Feeds:
Posts
Comments

Archive for the ‘Genomic Expression’ Category


Live Notes, Real Time Conference Coverage AACR 2020: Tuesday June 23, 2020 3:00 PM-5:30 PM Educational Sessions

Reporter: Stephen J. Williams, PhD

Follow Live in Real Time using

#AACR20

@pharma_BI

@AACR

Register for FREE at https://www.aacr.org/

uesday, June 23

3:00 PM – 5:00 PM EDT

Virtual Educational Session
Tumor Biology, Bioinformatics and Systems Biology

The Clinical Proteomic Tumor Analysis Consortium: Resources and Data Dissemination

This session will provide information regarding methodologic and computational aspects of proteogenomic analysis of tumor samples, particularly in the context of clinical trials. Availability of comprehensive proteomic and matching genomic data for tumor samples characterized by the National Cancer Institute’s Clinical Proteomic Tumor Analysis Consortium (CPTAC) and The Cancer Genome Atlas (TCGA) program will be described, including data access procedures and informatic tools under development. Recent advances on mass spectrometry-based targeted assays for inclusion in clinical trials will also be discussed.

Amanda G Paulovich, Shankha Satpathy, Meenakshi Anurag, Bing Zhang, Steven A Carr

Methods and tools for comprehensive proteogenomic characterization of bulk tumor to needle core biopsies

Shankha Satpathy
  • TCGA has 11,000 cancers with >20,000 somatic alterations but only 128 proteins as proteomics was still young field
  • CPTAC is NCI proteomic effort
  • Chemical labeling approach now method of choice for quantitative proteomics
  • Looked at ovarian and breast cancers: to measure PTM like phosphorylated the sample preparation is critical

 

Data access and informatics tools for proteogenomics analysis

Bing Zhang
  • Raw and processed data (raw MS data) with linked clinical data can be extracted in CPTAC
  • Python scripts are available for bioinformatic programming

 

Pathways to clinical translation of mass spectrometry-based assays

Meenakshi Anurag

·         Using kinase inhibitor pulldown (KIP) assay to identify unique kinome profiles

·         Found single strand break repair defects in endometrial luminal cases, especially with immune checkpoint prognostic tumors

·         Paper: JNCI 2019 analyzed 20,000 genes correlated with ET resistant in luminal B cases (selected for a list of 30 genes)

·         Validated in METABRIC dataset

·         KIP assay uses magnetic beads to pull out kinases to determine druggable kinases

·         Looked in xenografts and was able to pull out differential kinomes

·         Matched with PDX data so good clinical correlation

·         Were able to detect ESR1 fusion correlated with ER+ tumors

Tuesday, June 23

3:00 PM – 5:00 PM EDT

Virtual Educational Session
Survivorship

Artificial Intelligence and Machine Learning from Research to the Cancer Clinic

The adoption of omic technologies in the cancer clinic is giving rise to an increasing number of large-scale high-dimensional datasets recording multiple aspects of the disease. This creates the need for frameworks for translatable discovery and learning from such data. Like artificial intelligence (AI) and machine learning (ML) for the cancer lab, methods for the clinic need to (i) compare and integrate different data types; (ii) scale with data sizes; (iii) prove interpretable in terms of the known biology and batch effects underlying the data; and (iv) predict previously unknown experimentally verifiable mechanisms. Methods for the clinic, beyond the lab, also need to (v) produce accurate actionable recommendations; (vi) prove relevant to patient populations based upon small cohorts; and (vii) be validated in clinical trials. In this educational session we will present recent studies that demonstrate AI and ML translated to the cancer clinic, from prognosis and diagnosis to therapy.
NOTE: Dr. Fish’s talk is not eligible for CME credit to permit the free flow of information of the commercial interest employee participating.

Ron C. Anafi, Rick L. Stevens, Orly Alter, Guy Fish

Overview of AI approaches in cancer research and patient care

Rick L. Stevens
  • Deep learning is less likely to saturate as data increases
  • Deep learning attempts to learn multiple layers of information
  • The ultimate goal is prediction but this will be the greatest challenge for ML
  • ML models can integrate data validation and cross database validation
  • What limits the performance of cross validation is the internal noise of data (reproducibility)
  • Learning curves: not the more data but more reproducible data is important
  • Neural networks can outperform classical methods
  • Important to measure validation accuracy in training set. Class weighting can assist in development of data set for training set especially for unbalanced data sets

Discovering genome-scale predictors of survival and response to treatment with multi-tensor decompositions

Orly Alter
  • Finding patterns using SVD component analysis. Gene and SVD patterns match 1:1
  • Comparative spectral decompositions can be used for global datasets
  • Validation of CNV data using this strategy
  • Found Ras, Shh and Notch pathways with altered CNV in glioblastoma which correlated with prognosis
  • These predictors was significantly better than independent prognostic indicator like age of diagnosis

 

Identifying targets for cancer chronotherapy with unsupervised machine learning

Ron C. Anafi
  • Many clinicians have noticed that some patients do better when chemo is given at certain times of the day and felt there may be a circadian rhythm or chronotherapeutic effect with respect to side effects or with outcomes
  • ML used to determine if there is indeed this chronotherapy effect or can we use unstructured data to determine molecular rhythms?
  • Found a circadian transcription in human lung
  • Most dataset in cancer from one clinical trial so there might need to be more trials conducted to take into consideration circadian rhythms

Stratifying patients by live-cell biomarkers with random-forest decision trees

Stratifying patients by live-cell biomarkers with random-forest decision trees

Guy Fish CEO Cellanyx Diagnostics

 

Tuesday, June 23

3:00 PM – 5:00 PM EDT

Virtual Educational Session
Tumor Biology, Molecular and Cellular Biology/Genetics, Bioinformatics and Systems Biology, Prevention Research

The Wound Healing that Never Heals: The Tumor Microenvironment (TME) in Cancer Progression

This educational session focuses on the chronic wound healing, fibrosis, and cancer “triad.” It emphasizes the similarities and differences seen in these conditions and attempts to clarify why sustained fibrosis commonly supports tumorigenesis. Importance will be placed on cancer-associated fibroblasts (CAFs), vascularity, extracellular matrix (ECM), and chronic conditions like aging. Dr. Dvorak will provide an historical insight into the triad field focusing on the importance of vascular permeability. Dr. Stewart will explain how chronic inflammatory conditions, such as the aging tumor microenvironment (TME), drive cancer progression. The session will close with a review by Dr. Cukierman of the roles that CAFs and self-produced ECMs play in enabling the signaling reciprocity observed between fibrosis and cancer in solid epithelial cancers, such as pancreatic ductal adenocarcinoma.

Harold F Dvorak, Sheila A Stewart, Edna Cukierman

 

The importance of vascular permeability in tumor stroma generation and wound healing

Harold F Dvorak

Aging in the driver’s seat: Tumor progression and beyond

Sheila A Stewart

Why won’t CAFs stay normal?

Edna Cukierman

 

Tuesday, June 23

3:00 PM – 5:00 PM EDT

 

 

 

 

 

 

 

Other Articles on this Open Access  Online Journal on Cancer Conferences and Conference Coverage in Real Time Include

Press Coverage
Live Notes, Real Time Conference Coverage 2020 AACR Virtual Meeting April 28, 2020 Symposium: New Drugs on the Horizon Part 3 12:30-1:25 PM
Live Notes, Real Time Conference Coverage 2020 AACR Virtual Meeting April 28, 2020 Session on NCI Activities: COVID-19 and Cancer Research 5:20 PM
Live Notes, Real Time Conference Coverage 2020 AACR Virtual Meeting April 28, 2020 Session on Evaluating Cancer Genomics from Normal Tissues Through Metastatic Disease 3:50 PM
Live Notes, Real Time Conference Coverage 2020 AACR Virtual Meeting April 28, 2020 Session on Novel Targets and Therapies 2:35 PM

Read Full Post »


Live Conference Coverage AACR 2020 in Real Time: Monday June 22, 2020 Mid Day Sessions

Reporter: Stephen J. Williams, PhD

This post will be UPDATED during the next two days with notes from recordings from other talks

Follow Live in Real Time using

#AACR20

@pharma_BI

@AACR

 

 

 

 

 

 

 

Register for FREE at https://www.aacr.org/

 

AACR VIRTUAL ANNUAL MEETING II

 

June 22-24: Free Registration for AACR Members, the Cancer Community, and the Public
This virtual meeting will feature more than 120 sessions and 4,000 e-posters, including sessions on cancer health disparities and the impact of COVID-19 on clinical trials

 

This Virtual Meeting is Part II of the AACR Annual Meeting.  Part I was held online in April and was centered only on clinical findings.  This Part II of the virtual meeting will contain all the Sessions and Abstracts pertaining to basic and translational cancer research as well as clinical trial findings.

 

REGISTER NOW

 

Pezcoller Foundation-AACR International Award for Extraordinary Achievement in Cancer Research

The prestigious Pezcoller Foundation-AACR International Award for Extraordinary Achievement in Cancer Research was established in 1997 to annually recognize a scientist of international renown who has made a major scientific discovery in basic cancer research OR who has made significant contributions to translational cancer research; who continues to be active in cancer research and has a record of recent, noteworthy publications; and whose ongoing work holds promise for continued substantive contributions to progress in the field of cancer. For more information regarding the 2020 award recipient go to aacr.org/awards.

John E. Dick, Enzo Galligioni, David A Tuveson

DETAILS

Awardee: John E. Dick
Princess Anne Margaret Cancer Center, Toronto, Ontario
For determining how stem cells contribute to normal and leukemic hematopoeisis
  • not every cancer cell equal in their Cancer Hallmarks
  • how do we monitor and measure clonal dynamics
  • Barnie Clarkson did pivotal work on this
  • most cancer cells are post mitotic but minor populations of cells were dormant and survive chemotherapy
  •  only one cell is 1 in a million can regenerate and transplantable in mice and experiments with flow cytometry resolved the question of potency and repopulation of only small percentage of cells and undergo long term clonal population
  • so instead of going to cell lines and using thousands of shRNA looked at clinical data and deconvoluted the genetic information (RNASeq data) to determine progenitor and mature populations (how much is stem and how much is mature populations)
  • in leukemic patients they have seen massive expansion of a single stem cell population so only need one cell in AML if the stem cells have the mutational hits early on in their development
  • finding the “seeds of relapse”: finding the small subpopulation of stem cells that will relapse
  • they looked in BALL;;  there are cells resistant to l-aspariginase, dexamethasone, and vincristine
  • a lot of OXPHOS related genes (in DRIs) that may be the genes involved in this resistance
  • it a wonderful note of acknowledgement he dedicated this award to all of his past and present trainees who were the ones, as he said, made this field into what it is and for taking it into directions none of them could forsee

Monday, June 22

1:30 PM – 3:30 PM EDT

Virtual Educational Session

Experimental and Molecular Therapeutics, Drug Development, Cancer Chemistry

Chemistry to the Clinic: Part 1: Lead Optimization Case Studies in Cancer Drug Discovery

How can one continue to deliver innovative medicines to patients when biological targets are becoming ever scarcer and less amenable to therapeutic intervention? Are there sound strategies in place that can clear the path to targets previously considered “undruggable”? Recent advances in lead finding methods and novel technologies such as covalent screening and targeted protein degradation have enriched the toolbox at the disposal of drug discovery scientists to expand the druggable ta

Stefan N Gradl, Elena S Koltun, Scott D Edmondson, Matthew A. Marx, Joachim Rudolph

DETAILS

Monday, June 22

1:30 PM – 3:30 PM EDT

Virtual Educational Session

Bioinformatics and Systems Biology, Molecular and Cellular Biology/Genetics

Informatics Technologies for Cancer Research

Cancer researchers are faced with a deluge of high-throughput data. Using these data to advance understanding of cancer biology and improve clinical outcomes increasingly requires effective use of computational and informatics tools. This session will introduce informatics resources that support the data management, analysis, visualization, and interpretation. The primary focus will be on high-throughput genomic data and imaging data. Participants will be introduced to fundamental concepts

Rachel Karchin, Daniel Marcus, Andriy Fedorov, Obi Lee Griffith

DETAILS

  • Variant analysis is the big bottleneck, especially interpretation of variants
  • CIVIC resource is a network for curation, interpretation of genetic variants
  • CIVIC curators go through multiple rounds of editors review
  • gene summaries, variant summaries
  • curation follows ACSME guidelines
  • evidences are accumulated, categories by various ontologies and is the heart of the reports
  • as this is a network of curators the knowledgebase expands
  • CIVIC is linked to multiple external informatic, clinical, and genetic databases
  • they have curated 7017 clinical interpretations, 2527 variants, using 2578 papers, and over 1000 curators
  • they are currently integrating with COSMIC ClinVar, and UniProt
  • they are partnering with ClinGen to expand network of curators and their curation effort
  • CIVIC uses a Python interface; available on website

https://civicdb.org/home

The Precision Medicine Revolution

Precision medicine refers to the use of prevention and treatment strategies that are tailored to the unique features of each individual and their disease. In the context of cancer this might involve the identification of specific mutations shown to predict response to a targeted therapy. The biomedical literature describing these associations is large and growing rapidly. Currently these interpretations exist largely in private or encumbered databases resulting in extensive repetition of effort.

