Molecular profiling in cancer research and personalized medicine

2019 ◽  
pp. 347-362
Author(s):  
Pieter-Jan van Dam ◽  
Steven Van Laere
Keyword(s):  
Personalized Medicine ◽  
High Throughput ◽  
Cancer Biology ◽  
Complex Disease ◽  
Human Cancer ◽  
Molecular Profiling ◽  
Cancer Genome ◽  
The Cancer Genome Atlas ◽  
Molecular Heterogeneity ◽  
Profiling Techniques

Recent efforts by worldwide consortia such as The Cancer Genome Atlas and the International Cancer Genome Consortium have greatly accelerated our knowledge of human cancer biology. Nowadays, complete sets of human tumours that have been characterized at the genomic, epigenomic, transcriptomic, or proteomic level are available to the research community. The generation of these data was made possible thanks to the application of high-throughput molecular profiling techniques such as microarrays and next-generation sequencing. The primary conclusion from current profiling experiments is that human cancer is a complex disease characterized by extreme molecular heterogeneity, both between and within the classical, tissue-defined cancer types. This molecular variety necessitates a paradigm shift in patient management, away from generalized therapy schemes and towards more personalized treatments. This chapter provides an overview of how molecular cancer profiling can assist in facilitating this transition. First, the state-of-the-art of molecular breast cancer profiling is reviewed to provide a general background. Then, the most pertinent high-throughput molecular profiling techniques along with various data mining techniques (i.e. unsupervised clustering, statistical learning) are discussed. Finally, the challenges and perspectives with respect to molecular cancer profiling, also from the perspective of personalized medicine, are summarized.

scholarly journals FIREVAT: finding reliable variants without artifacts in human cancer samples using etiologically relevant mutational signatures

2019 ◽  
Vol 11 (1) ◽  
Author(s):  
Hyunbin Kim ◽  
Andy Jinseok Lee ◽  
Jongkeun Lee ◽  
Hyonho Chun ◽  
Young Seok Ju ◽  
...  
Keyword(s):  
Data Storage ◽  
Human Cancer ◽  
Point Mutations ◽  
Variant Calling ◽  
Human Tumor ◽  
Cancer Genome ◽  
The Cancer Genome Atlas ◽  
Precision Oncology ◽  
Mutational Signatures ◽  
Accurate Identification

Abstract Background Accurate identification of real somatic variants is a primary part of cancer genome studies and precision oncology. However, artifacts introduced in various steps of sequencing obfuscate confidence in variant calling. Current computational approaches to variant filtering involve intensive interrogation of Binary Alignment Map (BAM) files and require massive computing power, data storage, and manual labor. Recently, mutational signatures associated with sequencing artifacts have been extracted by the Pan-cancer Analysis of Whole Genomes (PCAWG) study. These spectrums can be used to evaluate refinement quality of a given set of somatic mutations. Results Here we introduce a novel variant refinement software, FIREVAT (FInding REliable Variants without ArTifacts), which uses known spectrums of sequencing artifacts extracted from one of the largest publicly available catalogs of human tumor samples. FIREVAT performs a quick and efficient variant refinement that accurately removes artifacts and greatly improves the precision and specificity of somatic calls. We validated FIREVAT refinement performance using orthogonal sequencing datasets totaling 384 tumor samples with respect to ground truth. Our novel method achieved the highest level of performance compared to existing filtering approaches. Application of FIREVAT on additional 308 The Cancer Genome Atlas (TCGA) samples demonstrated that FIREVAT refinement leads to identification of more biologically and clinically relevant mutational signatures as well as enrichment of sequence contexts associated with experimental errors. FIREVAT only requires a Variant Call Format file (VCF) and generates a comprehensive report of the variant refinement processes and outcomes for the user. Conclusions In summary, FIREVAT facilitates a novel refinement strategy using mutational signatures to distinguish artifactual point mutations called in human cancer samples. We anticipate that FIREVAT results will further contribute to precision oncology efforts that rely on accurate identification of variants, especially in the context of analyzing mutational signatures that bear prognostic and therapeutic significance. FIREVAT is freely available at https://github.com/cgab-ncc/FIREVAT

