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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?)

 

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THE 3RD STAT4ONC ANNUAL SYMPOSIUM APRIL 25-27, 2019, HILTON, HARTFORD, CONNECTICUT, 315 Trumbull St, Hartford, CT 06103

Reporter: Stephen J. Williams, Ph.D.

SYMPOSIUM OBJECTIVES

The three-day symposium aims to bring oncologists and statisticians together to share new research, discuss novel ideas, ask questions and provide solutions for cancer clinical trials. In the era of big data, precision medicine, and genomics and immune-based oncology, it is crucial to provide a platform for interdisciplinary dialogues among clinical and quantitative scientists. The Stat4Onc Annual Symposium serves as a venue for oncologists and statisticians to communicate their views on trial design and conduct, drug development, and translations to patient care. To be discussed includes big data and genomics for oncology clinical trials, novel dose-finding designs, drug combinations, immune oncology clinical trials, and umbrella/basket oncology trials. An important aspect of Stat4Onc is the participation of researchers across academia, industry, and regulatory agency.

Meeting Agenda will be announced coming soon. For Updated Agenda and Program Speakers please CLICK HERE

The registration of the symposium is via NESS Society PayPal. Click here to register.

Other  2019 Conference Announcement Posts on this Open Access Journal Include:

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Study Finds that Both Women and their Primary Care Physicians Confusion over Ovarian Cancer Symptoms May Lead to Misdiagnosis

Reporter: Stephen J. Williams, Ph.D.

This post discusses the recently released “The Every Woman Study” conducted by the World Ovarian Cancer Coalition.  For full PDF of the study please click here: WOCC-Every-Woman-Study-Summary-Report-Oct-16

The findings are summarized nicely in the NPR article from Joanne Silberner below but just want to list a few takeaways from the study

  1.  Ovarian Cancer, while not the most common cancer in women, is still one of the most deadly malignancies.  A major reason for this is the inability to catch the disease in its early, and most treatable stages.  Much work is being done on early detection (a few posts on this area from this online journal are given at the end of this post for reference)
  2. The symptoms of ovarian cancer closely mimic symptoms of gastrointestinal distress and disorders and many times these symptoms are overlooked by women as benign, temporary issues and may be mis-self diagnosed.  In addition, if mistaken for common gastrointestinal discomfort or gynecologic discomfort (cramping)  women may self-medicate with over the counter agents which mask the symptoms of ovarian cancer
  3. certain lessons can be learned from the experiences in other countries regarding access to healthcare and diagnosis. For instance

Looking at the key findings of the study it becomes clear that countries have significant potential to
learn from each other:
• Women in Germany had the shortest time to diagnosis, but much less access to
specialist clinicians that are key to successful treatment.
• Women in the UK have almost universal access to specialists but the lowest
proportion of women diagnosed within a month of visiting a doctor.
• Women in Japan had one of the shortest times to diagnosis, but very little access to
genetic testing, and were least likely to get the emotional support they needed.
• Women in the USA were most likely to wait more than three months before
consulting a doctor about symptoms, but most likely to receive genetic testing.
• Women with ovarian cancer in Hungary were most aware of ovarian cancer before
their diagnosis, but were much less likely to be offered surgery to treat their disease.

 

In summary it appears there are three key areas needing to be addressed with regard to improving early reporting of symptoms of ovarian cancer

  1. information and awareness of symptoms by BOTH women and their physicians
  2. family risk assessment programs are very important to make women aware of their risks and needs for screening
  3. access to specialist treatment is important in the early diagnosis and treatment of this disease

 

Learn the Symptoms

Symptoms (from the Sandy Rollman Ovarian Cancer Foundation)

Historically ovarian cancer was called the “silent killer” because symptoms were not thought to develop until the chance of cure was poor. However, recent studies have shown this term is untrue and that the following symptoms are much more likely to occur in women with ovarian cancer than women in the general population. These symptoms include:

  • Bloating
  • Pelvic or abdominal pain
  • Difficulty eating or feeling full quickly
  • Urinary symptoms (urgency or frequency)

Women with ovarian cancer report that symptoms are persistent and represent a change from normal for their bodies. The frequency and/or number of such symptoms are key factors in the diagnosis of ovarian cancer. Several studies show that even early stage ovarian cancer can produce these symptoms.

Women who have these symptoms almost daily for more than a few weeks should see their doctor, preferably a gynecologist. Prompt medical evaluation may lead to detection at the earliest possible stage of the disease. Early stage diagnosis is associated with an improved prognosis.