CIViC’s Role in Precision Medicine

Realizing precision medicine will require this information to be centralized, debated and interpreted for application in the clinic. CIViC is an open access, open source, community-driven web resource for Clinical Interpretation of Variants in Cancer. Our goal is to enable precision medicine by providing an educational forum for dissemination of knowledge and active discussion of the clinical significance of cancer genome alterations. For more details refer to the 2017 CIViC publication in Nature Genetics.

U24 funding announced: We are excited to announce that the Informatics Technology for Cancer Research (ICTR) program of the National Cancer Institute (NCI) has awarded funding to the CIViC team! Starting this year, a five-year, $3.7 million U24 award (CA237719), will support CIViC to develop Standardized and Genome-Wide Clinical Interpretation of Complex Genotypes for Cancer Precision Medicine.

Informatics tools for high-throughput analysis of cancer mutations

Rachel Karchin
  • CRAVAT is a platform to determine, categorize, and curate cancer mutations and cancer related variants
  • adding new tools used to be hard but having an open architecture allows for modular growth and easy integration of other tools
  • so they are actively making an open network using social media

Towards FAIR data in cancer imaging research

Andriy Fedorov, PhD

Towards the FAIR principles

While LOD has had some uptake across the web, the number of databases using this protocol compared to the other technologies is still modest. But whether or not we use LOD, we do need to ensure that databases are designed specifically for the web and for reuse by humans and machines. To provide guidance for creating such databases independent of the technology used, the FAIR principles were issued through FORCE11: the Future of Research Communications and e-Scholarship. The FAIR principles put forth characteristics that contemporary data resources, tools, vocabularies and infrastructures should exhibit to assist discovery and reuse by third-parties through the web. Wilkinson et al.,2016. FAIR stands for: Findable, Accessible, Interoperable and Re-usable. The definition of FAIR is provided in Table 1:

Number Principle
F Findable
F1 (meta)data are assigned a globally unique and persistent identifier
F2 data are described with rich metadata
F3 metadata clearly and explicitly include the identifier of the data it describes
F4 (meta)data are registered or indexed in a searchable resource
A Accessible
A1 (meta)data are retrievable by their identifier using a standardized communications protocol
A1.1 the protocol is open, free, and universally implementable
A1.2 the protocol allows for an authentication and authorization procedure, where necessary
A2 metadata are accessible, even when the data are no longer available
I Interoperable
I1 (meta)data use a formal, accessible, shared, and broadly applicable language for knowledge representation.
I2 (meta)data use vocabularies that follow FAIR principles
I3 (meta)data include qualified references to other (meta)data
R Reusable
R1 meta(data) are richly described with a plurality of accurate and relevant attributes
R1.1 (meta)data are released with a clear and accessible data usage license
R1.2 (meta)data are associated with detailed provenance
R1.3 (meta)data meet domain-relevant community standards

A detailed explanation of each of these is included in the Wilkinson et al., 2016 article, and the Dutch Techcenter for Life Sciences has a set of excellent tutorials, so we won’t go into too much detail here.

  • for outside vendors to access their data, vendors would need a signed Material Transfer Agreement but NCI had formulated a framework to facilitate sharing of data using a DIACOM standard for imaging data

Monday, June 22

1:30 PM – 3:01 PM EDT

Virtual Educational Session

Experimental and Molecular Therapeutics, Cancer Chemistry, Drug Development, Immunology

Engineering and Physical Sciences Approaches in Cancer Research, Diagnosis, and Therapy

The engineering and physical science disciplines have been increasingly involved in the development of new approaches to investigate, diagnose, and treat cancer. This session will address many of these efforts, including therapeutic methods such as improvements in drug delivery/targeting, new drugs and devices to effect immunomodulation and to synergize with immunotherapies, and intraoperative probes to improve surgical interventions. Imaging technologies and probes, sensors, and bioma

Claudia Fischbach, Ronit Satchi-Fainaro, Daniel A Heller

DETAILS

Monday, June 22

1:30 PM – 3:30 PM EDT

Virtual Educational Session

Survivorship

Exceptional Responders and Long-Term Survivors

How should we think about exceptional and super responders to cancer therapy? What biologic insights might ensue from considering these cases? What are ways in which considering super responders may lead to misleading conclusions? What are the pros and cons of the quest to locate exceptional and super responders?

Alice P Chen, Vinay K Prasad, Celeste Leigh Pearce

DETAILS

Monday, June 22

1:30 PM – 3:30 PM EDT

Virtual Educational Session

Tumor Biology, Immunology

Exploiting Metabolic Vulnerabilities in Cancer

The reprogramming of cellular metabolism is a hallmark feature observed across cancers. Contemporary research in this area has led to the discovery of tumor-specific metabolic mechanisms and illustrated ways that these can serve as selective, exploitable vulnerabilities. In this session, four international experts in tumor metabolism will discuss new findings concerning the rewiring of metabolic programs in cancer that support metabolic fitness, biosynthesis, redox balance, and the reg

Costas Andreas Lyssiotis, Gina M DeNicola, Ayelet Erez, Oliver Maddocks

DETAILS

Monday, June 22

1:30 PM – 3:30 PM EDT

Virtual Educational Session

Other Articles on this Open Access  Online Journal on Cancer Conferences and Conference Coverage in Real Time Include

Press Coverage

Live Notes, Real Time Conference Coverage 2020 AACR Virtual Meeting April 28, 2020 Symposium: New Drugs on the Horizon Part 3 12:30-1:25 PM

Live Notes, Real Time Conference Coverage 2020 AACR Virtual Meeting April 28, 2020 Session on NCI Activities: COVID-19 and Cancer Research 5:20 PM

Live Notes, Real Time Conference Coverage 2020 AACR Virtual Meeting April 28, 2020 Session on Evaluating Cancer Genomics from Normal Tissues Through Metastatic Disease 3:50 PM

Live Notes, Real Time Conference Coverage 2020 AACR Virtual Meeting April 28, 2020 Session on Novel Targets and Therapies 2:35 PM

Read Full Post »


Live Notes, Real Time Conference Coverage 2020 AACR Virtual Meeting April 28, 2020 Session on Evaluating Cancer Genomics from Normal Tissues Through Metastatic Disease 3:50 PM

Reporter: Stephen J. Williams, PhD

 Minisymposium: Evaluating Cancer Genomics from Normal Tissues through Evolution to Metastatic Disease

Oncologic therapy shapes the fitness landscape of clonal hematopoiesis

April 28, 2020, 4:10 PM – 4:20 PM

Presenter/Authors
Kelly L. Bolton, Ryan N. Ptashkin, Teng Gao, Lior Braunstein, Sean M. Devlin, Minal Patel, Antonin Berthon, Aijazuddin Syed, Mariko Yabe, Catherine Coombs, Nicole M. Caltabellotta, Mike Walsh, Ken Offit, Zsofia Stadler, Choonsik Lee, Paul Pharoah, Konrad H. Stopsack, Barbara Spitzer, Simon Mantha, James Fagin, Laura Boucai, Christopher J. Gibson, Benjamin Ebert, Andrew L. Young, Todd Druley, Koichi Takahashi, Nancy Gillis, Markus Ball, Eric Padron, David Hyman, Jose Baselga, Larry Norton, Stuart Gardos, Virginia Klimek, Howard Scher, Dean Bajorin, Eder Paraiso, Ryma Benayed, Maria Arcilla, Marc Ladanyi, David Solit, Michael Berger, Martin Tallman, Montserrat Garcia-Closas, Nilanjan Chatterjee, Luis Diaz, Ross Levine, Lindsay Morton, Ahmet Zehir, Elli Papaemmanuil. Memorial Sloan Kettering Cancer Center, New York, NY, University of North Carolina at Chapel Hill, Chapel Hill, NC, University of Cambridge, Cambridge, United Kingdom, Dana-Farber Cancer Institute, Boston, MA, Washington University, St Louis, MO, The University of Texas MD Anderson Cancer Center, Houston, TX, Moffitt Cancer Center, Tampa, FL, National Cancer Institute, Bethesda, MD

Abstract
Recent studies among healthy individuals show evidence of somatic mutations in leukemia-associated genes, referred to as clonal hematopoiesis (CH). To determine the relationship between CH and oncologic therapy we collected sequential blood samples from 525 cancer patients (median sampling interval time = 23 months, range: 6-53 months) of whom 61% received cytotoxic therapy or external beam radiation therapy and 39% received either targeted/immunotherapy or were untreated. Samples were sequenced using deep targeted capture-based platforms. To determine whether CH mutational features were associated with tMN risk, we performed Cox proportional hazards regression on 9,549 cancer patients exposed to oncologic therapy of whom 75 cases developed tMN (median time to transformation=26 months). To further compare the genetic and clonal relationships between tMN and the proceeding CH, we analyzed 35 cases for which paired samples were available. We compared the growth rate of the variant allele fraction (VAF) of CH clones across treatment modalities and in untreated patients. A significant increase in the growth rate of CH mutations was seen in DDR genes among those receiving cytotoxic (p=0.03) or radiation therapy (p=0.02) during the follow-up period compared to patients who did not receive therapy. Similar growth rates among treated and untreated patients were seen for non-DDR CH genes such as DNMT3A. Increasing cumulative exposure to cytotoxic therapy (p=0.01) and external beam radiation therapy (2×10-8) resulted in higher growth rates for DDR CH mutations. Among 34 subjects with at least two CH mutations in which one mutation was in a DDR gene and one in a non-DDR gene, we studied competing clonal dynamics for multiple gene mutations within the same patient. The risk of tMN was positively associated with CH in a known myeloid neoplasm driver mutation (HR=6.9, p<10-6), and increased with the total number of mutations and clone size. The strongest associations were observed for mutations in TP53 and for CH with mutations in spliceosome genes (SRSF2, U2AF1 and SF3B1). Lower hemoglobin, lower platelet counts, lower neutrophil counts, higher red cell distribution width and higher mean corpuscular volume were all positively associated with increased tMN risk. Among 35 cases for which paired samples were available, in 19 patients (59%), we found evidence of at least one of these mutations at the time of pre-tMN sequencing and in 13 (41%), we identified two or more in the pre-tMN sample. In all cases the dominant clone at tMN transformation was defined by a mutation seen at CH Our serial sampling data provide clear evidence that oncologic therapy strongly selects for clones with mutations in the DDR genes and that these clones have limited competitive fitness, in the absence of cytotoxic or radiation therapy. We further validate the relevance of CH as a predictor and precursor of tMN in cancer patients. We show that CH mutations detected prior to tMN diagnosis were consistently part of the dominant clone at tMN diagnosis and demonstrate that oncologic therapy directly promotes clones with mutations in genes associated with chemo-resistant disease such as TP53.

