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The Human Genome Gets Fully Sequenced: A Simplistic Take on Century Long Effort
Curator: Stephen J. Williams, PhD
Article ID #295: The Human Genome Gets Fully Sequenced: A Simplistic Take on Century Long Effort. Published on 6/14/2022
WordCloud Image Produced by Adam Tubman
Ever since the hard work by Rosalind Franklin to deduce structures of DNA and the coincidental work by Francis Crick and James Watson who modeled the basic building blocks of DNA, DNA has been considered as the basic unit of heredity and life, with the “Central Dogma” (DNA to RNA to Protein) at its core. These were the discoveries in the early twentieth century, and helped drive the transformational shift of biological experimentation, from protein isolation and characterization to cloning protein-encoding genes to characterizing how the genes are expressed temporally, spatially, and contextually.
Rosalind Franklin, who’s crystolagraphic data led to determination of DNA structure. Shown as 1953 Time cover as Time person of the Year
Dr Francis Crick and James Watson in front of their model structure of DNA
Up to this point (1970s-mid 80s) , it was felt that genetic information was rather static, and the goal was still to understand and characterize protein structure and function while an understanding of the underlying genetic information was more important for efforts like linkage analysis of genetic defects and tools for the rapidly developing field of molecular biology. But the development of the aforementioned molecular biology tools including DNA cloning, sequencing and synthesis, gave scientists the idea that a whole recording of the human genome might be possible and worth the effort.
How the Human Genome Project Expanded our View of Genes Genetic Material and Biological Processes
The Human Genome Project (HGP) refers to the international 13-year effort, formally begun in October 1990 and completed in 2003, to discover all the estimated 20,000-25,000 human genes and make them accessible for further biological study. Another project goal was to determine the complete sequence of the 3 billion DNA subunits (bases in the human genome). As part of the HGP, parallel studies were carried out on selected model organisms such as the bacterium E. coli and the mouse to help develop the technology and interpret human gene function. The DOE Human Genome Program and the NIH National Human Genome Research Institute (NHGRI) together sponsored the U.S. Human Genome Project.
Please see the following for goals, timelines, and funding for this project
Timeline: Major Events in the Human Genome Project
It is interesting to note that multiple government legislation is credited for the funding of such a massive project including
Project Enabling Legislation
The Atomic Energy Act of 1946 (P.L. 79-585) provided the initial charter for a comprehensive program of research and development related to the utilization of fissionable and radioactive materials for medical, biological, and health purposes.
The Atomic Energy Act of 1954 (P.L. 83-706) further authorized the AEC “to conduct research on the biologic effects of ionizing radiation.”
The Energy Reorganization Act of 1974 (P.L. 93-438) provided that responsibilities of the Energy Research and Development Administration (ERDA) shall include “engaging in and supporting environmental, biomedical, physical, and safety research related to the development of energy resources and utilization technologies.”
The Federal Non-nuclear Energy Research and Development Act of 1974 (P.L. 93-577) authorized ERDA to conduct a comprehensive non-nuclear energy research, development, and demonstration program to include the environmental and social consequences of the various technologies.
The DOE Organization Act of 1977 (P.L. 95-91) mandated the Department “to assure incorporation of national environmental protection goals in the formulation and implementation of energy programs; and to advance the goal of restoring, protecting, and enhancing environmental quality, and assuring public health and safety,” and to conduct “a comprehensive program of research and development on the environmental effects of energy technology and program.”
It should also be emphasized that the project was not JUST funded through NIH but also Department of Energy
For a great read on Dr. Craig Ventnor with interviews with the scientist see Dr. Larry Bernstein’s excellent post The Human Genome Project
By 2003 we had gained much information about the structure of DNA, genes, exons, introns and allowed us to gain more insights into the diversity of genetic material and the underlying protein coding genes as well as many of the gene-expression regulatory elements. However there was much uninvestigated material dispersed between genes, the then called “junk DNA” and, up to 2003 not much was known about the function of this ‘junk DNA’. In addition there were two other problems:
The reference DNA used was actually from one person (Craig Ventor who was the lead initiator of the project)
Multiple gaps in the DNA sequence existed, and needed to be filled in
It is important to note that a tremendous amount of diversity of protein has been realized from both transcriptomic and proteomic studies. Although about 20 to 25,000 coding genes exist the human proteome contains about 600,000 proteoforms (due to alternative splicing, posttranslational modifications etc.)
This expansion of the proteoform via alternate splicing into isoforms, gene duplication to paralogs has been shown to have major effects on, for example, cellular signaling pathways (1)
However just recently it has been reported that the FULL human genome has been sequenced and is complete and verified. This was the focus of a recent issue in the journal Science.
Since its initial release in 2000, the human reference genome has covered only the euchromatic fraction of the genome, leaving important heterochromatic regions unfinished. Addressing the remaining 8% of the genome, the Telomere-to-Telomere (T2T) Consortium presents a complete 3.055 billion–base pair sequence of a human genome, T2T-CHM13, that includes gapless assemblies for all chromosomes except Y, corrects errors in the prior references, and introduces nearly 200 million base pairs of sequence containing 1956 gene predictions, 99 of which are predicted to be protein coding. The completed regions include all centromeric satellite arrays, recent segmental duplications, and the short arms of all five acrocentric chromosomes, unlocking these complex regions of the genome to variational and functional studies.
The current human reference genome was released by the Genome Reference Consortium (GRC) in 2013 and most recently patched in 2019 (GRCh38.p13) (1). This reference traces its origin to the publicly funded Human Genome Project (2) and has been continually improved over the past two decades. Unlike the competing Celera effort (3) and most modern sequencing projects based on “shotgun” sequence assembly (4), the GRC assembly was constructed from sequenced bacterial artificial chromosomes (BACs) that were ordered and oriented along the human genome by means of radiation hybrid, genetic linkage, and fingerprint maps. However, limitations of BAC cloning led to an underrepresentation of repetitive sequences, and the opportunistic assembly of BACs derived from multiple individuals resulted in a mosaic of haplotypes. As a result, several GRC assembly gaps are unsolvable because of incompatible structural polymorphisms on their flanks, and many other repetitive and polymorphic regions were left unfinished or incorrectly assembled (5).
Fig. 1. Summary of the complete T2T-CHM13 human genome assembly. (A) Ideogram of T2T-CHM13v1.1 assembly features. For each chromosome (chr), the following information is provided from bottom to top: gaps and issues in GRCh38 fixed by CHM13 overlaid with the density of genes exclusive to CHM13 in red; segmental duplications (SDs) (42) and centromeric satellites (CenSat) (30); and CHM13 ancestry predictions (EUR, European; SAS, South Asian; EAS, East Asian; AMR, ad-mixed American). Bottom scale is measured in Mbp. (B and C) Additional (nonsyntenic) bases in the CHM13 assembly relative to GRCh38 per chromosome, with the acrocentrics highlighted in black (B) and by sequence type (C). (Note that the CenSat and SD annotations overlap.) RepMask, RepeatMasker. (D) Total nongap bases in UCSC reference genome releases dating back to September 2000 (hg4) and ending with T2T-CHM13 in 2021. Mt/Y/Ns, mitochondria, chrY, and gaps.
Note in Figure 1D the exponential growth in genetic information.
Also very important is the ability to determine all the paralogs, isoforms, areas of potential epigenetic regulation, gene duplications, and transposable elements that exist within the human genome.
Analyses and resources
A number of companion studies were carried out to characterize the complete sequence of a human genome, including comprehensive analyses of centromeric satellites (30), segmental duplications (42), transcriptional (49) and epigenetic profiles (29), mobile elements (49), and variant calls (25). Up to 99% of the complete CHM13 genome can be confidently mapped with long-read sequencing, opening these regions of the genome to functional and variational analysis (23) (fig. S38 and table S14). We have produced a rich collection of annotations and omics datasets for CHM13—including RNA sequencing (RNA-seq) (30), Iso-seq (21), precision run-on sequencing (PRO-seq) (49), cleavage under targets and release using nuclease (CUT&RUN) (30), and ONT methylation (29) experiments—and have made these datasets available via a centralized University of California, Santa Cruz (UCSC), Assembly Hub genome browser (54).
To highlight the utility of these genetic and epigenetic resources mapped to a complete human genome, we provide the example of a segmentally duplicated region of the chromosome 4q subtelomere that is associated with facioscapulohumeral muscular dystrophy (FSHD) (55). This region includes FSHD region gene 1 (FRG1), FSHD region gene 2 (FRG2), and an intervening D4Z4 macrosatellite repeat containing the double homeobox 4 (DUX4) gene that has been implicated in the etiology of FSHD (56). Numerous duplications of this region throughout the genome have complicated past genetic analyses of FSHD.
The T2T-CHM13 assembly reveals 23 paralogs of FRG1 spread across all acrocentric chromosomes as well as chromosomes 9 and 20 (Fig. 5A). This gene appears to have undergone recent amplification in the great apes (57), and approximate locations of FRG1 paralogs were previously identified by FISH (58). However, only nine FRG1 paralogs are found in GRCh38, hampering sequence-based analysis.
Future of the human reference genome
The T2T-CHM13 assembly adds five full chromosome arms and more additional sequence than any genome reference release in the past 20 years (Fig. 1D). This 8% of the genome has not been overlooked because of a lack of importance but rather because of technological limitations. High-accuracy long-read sequencing has finally removed this technological barrier, enabling comprehensive studies of genomic variation across the entire human genome, which we expect to drive future discovery in human genomic health and disease. Such studies will necessarily require a complete and accurate human reference genome.
CHM13 lacks a Y chromosome, and homozygous Y-bearing CHMs are nonviable, so a different sample type will be required to complete this last remaining chromosome. However, given its haploid nature, it should be possible to assemble the Y chromosome from a male sample using the same methods described here and supplement the T2T-CHM13 reference assembly with a Y chromosome as needed.
Extending beyond the human reference genome, large-scale resequencing projects have revealed genomic variation across human populations. Our reanalyses of the 1KGP (25) and SGDP (42) datasets have already shown the advantages of T2T-CHM13, even for short-read analyses. However, these studies give only a glimpse of the extensive structural variation that lies within the most repetitive regions of the genome assembled here. Long-read resequencing studies are now needed to comprehensively survey polymorphic variation and reveal any phenotypic associations within these regions.
Although CHM13 represents a complete human haplotype, it does not capture the full diversity of human genetic variation. To address this bias, the Human Pangenome Reference Consortium (59) has joined with the T2T Consortium to build a collection of high-quality reference haplotypes from a diverse set of samples. Ideally, all genomes could be assembled at the quality achieved here, but automated T2T assembly of diploid genomes presents a difficult challenge that will require continued development. Until this goal is realized, and any human genome can be completely sequenced without error, the T2T-CHM13 assembly represents a more complete, representative, and accurate reference than GRCh38.
This paper was the focus of a Time article and their basis for making the lead authors part of their Time 100 people of the year.
The first human genome was mapped in 2001 as part of the Human Genome Project, but researchers knew it was neither complete nor completely accurate. Now, scientists have produced the most completely sequenced human genome to date, filling in gaps and correcting mistakes in the previous version.
The sequence is the most complete reference genome for any mammal so far. The findings from six new papers describing the genome, which were published in Science, should lead to a deeper understanding of human evolution and potentially reveal new targets for addressing a host of diseases.
A more precise human genome
“The Human Genome Project relied on DNA obtained through blood draws; that was the technology at the time,” says Adam Phillippy, head of genome informatics at the National Institutes of Health’s National Human Genome Research Institute (NHGRI) and senior author of one of the new papers. “The techniques at the time introduced errors and gaps that have persisted all of these years. It’s nice now to fill in those gaps and correct those mistakes.”
“We always knew there were parts missing, but I don’t think any of us appreciated how extensive they were, or how interesting,” says Michael Schatz, professor of computer science and biology at Johns Hopkins University and another senior author of the same paper.
The work is the result of the Telomere to Telomere consortium, which is supported by NHGRI and involves genetic and computational biology experts from dozens of institutes around the world. The group focused on filling in the 8% of the human genome that remained a genetic black hole from the first draft sequence. Since then, geneticists have been trying to add those missing portions bit by bit. The latest group of studies identifies about an entire chromosome’s worth of new sequences, representing 200 million more base pairs (the letters making up the genome) and 1,956 new genes.
NOTE: In 2001 many scientists postulated there were as much as 100,000 coding human genes however now we understand there are about 20,000 to 25,000 human coding genes. This does not however take into account the multiple diversity obtained from alternate splicing, gene duplications, SNPs, and chromosomal rearrangements.
Scientists were also able to sequence the long stretches of DNA that contained repeated sequences, which genetic experts originally thought were similar to copying errors and dismissed as so-called “junk DNA”. These repeated sequences, however, may play roles in certain human diseases. “Just because a sequence is repetitive doesn’t mean it’s junk,” says Eichler. He points out that critical genes are embedded in these repeated regions—genes that contribute to machinery that creates proteins, genes that dictate how cells divide and split their DNA evenly into their two daughter cells, and human-specific genes that might distinguish the human species from our closest evolutionary relatives, the primates. In one of the papers, for example, researchers found that primates have different numbers of copies of these repeated regions than humans, and that they appear in different parts of the genome.
“These are some of the most important functions that are essential to live, and for making us human,” says Eichler. “Clearly, if you get rid of these genes, you don’t live. That’s not junk to me.”
Deciphering what these repeated sections mean, if anything, and how the sequences of previously unsequenced regions like the centromeres will translate to new therapies or better understanding of human disease, is just starting, says Deanna Church, a vice president at Inscripta, a genome engineering company who wrote a commentary accompanying the scientific articles. Having the full sequence of a human genome is different from decoding it; she notes that currently, of people with suspected genetic disorders whose genomes are sequenced, about half can be traced to specific changes in their DNA. That means much of what the human genome does still remains a mystery.
The investigators in the Telomere to Telomere Consortium made the Time 100 People of the Year.
Michael Schatz, Karen Miga, Evan Eichler, and Adam Phillippy
Illustration by Brian Lutz for Time (Source Photos: Will Kirk—Johns Hopkins University; Nick Gonzales—UC Santa Cruz; Patrick Kehoe; National Human Genome Research Institute)
BY JENNIFER DOUDNA
MAY 23, 2022 6:08 AM EDT
Ever since the draft of the human genome became available in 2001, there has been a nagging question about the genome’s “dark matter”—the parts of the map that were missed the first time through, and what they contained. Now, thanks to Adam Phillippy, Karen Miga, Evan Eichler, Michael Schatz, and the entire Telomere-to-Telomere Consortium (T2T) of scientists that they led, we can see the full map of the human genomic landscape—and there’s much to explore.
In the scientific community, there wasn’t a consensus that mapping these missing parts was necessary. Some in the field felt there was already plenty to do using the data in hand. In addition, overcoming the technical challenges to getting the missing information wasn’t possible until recently. But the more we learn about the genome, the more we understand that every piece of the puzzle is meaningful.
I admire the
T2T group’s willingness to grapple with the technical demands of this project and their persistence in expanding the genome map into uncharted territory. The complete human genome sequence is an invaluable resource that may provide new insights into the origin of diseases and how we can treat them. It also offers the most complete look yet at the genetic script underlying the very nature of who we are as human beings.
Doudna is a biochemist and winner of the 2020 Nobel Prize in Chemistry
P. Scalia, A. Giordano, C. Martini, S. J. Williams, Isoform- and Paralog-Switching in IR-Signaling: When Diabetes Opens the Gates to Cancer. Biomolecules10, (Nov 30, 2020).
Reporter: Danielle Smolyar, Research Assistant 3 – Text Analysis for 2.0 LPBI Group’s TNS #1 – 2020/2021 Academic Internship in Medical Test Analysis (MTA)
Recently, researchers have found many ways to manipulate and alter gene activity in specific cells. As a result of seeing this alteration, it has caused much development and progress in understanding cancer, brain function, and immunity.
IMAGE SOURCE: 3D-model of DNA. Credit: Michael Ströck/Wikimedia/ GNU Free Documentation Lic
Tissues and Organs are composed of cells that look the same but have different roles. For example, single-cell analysis allows us to research and test the cells within an organ or cancerous tumor. However, the single-cell study has its boundaries and limits in trying a more significant number of cells. This result is not an accurate data and analysis of the cells.
Mulqueen, R. M., Pokholok, D., O’Connell, B. L., Thornton, C. A., Zhang, F., O’Roak, B. J., Link, J., Yardımcı, G. G., Sears, R. C., Steemers, F. J., & Adey, A. C. (2021, July 5). High-content single-cell combinatorial indexing. Nature News. https://www.nature.com/articles/s41587-021-00962-z
states that the new method gives us the ability to have a ten-fold improvement in the amount of DNA produced from a single DNA sequence. A DNA sequence is composed of units which are called bases. The sequence puts the bases in chronological order for it to code correctly.
To understand cancer better, single-cell studies are a crucial factor in doing so. Different cells catch on to other mutations in the DNA sequence in a cancerous tumor, which ultimately alters the DNA sequence. This results in tumor cells with new alterations, which could eventually spread to the rest of the body.
Adey and his team provided evidence that the method they had created can show DNA alterations that have come from cells present in tumor samples from patients with pancreatic cancer. Adey stated,
quote “For example, you can potentially identify rare cell subtypes within a tumor that are resistant to therapy.”
Abey and his team have been working with OHSU Knight Cancer Institute, and with them, they are testing a single-cell method to see if patients’ tumors have changed by doing chemo or drug therapy.
This new method allows itself to create DNA libraries and fragments of DNA that helps analyze the different genes and mutations within the sequence. This method uses something called an enzymatic reaction that attaches primers to the end of each DNA fragment. For the cells to be analyzed, each primer must be present on both ends of the fragment.
As a result of this new method, all library fragments present must-have primers on both ends of the fragments. At the same time, it improves efficiency by reducing its sequencing price overall, that these adapters can be used instead of the regular custom workflows.
4.1.2 The race to map the human body — one cell at a time, A host of detailed cell atlases could revolutionize understanding of cancer and other diseases
4.1.3 Single-cell Genomics: Directions in Computational and Systems Biology – Contributions of Prof. Aviv Regev @Broad Institute of MIT and Harvard, Cochair, the Human Cell Atlas Organizing Committee with Sarah Teichmann of the Wellcome Trust Sanger Institute
4.1.7 Norwich Single-Cell Symposium 2019, Earlham Institute, single-cell genomics technologies and their application in microbial, plant, animal and human health and disease, October 16-17, 2019, 10AM-5PM
4.2.1 How to build a human cell atlas – Aviv Regev is a maven of hard-core biological analyses. Now she is part of an effort to map every cell in the human body.
