Personalized Medicine: The Use of Biomarkers and Molecularly Targeted Therapies for Patient Care and Cancer Intervention

Personalized medicine and targeted therapy have been emerging fields of study for the remediation and inhibition of cancer. Personalized medicine in the treatment of cancer involves using genetic, immune, and proteomic profiling to provide therapeutic options as well as prognostic background for every patient and their tumor’s genetic mutations. Targeted therapies allow researchers and medical personnel alike to determine the appropriate treatment for a patient based on the molecular basis and mechanistic actions of a cancerous tumor. The overall significance of this study was to express how these treatments use biomarkers to pinpoint the location, and severity of the cancer, and to administer the right treatment. Early detection of tumor‐specific biomarkers can allow the use of non‐invasive routine monitoring. The study aims to provide an elaborate explanation on the various biomarker classification and present the protocol on how they are sorted and validated to be a potential cancer biomarker used in clinical practice. Categorizing biomarkers relies on their characteristics. These classifiers will divide them into one of the following groups: general biomarkers, DNA biomarkers, and DNA tumor biomarker. The expressions of microRNA also play a role in the determination of cancer, as most of these clusters regulate the expression and transcriptional activity of various cancer cell lines. The expression of the ER receptors in mammalian cells classifies breast cancer into one of the following categories: triple negative, estrogen receptor (ER) negative, or (ER) positive. ER positive breast cancer patients can positively benefit from personalized medicine as these patients have to undergo specific hormonal therapy and supplementary adjuvant chemotherapy to eliminate the estrogen-induced proliferation of these mammalian cells. Drugs like tamoxifen function as antagonists to the ER receptor to inhibit the transcriptional activity of the ER receptors. Other cancer types such as colorectal cancer, and lung cancer may also benefit from such approaches. The limitations of the study include the unique genomic profiling of each patient, challenges in validation and implementation of drug combinations, and the deployment of technologies for DNA sequencing. Keywords: cancer, biomarkers, personalized medicine, gene therapy, targeted therapy, DNA Biomarker, RNA expressions, ER, colorectal cancer, lung cancer, prostate cancer, leukemia, targeted treatmen

Basic and Preclinical Research for Personalized Medicine

Basic and preclinical research founded the progress of personalized medicine by providing a prodigious amount of integrated profiling data and by enabling the development of biomedical applications to be implemented in patient-centered care and cures. If the rapid development of genomics research boosted the birth of personalized medicine, further development in omics technologies has more recently improved our understanding of the functional genome and its relevance in profiling patients\u2019 phenotypes and disorders. Concurrently, the rapid biotechnological advancement in diverse research areas enabled uncovering disease mechanisms and prompted the design of innovative biological treatments tailored to individual patient genotypes and phenotypes. Research in stem cells enabled clarifying their role in tissue degeneration and disease pathogenesis while providing novel tools toward the development of personalized regenerative medicine strategies. Meanwhile, the evolving field of integrated omics technologies ensured translating structural genomics information into actionable knowledge to trace detailed patients\u2019 molecular signatures. Finally, neuroscience research provided invaluable models to identify preclinical stages of brain diseases. This review aims at discussing relevant milestones in the scientific progress of basic and preclinical research areas that have considerably contributed to the personalized medicine revolution by bridging the bench-to-bed gap, focusing on stem cells, omics technologies, and neuroscience fields as paradigms

Corporate Capability for Personalized Medicine Development

学位の種別: 課程博士審査委員会委員 : (主査)東京大学准教授 加納 信吾, 東京大学教授 渡邉 俊樹, 東京大学教授 上田 卓也, 東京大学教授 馬場 靖憲, 東北大学教授 柴田 友厚University of Tokyo(東京大学

The efficacy of chemotherapeutic drug combinations may be predicted by concordance of gene response to the single agents

Determining the expression of genes in response to different classes of chemotherapeutic drugs may allow for a better understanding as to which may be used effectively in combination. In the present study, the human colorectal cancer cell line HCT116 was cultured with equi‑active concentrations of a series of anti‑cancer agents. Gene expression profiles were then measured by whole‑genome microarray. Although each drug induced a unique signature of gene expression in tumour cells, there were marked similarities between certain drugs, even in those from different classes. For example, the antimalarial agent artesunate and the platinum‑containing alkylating agent, oxaliplatin, produced a very similar mRNA expression pattern in HCT116 cells with ~14,000 genes being affected by the two drugs in the same way. Furthermore, the overall correlation of gene responses between two agents could predict whether their use in combination would lead to a greater or lesser effect on cell number, determined experimentally, than predicted by single agent experiments. The results indicated that even when working through different mechanisms, combining drugs that initiate a similar transcriptional response may constitute the best option for determining drug‑combination strategies for the treatment of cancer

