Treffer: Targeting Cancer Cell Signaling Using Precision Oncology Towards a Holistic Approach to Cancer Therapeutics

The TCGA Research Network started in 2005 has profiled and analyzed a large number of human tumors to discover molecular aberrations at the DNA, RNA, protein, and epigenetic levels and thereby provided reliable diagnostic and prognostic biomarkers for different cancer types since then.The presence of mutated genes is strongly correlated with cancer incidence, very specific causative genes or a small set of genes for most cancers have not been confirmed after decades of genomic studies. Nobel laureate James D. Watson opined at Cancer World 2013: "We can go ahead and sequence every piece of DNA that has ever existed, but I don't think we'll find the Achilles heel of cancer. Importantly, it is not only necessary to associate genetic mutations with different cancers but also to work on the mechanism of action of mutagens by focusing on enzymes which could invariably mediate oncogenic transformations. For example, overexpression of the ribonucleotide reductase (RnR) enzyme which catalyzes the formation of deoxyribonucleotides from ribonucleotides necessary for cell division, is implicated in many forms of cancer and the genes for the components of the enzyme are often mutated, leading to hyperactivity of the enzyme. But there are instances indicating that cytoplasmic material rather than the karyoplast would be responsible for cellular transformation that might be better explained as a consequence of certain epigenetic modulation than purely genetic changes. .RnR active site inhibitors have been developed accordingly to biophysically deactivate the enzyme when necessary, with positive results A comprehensive analysis of tumors based on their genomic studies must reveal the alterations in signaling pathways indicating patterns of vulnerabilities and the means to identify prospective targets for the development of personalized treatments and new combination therapies. In this regard, the Cancer Cell Map Initiative (CCMI), launched in 2015 by researchers at the University of California, San Francisco and the University of California, San Diego, allowed researchers to determine how hundreds of genetic mutations involved in a few types of cancer affect the activity of certain crucial proteins which ultimately lead to the manifestation of cancer. As there is a large amount of sequence data from many different cancer types, efforts are being made to extract mechanistic insights from the available information, requiring an integrated computational and experimental strategy that will help place these alterations in the higher order contexts of signaling mechanisms in cancer cells. This is the defined goal of the CCMI and has the potential to create a resource that can be used for cancer genome interpretation, enabling the identification of key complexes and pathways to better understand the biology underlying different cancer types and conditions for precise treatment of the disease.Multiomics: High-throughput sequencing technologies, also known as next-generation sequencing (NGS), are a comprehensive term used to describe technologies that sequence DNA and RNA rapidly and cost-effectively. It has revolutionized the field of genetics and molecular biology and aided in the study of biological sciences as never before. Technologies using NGS have been developed that measure some characteristics of a whole family of cellular molecules, such as genes, proteins, or metabolites, and have been named by appending the term "-omics. Multiomics refers to the approach where the data sets of different omics groups are combined during sample analysis to allow scientists to read the more complex and transient molecular changes that underpin the course of disease progression and response to treatment and to select the right drug target for desired results. It forms the basis of precision medicine in general and is at the core of the development of precision oncology. The breakthroughs in high- throughput technologies in recent years have led to the rapid accumulation of large-scale omics cancer data and brought an evolving concept of “big data” in cancer the analysis of which requires huge computational resources with the potential to bring new insights into critical problems. The combination of big data, bioinformatics, and artificial intelligence is thought to lead to notable advances in translational research in cancer.Artificial intelligence (AI): It encompasses multiple technologies with the common aim of computationally simulating human intelligence to solve complex problems. It is based on the principle that human intelligence can be defined in a way that a machine can easily mimic and execute tasks from the simpler to far more complex ones successfully. Broadly referred to as computer programming enabled to perform specific tasks, the term may be applied to any machine that displays traits associated with human understanding, such as learning and problem-solving. In regular programming, data are processed with well-defined rules to bring solutions, whereas AI relies on the learning process to devise rules for the efficient processing of data to yield smart results. AI and related technologies have increasingly been prevalent in finance, security, and society, and are now being applied to healthcare as well. It has been widely applied in precision medicine-based healthcare practices and is found to be greatly useful in medical oncology practice. Many artificial