4.2.2 Featuring Computational and Systems Biology Program at Memorial Sloan Kettering Cancer Center, Sloan Kettering Institute (SKI), The Dana Pe’er Lab
4.3.2 eProceedings 2019 Koch Institute Symposium – 18th Annual Cancer Research Symposium – Machine Learning and Cancer, June 14, 2019, 8:00 AM-5:00 PM ET MIT Kresge Auditorium, 48 Massachusetts Ave, Cambridge, MA
4.4.1 iBioChips integrate diagnostic assays and cellular engineering into miniaturized chips that achieve cutting-edge sensitivity and high-throughput. We have resolved traditional biotech challenges with innovative biochip approaches
4.4.2 Targeted Single-Cell Solutions for High Impact Applications – Mission Bio’s Tapestri® Platform is the only technology that provides single-cell targeted DNA sequencing at single-base resolution.
Agilent was created as a spin off from Hewlett-Packard Company in 1999.
Agilent Technologies Inc. is engaged in the life sciences, diagnostics and applied chemical markets. The Company provides application focused solutions that include instruments, software, services and consumables for the entire laboratory workflow. The Company has three business segments:
the life sciences and applied markets business,
the diagnostics and genomics business, and
the Agilent Cross Lab business
The Company’s life sciences and applied markets business segment brings together the Company’s analytical laboratory instrumentation and informatics.
The Company’s diagnostics and genomics business segment consists of three businesses: the Dako business, the genomics business and the nucleic acid solutions business.
The Company’s Agilent Cross Lab business segment combines its analytical laboratory services and consumables business
CARPINTERIA, Calif.–(BUSINESS WIRE)–Dako, an Agilent Technologies company and a worldwide provider of cancer diagnostics, today announced the U.S. Food and Drug Administration has approved a new test that can identify PD-L1 expression levels on the surface of non-small cell lung cancer tumor cells and provide information on the survival benefit with OPDIVO® (nivolumab) for patients with non-squamous NSCLC.
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Gen9 is building on advances in synthetic biology to power a scalable fabrication capability that will significantly increase the world’s capacity to produce DNA content. The privately held company’s next-generation gene synthesis technology allows for the high-throughput, automated production of DNA constructs at lower cost and higher accuracy than previous methods on the market. Founded by world leaders in synthetic biology, Gen9 aims to ensure the constructive application of synthetic biology in industries ranging from enzyme and chemical production to pharmaceuticals and biofuels.
SERVICES
Synthetic Biology
Gene Synthesis Services
Variant Libraries
Gene Sequence Design Services
INVESTORS
Agilent Technologies : Private Equity
CAMBRIDGE, Mass. and SANTA CLARA, Calif. — April 24, 2013 —Gen9 Receives $21 Million Strategic Investment from Agilent Technologies
GenScript is the largest gene synthesis provider in the USA
GenScript Corporation, a biology contract research organization, provides biological research and drug discovery services to pharmaceutical companies, biotech firms, and research institutions in the United States, Europe, and Japan. It offers bio-reagent, custom molecular biology, custom peptide, protein production, custom antibody production, drug candidates testing, assay development and screening, lead optimization, antibody drug development, gene synthesis, and assay-ready cell line production services.
The company also offers molecular biology, peptide, protein, immunoassay, chemicals, and cell biology products. It offers its products through distributors in Tokyo, Japan; and Seoul, Korea. GenScript Corporation has a strategic partnership with Immunologix, Inc. The company was founded in 2002 and is based in Piscataway, New Jersey. It has subsidiaries in France, Japan, and China.
Note: As of October 24, 2011, Immunologix, Inc. was acquired by Intrexon Corporation. Immunologix, Inc. develops and produces antibody-based therapeutics for various biological targets. It produces human monoclonal antibodies against viral, bacterial, and tumor antigens, as well as human auto antigens.
Intrexon Corporation, founded in 1998, is a leader in synthetic biology focused on collaborating with companies in Health, Food, Energy, Environment and Consumer sectors to create biologically based products that improve quality of life and the health of the planet.
PRODUCTS AND SERVICES
Gene synthesis
Antibody services
Protein Services
Peptide services
INVESTORS
Note: The Balloch Group (‘TBG’) was established in 2001 by Howard Balloch (Canada‘s ambassador to China from 1996 to 2001). TBG has since grown from a market-entry consultancy working with North American clients in China to a leading advisory and merchant banking firm serving both domestic Chinese companies and multinational corporations. TBG was ranked as the number one boutique investment bank in China by ChinaVenture in 2008.
Monica Heger : SAN FRANCISCO (GenomeWeb) – Illumina today announced two new next-generation sequencing platforms, a targeted sequencing system called MiniSeq and a semiconductor sequencer that is still under development.
Illumina disclosed the initiatives during a presentation at the JP Morgan Healthcare conference held here today. During the presentation, Illumina CEO Jay Flatley also announced a new genotyping array called Infinium XT; a partnership with Bio-Rad to develop a single-cell sequencing workflow; preliminary estimates of its fourth-quarter 2015 revenues; and an update on existing products. The presentation followed the company’s announcement on Sunday that it has launched a new company called Grail to develop a next-generation sequencing test for early cancer detection from patient blood samples.
The MiniSeq system, which is based on Illumina’s current sequencing technology, will begin shipping early this quarter and has a list price of $49,500. It can perform a variety of targeted DNA and RNA applications, from single-gene to pathway sequencing, and promises “all-in” prices, including library prep and sequencing, of $200 to $300 per sample, Flatley said during the JP Morgan presentation.
Integrated DNA Technologies, Inc. (IDT), the global leader in nucleic acid synthesis, serving all areas of life sciences research and development, offers products for a broad range of genomics applications. IDT’s primary business is the production of custom, synthetic nucleic acids for molecular biology applications, including qPCR, sequencing, synthetic biology, and functional genomics. The company manufactures and ships an average of 44,000 custom nucleic acids per day to more than 82,000 customers worldwide. For more information, visit idtdna.com.
Dyes GMP for Molecular Diagnostics Large Scale Oligo Synthesis
Note : Skokie, IL – December 1, 2015. Integrated DNA Technologies Inc. (“IDT”), the global leader in custom nucleic acid synthesis, has entered into a definitive agreement to acquire the oligonucleotide synthesis business of AITbiotech Pte. Ltd. in Singapore (“AITbiotech”). With this acquisition, IDT expands its customer base across Southeast Asia making it possible for these additional customers to now have access to its broad range of products for genomic applications. AITbiotech will continue operations in its other core business areas.
With over 20 years of experience in oligonucleotide development and production, and over 1000 sequences manufactured, Avecia has played an integral role in the advancing oligo therapeutic market. Our mission is to continue to build value for our customers, as they progress through drug development into commercialization. And as a member of the Nitto Denko Corporation (nitto.com), Avecia is committed to the future of the oligonucleotide market. We are driven by innovative ideas and flexible solutions, designed to provide our customers with the best in service, quality, and technology.
OriGene Technologies, Inc. develops, manufactures, and sells genome wide research and diagnostic products for pharmaceutical, biotechnology, and academic research applications. The company offers cDNA clones, including TrueORF cDNA, viral ORF, destination vectors, TrueClones (human), TrueClones (mouse), organelle marker plasmids, MicroRNA tools, mutant and variant clones, plasmid purification kits, transfection reagents, and gene synthesis service; and HuSH shRNA, siRNA, miRNA, qPCR reagents, plasmid purification products, transfection reagents, PolyA+ and total RNA products, first-strand cDNA synthesis, and CRISPR/Cas9 genome products. It also provides proteins and lysates, such as purified human proteins, over-expression cell lysates, mass spectrometry standard proteins, and protein purification reagents; UltraMAB IHC antibodies, TrueMAB primary antibodies, anti-tag and fluorescent proteins, ELISA antibodies, luminex antibodies, secondary antibodies, and controls and others; and anatomic pathology products, including IHC antibodies, detection systems, and IHC accessories
The company offers luminex and ELISA antibody pairs, autoantibody profiling arrays, ELISA kits, cell assay kits, assay reagents, custom development, and fluorogenic cell assays; TissueFocus search tools; tissue sections; tissue microarrays, cancer protein lysate arrays, TissueScan cDNA arrays, tissue blocks, and quality control products, as well as tissue RNA, DNA, and protein lysates; and lab essentials. Its research areas include cancer biomarker research, RNAi, pathology IHC, stem cell research, ion channels, and protein kinase products. The company provides gene synthesis and molecular biology services, genome editing, custom cloning, custom shRNA, purified protein, monoclonal antibody development, and assay development. It sells its products through distributors worldwide, as well as online. OriGene Technologies, Inc. was incorporated in 1995 and is based in Rockville, Maryland.
Louis, MO – November 18, 2015 Merck KGaA, Darmstadt, Germany, Completes Sigma-Aldrich Acquisition
Merck KGaA today announced the completion of its $17 billion acquisition of Sigma-Aldrich, creating one of the leaders in the $130 billion global industry to help solve the toughest problems in life science.
Press Release: 18-Nov-2015
Letter to our Life Science Customers from Dr. Udit Batra
The life science business of Merck KGaA, Darmstadt, Germany brings together the world-class products and services, innovative capabilities and exceptional talent of EMD Millipore and Sigma-Aldrich to create a global leader in the life science industry.
“Everything we do starts with our shared purpose – to solve the toughest problems in life science by collaborating with the global scientific community.
This combination is built on complementary strengths, which will enable us to serve you even better as one organization than either company could alone.
This means providing a broader portfolio with a catalog of more than 300,000 products, including many of the most respected brands in the industry, greater geographic reach, and an unmatched combination of industry-leading capabilities.”
Thermo Fisher Scientific Inc. is a provider of analytical instruments, equipment, reagents and consumables, software and services for research, manufacturing, analysis, discovery and diagnostics. The company operates through four segments: Life Sciences Solutions, provides reagents, instruments and consumables used in biological and medical research, discovery and production of new drugs and vaccines as well as diagnosis of disease; Analytical Instruments, provides instruments, consumables, software and services that are used in the laboratory; Specialty Diagnostics, offers diagnostic test kits, reagents, culture media, instruments and associated products, and Laboratory Products and Services, offers self-manufactured and sourced products for the laboratory.
WALTHAM, Mass. & SANTA CLARA, Calif.–(BUSINESS WIRE)–Jan. 8, 2016– Thermo Fisher Scientific Inc. (NYSE:TMO), the world leader in serving science, and Affymetrix Inc. (NASDAQ:AFFX), a leading provider of cellular and genetic analysis products, today announced that their boards of directors have unanimously approved Thermo Fisher’s acquisition of Affymetrix for $14.00 per share in cash. The transaction represents a purchase price of approximately $1.3 billion.
MinION could help achieve NIH’s goal of $1,000 human genome sequencing and in remote clinics and outbreak zones shift testing away from medical laboratories
Point-of-care DNA sequencing technology is edging ever closer to widespread commercial use as the Oxford Nanopore MinION sequencer draws praise and registers successes in pre-release testing.
A pocketsize gene-sequencing machine such as the MinION could transform the marketplace by shifting DNA testing to remote clinics and outbreak zones while eliminating the need to return samples to clinical laboratories for analysis. Such devices also are expected to increase the need for trained genetic pathologists andmedical technologists.
After Much Anticipation, MinION Delivers on Promises
The MinION, produced by United Kingdom-based Oxford Nanopore Technologies, is a miniaturized instrument about the size of a USB memory stick that plugs directly into a PC or laptop computer’s USB port. Unlike bench-top sequencers, the MinION uses nanopore “strand sequencing” technology to deliver ultra-long-read-length single-molecule sequence data.
“The USB-powered sequencer contains thousands of wells, each containing nanopores—narrow protein channels that are only wide enough for a single strand of DNA. When DNA enters the channels, each base gives off a unique electronic signature that can be detected by the system, providing a readout of the DNA sequence,” reported
After several years of unfulfilled promises, Oxford began delivering the MinION in the spring of 2014 to researchers participating in its early access program called MAP . For a $1,000 access fee, participants receive a starter kit and may purchase consumable supplies. The current price for additional flow cells ranges from $900 for one to $500 per piece when purchased in 48-unit quantities.
Nick Loman, an Independent Research Fellow in the Institute for Microbiology and Infection at the University of Birmingham, UK, had questioned if MinION’s promise would ever be realized. But the USB-size sequencer won him over after he used it to detect Salmonella within 15 minutes in samples sent from a local hospital.
Loman received the MinION in May 2014 as part of the MAP program and quickly tested its usefulness. After using the device to sequence a strain of Pseudomonas aeruginosa, a common hospital-acquired infection (HAI), he next helped solve the riddle of an outbreak of Salmonella infection in a Birmingham hospital that had affected 30 patients and staff.
“The hospital wanted to understand quickly what was happening,” Loman stated. “But routine genome sequencing is quite slow. It usually takes weeks or even months to get information back.”
Using MinION, Loman detected Salmonella in some of the samples sent from the hospital in less than 15 minutes. Ultimately, the main source of the outbreak was traced to a German egg supplier.
“The MinION just blew me away,” Loman stated in Wired. “The idea that you could do sequencing on a sort of USB stick that you can chuck around does stretch credulity.”
Portable Sequencing Opens Up Intriguing Possibilities for Pathologists
In May 2015, Oxford released a second version of the device, the MinION MkI. According to the company website, the updated MinION is a “full production device featuring improvements of performance and ease of use,” such as improved temperature control and updated mechanism to engage the device with the consumable flow cells.
“The bench-top sequencers opened up the market to a certain degree,” Loman says. “You started seeing [them] in intensive research groups and in the clinic. But what if anyone could have this hanging off their key ring and go do sequencing? That’s an insane idea, and we don’t really know what it’s going to mean in terms of the potential applications. We’re very much at the start of thinking about what we might be able to do, if anyone can just sequence anything, anywhere they are.”
Joshua Quick, a PhD candidate at the University of Birmingham, UK believes Oxford Nanopore Technologies’ portable and inexpensive device will change the gene sequencing landscape.
Accuracy One Trade-off for Portability
Beta-testers have shown that the miniature device can read out relatively long stretches of genetic sequence with increasing accuracy, but according to the report in the journal Nature , the MinION MkI will need to correct several shortcomings found in the original sequencer:
• It is not practical to sequence large genomes with the device, with some experts estimating it would take a year for the original version to sequence the equivalent of a human genome.
• The machine has a high error rate compared with those of existing full-sized sequencers, misidentifying DNA sequence 5%–30% of the time.
• It also has difficulties reading sections of genome that contain long stretches of a single DNA base.
Yet researchers who have used the device remain enthusiastic about the future of this fourth-generation sequencing technique, which may have the potential to achieve the $1,000-per-human-genome goal set by the National Institutes of Health (NIH).
“This is the democratization of sequencing,” Joshua Quick, a PhD candidate at the University of Birmingham, told Nature. “You don’t have to rely on expensive infrastructure and costly equipment.”
News accounts did not provide information about Oxford Nanopore’s plans to obtain an EU mark for its MinION device. That will be the next step to demonstrating that the device is ready for widespread clinical use. At the same time, clinical laboratory managers and pathologist should take note of the capabilities of the MinION MkI as described above. Researchers are already finding it useful to identify infectious diseases in clinical setting where other diagnostic methods have not yet identified the agent causing the infection.
Why Does Cytotoxic Chemotherapy Still Remain a Mainstay in Many Chemotherapeutic Regimens? [6.1.1]
Reporter: Stephen J. Williams, Ph.D.
At the 2015 AACR National Meeting, Drs. Anthony Letai, Dr. Michael Hermann, Dr. Rene Bernards, and Dr. Guido Kroemer gave The 2015 Stanley J. Korsmeyer Memorial Symposium: Cell Death and Cancer Therapy: Why Has Conventional Chemotherapy Been So Successful?
Cytotoxic chemotherapy, for which the mechanism of action is centered on the ability of the drug to kill a cell by either necrosis, genotoxic, apoptosis, or autophagy mechanisms rather than just halting cell growth, is still, in this era of personalized and cytostatic therapies, is still a mainstay in many treatment regimens for a majority of cancers. Treatment regimens such as MOPP (mechlorethamine, Oncovin, procarbazine, prednisone), CMF (cyclophosphamide, methotrexate, 5-fluorouracil) , carboplatin with taxol, and even with personalized therapies, which usually are given in combination with a cytotoxic agent. However treatment regimens containing these cytotoxic chemotherapeutics show some of the best survival rates. The abstract for the Symposium is given below:
In this current era of precisely targeted therapies and –omics technologies, it is often forgotten that no medical therapy has cured, and continues to cure, more people of cancer than conventional chemotherapy. Notwithstanding its superior performance across many cancer types, the mechanism of the therapeutic index of conventional agents, largely targeting ubiquitous elements like DNA and microtubules, is poorly understood. The textbook explanation of conventional chemotherapy’s working by killing supposedly rapidly dividing cancer cells lacks clinical evidence and flies in the face of many obvious clinical counter-examples. In the session,m the speakers will describe how conventional cytotoxic chemotherapy preferentially kills cancer cells. Moreover, they will describe how clinical response to chemotherapy might be better predicted.
This post is presented as the speakers titles and a brief curation of their papers related to the subject matter.
Anthony G. Letai, Dana-Farber Cancer Institute, Boston, MA. Conventional chemotherapy cures people by exploiting apoptotic priming.
Conventional chemotherapy has an amazing track record that is often under-appreciated in today’s world of genomics and targeted pathway inhibitors. Conventional chemotherapy is responsible for curing millions of cancer patients over the past 5 decades. That is, millions of patients have presented to their doctors with an otherwise fatal malignancy, were given a finite course of chemotherapy (largely DNA and microtubule perturbing agents) and had their cancer eradicated, never to return. Perhaps as remarkable as the magnitude of the achievement of conventional chemotherapy is the magnitude of our ignorance of why it should ever work, and why it works far better in some tumors than in others. Textbook explanations rely on concepts of differential proliferation rates in cancers that are incompletely supported in the clinical literature. Successful chemotherapy treatments usually kill via the mitochondrial pathway of apoptosis. We have found that simple functional measurements of the pre-treatment state of the tumor cell can be rapidly made with BH3 profiling. These measurements demonstrate that a major, if not the major, reason for a therapeutic index for cancer chemotherapy is that chemo-sensitive cancer cells are simply more primed for apoptosis than normal cells. Moreover, apoptotic priming can be measured to make clinical predictions regarding quality of response on an individualized basis. Enhancing pretreatment priming of cancer cells with selectively acting targeted agents is a promising strategy to extend the demonstrated curative power of conventional chemotherapy.
Triona Ni Chonghaile, Justine E. Roderick, Cian Glenfield, Jeremy Ryan, Stephen E. Sallan, Lewis B. Silverman, Mignon L. Loh, Stephen P. Hunger, Brent Wood, Daniel J. DeAngelo, Richard Stone, Marian Harris, Alejandro Gutierrez, Michelle A. Kelliher, Anthony Letai
Cancer Discov. Author manuscript; available in PMC 2015 March 1.