  • therapy resulted also in clonal evolution and saw changes in splice variants and spliceosome
  • therapy promotes current DDR mutations
  • clonal hematopoeisis due to selective pressures
  • mutations, variants number all predictive of myeloid disease
  • deferring adjuvant therapy for breast cancer patients with patients in highest MDS risk group based on biomarkers, greatly reduced their risk for MDS

5704 – Pan-cancer genomic characterization of patient-matched primary, extracranial, and brain metastases

Presenter/AuthorsOlivia W. Lee, Akash Mitra, Won-Chul Lee, Kazutaka Fukumura, Hannah Beird, Miles Andrews, Grant Fischer, John N. Weinstein, Michael A. Davies, Jason Huse, P. Andrew Futreal. The University of Texas MD Anderson Cancer Center, TX, The University of Texas MD Anderson Cancer Center, TX, Olivia Newton-John Cancer Research Institute and School of Cancer Medicine, La Trobe University, AustraliaDisclosures O.W. Lee: None. A. Mitra: None. W. Lee: None. K. Fukumura: None. H. Beird: None. M. Andrews: ; Merck Sharp and Dohme. G. Fischer: None. J.N. Weinstein: None. M.A. Davies: ; Bristol-Myers Squibb. ; Novartis. ; Array BioPharma. ; Roche and Genentech. ; GlaxoSmithKline. ; Sanofi-Aventis. ; AstraZeneca. ; Myriad Genetics. ; Oncothyreon. J. Huse: None. P. Futreal: None.

Abstract: Brain metastases (BM) occur in 10-30% of patients with cancer. Approximately 200,000 new cases of brain metastases are diagnosed in the United States annually, with median survival after diagnosis ranging from 3 to 27 months. Recently, studies have identified significant genetic differences between BM and their corresponding primary tumors. It has been shown that BM harbor clinically actionable mutations that are distinct from those in the primary tumor samples. Additional genomic profiling of BM will provide deeper understanding of the pathogenesis of BM and suggest new therapeutic approaches.
We performed whole-exome sequencing of BM and matched tumors from 41 patients collected from renal cell carcinoma (RCC), breast cancer, lung cancer, and melanoma, which are known to be more likely to develop BM. We profiled total 126 fresh-frozen tumor samples and performed subsequent analyses of BM in comparison to paired primary tumor and extracranial metastases (ECM). We found that lung cancer shared the largest number of mutations between BM and matched tumors (83%), followed by melanoma (74%), RCC (51%), and Breast (26%), indicating that cancer type with high tumor mutational burden share more mutations with BM. Mutational signatures displayed limited differences, suggesting a lack of mutagenic processes specific to BM. However, point-mutation heterogeneity revealed that BM evolve separately into different subclones from their paired tumors regardless of cancer type, and some cancer driver genes were found in BM-specific subclones. These models and findings suggest that these driver genes may drive prometastatic subclones that lead to BM. 32 curated cancer gene mutations were detected and 71% of them were shared between BM and primary tumors or ECM. 29% of mutations were specific to BM, implying that BM often accumulate additional cancer gene mutations that are not present in primary tumors or ECM. Co-mutation analysis revealed a high frequency of TP53 nonsense mutation in BM, mostly in the DNA binding domain, suggesting TP53 nonsense mutation as a possible prerequisite for the development of BM. Copy number alteration analysis showed statistically significant differences between BM and their paired tumor samples in each cancer type (Wilcoxon test, p < 0.0385 for all). Both copy number gains and losses were consistently higher in BM for breast cancer (Wilcoxon test, p =1.307e-5) and lung cancer (Wilcoxon test, p =1.942e-5), implying greater genomic instability during the evolution of BM.
Our findings highlight that there are more unique mutations in BM, with significantly higher copy number alterations and tumor mutational burden. These genomic analyses could provide an opportunity for more reliable diagnostic decision-making, and these findings will be further tested with additional transcriptomic and epigenetic profiling for better characterization of BM-specific tumor microenvironments.

  • are there genomic signatures different in brain mets versus non metastatic or normal?
  • 32 genes from curated databases were different between brain mets and primary tumor
  • frequent nonsense mutations in TP53
  • divergent clonal evolution of drivers in BMets from primary
  • they were able to match BM with other mutational signatures like smokers and lung cancer signatures

5707 – A standard operating procedure for the interpretation of oncogenicity/pathogenicity of somatic mutations

Presenter/AuthorsPeter Horak, Malachi Griffith, Arpad Danos, Beth A. Pitel, Subha Madhavan, Xuelu Liu, Jennifer Lee, Gordana Raca, Shirley Li, Alex H. Wagner, Shashikant Kulkarni, Obi L. Griffith, Debyani Chakravarty, Dmitriy Sonkin. National Center for Tumor Diseases, Heidelberg, Germany, Washington University School of Medicine, St. Louis, MO, Mayo Clinic, Rochester, MN, Georgetown University Medical Center, Washington, DC, Dana-Farber Cancer Institute, Boston, MA, Frederick National Laboratory for Cancer Research, Rockville, MD, University of Southern California, Los Angeles, CA, Sunquest, Boston, MA, Baylor College of Medicine, Houston, TX, Memorial Sloan Kettering Cancer Center, New York, NY, National Cancer Institute, Rockville, MDDisclosures P. Horak: None. M. Griffith: None. A. Danos: None. B.A. Pitel: None. S. Madhavan: ; Perthera Inc. X. Liu: None. J. Lee: None. G. Raca: None. S. Li: ; Sunquest Information Systems, Inc. A.H. Wagner: None. S. Kulkarni: ; Baylor Genetics. O.L. Griffith: None. D. Chakravarty: None. D. Sonkin: None.AbstractSomatic variants in cancer-relevant genes are interpreted from multiple partially overlapping perspectives. When considered in discovery and translational research endeavors, it is important to determine if a particular variant observed in a gene of interest is oncogenic/pathogenic or not, as such knowledge provides the foundation on which targeted cancer treatment research is based. In contrast, clinical applications are dominated by diagnostic, prognostic, or therapeutic interpretations which in part also depends on underlying variant oncogenicity/pathogenicity. The Association for Molecular Pathology, the American Society of Clinical Oncology, and the College of American Pathologists (AMP/ASCO/CAP) have published structured somatic variant clinical interpretation guidelines which specifically address diagnostic, prognostic, and therapeutic implications. These guidelines have been well-received by the oncology community. Many variant knowledgebases, clinical laboratories/centers have adopted or are in the process of adopting these guidelines. The AMP/ASCO/CAP guidelines also describe different data types which are used to determine oncogenicity/pathogenicity of a variant, such as: population frequency, functional data, computational predictions, segregation, and somatic frequency. A second collaborative effort created the European Society for Medical Oncology (ESMO) Scale for Clinical Actionability of molecular Targets to provide a harmonized vocabulary that provides an evidence-based ranking system of molecular targets that supports their value as clinical targets. However, neither of these clinical guideline systems provide systematic and comprehensive procedures for aggregating population frequency, functional data, computational predictions, segregation, and somatic frequency to consistently interpret variant oncogenicity/pathogenicity, as has been published in the ACMG/AMP guidelines for interpretation of pathogenicity of germline variants. In order to address this unmet need for somatic variant oncogenicity/pathogenicity interpretation procedures, the Variant Interpretation for Cancer Consortium (VICC, a GA4GH driver project) Knowledge Curation and Interpretation Standards (KCIS) working group (WG) has developed a Standard Operating Procedure (SOP) with contributions from members of ClinGen Somatic Clinical Domain WG, and ClinGen Somatic/Germline variant curation WG using an approach similar to the ACMG/AMP germline pathogenicity guidelines to categorize evidence of oncogenicity/pathogenicity as very strong, strong, moderate or supporting. This SOP enables consistent and comprehensive assessment of oncogenicity/pathogenicity of somatic variants and latest version of an SOP can be found at https://cancervariants.org/wg/kcis/.

  • best to use this SOP for somatic mutations and not rearangements
  • variants based on oncogenicity as strong to weak
  • useful variant knowledge on pathogenicity curated from known databases
  • the recommendations would provide some guideline on curating unknown somatic variants versus known variants of hereditary diseases
  • they have not curated RB1 mutations or variants (or for other RBs like RB2? p130?)

 

Follow on Twitter at:

@pharma_BI

@AACR

@CureCancerNow

@pharmanews

@BiotechWorld

#AACR20

 

Read Full Post »


scPopCorn: A New Computational Method for Subpopulation Detection and their Comparative Analysis Across Single-Cell Experiments

Reporter and Curator: Dr. Sudipta Saha, Ph.D.

 

Present day technological advances have facilitated unprecedented opportunities for studying biological systems at single-cell level resolution. For example, single-cell RNA sequencing (scRNA-seq) enables the measurement of transcriptomic information of thousands of individual cells in one experiment. Analyses of such data provide information that was not accessible using bulk sequencing, which can only assess average properties of cell populations. Single-cell measurements, however, can capture the heterogeneity of a population of cells. In particular, single-cell studies allow for the identification of novel cell types, states, and dynamics.

 

One of the most prominent uses of the scRNA-seq technology is the identification of subpopulations of cells present in a sample and comparing such subpopulations across samples. Such information is crucial for understanding the heterogeneity of cells in a sample and for comparative analysis of samples from different conditions, tissues, and species. A frequently used approach is to cluster every dataset separately, inspect marker genes for each cluster, and compare these clusters in an attempt to determine which cell types were shared between samples. This approach, however, relies on the existence of predefined or clearly identifiable marker genes and their consistent measurement across subpopulations.

 

Although the aligned data can then be clustered to reveal subpopulations and their correspondence, solving the subpopulation-mapping problem by performing global alignment first and clustering second overlooks the original information about subpopulations existing in each experiment. In contrast, an approach addressing this problem directly might represent a more suitable solution. So, keeping this in mind the researchers developed a computational method, single-cell subpopulations comparison (scPopCorn), that allows for comparative analysis of two or more single-cell populations.

 

The performance of scPopCorn was tested in three distinct settings. First, its potential was demonstrated in identifying and aligning subpopulations from single-cell data from human and mouse pancreatic single-cell data. Next, scPopCorn was applied to the task of aligning biological replicates of mouse kidney single-cell data. scPopCorn achieved the best performance over the previously published tools. Finally, it was applied to compare populations of cells from cancer and healthy brain tissues, revealing the relation of neoplastic cells to neural cells and astrocytes. Consequently, as a result of this integrative approach, scPopCorn provides a powerful tool for comparative analysis of single-cell populations.

 

This scPopCorn is basically a computational method for the identification of subpopulations of cells present within individual single-cell experiments and mapping of these subpopulations across these experiments. Different from other approaches, scPopCorn performs the tasks of population identification and mapping simultaneously by optimizing a function that combines both objectives. When applied to complex biological data, scPopCorn outperforms previous methods. However, it should be kept in mind that scPopCorn assumes the input single-cell data to consist of separable subpopulations and it is not designed to perform a comparative analysis of single cell trajectories datasets that do not fulfill this constraint.

 

Several innovations developed in this work contributed to the performance of scPopCorn. First, unifying the above-mentioned tasks into a single problem statement allowed for integrating the signal from different experiments while identifying subpopulations within each experiment. Such an incorporation aids the reduction of biological and experimental noise. The researchers believe that the ideas introduced in scPopCorn not only enabled the design of a highly accurate identification of subpopulations and mapping approach, but can also provide a stepping stone for other tools to interrogate the relationships between single cell experiments.