scholarly journals Organoid Models for Cancer Research

2019 ◽  
Vol 3 (1) ◽  
pp. 223-234 ◽  
Author(s):  
Hans Clevers ◽  
David A. Tuveson
Keyword(s):  
Cancer Research ◽  
Tumor Microenvironment ◽  
Personalized Medicine ◽  
High Throughput ◽  
Stromal Cells ◽  
Transcriptome Sequencing ◽  
Cancer Biology ◽  
Three Dimensional ◽  
Model Systems ◽  
Liver Cancers

Organoid cultures have emerged as powerful model systems accelerating discoveries in cellular and cancer biology. These three-dimensional cultures are amenable to diverse techniques, including high-throughput genome and transcriptome sequencing, as well as genetic and biochemical perturbation, making these models well suited to answer a variety of questions. Recently, organoids have been generated from diverse human cancers, including breast, colon, pancreas, prostate, bladder, and liver cancers, and studies involving these models are expanding our knowledge of the etiology and characteristics of these malignancies. Co-cultures of cancer organoids with non-neoplastic stromal cells enable investigation of the tumor microenvironment. In addition, recent studies have established that organoids have a place in personalized medicine approaches. Here, we describe the application of organoid technology to cancer discovery and treatment.

Detection of breast cancer-related antigens through cDNA phage-displayed protein microarray

2010 ◽  
Vol 391 (7) ◽  
Author(s):  
Qun Bi ◽  
Taochao Tan ◽  
Xi Xiang ◽  
Aiping Lu ◽  
Shenggeng Zhu
Keyword(s):  
Breast Cancer ◽  
Cancer Research ◽  
High Throughput ◽  
Breast Tumor ◽  
Protein Microarray ◽  
Molecular Profiling ◽  
Multifactorial Disease ◽  
Cdna Sequences ◽  
Profiling Techniques ◽  
Related Proteins

AbstractHigh-throughput molecular profiling techniques are helpful in the diagnosis of multifactorial disease. In this study, a cDNA-phage-displayed protein microarray using phage particles spotted directly onto it as sensors was used to detect related antigens in breast tumor sera. cDNA sequences from 17 positive clones were determined, which included some sequences encoding known breast cancer-related antigens and proteins related to other diseases, as well as proteins with unknown functions. Our results not only provide some useful information for breast cancer research, but also suggest that the strategy used here would be efficient to search for disease-related proteins and other functional target proteins.

High-throughput mutational analysis of the human cancer genome

2006 ◽  
Vol 7 (4) ◽  
pp. 597-612 ◽  
Author(s):  
Patrick C Ma ◽  
Xiaodong Zhang ◽  
Zhenghe J Wang
Keyword(s):  
High Throughput ◽  
Mutational Analysis ◽  
Human Cancer ◽  
Cancer Genome

Future Directions of Next-Generation Novel Therapies, Combination Approaches, and the Development of Personalized Medicine in Myeloma

2011 ◽  
Vol 29 (14) ◽  
pp. 1916-1923 ◽  
Author(s):  
Constantine S. Mitsiades ◽  
Faith E. Davies ◽  
Jacob P. Laubach ◽  
Douglas Joshua ◽  
Jesus San Miguel ◽  
...  
Keyword(s):  
Personalized Medicine ◽  
Molecular Heterogeneity ◽  
Clinical Safety ◽  
Resistant Disease ◽  
Molecular Features ◽  
Effective Combination ◽  
Profiling Techniques ◽  
Investigational Agents ◽  
Combination Regimens

