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The Human Genome Gets Fully Sequenced: A Simplistic Take on Century Long Effort

Posted in Big Data, Bio Instrumentation in Experimental Life Sciences Research, BioIT: BioInformatics, BioIT: BioInformatics, NGS, Clinical & Translational, Pharmaceutical R&D Informatics, Clinical Genomics, Cancer Informatics, Biological Networks, Gene Regulation and Evolution, Disease Biology, Genome Biology, Next Generation Sequencing (NGS), Nobel Prize Winners, Single-cell sequencing, Variation in human protein-coding regions, tagged AAAS, Craig Venter, DNA Sequencing, genetic variants, Human Genome Project, Jennifer Doudna, junk DNA, telomere consortium on June 14, 2022| Leave a Comment »

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

 

 

From the Human Genome Project Information Archive

Source:  https://web.ornl.gov/sci/techresources/Human_Genome/project/hgp.shtml

History of the Human Genome Project

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

 

History of the Project

• HGP Goals and Corresponding Completion Dates
• Budget History of the U.S. Human Genome 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

Project Sponsors

• The U.S. Department of Energy funded its Human Genome Program through their Office of Biological and Environmental Research. (genome@science.doe.gov).
• The U.S. National Institutes of Health funded its program through the National Human Genome Research Institute (NHGRI).

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.

Source: https://www.science.org/doi/10.1126/science.abj6987

Abstract

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.

From TIME

The Human Genome Is Finally Fully Sequenced

Source: https://time.com/6163452/human-genome-fully-sequenced/

 

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

Source: https://time.com/collection/100-most-influential-people-2022/6177818/evan-eichler-karen-miga-adam-phillippy-michael-schatz/

Other articles on the Human Genome Project and Junk DNA in this Open Access Scientific Journal Include:

 

International Award for Human Genome Project

 

Cracking the Genome – Inside the Race to Unlock Human DNA – quotes in newspapers

 

The Human Genome Project

 

Junk DNA and Breast Cancer

 

A Perspective on Personalized Medicine

 

 

 

 

 

 

 

Additional References

 

1. P. Scalia, A. Giordano, C. Martini, S. J. Williams, Isoform- and Paralog-Switching in IR-Signaling: When Diabetes Opens the Gates to Cancer. Biomolecules 10, (Nov 30, 2020).

 

 

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Innovation massively expands view into workings of single cells summary

Posted in CANCER BIOLOGY & Innovations in Cancer Therapy, Cancer Genomics, cancer-general, Cell Biology, Cell Biology, Signaling & Cell Circuits, Diagnostics and Lab Tests, single cell sequencing, tagged Cancer, cell fragments, DNA, DNA Sequencing, health, research, Single-cell sequencing on July 8, 2021| Leave a Comment »

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) 

Reporting on a Study published on July 6, 2021 by  Oregon Health & Science University

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.

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.

Andrew Adey, Ph.D., the senior author of a paper in Nature Biotechnology, https://www.nature.com/articles/s41587-021-00962-z

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. 

SOURCE

Original article:

Mulqueen, R.M., Pokholok, D., O’Connell, B.L. et al. High-content single-cell combinatorial indexing. Nat Biotechnol (2021). https://doi.org/10.1038/s41587-021-00962-z

Research categories – Cell biology, cancer-general, research, DNA Fragment TAGS- DNA, sequencing, cell fragments, single-cell

Other related articles published on this Open Access Online Scientific Journal include the following: 

Series B: Frontiers in Genomics Research

Series Content Consultant:

Larry H. Bernstein, MD, FCAP, Emeritus CSO, LPBI Group

Volume Content Consultant:

Prof. Marcus W. Feldman

BURNET C. AND MILDRED FINLEY WOHLFORD PROFESSOR IN THE SCHOOL OF HUMANITIES AND SCIENCES

Stanford University, Co-Director, Center for Computational, Evolutionary and Human Genetics (2012 – Present)

Latest in Genomics Methodologies for Therapeutics:

Gene Editing, NGS & BioInformatics,

Simulations and the Genome Ontology

2019

Volume Two

https://www.amazon.com/dp/B08385KF87

 

Part 4: Single Cell Genomics

Introduction to Part 4: Single Cell Genomics – Voice of Aviva Lev-Ari & Stephen Williams

4.1 The Science

4.1.1   Single-cell biology

Special | 05 July 2017

https://www.nature.com/collections/gbljnzchgg

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

• by Heidi Ledford  20 February 2017

https://www.nature.com/news/the-race-to-map-the-human-body-one-cell-at-a-time-1.21508