Published in final edited form as: Cancer Discov. 2014 September; 4(9): 1074–1087. Published online 2014 July 3. doi: 10.1158/2159-8290.CD-14-0353
Sidong Huang, Michael Hölzel, Theo Knijnenburg, Andreas Schlicker, Paul Roepman, Ultan McDermott, Mathew Garnett, Wipawadee Grernrum, Chong Sun, Anirudh Prahallad, Floris H. Groenendijk, Lorenza Mittempergher, Wouter Nijkamp, Jacques Neefjes, Ramon Salazar, Peter ten Dijke, Hidetaka Uramoto, Fumihiro Tanaka, Roderick L. Beijersbergen, Lodewyk F.A. Wessels, René Bernards
Cell. Author manuscript; available in PMC 2013 June 5.
Published in final edited form as: Cell. 2012 November 21; 151(5): 937–950.
Floris H Groenendijk, Wouter W Mellema, Eline van der Burg, Eva Schut, Michael Hauptmann, Hugo M Horlings, Stefan M Willems, Michel M van den Heuvel, Jos Jonkers, Egbert F Smit, René Bernards
Int J Cancer. 2015 March 15; 136(6): 1434–1444. Published online 2014 August 1.
Prashanth Kumar Bajpe, Guus J. J. E. Heynen, Lorenza Mittempergher, Wipawadee Grernrum, Iris A. de Rink, Wouter Nijkamp, Roderick L. Beijersbergen, Rene Bernards, Sidong Huang
Andreas I Papadakis, Chong Sun, Theo A Knijnenburg, Yibo Xue, Wipawadee Grernrum, Michael Hölzel, Wouter Nijkamp, Lodewyk FA Wessels, Roderick L Beijersbergen, Rene Bernards, Sidong Huang
Cell Res. 2015 April; 25(4): 445–458. Published online 2015 February 6.
Katherine Stemke-Hale, Ana Maria Gonzalez-Angulo, Ana Lluch, Richard M. Neve, Wen-Lin Kuo, Michael Davies, Mark Carey, Zhi Hu, Yinghui Guan, Aysegul Sahin, W. Fraser Symmans, Lajos Pusztai, Laura K. Nolden, Hugo Horlings, Katrien Berns, Mien-Chie Hung, Marc J. van de Vijver, Vicente Valero, Joe W. Gray, René Bernards, Gordon B. Mills, Bryan T. Hennessy
Cancer Res. Author manuscript; available in PMC 2009 August 1.
Published in final edited form as: Cancer Res. 2008 August 1; 68(15): 6084–6091.
Brigitte C. Widemann, Maria T. Acosta, Sylvia Ammoun, Allan J. Belzberg, Andre Bernards, Jaishri Blakeley, Antony Bretscher, Karen Cichowski, D. Wade Clapp, Eva Dombi, Gareth D. Evans, Rosalie Ferner, Cristina Fernandez-Valle, Michael J. Fisher, Marco Giovannini, David H. Gutmann, C. Oliver Hanemann, Robert Hennigan, Susan Huson, David Ingram, Joe Kissil, Bruce R. Korf, Eric Legius, Roger J. Packer, Andrea I McClatchey, Frank McCormick, Kathryn North, Minja Pehrsson, Scott R. Plotkin, Vijaya Ramesh, Nancy Ratner, Susann Schirmer, Larry Sherman, Elizabeth Schorry, David Stevenson, Douglas R. Stewart, Nicole Ullrich, Annette C. Bakker, Helen Morrison
Am J Med Genet A. Author manuscript; available in PMC 2014 September 1.
Published in final edited form as: Am J Med Genet A. 2014 March; 0(3): 563–578. Published
Ben S. Wittner, Dennis C. Sgroi, Paula D. Ryan, Tako J. Bruinsma, Annuska M. Glas, Anitha Male, Sonika Dahiya, Karleen Habin, Rene Bernards, Daniel A. Haber, Laura J. Van’t Veer, Sridhar Ramaswamy Clin Cancer Res. Author manuscript; available in PMC 2011 May 7.
Guido Kroemer, INSERM U848- Institute Gustave-Roussy, Villejuif, France. A hallmark of successful cancer therapies: Reinstatement of immunosurvelliance.
Cancer Biology and Genomics for Disease Diagnosis (Vol. I) Now Available for Amazon Kindle
Reporter: Stephen J Williams, PhD
Article ID #179: Cancer Biology and Genomics for Disease Diagnosis (Vol. I) Now Available for Amazon Kindle. Published on 8/14/2015
WordCloud Image Produced by Adam Tubman
Leaders in Pharmaceutical Business Intelligence would like to announce the First volume of their BioMedical E-Book Series C: e-Books on Cancer & Oncology
This e-Book is a comprehensive review of recent Original Research on Cancer & Genomics including related opportunities for Targeted Therapy written by Experts, Authors, Writers. This ebook highlights some of the recent trends and discoveries in cancer research and cancer treatment, with particular attention how new technological and informatics advancements have ushered in paradigm shifts in how we think about, diagnose, and treat cancer. The results of Original Research are gaining value added for the e-Reader by the Methodology of Curation.The e-Book’s articles have been published on the Open Access Online Scientific Journal, since April 2012. All new articles on this subject, will continue to be incorporated, as published with periodical updates.
We invite e-Readers to write an Article Reviews on Amazon for this e-Book on Amazon. All forthcoming BioMed e-Book Titles can be viewed at:
Leaders in Pharmaceutical Business Intelligence, launched in April 2012 an Open Access Online Scientific Journal is a scientific, medical and business multi expert authoring environment in several domains of life sciences, pharmaceutical, healthcare & medicine industries. The venture operates as an online scientific intellectual exchange at their website http://pharmaceuticalintelligence.com and for curation and reporting on frontiers in biomedical, biological sciences, healthcare economics, pharmacology, pharmaceuticals & medicine. In addition the venture publishes a Medical E-book Series available on Amazon’s Kindle platform.
Analyzing and sharing the vast and rapidly expanding volume of scientific knowledge has never been so crucial to innovation in the medical field. WE are addressing need of overcoming this scientific information overload by:
delivering curation and summary interpretations of latest findings and innovations
on an open-access, Web 2.0 platform with future goals of providing primarily concept-driven search in the near future
providing a social platform for scientists and clinicians to enter into discussion using social media
compiling recent discoveries and issues in yearly-updated Medical E-book Series on Amazon’s mobile Kindle platform
This curation offers better organization and visibility to the critical information useful for the next innovations in academic, clinical, and industrial research by providing these hybrid networks.
Table of Contents for Cancer Biology and Genomics for Disease Diagnosis
Preface
Introduction The evolution of cancer therapy and cancer research: How we got here?
Part I. Historical Perspective of Cancer Demographics, Etiology, and Progress in Research
Chapter 1: The Occurrence of Cancer in World Populations
Chapter 2. Rapid Scientific Advances Changes Our View on How Cancer Forms
Chapter 3: A Genetic Basis and Genetic Complexity of Cancer Emerge
Chapter 4: How Epigenetic and Metabolic Factors Affect Tumor Growth
Chapter 5: Advances in Breast and Gastrointestinal Cancer Research Supports Hope for Cure
Part II. Advent of Translational Medicine, “omics”, and Personalized Medicine Ushers in New Paradigms in Cancer Treatment and Advances in Drug Development
Chapter 6: Treatment Strategies
Chapter 7: Personalized Medicine and Targeted Therapy
Part III.Translational Medicine, Genomics, and New Technologies Converge to Improve Early Detection
Chapter 8: Diagnosis
Chapter 9: Detection
Chapter 10: Biomarkers
Chapter 11: Imaging In Cancer
Chapter 12: Nanotechnology Imparts New Advances in Cancer Treatment, Detection, & Imaging
Epilogue by Larry H. Bernstein, MD, FACP: Envisioning New Insights in Cancer Translational Biology
It is estimated that the medical imaging market will exceed $30 billion in 2014 (FierceMedicalImaging). To put this amount in perspective; the global pharmaceutical market size for the same year is expected to be ~$1 trillion (IMS) while the global health care spending as a percentage of Gross Domestic Product (GDP) will average 10.5% globally in 2014 (Deloitte); it will reach ~$3 trillion in the USA.
Recent technology-advances, mainly miniaturization and improvement in electronic-processing components is driving increased introduction of innovative medical-imaging devices into critical nodes of major-diseases’ management pathways. Consequently, in contrast to it’s very small contribution to global health costs, medical imaging bears outstanding potential to reduce the future growth in spending on major segments in this market mainly: Drugs development and regulation (e.g. companion diagnostics and imaging surrogate markers); Disease management (e.g. non-invasive diagnosis, guided treatment and non-invasive follow-ups); and Monitoring aging-population (e.g. Imaging-based domestic sensors).
In; The Role of Medical Imaging in Personalized Medicine I discussed in length the role medical imaging assumes in drugs development. Integrating imaging into drug development processes, specifically at the early stages of drug discovery, as well as for monitoring drug delivery and the response of targeted processes to the therapy is a growing trend. A nice (and short) review highlighting the processes, opportunities, and challenges of medical imaging in new drug development is: Medical imaging in new drug clinical development.
The following is dedicated to the role of imaging in guiding treatment.
Precise treatment is a major pillar of modern medicine. An important aspect to enable accurate administration of treatment is complementing the accurate identification of the organ location that needs to be treated with a system and methods that ensure application of treatment only, or mainly to, that location. Imaging is off-course, a major component in such composite systems. Amongst the available solution, functional-imaging modalities are gaining traction. Specifically, molecular imaging (e.g. PET, MRS) allows the visual representation, characterization, and quantification of biological processes at the cellular and subcellular levels within intact living organisms. In oncology, it can be used to depict the abnormal molecules as well as the aberrant interactions of altered molecules on which cancers depend. Being able to detect such fundamental finger-prints of cancer is key to improved matching between drugs-based treatment and disease. Moreover, imaging-based quantified monitoring of changes in tumor metabolism and its microenvironment could provide real-time non-invasive tool to predict the evolution and progression of primary tumors, as well as the development of tumor metastases.
A recent review-paper: Image-guided interventional therapy for cancer with radiotherapeutic nanoparticles nicely illustrates the role of imaging in treatment guidance through a comprehensive discussion of; Image-guided radiotherapeutic using intravenous nanoparticles for the delivery of localized radiation to solid cancer tumors.
Abstract
One of the major limitations of current cancer therapy is the inability to deliver tumoricidal agents throughout the entire tumor mass using traditional intravenous administration. Nanoparticles carrying beta-emitting therapeutic radionuclides[DN: radioactive isotops that emits electrons as part of the decay process a list of β-emitting radionuclides used in radiotherapeutic nanoparticle preparation is given in table1 of this paper.) that are delivered using advanced image-guidance have significant potential to improve solid tumor therapy. The use of image-guidance in combination with nanoparticle carriers can improve the delivery of localized radiation to tumors. Nanoparticles labeled with certain beta-emitting radionuclides are intrinsically theranostic agents that can provide information regarding distribution and regional dosimetry within the tumor and the body. Image-guided thermal therapy results in increased uptake of intravenous nanoparticles within tumors, improving therapy. In addition, nanoparticles are ideal carriers for direct intratumoral infusion of beta-emitting radionuclides by convection enhanced delivery, permitting the delivery of localized therapeutic radiation without the requirement of the radionuclide exiting from the nanoparticle. With this approach, very high doses of radiation can bedelivered to solid tumors while sparing normal organs. Recent technological developments in image-guidance, convection enhanced delivery and newly developed nanoparticles carrying beta-emitting radionuclides will be reviewed. Examples will be shown describing how this new approach has promise for the treatment of brain, head and neck, and other types of solid tumors.
The challenges this review discusses
intravenously administered drugs are inhibited in their intratumoral penetration by high interstitial pressures which prevent diffusion of drugs from the blood circulation into the tumor tissue [1–5].
relatively rapid clearance of intravenously administered drugs from the blood circulation by kidneys and liver.
drugs that do reach the solid tumor by diffusion are inhomogeneously distributed at the micro-scale – This cannot be overcome by simply administering larger systemic doses as toxicity to normal organs is generally the dose limiting factor.
even nanoparticulate drugs have poor penetration from the vascular compartment into the tumor and the nanoparticles that do penetrate are most often heterogeneously distributed
How imaging could mitigate the above mentioned challenges
The inclusion of an imaging probe during drug development can aid in determining the clearance kinetics and tissue distribution of the drug non-invasively. Such probe can also be used to determine the likelihood of the drug reaching the tumor and to what extent.
Note:Drugs that have increased accumulation within the targeted site are likely to be more effective as compared with others. In that respect, Nanoparticle-based drugs have an additional advantage over free drugs with their potential to be multifunctional carriers capable of carrying both therapeutic and diagnostic imaging probes (theranostic) in the same nanocarrier. These multifunctional nanoparticles can serve as theranostic agents and facilitate personalized treatment planning.
Imaging can also be used for localization of the tumor to improve the placement of a catheter or external device within tumors to cause cell death through thermal ablation or oxidative stress secondary to reactive oxygen species.
Note: Image guided thermal ablation methods include radiofrequency (RF) ablation, microwave ablation or high intensity focused ultrasound (HIFU). Photodynamic therapy methods using external light devices to activate photosensitizing agents can also be used to treat superficial tumors or deeper tumors when used with endoscopic catheters.
Quality control during and post treatment
For example: The use of high intensity focused ultrasound (HIFU) combined with nanoparticle therapeutics: HIFU is applied to improve drug delivery and to trigger drug release from nanoparticles. Gas-bubbles are playing the role of the drug’s nano-carrier. These are used both to increase the drug transport into the cell and as ultrasound-imaging contrast material. The ultrasound is also used for processes of drug-release and ablation.
Additional example; Multifunctional nanoparticles for tracking CED (convection enhanced delivery) distribution within tumors: Nanoparticle that could serve as a carrier not only for the therapeutic radionuclides but simultaneously also for a therapeutic drug and 4 different types of imaging contrast agents including an MRI contrast agent, PET and SPECT nuclear diagnostic imaging agents and optical contrast agents as shown below. The ability to perform multiple types of imaging on the same nanoparticles will allow studies investigating the distribution and retention of nanoparticles initially in vivo using non-invasive imaging and later at the histological level using optical imaging.
Conclusions
Image-guided radiotherapeutic nanoparticles have significant potential for solid tumor cancer therapy. The current success of this therapy in animals is most likely due to the improved accumulation, retention and dispersion of nanoparticles within solid tumor following image-guided therapies as well as the micro-field of the β-particle which reduces the requirement of perfectly homogeneous tumor coverage. It is also possible that the intratumoral distribution of nanoparticles may benefit from their uptake by intratumoral macrophages although more research is required to determine the importance of this aspect of intratumoral radionuclide nanoparticle therapy. This new approach to cancer therapy is a fertile ground for many new technological developments as well as for new understandings in the basic biology of cancer therapy. The clinical success of this approach will depend on progress in many areas of interdisciplinary research including imaging technology, nanoparticle technology, computer and robot assisted image-guided application of therapies, radiation physics and oncology. Close collaboration of a wide variety of scientists and physicians including chemists, nanotechnologists, drug delivery experts, radiation physicists, robotics and software experts, toxicologists, surgeons, imaging physicians, and oncologists will best facilitate the implementation of this novel approach to the treatment of cancer in the clinical environment. Image-guided nanoparticle therapies including those with β-emission radionuclide nanoparticles have excellent promise to significantly impact clinical cancer therapy and advance the field of drug delivery.
This update was performed by the following methods:
A. GPT 5 Text analysis and Reasoning
B. Insertion of Knowledge Graph on topic Curation of Genomic Analysis from Non Small Cell Lung Cancer Studies from Nodus Labs using InfraNodus software
C. Domain Knowledge Expert evaluation of the Update outcomes
This article has the following Structure:
Part A: Introduction to LLM, Knowledge Graph software InfraNodus, ChatGPT5 and Background Information on curated material for Test Case
Part B: InfraNodus Analysis of manual curation and Knowledge Graph Creation
Part C: Chat GPT 5 Analysis of Manually Curated Material
Part D: Curation entitled Multiple Lung Cancer Genomic Projects Suggest New Targets, Research Directions for Non-Small Cell Lung Cancer originally published on 09/05/2014
Results of Article Update with GPT 5
1. GPT5 alone was not able to understand the goal of the article, namely to determine knowledge gaps in a particular research area involving 5 genomic studies on lung cancer patients
2. GPT5 alone was not able to group concepts or comonalities between biological pathways unless supplied with a manually curated list of KEGG pathways from a list of mutated genes. However this precluded any effect that fusion proteins had on the analysis and so GPT5 would only concentrate on mutated genes commonly found in literature
3. GPT was not able to access some of the open Access databases like NCBI Gene Ontology database
Results of Article Update with KnowledgeGraph presentation to GPT 5
4. As the Knowledge Graph understood the importance of fusion proteins and transversions, the knowledgegraph augmented the GPT analysis and so enriched the known pathways as well as could correctly identify the less represented pathways in the knowledge graph
5. This led to the identification of many novel signaling pathways not identified in the original analysis, and was able to perform this task with ease and speed
6. GPT with InfraNodus Analysis was able to propose pertinent questions for future research (the goal of the original curation) such as:
How does the interaction between [[EGFR]] mutations and sex-specific gene alterations, including [[RBM10]], influence treatment outcomes in lung adenocarcinoma?
How does the intersection of mutational patterns from smoking influence pathway activation in NSCLC, and can identifying these interactions improve targeted therapy development?
Novelty in comparison to Original article published on 09/05/2014
7. it appears that manual curation is necessary to assist in the building of relevant knowledge graphs in the biomedical fields to augment generative AI analysis
8. by itself, generative AI is not optimized for inference of higher concepts from biomedical text, and therefore, at this point, requires the input from human curators developing domain-specific knowledge graphs
9. The combination of ChatGPT5 and Knowledge graphs of this manually curated biomedical text added a further layer of complexity of gaps of knowledge not seen in the original curations including the need to study noncanonical signaling pathways like WNT and Hedgehog in smoker versus nonsmoker cohorts of lung cancer patients
A Comparison of Manual Expert-Curative and an LLM-based analysis of Knowledge Gaps in Non Small Lung Cancer Whole Exome Sequencing Studies and a Use Case Example of Chat GPT 5
Part A: Introduction to LLM, Knowledge Graph software InfraNodus, ChatGPT5 and Background Information on curated material for Test Case
The development of Large Language Models (LLMs), together with development of knowledge graphs, have facilitated the ability to analyze text and determine the relationships among the various concepts contained within series of texts. These concepts and relationships can be visualized, and new insights inferred from these visualizations. As a result, this type of analysis suggests new directions and lines of research.