Despite tangible progress in recent years, substantial therapeutic challenges remain in multiple myeloma (MM), particularly for patients at high risk for early relapse or death and for those with advanced multi-drug resistant disease and refractoriness to currently available combination regimens. Addressing these challenges requires identification of novel classes of anti-MM agents, their incorporation into safe and more effective combination regimens, and development of efficient algorithms to select the most appropriate therapeutic options for the clinical and molecular features of individual patients at a given time during their disease. Ideally, these goals can be facilitated by preclinical identification of the “driver” molecular lesions on which different myeloma subtypes exquisitely depend, and by informative preclinical models simulating the clinical setting(s) in which trials will be conducted. Large prospective studies of patients treated uniformly with contemporary clinical regimens are essential, but there is also a major need for flexibility in studying new regimens in the future. Long-term patient follow-up and integrated annotation of clinical (safety and efficacy) and correlative (molecular, biochemical, etc) data are also critical. Novel molecular profiling techniques will likely identify more clinically and biologically discrete subsets of patients with recurrent, even if infrequent, lesions. This molecular heterogeneity, combined with the increasing numbers of candidate therapeutic targets and respective investigational agents, may pose formidable challenges for the development and implementation of personalized medicine in MM. This review discusses these challenges, as well as potential strategies to address them, with the aim of making significant improvement in the clinical outcome of patients with MM.

scholarly journals Targeting STAT3 and STAT5 in Cancer

Cancers ◽  
2020 ◽  
Vol 12 (8) ◽  
pp. 2002 ◽  
Author(s):  
Elvin D. de Araujo ◽  
György M. Keserű ◽  
Patrick T. Gunning ◽  
Richard Moriggl
Keyword(s):  
Cancer Biology ◽  
Human Cancer ◽  
Cancer Genome ◽  
Driver Genes ◽  
Cancer Driver ◽  
Mutational Landscape ◽  
Coding Regions ◽  
Cancer Driver Genes

Insights into the mutational landscape of the human cancer genome coding regions defined about 140 distinct cancer driver genes in 2013, which approximately doubled to 300 in 2018 following advances in systems cancer biology studies [...]

scholarly journals Convergent architecture of the transcriptome of human cancer

2015 ◽  
Author(s):  
Lihua Zou
Keyword(s):  
Large Scale ◽  
Functional Organization ◽  
Human Cancer ◽  
Adaptive Immune Response ◽  
Cancer Genome ◽  
The Cancer Genome Atlas ◽  
System Level ◽  
Data Sets ◽  
Using Data ◽  
Conserved Gene

Despite large-scale efforts to systematically map the cancer genome, little is known about how the interplay of genetic and epigenetic alternations shapes the architecture of the transcriptome of human cancer. With the goal of constructing a system-level view of the deregulated pathways in cancer cells, we systematically investigated the functional organization of the transcriptomes of 10 tumor types using data sets generated by The Cancer Genome Atlas project (TCGA). Our analysis indicates that the human cancer transcriptome is organized into well-conserved modules of co-expressed genes. In particular, our analysis identified a set of conserved gene modules with distinct cancer hallmark themes involving cell cycle regulation, angiogenesis, innate and adaptive immune response, differentiation, metabolism and regulation of protein phosphorylation. Our analysis provided global views of convergent transcriptome architecture of human cancer. The result of our analysis can serve as a foundation to link diverse genomic alternations to common transcriptomic features in human cancer.

scholarly journals Using The Cancer Genome Atlas as an Inquiry Tool in the Undergraduate Classroom

2020 ◽  
Vol 11 ◽  
Author(s):  
William Hankey ◽  
Nicholas Zanghi ◽  
Mackenzie M. Crow ◽  
Whitney H. Dow ◽  
Austin Kratz ◽  
...  
Keyword(s):  
Cancer Biology ◽  
Statistical Tests ◽  
Cancer Genome ◽  
The Cancer Genome Atlas ◽  
Biomedical Science ◽  
Biomedical Sciences ◽  
Future Health ◽  
Molecular Features ◽  
Cancer Genome Atlas ◽  
Genome Atlas