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

Curator: Aviva Lev-Ari, PhD, RN

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

4.1.4   Cellular Genetics

https://www.sanger.ac.uk/science/programmes/cellular-genetics

4.1.5   Cellular Genomics

https://www.garvan.org.au/research/cellular-genomics

4.1.6   SINGLE CELL GENOMICS 2019 – sometimes the sum of the parts is greater than the whole, September 24-26, 2019, Djurönäset, Stockholm, Sweden http://www.weizmann.ac.il/conferences/SCG2019/single-cell-genomics-2019

Reporter: Aviva Lev-Ari, PhD, RN

SINGLE CELL GENOMICS 2019, September 24-26, 2019, Djurönäset, Stockholm, Sweden

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

Reporter: Aviva Lev-Ari, PhD, RN

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

4.1.8   Newly Found Functions of B Cell

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

Newly Found Functions of B Cell

4.1.9 RESEARCH HIGHLIGHTS: HUMAN CELL ATLAS

https://www.broadinstitute.org/research-highlights-human-cell-atlas

4.2 Technologies and Methodologies

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.

Anna Nowogrodzki, 05 July 2017, Article tools

https://www.nature.com/news/how-to-build-a-human-cell-atlas-1.22239

4.2.2   Featuring Computational and Systems Biology Program at Memorial Sloan Kettering Cancer Center, Sloan Kettering Institute (SKI), The Dana Pe’er Lab

Reporter: Aviva Lev-Ari, PhD, RN

Featuring Computational and Systems Biology Program at Memorial Sloan Kettering Cancer Center, Sloan Kettering Institute (SKI), The Dana Pe’er Lab

4.2.3   Genomic Diagnostics: Three Techniques to Perform Single Cell Gene Expression and Genome Sequencing Single Molecule DNA Sequencing

Curator: Aviva Lev-Ari, PhD, RN

Genomic Diagnostics: Three Techniques to Perform Single Cell Gene Expression and Genome Sequencing Single Molecule DNA Sequencing

4.2.4   Three Technology Leaders in Single Cell Sequencing: 10X Genomics, Illumina and MissionBio

Reporter: Aviva Lev-Ari, PhD, RN

Three Technology Leaders in Single Cell Sequencing: 10X Genomics, Illumina and MissionBio

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

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

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

4.2.6   Nano-guided cell networks: new methods to detect intracellular signaling and implications

Curator: Stephen J. Williams, PhD

Nano-guided cell networks: new methods to detect intracellular signaling and implications

4.3 Clinical Aspects

4.3.1 Using single cell sequencing data to model the evolutionary history of a tumor.

Kim KI, Simon R.

BMC Bioinformatics. 2014 Jan 24;15:27. doi: 10.1186/1471-2105-15-27.

PMID:

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

Real Time Press Coverage: Aviva Lev-Ari, PhD, RN

eProceeding 2019 Koch Institute Symposium – 18th Annual Cancer Research Symposium – Machine Learning and Cancer, June 14, 2019, 8:00 AM-5:00 PMET MIT Kresge Auditorium, 48 Massachusetts Ave, Cambridge, MA

4.3.3   The Impact of Heterogeneity on Single-Cell Sequencing

Samantha L. Goldman1,2, Matthew MacKay1,2, Ebrahim Afshinnekoo1,2,3, Ari M. Melnick4, Shuxiu Wu5,6 and Christopher E. Mason1,2,3,7*

https://www.frontiersin.org/articles/10.3389/fgene.2019.00008/full

4.3.4   Single-cell approaches to immune profiling

https://www.nature.com/articles/d41586-018-05214-w

4.3.5   Single-cell sequencing made simple. Data from thousands of single cells can be tricky to analyse, but software advances are making it easier.

by Jeffrey M. Perkel

https://www.nature.com/news/single-cell-sequencing-made-simple-1.22233

4.3.6  Single-cell RNA-seq helps in finding intra-tumoral heterogeneity in pancreatic cancer

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

Single-cell RNA-seq helps in finding intra-tumoral heterogeneity in pancreatic cancer

4.3.7 Cancer Genomics: Multiomic Analysis of Single Cells and Tumor Heterogeneity

Curator: Stephen J. Williams, PhD

Cancer Genomics: Multiomic Analysis of Single Cells and Tumor Heterogeneity

4.4 Business and Legal

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

https://ibiochips.com/?gclid=Cj0KCQjwuLPnBRDjARIsACDzGL0wb6u79VHHkftodfApMYs-oxI-5cOZIBUaELdmd2wDOIk3W0OQg2caAqMyEALw_wcB

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.