intelligence algorithms have been developed and applied in cancer research in recent years. An exact understanding of the structure of a protein remains the first step to knowing all about its roles in cancer progression and therapeutic drugs are also designed using structural information of the target proteins where AI-based techniques can be used for the solutions. The advances in NGS have led multi-omics data on cancer to become available to researchers providing them with opportunities to explore the genetic risk and reveal underlying cancer mechanisms to help early diagnosis, exact prognosis, and the discovery, design, and application of specific targeted drugs against cancer. Thus, integrating multi-omics-related studies with artificial intelligence is the need of the hour and is likely to serve the purpose well with time. Taking the help of large datasets from multi-omics platforms, imaging techniques, and biomarkers found and mined by artificial intelligence algorithms, oncologists can diagnose cancer early at its onset and help direct treatment options for individualized cancer therapy for anticipated results. Thus, the advances in AI present an opportunity to perfect the methods of diagnosis and prognosis and develop strategies for personalized treatment using large datasets, and future developments in AI technologies are most likely to help many more problems in this direction to be resolved swiftly. In this way, AI is thought to be the future of precision oncology towards the prevention, detection, risk assessment, and treatment of cancer.Machine learning (ML): It is a branch of artificial intelligence that aims to develop computational systems with advanced analytical capabilities. It is concerned with the development of domain-specific programming algorithms with the ability to learn from data to solve a class of problems. Therefore, the most common and purposeful application of traditional machine learning in healthcare seems to be in the area of precision medicine and is most suited for the data-driven identification of cancer states and designing treatment options that is crucial to precision oncology-based cancer treatment.Deep Learning (DL): Ii is a sub-branch of ML that uses statistics and predictive modeling to extract patterns from large data sets to precisely predict a result. A variety of data have been appearing in modern biomedical research, including electronic health records, imaging, multi-omics-based reports, sensor data, etc., which are complex, heterogeneous, and poorly defined and need to be mined efficiently to bring correct results. To meet this end, DL uses a machine learning program called artificial neural networks modeled on the human brain that forms a diverse family of computational models consisting of many deep data processing layers for automated feature extraction and pattern recognition in large datasets to efficiently answer the problems. The human brain consists of neurons arranged together as a network of nerves processing several pieces of information received from many different sources to translate into a particular reflex action. In DL, the same concept of a network of neurons is imitated on a machine learning platform to emulate human understanding to bring perfect solutions. The neurons are created artificially in a computer system and the data processing layers work together to create an artificial neural network where the working of an artificial neuron could be taken as like that of a neuron present in the brain. Thus, DL is designed to use a complex set of algorithms enabling it to process unstructured data such as documents, images, and text to find efficient results.Drug development for effective cancer treatment: It is a major problem in cancer research and DL provides immense help to researchers in this regard. Changes in the genetic composition of tumors translate into structural changes in cellular subsystems that require to be integrated into drug design to predict therapy response and concurrently learn about the mechanism underlying a particular drug response. Importantly, epidemiological studies have consistently shown that environmental factors or lifestyle changes involving mutagenic agents are the primary culprits. Thus, it is not only necessary to associate genetic mutations with different cancers but also to work on the mechanism of action of mutagens by focusing on enzymes which could invariably mediate oncogenic transformations. For example, overexpression of the ribonucleotide reductase (RnR) enzyme which catalyzes the formation of deoxyribonucleotides from ribonucleotides necessary for cell division, is implicated in many forms of cancer and the genes for the components of the enzyme are often mutated, leading to hyperactivity of the enzyme. But there are instances indicating that cytoplasmic material rather than the karyoplast would be responsible for cellular transformation that might be better explained as a consequence of certain epigenetic modulation than purely genetic changes. RnR active site inhibitors have been developed accordingly to biophysically deactivate the enzyme when necessary, with positive results A proper understanding of the mechanism of drug action can lead researchers to understand the importance of the different signaling pathways, including some new and uncommon pathways associated with tumors to help develop novel drugs for the therapeutic targeting of diverse forms of cancer. Drug combinations targeting multiple pathways are thought to be the answers