Several other symptoms have been commonly reported by women with ovarian cancer. These symptoms include fatigue, indigestion, back pain, pain with intercourse, constipation and menstrual irregularities. However, these other symptoms are not as useful in identifying ovarian cancer because they are also found in equal frequency in women in the general population who do not have ovarian cancer.

 

In addition there are serum biomarker tests which have shown useful in the screening for ovarian cancer however these tests have their caveats and not generally suggested for whole population screening due to number of false postitives which may occur (these tests will be discussed in further posts)

Serum biomarker tests include:

 Taken From NPR at https://www.npr.org/sections/goatsandsoda/2018/10/21/658798956/report-women-everywhere-dont-know-enough-about-ovarian-cancer

Report: Women Everywhere Don’t Know Enough About Ovarian Cancer

Colored scanning electron micrograph of dividing ovarian cancer cells.

Steve Gschmeissner/Science Source

new study of women with ovarian cancer shows that ignorance about the condition is common among patients in all 44 countries surveyed. And that ignorance has a cost. The disease is more treatable, even potentially curable, in its early stages.

The women’s answers also suggested their doctors were ignorant. Many of them reported that diagnosis took a long time and that they weren’t referred to proper specialists.

The study was based on an online survey of 1,531 women who had been diagnosed with the cancer and was conducted by the World Ovarian Cancer Coalition, a nonprofit support group between March and May of this year.

Ovarian cancer is the eighth leading cause of cancer in women, according to the World Health Organization. Nearly 300,000 women will develop it this year. The World Ovarian Cancer Coalition estimates that one in six will die within three months of diagnosis and fewer than half will be alive in five years.

Prior to their diagnosis, two-thirds of the women surveyed either had never heard of ovarian cancer or were familiar with the name but didn’t know anything about the disease.

 

Other articles related to Ovarian Cancer on this online Open Access Journal Include:

Model mimicking clinical profile of patients with ovarian cancer @ Yale School of Medicine

New Findings in Endometrial Cancer: Mutations, Molecular Types and Immune Responses Evoked by Mutation-prone Endometrial, Ovarian Cancer Subtypes

Good and Bad News Reported for Ovarian Cancer Therapy

Efficacy of Ovariectomy in Presence of BRCA1 vs BRCA2 and the Risk for Ovarian Cancer

Testing for Multiple Genetic Mutations via NGS for Patients: Very Strong Family History of Breast & Ovarian Cancer, Diagnosed at Young Ages, & Negative on BRCA Test

Ultrasound-based Screening for Ovarian Cancer

Warning signs may lead to better early detection of ovarian cancer

Epigenetics, Environment and Cancer: Articles of Note @PharmaceuticalIntelligence.com

Early Diagnosis [Early Detection Research Networks]

 

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The Role of Exosomes in Metabolic Regulation

Author: Larry H. Bernstein, MD, FCAP

 

On 9/25/2017, Aviva Lev-Ari, PhD, RN commissioned Dr. Larry H. Bernstein to write a short article on the following topic reported on 9/22/2017 in sciencemission.com

 

We are publishing, below the new article created by Larry H. Bernstein, MD, FCAP.

 

Background

During the period between 9/2015  and 6/2017 the Team at Leaders in Pharmaceutical Business Intelligence (LPBI)  has launched an R&D effort lead by Aviva Lev-Ari, PhD, RN in conjunction with SBH Sciences, Inc. headed by Dr. Raphael Nir.

This effort, also known as, “DrugDiscovery @LPBI Group”  has yielded several publications on EXOSOMES on this Open Access Online Scientific Journal. Among them are included the following:

 

QIAGEN – International Leader in NGS and RNA Sequencing, 10/08/2017

Reporter: Aviva Lev-Ari, PhD, RN

 

cell-free DNA (cfDNA) tests could become the ultimate “Molecular Stethoscope” that opens up a whole new way of practicing Medicine, 09/08/2017

Reporter: Aviva Lev-Ari, PhD, RN

 

Detecting Multiple Types of Cancer With a Single Blood Test (Human Exomes Galore), 07/02/2017

Reporter and Curator: Irina Robu, PhD

 

Exosomes: Natural Carriers for siRNA Delivery, 04/24/2017

Reporter: Aviva Lev-Ari, PhD, RN

 

One blood sample can be tested for a comprehensive array of cancer cell biomarkers: R&D at WPI, 01/05/2017

Curator: Marzan Khan, B.Sc

 

SBI’s Exosome Research Technologies, 12/29/2016

Reporter: Aviva Lev-Ari, PhD, RN

 