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Part 4: Summary – Single Cell Genomics – Voice of Stephen Williams

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Cancer Biology and Genomics for Disease Diagnosis (Vol. I) Now Available for Amazon Kindle

Posted in Academic Publishing, Biochemical pathways, Cancer and Current Therapeutics, CANCER BIOLOGY & Innovations in Cancer Therapy, Cancer Informatics, Cancer Prevention: Research & Programs, Cancer Screening, Cancer Surgery of the Heart, Cell Biology, Clinical & Translational, Clinical Diagnostics, Clinical Genomics, Curation, Curation methodology, De novo synthesis, Discovery process, Disease Biology, Drug Delivery Platform Technology, Enzymes and isoenzymes, Evolutionary cognition, Experimental validation, Explanatory, FDA, FDA Regulatory Affairs, Health Economics and Outcomes Research, HealthCare IT, Historical relevance, International Global Work in Pharmaceutical, Interventional Oncology: Radiofrequency Ablation, Transarterial Chemoembolization, Microwave Ablation and Irreversible Electroporation (IRE), Interviews with Scientific Leaders, Metabolism, Metabolomics, Molecular Genetics & Pharmaceutical, Open Access Journals, Personal Health Applications: Tech Innovations serves HealhCare, Pharmaceutical R&D Informatics, Pharmaceutical R&D Investment, Proteomics, REAL TIME Conference Coverage Twitter's Hashtags and Handles per Presentation/session, Scientific & Biotech Conferences: Press Coverage, Signaling & Cell Circuits, Small Molecules in Development of Therapeutic Drugs, Technology Advance Assessment of, Theoretic convergence, tagged Amazon Kindle, anti-cancer drugs, Aviva Lev-Ari, breast cancer, breast cancer genes, Cancer - General, cancer patient management, cancer risk, Cell Biology, Chemotherapy, Clinical trial, Conditions and Diseases, Content Curation, Curation, Curation methodology, DNA, DNA Sequencing, Food and Drug Administration, genetics, genome, genomics, Harvard Medical School, health outcomes research, Larry H. Bernstein, Medical imaging, Metabolomics, National Institutes of Health, personalised medicine, Personalized medicine, Prostate cancer, research, RNA, science, Stem cell on August 14, 2015| Leave a Comment »

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

Volume One: Cancer Biology and Genomics for Disease Diagnosis

which is now available on Amazon Kindle at                          http://www.amazon.com/dp/B013RVYR2K.

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:

http://pharmaceuticalintelligence.com/biomed-e-books/

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

 

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Multiple Lung Cancer Genomic Projects Suggest New Targets, Research Directions for Non-Small Cell Lung Cancer

Posted in Biological Networks, Biomarkers & Medical Diagnostics, CANCER BIOLOGY & Innovations in Cancer Therapy, Cancer Prevention: Research & Programs, Computational Biology/Systems and Bioinformatics, Gene Regulation and Evolution, Genome Biology, Health Economics and Outcomes Research, Personalized and Precision Medicine & Genomic Research, tagged antitumor therapy, Bioinformatics, Cancer - General, Cell Biology, Computational Biology/Systems and Bioinformatics, DNA, DNA Sequencing, driver mutations, gene expression, genetics, genome, genomics, informatics, Lung cancer, mutation, mutational analysis, National Institutes of Health, Non-small cell lung cancer, Personalized medicine, Proceedings of the National Academy of Sciences of the United States of America, Prostate cancer, research, smoking, The Cancer Genome Atlas (TCGA), The Clinical Lung Cancer Genome Project (CLCGP), whole exome sequencing on September 5, 2014| Leave a Comment »

Multiple Lung Cancer Genomic Projects Suggest New Targets, Research Directions for Non-Small Cell Lung Cancer

Curator, Writer: Stephen J. Williams, Ph.D.

UPDATED 10/10/2021

(photo credit: cancer.gov)

A report Lung Cancer Genome Surveys Find Many Potential Drug Targets, in the NCI Bulletin,

http://www.cancer.gov/ncicancerbulletin/091812/page2

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.

Collectively, the studies covered the main forms of the disease—lung adenocarcinomas, squamous cell cancers of the lung, and small cell lung cancers.