Alternatively, these types of visualizations can also reveal gaps in knowledge which should be addressed. A new type of LLM and visualization tools have been developed to understand the gaps in knowledge in biomedical text.
Nodus Labs InfrNodus AI Knowledge Graph Software Tools Allow Text Relationship Visualization and Integrated AI Functionality
Infranodus makes knowlegde graphs from text and then is able to visualize the relationships between concepts (or nodes). In doing so, the tool also highlights the various knowledge gaps (or large differences between nodes) which can be used to investigate new hypotheses and research directions of previously univestigated relationships between concepts. This generates new research questions, in which these gaps can be used as prompts in the software’s integrated AI tool. The AI tool, much like a GPT, returns recommendations for research to be conducted in the area.
In addition, the InfraNodus software can detect if text is too biased on a particular concept or conclusion, and using a GPT3 or GPT4, can determine if the nodes are too dispersed and will recommend which gaps should be focused on.
The software can upload any biomedical text in various formats
A full demonstration is on their website but a good summary is found on their Youtube site at
Previously we had manually curated and analyzed the knowledge gaps from a series of publications on whole exome sequencing of biopsied tumors from cohorts of non small lung cancer patients. This curation (from 2016) is seen in the lower half of this updated link below and I separated with a bar and highlighted in Yellow as Text for AI Analysis.
Govindan R, Ding L, Griffith M, Subramanian J, Dees ND, Kanchi KL, Maher CA, Fulton R, Fulton L, Wallis J et al: Genomic landscape of non-small cell lung cancer in smokers and never-smokers. Cell 2012, 150(6):1121-1134.
Imielinski M, Berger AH, Hammerman PS, Hernandez B, Pugh TJ, Hodis E, Cho J, Suh J, Capelletti M, Sivachenko A et al: Mapping the hallmarks of lung adenocarcinoma with massively parallel sequencing. Cell 2012, 150(6):1107-1120.
Peifer M, Fernandez-Cuesta L, Sos ML, George J, Seidel D, Kasper LH, Plenker D, Leenders F, Sun R, Zander T et al: Integrative genome analyses identify key somatic driver mutations of small-cell lung cancer. Nature genetics 2012, 44(10):1104-1110.
were performed.
The purpose of this analysis was to uncover biological functions related to the sets of mutated genes with limited research publications in the area of non small cell lung cancer. The identification of such biological functions would represent a gap in knowledge in this disease. In addition, this analysis attempted to find new lines of research or potential new biotargets to investigate for lung cancer therapy.
However this manual method is time consuming and may miss relationships not defined in a GO ontology or gene knowledgebases.
Therefore we turned to an AI-driven approach:
Using InfraNodus ability to develop a knowledge graph based on our curation and determine if the AI platform could infer knowledge gaps
Utilize Chat GPT5 to analyze the same curated set to determine if OpenAI analysis would lead to the similar analysis from curated material
Determine if combining a knowledge graph within GPT would lead to a higher level of analysis
See below (Part D) of this update for the curated studies which were included in this analysis and the text which was entered into both InfraNodus and Chat GPT5.
As a summary, it seems that manual curation is necessary to assist in the building of relevant knowledge graphs in the biomedical fields to augment generative AI analysis. In addition, it appears that , by itself, generative AI is not optimized for inference of higher concepts from biomedical text, and therefore, at this point, requires the input from human curators developing domain-specific knowledge graphs.
Part B. InfraNodus Analysis of manual curation and Knowledge Graph Creation
Methods:
Text of the curation was copied and directly pasted into the text analysis module of InfraNodus. There was no editing of words however genes in the curation were linked to their GeneCard entry. GeneCards is a database run by the Weizmann Institute. InfraNodus utilizes a combination of LLMs and its own GraphRAG system to provide insights from text analysis. While it leverages various models, including those from OpenAI and Anthropic, it’s not limited to a single LLM. Instead, InfraNodus integrates these models within its GraphRAG framework, which enhances their capabilities by adding a relational understanding of the context through a knowledge graph.
InfraNodus then autogenerates a knowledge graph and returns entities and relationships between entities. InfraNodus offers the opportunity to modify the knowledge graph however for this analysis we used the first graph InfraNodus generated. Inspection of this graph (as shown below) was deemed reasonable.
Results
The knowledge graph of the input text is shown below:
InfraNodus generated Knowledge Graph of 5 WES Non Smal Cell Lung Cancer studies involving smokers and non smokers
Four main concepts were returned: tumors, genes, literature, and mutations.
A snapshot of the Analysis window is given below. It should be noted that InfraNodus felt there needed to be more connections between Pathway and Mutational Patterns.
An InfraNodus reposrt with Knowlege Graph on Whole Exome Sequencing studies in NSCLC to determine mutational spectrum in smokers versus non smokers
alk clinical [[egfr]] mutational pathway [[paper]] found key literature study [[genomic]] reveal [[transversion]]
Top relations / ngrams:
1) [[lung]] [[tumors]]
2) alk fusion
3) link function
4) eml alk
5) function [[gene_ontology]]
Modulary: 0.47
Relations:
InfraNodus identified 744 relations between entities (nodes)
A list of some of the more frequent are given here:
source
target
occurrences
weight
betweenness
[[lung]]
[[tumors]]
8
24
0.4676
analysis
pathway
5
12
0.2291
significantly
[[genes]]
5
9
0.1074
significantly
[[mutated]]
4
12
0.0281
[[mutated]]
[[genes]]
4
12
0.0847
[[transversion]]
high
3
12
0.0329
[[smoking]]
history
3
10
0.0352
study
identify
3
9
0.2051
mutational
pattern
3
9
0.0921
[[rbm10]]
[[mutations]]
3
8
0.1776
literature
analysis
3
7
0.2218
[[egfr]]
[[mutations]]
3
7
0.2139
[[transversion]]
group
3
7
0.0259
enriched
cohort
3
6
0.0219
[[whole_exome_sequencing]]
[[tumors]]
3
6
0.3485
identify
[[genes]]
3
6
0.2268
including
analysis
3
5
0.1985
alteration
[[genes]]
3
4
0.1298
[[tumors]]
analysis
3
4
0.5192
alk
fusion
2
15
0.0671
link
function
2
14
0.0269
function
[[gene_ontology]]
2
13
0.0054
Notice how the betweenness or importance of connection of disparate concepts vary but are high between concepts like tumors and analysis, or lung and tumor, however many important linked concepts like alk and fusion may have low betweenness but are mentioned frequently and have a much higher weight or closeness to each other. Gene-mutations-transversions-smoking seem to have a high correspondence to each other
Genetic Alterations: identify, [[genes]], study:The recent comprehensive studies on lung adenocarcinoma have significantly advanced our understanding of the genetic landscape by identifying key mutations and their intricate interactions. Notably, EGFR and RBM10 exhibit distinct mutational patterns, with RBM10 inactivations being notably enriched in male cohorts. This gender-linked enrichment underscores a potential differential oncogenic pathway involving ERBB2 and RB1 alterations.Moreover, these projects emphasize the quest to map significant gene alterations within lung adenocarcinoma. The identification of such genes not only corroborates prior reports but also expands upon them by highlighting new connections between mutation signatures and clinical factors like smoking history. These findings are crucial as they can inform future therapeutic targeting strategies, ensuring that personalized treatment approaches consider both gender-specific genomic enrichments and mutation-driven tumorigenesis pathways elucidated through rigorous analyses.elaborate
questions generated using AI to help you explore “alk, clinical, [[egfr]], mutational, pathway, [[paper]], found, key, literature, study, [[genomic]], reveal, [[transversion]]…”:How do mutational patterns, specifically EGFR mutations and transversions related to smoking history, influence the effectiveness of targeted therapies in NSCLC patients?elaborate
ideas generated using AI to help you explore “alk, clinical, [[egfr]], mutational, pathway, [[paper]], found, key, literature, study, [[genomic]], reveal, [[transversion]]…”:Develop a predictive model that utilizes genomic data and smoking history to forecast patient response to targeted therapies. This model would identify key mutational signatures linked to EGFR and other genes, highlighting the impact of smoking-induced transversions on drug efficacy.elaborate
Project Notes
”
The recent comprehensive studies on lung adenocarcinoma have significantly advanced our understanding of the genetic landscape by identifying key mutations and their intricate interactions. Notably, EGFR and RBM10 exhibit distinct mutational patterns, with RBM10 inactivations being notably enriched in male cohorts. This gender-linked enrichment underscores a potential differential oncogenic pathway involving ERBB2 and RB1 alterations.
Moreover, these projects emphasize the quest to map significant gene alterations within lung adenocarcinoma. The identification of such genes not only corroborates prior reports but also expands upon them by highlighting new connections between mutation signatures and clinical factors like smoking history. These findings are crucial as they can inform future therapeutic targeting strategies, ensuring that personalized treatment approaches consider both gender-specific genomic enrichments and mutation-driven tumorigenesis pathways elucidated through rigorous analyses.”
<ConceptualGateways>
alk
clinical
[[egfr]]
mutational
pathway
[[paper]]
found
key
literature
study
[[genomic]]
reveal
[[transversion]]
</ConceptualGateways>
How do mutational patterns, specifically EGFR mutations and transversions related to smoking history, influence the effectiveness of targeted therapies in NSCLC patients?
The report from the NCI Bulletin outlines significant advancements in understanding lung cancer through genome sequencing projects. These studies have revealed a plethora of genetic and epigenetic alterations across various forms of lung tumors, including adenocarcinomas, squamous cell carcinomas, and small cell lung cancers. Notably, some identified alterations could be targeted by existing therapies, providing potential new avenues for treatment.Dr. Meyerson emphasizes the complexity of these genetic changes, highlighting that distinct mechanisms inactivating genes can vary between tumors. The report also notes gaps in knowledge regarding non-coding DNA alterations, which comprise a major part of the human genome.Key findings include:1. Comprehensive genomic analyses revealing unique driver mutations in lung adenocarcinoma, such as those affecting MET and ERBB2, alongside significant mutations in known cancer drivers like TP53 and KRAS.2. A classification system based on genomic data enabling more accurate patient stratification—achieving a 75% classification rate of lung cancer subtypes.3. Smoking history is shown to influence mutational patterns significantly, with smokers exhibiting a higher incidence of point mutations compared to never-smokers.Moreover, the integration of genomic data and pathway analysis highlighted recurrent mutations across various pathways related to tumorigenesis, suggesting new therapeutic targets and underscoring the importance of personalized medicine approaches that factor in gender-specific mutation distributions.This synthesis of findings not only corroborates earlier studies but also extends our understanding of the interplay between genomic alterations, smoking habits, and clinical outcomes in lung cancer. Future research is needed to explore the implications of these findings further and to develop targeted therapies that leverage the identified genetic alterations.
Tumor Genomics
Genetic Alterations
Pathway Analysis
Mutation Patterns
Main Ideas Summary from Infranodus:
The report from the NCI Bulletin highlights significant developments in lung cancer genomics through five genome sequencing projects. These studies have uncovered various genetic and epigenetic alterations across multiple forms of lung cancer, including adenocarcinomas, squamous cell cancers, and small cell lung cancers. Notably, these alterations may correspond to existing drugs, offering new avenues for targeted therapy.
Key findings include the identification of unique mutations in genes like NF1, MET, and ERBB2, with particular attention to smoking’s role in mutational patterns. The analysis shows that smokers exhibit a higher incidence of specific point mutations, marking a distinct divergence in mutation profiles compared to never-smokers.
The research also emphasizes the need for continued exploration of the data to link mutation signatures with clinical outcomes, particularly regarding gender-specific differences in mutation prevalence. Emerging insights suggest that pathways involving MYC and alterations in splicing factors could be vital targets for future therapeutic strategies.
Overall, the studies collectively underscore an urgent need for integrating genomic data into clinical practice, which can lead to more personalized treatment approaches for lung cancer patients by considering both the genetic landscape of tumors and their histories, such as smoking exposure. The ongoing efforts to map gene alterations within lung adenocarcinoma not only reinforce existing knowledge but also reveal new potential therapeutic targets that can significantly impact patient outcomes.
Questions to Explore from InfraNodus:
How does the interaction between [[EGFR]] mutations and sex-specific gene alterations, including [[RBM10]], influence treatment outcomes in lung adenocarcinoma?
How does the intersection of mutational patterns from smoking influence pathway activation in NSCLC, and can identifying these interactions improve targeted therapy development?
Part C: Chat GPT 5 Analysis of Manually Curated Material
Now let us see how Chat GPT5 analyzed the same material.
Methods
Nice — I’ll work from the KEGG-pathway list shown in your text (the table of pathways with PubMed counts) and call out major KEGG pathways that are not represented there.
Important caveat: “Not represented in the table” below means not listed in the KEGG-pathway hits shown in your pasted literature analysis, not that these pathways never play a role in lung cancer. Absence from that table can reflect lack of reporting in the specific WES/WGS studies or the PubMed keyword search used — it’s not proof the pathway is uninvolved biologically.
The above Important caveat was very important because it showed that ChatGPT5 could understand what my experimental goals were, or at least understood the problem I was presenting to ChatGPT5!
Major KEGG pathways missing from the provided KEGG list
(However Note that GPT did NOT take into account the mutational spectrum of the studies or the gene lists supplied in the curation)
These are commonly cited cancer-related KEGG pathways that do not appear in the table you gave:
Wnt signaling pathway
Notch signaling pathway
Hedgehog signaling pathway
TGF-beta signaling pathway
Hippo signaling pathway
p53 signaling pathway — (note: TP53 is mutated in many tumors, so p53 biology is implicated in the text, but “p53 signaling” does not appear in that KEGG hits table)
Autocrine/paracrine inflammation/chemokine signaling beyond the one low-count entry (general cytokine networks other than the single “cytokine–cytokine receptor interaction” hit)
Signal transduction modules sometimes separate in KEGG:g. Calcium signaling was listed in your table, but other modules like cGMP–PKG signaling or PKA/PKC broader signaling networks are not.
Quick summary
The KEGG table in your text highlights many cancer and receptor-tyrosine/MAPK/PI3K related pathways (cell cycle, MAPK, ErbB, JAK-STAT, focal adhesion, etc.). What’s not listed are numerous developmental morphogen pathways (Wnt/Notch/Hedgehog/TGF-β/Hippo), many DNA-repair and core metabolic pathways, canonical apoptosis/autophagy/ubiquitin pathways, and broad adaptive-immune signaling pathways.
If you want, I can:
(A) cross-check this against the full KEGG pathway list and produce a definitive “KEGG universe → present vs absent in your table” spreadsheet, or
(B) prioritize which of the missing pathways you’d most want investigated in lung tumor sequencing data and suggest genes to look for.
Which follow-up would you prefer?
Now taking out the KEGG list I gave GPT in order to force GPT to do a similar but unbiased analysis
From the studies you’ve shared, we can identify which KEGG pathways are under-represented (i.e., appear less frequently altered than expected) in the altered genes in lung cancer.
To do this, we note that the researchers used KEGG and other pathway analysis tools (PathScan, literature curation, proteomics) to see which biological pathways the mutated genes mapped to.
While many well-known cancer pathways were found to be activated (MAPK, PI3K-AKT, mTOR, cell cycle, oxidative stress, etc.), a few key pathways were reported as unexpectedly low in mutation frequency despite their known role in cancer biology.
KEGG Pathways Under-Represented in Altered Genes in Lung Cancer
From the descriptions in Papers 1–5, these are the main pathways that appear under-represented:
MAPK signaling pathway
Normally, MAPK is a major cancer driver pathway. In this dataset, MAPK activity was high in proteomics, but many tumors lacked mutations in MAPK pathway genes.
This means MAPK activation is often non-mutational (possibly via upstream signaling, epigenetic changes, or ligand activation), so in mutation-based KEGG enrichment it is under-represented.
PI3K-Akt signaling pathway
Like MAPK, the PI3K-Akt pathway was highly activated in proteomics, but often without mutations in PI3K pathway genes.
Under-represented in gene mutation lists, despite strong functional activation.
mTOR signaling pathway
Functional activation was observed (especially when STK11 was mutated), but most tumors had no mTOR-pathway gene mutations.
Under-represented when looking only at altered genes.
JAK-STAT signaling pathway
Mentioned as novel pathway alterations in some tumors, but in KEGG enrichment analysis, JAK-STAT genes did not appear as a dominant mutated group—implying low mutation frequency compared to other cancer types.
Oxidative phosphorylation & metabolic pathways
While oxidative stress response genes (e.g., KEAP1) were sometimes mutated, the oxidative phosphorylation KEGG pathway itself did not emerge as mutation-enriched, suggesting functional changes occur via other mechanisms (e.g., hypoxia signaling).
While some epigenetic regulators (ARID1A, CREBBP, EP300, MLL) were mutated, KEGG’s “Chromatin remodeling” and “Histone modification” groupings were less represented than expected given the role of epigenetics in lung cancer, indicating epigenetic dysregulation is often non-mutational.
Why They Are Under-Represented
Proteomics revealed activation without DNA mutation (e.g., ligand-driven activation, phosphorylation cascades).
Epigenetic or post-translational modifications can activate pathways without altering the coding sequence.
Tumor heterogeneity: some pathways are only altered in subsets of tumors and thus don’t reach statistical enrichment.
Limitations of mutation-based KEGG analysis: pathways that are activated downstream or by non-coding alterations are missed.
✅ Summary Table
Pathway (KEGG ID)
Representation in Mutated Genes
Reason for Under-Representation
hsa04010 MAPK signaling
Low
Activated via non-mutational mechanisms
hsa04151 PI3K-Akt signaling
Low
Functional activation w/o gene mutations
hsa04150 mTOR signaling
Low
Activation often via upstream STK11 loss or growth factor signaling
hsa04630 JAK-STAT signaling
Low
Rarely mutated, altered mostly via cytokine/growth factor changes
hsa00190 Oxidative phosphorylation
Low
Functional metabolic shift via hypoxia & ROS stress, not mutations
Chromatin remodeling / histone modification
Low
Mostly altered via epigenetic changes, not coding mutations
If you want, I can cross-reference the exact KEGG IDs and run a quick gene-to-pathway mapping using the genes listed in the reports so we can get a precise statistical under-representation list. That way it’s backed by an enrichment p-value rather than just qualitative observation.
I tried to re-run the KEGG-backed analysis you requested, but the KEGG REST endpoints/web pages aren’t reachable from my browsing tool right now (the web.run calls to KEGG returned an internal/safety error). I can’t produce a properly-sourced, KEGG-ID–backed enrichment report unless I can successfully query KEGG (or a comparable pathway service) for the exact pathway membership.
Two clear, immediate options — tell me which you prefer and I’ll proceed right away:
Option 1 — Proceed with a KEGG-backed run using an alternate route (recommended)
I’ll attempt a small number of web.run search queries (to public pathway tools that mirror KEGG membership, e.g., g:Profiler / Reactome / Enrichr / DAVID) and then follow links to KEGG pathway pages. This often works even when direct KEGG REST calls fail. It will take a few web.run calls and I’ll cite the pages used. (If you want me to prefer a particular service — e.g., g:Profiler, Enrichr, DAVID — say so; otherwise I’ll pick g:Profiler as it’s robust.)
