Clinical proteomics for precision medicine: the bladder cancer case

Precision medicine can improve patient management by guiding therapeutic decision based on molecular characteristics. The concept has been extensively addressed through the application of –omics based approaches. Proteomics attract high interest, as proteins reflect a “real-time” dynamic molecular phenotype. Focusing on proteomics applications for personalized medicine, a literature search was conducted to cover: a) disease prevention, b) monitoring/ prediction of treatment response, c) stratification to guide intervention and d) identification of drug targets. The review indicates the potential of proteomics for personalized medicine by also highlighting multiple challenges to be addressed prior to actual implementation. In oncology, particularly bladder cancer, application of precision medicine appears especially promising. The high heterogeneity and recurrence rates together with the limited treatment options, suggests that earlier and more efficient intervention, continuous monitoring and the development of alternative therapies could be accomplished by applying proteomics-guided personalized approaches. This notion is backed by studies presenting biomarkers that are of value in patient stratification and prognosis, and by recent studies demonstrating the identification of promising therapeutic targets. Herein, we aim to present an approach whereby combining the knowledge on biomarkers and therapeutic targets in bladder cancer could serve as basis towards proteomics- guided personalized patient management

Modeling cancer metabolism on a genome scale

Cancer cells have fundamentally altered cellular metabolism that is associated with their tumorigenicity and malignancy. In addition to the widely studied Warburg effect, several new key metabolic alterations in cancer have been established over the last decade, leading to the recognition that altered tumor metabolism is one of the hallmarks of cancer. Deciphering the full scope and functional implications of the dysregulated metabolism in cancer requires both the advancement of a variety of omics measurements and the advancement of computational approaches for the analysis and contextualization of the accumulated data. Encouragingly, while the metabolic network is highly interconnected and complex, it is at the same time probably the best characterized cellular network. Following, this review discusses the challenges that genome‐scale modeling of cancer metabolism has been facing. We survey several recent studies demonstrating the first strides that have been done, testifying to the value of this approach in portraying a network‐level view of the cancer metabolism and in identifying novel drug targets and biomarkers. Finally, we outline a few new steps that may further advance this field

Developing a 'personalome' for precision medicine: emerging methods that compute interpretable effect sizes from single-subject transcriptomes

The development of computational methods capable of analyzing -omics data at the individual level is critical for the success of precision medicine. Although unprecedented opportunities now exist to gather data on an individual's -omics profile (personalome'), interpreting and extracting meaningful information from single-subject -omics remain underdeveloped, particularly for quantitative non-sequence measurements, including complete transcriptome or proteome expression and metabolite abundance. Conventional bioinformatics approaches have largely been designed for making population-level inferences about average' disease processes; thus, they may not adequately capture and describe individual variability. Novel approaches intended to exploit a variety of -omics data are required for identifying individualized signals for meaningful interpretation. In this review-intended for biomedical researchers, computational biologists and bioinformaticians-we survey emerging computational and translational informatics methods capable of constructing a single subject's personalome' for predicting clinical outcomes or therapeutic responses, with an emphasis on methods that provide interpretable readouts. Key points: (i) the single-subject analytics of the transcriptome shows the greatest development to date and, (ii) the methods were all validated in simulations, cross-validations or independent retrospective data sets. This survey uncovers a growing field that offers numerous opportunities for the development of novel validation methods and opens the door for future studies focusing on the interpretation of comprehensive personalomes' through the integration of multiple -omics, providing valuable insights into individual patient outcomes and treatments.National Institute of Health (NIH)/Office of the Director Precision Medicine Initiative [1UG3OD023171-01]; Precision Medicine Initiative of the Center for Biomedical Informatics and Biostatistics of the University of Arizona Health Sciences; NIH/National Heart, Lung, and Blood Institute [HL126609-01, HL132523, U01 HL125208]; NIH/National Cancer Institute [P30CA023074, 1R01CA190696-01]; NIH/National Institute of Allergy and Infectious Diseases [U01AI122275-01]Open access articleThis item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at [email protected]

EPMA position paper in cancer:current overview and future perspectives

At present, a radical shift in cancer treatment is occurring in terms of predictive, preventive, and personalized medicine (PPPM). Individual patients will participate in more aspects of their healthcare. During the development of PPPM, many rapid, specific, and sensitive new methods for earlier detection of cancer will result in more efficient management of the patient and hence a better quality of life. Coordination of the various activities among different healthcare professionals in primary, secondary, and tertiary care requires well-defined competencies, implementation of training and educational programs, sharing of data, and harmonized guidelines. In this position paper, the current knowledge to understand cancer predisposition and risk factors, the cellular biology of cancer, predictive markers and treatment outcome, the improvement in technologies in screening and diagnosis, and provision of better drug development solutions are discussed in the context of a better implementation of personalized medicine. Recognition of the major risk factors for cancer initiation is the key for preventive strategies (EPMA J. 4(1):6, 2013). Of interest, cancer predisposing syndromes in particular the monogenic subtypes that lead to cancer progression are well defined and one should focus on implementation strategies to identify individuals at risk to allow preventive measures and early screening/diagnosis. Implementation of such measures is disturbed by improper use of the data, with breach of data protection as one of the risks to be heavily controlled. Population screening requires in depth cost-benefit analysis to justify healthcare costs, and the parameters screened should provide information that allow an actionable and deliverable solution, for better healthcare provision