 

References:

 

https://www.sciencedirect.com/science/article/pii/S2405471219301887

 

https://www.tandfonline.com/doi/abs/10.1080/23307706.2017.1397554

 

https://ieeexplore.ieee.org/abstract/document/4031383

 

https://genomebiology.biomedcentral.com/articles/10.1186/s13059-016-0927-y

 

https://www.sciencedirect.com/science/article/pii/S2405471216302666

 

 

Read Full Post »


First Cost-Effectiveness Study of Multi-Gene Panel Sequencing in Advanced Non-Small Cell Lung Cancer Shows Moderate Cost-Effectiveness, Exposes Crucial Practice Gap

WASHINGTON (June 27, 2019) — The results of the first economic modeling study to estimate the cost-effectiveness of “multi-gene panel sequencing” (MGPS) as compared to standard-of-care, single-gene tests for patients with advanced non-small cell lung cancer (aNSCLC) show that the MGPS tests are moderately cost-effective but could deliver more value if patients with test results identifying actionable genetic mutations consistently received genetically guided treatments. The results of the study, which was commissioned by the Personalized Medicine Coalition (PMC), underline the need to align clinical practices with an era of personalized medicine in which physicians can use diagnostic tests to identify specific biological markers that inform targeted prevention and treatment plans.

The study, which was published yesterday in JCO Clinical Cancer Informatics, analyzed the clinical and economic value of using MGPS testing to identify patients with tumors that over-express genetic mutations that could be targeted by available therapies designed to inhibit the function of those genes — a mainstay of modern care for aNSCLC patients. Using data provided by Flatiron Health, researchers examined clinical and cost information associated with the care of 5,688 patients with aNSCLC treated between 2011 – 2016, separating them into cohorts who received MGPS tests that assess at least 30 genetic mutations at once and those who received only “single-marker genetic testing” (SMGT) of less than 30 genes.

Compared to SMGT, the MGPS testing strategy, including downstream treatment and monitoring of disease, incurred costs equal to $148,478 for each year of life that it facilitated, a level suggesting that MGPS is moderately cost-effective compared to commonly cited thresholds in the U.S., which range from $50,000 to $200,000 per life year (LY) gained.

The authors of the study point out, however, that physicians only prescribed a targeted therapy to some of the patients whose MGPS test results revealed actionable mutations. MGPS tests can only improve downstream patient outcomes if actionable results are used to put the patient on a targeted treatment regimen that is more effective than the therapy they would otherwise have been prescribed. It is therefore impossible for the cost of an MGPS test to translate into additional LYs if actionable results do not result in the selection of a targeted treatment regimen.

Although MGPS testing revealed actionable mutations in 30.1 percent of the patients in the study cohort, only 21.4 percent of patients who underwent MGPS testing received a targeted treatment.

The study’s authors calculated that if all MGPS-tested patients with actionable mutations had received a targeted therapy, MGPS testing would deliver measurably better value ($110,000 per LY gained).

“This research underlines the importance of ensuring that clinical practices keep pace with scientific progress in personalized medicine so that we can maximize the benefits of diagnostic tests that can improve patient care and make the health system more efficient by ensuring that safe and effective targeted therapies are prescribed to those patients who will benefit,” said PMC President Edward Abrahams.

The study’s authors include Dr. Lotte Steuten, Vice President and Head of Consulting, The Office of Health Economics, London, U.K., and Affiliate Associate Faculty Member, Hutchinson Institute for Cancer Outcomes Research, Fred Hutchinson Cancer Research Center; Dr. Bernardo Goulart, Associate Faculty Member, Hutchinson Institute for Cancer Outcomes Research, Fred Hutchinson Cancer Research Center; Dr. Neal Meropol, Vice President, Research Oncology, Flatiron Health; Dr. Daryl Pritchard, Senior Vice President, Science Policy, Personalized Medicine Coalition; and Dr. Scott Ramsey, Director, Hutchinson Institute for Cancer Outcomes Research, Fred Hutchinson Cancer Research Center.

###

About the Personalized Medicine Coalition:

The Personalized Medicine Coalition, representing innovators, scientists, patients, providers and payers, promotes the understanding and adoption of personalized medicine concepts, services and products to benefit patients and the health system. For more information, please visit www.personalizedmedicinecoalition.org.

SOURCE

From: Personalized Medicine Coalition <pmc@personalizedmedicinecoalition.org>

Reply-To: “Christopher Wells (PMC)” <cwells@personalizedmedicinecoalition.org>

Date: Thursday, June 27, 2019 at 9:32 AM

To: Aviva Lev-Ari <AvivaLev-Ari@alum.berkeley.edu>

Subject: First Cost-Effectiveness Study of MGPS in aNSCLC Shows Moderate Cost-Effectiveness, Exposes Crucial Practice Gap

Read Full Post »


A Nonlinear Methodology to Explain Complexity of the Genome and Bioinformatic Information

Reporter: Stephen J. Williams, Ph.D.

Multifractal bioinformatics: A proposal to the nonlinear interpretation of genome

The following is an open access article by Pedro Moreno on a methodology to analyze genetic information across species and in particular, the evolutionary trends of complex genomes, by a nonlinear analytic approach utilizing fractal geometry, coined “Nonlinear Bioinformatics”.  This fractal approach stems from the complex nature of higher eukaryotic genomes including mosaicism, multiple interdispersed  genomic elements such as intronic regions, noncoding regions, and also mobile elements such as transposable elements.  Although seemingly random, there exists a repetitive nature of these elements. Such complexity of DNA regulation, structure and genomic variation is felt best understood by developing algorithms based on fractal analysis, which can best model the regionalized and repetitive variability and structure within complex genomes by elucidating the individual components which contributes to an overall complex structure rather than using a “linear” or “reductionist” approach looking at individual coding regions, which does not take into consideration the aforementioned factors leading to genetic complexity and diversity.

Indeed, many other attempts to describe the complexities of DNA as a fractal geometric pattern have been described.  In a paper by Carlo Cattani “Fractals and Hidden Symmetries in DNA“, Carlo uses fractal analysis to construct a simple geometric pattern of the influenza A virus by modeling the primary sequence of this viral DNA, namely the bases A,G,C, and T. The main conclusions that

fractal shapes and symmetries in DNA sequences and DNA walks have been shown and compared with random and deterministic complex series. DNA sequences are structured in such a way that there exists some fractal behavior which can be observed both on the correlation matrix and on the DNA walks. Wavelet analysis confirms by a symmetrical clustering of wavelet coefficients the existence of scale symmetries.

suggested that, at least, the viral influenza genome structure could be analyzed into its basic components by fractal geometry.
This approach has been used to model the complex nature of cancer as discussed in a 2011 Seminars in Oncology paper
Abstract: Cancer is a highly complex disease due to the disruption of tissue architecture. Thus, tissues, and not individual cells, are the proper level of observation for the study of carcinogenesis. This paradigm shift from a reductionist approach to a systems biology approach is long overdue. Indeed, cell phenotypes are emergent modes arising through collective non-linear interactions among different cellular and microenvironmental components, generally described by “phase space diagrams”, where stable states (attractors) are embedded into a landscape model. Within this framework, cell states and cell transitions are generally conceived as mainly specified by gene-regulatory networks. However, the system s dynamics is not reducible to the integrated functioning of the genome-proteome network alone; the epithelia-stroma interacting system must be taken into consideration in order to give a more comprehensive picture. Given that cell shape represents the spatial geometric configuration acquired as a result of the integrated set of cellular and environmental cues, we posit that fractal-shape parameters represent “omics descriptors of the epithelium-stroma system. Within this framework, function appears to follow form, and not the other way around.

As authors conclude

” Transitions from one phenotype to another are reminiscent of phase transitions observed in physical systems. The description of such transitions could be obtained by a set of morphological, quantitative parameters, like fractal measures. These parameters provide reliable information about system complexity. “

Gene expression also displays a fractal nature. In a Frontiers in Physiology paper by Mahboobeh Ghorbani, Edmond A. Jonckheere and Paul Bogdan* “Gene Expression Is Not Random: Scaling, Long-Range Cross-Dependence, and Fractal Characteristics of Gene Regulatory Networks“,

the authors describe that gene expression networks display time series display fractal and long-range dependence characteristics.

Abstract: Gene expression is a vital process through which cells react to the environment and express functional behavior. Understanding the dynamics of gene expression could prove crucial in unraveling the physical complexities involved in this process. Specifically, understanding the coherent complex structure of transcriptional dynamics is the goal of numerous computational studies aiming to study and finally control cellular processes. Here, we report the scaling properties of gene expression time series in Escherichia coliand Saccharomyces cerevisiae. Unlike previous studies, which report the fractal and long-range dependency of DNA structure, we investigate the individual gene expression dynamics as well as the cross-dependency between them in the context of gene regulatory network. Our results demonstrate that the gene expression time series display fractal and long-range dependence characteristics. In addition, the dynamics between genes and linked transcription factors in gene regulatory networks are also fractal and long-range cross-correlated. The cross-correlation exponents in gene regulatory networks are not unique. The distribution of the cross-correlation exponents of gene regulatory networks for several types of cells can be interpreted as a measure of the complexity of their functional behavior.

 

Given that multitude of complex biomolecular networks and biomolecules can be described by fractal patterns, the development of bioinformatic algorithms  would enhance our understanding of the interdependence and cross funcitonality of these mutiple biological networks, particularly in disease and drug resistance.  The article below by Pedro Moreno describes the development of such bioinformatic algorithms.

Pedro A. Moreno
Escuela de Ingeniería de Sistemas y Computación, Facultad de Ingeniería, Universidad del Valle, Cali, Colombia
E-mail: pedro.moreno@correounivalle.edu.co

Eje temático: Ingeniería de sistemas / System engineering
Recibido: 19 de septiembre de 2012
Aceptado: 16 de diciembre de 2013


 

 


Abstract

The first draft of the human genome (HG) sequence was published in 2001 by two competing consortia. Since then, several structural and functional characteristics for the HG organization have been revealed. Today, more than 2.000 HG have been sequenced and these findings are impacting strongly on the academy and public health. Despite all this, a major bottleneck, called the genome interpretation persists. That is, the lack of a theory that explains the complex puzzles of coding and non-coding features that compose the HG as a whole. Ten years after the HG sequenced, two recent studies, discussed in the multifractal formalism allow proposing a nonlinear theory that helps interpret the structural and functional variation of the genetic information of the genomes. The present review article discusses this new approach, called: “Multifractal bioinformatics”.

Keywords: Omics sciences, bioinformatics, human genome, multifractal analysis.


1. Introduction

Omic Sciences and Bioinformatics

In order to study the genomes, their life properties and the pathological consequences of impairment, the Human Genome Project (HGP) was created in 1990. Since then, about 500 Gpb (EMBL) represented in thousands of prokaryotic genomes and tens of different eukaryotic genomes have been sequenced (NCBI, 1000 Genomes, ENCODE). Today, Genomics is defined as the set of sciences and technologies dedicated to the comprehensive study of the structure, function and origin of genomes. Several types of genomic have arisen as a result of the expansion and implementation of genomics to the study of the Central Dogma of Molecular Biology (CDMB), Figure 1 (above). The catalog of different types of genomics uses the Latin suffix “-omic” meaning “set of” to mean the new massive approaches of the new omics sciences (Moreno et al, 2009). Given the large amount of genomic information available in the databases and the urgency of its actual interpretation, the balance has begun to lean heavily toward the requirements of bioinformatics infrastructure research laboratories Figure 1 (below).

The bioinformatics or Computational Biology is defined as the application of computer and information technology to the analysis of biological data (Mount, 2004). An interdisciplinary science that requires the use of computing, applied mathematics, statistics, computer science, artificial intelligence, biophysical information, biochemistry, genetics, and molecular biology. Bioinformatics was born from the need to understand the sequences of nucleotide or amino acid symbols that make up DNA and proteins, respectively. These analyzes are made possible by the development of powerful algorithms that predict and reveal an infinity of structural and functional features in genomic sequences, as gene location, discovery of homologies between macromolecules databases (Blast), algorithms for phylogenetic analysis, for the regulatory analysis or the prediction of protein folding, among others. This great development has created a multiplicity of approaches giving rise to new types of Bioinformatics, such as Multifractal Bioinformatics (MFB) that is proposed here.