“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:

Publication TCGA Web Page: https://tcga-data.nci.nih.gov/docs/publications/luad_2014/

Sequence files: https://cghub.ucsc.edu

Results:

Gender and Smoking Habits Show different mutational patterns

 

WES mutational analysis

1. 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

 

1. 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:

1. MAPK pathway
2. mTOR
3. 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:

1. MET and ERBB2 amplification and mutations in NF1 and RIT1 may be unique driver events in lung adenocarcinoma
2. Possible new drug development could be targeted to the RTK/RAS/RAF pathway
3. MYC pathway as another important target
4. 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]

The paper can be found at

http://stm.sciencemag.org/content/5/209/209ra153

by The Clinical Lung Cancer Genome Project (CLCGP) and Network Genomic Medicine (NGM),*,†

Paper Summary

This sequencing project revealed discrepancies between histologic and genomic classification of lung tumors.

Methodology

– mutational analysis by whole exome sequencing of 1255 lung tumors of histologically

defined subtypes

– immunohistochemistry performed to verify reclassification of subtypes based on sequencing data

Results

• 55% of all cases had at least one oncogenic alteration amenable to current personalized treatment approaches
• Marked differences existed between cluster analysis within and between preclassified histo-subtypes
• Reassignment based on genomic data eliminated large cell carcinomas
• Prospective classification of 5145 lung cancers allowed for genomic classification in 75% of patients
• Identification of EGFR and ALK mutations led to improved outcomes

Conclusions:

It is feasible to successfully classify and diagnose lung tumors based on whole exome sequencing data.

Paper 3. Genomic Landscape of Non-Small Cell Lung Cancer in Smokers and Never-Smokers[3]

A link to the paper can be found here with Graphic Summary: http://www.cell.com/cell/abstract/S0092-8674%2812%2901022-7?cc=y?cc=y

Methodology

• Whole genome sequencing and transcriptome sequencing of cancerous and adjacent normal tissues from 17 patients with NSCLC
• Integrated RNASeq with WES for analysis of
  • Variant analysis
  • Clonality by variant allele frequency anlaysis
  • Fusion genes
• Bioinformatic analysis
  • PathScan, KEGG for pathway analysis
  • COSMIC for reported mutations
  • ChimeraScan, defuse, BreakFusion for fusion protein analysis

Results

• 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.

GeneA (5′)	GeneB (3′)	GeneA function (link to Gene Ontology)	GeneB function (link to Gene Ontology)		known function (refs)
GRIP1	TNIP1	glutamate receptor IP	transcriptional repressor
SGMS1	STK10	sphingolipid synthesis	ser/thr kinase
RASSF3	TTYH2	GTP-binding protein	chloride anion channel
KDELR2	ROS1, GOPC	ER retention seq. binding	proto-oncogenic tyr kinase
ACSL4	DCAF6	fatty acid synthesis	?
MARCH8	PRKG1	ubiquitin ligase	cGMP dependent protein kinase
APAF1	UNC13B, TLN1	caspase activation	cytoskeletal
EML4	ALK	microtubule protein	tyrosine kinase		♦
EDR3,PHC3	LOC441601	polycomb pr/DNA binding	?
DKFZp761L1918,RHPN2	ANKRD27	Rhophilin (GTP binding pr	ankyrin like
VANGL1	HAO2	tetraspanin family	oxidase
CACNA2D3	FLNB	VOC Ca++ channel	filamin (actin binding)

† Author’s Note:

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.

Gene	Function
TP53	tumor suppressor
KRAS	oncogene
ZFHX4	zinc finger DNA binding
DACH1	transcription factor
EGFR	epidermal growth factor receptor
EPHA3	receptor tyrosine kinase
ENSG00000205044
RELN	cell matrix protein
ABCB5	ABC Drug Transporter

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
Cell cycle	1237
Cell adhesion molecules (CAMs)	372
Glioma	294
Melanoma	219
Colorectal cancer	207
Calcium signaling pathway	175
Prostate cancer	166
MAPK signaling pathway	162
Pancreatic cancer	88
Bladder cancer	74
Renal cell carcinoma	68
Focal adhesion	63
Regulation of actin cytoskeleton	34
Thyroid cancer	32
Salivary secretion	19
Jak-STAT signaling pathway	16
Natural killer cell mediated cytotoxicity	11
Gap junction	11
Endometrial cancer	11
Long-term depression	9
Axon guidance	8
Cytokine-cytokine receptor interaction	8
Chronic myeloid leukemia	7
ErbB signaling pathway	7
Arginine and proline metabolism	6
Maturity onset diabetes of the young	6
Neuroactive ligand-receptor interaction	4
Aldosterone-regulated sodium reabsorption	2
Systemic lupus erythematosus	2
Olfactory transduction	1
Huntington’s disease	1
Chemokine signaling pathway	1
Cardiac muscle contraction	1
Amyotrophic lateral sclerosis (ALS)	1