Option 2 — Run an immediate local (best-effort) analysis without web lookups
I’ll map the genes you listed to likely KEGG pathways from internal knowledge, run hypergeometric enrichment using a standard human background (20,000 genes), and give a ranked list of under-represented KEGG pathways with p-values — but I must be clear this is approximate (I cannot provide verified KEGG IDs or exact pathway sizes).
If you pick Option 1, I’ll start the web lookups now and produce the KEGG-ID mapped, statistically precise under-representation list with citations.
If you pick Option 2, I’ll run the approximation immediately and give results plus a confidence caveat.
Which do you want?
Sources
Ask ChatGPT
You said:
Use Option 1 to Proceed with a KEGG-backed run using an alternate route
ChatGPT said:
Thought for 37s
Thanks — I followed Option 1 trying to do a KEGG-backed run, but hit the same problem: my web tool (web.run) can reach g:Profiler/KEGG reliably in some contexts but it failed for the programmatic queries I attempted (internal / non-retryable errors). Because you explicitly requested a KEGG-backed run, I tried an alternate route (g:Profiler web query) and the site blocked the automated call.
Rather than leave you waiting, I made a transparent, reproducible best-effort analysis right now so you have immediate, concrete results — but please read the very important limitations below before using these numbers: (GPT having problems accessing material behind firewalls and paywalls)
What I did (short)
I extracted a conservative set of 48 unique genes from the papers you supplied (recurrent drivers, fusion partners, and the druggable gene list). Gene list used (48 genes):
TP53, KRAS, EGFR, STK11, KEAP1, SMARCA4, RBM10, NF1, MET, ERBB2, RIT1, BRAF, PIK3CA, PTEN, ARID1A, U2AF1, CREBBP, EP300, MLL, ALK, ROS1, DACH1, CFTR, RELN, ABCB5, HGF, JAK2, JAK3, HDAC2, HDAC6, HDAC9, BIRC6, ITGB1, ITGB3, MMP2, PRKCB, PIK3CG, TERT, MMP14, AURKC, ERBB4, FGFR1, EML4, SIK2, ZFHX4, EPHA3, EPHA7, SLIT2.
Because direct KEGG lookups failed, I mapped those genes to a short list of common KEGG pathways by manual curation (based on canonical pathway membership knowledge) and used conservative, literature-style approximate pathway sizes (number of genes in that KEGG pathway) to compute exact hypergeometric lower-tail p-values (i.e., probability of observing as few or fewer genes in the pathway than we did — an under-representation test).
I computed exact hypergeometric probabilities (sum of combinations) with a standard human gene background of N = 20,000. All calculations were executed programmatically (so no hand-approximate combinatorics).
Important limitations (please read)
This is NOT a true KEGG-backed run. I could not fetch KEGG pathway membership lists or KEGG IDs because the web tool could not access the KEGG/g:Profiler APIs in this session. (more GPT issues with access to data)
I used manually mapped genes → pathways (conservative, but not exhaustive) and approximate pathway sizes (estimates). Because both gene membership and exact pathway sizes matter a lot for enrichment statistics, the p-values below are illustrative only — they are useful to prioritize where to retry a proper KEGG query, but should not be taken as authoritative.
The correct, defensible way to produce what you asked is to re-run this on g:Profiler/Enrichr/DAVID (or download KEGG GMTs) so pathway membership and KEGG IDs are exact. I can do that as soon as the web tool can access those services (or you can paste exact KEGG gene sets if you prefer).
Results — immediate best-effort under-representation test
For each pathway I show: pathway size (K, approximate), number of genes from your list found in that pathway (k), and the hypergeometric lower-tail p-value P(X ≤ k) (probability of seeing k or fewer pathway genes by chance). Lower p-values (e.g., < 0.05) would suggest significant under-representation.
Interpretation: none of the pathways above reach statistical significance for under-representation under the assumptions I used. The closest to a (moderate) signal is oxidative phosphorylation where we observed 0 genes from the list (p ≈ 0.73) — but that p-value is still far from being significant (p < 0.05 would be needed). In short: no pathway is robustly under-represented in this small gene set using the conservative approximations above.
What I recommend (next steps to get the exact KEGG-ID–backed answer you originally asked for)
Allow me to retry the web-backed run (g:Profiler / Enrichr / DAVID) — I’ll re-run the enrichment and return:
gene → exact KEGG IDs mapping,
KEGG pathway sizes (exact K),
over- and under-representation p-values (Fisher/hypergeometric), and
multiple-testing corrected q-values (Benjamini–Hochberg). (This requires the web tool to be able to reach g:Profiler/KEGG; I already tried and the call failed. If you want, I’ll attempt again immediately.)
If you prefer not to rely on the web tool, you can paste your final canonical gene list (e.g., a CSV of the exact genes you want analyzed) and I will run a conservative local analysis like the above but I will be explicit that KEGG IDs and pathway sizes are approximations.
If you’d like, I can also send you the exact code/notebook I used for the above best-effort run so you (or your bioinformatics person) can re-run it locally against an up-to-date KEGG GMT file.
If you want me to retry the web-backed run now, say “Retry web run now” and I’ll immediately attempt g:Profiler/Enrichr again and produce the precise KEGG-ID–backed enrichment with citations. If you prefer I proceed without web access (i.e., run a local approximation on a larger set of genes or with different pathway groupings), tell me what gene list to use and I’ll compute immediately.
Top of Form
Bottom of Form
Done — I created a bar chart showing estimated PubMed article counts (prior to 2018) for “lung cancer” mentions of the pathways we discussed: MAPK, PI3K-Akt, mTOR, JAK-STAT, and Oxidative phosphorylation.
Important caveats (please read)
I could not access PubMed programmatically from this session (the web tool failed on PubMed/NCBI requests), so the numbers used are illustrative estimates, not exact counts.
If you need exact PubMed counts (filtered to publications before 2018), I can re-run the web-backed PubMed queries and regenerate the graph as soon as web access to NCBI is available — just say “Retry PubMed queries” and I’ll attempt that immediately.
Re-run the exact PubMed queries and update the chart with real counts, or
Expand the chart to include more pathways (e.g., chromatin modifiers, spliceosome, cell cycle), or
Provide the code/notebook used so you can run it locally against PubMed.
Here’s the expanded bar chart with additional pathways — Spliceosome, Chromatin remodeling / histone modification, ECM / focal adhesion, and Cell cycle — alongside the original set.
Would you like me to now replace these illustrative numbers with exact PubMed counts (filtered to before 2018) by running targeted PubMed searches?
Yes — even though the dataset and pathway lists we discussed focused mostly on signaling, metabolic, and genomic alterations, there is a clear immune component to lung cancer supported by both your provided text and the broader literature. (Now note that GPT noticed a clear literature coverage of cell cycle in lung cancer however it is claiming there was evidence of an immune component in the literature before 2018).
Evidence from your provided studies (Note GPT is using recent evidence not just restricting to periods before 2018)
Pathways with immune relevance
JAK-STAT signaling (detected in the analysis)
Central to cytokine signaling, immune cell activation, and inflammatory responses in the tumor microenvironment.
Altered JAK-STAT can drive immune evasion or chronic inflammation that promotes tumor growth.
RTK/RAS/RAF pathway indirectly affects immune signaling by altering cytokine/chemokine secretion from tumor cells.
ROS1-ALK fusions
These can alter the tumor’s immunogenic profile; your text notes they may create epitopes that make tumors amenable to immunotherapy.
Shown in cited literature to be less chemosensitive but potentially sensitive to immune-based approaches when certain epitopes are exposed.
Also exhibit HSP90 chaperone dependence, which links to immune stress response pathways.
Tumor heterogeneity and immune escape
The genomic heterogeneity reported (monoclonal vs biclonal tumors) suggests different subclones may evade immune surveillance differently — a hallmark of tumor–immune system interaction.
From broader lung cancer research
Even when not highlighted in mutation frequency lists, immune-related processes in lung cancer are driven by:
Tumor-infiltrating lymphocytes (TILs) and their exhaustion profiles.
Immune checkpoints (PD-1/PD-L1, CTLA-4) — a major therapeutic target in NSCLC.
Inflammatory cytokine networks (many driven via JAK-STAT, NF-κB, MAPK cross-talk).
Neoantigen load (especially in smokers, due to high mutation rates) that can trigger immune recognition — unless suppressed by tumor-mediated immunosuppression.
Conclusion
Yes — there is an immune component to lung cancer. It may not appear as strongly in mutation-based KEGG under-representation analysis (because immune evasion is often mediated by gene expression, epigenetics, and tumor–immune interactions rather than recurrent coding mutations), but immune signaling pathways like JAK-STAT and cytokine-related signaling are still altered and clinically relevant.
(Remember our initial test was to see if GPT could do our manual curative analysis without relying on current information!)
That’s why immune checkpoint inhibitors (anti–PD-1, anti–PD-L1, anti–CTLA-4) are now standard-of-care in many lung cancer treatment regimens (The evidence that led to immune checkpoint inhibitors was not based just on JAK-STAT or cytokine signaling).
Now Let Us See What Happens When a Knowledge Graph from InfraNodus is Imported into Chat GPT5
The import of a knowledge graph as a CSV from InfraNodus into ChatGPT5 is rather straightforward as can directly upload into the GPT interface.
ChatGPT was prompted with: “based on the imported knowledge graph tell me what Gene ontology biologic functions are missing?”
ChatGPT said:
Your imported graph isn’t labeled with standard Gene Ontology (GO) IDs or names, so I can’t directly map it to “biological process” terms yet.
The next step is to align your node name column with the official GO Biological Process ontology and then report which expected functions are missing.
I can:
Pull the latest GO Biological Process list.
Match your nodes against it.
Output the GO BP terms absent from your network.
Do you want me to fetch the current GO BP ontology from the Gene Ontology database and run that comparison?
You said:
based on the imported knowledge graph tell me what KEGG processes are missing?
ChatGPT said:
To identify which KEGG processes are missing from your imported knowledge graph, the first step is to retrieve an up-to-date list of KEGG human pathways and then compare that against the nodes present in your graph. Here’s how I can help:
Fetch the current list of KEGG human pathways
I can pull the latest catalog of KEGG pathways for Homo sapiens, which includes major metabolic routes (e.g., purine metabolism, glycolysis) as well as signaling pathways (e.g., JAK-STAT, Wnt, PI3K-Akt) KEGG+1.
Compare with your knowledge graph nodes
Once I have that KEGG list, I’ll match it against your graph’s node name entries. Any pathway names or identifiers (like hsa04110 for “Cell cycle”) not present in your graph would be flagged as missing.
Deliver the findings
I’ll provide you with:
A list of KEGG processes present in your graph
A list of those missing, enabling you to fill in gaps or trigger further investigation
Does that sound good? If you’d like, please share a sample or the full list of your graph’s node name entries, especially if they include KEGG-style labels or pathway names—it’ll help speed up the matching process.
Sources
You said:
Fetch the current list of KEGG human pathways and compare with my knowledge graph nodes and provide me with a list of KEGG processes present and a list of KEGG processes missing.
ChatGPT said:
I’ve fetched the current list of KEGG human pathways (hsa) and compared it to the nodes in your imported knowledge graph. Here’s what we can do next—and what I need from you to complete the analysis:
KEGG Human Pathway List
Below are just a few highlights from the complete KEGG human pathway list (retrieved via KEGG REST /list/pathway/hsa) rest.kegg.jp:
hsa00010 – Glycolysis / Gluconeogenesis
hsa00020 – Citrate cycle (TCA cycle)
hsa01100 – Metabolic pathways
hsa04010 – MAPK signaling pathway
hsa04151 – PI3K-Akt signaling pathway
hsa04910 – Insulin signaling pathway
hsa05200 – Pathways in cancer
…plus hundreds more spanning metabolism, genetic information processing, organismal systems, disease, and more.
You can view the full list in the KEGG API or in your local copy if you’ve grabbed it via /list/pathway/hsa.
What I Need From You
To identify which KEGG pathways are present or missing in your graph, please provide the list of node names (or IDs) from your knowledge graph that correspond to KEGG pathways. For example, this might include:
KEGG identifiers like hsa04110
Pathway names like “Cell cycle” or “Glycolysis / Gluconeogenesis”—ideally matching exact KEGG naming
With those node names in hand, I can:
Match them against the official KEGG list.
Generate two clear lists:
Present in your graph
Missing from your graph (i.e., not represented)
If you can share your node set (even a sample if too large), we can proceed immediately. Let me know when you’re ready!
Sources
You said:
based on my knowledge graph I uploaded, is there any relation between lung cancer and the immune system?
ChatGPT said:
In the knowledge graph you uploaded, there aren’t any nodes explicitly labeled with immune-related terms (like “immune,” “T cell,” “macrophage,” “cytokine,” etc.) that directly link to lung cancer concepts.
That means your graph doesn’t appear to contain an obvious connection between lung cancer and the immune system—at least not through explicitly named immune processes or cell types.
If you want, I can run a broader search for indirect immune-related concepts (e.g., inflammation, checkpoint pathways like PD-1/PD-L1, MHC) to see if they’re present but labeled differently.
You said:
Based on my knowledge graph I uploaded is there any relation between lung cancer and inflammation or genes related to inflammation?
ChatGPT said:
Your uploaded knowledge graph doesn’t contain any nodes that explicitly mention inflammation or common inflammation-related genes (e.g., TNF, interleukins, NF-κB, COX-2, JAK/STAT, chemokines).
So based on the current data, there’s no recorded link between lung cancer and inflammation in your network—either directly or via inflammation-related genes.
If you want, I can try mapping your existing nodes to KEGG’s inflammation and immune-related pathways to catch indirect associations that aren’t obvious from node names alone.
You said:
Are there any nodes in my graph that maps to KEGG inflammation and immune-related pathways?
ChatGPT said:
Here’s what I discovered regarding KEGG immune- and inflammation-related pathways, and how they relate (or don’t) to the nodes in your uploaded knowledge graph:
Key KEGG Immune-Related Pathways
KEGG organizes numerous human pathways under the “Immune System” category, including:
From your graph’s node name entries, I didn’t see any nodes that correspond to the KEGG immune or inflammation pathways listed above. There are no direct matches like:
“Toll-like receptor signaling”
“Chemokine signaling”
“Antigen processing and presentation”
“Th1 cell differentiation,” etc.
So, based solely on node names, none of the canonical KEGG immune or inflammation pathways appear to be represented in your graph.
Next Steps to Explore Potential Indirect Links
Even if those pathways aren’t explicitly named, your graph might still include immune- or inflammation-related genes under alternate labels. If you’d like, I can help:
Search for common immune-related genes in your graph (e.g., TLR4, NLRP3, interleukins, NF-κB) to spot indirect associations;
Perform pathway mapping, using tools like KEGG Mapper or Reactome, to see if the gene set from your graph corresponds to any immune-related pathways—even if they’re not explicitly named.
Would you like me to proceed with one of those approaches to dig deeper?
Sources
You said:
Search for common immune-related genes in my graph and pathways using KEGG Mapper
ChatGPT said:
Your uploaded knowledge graph doesn’t contain any nodes that match common immune-related genes from the KEGG immune and inflammation pathways list—at least not under standard gene symbols like IL6, TNF, TLR4, STAT3, etc.
This means KEGG Mapper wouldn’t find direct immune pathway hits from your current node set unless those genes are present under alternate labels or embedded inside other entity names.
If you want, I can:
Expand the search to look for partial matches (e.g., “IL-6” or “interleukin 6” instead of strictly “IL6”)
Try mapping all your nodes to KEGG to see if indirect immune pathway associations appear
You said:
Based on the above analysis and my knowledge graph would you say investigating the role of the immune system in lung cancer would uncover interesting therapeutic targets?
ChatGPT said:
Thought for 9s
Short answer: Yes — very likely.
Even though your uploaded graph does not currently show immune or inflammation nodes, independent evidence strongly supports that the immune system and inflammatory programs are central to lung-cancer biology and have already yielded major therapeutic targets (and will likely yield more). PMC+1
Why I say that (big points, with sources):
Immune checkpoint therapy (anti-PD-1/PD-L1, anti-CTLA-4) is an established, practice-changing class of treatments in non-small cell lung cancer (NSCLC) — i.e., the immune axis is clinically actionable in lung cancer. PMCAACR Journals
The tumor immune microenvironment (TIME) — infiltrating T cells, myeloid cells, cytokines and stromal factors — strongly influences lung cancer progression and patient outcome, and is a major focus for biomarker/target discovery. PMC+1
Chronic inflammation (IL-1, IL-6, TNF, NF-κB, STAT3 and related signaling) promotes tumor initiation/progression and shapes the microenvironment in lung cancer; those pathways are plausible therapeutic targets or combinatorial partners with immunotherapy. FrontiersPMC
Beyond PD-1/PD-L1, many immune/TME components are under active translational study (co-inhibitory/activating receptors, cytokines, myeloid suppressor cells, TME remodeling) — so there’s real opportunity for new targets or combination strategies. NatureBioMed Central
What this means for your graph / next steps I recommend (practical, doable):
Expand node matching to include gene name variants and immune terms (e.g., IL6, IL-6, interleukin 6, PDCD1, PD-1, CD274, PD-L1, TLR4, NFKB1, STAT3) — you may have immune hits under alternate labels. I can do this mapping for you and return matches.
Map your gene set (all nodes that are genes/keywords) to pathway databases (KEGG, Reactome) and to immune gene lists — that will reveal indirect associations even if pathway names aren’t nodes. I can run KEGG Mapper / Reactome mapping and give a present/missing report.
If you have expression / mutation / clinical metadata, do: (a) differential expression / mutation enrichment for immune genes, (b) deconvolution to estimate immune cell infiltration, (c) network-neighborhood expansion to find immune neighbors — each can point to actionable targets.
Prioritize candidate targets by combining evidence: presence in your graph, pathway membership, druggability, and clinical relevance (e.g., known checkpoint ligands, cytokines, myeloid regulators).
Would you like me to (A) run a relaxed search of your graph for immune/inflammation gene name variants now, and then (B) map any hits to KEGG/Reactome so we can produce a ranked list of immune-related targets and pathways? If yes I’ll start immediately and return the matched nodes + pathway hits.
Sources
Short answer: Yes — very likely.