1.1 Multifractal Bioinformatics and Theoretical Background

MFB is a proposal to analyze information content in genomes and their life properties in a non-linear way. This is part of a specialized sub-discipline called “nonlinear Bioinformatics”, which uses a number of related techniques for the study of nonlinearity (fractal geometry, Hurts exponents, power laws, wavelets, among others.) and applied to the study of biological problems (https://pharmaceuticalintelligence.com/tag/fractal-geometry/). For its application, we must take into account a detailed knowledge of the structure of the genome to be analyzed and an appropriate knowledge of the multifractal analysis.

1.2 From the Worm Genome toward Human Genome

To explore a complex genome such as the HG it is relevant to implement multifractal analysis (MFA) in a simpler genome in order to show its practical utility. For example, the genome of the small nematode Caenorhabditis elegans is an excellent model to learn many extrapolated lessons of complex organisms. Thus, if the MFA explains some of the structural properties in that genome it is expected that this same analysis reveals some similar properties in the HG.

The C. elegans nuclear genome is composed of about 100 Mbp, with six chromosomes distributed into five autosomes and one sex chromosome. The molecular structure of the genome is particularly homogeneous along with the chromosome sequences, due to the presence of several regular features, including large contents of genes and introns of similar sizes. The C. elegans genome has also a regional organization of the chromosomes, mainly because the majority of the repeated sequences are located in the chromosome arms, Figure 2 (left) (C. elegans Sequencing Consortium, 1998). Given these regular and irregular features, the MFA could be an appropriate approach to analyze such distributions.

Meanwhile, the HG sequencing revealed a surprising mosaicism in coding (genes) and noncoding (repetitive DNA) sequences, Figure 2 (right) (Venter et al., 2001). This structure of 6 Gbp is divided into 23 pairs of chromosomes (diploid cells) and these highly regionalized sequences introduce complex patterns of regularity and irregularity to understand the gene structure, the composition of sequences of repetitive DNA and its role in the study and application of life sciences. The coding regions of the genome are estimated at ~25,000 genes which constitute 1.4% of GH. These genes are involved in a giant sea of various types of non-coding sequences which compose 98.6% of HG (misnamed popularly as “junk DNA”). The non-coding regions are characterized by many types of repeated DNA sequences, where 10.6% consists of Alu sequences, a type of SINE (short and dispersed repeated elements) sequence and preferentially located towards the genes. LINES, MIR, MER, LTR, DNA transposons and introns are another type of non-coding sequences which form about 86% of the genome. Some of these sequences overlap with each other; as with CpG islands, which complicates the analysis of genomic landscape. This standard genomic landscape was recently clarified, the last studies show that 80.4% of HG is functional due to the discovery of more than five million “switches” that operate and regulate gene activity, re-evaluating the concept of “junk DNA”. (The ENCODE Project Consortium, 2012).

Given that all these genomic variations both in worm and human produce regionalized genomic landscapes it is proposed that Fractal Geometry (FG) would allow measuring how the genetic information content is fragmented. In this paper the methodology and the nonlinear descriptive models for each of these genomes will be reviewed.

1.3 The MFA and its Application to Genome Studies

Most problems in physics are implicitly non-linear in nature, generating phenomena such as chaos theory, a science that deals with certain types of (non-linear) but very sensitive dynamic systems to initial conditions, nonetheless of deterministic rigor, that is that their behavior can be completely determined by knowing initial conditions (Peitgen et al, 1992). In turn, the FG is an appropriate tool to study the chaotic dynamic systems (CDS). In other words, the FG and chaos are closely related because the space region toward which a chaotic orbit tends asymptotically has a fractal structure (strange attractors). Therefore, the FG allows studying the framework on which CDS are defined (Moon, 1992). And this is how it is expected for the genome structure and function to be organized.

The MFA is an extension of the FG and it is related to (Shannon) information theory, disciplines that have been very useful to study the information content over a sequence of symbols. Initially, Mandelbrot established the FG in the 80’s, as a geometry capable of measuring the irregularity of nature by calculating the fractal dimension (D), an exponent derived from a power law (Mandelbrot, 1982). The value of the D gives us a measure of the level of fragmentation or the information content for a complex phenomenon. That is because the D measures the scaling degree that the fragmented self-similarity of the system has. Thus, the FG looks for self-similar properties in structures and processes at different scales of resolution and these self-similarities are organized following scaling or power laws.

Sometimes, an exponent is not sufficient to characterize a complex phenomenon; so more exponents are required. The multifractal formalism allows this, and applies when many subgroups of fractals with different scalar properties with a large number of exponents or fractal dimensions coexist simultaneously. As a result, when a spectrum of multifractal singularity measurement is generated, the scaling behavior of the frequency of symbols of a sequence can be quantified (Vélez et al, 2010).

The MFA has been implemented to study the spatial heterogeneity of theoretical and experimental fractal patterns in different disciplines. In post-genomics times, the MFA was used to study multiple biological problems (Vélez et al, 2010). Nonetheless, very little attention has been given to the use of MFA to characterize the content of the structural genetic information of the genomes obtained from the images of the Chaos Representation Game (CRG). First studies at this level were made recently to the analysis of the C. elegans genome (Vélez et al, 2010) and human genomes (Moreno et al, 2011). The MFA methodology applied for the study of these genomes will be developed below.

2. Methodology

The Multifractal Formalism from the CGR

2.1 Data Acquisition and Molecular Parameters

Databases for the C. elegans and the 36.2 Hs_ refseq HG version were downloaded from the NCBI FTP server. Then, several strategies were designed to fragment the genomic DNA sequences of different length ranges. For example, the C. elegans genome was divided into 18 fragments, Figure 2 (left) and the human genome in 9,379 fragments. According to their annotation systems, the contents of molecular parameters of coding sequences (genes, exons and introns), noncoding sequences (repetitive DNA, Alu, LINES, MIR, MER, LTR, promoters, etc.) and coding/ non-coding DNA (TTAGGC, AAAAT, AAATT, TTTTC, TTTTT, CpG islands, etc.) are counted for each sequence.

2.2 Construction of the CGR 2.3 Fractal Measurement by the Box Counting Method

Subsequently, the CGR, a recursive algorithm (Jeffrey, 1990; Restrepo et al, 2009) is applied to each selected DNA sequence, Figure 3 (above, left) and from which an image is obtained, which is quantified by the box-counting algorithm. For example, in Figure 3 (above, left) a CGR image for a human DNA sequence of 80,000 bp in length is shown. Here, dark regions represent sub-quadrants with a high number of points (or nucleotides). Clear regions, sections with a low number of points. The calculation for the D for the Koch curve by the box-counting method is illustrated by a progression of changes in the grid size, and its Cartesian graph, Table 1

The CGR image for a given DNA sequence is quantified by a standard fractal analysis. A fractal is a fragmented geometric figure whose parts are an approximated copy at full scale, that is, the figure has self-similarity. The D is basically a scaling rule that the figure obeys. Generally, a power law is given by the following expression:

Where N(E) is the number of parts required for covering the figure when a scaling factor E is applied. The power law permits to calculate the fractal dimension as:

The D obtained by the box-counting algorithm covers the figure with disjoint boxes ɛ = 1/E and counts the number of boxes required. Figure 4 (above, left) shows the multifractal measure at momentum q=1.

2.4 Multifractal Measurement

When generalizing the box-counting algorithm for the multifractal case and according to the method of moments q, we obtain the equation (3) (Gutiérrez et al, 1998; Yu et al, 2001):

Where the Mi number of points falling in the i-th grid is determined and related to the total number Mand ɛ to box size. Thus, the MFA is used when multiple scaling rules are applied. Figure 4 (above, right) shows the calculation of the multifractal measures at different momentum q (partition function). Here, linear regressions must have a coefficient of determination equal or close to 1. From each linear regression D are obtained, which generate an spectrum of generalized fractal dimensions Dfor all q integers, Figure 4 (below, left). So, the multifractal spectrum is obtained as the limit:

The variation of the q integer allows emphasizing different regions and discriminating their fractal a high Dq is synonymous of the structure’s richness and the properties of these regions. Negative values emphasize the scarce regions; a high Dindicates a lot of structure and properties in these regions. In real world applications, the limit Dqreadily approximated from the data using a linear fitting: the transformation of the equation (3) yields:

Which shows that ln In(Mi )= for set q is a linear function in the ln(ɛ), Dq can therefore be evaluated as q the slope of a fixed relationship between In(Mi )= and (q-1) ln(ɛ). The methodologies and approaches for the method of box-counting and MFA are detailed in Moreno et al, 2000, Yu et al, 2001; Moreno, 2005. For a rigorous mathematical development of MFA from images consult Multifractal system, wikipedia.

2.5 Measurement of Information Content

Subsequently, from the spectrum of generalized dimensions Dq, the degree of multifractality ΔDq(MD) is calculated as the difference between the maximum and minimum values of : ΔD qq Dqmax – Dqmin (Ivanov et al, 1999). When qmaxqmin ΔDis high, the multifractal spectrum is rich in information and highly aperiodic, when ΔDq is small, the resulting dimension spectrum is poor in information and highly periodic. It is expected then, that the aperiodicity in the genome would be related to highly polymorphic genomic aperiodic structures and those periodic regions with highly repetitive and not very polymorphic genomic structures. The correlation exponent t(q) = (– 1)DqFigure 4 (below, right ) can also be obtained from the multifractal dimension Dq. The generalized dimension also provides significant specific information. D(q = 0) is equal to the Capacity dimension, which in this analysis is the size of the “box count”. D(q = 1) is equal to the Information dimension and D(q = 2) to the Correlation dimension. Based on these multifractal parameters, many of the structural genomic properties can be quantified, related, and interpreted.

2.6 Multifractal Parameters and Statistical and Discrimination Analyses

Once the multifractal parameters are calculated (D= (-20, 20), ΔDq, πq, etc.), correlations with the molecular parameters are sought. These relations are established by plotting the number of genome molecular parameters versus MD by discriminant analysis with Cartesian graphs in 2-D, Figure 5 (below, left) and 3-D and combining multifractal and molecular parameters. Finally, simple linear regression analysis, multivariate analysis, and analyses by ranges and clusterings are made to establish statistical significance.

3 Results and Discussion

3.1 Non-linear Descriptive Model for the C. elegans Genome

When analyzing the C. elegans genome with the multifractal formalism it revealed what symmetry and asymmetry on the genome nucleotide composition suggested. Thus, the multifractal scaling of the C. elegans genome is of interest because it indicates that the molecular structure of the chromosome may be organized as a system operating far from equilibrium following nonlinear laws (Ivanov et al, 1999; Burgos and Moreno-Tovar, 1996). This can be discussed from two points of view:

1) When comparing C. elegans chromosomes with each other, the X chromosome showed the lowest multifractality, Figure 5 (above). This means that the X chromosome is operating close to equilibrium, which results in an increased genetic instability. Thus, the instability of the X could selectively contribute to the molecular mechanism that determines sex (XX or X0) during meiosis. Thus, the X chromosome would be operating closer to equilibrium in order to maintain their particular sexual dimorphism.

2) When comparing different chromosome regions of the C. elegans genome, changes in multifractality were found in relation to the regional organization (at the center and arms) exhibited by the chromosomes, Figure 5 (below, left). These behaviors are associated with changes in the content of repetitive DNA, Figure 5 (below, right). The results indicated that the chromosome arms are even more complex than previously anticipated. Thus, TTAGGC telomere sequences would be operating far from equilibrium to protect the genetic information encoded by the entire chromosome.