A novel 5-gene pancreatic adenocarcinoma classifier: Meta-analysis of transcriptome data – Clinical Genomics Research @BIDMC, 12/28/2016

Curator: Tilda Barliya, PhD

 

Liquid Biopsy Chip detects an array of metastatic cancer cell markers in blood – R&D @Worcester Polytechnic Institute, Micro and Nanotechnology Lab, 12/28/2016

Reporters: Tilda Barliya, PhD and Aviva Lev-Ari, PhD, RN

 

Exosomes – History and Promise, 04/28/2016

Reporter: Aviva Lev-Ari, PhD, RN

 

Exosomes, 11/17/2015

Curator: Larry H. Bernstein, MD, FCAP

 

Liquid Biopsy Assay May Predict Drug Resistance, 11/16/2015

Curator: Larry H. Bernstein, MD, FCAP

 

Glypican-1 identifies cancer exosomes, 10/31/2015

Curator: Larry H. Bernstein, MD, FCAP

 

Circulating Biomarkers World Congress, March 23-24, 2015, Boston: Exosomes, Microvesicles, Circulating DNA, Circulating RNA, Circulating Tumor Cells, Sample Preparation, 03/24/2015

Reporter: Aviva Lev-Ari, PhD, RN

 

Cambridge Healthtech Institute’s Second Annual Exosomes and Microvesicles as Biomarkers and Diagnostics Conference, March 16-17, 2015 in Cambridge, MA, 03/17, 2015

Reporter: Aviva Lev-Ari, PhD, RN

 

The newly created think-piece on the relationship between regulatory functions of Exosomes and Metabolic processes is developed conceptually, below.

 

The Role of Exosomes in Metabolic Regulation

Author: Larry H. Bernstein, MD, FCAP

We have had more than a half century of research into the genetic code and transcription leading to abundant work on RNA and proteomics. However, more recent work in the last two decades has identified RNA interference in siRNA. These molecules may be found in the circulation, but it has been a challenge to find their use in therapeutics. Exosomes were first discovered in the 1980s, but only recently there has been a huge amount of research into their origin, structure and function. Exosomes are 30–120 nm endocytic membrane-bound extracellular vesicles (EVs)(1-23) , and more specifically multiple vesicle bodies (MVBs) by a budding process from invagination of the outer cell membrane that carry microRNA (miRNA), and have structures composed of protein and lipids (1,23-27 ). EVs are the membrane vesicles secreted by eukaryotic cells for intracellular communication by transferring the proteins, lipids, and RNA under various physiologic conditions as well as during the disease stage. EVs also act as a signalosomes in many biological processes. Inward budding of the plasma membrane forms small vesicles that fuse. Intraluminal vesicles (ILVs) are formed by invagination of the limiting endosomal membrane during the maturation process of early endosome.

EVs are the MVBs secreted that serve in intracellular communication by transferring a cargo consisting of proteins, lipids, and RNA under various physiologic conditions (4, 23). Exosome-mediated miRNA transfer between cells is considered to be necessary for intercellular signaling and exosome-associated miRNAs in biofluids (23). Exosomes carry various molecular constituents of their cell of origin, including proteins, lipids, mRNAs, and microRNAs (miRNAs) (. They are released from many cell types, such as dendritic cells (DCs), lymphocytes, platelets, mast cells, epithelial cells, endothelial cells, and neurons, and can be found in most bodily fluids including blood, urine, saliva, amniotic fluid, breast milk, hydrothoracic fluid, and ascitic fluid, as well as in culture medium of most cell types.Exosomes have also been shown to be involved in noncoding RNA surveillance machinery in generating antibody diversity (24). There are also a vast number of long non-coding RNAs (lncRNAs) and enhancer RNAs (eRNAs) that accumulate R-loop structures upon RNA exosome ablation, thereby, resolving deleterious DNA/RNA hybrids arising from active enhancers and distal divergent eRNA-expressing elements (lncRNA-CSR) engaged in long-range DNA interactions (25). RNA exosomes are large multimeric 3′-5′ exo- and endonucleases representing the central RNA 3′-end processing factor and are implicated in processing, quality control, and turnover of both coding and noncoding RNAs. They are large macromolecular cages that channel RNA to the ribonuclease sites (29). A major interest has been developed to characterize of exosomal cargo, which includes numerous non-randomly packed proteins and nucleic acids (1). Moreover, exosomes play an active role in tumorigenesis, metastasis, and response to therapy through the transfer of oncogenes and onco-miRNAs between cancer cells and the tumor stroma. Blood cells and the vascular endothelium is also exosomal shedding, which has significance for cardiovascular,   neurologicological disorders, stroke, and antiphospholipid syndrome (1). Dysregulation of microRNAs and the affected pathways is seen in numerous pathologies their expression can reflect molecular processes of tumor onset and progression qualifying microRNAs as potential diagnostic and prognostic biomarkers (30).