to the incidences of drug resistance in cancer therapy where computational models could be used to find solutions. Occupation-oriented pharmacology is the dominant paradigm of drug discovery for the treatment of cancer. It relies on the use of inhibitors that occupy the functional binding site of a protein and can disrupt protein interactions and their functions. New advances in AI have enabled researchers to develop DL-based models to predict tumor cell response to synergistic drug combinations to be employed effectively in precision oncology. Researchers continue to discover proteins that may be the key drivers of cancer and need a fuller understanding of the 3D shape, or structure, of these proteins to decide their exact functions in the cell. A recent development in the DL system is AlphaFold, which is being used to predict the structures of different proteins, and the tool has already determined the structures of around 200 million proteins, from almost every known organism on the planet. This revolutionary new development in DL is going to be of great use in understanding the roles of suspected proteins in cancer development and in anticancer drug design. A newly developed DL system called PocketMiner is an efficient tool for predicting the locations of bonding sites on proteins. Proteins exist in a state of dynamic equilibrium with their different conformational structures, including experimentally determined structures that may not have targetable pockets. PocketMiner uses graph neural networks to find hidden areas or pocket formation from a single protein and is thought to be 1,000 times faster than existing methods of finding binding sites on proteins. This technology has made researchers understand that around half of proteins that were earlier considered undruggable might have ‘cryptic pockets’ that could be targeted successfully by anticancer agents. The AI-based system finds multiple uses in cancer management like the prediction of treatment response, estimation of survival analysis, risk estimation, and treatment planning, and is becoming the central approach in precision oncology.The Cancer Genome Atlas (TCGA) Program and Related Cancer Initiatives: The landmark Cancer Genomics Program launched in 2006 has contributed immensely to the awareness of the importance of cancer genomics in our understanding of cancer over the past decade and has begun to change the way the disease is treated in clinic. A large number of mutations contribute to cancer and predicting the effects of mutations using in silico tools has become a frequently used approach, but the use of next-generation sequencing-based approaches in clinical diagnosis has also led to a considerable increase in data and a vast number of variants of uncertain significance that require further analysis and validation to achieve the development goals. These data cannot be analyzed simply by using the tools and techniques traditionally available to better understand the origin and evolution of cancer and therefore to achieve this goal, a cancer reference framework through modeling of genome sequencing data has been proposed for the systematic identification of representative driver networks to predict cancer progression and associated clinical phenotypes. It is based on the consideration that possible observable combinations of these mutations must converge on a few common signaling pathways and networks responsible for tumor growth and cancer progression. In this way, it aims to analyze data to explain how different genetic mutations in different patients have the same downstream effects on the protein machinery, ultimately leading to the analysis of the characteristic pathway of cancer progression. The Cancer Genome Atlas (TCGA) Program is the landmark cancer genomics program initiated by the NIH, and has contributed immensely to realizing the importance of genomics in cancer research. The synchronized vision of oncogenic processes based on PanCancer Atlas analyzes attempts to elucidate the possible consequences of genome alterations on the different signaling pathways involved in human cancers, also reflecting on their influence on the tumor microenvironment and immune cells, to provide new information about development of new forms of targeted drugs and immunotherapies. Thus, considering the genes and pathways affecting different cancer types and individual tumors vary considerably, a complete understanding of these alterations becomes essential to identify vulnerabilities and discover precise therapeutic solutions. Although the presence of mutated genes is strongly correlated with cancer incidence, very specific causative genes or a small set of genes for most cancers have not been confirmed after decades of genomic studies. Nobel laureate James D. Watson spoke at Cancer World 2013: "We can go ahead and sequence every piece of DNA that has ever existed, but I don't think we'll find the Achilles heel of cancer. A comprehensive analysis of tumors based on their genomic studies must reveal the alterations in signaling pathways indicating patterns of vulnerabilities and the means to identify prospective targets for the development of personalized treatments and new combination therapies. The TCGA Research Network has profiled and analyzed a large number of human tumors to discover molecular aberrations at the DNA, RNA, protein, and epigenetic levels and thereby provided reliable diagnostic and prognostic biomarkers for different cancer types since then. Further, the Cancer Cell Map