All these biological arguments may explain why C. elegans genome is organized in a nonlinear way. These findings provide insight to quantify and understand the organization of the non-linear structure of the C. elegans genome, which may be extended to other genomes, including the HG (Vélez et al, 2010).

3.2 Nonlinear Descriptive Model for the Human Genome

Once the multifractal approach was validated in C. elegans genome, HG was analyzed exhaustively. This allowed us to propose a nonlinear model for the HG structure which will be discussed under three points of view.

1) It was found that the HG high multifractality depends strongly on the contents of Alu sequences and to a lesser extent on the content of CpG islands. These contents would be located primarily in highly aperiodic regions, thus taking the chromosome far from equilibrium and giving to it greater genetic stability, protection and attraction of mutations, Figure 6 (A-C). Thus, hundreds of regions in the HG may have high genetic stability and the most important genetic information of the HG, the genes, would be safeguarded from environmental fluctuations. Other repeated elements (LINES, MIR, MER, LTRs) showed no significant relationship,

Figure 6 (D). Consequently, the human multifractal map developed in Moreno et al, 2011 constitutes a good tool to identify those regions rich in genetic information and genomic stability. 2) The multifractal context seems to be a significant requirement for the structural and functional organization of thousands of genes and gene families. Thus, a high multifractal context (aperiodic) appears to be a “genomic attractor” for many genes (KOGs, KEEGs), Figure 6 (E) and some gene families, Figure 6 (F) are involved in genetic and deterministic processes, in order to maintain a deterministic regulation control in the genome, although most of HG sequences may be subject to a complex epigenetic control.

3) The classification of human chromosomes and chromosome regions analysis may have some medical implications (Moreno et al, 2002; Moreno et al, 2009). This means that the structure of low nonlinearity exhibited by some chromosomes (or chromosome regions) involve an environmental predisposition, as potential targets to undergo structural or numerical chromosomal alterations in Figure 6 (G). Additionally, sex chromosomes should have low multifractality to maintain sexual dimorphism and probably the X chromosome inactivation.

All these fractals and biological arguments could explain why Alu elements are shaping the HG in a nonlinearly manner (Moreno et al, 2011). Finally, the multifractal modeling of the HG serves as theoretical framework to examine new discoveries made by the ENCODE project and new approaches about human epigenomes. That is, the non-linear organization of HG might help to explain why it is expected that most of the GH is functional.

4. Conclusions

All these results show that the multifractal formalism is appropriate to quantify and evaluate genetic information contents in genomes and to relate it with the known molecular anatomy of the genome and some of the expected properties. Thus, the MFB allows interpreting in a logic manner the structural nature and variation of the genome.

The MFB allows understanding why a number of chromosomal diseases are likely to occur in the genome, thus opening a new perspective toward personalized medicine to study and interpret the GH and its diseases.

The entire genome contains nonlinear information organizing it and supposedly making it function, concluding that virtually 100% of HG is functional. Bioinformatics in general, is enriched with a novel approach (MFB) making it possible to quantify the genetic information content of any DNA sequence and their practical applications to different disciplines in biology, medicine and agriculture. This novel breakthrough in computational genomic analysis and diseases contributes to define Biology as a “hard” science.

MFB opens a door to develop a research program towards the establishment of an integrative discipline that contributes to “break” the code of human life. (http://pharmaceuticalintelligence. com/page/3/).

5. Acknowledgements

Thanks to the directives of the EISC, the Universidad del Valle and the School of Engineering for offering an academic, scientific and administrative space for conducting this research. Likewise, thanks to co authors (professors and students) who participated in the implementation of excerpts from some of the works cited here. Finally, thanks to Colciencias by the biotechnology project grant # 1103-12-16765.


6. References

Blanco, S., & Moreno, P.A. (2007). Representación del juego del caos para el análisis de secuencias de ADN y proteínas mediante el análisis multifractal (método “box-counting”). In The Second International Seminar on Genomics and Proteomics, Bioinformatics and Systems Biology (pp. 17-25). Popayán, Colombia.         [ Links ]

Burgos, J.D., & Moreno-Tovar, P. (1996). Zipf scaling behavior in the immune system. BioSystem , 39, 227-232.         [ Links ]

C. elegans Sequencing Consortium. (1998). Genome sequence of the nematode C. elegans: a platform for investigating biology. Science , 282, 2012-2018.         [ Links ]

Gutiérrez, J.M., Iglesias A., Rodríguez, M.A., Burgos, J.D., & Moreno, P.A. (1998). Analyzing the multifractals structure of DNA nucleotide sequences. In, M. Barbie & S. Chillemi (Eds.) Chaos and Noise in Biology and Medicine (cap. 4). Hackensack (NJ): World Scientific Publishing Co.         [ Links ]

Ivanov, P.Ch., Nunes, L.A., Golberger, A.L., Havlin, S., Rosenblum, M.G., Struzikk, Z.R., & Stanley, H.E. (1999). Multifractality in human heartbeat dynamics. Nature , 399, 461-465.         [ Links ]

Jeffrey, H.J. (1990). Chaos game representation of gene structure. Nucleic Acids Research , 18, 2163-2175.         [ Links ]

Mandelbrot, B. (1982). La geometría fractal de la naturaleza. Barcelona. España: Tusquets editores.         [ Links ]

Moon, F.C. (1992). Chaotic and fractal dynamics. New York: John Wiley.         [ Links ]

Moreno, P.A. (2005). Large scale and small scale bioinformatics studies on the Caenorhabditis elegans enome. Doctoral thesis. Department of Biology and Biochemistry, University of Houston, Houston, USA.         [ Links ]

Moreno, P.A., Burgos, J.D., Vélez, P.E., Gutiérrez, J.M., & et al., (2000). Multifractal analysis of complete genomes. In P roceedings of the 12th International Genome Sequencing and Analysis Conference (pp. 80-81). Miami Beach (FL).         [ Links ]

Moreno, P.A., Rodríguez, J.G., Vélez, P.E., Cubillos, J.R., & Del Portillo, P. (2002). La genómica aplicada en salud humana. Colombia Ciencia y Tecnología. Colciencias , 20, 14-21.         [ Links ]

Moreno, P.A., Vélez, P.E., & Burgos, J.D. (2009). Biología molecular, genómica y post-genómica. Pioneros, principios y tecnologías. Popayán, Colombia: Editorial Universidad del Cauca.         [ Links ]

Moreno, P.A., Vélez, P.E., Martínez, E., Garreta, L., Díaz, D., Amador, S., Gutiérrez, J.M., et. al. (2011). The human genome: a multifractal analysis. BMC Genomics , 12, 506.         [ Links ]

Mount, D.W. (2004). Bioinformatics. Sequence and ge nome analysis. New York: Cold Spring Harbor Laboratory Press.         [ Links ]

Peitgen, H.O., Jürgen, H., & Saupe D. (1992). Chaos and Fractals. New Frontiers of Science. New York: Springer-Verlag.         [ Links ]

Restrepo, S., Pinzón, A., Rodríguez, L.M., Sierra, R., Grajales, A., Bernal, A., Barreto, E. et. al. (2009). Computational biology in Colombia. PLoS Computational Biology, 5 (10), e1000535.         [ Links ]

The ENCODE Project Consortium. (2012). An integrated encyclopedia of DNA elements in the human genome. Nature , 489, 57-74.         [ Links ]

Vélez, P.E., Garreta, L.E., Martínez, E., Díaz, N., Amador, S., Gutiérrez, J.M., Tischer, I., & Moreno, P.A. (2010). The Caenorhabditis elegans genome: a multifractal analysis. Genet and Mol Res , 9, 949-965.         [ Links ]

Venter, J.C., Adams, M.D., Myers, E.W., Li, P.W., & et al. (2001). The sequence of the human genome. Science , 291, 1304-1351.         [ Links ]

Yu, Z.G., Anh, V., & Lau, K.S. (2001). Measure representation and multifractal analysis of complete genomes. Physical Review E: Statistical, Nonlinear, and Soft Matter Physics , 64, 031903.         [ Links ]

 

Other articles on Bioinformatics on this Open Access Journal include:

Bioinformatics Tool Review: Genome Variant Analysis Tools

2017 Agenda – BioInformatics: Track 6: BioIT World Conference & Expo ’17, May 23-35, 2017, Seaport World Trade Center, Boston, MA

Better bioinformatics

Broad Institute, Google Genomics combine bioinformatics and computing expertise

Autophagy-Modulating Proteins and Small Molecules Candidate Targets for Cancer Therapy: Commentary of Bioinformatics Approaches

CRACKING THE CODE OF HUMAN LIFE: The Birth of BioInformatics & Computational Genomics

Read Full Post »


Live 12:00 – 1:00 P.M  Mediterranean Diet and Lifestyle: A Symposium on Diet and Human Health : October 19, 2018

Reporter: Stephen J. Williams, Ph.D.

12.00 The Italian Mediterranean Diet as a Model of Identity of a People with a Universal Good to Safeguard Health?

Prof. Antonino De Lorenzo, MD, PhD.

Director of the School of Specialization in Clinical Nutrition, University of Rome “Tor Vergata”

It is important to determine how our bodies interacts with the environment, such as absorption of nutrients.

Studies shown here show decrease in life expectancy of a high sugar diet, but the quality of the diet, not just the type of diet is important, especially the role of natural probiotics and phenolic compounds found in the Mediterranean diet.

The WHO report in 2005 discusses the unsustainability of nutrition deficiencies and suggest a proactive personalized and preventative/predictive approach of diet and health.

Most of the noncommunicable diseases like CV (46%) cancer 21% and 11% respiratory and 4% diabetes could be prevented and or cured with proper dietary approaches

Italy vs. the US diseases: in Italy most disease due to environmental contamination while US diet plays a major role

The issue we are facing in less than 10% of the Italian population (fruit, fibers, oils) are not getting the proper foods, diet and contributing to as we suggest 46% of the disease

The Food Paradox: 1.5 billion are obese; we notice we are eating less products of quality and most quality produce is going to waste;

  •  growing BMI and junk food: our studies are correlating the junk food (pre-prepared) and global BMI
  • modern diet and impact of human health (junk food high in additives, salt) has impact on microflora
  • Western Diet and Addiction: We show a link (using brain scans) showing correlation of junk food, sugar cravings, and other addictive behaviors by affecting the dopamine signaling in the substantia nigra
  • developed a junk food calculator and a Mediterranean diet calculator
  • the intersection of culture, food is embedded in the Mediterranean diet; this is supported by dietary studies of two distinct rural Italian populations (one of these in the US) show decrease in diet
  • Impact of diet: have model in Germany how this diet can increase health and life expectancy
  • from 1950 to present day 2.7 unit increase in the diet index can increase life expectancy by 26%
  • so there is an inverse relationship with our index and breast cancer

Environment and metal contamination and glyphosate: contribution to disease and impact of maintaining the healthy diet

  • huge problem with use of pesticides and increase in celiac disease

12:30 Environment and Health

Dr. Iris Maria Forte, PhD.

National Cancer Institute “Pascale” Foundation | IRCCS · Department of Research, Naples, Italy

Cancer as a disease of the environment.  Weinberg’s hallmarks of Cancer reveal how environment and epigenetics can impact any of these hallmarks.

