A New Approach to Deriving Prognostic Gene Pairs from Cancer Patient-specific Gene Correlation Networks

Abstract Background Molecular characterization of individual cancer patients is important because cancer is a complex and heterogeneous disease with many possible genetic and environmental causes. Many studies have been conducted to identify diagnostic or prognostic gene signatures for cancer from gene expression profiles. However, some gene signatures may fail to serve as diagnostic or prognostic biomarkers and gene signatures may not be found in gene expression profiles. Methods In this study, we developed a general method for constructing patient-specific gene correlation networks and for identifying prognostic gene pairs from the networks. A patient-specific gene correlation network was constructed by comparing a reference gene correlation network from normal samples to a network perturbed by a single patient sample. The main difference of our method from previous ones includes (1) it is focused on finding prognostic gene pairs rather than prognostic genes and (2) it can identify prognostic gene pairs from gene expression profiles even when no significant prognostic genes exist. Results Evaluation of our method with extensive data sets of three cancer types (breast invasive carcinoma, colon adenocarcinoma, and lung adenocarcinoma) showed that our approach is general and that gene pairs can serve as more reliable prognostic signatures for cancer than genes. Conclusions Our study revealed that prognosis of individual cancer patients is associated with the existence of prognostic gene pairs in the patient-specific network and the size of a subnetwork of the prognostic gene pairs in the patient-specific network. Although preliminary, our approach will be useful for finding gene pairs to predict survival time of patients and to tailor treatments to individual characteristics. The program for dynamically constructing patient-specific gene networks and for finding prognostic gene pairs is available at http://bclab.inha.ac.kr/pancancer.

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Constructing cancer patient-specific and group-specific gene networks with multi-omics data

BMC Medical Genomics ◽

10.1186/s12920-020-00736-7 ◽

2020 ◽

Vol 13 (S6) ◽

Author(s):

Wook Lee ◽

De-Shuang Huang ◽

Kyungsook Han

Keyword(s):

Cancer Patient ◽

Gene Networks ◽

Patient Specific ◽

Specific Gene ◽

Omics Data

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Bayesian adaptive design of early-phase clinical trials for precision medicine based on cancer biomarkers

The International Journal of Biostatistics ◽

10.1515/ijb-2021-0009 ◽

2021 ◽

Vol 0 (0) ◽

Author(s):

Shinjo Yada

Keyword(s):

Neural Network ◽

Clinical Trials ◽

Precision Medicine ◽

Early Phase ◽

Patient Specific ◽

Specific Gene ◽

Cancer Type ◽

Background Characteristics ◽

Number Of Patients ◽

Early Phase Clinical Trials

Abstract Cancer tissue samples obtained via biopsy or surgery were examined for specific gene mutations by genetic testing to inform treatment. Precision medicine, which considers not only the cancer type and location, but also the genetic information, environment, and lifestyle of each patient, can be applied for disease prevention and treatment in individual patients. The number of patient-specific characteristics, including biomarkers, has been increasing with time; these characteristics are highly correlated with outcomes. The number of patients at the beginning of early-phase clinical trials is often limited. Moreover, it is challenging to estimate parameters of models that include baseline characteristics as covariates such as biomarkers. To overcome these issues and promote personalized medicine, we propose a dose-finding method that considers patient background characteristics, including biomarkers, using a model for phase I/II oncology trials. We built a Bayesian neural network with input variables of dose, biomarkers, and interactions between dose and biomarkers and output variables of efficacy outcomes for each patient. We trained the neural network to select the optimal dose based on all background characteristics of a patient. Simulation analysis showed that the probability of selecting the desirable dose was higher using the proposed method than that using the naïve method.

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Depression patient-derived cortical neurons reveal potential biomarkers for antidepressant response

Translational Psychiatry ◽

10.1038/s41398-021-01319-5 ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Yishai Avior ◽

Shiri Ron ◽

Dana Kroitorou ◽

Claudia Albeldas ◽

Vitaly Lerner ◽

...