Exosomes are secreted by many cells like B lymphocytes and dendritic cells of hematopoietic and non-hematopoietic origin viz. platelets, Schwann cells, neurons, mast cells, cytotoxic T cells, oligodendrocytes, intestinal epithelial cells were also found to be releasing exosomes (4). They are engaged in complex functions like persuading immune response as the exosomes secreted by antigen presenting cells activate T cells (4). They all have a common set of proteins e.g. Rab family of GTPases, Alix and ESCRT (required for transport) protein and they maintain their cytoskeleton dynamics and participate in membrane fusion. However, they are involved in retrovirus disease pathology as a result of recruitment of the host`s endosomal compartments in order to generate viral vesicles, and they can either spread or limit an infection based on the type of pathogen and its target cells (5).

Upon further consideration, it is understandable how this growing biological work on exosomes has enormous significance for laboratory diagnostics (1, 3, 5, 6, 11, 14, 15, 17-20, 23,30-41) . They are released from many cell types, such as dendritic cells (DCs), lymphocytes, platelets, mast cells, epithelial cells, endothelial cells, and neurons, and can be found in most bodily fluids including blood, urine, saliva, amniotic fluid, breast milk, thoracic and abdominal effusions, and ascitic fluid (1). The involvement of exosomes in disease is broad, and includes: cancer, autoimmune and infectious disease, hematologic disorders, neurodegenerative diseases, and cardiovascular disease. Proteins frequently identified in exosomes include membrane transporters and fusion proteins (e.g., GTPases, annexins, and flotillin), heat shock proteins (e.g., HSC70), tetraspanins (e.g., CD9, CD63, and CD81), MVB biogenesis proteins (e.g., alix and TSG101), and lipid-related proteins and phospholipases. The exosomal lipid composition has been thoroughly analyzed in exosomes secreted from several cell types including DCs and mast cells, reticulocytes, and B-lymphocytes (1). Dysregulation of microRNAs of pathways observed in numerous pathologies (5, 10, 12, 21, 27, 35, 37) including cancers (30), particularly, colon, pancreas, breast, liver, brain, lung (2, 6, 17-20, 30, 33-36, 38, 39). Following these considerations, it is important that we characterize the content of exosomal cargo to gain clues to their biogenesis, targeting, and cellular effects which may lead to identification of biomarkers for disease diagnosis, prognosis and response to treatment (42).

We might continue in pursuit of a particular noteworthy exosome, the NLRP3 inflammasome, which is activated by a variety of external or host-derived stimuli, thereby, initiating an inflammatory response through caspase-1 activation, resulting in inflammatory cytokine IL-1b maturation and secretion (43).
Inflammasomes are multi-protein signaling complexes that activate the inflammatory caspases and the maturation of interleukin-1b. The NLRP3 inflammasome is linked with human autoinflammatory and autoimmune diseases (44). This makes the NLRP3 inflammasome a promising target for anti-inflammatory therapies. The NLRP3 inflammasome is activated in response to a variety of signals that indicate tissue damage, metabolic stress, and infection (45). Upon activation, the NLRP3 inflammasome serves as a platform for activation of the cysteine protease caspase-1, which leads to the processing and secretion of the proinflammatory cytokines interleukin-1β (IL-1β) and IL-18. Heritable and acquired inflammatory diseases are both characterized by dysregulation of NLRP3 inflammasome activation (45).
Receptors of innate immunity recognize conserved moieties associated with either cellular damage [danger-associated molecular patterns (DAMPs)] or invading organisms [pathogen-associated molecular patterns (PAMPs)](45). Either chronic stimulation or overwhelming tissue damage is injurious and responsible for the pathology seen in a number of autoinflammatory and autoimmune disorders, such as arthritis and diabetes. The nucleotide-binding domain leucine-rich repeat (LRR)-containing receptors (NLRs) are PRRs are found intracellularly and they share a unique domain architecture. It consists of a central nucleotide binding and oligomerization domain called the NACHT domain that is located between an N-terminal effector domain and a C-terminal LRR domain (45). The NLR family members NLRP1, NLRP3, and NLRC4 are capable of forming multiprotein complexes called inflammasomes when activated.