Personalized identification of altered pathway using accumulated data

학위논문 (박사)-- 서울대학교 대학원 : 협동과정 생물정보학전공, 2014. 8. 박태성.유전자 네트웍의 이상을 탐지하는 것은 질병의 기작을 이해하고 나아가 개인의 유전자 결함에 맞춤 치료를 선정하는 일에 중요하다. 현재 존재하는 유전자 조절/생체 대사 경로 분석 알고리즘은 대부분 정상과 대조군 집단에서의 차이를 판별하는 데에 초점이 맞추어져 있다. 이러한 방법은 한 개인에 초점을 맞추어 분석을 하는 용도로는 적합하지 못하다. 한 개인의 유전자 네트웍의 이상을 분석함에 있어 가장 이상적인 방법은 같은 환자의 정상 조직과 질병 조직의 정보를 비교하는 것이다. 하지만, 임상적인 이유에서 환자의 정상 조직의 정보는 항상 가용한 것은 아니다. 정상 조직을 채취 하는 것은 임상적인 위험이 따르며, 특별하고 명확한 이유가 없는 한 권장되지 않는다. 따라서 질병 시료의 개인 맞춤 분석에 있어서, 같은 사람의 정상 조직 정보는 가용하지 않은 경우가 많다. 본 논문에서는 개인 분석이라는 측면과 해당 환자의 정상 조직 정보가 가용하지 않을 때 유전자 네트웍을 분석하는 것에 초점을 맞추었다. 본 논문의 방법의 철학은 한 사람의 암 환자 유전자 정보를 많은 수의 집적된 정상 조직의 유전자 정보와 비교하여 이상 유무를 판단하는 것에 있다. 본 논문은 Over-Representation Analysis (ORA), Functional Class Score (FCS) 등의 기존에 알려진 그룹 대 그룹에서의 유전자 네트웍 분석법의 개인향 분석법을 제공한다. 이 방법을 사용하여 본 논문에서는 개인의 유전자 네트웍 이상 점수 (individualized pathway aberrance score : iPAS)를 제시 한다. 본 논문의 방법을 두가지 종류의 암종 (폐 선암종, 대장암) 유전자 발현 데이터에 적용하여 유용성을 보였다. 페 정상 조직과 대장 점막 정상 조직의 유전자 발현 데이터를 참조 표준으로 삼고, 각 암 환자 한 사람씩의 유전자 네트웍의 이상을 분석 하였다. 본 논문의 방법은 기존의 연구에서 밝혀진 환자 생존률과 관련된 유전자 네트웍 이상을 정확히 탐지 하였다. 본 논문의 방법은 기존에 방법이라고 할 수 있는, 환자 한명의 정보를 해당 환자가 속한 코호트의 정보를 참조 표준으로 사용하여 해석하는 것 보다, 더 높은 재현성을 보였다. 재현성 측정은 서로 다른 데이터군을 사용하여, 유전자 네트웍 발굴군에서 발굴한 생존 관련 유전자 네트웍이, 발굴에 사용되지 않았던 데이터군에서도 생존에 유의한 영향을 미치는지 측정하였다. 또한 해당 방법은 유전자 네트웍의 특징을 기반으로 환자와 정상을 구분할 수 있다. 특별히 amino acid synthesis and interconversion pathway의 경우 폐 선암을 독립적인 검증을 위한 데이터군에서도 AUC 0.982로 잘 구분할 수 있다. 또한 본 논문에서 제시한 방법은 돌연변이가 유전자 발현 네트웍에 미치는 영향을 정량화 할 수 있는 방법으로 사용될 수 있다. 본 방법을 사용하였을 때 유방암의 유전자 발현 네트웍에 통계적으로 유의한 영향을 미치는 PI3KCA, TP53, RB1 의 세 유전자를 찾을 수 있었고, 이는 알려진 유방암의 지식과 일치한다. 본 논문의 임상적인 의의는 환자 한 사람에서 정상 조직 정보가 없을 때, 한 사람의 암을 유전자 네트웍 측면에서 해석 할 수 있도록 한 것이다. 이러한 방법은 데이터에 기반한 것으로서, 축적되고 있는 정상 조직 데이터를 사용하여, 더욱 정확한 데이터 기반 의사 결정을 하는 데에 기여할 수 있다. 본 논문의 방법은 유전자 발현 뿐 아니라 돌연 변이 분석과도 연계되어, 환자의 암을 유발하는 유전자 네트웍을 발굴하고, 맞춤 치료제를 선정하는 일에 기여할 수 있다.Identifying altered pathways in an individual is important for understanding disease mechanisms and for the future application of custom therapeutic decisions. Existing pathway analysis techniques are mainly focused on discovering altered pathways between normal and cancer groups and are not suitable for identifying the pathway aberrance that may occur in an individual sample. A simple way to identify individuals pathway aberrance is to compare normal and tumor data from the same individual. However, the matched normal data from the same individual is often unavailable in clinical situation. We therefore suggest a new approach for the personalized identification of altered pathways, making special use of accumulated normal data in cases when a patients matched normal data is unavailable. The philosophy behind our method is to quantify the aberrance of an individual sample's pathway by comparing it to accumulated normal samples. We propose and examine personalized extensions of pathway statistics, Over-Representation Analysis (ORA) and Functional Class Scoring (FCS), to generate individualized pathway aberrance score (iPAS). Collected microarray data of normal tissue of lung and colon mucosa is served as reference to investigate a number of cancer individuals of lung adenocarcinoma and colon cancer, respectively. Our method concurrently captures known facts of cancer survival pathways and identifies the pathway aberrances that represent cancer differentiation status and survival. It also provides more improved validation rate of survival related pathways than when a single cancer sample is interpreted in the context of cancer-only cohort. In addition, our method is useful in classifying unknown samples into cancer or normal groups. Particularly, we identified amino acid synthesis and interconversion pathway is a good indicator of lung adenocarcinoma (AUC 0.982 at independent validation). We also suggest a new approach for discovering rare mutations that have functional impact in the context of pathway by iteratively combining rare mutations until no more mutations with pathway impact can be added. The approach is shown to sensitively capture mutations that change pathway level gene expression at breast cancer data. Clinical importance of the method is providing pathway interpretation of single cancer even though its matched normal data is unavailable.Abstract 1 List of Figures 5 List of Tables 6 1. Introduction 7 1.1 Existing pathway analysis approaches (Group to group) 7 1.1.1 Importance of pathway analysis 8 1.1.2 Component of pathway analysis 9 1.1.3 Classification of existing pathway analysis approaches 17 1.2 Personalized pathway analysis 32 1.3 Purpose and novelty of this study 36 1.4 Outline of thesis 37 2. Methods and materials 39 2.1 Gene expression data 39Docto