Keyword(s):

Cortical Neurons ◽

Antidepressant Treatment ◽

Patient Specific ◽

Specific Gene ◽

Specific Information ◽

Patient Response ◽

Patient Will ◽

Gene Expression Alterations ◽

Treatment Alternatives

AbstractMajor depressive disorder is highly prevalent worldwide and has been affecting an increasing number of people each year. Current first line antidepressants show merely 37% remission, and physicians are forced to use a trial-and-error approach when choosing a single antidepressant out of dozens of available medications. We sought to identify a method of testing that would provide patient-specific information on whether a patient will respond to a medication using in vitro modeling. Patient-derived lymphoblastoid cell lines from the Sequenced Treatment Alternatives to Relieve Depression study were used to rapidly generate cortical neurons and screen them for bupropion effects, for which the donor patients showed remission or non-remission. We provide evidence for biomarkers specific for bupropion response, including synaptic connectivity and morphology changes as well as specific gene expression alterations. These biomarkers support the concept of personalized antidepressant treatment based on in vitro platforms and could be utilized as predictors to patient response in the clinic.

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Abstract 289: Transcriptomic Analysis of Inter- and Intra-patient Variation in Human iPSCs: Platform for Precision Medicine to Predict Drug Toxicity

Circulation Research ◽

10.1161/res.119.suppl_1.289 ◽

2016 ◽

Vol 119 (suppl_1) ◽

Author(s):

Elena Matsa ◽

Paul W Burridge ◽

Kun-Hsing Yu ◽

Haodi Wu ◽

Vittavat Termglinchan ◽

...

Keyword(s):

Gene Expression ◽

Precision Medicine ◽

Disease Modeling ◽

Sendai Virus ◽

Expression Profiles ◽

Cardiac Differentiation ◽

Patient Specific ◽

Specific Gene ◽

Drug Induced ◽

Human Ipscs

Rapid improvements in human induced pluripotent stem cell (hiPSC) differentiation methodologies have allowed previously unattainable access to high-purity, patient-specific cardiomyocytes (CMs) for use in disease modeling, cardiac regeneration, and drug testing. In the present study, we investigate the ability of hiPSC-derived cardiomyocytes (hiPSC-CMs) to reflect the donor’s genetic identity and serve as preclinical functional readout platforms for precision medicine. We used footprint-free Sendai virus to create two separate hiPSC clones from the fibroblasts of five different individuals lacking known mutations associated with cardiovascular disease. Whole genome expression profiling of hiPSC-CMs showed that inter-patient variation was greater than intra-patient variation, thereby verifying that reprogramming and cardiac differentiation technologies can preserve patient-specific gene expression signatures. Gene ontologies (GOs) accounting for inter-patient variation were mostly metabolic or epigenetic. Toxicology analysis based on gene expression profiles predicted patient-specific susceptibility of hiPSC-CMs to cardiotoxicity, and functional assays using drugs targeting key regulators in pathways predicted to produce cardiotoxicity showed inter-patient differential responses in hiPSC-CMs. Our data suggest that hiPSC-CMs can be used in vitro to predict and help prevent patient-specific drug-induced cardiotoxicity, potentially enabling personalized patient consultation in the future.

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Novel cancer subtyping method based on patient-specific gene regulatory network

10.1101/2021.03.24.436731 ◽

2021 ◽

Author(s):

Mai Adachi Nakazawa ◽

Yoshinori Tamada ◽

Yoshihisa Tanaka ◽

Marie Ikeguchi ◽

Kako Higashihara ◽

...

Keyword(s):

Gene Networks ◽

Regulatory Networks ◽

The Cancer Genome Atlas ◽

Patient Specific ◽

Specific Gene ◽

Omics Data ◽

Cancer Subtypes ◽

Molecular Systems ◽

Molecular Features ◽

Cancer Genome Atlas

The identification of cancer subtypes is important for the understanding of tumor heterogeneity. In recent years, numerous computational methods have been proposed for this problem based on the multi-omics data of patients. It is widely accepted that different cancer subtypes are induced by different molecular regulatory networks. However, only a few incorporate the differences between their molecular systems into the classification processes. In this study, we present a novel method to classify cancer subtypes based on patient-specific molecular systems. Our method quantifies patient-specific gene networks, which are estimated from their transcriptome data. By clustering their quantified networks, our method allows for cancer subtyping, taking into consideration the differences in the molecular systems of patients. Comprehensive analyses of The Cancer Genome Atlas (TCGA) datasets applied to our method confirmed that they were able to identify more clinically meaningful cancer subtypes than the existing subtypes and found that the identified subtypes comprised different molecular features. Our findings show that the proposed method, based on a simple classification using the patient-specific molecular systems, can identify cancer subtypes even with single omics data, which cannot otherwise be captured by existing methods using multi-omics data.