The (NLRP3) inflammasome is important in chronic airway diseases such as asthma and chronic obstructive pulmonary disease because the activation results, in pro-IL-1β processing and the secretion of the proinflammatory cytokine IL-1β (46). It has been proposed that Activation of the NLRP3 inflammasome by invading pathogens may prove cell type-specific in exacerbations of airway inflammation in asthma (46). First, NLRP3 interacts with the adaptor protein ASC by sensing microbial pathogens and self-danger signals. Then pro-caspase-1 is recruited and the large protein complex called the NLRP3 inflammasome is formed. This is followed by autocleavage and activation of caspase-1, after which pro-IL-1β and pro-IL-18 are converted into their mature forms. Ion fluxes disrupt membrane integrity, and also mitochondrial damage both play key roles in NLRP3 inflammasome activation (47). Depletion of mitochondria as well as inhibitors that block mitochondrial respiration and ROS production prevented NLRP3 inflammasome activation. Futhermore, genetic ablation of VDAC channels (namely VDAC1 and VDAC3) that are located on the mitochondrial outer membrane and that are responsible for exchanging ions and metabolites with the cytoplasm, leads to diminished mitochondrial (mt) ROS production and inhibition of NLRP3 inflammasome activation (47). Inflammasome activation not only occurs in immune cells, primarily macrophages and dendritic cells, but also in kidney cells, specifically the renal tubular epithelium. The NLRP3 inflammasome is probably involved in the pathogenesis of acute kidney injury, chronic kidney disease, diabetic nephropathy and crystal-related nephropathy (48). The inflammasome also plays a role in autoimmune kidney disease. IL-1 blockade and two recently identified specific NLRP3 inflammasome blockers, MCC950 and β-hydroxybutyrate, may prove to have value in the treatment of inflammasome-mediated conditions.

Autophagosomes derived from tumor cells are referred to as defective ribosomal products in blebs (DRibbles). DRibbles mediate tumor regression by stimulating potent T-cell responses and, thus, have been used as therapeutic cancer vaccines in multiple preclinical cancer models (49). It has been found that DRibbles could induce a rapid differentiation of monocytes and DC precursor (pre-DC) cells into functional APCs (49). Consequently, DRibbles could potentially induce strong innate immune responses via multiple pattern recognition receptors. This explains why DRibbles might be excellent antigen carriers to induce adaptive immune responses to both tumor cells and viruses. This suggests that isolated autophagosomes (DRibbles) from antigen donor cells activate inflammasomes by providing the necessary signals required for IL-1β production.

The Hsp90 system is characterized by a cohort of co-chaperones that bind to Hsp90 and affect its function (50). The co-chaperones enable Hsp90 to chaperone structurally and functionally diverse client proteins. Sahasrabudhe et al. (50) show that the nature of the client protein dictates the contribution of a co-chaperone to its maturation. The study reveals the general importance of the cochaperone Sgt1 (50). In addition to Hsp90, we have to consider Hsp60. Adult cardiac myocytes release heat shock protein (HSP)60 in exosomes. Extracellular HSP60, when not in exosomes, causes cardiac myocyte apoptosis via the activation of Toll-like receptor 4. the protein content of cardiac exosomes differed significantly from other types of exosomes in the literature and contained cytosolic, sarcomeric, and mitochondrial proteins (21).

A new Protein Organic Solvent Precipitation (PROSPR) method efficiently isolates the EV repertoire from human biological samples. Proteomic profiling of PROSPR-enriched CNS EVs indicated that > 75 % of the proteins identified matched previously reported exosomal and microvesicle cargoes. In addition lipidomic characterization of enriched CNS vesicles identified previously reported EV-specific lipid families and novel lipid isoforms not previously detected in human EVs. The characterization of these structures from central nervous system (CNS) tissues is relevant to current neuroscience, especially to advance the understanding of neurodegeneration in amyotrophic lateral sclerosis (ALS), Parkinson’s disease (PD) and Alzheimer’s disease (AD)(15). In addition, study of EVs in brain will enable characterization of the degenerative posttranslational modifications (DPMs) occurring in those proteins.
Neurodegenerative disease is characterized by dysregulation because of NLRP3 inflammasome activation. Alzheimer’s disease (AD) and Parkinson’s disease (PD), both neurodegenerative diseases are associated with the NLRP3 inflammasome. PD is characterized by accumulation of Lewy bodies (LB) formed by a-synuclein (aSyn) aggregation. A recent study revealed that aSyn induces synthesis of pro-IL-1b by an interaction with TLR2 and activates NLRP3 inflammasome resulting in caspase-1 activation and IL-1b maturation in human primary monocytes (43). In addition mitophagy downregulates NLRP3 inflammasome activation by eliminating damaged mitochondria, blocking NLRP3 inflammasome activating signals. It is notable that in this aberrant activation mitophagy downregulates NLRP3 inflammasome activation by eliminating damaged mitochondria, blocking NLRP3 inflammasome activating signals (43).