Epigenetic effects

  • gene gatekeepers (Rb and P53)
  • DNA repair and damage stabilization

Heavy Metals and Dioxins:( alterations of the immune system as well as epigenetic regulations)

Asbestos and Mesothelioma:  they have demonstrated that p53 can be involved in development of mesothelioma as reactivating p53 may be a suitable strategy for therapy

Diet, Tomato and Cancer

  • looked at tomato extract on p53 function in gastric cancer: tomato extract had a growth reduction effect and altered cell cycle regulation and results in apoptosis
  • RBL2 levels are increased in extract amount dependent manner so data shows effect of certain tomato extracts of the southern italian tomato (     )

Antonio Giordano: we tested whole extracts of almost 30 different varieties of tomato.  The tomato variety  with highest activity was near Ravela however black tomatoes have shown high antitumor activity.  We have done a followup studies showing that these varieties, if grow elsewhere lose their antitumor activity after two or three generations of breeding, even though there genetics are similar.  We are also studying the effects of different styles of cooking of these tomatoes and if it reduces antitumor effect

please see post https://news.temple.edu/news/2017-08-28/muse-cancer-fighting-tomatoes-study-italian-food

 

To follow or Tweet on Twitter please use the following handles (@) and hashtags (#):

@ handles


@S_H_R_O 

@SbarroHealth

@Pharma_BI 

@ItalyinPhilly

@WHO_Europe

@nutritionorg

# hashtags


#healthydiet

#MediterraneanDiet

#health

#nutrition

Please see related articles on Live Coverage of Previous Meetings on this Open Access Journal

Real Time Conference Coverage for Scientific and Business Media: Unique Twitter Hashtags and Handles per Conference Presentation/Session

LIVE – Real Time – 16th Annual Cancer Research Symposium, Koch Institute, Friday, June 16, 9AM – 5PM, Kresge Auditorium, MIT

Real Time Coverage and eProceedings of Presentations on 11/16 – 11/17, 2016, The 12th Annual Personalized Medicine Conference, HARVARD MEDICAL SCHOOL, Joseph B. Martin Conference Center, 77 Avenue Louis Pasteur, Boston

Tweets Impression Analytics, Re-Tweets, Tweets and Likes by @AVIVA1950 and @pharma_BI for 2018 BioIT, Boston, 5/15 – 5/17, 2018

BIO 2018! June 4-7, 2018 at Boston Convention & Exhibition Center

LIVE 2018 The 21st Gabay Award to LORENZ STUDER, Memorial Sloan Kettering Cancer Center, contributions in stem cell biology and patient-specific, cell-based therapy

HUBweek 2018, October 8-14, 2018, Greater Boston – “We The Future” – coming together, of breaking down barriers, of convening across disciplinary lines to shape our future

Read Full Post »


2018 Nobel Prize in Physiology or Medicine for contributions to Cancer Immunotherapy to James P. Allison, Ph.D., of the University of Texas, M.D. Anderson Cancer Center, Houston, Texas. Dr. Allison shares the prize with Tasuku Honjo, M.D., Ph.D., of Kyoto University Institute, Japan

Reporter: Aviva Lev-Ari, PhD, RN

 

See

Immune System Stimulants: Articles of Note @pharmaceuticalintelligence.com

Curators: Larry H. Bernstein, MD, FCAP and Aviva Lev-Ari, PhD, RN

https://pharmaceuticalintelligence.com/2016/05/01/immune-system-stimulants-articles-of-note-pharmaceuticalintelligence-com/

 

Immune-Oncology Molecules In Development & Articles on Topic in @pharmaceuticalintelligence.com

Curators: Stephen J Williams, PhD and Aviva Lev-Ari, PhD, RN

https://pharmaceuticalintelligence.com/2016/01/11/articles-on-immune-oncology-molecules-in-development-pharmaceuticalintelligence-com/

 

 

Monday, October 1, 2018

NIH grantees win 2018 Nobel Prize in Physiology or Medicine.

The 2018 Nobel Prize in Physiology or Medicine has been awarded to National Institutes of Health grantee James P. Allison, Ph.D., of the University of Texas, M.D. Anderson Cancer Center, Houston, Texas. Dr. Allison shares the prize with Tasuku Honjo, M.D., Ph.D., of Kyoto University Institute, Japan, for their discovery of cancer therapy by inhibition of negative immune regulation.

The Royal Swedish Academy of Sciences said, “by stimulating the inherent ability of our immune system to attack tumor cells this year’s Nobel Laureates have established an entirely new principle for cancer therapy.”

Dr. Allison discovered that a particular protein (CTLA-4) acts as a braking system, preventing full activation of the immune system when a cancer is emerging. By delivering an antibody that blocks that protein, Allison showed the brakes could be released. The discovery has led to important developments in cancer drugs called checkpoint inhibitors and dramatic responses to previously untreatable cancers. Dr. Honjo discovered a protein on immune cells and revealed that it also operates as a brake, but with a different mechanism of action.

“Jim’s work was pivotal for cancer therapy by enlisting our own immune systems to launch an attack on cancer and arrest its development,” said NIH Director Francis S. Collins, M.D., Ph.D. “NIH is proud to have supported this groundbreaking research.”

Dr. Allison has received continuous funding from NIH since 1979, receiving more than $13.7 million primarily from NIH’s National Cancer Institute (NCI) and National Institute of Allergy and Infectious Diseases (NIAID).

“This work has led to remarkably effective, sometime curative, therapy for patients with advanced cancer, who we were previously unable to help,” said NCI Director Ned Sharpless, M.D. “Their findings have ushered in the era of cancer immunotherapy, which along with surgery, radiation and cytotoxic chemotherapy, represents a ‘fourth modality’ for treating cancer. A further understanding of the biology underlying the immune system and cancer has the potential to help many more patients.”

“Dr. Allison’s elegant and groundbreaking work in basic immunology over four decades and its important applicability to cancer is a vivid demonstration of the critical nature of interdisciplinary biomedical research supported by NIH,” says NIAID Director Anthony S. Fauci, M.D.

About the National Institutes of Health (NIH): NIH, the nation’s medical research agency, includes 27 Institutes and Centers and is a component of the U.S. Department of Health and Human Services. NIH is the primary federal agency conducting and supporting basic, clinical, and translational medical research, and is investigating the causes, treatments, and cures for both common and rare diseases. For more information about NIH and its programs, visit www.nih.gov.

SOURCE

https://www.nih.gov/news-events/news-releases/nih-grantees-win-2018-nobel-prize-physiology-or-medicine

 

Dr. Lev-Ari covered in person the following curated articles about James Allison, PhD since his days at University of California, Berkeley, including the prizes awarded prior to the 2018 Nobel Prize in Physiology.

 

2018 Albany Medical Center Prize in Medicine and Biomedical Research goes to NIH’s Dr. Rosenberg and fellow immunotherapy researchers James P. Allison, Ph.D., and Carl H. June, M.D.

Reporter: Aviva Lev-Ari, PhD, RN

https://pharmaceuticalintelligence.com/2018/08/15/2018-albany-medical-center-prize-in-medicine-and-biomedical-research-goes-to-nihs-dr-rosenberg-and-fellow-immunotherapy-researchers-james-p-allison-ph-d-and-carl-h-june-m-d/

 

Lectures by The 2017 Award Recipients of Warren Alpert Foundation Prize in Cancer Immunology, October 5, 2017, HMS, 77 Louis Paster, Boston

REAL TIME Reporter: Aviva Lev-Ari, PhD, RN

https://pharmaceuticalintelligence.com/2017/09/08/lectures-by-the-2017-award-recipients-of-warren-alpert-foundation-prize-in-cancer-immunology-october-5-2017-hms-77-louis-paster-boston/

 

Cancer-free after immunotherapy treatment: Treating advanced colon cancer – targeting KRAS gene mutation by tumor-infiltrating lymphocytes (TILs) and Killer T-cells (NK)

Reporter: Aviva Lev-Ari, PhD, RN

https://pharmaceuticalintelligence.com/2016/12/08/cancer-free-after-immunotherapy-treatment-treating-advanced-colon-cancer-targeting-kras-gene-mutation-by-tumor-infiltrating-lymphocytes-tils-and-killer-t-cells-nk/

 

New Class of Immune System Stimulants: Cyclic Di-Nucleotides (CDN): Shrink Tumors and bolster Vaccines, re-arm the Immune System’s Natural Killer Cells, which attack Cancer Cells and Virus-infected Cells

Reporter: Aviva Lev-Ari, PhD, RN

https://pharmaceuticalintelligence.com/2016/04/24/new-class-of-immune-system-stimulants-cyclic-di-nucleotides-cdn-shrink-tumors-and-bolster-vaccines-re-arm-the-immune-systems-natural-killer-cells-which-attack-cancer-cells-and-virus-inf/

 

UC Berkeley research led to Nobel Prize-winning immunotherapy

Immunologist James P. Allison today shared the 2018 Nobel Prize in Physiology or Medicine for groundbreaking work he conducted on cancer immunotherapy at UC Berkeley during his 20 years as director of the campus’s Cancer Research Laboratory.

James Allison

James Allison, who for 20 years was a UC Berkeley immunologist conducting fundamental research on cancer, is now at the M.D. Anderson Cancer Center in Houston, Texas.

Now at the University of Texas M.D. Anderson Cancer Center in Houston, Allison shared the award with Tasuku Honjo of Kyoto University in Japan “for their discovery of cancer therapy by inhibition of negative immune regulation.”

Allison, 70, conducted basic research on how the immune system – in particular, a cell called a T cell – fights infection. His discoveries led to a fundamentally new strategy for treating malignancies that unleashes the immune system to kill cancer cells. A monoclonal antibody therapy he pioneered was approved by the Food and Drug Administration in 2011 to treat malignant melanoma, and spawned several related therapies now being used against lung, prostate and other cancers.

“Because this approach targets immune cells rather than specific tumors, it holds great promise to thwart diverse cancers,” the Lasker Foundation wrote when it awarded Allison its 2015 Lasker-DeBakey Clinical Medical Research Award.

Allison’s work has already benefited thousands of people with advanced melanoma, a disease that used to be invariably fatal within a year or so of diagnosis. The therapy he conceived has resulted in elimination of cancer in a significant fraction of patients for a decade and counting, and it appears likely that many of these people are cured.

“Targeted therapies don’t cure cancer, but immunotherapy is curative, which is why many consider it the biggest advance in a generation,” Allison said in a 2015 interview. “Clearly, immunotherapy now has taken its place along with surgery, chemotherapy and radiation as a reliable and objective way to treat cancer.”

“We are thrilled to see Jim’s work recognized by the Nobel Committee,” said Russell Vance, the current director of the Cancer Research Laboratory and a UC Berkeley professor of molecular and cell biology. “We congratulate him on this highly deserved honor. This award is a testament to the incredible impact that the fundamental research Jim conducted at Berkeley has had on the lives of cancer patients”

“I don’t know if I could have accomplished this work anywhere else than Berkeley,” Allison said. “There were a lot of smart people to work with, and it felt like we could do almost anything. I always tell people that it was one of the happiest times of my life, with the academic environment, the enthusiasm, the students, the faculty.”

In this video about UC Berkeley’s new Immunotherapeutics and Vaccine Research Initiative (IVRI), Allison discusses his groundbreaking work on cancer immunotherapy.

In fact, Allison was instrumental in creating the research environment of the current Department of Molecular and Cell Biology at UC Berkeley as well as the department’s division of immunology, in which he served stints as chair and division head during his time at Berkeley, said David Raulet, director of Berkeley’s Immunotherapeutics and Vaccine Research Initiative (IVRI).

“His actions helped create the superb research environment here, which is so conducive to making the fundamental discoveries that will be the basis of the next generation of medical breakthroughs,” Raulet said.

Self vs. non-self

Allison joined the UC Berkeley faculty as a professor of molecular and cell biology and director of the Cancer Research Laboratory in 1985. An immunologist with a Ph.D. from the University of Texas, Austin, he focused on a type of immune system cell called the T cell or T lymphocyte, which plays a key role in fighting off bacterial and viral infections as well as cancer.

Supercharging the immune system to cure disease: immunotherapy research at UC Berkeley. (UC Berkeley video by Roxanne Makasdjian and Stephen McNally)

At the time, most doctors and scientists believed that the immune system could not be exploited to fight cancer, because cancer cells look too much like the body’s own cells, and any attack against cancer cells would risk killing normal cells and creating serious side effects.