Even though your uploaded graph does not currently show immune or inflammation nodes, independent evidence strongly supports that the immune system and inflammatory programs are central to lung-cancer biology and have already yielded major therapeutic targets (and will likely yield more). (PMC)
Why I say that (big points, with sources):
Immune checkpoint therapy (anti-PD-1/PD-L1, anti-CTLA-4) is an established, practice-changing class of treatments in non-small cell lung cancer (NSCLC) — i.e., the immune axis is clinically actionable in lung cancer. (PMC, AACR Journals)
The tumor immune microenvironment (TIME) — infiltrating T cells, myeloid cells, cytokines and stromal factors — strongly influences lung cancer progression and patient outcome, and is a major focus for biomarker/target discovery. (PMC)
Chronic inflammation (IL-1, IL-6, TNF, NF-κB, STAT3 and related signaling) promotes tumor initiation/progression and shapes the microenvironment in lung cancer; those pathways are plausible therapeutic targets or combinatorial partners with immunotherapy. (Frontiers, PMC)
Beyond PD-1/PD-L1, many immune/TME components are under active translational study (co-inhibitory/activating receptors, cytokines, myeloid suppressor cells, TME remodeling) — so there’s real opportunity for new targets or combination strategies. (Nature, BioMed Central)
What this means for your graph / next steps I recommend (practical, doable):
Expand node matching to include gene name variants and immune terms (e.g., IL6, IL-6, interleukin 6, PDCD1, PD-1, CD274, PD-L1, TLR4, NFKB1, STAT3) — you may have immune hits under alternate labels. I can do this mapping for you and return matches.
Map your gene set (all nodes that are genes/keywords) to pathway databases (KEGG, Reactome) and to immune gene lists — that will reveal indirect associations even if pathway names aren’t nodes. I can run KEGG Mapper / Reactome mapping and give a present/missing report.
If you have expression / mutation / clinical metadata, do: (a) differential expression / mutation enrichment for immune genes, (b) deconvolution to estimate immune cell infiltration, (c) network-neighborhood expansion to find immune neighbors — each can point to actionable targets.
Prioritize candidate targets by combining evidence: presence in your graph, pathway membership, druggability, and clinical relevance (e.g., known checkpoint ligands, cytokines, myeloid regulators).
Would you like me to (A) run a relaxed search of your graph for immune/inflammation gene name variants now, and then (B) map any hits to KEGG/Reactome so we can produce a ranked list of immune-related targets and pathways? If yes I’ll start immediately and return the matched nodes + pathway hits.
Based on the uploaded knowledge graph would there be any plausible reason to develop PD-L1 inhibitors for treatment of lung cancer?
ChatGPT said:
Thought for a few seconds
Short answer: Not from the uploaded graph alone.
Your current knowledge graph contains no node matching PD-L1 (CD274) or close synonyms (PD-L1, PDL1, PDCD1LG1/2) and—more broadly—no explicit immune/inflammation gene nodes were found. So, based solely on the graph you uploaded there’s no direct evidence in that dataset that would, by itself, justify developing PD-L1 inhibitors.
That said — important context and why the answer is still “probably yes” in real biology:
Clinically, PD-1/PD-L1 blockade is already an established, effective therapy in many lung cancers (especially NSCLC). The absence of PD-L1 in your graph likely reflects a gap in the data you uploaded (labels, scope, or data sources), not a biological counterargument.
Rationale for PD-L1 targeting typically depends on features that your graph currently lacks: PD-L1 (CD274) expression on tumor or immune cells, presence of PD-1 on T cells, tumor mutational burden/neoantigens, and evidence of T cell infiltration or an immunosuppressive microenvironment. None of those features appear in your nodes.
If you want to pursue PD-L1 as a target for this dataset, here are concrete next steps I recommend (I can do these for you):
Search the graph for PD-L1 synonyms and related immune genes (CD274, PDCD1, PDCD1LG2, PD1, PDL1, PD-L2, CD8A, CTLA4, etc.) — I’ll run a relaxed search (IL-6 vs IL6, hyphens, full names) to catch alternate labels.
Map all gene nodes to standard gene symbols (HGNC) and then run pathway mapping (KEGG/Reactome) to reveal immune pathway hits even if pathway names aren’t nodes.
If you have expression or mutation metadata (linked to nodes), check: PD-L1 expression levels, TMB/neoantigen load, and immune-deconvolution signatures — these are strong predictors of benefit from PD-1/PD-L1 therapies.
Network neighborhood expansion: find genes directly connected to known immune regulators — that can reveal indirect immune associations missed by exact-name matching.
If no immune content is present, consider integrating orthogonal data (TCGA expression for lung cancer, published PD-L1 expression datasets) to augment the graph and inform target prioritization.
Summary and Discussions: The goal of our original curation was to determine if gaps of knowledge exists between five whole exome studies on mutational spectrum in non small cell lung cancer (NSCLC) and if differences in mutational spectrum in smokers versus non-smokers. Our manual curation involved a process in which individual genes and mutations were analayzed for literature coverage in Pubmed and if the potential affected pathways were also being investigated in lung cancer research (what we called a literomics approach). Our manual analysis (as of 2016) revealed while many mutated genes were involved in the well researched fields of Cell Cycle, there were substantial gaps in knowledge of the role of the immune system in lung cancer, especially given the mutational spectrum seen in these studies. We had also noticed a number of fusion proteins which may be interesting for further (post 2016) investigation. This involved some inference into the use of ALK inhibitors and a suggestion of noncanonical pathways of EGFR to smoker versus nonsmoker patients, based on differences in mutational spectrum and KEGG analysis.
Using both an AI tool to generate knowledge graphs and gain insights into knowledge gaps (InfraNodus) and a generative AI new tool (Chat GPT5) we attempted to determine if our inital analysis in 2016 using more labor intensive manual curation methods could be similar to results that both AI tools could infer. It is interesting to note that InfraNodus generated knowledge graphs could generate concepts and relationships pertinent to lung cancer, mutational spectrum and gave some interesting insights into the importance of transversions, especially relating to fusion proteins. InfraNodus did not see much relations to immune functions however to further probe this we asked the same question to GPT5 in two different formats: with text alone and text with uploaded knowledge graph. Surprisingly Chat GPT had some issues retrieving data from certain online open access databases such as NCBI GO but better luck with the KEGG database. However GPT, being trained on the most recent data inferred there must be an immune component of lung cancer, although it admitted this was from recent studies; not the studies we supplied to it. When we narrowed down GPT to look at studies before 2018 there was similarities in the relations and lack of relations we had found in our previous manual method. We then supplied GPT with our knowledge graph and forced GPT to focus on our knowledge graph from older studies. Under these constraints GPT correctly admitted there were no links between the immune system and lung cancer mutational specrum although it did give some interesting insights into the role of fusion proteins and reactive oxygen signaling. After our intial curation, one of our experts Dr. Larry Bernstein had noticed that KEAP1 and 2 showed genetic alterations in the studies, as he suggested there were differences in redox signaling between smokers and nonsmokers. KEAP1 and 2 are intracellular redox sensors.
Therefore it is possible that GPT alone, including the new 5 version, may not be as effective in complex inference into biomedical literature analysis, and a human expert curated knowledge graph incorporated into GPT analysis returns better inference and more novel insights than either modality alone.
For further reading on Artificial Intelligence, Machine Learning and Immunotherapy on this Open Access Scientific Journal please read these articles:
Part D: Curation entitled Multiple Lung Cancer Genomic Projects Suggest New Targets, Research Directions for Non-Small Cell Lung Cancer originally published on 09/05/2014
Note the text below this point was used for all AI-based text analsysis
summarizes the clinical importance of five new lung cancer genome sequencing projects. These studies have identified genetic and epigenetic alterations in hundreds of lung tumors, of which some alterations could be taken advantage of using currently approved medications.
The reports, all published this month, included genomic information on more than 400 lung tumors. In addition to confirming genetic alterations previously tied to lung cancer, the studies identified other changes that may play a role in the disease.
“All of these studies say that lung cancers are genomically complex and genomically diverse,” said Dr. Matthew Meyerson of Harvard Medical School and the Dana-Farber Cancer Institute, who co-led several of the studies, including a large-scale analysis of squamous cell lung cancer by The Cancer Genome Atlas (TCGA) Research Network.
Some genes, Dr. Meyerson noted, were inactivated through different mechanisms in different tumors. He cautioned that little is known about alterations in DNA sequences that do not encode genes, which is most of the human genome.
Four of the papers are summarized below, with the first described in detail, as the Nature paper used a multi-‘omics strategy to evaluate expression, mutation, and signaling pathway activation in a large cohort of lung tumors. A literature informatics analysis is given for one of the papers. Please note that links on GENE names usually refer to the GeneCard entry.
Paper 1. Comprehensive genomic characterization of squamous cell lung cancers[1]
The Cancer Genome Atlas Research Network Project just reported, in the journal Nature, the results of their comprehensive profiling of 230 resected lung adenocarcinomas. The multi-center teams employed analyses of
microRNA
Whole Exome Sequencing including
Exome mutation analysis
Gene copy number
Splicing alteration
Methylation
Proteomic analysis
Summary:
Some very interesting overall findings came out of this analysis including:
High rates of somatic mutations including activating mutations in common oncogenes
Newly described loss of function MGA mutations
Sex differences in EGFR and RBM10 mutations
driver roles for NF1, MET, ERBB2 and RITI identified in certain tumors
differential mutational pattern based on smoking history
splicing alterations driven by somatic genomic changes
MAPK and PI3K pathway activation identified by proteomics not explained by mutational analysis = UNEXPLAINED MECHANISM of PATHWAY ACTIVATION
however, given the plethora of data, and in light of a similar study results recently released, there appears to be a great need for additional mining of this CGAP dataset. Therefore I attempted to curate some of the findings along with some other recent news relevant to the surprising findings with relation to biomarker analysis.
Makeup of tumor samples
230 lung adenocarcinomas specimens were categorized by:
Subtype
33% acinar
25% solid
14% micro-papillary
9% papillary
8% unclassified
5% lepidic
4% invasive mucinous
Gender
Smoking status
81% of patients reported past of present smoking
The authors note that TCGA samples were combined with previous data for analysis purpose.
A detailed description of Methodology and the location of deposited data are given at the following addresses:
Gender and Smoking Habits Show different mutational patterns
WES mutational analysis
a) smoking status
– there was a strong correlations of cytosine to adenine nucleotide transversions with past or present smoking. In fact smoking history separated into transversion high (past and previous smokers) and transversion low (never smokers) groups, corroborating previous results.
→ mutations in groups Transversion High Transversion Low
TP53, KRAS, STK11, EGFR, RB1, PI3CA
KEAP1, SMARCA4 RBM10
b) Gender
Although gender differences in mutational profiles have been reported, the study found minimal number of significantly mutated genes correlated with gender. Notably:
EGFR mutations enriched in female cohort
RBM10 loss of function mutations enriched in male cohort
Although the study did not analyze the gender differences with smoking patterns, it was noted that RBM10 mutations among males were more prevalent in the transversion high group.
Whole exome Sequencing and copy number analysis reveal Unique, Candidate Driver Genes
Whole exome sequencing revealed that 62% of tumors contained mutations (either point or indel) in known cancer driver genes such as:
KRAS, EGFR, BRMF, ERBB2
However, authors looked at the WES data from the oncogene-negative tumors and found unique mutations not seen in the tumors containing canonical oncogenic mutations.
Unique potential driver mutations were found in
TP53, KEAP1, NF1, and RIT1
The genomics and expression data were backed up by a proteomics analysis of three pathways:
MAPK pathway
mTOR
PI3K pathway
…. showing significant activation of all three pathways HOWEVER the analysis suggested that activation of signaling pathways COULD NOT be deduced from DNA sequencing alone. Phospho-proteomic analysis was required to determine the full extent of pathway modification.
For example, many tumors lacked an obvious mutation which could explain mTOR or MAPK activation.
Altered cell signaling pathways included:
Increased MAPK signaling due to activating KRAS
Higher mTOR due to inactivating STK11 leading to increased proliferation, translation
Pathway analysis of mutations revealed alterations in multiple cellular pathways including:
Reduced oxidative stress response
Nucleosome remodeling
RNA splicing
Cell cycle progression
Histone methylation
Summary:
Authors noted some interesting conclusions including:
MET and ERBB2 amplification and mutations in NF1 and RIT1 may be unique driver events in lung adenocarcinoma
Possible new drug development could be targeted to the RTK/RAS/RAF pathway
MYC pathway as another important target
Cluster analysis using multimodal omics approach identifies tumors based on single-gene driver events while other tumor have multiple driver mutational events (TUMOR HETEROGENEITY)
Paper 2. A Genomics-Based Classification of Human Lung Tumors[2]
3,726 point mutations and more than 90 indels in the coding sequence
Smokers with lung cancer show 10× the number of point mutations than never-smokers
Novel lung cancer genes, including DACH1, CFTR, RELN, ABCB5, and HGF were identified
Tumor samples from males showed high frequency of MYCBP2 MYCBP2 involved in transcriptional regulation of MYC.
Variant allele frequency analysis revealed 10/17 tumors were at least biclonal while 7/17 tumors were monoclonal revealing majority of tumors displayed tumor heterogeneity
Novel pathway alterations in lung cancer include cell-cycle and JAK-STAT pathways
14 fusion proteins found, including ROS1-ALK fusion. ROS1-ALK fusions have been frequently found in lung cancer and is indicative of poor prognosis[4].
Novel metabolic enzyme fusions
Alterations were identified in 54 genes for which targeted drugs are available. Drug-gable mutant targets include: AURKC, BRAF, HGF, EGFR, ERBB4, FGFR1, MET, JAK2, JAK3, HDAC2, HDAC6, HDAC9, BIRC6, ITGB1, ITGB3, MMP2, PRKCB, PIK3CG, TERT, KRAS, MMP14
Table. Validated Gene-Fusions Obtained from Ref-Seq Data
Note: Gene columns contain links for GeneCard while Gene function links are to the gene’s GO (Gene Ontology) function.
There has been a recent literature on the importance of the EML4-ALK fusion protein in lung cancer. EML4-ALK positive lung tumors were found to be les chemo sensitive to cytotoxic therapy[5] and these tumor cells may exhibit an epitope rendering these tumors amenable to immunotherapy[6]. In addition, inhibition of the PI3K pathway has sensitized EMl4-ALK fusion positive tumors to ALK-targeted therapy[7]. EML4-ALK fusion positive tumors show dependence on the HSP90 chaperone, suggesting this cohort of patients might benefit from the new HSP90 inhibitors recently being developed[8].
Table. Significantly mutated genes (point mutations, insertions/deletions) with associated function.
Table. Literature Analysis of pathways containing significantly altered genes in NSCLC reveal putative targets and risk factors, linkage between other tumor types, and research areas for further investigation.
Note: Significantly mutated genes, obtained from WES, were subjected to pathway analysis (KEGG Pathway Analysis) in order to see which pathways contained signicantly altered gene networks. This pathway term was then used for PubMed literature search together with terms “lung cancer”, “gene”, and “NOT review” to determine frequency of literature coverage for each pathway in lung cancer. Links are to the PubMEd search results.
KEGG pathway Name
# of PUBMed entries containing Pathway Name, Gene ANDLung Cancer
A few interesting genetic risk factors and possible additional targets for NSCLC were deduced from analysis of the above table of literature including HIF1-α, mIR-31, UBQLN1, ACE, mIR-193a, SRSF1. In addition, glioma, melanoma, colorectal, and prostate and lung cancer share many validated mutations, and possibly similar tumor driver mutations.
please click on graph for larger view
Paper 4. Mapping the Hallmarks of Lung Adenocarcinoma with Massively Parallel Sequencing[9]
Exome and genome characterization of somatic alterations in 183 lung adenocarcinomas
12 somatic mutations/megabase
U2AF1, RBM10, and ARID1A are among newly identified recurrently mutated genes
Structural variants include activating in-frame fusion of EGFR
Epigenetic and RNA deregulation proposed as a potential lung adenocarcinoma hallmark
Summary
Lung adenocarcinoma, the most common subtype of non-small cell lung cancer, is responsible for more than 500,000 deaths per year worldwide. Here, we report exome and genome sequences of 183 lung adenocarcinoma tumor/normal DNA pairs. These analyses revealed a mean exonic somatic mutation rate of 12.0 events/megabase and identified the majority of genes previously reported as significantly mutated in lung adenocarcinoma. In addition, we identified statistically recurrent somatic mutations in the splicing factor gene U2AF1 and truncating mutations affecting RBM10 and ARID1A. Analysis of nucleotide context-specific mutation signatures grouped the sample set into distinct clusters that correlated with smoking history and alterations of reported lung adenocarcinoma genes. Whole-genome sequence analysis revealed frequent structural rearrangements, including in-frame exonic alterations within EGFR and SIK2 kinases. The candidate genes identified in this study are attractive targets for biological characterization and therapeutic targeting of lung adenocarcinoma.
Paper 5. Integrative genome analyses identify key somatic driver mutations of small-cell lung cancer[10]
Highlights
Whole exome and transcriptome (RNASeq) sequencing 29 small-cell lung carcinomas
High mutation rate 7.4 protein-changing mutations/million base pairs
Inactivating mutations in TP53 and RB1
Functional mutations in CREBBP, EP300, MLL, PTEN, SLIT2, EPHA7, FGFR1 (determined by literature and database mining)
The mutational spectrum seen in human data also present in a Tp53-/- Rb1-/- mouse lung tumor model
Curator Graphical Summary of Interesting Findings From the Above Studies
The above figure (please click on figure) represents themes and findings resulting from the aforementioned studies including
questions which will be addressed in Future Postson this site.
UPDATED 10/10/2021
The following article uses RNASeq to screen lung adenocarcinomas for fusion proteins in patients with either low or high tumor mutational burden. Findings included presence of MET fusion proteins in addition to other fusion proteins irrespective if tumors were driver negative by DNASeq screening.
High Yield of RNA Sequencing for Targetable Kinase Fusions in Lung Adenocarcinomas with No Mitogenic Driver Alteration Detected by DNA Sequencing and Low Tumor Mutation Burden
Source:
High Yield of RNA Sequencing for Targetable Kinase Fusions in Lung Adenocarcinomas with No Mitogenic Driver Alteration Detected by DNA Sequencing and Low Tumor Mutation Burden
RymaBenayed, MichaelOffin, KerryMullaney, PurvilSukhadia, KellyRios, PatriceDesmeules, RyanPtashkin, HelenWon, JasonChang, DarraghHalpenny, Alison M.Schram, Charles M.Rudin, David M.Hyman, Maria E.Arcila, Michael F.Berger, AhmetZehir, Mark G.Kris, AlexanderDrilon and MarcLadanyi
Purpose: Targeted next-generation sequencing of DNA has become more widely used in the management of patients with lung adenocarcinoma; however, no clear mitogenic driver alteration is found in some cases. We evaluated the incremental benefit of targeted RNA sequencing (RNAseq) in the identification of gene fusions and MET exon 14 (METex14) alterations in DNA sequencing (DNAseq) driver–negative lung cancers.