REFERENCES

  1. Lin J, Li J, Huang B, Liu J, Chen X. Exosomes: Novel Biomarkers for Clinical Diagnosis. Scie World J 2015; Article ID 657086, 8 pages http://dx.doi.org/10.1155/2015/657086
  2. Kahlert C, Melo SA, Protopopov A, Tang J, Seth S, et al. Identification of Double-stranded Genomic DNA Spanning All Chromosomes with Mutated KRAS and p53 DNA in the Serum Exosomes of Patients with Pancreatic Cancer. J Biol Chem 2014; 289: 3869-3875. doi: 10.1074/jbc.C113.532267.
  3. Lässer C, Eldh M, Lötvall J. Isolation and Characterization of RNA-Containing Exosomes. J. Vis. Exp. 2012; 59, e3037. doi:10.3791/3037(2012).
  4. Kaur A, Leishangthem GD, Bhat P, et al. Role of Exosomes in Pathology – A Review. Journal of Pathology and Toxicology 2014; 1: 07-11
  5. Hosseini HM, Fooladi AAI, Nourani MR and Ghanezadeh F. The Role of Exosomes in Infectious Diseases. Inflammation & Allergy – Drug Targets 2013; 12:29-37.
  6. Ciregia F, Urbani A and Palmisano G. Extracellular Vesicles in Brain Tumors and Neurodegenerative Diseases. Front. Mol. Neurosci. 2017;10:276. doi: 10.3389/fnmol.2017.00276
  7. Zhang B, Yin Y, Lai RC, Lim SK. Immunotherapeutic potential of extracellular vesicles. Front Immunol (2014)
  8. Kowal J, Tkach M, Théry C. Biogenesis and secretion of exosomes. Current Opin in Cell Biol 2014 Aug; 29: 116-125. https://doi.org/10.1016/j.ceb.2014.05.004
  9. McKelvey KJ, Powell KL, Ashton AW, Morris JM and McCracken SA. Exosomes: Mechanisms of Uptake. J Circ Biomark, 2015; 4:7   DOI: 10.5772/61186
  10. Xiao T, Zhang W, Jiao B, Pan C-Z, Liu X and Shen L. The role of exosomes in the pathogenesis of Alzheimer’ disease. Translational Neurodegen 2017; 6:3. DOI 10.1186/s40035-017-0072-x
  11. Gonzales PA, Pisitkun T, Hoffert JD, et al. Large-Scale Proteomics and Phosphoproteomics of Urinary Exosomes. J Am Soc Nephrol 2009; 20: 363–379. doi: 10.1681/ASN.2008040406
  12. Waldenström A, Ronquist G. Role of Exosomes in Myocardial Remodeling. Circ Res. 2014; 114:315-324.
  13. Xin H, Li Y and Chopp M. Exosomes/miRNAs as mediating cell-based therapy of stroke. Front. Cell. Neurosci. 10 Nov, 2014; 8(377) doi: 10.3389/fncel.2014.00377
  14. Wang S, Zhang L, Wan S, Cansiz S, Cui C, et al. Aptasensor with Expanded Nucleotide Using DNA Nanotetrahedra for Electrochemical Detection of Cancerous Exosomes. ACS Nano, 2017; 11(4):3943–3949 DOI: 10.1021/acsnano.7b00373
  15. Gallart-Palau X, Serra A, Sze SK. (2016) Enrichment of extracellular vesicles from tissues of the central nervous system by PROSPR. Mol Neurodegener 11(1):41.
  16. Simpson RJ, Jensen SS, Lim JW. Proteomic profiling of exosomes: current perspectives. Proteomics. 2008 Oct; 8(19):4083-99. doi: 10.1002/pmic.200800109.
  17. Sandfeld-Paulsen R, Aggerholm-Pedersen N, Bæk R, Jakobs KR, et al. Exosomal proteins as prognostic biomarkers in non-small cell lung cancer. Mol Onc 2016 Dec; 10(10):1595-1602.
  18. Li W, Li C, Zhou T, et al. Role of exosomal proteins in cancer diagnosis. Molecular Cancer 2017; 16:145 DOI 10.1186/s12943-017-0706-8
  19. Zhang W, Xia W, Lv Z, Xin Y, Ni C, Yang L. Liquid Biopsy for Cancer: Circulating Tumor Cells, Circulating Free DNA or Exosomes? Cell Physiol Biochem 2017; 41:755-768. DOI: 10.1159/00045873