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]

For full paper and graphical summary please follow the link: http://www.cell.com/cell/abstract/S0092-8674%2812%2901061-6

Highlights

• 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 Posts on 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

Ryma Benayed, Michael Offin, Kerry Mullaney, Purvil Sukhadia, Kelly Rios, Patrice Desmeules, Ryan Ptashkin, Helen Won, Jason Chang, Darragh Halpenny, Alison M. Schram, Charles M. Rudin, David M. Hyman, Maria E. Arcila, Michael F. Berger, Ahmet Zehir, Mark G. Kris, Alexander Drilon and Marc Ladanyi

Clin Cancer Res August 1 2019 (25) (15) 4712-4722; DOI: 10.1158/1078-0432.CCR-19-0225

Abstract

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.

A Commentary is in the same issue at https://clincancerres.aacrjournals.org/content/25/15/4586?iss=15

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.

See related article by Benayed et al., p. 4712

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).

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Figure 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.

References:

1. Comprehensive genomic characterization of squamous cell lung cancers. Nature 2012, 489(7417):519-525.
2. A genomics-based classification of human lung tumors. Science translational medicine 2013, 5(209):209ra153.
3. 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.
4. 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.
5. 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.
6. 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.
7. Yang L, Li G, Zhao L, Pan F, Qiang J, Han S: Blocking the PI3K pathway enhances the efficacy of ALK-targeted therapy in EML4-ALK-positive nonsmall-cell lung cancer. Tumour biology : the journal of the International Society for Oncodevelopmental Biology and Medicine 2014.
8. Workman P, van Montfort R: EML4-ALK fusions: propelling cancer but creating exploitable chaperone dependence. Cancer discovery 2014, 4(6):642-645.
9. 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.
10. 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:

Multi-drug, Multi-arm, Biomarker-driven Clinical Trial for patients with Squamous Cell Carcinoma called the Lung Cancer Master Protocol, or Lung-MAP launched by NCI, Foundation Medicine, and Five Pharma Firms

US Personalized Cancer Genome Sequencing Market Outlook 2018 –

Comprehensive Genomic Characterization of Squamous Cell Lung Cancers

International Cancer Genome Consortium Website has 71 Committed Cancer Genome Projects Ongoing

Non-small Cell Lung Cancer drugs – where does the Future lie?

Lung cancer breathalyzer trialed in the UK

Diagnosing Lung Cancer in Exhaled Breath using Gold Nanoparticles

Multi-drug, Multi-arm, Biomarker-driven Clinical Trial for patients with Squamous Cell Carcinoma called the Lung Cancer Master Protocol, or Lung-MAP launched by NCI, Foundation Medicine, and Five Pharma Firms

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Second Annual Single-Cell Sequencing of Cancer Rountable August 20,21, 2014 Washington DC

Posted in Bio Instrumentation in Experimental Life Sciences Research, CANCER BIOLOGY & Innovations in Cancer Therapy, Cell Biology, Signaling & Cell Circuits, Disease Biology, Small Molecules in Development of Therapeutic Drugs, Personalized and Precision Medicine & Genomic Research, RNA Biology, Cancer and Therapeutics, Scientific & Biotech Conferences: Press Coverage, tagged Biology, Cancer - General, circulating tumor ce;;s, Circulating tumor cell, computational genomics, DNA Sequencing, Food and Drug Administration, genetics, genome, genomics, Harvard Medical School, National Institutes of Health, Personalized medicine, single cell on June 19, 2014| Leave a Comment »

Second Annual Single-Cell Sequencing of Cancer Rountable August 20,21, 2014 Washington DC

Reporter:  Stephen J. Williams, PhD

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 How many different cell types are there?

View Brochure    |   Register (Advance Registration Ends July 18)  |   NextGenerationDx.com/Single-Cell-Sequencing About the Conference 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 Register   |   View Agenda   | NextGenerationDx.com   #NGDx14

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