Initiative (CCMI), launched in 2015 by researchers at the University of California, San Francisco and the University of California, San Diego, allowed researchers to determine how hundreds of genetic mutations involved in a few types of cancer affect the activity of certain crucial proteins which ultimately lead to the manifestation of cancer. As there is a large amount of sequence data from many different cancer types, efforts are being made to extract mechanistic insights from the available information, requiring an integrated computational and experimental strategy that will help place these alterations in the higher order contexts of signaling mechanisms in cancer cells. This is the defined goal of the CCMI and has the potential to create a resource that can be used for cancer genome interpretation, enabling the identification of key complexes and pathways to be studied mechanistically to better understand the biology underlying different cancer types and conditions. Additionally, the Cancer Dependency Map (DepMap) initiative at the Broad Institute of MIT and Harvard, an academic-industry partnership program officially announced in 2019, is devoted to cancer research aimed at accelerating precision cancer medicine by creating a comprehensive map of tumor vulnerabilities and identifying key cancer biomarkers. This program focuses on screening thousands of cancer cell lines using RNA interference (RNAi) and CRISPR-Cas9 gene editing strategies to identify genes whose expression may be affected and found to be essential for cell transformation. CRISPR-Cas9 gene editing is an efficient method of modifying the genome of almost any cell type. CRISPR editing and screening have emerged as powerful tools for studying nearly all aspects of cellular behaviors which have greatly influenced our understanding of cancer biology and continue to contribute to new discoveries. A related project called Cancer Cell Line Encyclopedia (CCLE), started as a collaboration between the Broad Institute and the Novartis Institute for Biomedical Research in 2008, appears well suited for large-scale genetic characterization of thousands of cancer cell lines in order to link characteristics genetic alterations with distinct pharmacological vulnerabilities, and to translate integrative genomics into patients stratification for personalized cancer treatments. By accessing critical genomic data via CCLE, such as gene mutations, chromosome copy number, gene expression and methylation profiles, scientists can now predict new synthetic lethality and identify new molecular markers for selectively targeting cells with specific genetic changes. Thus, the initiative provides a rigorous foundation on which to study genetic variants and candidate targets, identify new marker-based cancer diagnostics, and design anticancer agents for critical cancers therapies. The challenge of identifying relevant genes and signaling molecules for different types of cancer using cutting-edge technologies will remain an essential part of cancer research and precision oncology and will most likely help vulnerable individuals receive effective treatment for cancer. The related article aims to fully determine the landscape of precision oncology research and seek solutions based on these initiatives in cancer research.
Cancer is a complex disease having a number of composite problems to be considered including cancer immune evasion, therapy resistance, and recurrence for a cure. Fundamentally, it remains a genetic disease as diverse aspects of the complexity of tumor growth and cancer development relate to its genetic machinery and require addressing the problems at the level of genome and epigenome. Importantly, patients with the same cancer types respond differently to cancer therapies indicating the need for patient-specific treatment options. Precision oncology is a form of cancer therapy that focuses on the genetic profiling of tumors to identify molecular alterations involved in cancer development for custom-tailored personalized treatment of the deadly disease. This article aims to briefly explain the foundations and frontiers of precision oncology in the context of ongoing technological advances in this regard to assess its scope and importance in the realization of a proper cure for cancer.
Le cancer est une maladie complexe qui présente un certain nombre de problèmes composites à prendre en compte, notamment l'évasion immunitaire du cancer, la résistance au traitement et la récidive pour trouver un remède. Fondamentalement, il s’agit d’une maladie génétique, car divers aspects de la complexité de la croissance tumorale et du développement du cancer sont liés à sa machinerie génétique et nécessitent d’aborder les problèmes au niveau du génome et de l’épigénome. Il est important de noter que les patients atteints des mêmes types de cancer réagissent différemment aux traitements anticancéreux, ce qui indique la nécessité d’options thérapeutiques spécifiques au patient. L'oncologie de précision est une forme de thérapie anticancéreuse qui se concentre sur le profilage génétique des tumeurs afin d'identifier les altérations moléculaires impliquées dans le développement du cancer en vue d'un traitement personnalisé sur mesure de cette maladie mortelle. Cet article vise à expliquer brièvement les fondements et les frontières de l’oncologie de précision dans le contexte des progrès technologiques en cours à cet égard afin d’évaluer sa portée et son importance dans la réalisation d’un remède approprié contre le cancer.