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COMP-04. MODELING PRECISION ONCOLOGY FOR GLIOBLASTOMA THROUGH INTEGRATION OF DESCRIPTIVE, FUNCTIONAL, AND NETWORK-BASED GENOMICS

Neuro-Oncology ◽

10.1093/neuonc/noz175.247 ◽

2019 ◽

Vol 21 (Supplement_6) ◽

pp. vi61-vi62

Author(s):

Pia Hoellerbauer ◽

Megan Kufeld ◽

Sonali Arora ◽

Emily Girard ◽

James Olson ◽

...

Keyword(s):

Membrane Trafficking ◽

Mitochondrial Protein ◽

Survival Rates ◽

Mitotic Catastrophe ◽

Patient Specific ◽

Specific Gene ◽

Precision Oncology ◽

Genome Wide ◽

F Box Protein ◽

Phosphatidylinositol 4

Abstract Precision oncology is largely based on the notion that identification and targeting of oncogenic drivers will lead to improved clinical outcomes. However, the promise of precision oncology awaits to be fulfilled for many cancers, including Glioblastoma (GBM), where identification of oncogenic drivers has yet to improve survival rates. Here, we have attempted to systematically identify GBM vulnerabilities by performing genome-wide CRISRP-Cas9 lethality screens in patient-derived GBM stem-like cells (GSCs). In validation studies, we comprehensively retested GSC-specific hits in multiple GSC isolates, which were also genomically profiled (e.g. RNA-seq, exome-seq, CNV), and further integrated these data with CRISPR-Cas9 lethality screens from over 500 human cell lines from the Broad Institute’s CRISPR Avana dataset. As a result, we have begun making GBM dependency predictions and functional associations for top scoring hits, including: tumor developmental subtype; loss of functional redundancy with other genes/proteins; cancer-specific subnetworks of genes involved in mitochondrial protein turnover and membrane trafficking; and genes of unknown function essential for subset of GBMs. A few examples of these categories include the following scenarios. We find ADAR (Adenosine Deaminase RNA Specific) gene dependency is associated with the mesenchymal GBM subtype. The EFR3Agene, which has roles in maintaining active pools of phosphatidylinositol 4-kinase, appears required when the expression of its paralog EFR3Bis low or absent in tumor cells. The F-box protein-encoding gene FBXO42appears non-essential to most human cells lines and neural stem cells, but when knocked out in sensitive GSCs causes mitotic arrest, mitotic catastrophe, and cell death. While still a work in progress, we hope to use these results as a foundation for exploring and illuminating patient-specific molecular vulnerabilities for brain tumors. The results also underscore the need for integration of functional genetic approaches, where gene activities are inhibited, into precision oncology paradigms.

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In vivo CRISPR/Cas9 targeting of fusion oncogenes for selective elimination of cancer cells

Nature Communications ◽

10.1038/s41467-020-18875-x ◽

2020 ◽

Vol 11 (1) ◽

Author(s):

M. Martinez-Lage ◽

R. Torres-Ruiz ◽

P. Puig-Serra ◽

P. Moreno-Gaona ◽

M. C. Martin ◽

...

Keyword(s):

Cancer Cells ◽

Tumor Development ◽

Tumor Burden ◽

Patient Specific ◽

Specific Gene ◽

In Vivo Models ◽

New Horizons ◽

Selective Elimination ◽

Cancer Types

Abstract Fusion oncogenes (FOs) are common in many cancer types and are powerful drivers of tumor development. Because their expression is exclusive to cancer cells and their elimination induces cell apoptosis in FO-driven cancers, FOs are attractive therapeutic targets. However, specifically targeting the resulting chimeric products is challenging. Based on CRISPR/Cas9 technology, here we devise a simple, efficient and non-patient-specific gene-editing strategy through targeting of two introns of the genes involved in the rearrangement, allowing for robust disruption of the FO specifically in cancer cells. As a proof-of-concept of its potential, we demonstrate the efficacy of intron-based targeting of transcription factors or tyrosine kinase FOs in reducing tumor burden/mortality in in vivo models. The FO targeting approach presented here might open new horizons for the selective elimination of cancer cells.

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