Advanced Optical Imaging-Guided Nanotheranostics toward Personalized Cancer Drug Delivery

Nanomedicine involves the use of nanotechnology for clinical applications and holds promise to improve treatments. Recent developments offer new hope for cancer detection, prevention and treatment; however, being a heterogenous disorder, cancer calls for a more targeted treatment approach. Personalized Medicine (PM) aims to revolutionize cancer therapy by matching the most effective treatment to individual patients. Nanotheranostics comprise a combination of therapy and diagnostic imaging incorporated in a nanosystem and are developed to fulfill the promise of PM by helping in the selection of treatments, the objective monitoring of response and the planning of follow-up therapy. Although well-established imaging techniques, such as Magnetic Resonance Imaging (MRI), Computed Tomography (CT), Positron Emission Tomography (PET) and Single-Photon Emission Computed Tomography (SPECT), are primarily used in the development of theranostics, Optical Imaging (OI) offers some advantages, such as high sensitivity, spatial and temporal resolution and less invasiveness. Additionally, it allows for multiplexing, using multi-color imaging and DNA barcoding, which further aids in the development of personalized treatments. Recent advances have also given rise to techniques permitting better penetration, opening new doors for OI-guided nanotheranostics. In this review, we describe in detail these recent advances that may be used to design and develop efficient and specific nanotheranostics for personalized cancer drug delivery. © 2022 by the authors. Licensee MDPI, Basel, Switzerland

Joint transcriptomic analysis of lung cancer and other lung diseases

Q2Q1Completo1-18Background: Epidemiological and clinical evidence points cancer comorbidity with pulmonary chronic disease. The acquisition of some hallmarks of cancer by cells affected with lung pathologies as a cell adaptive mechanism to a shear stress, suggests that could be associated with the establishment of tumoral processes. Objective: To propose a bioinformatic pipeline for the identification of all deregulated genes and the transcriptional regulators (TFs) that are coexpressed during lung cancer establishment, and therefore could be important for the acquisition of the hallmarks of cancer. Methods: Ten microarray datasets (six of lung cancer, four of lung diseases) comparing normal and diseases-related lung tissue were selected to identify hub differentiated expressed genes (DEGs) in common between lung pathologies and lung cancer, along with transcriptional regulators through the utilization of specialized libraries from R language. DAVID bioinformatics tool for gene enrichment analyses was used to identify genes with experimental evidence associated to tumoral processes and signaling pathways. Coexpression networks of DEGs and TFs in lung cancer establishment were created with Coexnet library, and a survival analysis of the main hub genes was made. Results: Two hundred ten DEGs were identified in common between lung cancer and other lung diseases related to the acquisition of tumoral characteristics, which are coexpressed in a lung cancer network with TFs, suggesting that could be related to the establishment of the tumoral pathology in lung. The comparison of the coexpression networks of lung cancer and other lung diseases allowed the identification of common connectivity patterns (CCPs) with DEGs and TFs correlated to important tumoral processes and signaling pathways, that haven´t been studied to experimentally validate their role in the early stages of lung cancer. Some of the TFs identified showed a correlation between its expression levels and the survival of lung cancer patients. Conclusion: Our findings indicate that lung diseases share genes with lung cancer which are coexpressed in lung cancer, and might be able to explain the epidemiological observations that point to direct and inverse comorbid associations between some chronic lung diseases and lung cancer and represent a complex transcriptomic scenario

Intellectual Property, Surrogate Licensing, and Precision Medicine

The fruits of the biotechnology revolution are beginning to be harvested. Recent regulatory approvals of a variety of advanced therapies—Keytruda (pembrolizumab), Kymriah (tisagenlecleucel), and patisiran—have ushered in an age of “precision medicine” treatments that target patients’ specific genetic, physiological, and environmental profiles rather than generalized diagnoses of disease. Therapies like these may soon be supplemented by gene editing technologies such as CRISPR, which could enable the targeted eradication of deleterious genetic variants to improve human health. But the intellectual property (IP) surrounding precision therapies and their foundational technology remain controversial. Precision therapies ultimately rely—and are roughly congruent with—basic scientific information developed in the service of academic research. Much of precision medicine’s IP, however, is held by academic research institutions that employ for-profit surrogate companies, companies responsible both for commercially developing university research and sublicensing university IP to others. This creates an uneasy tension between the public missions of universities and the commercial motives of surrogates, particularly universities’ goals of producing and disclosing scientific information, and surrogates’ goals of exploiting that information for commercial gain. This essay examines the challenges that surrogate licensing poses for the future of precision medicine. It begins by providing a brief summary of precision medicine and its recent developments. Next, it provides an overview of university patenting and the shift toward surrogate licensing. It then explores some of the difficulties concerning surrogate licensing in the context of precision medicine and, later, suggests modified licensing approaches and best practices that may better promote scientific discovery, the development of human therapies, and overall social welfare. Lastly, the essay discusses some larger doctrinal and theoretical implications arising from surrogate licensing in informationally intensive fields, like precision medicine