  20. Thakur BK ,…, Williams C, Rodriguez-Barrueco R, Silva JM, Zhang W, et al. Double-stranded DNA in exosomes: a novel biomarker in cancer detection. Cell Research 2014 June; 24(6):766-769. doi:10.1038/cr.2014.44.
  21. Malik ZA, Kott KS, Poe AJ, Kuo T, Chen L, Ferrara KW, Knowlton AA. Cardiac myocyte exosomes: stability, HSP60, and proteomics. Am J Physiol Heart Circ Physiol 304: H954–H965, 2013. doi:10.1152/ajpheart.00835.2012.
  22. De Toro J, Herschlik L, Waldner C and Mongini C. Emerging roles of exosomes in normal and pathological conditions: new insights for diagnosis and therapeutic applications. Front. Immunol. 2015; 6:203. doi: 10.3389/fimmu.2015.00203
  23. Chevilleta JR, Kanga Q, Rufa IK, Briggs HA, et al. Quantitative and stoichiometric analysis of the microRNA content of exosomes. PNAS 2014 Oct 14; 111(41): 14888–14893. pnas.org/cgi/doi/10.1073/pnas.1408301111
  24. Basu U, Meng F-L, Keim C, Grinstein V, Pefanis E, et al. The RNA Exosome Targets the AID Cytidine Deaminase to Both Strands of Transcribed Duplex DNA Substrates. Cell 2011; 144: 353–363, DOI 10.1016/j.cell.2011.01.001
  25. Pefanis E, Wang J, …, Rabadan R, Basu U. RNA Exosome-Regulated Long Non-Coding RNA Transcription Controls Super-Enhancer Activity. Cell 2015; 161: 774–789. http://dx.doi.org/10.1016/j.cell.2015.04.034
  26. Kilchert C,Wittmann S & Vasiljeva L. The regulation and functions of the nuclear RNA exosome complex. In RNA processing and modifications. Nature Reviews Molecular Cell Biology 17, 227–239 (2016) doi:10.1038/nrm.2015.15
  27. Guay C, Regazzi R. Exosomes as new players in metabolic organ cross-talk. Diabetes Obes Metab. 2017;19(Suppl. 1):137–146. DOI: 10.1111/dom.13027.
  28. Abramowicz A, Widlak P, Pietrowska M. Proteomic analysis of exosomal cargo: the challenge of high purity vesicle isolation. Molecular BioSystems MB-REV-02-2016-000082.R1
  29. Hopfner K-P, Hartung S. The RNA Exosomes. In Nucleic Acids and Molecular Biology. 2011. Ribonucleases pp 223-244. https://link.springer.com/chapter/10.1007/978-3-642-21078-5_9/fulltext.html
  30. Fuessel S, Lohse-Fischer A, Vu Van D, Salomo K, Erdmann K, Wirth MP. (2017) Quantification of MicroRNAs in Urine-Derived Specimens. In Urothelial Carcinoma, Methods Mol Biol 1655:201-226.
  31. Street JM, Barran PE, Mackay CL, Weidt S, et al. Identification and proteomic profiling of exosomes in human cerebrospinal fluid. Journal of Translational Medicine 2012; 10:5. http://www.translational-medicine.com/content/10/1/5
  32. Pisitkun T, Shen R-F, and Knepper MA. Identification and proteomic profiling of exosomes in human urine. PNAS 2004, Sept 7; 101(36): 13368–13373. http://www.pnas.org/cgi/doi/10.1073/pnas.0403453101
  33. Duijvesz D, Burnum-Johnson KE, Gritsenko MA, Hoogland AM, Vredenbregt-van den Berg MS, et al. Proteomic Profiling of Exosomes Leads to the Identification of Novel Biomarkers for Prostate Cancer. PLoS ONE 2013; 8(12): e82589. doi:10.1371/journal.pone.0082589
  34. Welton JL, Khanna S, Giles PJ, Brennan P, et al. Proteomics Analysis of Bladder Cancer Exosomes. Molecular & Cellular Proteomics 2010; 9:1324–1338. DOI 10.1074/mcp.M000063-MCP201
  35. Lee S, Suh G-Y, Ryter SW, and Choi AMK. Regulation and Function of the Nucleotide Binding Domain Leucine-Rich Repeat-Containing Receptor, PyrinDomain-Containing-3 Inflammasome in Lung Disease. Am J Respir Cell Mol Biol 2016 Feb; 54(2):151–160. DOI: 10.1165/rcmb.2015-0231TR.