Dynamic changes during the treatment of pancreatic cancer

This manuscript follows a single patient with pancreatic adenocarcinoma for a five year period, detailing the clinical record, pathology, the dynamic evolution of molecular and cellular alterations as well as the responses to treatments with chemotherapies, targeted therapies and immunotherapies. DNA and RNA samples from biopsies and blood identified a dynamic set of changes in allelic imbalances and copy number variations in response to therapies. Organoid cultures established from biopsies over time were employed for extensive drug testing to determine if this approach was feasible for treatments. When an unusual drug response was detected, an extensive RNA sequencing analysis was employed to establish novel mechanisms of action of this drug. Organoid cell cultures were employed to identify possible antigens associated with the tumor and the patient\u27s T-cells were expanded against one of these antigens. Similar and identical T-cell receptor sequences were observed in the initial biopsy and the expanded T-cell population. Immunotherapy treatment failed to shrink the tumor, which had undergone an epithelial to mesenchymal transition prior to therapy. A warm autopsy of the metastatic lung tumor permitted an extensive analysis of tumor heterogeneity over five years of treatment and surgery. This detailed analysis of the clinical descriptions, imaging, pathology, molecular and cellular evolution of the tumors, treatments, and responses to chemotherapy, targeted therapies, and immunotherapies, as well as attempts at the development of personalized medical treatments for a single patient should provide a valuable guide to future directions in cancer treatment

Molecular Fingerprints and Biomarkers of Breast Cancer

Substantial progress has been made over the past three decades in understanding breast cancer (BC) molecular biology, genomics, and targeted therapy. The recent comprehensive molecular and pathological diversity observed in BC patients indicates that BC is not a homogeneous disease; It may be appropriately defined as a myriad of diseases. The explosion of molecular information in the past 10 years has led to a better understanding of the biologic diversity of breast cancers (BCs), and clues to the different etiologic pathways to BC development. It will be useful to study the epigenetics of BC cells and define the mechanisms of both genetic and epigenetic driving alterations beside the mutations. Identifying the oncogenes and tumor suppressor genes is the purpose cancer diagnostics and therapeutics. Oncogenes as well as novel ones involved in the significantly altered regions would enable researchers to identify new causes and molecular pathways that may be targeted at BC treatment. Our main goal is to provide comprehensive understanding of underlying molecular mechanisms and hallmarks of BC, focusing on the identification of fingerprints and novel molecular targets that will greatly improve the cancer predictive, prognostic, and diagnostic biomarkers and, in addition, the possible targets for novel therapies