“The community of cancer biologists was not convinced that you could even use the immune system to alter cancer’s outcome, because cancer was too much like self,” said Matthew “Max” Krummel, who was a graduate student and postdoctoral fellow with Allison in the 1990s and is now a professor of pathology and a member of the joint immunology group at UCSF. “The dogma at the time was, ‘Don’t even bother.’ ”

“What was heady about the moment was that we didn’t really listen to the dogma, we just did it,” Krummel added. Allison, in particular, was a bit “irreverent, but in a productive way. He didn’t suffer fools easily.” This attitude rubbed off on the team.

Trying everything they could in mice to tweak the immune system, Krummel and Allison soon found that a protein receptor called CTLA-4 seemed to be holding T cells back, like a brake in a car.

Postdoctoral fellow Dana Leach then stepped in to see if blocking the receptor would unleash the immune system to actually attack a cancerous tumor. In a landmark paper published in Science in 1996, Allison, Leach and Krummel showed not only that antibodies against CTLA-4 released the brake and allowed the immune system to attack the tumors, but that the technique was effective enough to result in long-term disappearance of the tumors.

“When Dana showed me the results, I was really surprised,” Allison said. “It wasn’t that the anti-CTLA-4 antibodies slowed the tumors down. The tumors went away.”

After Allison himself replicated the experiment, “that’s when I said, OK, we’ve got something here.”

Checkpoint blockade

The discovery led to a concept called “checkpoint blockade.” This holds that the immune system has many checkpoints designed to prevent it from attacking the body’s own cells, which can lead to autoimmune disease. As a result, while attempts to rev up the immune system are like stepping on the gas, they won’t be effective unless you also release the brakes.

Allison in 1993

James Allison in 1993, when he was conducting research at UC Berkeley on a promising immunotherapy now reaching fruition. (Jane Scherr photo)

“The temporary activation of the immune system though ‘checkpoint blockade’ provides a window of opportunity during which the immune system is mobilized to attack and eliminate tumors,” Vance said.

Allison spent the next few years amassing data in mice to show that anti-CTLA-4 antibodies work, and then, in collaboration with a biotech firm called Medarex, developed human antibodies that showed promise in early clinical trials against melanoma and other cancers. The therapy was acquired by Bristol-Myers Squibb in 2011 and approved by the FDA as ipilimumab (trade name Yervoy), which is now used to treat skin cancers that have metastasized or that cannot be removed surgically.

Meanwhile, Allison left UC Berkeley in 2004 for Memorial Sloan Kettering research center in New York to be closer to the drug companies shepherding his therapy through clinical trials, and to explore in more detail how checkpoint blockade works.

“Berkeley was my favorite place, and if I could have stayed there, I would have,” he said. “But my research got to the point where all the animal work showed that checkpoint blockade had a lot of potential in people, and working with patients at Berkeley wasn’t possible. There’s no hospital, no patients.”

Thanks to Allison’s doggedness, anti-CTLA-4 therapy is now an accepted therapy for cancer and it opened the floodgates for a slew of new immunotherapies, Krummel said. There now are several hundred ongoing clinical trials involving monoclonal antibodies to one or more receptors that inhibit T cell activity, sometimes combined with lower doses of standard chemotherapy.

Antibodies against one such receptor, PD-1, which Honjo discovered in 1992, have given especially impressive results. Allison’s initial findings can be credited for prompting researchers, including Allison himself, to carry out the studies that have demonstrated the potent anti-cancer effects of PD-1 antibodies. In 2015, the FDA approved anti-PD-1 therapy for malignant melanoma, and has since approved it for non-small-cell lung, gastric and several other cancers.

Science magazine named cancer immunotherapy its breakthrough of 2013 because that year, “clinical trials … cemented its potential in patients and swayed even the skeptics. The field hums with stories of lives extended: the woman with a grapefruit-size tumor in her lung from melanoma, alive and healthy 13 years later; the 6-year-old near death from leukemia, now in third grade and in remission; the man with metastatic kidney cancer whose disease continued fading away even after treatment stopped.”

Allison pursued more clinical trials for immunotherapy at Sloan-Kettering and then in 2012 returned to his native Texas.

Born in Alice, Texas, on Aug. 7, 1948, Allison earned a B.S. in microbiology in 1969 and a Ph.D. in biological science in 1973 from the University of Texas, Austin.

RELATED INFORMATION

SOURCE

http://news.berkeley.edu/2018/10/01/uc-berkeley-research-led-to-nobel-prize-winning-immunotherapy/

Read Full Post »


Centers for Medicare & Medicaid Services announced that the federal healthcare program will cover the costs of cancer gene tests that have been approved by the Food and Drug Administration

 

Reporter: Aviva Lev-Ari, PhD, RN

genetic testing just became routine care for patients with advanced cancers. And that means precision medicine has finally broken into the mainstream.

Any tests that gain FDA clearance in the future will automatically receive full coverage.

In 3/2018 there are three FDA approved Genetic Tests for Cancer:

UNDER development and not included in the agreement , above, includes:

  • Olivier Elemento, Director of the Caryl and Israel Englander Institute for Precision Medicine at Cornell, the team at Cornell, for example, has developed a whole exome test that compares mutations in tumors against healthy cells across 22,000 genes. To date, it’s been used to help match more than 1,000 patients in New York state with the best available treatment options.

Under the final decision, doctors are still free to order non-FDA approved tests, but coverage isn’t guaranteed; each case will be evaluated by local Medicare administrative contractors. Which means Elemento’s test could still be covered. “To me this is a vote of confidence that next generation sequencing is useful for cancer patients,” says Elemento.

So far, CMS is only covering these tests for stage three and stage four metastatic cancer sufferers. Most of them aren’t going to be cured. They might get a few more good months, maybe a year, tops.

Cancerous Genes

SOURCE

WITH MEDICARE SUPPORT, GENETIC CANCER TESTING GOES MAINSTREAM

https://www.wired.com/story/with-medicare-support-genetic-cancer-testing-goes-mainstream/?mbid=social_twitter_onsiteshare

Read Full Post »


Emerging STAR in Molecular Biology, Synthetic Virology and Genomics: Clodagh C. O’Shea: ChromEMT – Visualizing 3D chromatin structure

 

Curator: Aviva Lev-Ari, PhD, RN

 

On 8/28/2017, I attend and covered in REAL TIME the CHI’s 5th Immune Oncology Summit – Oncolytic Virus Immunotherapy, August 28-29, 2017 Sheraton Boston Hotel | Boston, MA

https://pharmaceuticalintelligence.com/2017/08/28/live-828-chis-5th-immune-oncology-summit-oncolytic-virus-immunotherapy-august-28-29-2017-sheraton-boston-hotel-boston-ma/

 

I covered in REAL TIME this event and Clodagh C. O’Shea talk at the conference.

On that evening, I e-mailed my team that

“I believe that Clodagh C. O’Shea will get the Nobel Prizebefore CRISPR

 

11:00 Synthetic Virology: Modular Assembly of Designer Viruses for Cancer Therapy

Clodagh_OShea

Clodagh O’Shea, Ph.D., Howard Hughes Medical Institute Faculty Scholar; Associate Professor, William Scandling Developmental Chair, Molecular and Cell Biology Laboratory, Salk Institute for Biological Studies

Design is the ultimate test of understanding. For oncolytic therapies to achieve their potential, we need a deep mechanistic understanding of virus and tumor biology together with the ability to confer new properties.

To achieve this, we have developed

  • combinatorial modular genome assembly (ADsembly) platforms,
  • orthogonal capsid functionalization technologies (RapAd) and
  • replication assays that have enabled the rational design, directed evolution, systematic assembly and screening of powerful new vectors and oncolytic viruses.

 

Clodagh O’Shea’s Talk In Real Time:

  • Future Cancer therapies to be sophisticated as Cancer is
  • Targer suppresor pathways (Rb/p53)
  • OV are safe their efficacy ishas been limited
  • MOA: Specify Oncolytic Viral Replication in Tumor cells Attenuate – lack of potency
  • SOLUTIONS: Assembly: Assmble personalized V Tx fro libraries of functional parts
  • Adenovirus – natural & clinical advantages
  • Strategy: Technology for Assmbling Novel Adenovirus Genomes using Modular Genomic Parts
  • E1 module: Inactives Rb & p53
  • core module:
  • E3 Module Immune Evasion Tissue targeting
  • E4 Module Activates E2F (transcription factor TDP1/2), PI3K
  • Adenovirus promoters for Cellular viral replication — Tumor Selective Replication: Novel Viruses Selective Replicate in RB/p16
  • Engineering Viruses to overcome tumor heterogeneity
  • Target multiple & Specific Tumor Cel Receptors – RapAd Technology allows Re-targeting anti Rapamycin – induced targeting of adenovirus
  • Virus Genome: FKBP-fusion FRB-Fiber
  • Engineer Adenovirus Caspids that prevent Liver uptake and Sequestration – Natural Ad5 Therapies 
  • Solution: AdSyn335 Lead candidat AdSyn335 Viruses targeting multiple cells
  • Engineering Mutations that enhanced potency
  • Novel Vector: Homes and targets
  • Genetically engineered PDX1 – for Pancreatic Cancer Stroma: Early and Late Stage
On Twitter:

Engineer Adenovirus Caspids prevent Liver uptake and Sequestration – Natural Ad5 Therapies C. O’Shea, HHDI

Scientist’s Profile: Clodagh C. O’Shea

http://www.salk.edu/scientist/clodagh-oshea/

EDUCATION

BS, Biochemistry and Microbiology, University College Cork, Ireland
PhD, Imperial College London/Imperial Cancer Research Fund, U.K.
Postdoctoral Fellow, UCSF Comprehensive Cancer Center, San Francisco, U.S.A

VIDEOS

http://www.salk.edu/scientist/clodagh-oshea/videos/

O’Shea Lab @Salk

http://oshea.salk.edu/

AWARDS & HONORS

  • 2016 Howard Hughes Medical Institute Faculty Scholar
  • 2014 W. M. Keck Medical Research Program Award
  • 2014 Rose Hills Fellow
  • 2011Science/NSF International Science & Visualization Challenge, People’s Choice
  • 2011 Anna Fuller Award for Cancer Research
  • 2010, 2011, 2012 Kavli Frontiers Fellow, National Academy of Sciences
  • 2009 Sontag Distinguished Scientist Award
  • 2009 American Cancer Society Research Scholar Award
  • 2008 ACGT Young Investigator Award for Cancer Gene Therapy
  • 2008 Arnold and Mabel Beckman Young Investigator Award
  • 2008 William Scandling Assistant Professor, Developmental Chair
  • 2007 Emerald Foundation Schola

READ 

Clodagh C. O’Shea: ChromEMT: Visualizing 3D chromatin structure and compaction in interphase and mitotic cells | Science

http://science.sciencemag.org/content/357/6349/eaag0025

and 

https://www.readbyqxmd.com/keyword/93030

Clodagh C. O’Shea

In Press

Jul 27, 2017 – Salk scientists solve longstanding biological mystery of DNA organization

Sep 22, 2016 – Clodagh O’Shea named HHMI Faculty Scholar for groundbreaking work in designing synthetic viruses to destroy cancer

Oct 05, 2015 – Clodagh O’Shea awarded $3 million to unlock the “black box” of the nucleus

Aug 27, 2015 – The DNA damage response goes viral: a way in for new cancer treatments

Apr 12, 2013 – Salk Institute promotes three top scientists

Oct 16, 2012 – Cold viruses point the way to new cancer therapies

Aug 25, 2010 – Use the common cold virus to target and disrupt cancer cells?

Oct 22, 2009 – Salk scientist receives The Sontag Foundation’s Distinguished Scientist Award

May 15, 2008 – Salk scientist wins 2008 Beckman Young Investigator Award

Mar 24, 2008 – Salk scientist wins 2007 Young Investigator’s Award in Gene Therapy for Cancer

Read Full Post »

Older Posts »