimensional datasets in different biomedical domains, there is an urgent need to develop predictive methods able to deal with this complexity. Feature selection is a relevant strategy in machine learning to address this challenge. We introduce a novel feature selection algorithm for linear regression called BOSO (Bilevel Optimization Selector Operator). We conducted a benchmark of BOSO with key algorithms in the literature, finding a superior accuracy for feature selection in high-dimensional datasets. Proof-of-concept of BOSO for predicting drug sensitivity in cancer is presented. A detailed analysis is carried out for methotrexate, a well-studied drug targeting cancer metabolism
BOSO: A novel feature selection algorithm for linear regression with high-dimensional data
With the frenetic growth of high-dimensional datasets in different biomedical domains, there is an urgent need to develop predictive methods able to deal with this complexity. Feature selection is a relevant strategy in machine learning to address this challenge. We introduce a novel feature selection algorithm for linear regression called BOSO (Bilevel Optimization Selector Operator). We conducted a benchmark of BOSO with key algorithms in the literature, finding a superior accuracy for feature selection in high-dimensional datasets. Proof-of-concept of BOSO for predicting drug sensitivity in cancer is presented. A detailed analysis is carried out for methotrexate, a well-studied drug targeting cancer metabolism
DrugSniper, a Tool to Exploit Loss-Of-Function Screens, Identifies CREBBP as a Predictive Biomarker of VOLASERTIB in Small Cell Lung Carcinoma (SCLC)
The development of predictive biomarkers of response to targeted therapies is an unmet
clinical need for many antitumoral agents. Recent genome-wide loss-of-function screens, such as
RNA interference (RNAi) and CRISPR-Cas9 libraries, are an unprecedented resource to identify
novel drug targets, reposition drugs and associate predictive biomarkers in the context of precision
oncology. In this work, we have developed and validated a large-scale bioinformatics tool named
DrugSniper, which exploits loss-of-function experiments to model the sensitivity of 6237 inhibitors
and predict their corresponding biomarkers of sensitivity in 30 tumor types. Applying DrugSniper to
small cell lung cancer (SCLC), we identified genes extensively explored in SCLC, such as Aurora
kinases or epigenetic agents. Interestingly, the analysis suggested a remarkable vulnerability to
polo-like kinase 1 (PLK1) inhibition in CREBBP-mutant SCLC cells. We validated this association
in vitro using four mutated and four wild-type SCLC cell lines and two PLK1 inhibitors (Volasertib
and BI2536), confirming that the effect of PLK1 inhibitors depended on the mutational status of
CREBBP. Besides, DrugSniper was validated in-silico with several known clinically-used treatments,
including the sensitivity of Tyrosine Kinase Inhibitors (TKIs) and Vemurafenib to FLT3 and BRAF
mutant cells, respectively. These findings show the potential of genome-wide loss-of-function screens
to identify new personalized therapeutic hypotheses in SCLC and potentially in other tumors, which
is a valuable starting point for further drug development and drug repositioning projects