Combining the amplification refractory mutation system and high-resolution melting analysis for KRAS mutation detection in clinical samples

© The Author(s) 2023. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.The success of personalized medicine depends on the discovery of biomarkers that allow oncologists to identify patients that will benefit from a particular targeted drug. Molecular tests are mostly performed using tumor samples, which may not be representative of the tumor's temporal and spatial heterogeneity. Liquid biopsies, and particularly the analysis of circulating tumor DNA, are emerging as an interesting means for diagnosis, prognosis, and predictive biomarker discovery. In this study, the amplification refractory mutation system (ARMS) coupled with high-resolution melting analysis (HRMA) was developed for detecting two of the most relevant KRAS mutations in codon 12. After optimization with commercial cancer cell lines, KRAS mutation screening was validated in tumor and plasma samples collected from patients with pancreatic ductal adenocarcinoma (PDAC), and the results were compared to those obtained by Sanger sequencing (SS) and droplet digital polymerase chain reaction (ddPCR). The developed ARMS-HRMA methodology stands out for its simplicity and reduced time to result when compared to both SS and ddPCR but showing high sensitivity and specificity for the detection of mutations in tumor and plasma samples. In fact, ARMS-HRMA scored 3 more mutations compared to SS (tumor samples T6, T7, and T12) and one more compared to ddPCR (tumor sample T7) in DNA extracted from tumors. For ctDNA from plasma samples, insufficient genetic material prevented the screening of all samples. Still, ARMS-HRMA allowed for scoring more mutations in comparison to SS and 1 more mutation in comparison to ddPCR (plasma sample P7). We propose that ARMS-HRMA might be used as a sensitive, specific, and simple method for the screening of low-level mutations in liquid biopsies, suitable for improving diagnosis and prognosis schemes.This work is financed by national funds from FCT—Fundação para a Ciência e a Tecnologia, I.P., in the scope of the project UIDP/04378/2020 and UIDB/04378/2020 of the Research Unit on Applied Molecular Biosciences—UCIBIO and the project LA/P/0140/2020 of the Associate Laboratory Institute for Health and Bioeconomy—i4HB. FCT-MCTES is also acknowledged for 2020.07660.BD for BBO. Open access funding provided by FCT|FCCN (b-on).info:eu-repo/semantics/publishedVersio

Co-expression based cancer staging and application

A novel method is developed for predicting the stage of a cancer tissue based on the consistency level between the co-expression patterns in the given sample and samples in a specific stage. The basis for the prediction method is that cancer samples of the same stage share common functionalities as reflected by the co-expression patterns, which are distinct from samples in the other stages. Test results reveal that our prediction results are as good or potentially better than manually annotated stages by cancer pathologists. This new co-expression-based capability enables us to study how functionalities of cancer samples change as they evolve from early to the advanced stage. New and exciting results are discovered through such functional analyses, which offer new insights about what functions tend to be lost at what stage compared to the control tissues and similarly what new functions emerge as a cancer advances. To the best of our knowledge, this new capability represents the first computational method for accurately staging a cancer sample

The Era of Radiogenomics in Precision Medicine: An Emerging Approach to Support Diagnosis, Treatment Decisions, and Prognostication in Oncology

With the rapid development of new technologies, including artificial intelligence and genome sequencing, radiogenomics has emerged as a state-of-the-art science in the field of individualized medicine. Radiogenomics combines a large volume of quantitative data extracted from medical images with individual genomic phenotypes and constructs a prediction model through deep learning to stratify patients, guide therapeutic strategies, and evaluate clinical outcomes. Recent studies of various types of tumors demonstrate the predictive value of radiogenomics. And some of the issues in the radiogenomic analysis and the solutions from prior works are presented. Although the workflow criteria and international agreed guidelines for statistical methods need to be confirmed, radiogenomics represents a repeatable and cost-effective approach for the detection of continuous changes and is a promising surrogate for invasive interventions. Therefore, radiogenomics could facilitate computer-aided diagnosis, treatment, and prediction of the prognosis in patients with tumors in the routine clinical setting. Here, we summarize the integrated process of radiogenomics and introduce the crucial strategies and statistical algorithms involved in current studies