Undergraduate students in the biomedical sciences are often interested in future health-focused careers. This presents opportunities for instructors in genetics, molecular biology, and cancer biology to capture their attention using lab experiences built around clinically relevant data. As biomedical science in general becomes increasingly dependent on high-throughput data, well-established scientific databases such as The Cancer Genome Atlas (TCGA) have become publicly available tools for medically relevant inquiry. The best feature of this database is that it bridges the molecular features of cancer to human clinical outcomes—allowing students to see a direct connection between the molecular sciences and their future professions. We have developed and tested a learning module that leverages the power of TCGA datasets to engage students to use the data to generate and test hypotheses and to apply statistical tests to evaluate significance.

scholarly journals Improved detection of gene fusions by applying statistical methods reveals new oncogenic RNA cancer drivers

2019 ◽  
Author(s):  
Roozbeh Dehghannasiri ◽  
Donald Eric Freeman ◽  
Milos Jordanski ◽  
Gillian L. Hsieh ◽  
Ana Damljanovic ◽  
...  
Keyword(s):  
False Positive ◽  
Cancer Biology ◽  
Cancer Genome ◽  
The Cancer Genome Atlas ◽  
Gene Fusions ◽  
Cancer Genome Atlas ◽  
Selection For ◽  
Tumor Types ◽  
Fusion Detection ◽  
Genome Atlas

Short AbstractThe extent to which gene fusions function as drivers of cancer remains a critical open question. Current algorithms do not sufficiently identify false-positive fusions arising during library preparation, sequencing, and alignment. Here, we introduce a new algorithm, DEEPEST, that uses statistical modeling to minimize false-positives while increasing the sensitivity of fusion detection. In 9,946 tumor RNA-sequencing datasets from The Cancer Genome Atlas (TCGA) across 33 tumor types, DEEPEST identifies 31,007 fusions, 30% more than identified by other methods, while calling ten-fold fewer false-positive fusions in non-transformed human tissues. We leverage the increased precision of DEEPEST to discover new cancer biology. For example, 888 new candidate oncogenes are identified based on over-representation in DEEPEST-Fusion calls, and 1,078 previously unreported fusions involving long intergenic noncoding RNAs partners, demonstrating a previously unappreciated prevalence and potential for function. Specific protein domains are enriched in DEEPEST calls, demonstrating a global selection for fusion functionality: kinase domains are nearly 2-fold more enriched in DEEPEST calls than expected by chance, as are domains involved in (anaerobic) metabolism and DNA binding. DEEPEST also reveals a high enrichment for fusions involving known and novel oncogenes in diseases including ovarian cancer, which has had minimal treatment advances in recent decades, finding that more than 50% of tumors harbor gene fusions predicted to be oncogenic. The statistical algorithms, population-level analytic framework, and the biological conclusions of DEEPEST call for increased attention to gene fusions as drivers of cancer and for future research into using fusions for targeted therapy.SignificanceGene fusions are tumor-specific genomic aberrations and are among the most powerful biomarkers and drug targets in translational cancer biology. The advent of RNA-Seq technologies over the past decade has provided a unique opportunity for detecting novel fusions via deploying computational algorithms on public sequencing databases. Yet, precise fusion detection algorithms are still out of reach. We develop DEEPEST, a highly specific and efficient statistical pipeline specially designed for mining massive sequencing databases, and apply it to all 33 tumor types and 10,500 samples in The Cancer Genome Atlas database. We systematically profile the landscape of detected fusions via employing classic statistical models and identify several signatures of selection for fusions in tumors.Software availabilityDEEPEST-Fusion workflow with a detailed readme file is available as a Github repository:https://github.com/salzmanlab/DEEPEST-Fusion. In addition to the main workflow, which is based on CWL, example input and batch scripts (for job submission on local clusters), and codes for building the SBT files and SBT querying are provided in the repository. All custom scripts used for systematic analysis of fusions are also available in the same repository.

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