  36. Zhang X, Yuan X, Shi H, Wu L, Qian H, Xu W. Exosomes in cancer: small particle, big player. J Hematol Oncol (2015)
  37. Zhao X, Wu Y, Duan J, Ma Y, Shen Z, et al. Quantitative Proteomic Analysis of Exosome Protein Content Changes Induced by Hepatitis B Virus in Huh-7 Cells Using SILAC Labeling and LC–MS/MS. J. Proteome Res.; 2014, 13 (12):5391–5402. DOI: 10.1021/pr5008703
  38. Liang B, Peng P, et al. Characterization and proteomic analysis of ovarian cancer-derived exosomes. J Proteomics. 2013 Mar; 80:171-182. https://doi.org/10.1016/j.jprot.2012.12.029
  39. Beckler MD, Higginbotham JN, Franklin JL,…, Li M, Liebler DC, Coffey RJ. Proteomic analysis of exosomes from mutant KRAS colon cancer cells identifies intercellular transfer of mutant KRAS. Mol. Cell Proteomics. 2013 Feb 12; (2). https://edrn.nci.nih.gov/publications/23161513-proteomic-analysis-of-exosomes
  40. Alvarez-Llamas G, Díaz J, Zubiri I. Proteome of Human Urinary Exosomes in Diabetic Nephropathy. In Biomarkers in Kidney Disease. Vinood B. Patel, Ed. Springer Science 2015; pp 1-21. DOI 10.1007/978-94-007-7743-9_22-1
  41. Simpson RJ, Jensen SS, Lim JW. Proteomic profiling of exosomes: current perspectives. Proteomics. 2008 Oct; 8(19):4083-99. doi: 10.1002/pmic.200800109.
  42. Scheya JKL, Luther M, Rose KL. Proteomics characterization of exosome cargo. Methods 2015 Oct; 87(1): 75-82. https://doi.org/10.1016/j.ymeth.2015.03.018
  43. Kim M-J, Yoon J-H & Ryu J-H. Mitophagy: a balance regulator of NLRP3 inflammasome Activation. BMB Rep. 2016; 49(10): 529-535. https://doi.org/10.5483/BMBRep.2016.49.10.115
  44. Eun-Kyeong Jo, Kim JK, Shin D-M and C Sasakawa. Molecular mechanisms regulating NLRP3 inflammasome activation. Cell Molec Immunol 2016; 13: 148–159. doi:10.1038/cmi.2015.95
  45. Leemans JC, Cassel SL, and Sutterwala FS. Sensing damage by the NLRP3 inflammasome. Immunol Rev. 2011 Sept; 243(1): 152–162. doi:10.1111/j.1600-065X.2011.01043.x.
  46. Hirota JA, Im H, Rahman MM, Rumzhum NN, Manetsch M, Pascoe CD, Bunge K, Alkhouri H, Oliver BG, Ammit AJ. The nucleotide-binding domain and leucine-rich repeat protein-3 inflammasome is not activated in airway smooth muscle upon toll-like receptor-2 ligation. Am J Respir Cell Mol Biol. 2013 Oct; 49(4):517-24. doi: 10.1165/rcmb.2013-0047OC.
  47. Zhong Z, Sanchez-Lopez E, Karin M. Autophagy, NLRP3 inflammasome and auto-inflammatory immune diseases. Clin Exp Rheumatol. 2016 Jul-Aug; 34(4 Suppl 98):12-6. Epub 2016 Jul 21.
  48. Hutton HL, Ooi JD, Holdsworth SR, Kitching AR. The NLRP3 inflammasome in kidney disease and autoimmunity. Nephrology (Carlton). 2016 Sep; 21(9):736-44. doi: 10.1111/nep.12785
  49. Xing Y, Cao R and Hu H-M. TLR and NLRP3 inflammasome-dependent innate immune responses to tumor-derived autophagosomes (DRibbles). Cell Death and Disease (2016) 7, e2322; doi:10.1038/cddis.2016.206
  50. Sahasrabudhe P, Rohrberg J, Biebl MM, Rutz DA, Buchner J. The Plasticity of the Hsp90 Co-chaperone System. Molecular Cell 2017 Sept; 67:947–961. http://dx.doi.org/10.1016/j.molcel.2017.08.004

 

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