Experimental Design: Lung cancers driver negative by MSK-IMPACT underwent further analysis using a custom RNAseq panel (MSK-Fusion). Tumor mutation burden (TMB) was assessed as a potential prioritization criterion for targeted RNAseq.
Results: As part of prospective clinical genomic testing, we profiled 2,522 lung adenocarcinomas using MSK-IMPACT, which identified 195 (7.7%) fusions and 119 (4.7%) METex14 alterations. Among 275 driver-negative cases with available tissue, 254 (92%) had sufficient material for RNAseq. A previously undetected alteration was identified in 14% (36/254) of cases, 33 of which were actionable (27 in-frame fusions, 6 METex14). Of these 33 patients, 10 then received matched targeted therapy, which achieved clinical benefit in 8 (80%). In the 32% (81/254) of DNAseq driver–negative cases with low TMB [0–5 mutations/Megabase (mut/Mb)], 25 (31%) were positive for previously undetected gene fusions on RNAseq, whereas, in 151 cases with TMB >5 mut/Mb, only 7% were positive for fusions (P < 0.0001).
Conclusions: Targeted RNAseq assays should be used in all cases that appear driver negative by DNAseq assays to ensure comprehensive detection of actionable gene rearrangements. Furthermore, we observed a significant enrichment for fusions in DNAseq driver–negative samples with low TMB, supporting the prioritization of such cases for additional RNAseq.
Translational Relevance
Inhibitors targeting kinase fusions have shown dramatic and durable responses in lung cancer patients, making their comprehensive detection critical. Here, we evaluated the incremental benefit of targeted RNA sequencing (RNAseq) in the identification of gene fusions in patients where no clear mitogenic driver alteration is found by DNA sequencing (DNAseq)–based panel testing. We found actionable alterations (kinase fusions or MET exon 14 skipping) in 13% of cases apparently driver negative by previous DNAseq testing. Among the driver-negative samples tested by RNAseq, those with low tumor mutation burden (TMB) were significantly enriched for gene fusions when compared with the ones with higher TMB. In a clinical setting, such patients should be prioritized for RNAseq. Thus, a rational, algorithmic approach to the use of targeted RNA-based next-generation sequencing (NGS) to complement large panel DNA-based NGS testing can be highly effective in comprehensively uncovering targetable gene fusions or oncogenic isoforms not just in lung cancer but also more generally across different tumor types.
Wake Up and Smell the Fusions: Single-Modality Molecular Testing Misses Drivers
by Kurtis D.Davies and Dara L.Aisner
Abstract
Multitarget assays have become common in clinical molecular diagnostic laboratories. However, all assays, no matter how well designed, have inherent gaps due to technical and biological limitations. In some clinical cases, testing by multiple methodologies is needed to address these gaps and ensure the most accurate molecular diagnoses.
In this issue of Clinical Cancer Research, Benayed and colleagues illustrate the growing need to consider multiple molecular testing methodologies for certain clinical specimens (1). The rapidly expanding list of actionable molecular alterations across cancer types has resulted in the wide adoption of multitarget testing approaches, particularly those based on next-generation sequencing (NGS). NGS-based assays are commonly viewed as “one-stop shops” to detect a vast array of molecular variants. However, as Benayed and colleagues discuss, even well-designed and highly vetted NGS assays have inherent gaps that, under certain circumstances, are ideally addressed by analyzing the sample using an alternative approach.
In the article, the authors examined a cohort of lung adenocarcinoma patient samples that had been deemed “driver- negative” via MSK-IMPACT, an FDA-cleared test that is widely considered by experts in the field to be one of the best examples of a DNA-based large gene panel NGS assay (2). Of 589 driver-negative cases, 254 had additional material amenable for a different approach: RNA-based NGS designed specifically for gene fusion and oncogenic gene isoform detection. After accounting for quality control failures, 232 samples were successfully sequenced, and, among these, 36 samples (representing an astonishing 15.5% of tested cases) were found to be positive for a driver gene fusion or oncogenic isoform that had not been detected by DNA-based NGS. The real-world value derived from this orthogonal testing schema was more than theoretical, with 8 of 10 (80%) patients demonstrating clinical benefit when treated according to the alteration identified via the RNA-based approach.
To detect gene rearrangements that lead to oncogenic gene fusions (and to detect mutations and insertions/deletions that lead to MET exon 14 skipping), MSK-IMPACT employs hybrid capture-based enrichment of selected intronic regions from genomic DNA. While this approach has proven to be successful in a variety of settings, there are associated limitations that were determined in this study to underlie the discrepancies between MSK-IMPACT and the RNA-based assay. First, some introns that are involved in clinically actionable rearrangement events are very large, thus requiring substantial sequencing capital that can represent a disproportionate fraction of the assay. Despite the ability via NGS to perform sequencing at a large scale, this sequencing capacity is still finite, and thus decisions must be made to sacrifice coverage of certain large genomic regions to ensure sufficient sequencing depth for other desired genomic targets. In the case of MSK-IMPACT (and most other DNA-based NGS assays), certain important introns in NTRK3 and NRG1 are not included in covered content, simply because they are too large (>90 Kb each). The second primary problem with DNA-based analysis of introns is that they often contain highly repetitive elements that are extremely difficult to assess via NGS due to their recurring presence across the genome. Attempts to sequence these regions are largely unfruitful because any sequencing data obtained cannot be specifically aligned/mapped to the desired targeted region of the genome (3). This is particularly true for intron 31 of ROS1, because it contains two repetitive long interspersed nuclear elements, and many DNA-based assays, including MSK-IMPACT, poorly cover this intron (4). In this study by Benayed and colleagues, the most common discrepant alteration was fusion involving ROS1, which accounted for 10 of 36 (28%) cases. At least six of these, those that demonstrated fusion to ROS1 exon 32, were likely directly explained by incomplete intron 31 sequencing. RNA-based analysis is able to overcome the above described limitations owing to the simple fact that sequencing is focused on exons post-splicing and the need to sequence introns is entirely avoided (Fig. 1).
Schematic representation of underlying genomic complexities that can lead to false-negative gene fusion results in DNA-based NGS analysis. In some cases, RNA-based approaches may overcome the limitations of DNA-based testing.
Lack of sufficient intronic coverage could not account for all of the discrepancies between DNA-based and RNA-based analysis however. Six samples in the cohort were found to be positive for MET exon 14 skipping based on RNA. In five of these, genomic alterations in MET introns 13 or 14 were observed, however they did not conform to canonical splice site alterations and thus were not initially called (although this was addressed by bioinformatics updates). In RNA-based testing, however, determination of exon skipping is simplified such that, regardless of the specific genomic alteration that interferes with splicing, absence of the exon in the transcript is directly observed (5). In another two of the discrepant cases, tumor purity was observed to be low in the sample, meaning that the expected variant allele frequency (VAF) for a genomic event would also likely be low, potentially below detectable levels. However, overexpression of the fusions at the transcript level was theorized to compensate for low VAF (Fig. 1). Additional explanations for discordant findings between the assays included sample-specific poor sequencing in selected introns and complex rearrangements that hindered proper capture (Fig. 1).
The take home message from Benayed and colleagues is simply this: there is no perfect assay that will detect 100% of the potential actionable alterations in patient samples. Even an extremely well designed, thoroughly vetted, and FDA-cleared assay such as MSK-IMPACT will have inherent and unavoidable “holes” due to intrinsic limitations. The solution to this dilemma, as adeptly described by Benayed and colleagues, is additional testing using a different approach. While in an ideal world every clinical tumor sample would be tested by multiple modalities to ensure the most comprehensive clinical assessment, the reality is that these samples are often scant and testing is fiscally burdensome (and often not reimbursed). Therefore, algorithms to determine which samples should be reflexed to secondary assays after testing with a primary assay are critical for maximizing benefit. In this study, the first algorithmic step was lack of an identified driver (because activated oncogenic drivers tend to exist exclusively of each other), which amounted to 23% of samples tested with the primary assay. In addition, the authors found a significantly higher rate of actionable gene fusions in samples with a low (<5 mut/Mb) tumor mutational burden, meaning that this metric, which was derived from the primary assay, could also be used to help inform decision making regarding additional testing. While this scenario is somewhat specific to lung cancer, similar approaches could be prescribed on a cancer type–specific basis.
These findings should be considered a “wake-up call” for oncologists in regard to the ordering and interpretation of molecular testing. It is clear from these and other published findings that advanced molecular analysis has limitations that require nuanced technical understanding. As this arena evolves, it is critical for oncologists (and trainees) to gain an increased comprehension of how to identify when the “gaps” in a test might be most clinically relevant. This requires a level of technical cognizance that has been previously unexpected of clinical practitioners, yet is underscored by the reality that opportunities for effective targeted therapy can and will be missed if the treating oncologist is unaware of how to best identify patients for whom additional testing is warranted. This study also highlights the mantra of “no test is perfect” regardless of prestige of the testing institution, number of past tests performed, or regulatory status. NGS, despite its benefits, does not mean all-encompassing. It is only through the adaptability of laboratories to utilize knowledge such as is provided by Benayed and colleagues that advances in laboratory medicine can be quickly deployed to maximize benefits for oncology patients.
Govindan R, Ding L, Griffith M, Subramanian J, Dees ND, Kanchi KL, Maher CA, Fulton R, Fulton L, Wallis J et al: Genomic landscape of non-small cell lung cancer in smokers and never-smokers. Cell 2012, 150(6):1121-1134.
Takeuchi K, Soda M, Togashi Y, Suzuki R, Sakata S, Hatano S, Asaka R, Hamanaka W, Ninomiya H, Uehara H et al: RET, ROS1 and ALK fusions in lung cancer. Nature medicine 2012, 18(3):378-381.
Morodomi Y, Takenoyama M, Inamasu E, Toyozawa R, Kojo M, Toyokawa G, Shiraishi Y, Takenaka T, Hirai F, Yamaguchi M et al: Non-small cell lung cancer patients with EML4-ALK fusion gene are insensitive to cytotoxic chemotherapy. Anticancer research 2014, 34(7):3825-3830.
Yoshimura M, Tada Y, Ofuzi K, Yamamoto M, Nakatsura T: Identification of a novel HLA-A 02:01-restricted cytotoxic T lymphocyte epitope derived from the EML4-ALK fusion gene. Oncology reports 2014, 32(1):33-39.
Workman P, van Montfort R: EML4-ALK fusions: propelling cancer but creating exploitable chaperone dependence. Cancer discovery 2014, 4(6):642-645.
Imielinski M, Berger AH, Hammerman PS, Hernandez B, Pugh TJ, Hodis E, Cho J, Suh J, Capelletti M, Sivachenko A et al: Mapping the hallmarks of lung adenocarcinoma with massively parallel sequencing. Cell 2012, 150(6):1107-1120.
Peifer M, Fernandez-Cuesta L, Sos ML, George J, Seidel D, Kasper LH, Plenker D, Leenders F, Sun R, Zander T et al: Integrative genome analyses identify key somatic driver mutations of small-cell lung cancer. Nature genetics 2012, 44(10):1104-1110.
Other posts on this site which refer to Lung Cancer and Cancer Genome Sequencing include:
CSHL, UCLA & Einstein to Lead Roundtable Discussions on Single-Cell Sequencing
Interactive discussions on three of the key questions researchers are facing when considering single-cell analysis will be held on the second day of the Single-Cell Sequencing Conference at Next Generation Dx Summit, taking place August 20-21, 2014 in Washington, DC. For full program details and to register, please visit NextGenerationDx.com/Single-Cell-Sequencing.Making Single-Cell Analysis Cost Effective for Clinical Use
Moderator: James Hicks, Ph.D., Research Professor, Cancer Genomics, Cold Spring Harbor Laboratory
Methods for capture: What are the tradeoffs?
Combining RNA, DNA and protein analysis
What genomic assays are most informative?
Can assays be certifiable?
Finding a Needle in a Haystack: Towards Diagnosing Rare Soft Tissue Cancer Stem Cells (CSCs) Moderator: Michael Masterman-Smith, Ph.D., Entrepreneurial Scientist, UCLA California NanoSystems Institute
Rethinking companion diagnostics for cancer to incorporate analysis of CSCs
Current direct methodologies of CSC detection/isolation
Current proxy methodologies of CSC detection/isolation
The hope and promise of single-cell assay tools and technologies
Why Single-Cell Sequencing? Moderator: Jan Vijg, Ph.D., Professor and Chairman, Genetics, Albert Einstein College of Medicine
Sample limitations, e.g., prenatal diagnostics and CTCs
Sample limitations, e.g., prenatal diagnostics and CTCs
To study cell-to-cell variation, e.g., in tumors as well as normal tissues
To overcome technological constraints, e.g., detecting somatic mutations
Cell-to-cell fluctuations in gene expression can easily impair function, yet can be undetectable by measuring averages
Sequencing data from bulk DNA or RNA from multiple cells provide global information on average states of cell populations. But with whole-genome amplification and NGS, researchers can detect variation in individual cancer cells and dissect tumor evolution. Such cancer genome sequencing will improve oncology by detecting rare tumor cells early, measuring intra-/intertumor heterogeneity, guiding chemotherapy and controlling drug resistance. The Single-Cell Sequencing conference explores the latest strategies, data analyses and clinical considerations that influence and aid cancer diagnosis, prognosis and prediction and will lead to individualized cancer therapy.
Sessions include presentations spanning the opportunities of clinical single-cell analysis from:
Sunney Xie, Ph.D., Mallinckrodt Professor. Chemistry and Chemical Biology, Harvard University
Maximilian Diehn, M.D., Ph.D., Assistant Professor, Radiation Oncology, Stanford Cancer Institute, Institute for Stem Cell Biology & Regenerative Medicine, Stanford University
Denis Smirnov, Associate Scientific Director, US Biomarker Oncology, Janssen R&D US
James Hicks, Ph.D., Research Professor, Cancer Genomics, Cold Spring Harbor Laboratory
Jan Vijg, Ph.D., Professor and Chairman, Genetics, Albert Einstein College of Medicine
John F. Zhong, Ph.D., Associate Professor, Pathology, University of Southern California School of Medicine
Mark Hills, Ph.D., Research Scientist, Peter M. Lansdorp Laboratory, BC Cancer Research Centre
Michael Masterman-Smith, Ph.D., Entrepreneurial Scientist, UCLA California NanoSystems Institute
Parveen Kumar, Research Scientist, Thierry Voet Laboratory, Human Genetics, University of Leuven
Peter Nemes, Ph.D., Assistant Professor, Chemistry, George Washington University
Theresa Zhang, Ph.D., Vice President, Research Services, Personal Genome Diagnostics
Yong Wang, Ph.D., Senior Postdoctoral Fellow, Nicholas E. Navin Laboratory, Genetics, Bioinformatics, MD Anderson Cancer Center
Zivana Tezak, Ph.D., Associate Director, Science and Technology, Personalized Medicine, Office of In Vitro Diagnostic Device Evaluation and Safety (OIVD), Center for Devices and Radiological Health (CDRH), FDA
Recommended Pre-Conference Courses
NGS Data Analysis – Determining Clinical Utility of Genome Variants Monday, August 18 | 9:00am – 12:00pm This course will explore the strategies of genomic data analysis and interpretation, an emergent discipline that seeks to deliver better answers from NGS data so that patients and their physicians can determine informed healthcare decisions. View Details
NGS as a Diagnostics Platform Monday, August 18 | 2:00pm – 5:00pm The focus of this short course will be on understanding the use of NGS in clinical diagnosis, practical implementation of NGS in clinical laboratories and analysis of large data sets by using bioinformatics tools to parse and interpret data in relation to the clinical phenotype. The concluding presentation will be dedicated to quality and standardization of NGS assays. View Details
Summary – Volume 4, Part 2: Translational Medicine in Cardiovascular Diseases
Author and Curator: Larry H Bernstein, MD, FCAP
We have covered a large amount of material that involves
the development,
application, and
validation of outcomes of medical and surgical procedures
that are based on translation of science from the laboratory to the bedside, improving the standards of medical practice at an accelerated pace in the last quarter century, and in the last decade. Encouraging enabling developments have been:
1. The establishment of national and international outcomes databases for procedures by specialist medical societies
2. The identification of problem areas, particularly in activation of the prothrombotic pathways, infection control to an extent, and targeting of pathways leading to progression or to arrythmogenic complications.
5. This has become possible because of the advances in our knowledge of key related pathogenetic mechanisms involving gene expression and cellular regulation of complex mechanisms.
This completes what has been presented in Part 2, Vol 4 , and supporting references for the main points that are found in the Leaders in Pharmaceutical Intelligence Cardiovascular book. Part 1 was concerned with Posttranslational Modification of Proteins, vital for understanding cellular regulation and dysregulation. Part 2 was concerned with Translational Medical Therapeutics, the efficacy of medical and surgical decisions based on bringing the knowledge gained from the laboratory, and from clinical trials into the realm opf best practice. The time for this to occur in practice in the past has been through roughly a generation of physicians. That was in part related to the busy workload of physicians, and inability to easily access specialty literature as the volume and complexity increased. This had an effect of making access of a family to a primary care provider through a lifetime less likely than the period post WWII into the 1980s.
However, the growth of knowledge has accelerated in the specialties since the 1980’s so that the use of physician referral in time became a concern about the cost of medical care. This is not the place for or a matter for discussion here. It is also true that the scientific advances and improvements in available technology have had a great impact on medical outcomes. The only unrelated issue is that of healthcare delivery, which is not up to the standard set by serial advances in therapeutics, accompanied by high cost due to development costs, marketing costs, and development of drug resistance.
I shall identify continuing developments in cardiovascular diagnostics, therapeutics, and bioengineering that is and has been emerging.
Abstract:Genome-wide characterization of the in vivo cellular response to perturbation is fundamental to understanding how cells survive stress. Identifying the proteins and pathways perturbed by small molecules affects biology and medicine by revealing the mechanisms of drug action. We used a yeast chemogenomics platform that quantifies the requirement for each gene for resistance to a compound in vivo to profile 3250 small molecules in a systematic and unbiased manner. We identified 317 compounds that specifically perturb the function of 121 genes and characterized the mechanism of specific compounds. Global analysis revealed that the cellular response to small molecules is limited and described by a network of 45 major chemogenomic signatures. Our results provide a resource for the discovery of functional interactions among genes, chemicals, and biological processes.
In order to identify how chemical compounds target genes and affect the physiology of the cell, tests of the perturbations that occur when treated with a range of pharmacological chemicals are required. By examining the haploinsufficiency profiling (HIP) and homozygous profiling (HOP) chemogenomic platforms, Lee et al.(p. 208) analyzed the response of yeast to thousands of different small molecules, with genetic, proteomic, and bioinformatic analyses. Over 300 compounds were identified that targeted 121 genes within 45 cellular response signature networks. These networks were used to extrapolate the likely effects of related chemicals, their impact upon genetic pathways, and to identify putative gene functions
A team of cardiovascular researchers from the Cardiovascular Research Center at Icahn School of Medicine at Mount Sinai, Sanford-Burnham Medical Research Institute, and University of California, San Diego have identified a small, but powerful, new player in thIe onset and progression of heart failure. Their findings, published in the journal Nature on March 12, also show how they successfully blocked the newly discovered culprit.
Investigators identified a tiny piece of RNA called miR-25 that blocks a gene known as SERCA2a, which regulates the flow of calcium within heart muscle cells. Decreased SERCA2a activity is one of the main causes of poor contraction of the heart and enlargement of heart muscle cells leading to heart failure.
Using a functional screening system developed by researchers at Sanford-Burnham, the research team discovered miR-25 acts pathologically in patients suffering from heart failure, delaying proper calcium uptake in heart muscle cells. According to co-lead study authors Christine Wahlquist and Dr. Agustin Rojas Muñoz, developers of the approach and researchers in Mercola’s lab at Sanford-Burnham, they used high-throughput robotics to sift through the entire genome for microRNAs involved in heart muscle dysfunction.
Subsequently, the researchers at the Cardiovascular Research Center at Icahn School of Medicine at Mount Sinai found that injecting a small piece of RNA to inhibit the effects of miR-25 dramatically halted heart failure progression in mice. In addition, it also improved their cardiac function and survival.
“In this study, we have not only identified one of the key cellular processes leading to heart failure, but have also demonstrated the therapeutic potential of blocking this process,” says co-lead study author Dr. Dongtak Jeong, a post-doctoral fellow at the Cardiovascular Research Center at Icahn School of Medicine at Mount Sinai in the laboratory of the study’s co-senior author Dr. Roger J. Hajjar.
Publication: Inhibition of miR-25 improves cardiac contractility in the failing heart.Christine Wahlquist, Dongtak Jeong, Agustin Rojas-Muñoz, Changwon Kho, Ahyoung Lee, Shinichi Mitsuyama, Alain Van Mil, Woo Jin Park, Joost P. G. Sluijter, Pieter A. F. Doevendans, Roger J. : Hajjar & Mark Mercola. Nature (March 2014) http://www.nature.com/nature/journal/vaop/ncurrent/full/nature13073.html
“Junk” DNA Tied to Heart Failure
Deep RNA Sequencing Reveals Dynamic Regulation of Myocardial Noncoding RNAs in Failing Human Heart and Remodeling With Mechanical Circulatory Support
The myocardial transcriptome is dynamically regulated in advanced heart failure and after LVAD support. The expression profiles of lncRNAs, but not mRNAs or miRNAs, can discriminate failing hearts of different pathologies and are markedly altered in response to LVAD support. These results suggest an important role for lncRNAs in the pathogenesis of heart failure and in reverse remodeling observed with mechanical support.
Junk DNA was long thought to have no important role in heredity or disease because it doesn’t code for proteins. But emerging research in recent years has revealed that many of these sections of the genome produce noncoding RNA molecules that still have important functions in the body. They come in a variety of forms, some more widely studied than others. Of these, about 90% are called long noncoding RNAs (lncRNAs), and exploration of their roles in health and disease is just beginning.
The Washington University group performed a comprehensive analysis of all RNA molecules expressed in the human heart. The researchers studied nonfailing hearts and failing hearts before and after patients received pump support from left ventricular assist devices (LVAD). The LVADs increased each heart’s pumping capacity while patients waited for heart transplants.
In their study, the researchers found that unlike other RNA molecules, expression patterns of long noncoding RNAs could distinguish between two major types of heart failure and between failing hearts before and after they received LVAD support.
“The myocardial transcriptome is dynamically regulated in advanced heart failure and after LVAD support. The expression profiles of lncRNAs, but not mRNAs or miRNAs, can discriminate failing hearts of different pathologies and are markedly altered in response to LVAD support,” wrote the researchers. “These results suggest an important role for lncRNAs in the pathogenesis of heart failure and in reverse remodeling observed with mechanical support.”
‘Junk’ Genome Regions Linked to Heart Failure
In a recent issue of the journal Circulation, Washington University investigators report results from the first comprehensive analysis of all RNA molecules expressed in the human heart. The researchers studied nonfailing hearts and failing hearts before and after patients received pump support from left ventricular assist devices (LVAD). The LVADs increased each heart’s pumping capacity while patients waited for heart transplants.
“We took an unbiased approach to investigating which types of RNA might be linked to heart failure,” said senior author Jeanne Nerbonne, the Alumni Endowed Professor of Molecular Biology and Pharmacology. “We were surprised to find that long noncoding RNAs stood out.
In the new study, the investigators found that unlike other RNA molecules, expression patterns of long noncoding RNAs could distinguish between two major types of heart failure and between failing hearts before and after they received LVAD support.
“We don’t know whether these changes in long noncoding RNAs are a cause or an effect of heart failure,” Nerbonne said. “But it seems likely they play some role in coordinating the regulation of multiple genes involved in heart function.”
Nerbonne pointed out that all types of RNA molecules they examined could make the obvious distinction: telling the difference between failing and nonfailing hearts. But only expression of the long noncoding RNAs was measurably different between heart failure associated with a heart attack (ischemic) and heart failure without the obvious trigger of blocked arteries (nonischemic). Similarly, only long noncoding RNAs significantly changed expression patterns after implantation of left ventricular assist devices.
Heart failure is a complex disease with a broad spectrum of pathological features. Despite significant advancement in clinical diagnosis through improved imaging modalities and hemodynamic approaches, reliable molecular signatures for better differential diagnosis and better monitoring of heart failure progression remain elusive. The few known clinical biomarkers for heart failure, such as plasma brain natriuretic peptide and troponin, have been shown to have limited use in defining the cause or prognosis of the disease.1,2 Consequently, current clinical identification and classification of heart failure remain descriptive, mostly based on functional and morphological parameters. Therefore, defining the pathogenic mechanisms for hypertrophic versus dilated or ischemic versus nonischemic cardiomyopathies in the failing heart remain a major challenge to both basic science and clinic researchers. In recent years, mechanical circulatory support using left ventricular assist devices (LVADs) has assumed a growing role in the care of patients with end-stage heart failure.3 During the earlier years of LVAD application as a bridge to transplant, it became evident that some patients exhibit substantial recovery of ventricular function, structure, and electric properties.4 This led to the recognition that reverse remodeling is potentially an achievable therapeutic goal using LVADs. However, the underlying mechanism for the reverse remodeling in the LVAD-treated hearts is unclear, and its discovery would likely hold great promise to halt or even reverse the progression of heart failure.
Efficacy and Safety of Dabigatran Compared With Warfarin in Relation to Baseline Renal Function in Patients With Atrial Fibrillation: A RE-LY (Randomized Evaluation of Long-term Anticoagulation Therapy) Trial Analysis
In patients with atrial fibrillation, impaired renal function is associated with a higher risk of thromboembolic events and major bleeding. Oral anticoagulation with vitamin K antagonists reduces thromboembolic events but raises the risk of bleeding. The new oral anticoagulant dabigatran has 80% renal elimination, and its efficacy and safety might, therefore, be related to renal function. In this prespecified analysis from the Randomized Evaluation of Long-Term Anticoagulant Therapy (RELY) trial, outcomes with dabigatran versus warfarin were evaluated in relation to 4 estimates of renal function, that is, equations based on creatinine levels (Cockcroft-Gault, Modification of Diet in Renal Disease (MDRD), Chronic Kidney Disease Epidemiology Collaboration [CKD-EPI]) and cystatin C. The rates of stroke or systemic embolism were lower with dabigatran 150 mg and similar with 110 mg twice daily irrespective of renal function. Rates of major bleeding were lower with dabigatran 110 mg and similar with 150 mg twice daily across the entire range of renal function. However, when the CKD-EPI or MDRD equations were used, there was a significantly greater relative reduction in major bleeding with both doses of dabigatran than with warfarin in patients with estimated glomerular filtration rate ≥80 mL/min. These findings show that dabigatran can be used with the same efficacy and adequate safety in patients with a wide range of renal function and that a more accurate estimate of renal function might be useful for improved tailoring of anticoagulant treatment in patients with atrial fibrillation and an increased risk of stroke.
Aldosterone Regulates MicroRNAs in the Cortical Collecting Duct to Alter Sodium Transport.
ABSTRACT A role for microRNAs (miRs) in the physiologic regulation of sodium transport in the kidney has not been established. In this study, we investigated the potential of aldosterone to alter miR expression in mouse cortical collecting duct (mCCD) epithelial cells. Microarray studies demonstrated the regulation of miR expression by aldosterone in both cultured mCCD and isolated primary distal nephron principal cells.
Aldosterone regulation of the most significantly downregulated miRs, mmu-miR-335-3p, mmu-miR-290-5p, and mmu-miR-1983 was confirmed by quantitative RT-PCR. Reducing the expression of these miRs separately or in combination increased epithelial sodium channel (ENaC)-mediated sodium transport in mCCD cells, without mineralocorticoid supplementation. Artificially increasing the expression of these miRs by transfection with plasmid precursors or miR mimic constructs blunted aldosterone stimulation of ENaC transport.
Using a newly developed computational approach, termed ComiR, we predicted potential gene targets for the aldosterone-regulated miRs and confirmed ankyrin 3 (Ank3) as a novel aldosterone and miR-regulated protein.
A dual-luciferase assay demonstrated direct binding of the miRs with the Ank3-3′ untranslated region. Overexpression of Ank3 increased and depletion of Ank3 decreased ENaC-mediated sodium transport in mCCD cells. These findings implicate miRs as intermediaries in aldosterone signaling in principal cells of the distal kidney nephron.
2. Diagnostic Biomarker Status
A prospective study of the impact of serial troponin measurements on the diagnosis of myocardial infarction and hospital and 6-month mortality in patients admitted to ICU with non-cardiac diagnoses.
ABSTRACT Troponin T (cTnT) elevation is common in patients in the Intensive Care Unit (ICU) and associated with morbidity and mortality. Our aim was to determine the epidemiology of raised cTnT levels and contemporaneous electrocardiogram (ECG) changes suggesting myocardial infarction (MI) in ICU patients admitted for non-cardiac reasons.
cTnT and ECGs were recorded daily during week 1 and on alternate days during week 2 until discharge from ICU or death. ECGs were interpreted independently for the presence of ischaemic changes. Patients were classified into 4 groups: (i) definite MI (cTnT >=15 ng/L and contemporaneous changes of MI on ECG), (ii) possible MI (cTnT >=15 ng/L and contemporaneous ischaemic changes on ECG), (iii) troponin rise alone (cTnT >=15 ng/L), or (iv) normal. Medical notes were screened independently by two ICU clinicians for evidence that the clinical teams had considered a cardiac event.
Data from 144 patients were analysed [42% female; mean age 61.9 (SD 16.9)]. 121 patients (84%) had at least one cTnT level >=15 ng/L. A total of 20 patients (14%) had a definite MI, 27% had a possible MI, 43% had a cTNT rise without contemporaneous ECG changes, and 16% had no cTNT rise. ICU, hospital and 180 day mortality were significantly higher in patients with a definite or possible MI.Only 20% of definite MIs were recognised by the clinical team. There was no significant difference in mortality between recognised and non-recognised events.At time of cTNT rise, 100 patients (70%) were septic and 58% were on vasopressors. Patients who were septic when cTNT was elevated had an ICU mortality of 28% compared to 9% in patients without sepsis. ICU mortality of patients who were on vasopressors at time of cTNT elevation was 37% compared to 1.7% in patients not on vasopressors.
The majority of critically ill patients (84%) had a cTnT rise and 41% met criteria for a possible or definite MI of whom only 20% were recognised clinically. Mortality up to 180 days was higher in patients with a cTnT rise.
Prognostic performance of high-sensitivity cardiac troponin T kinetic changes adjusted for elevated admission values and the GRACE score in an unselected emergency department population.
ABSTRACT To test the prognostic performance of rising and falling kinetic changes of high-sensitivity cardiac troponin T (hs-cTnT) and the GRACE score.
Rising and falling hs-cTnT changes in an unselected emergency department population were compared.
635 patients with a hs-cTnT >99th percentile admission value were enrolled. Of these, 572 patients qualified for evaluation with rising patterns (n=254, 44.4%), falling patterns (n=224, 39.2%), or falling patterns following an initial rise (n=94, 16.4%). During 407days of follow-up, we observed 74 deaths, 17 recurrent AMI, and 79 subjects with a composite of death/AMI. Admission values >14ng/L were associated with a higher rate of adverse outcomes (OR, 95%CI:death:12.6, 1.8-92.1, p=0.01, death/AMI:6.7, 1.6-27.9, p=0.01). Neither rising nor falling changes increased the AUC of baseline values (AUC: rising 0.562 vs 0.561, p=ns, falling: 0.533 vs 0.575, p=ns). A GRACE score ≥140 points indicated a higher risk of death (OR, 95%CI: 3.14, 1.84-5.36), AMI (OR,95%CI: 1.56, 0.59-4.17), or death/AMI (OR, 95%CI: 2.49, 1.51-4.11). Hs-cTnT changes did not improve prognostic performance of a GRACE score ≥140 points (AUC, 95%CI: death: 0.635, 0.570-0.701 vs. 0.560, 0.470-0.649 p=ns, AMI: 0.555, 0.418-0.693 vs. 0.603, 0.424-0.782, p=ns, death/AMI: 0.610, 0.545-0.676 vs. 0.538, 0.454-0.622, p=ns). Coronary angiography was performed earlier in patients with rising than with falling kinetics (median, IQR [hours]:13.7, 5.5-28.0 vs. 20.8, 6.3-59.0, p=0.01).
Neither rising nor falling hs-cTnT changes improve prognostic performance of elevated hs-cTnT admission values or the GRACE score. However, rising values are more likely associated with the decision for earlier invasive strategy.
ABSTRACT: Under normal circumstances, most intracellular troponin is part of the muscle contractile apparatus, and only a small percentage (< 2-8%) is free in the cytoplasm. The presence of a cardiac-specific troponin in the circulation at levels above normal is good evidence of damage to cardiac muscle cells, such as myocardial infarction, myocarditis, trauma, unstable angina, cardiac surgery or other cardiac procedures. Troponins are released as complexes leading to various cut-off values depending on the assay used. This makes them very sensitive and specific indicators of cardiac injury. As with other cardiac markers, observation of a rise and fall in troponin levels in the appropriate time-frame increases the diagnostic specificity for acute myocardial infarction. They start to rise approximately 4-6 h after the onset of acute myocardial infarction and peak at approximately 24 h, as is the case with creatine kinase-MB. They remain elevated for 7-10 days giving a longer diagnostic window than creatine kinase. Although the diagnosis of various types of acute coronary syndrome remains a clinical-based diagnosis, the use of troponin levels contributes to their classification. This Editorial elaborates on the nature of troponin, its classification, clinical use and importance, as well as comparing it with other currently available cardiac markers.
ABSTRACT: Although redefinition for acute myocardial infarction (AMI) has been proposed few years ago, to date it has not been universally adopted by many institutions. The purpose of this study is to evaluate the diagnostic, prognostic and economical impact of the new diagnostic criteria for AMI. Patients consecutively admitted to the emergency department with suspected acute coronary syndromes were enrolled in this study. Troponin T (cTnT) was measured in samples collected for routine CK-MB analyses and results were not available to physicians. Patients without AMI by traditional criteria and cTnT > or = 0.035 ng/mL were coded as redefined AMI. Clinical outcomes were hospital death, major cardiac events and revascularization procedures. In-hospital management and reimbursement rates were also analyzed. Among 363 patients, 59 (16%) patients had AMI by conventional criteria, whereas additional 75 (21%) had redefined AMI, an increase of 127% in the incidence. Patients with redefined AMI were significantly older, more frequently male, with atypical chest pain and more risk factors. In multivariate analysis, redefined AMI was associated with 3.1 fold higher hospital death (95% CI: 0.6-14) and a 5.6 fold more cardiac events (95% CI: 2.1-15) compared to those without AMI. From hospital perspective, based on DRGs payment system, adoption of AMI redefinition would increase 12% the reimbursement rate [3552 Int dollars per 100 patients evaluated]. The redefined criteria result in a substantial increase in AMI cases, and allow identification of high-risk patients. Efforts should be made to reinforce the adoption of AMI redefinition, which may result in more qualified and efficient management of ACS.
Acellular biomaterials can stimulate the local environment to repair tissues without the regulatory and scientific challenges of cell-based therapies. A greater understanding of the mechanisms of such endogenous tissue repair is furthering the design and application of these biomaterials. We discuss recent progress in acellular materials for tissue repair, using cartilage and cardiac tissues as examples of application with substantial intrinsic hurdles, but where human translation is now occurring.
Acellular Biomaterials: An Evolving Alternative to Cell-Based Therapies
Acellular biomaterials can stimulate the local environment to repair tissues without the regulatory and scientific challenges of cell-based therapies. A greater understanding of the mechanisms of such endogenous tissue repair is furthering the design and application of these biomaterials. We discuss recent progress in acellular materials for tissue repair, using cartilage and cardiac tissues as examples of applications with substantial intrinsic hurdles, but where human translation is now occurring.
Instructive Nanofiber Scaffolds with VEGF Create a Microenvironment for Arteriogenesis and Cardiac Repair
Angiogenic therapy is a promising approach for tissue repair and regeneration. However, recent clinical trials with protein delivery or gene therapy to promote angiogenesis have failed to provide therapeutic effects. A key factor for achieving effective revascularization is the durability of the microvasculature and the formation of new arterial vessels. Accordingly, we carried out experiments to test whether intramyocardial injection of self-assembling peptide nanofibers (NFs) combined with vascular endothelial growth factor (VEGF) could create an intramyocardial microenvironment with prolonged VEGF release to improve post-infarct neovascularization in rats. Our data showed that when injected with NF, VEGF delivery was sustained within the myocardium for up to 14 days, and the side effects of systemic edema and proteinuria were significantly reduced to the same level as that of control. NF/VEGF injection significantly improved angiogenesis, arteriogenesis, and cardiac performance 28 days after myocardial infarction. NF/VEGF injection not only allowed controlled local delivery but also transformed the injected site into a favorable microenvironment that recruited endogenous myofibroblasts and helped achieve effective revascularization. The engineered vascular niche further attracted a new population of cardiomyocyte-like cells to home to the injected sites, suggesting cardiomyocyte regeneration. Follow-up studies in pigs also revealed healing benefits consistent with observations in rats. In summary, this study demonstrates a new strategy for cardiovascular repair with potential for future clinical translation.
Along with scientific and regulatory issues, the translation of cell and tissue therapies in the routine clinical practice needs to address standardization and cost-effectiveness through the definition of suitable manufacturing paradigms.
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