Genomic Testing: Methodology for Diagnosis | Leaders in Pharmaceutical Business Intelligence (LPBI) Group

Archive for the ‘Genomic Testing: Methodology for Diagnosis’ Category

Genomic data can predict miscarriage and IVF failure

Posted in Advanced Computing Platform, Artificial Intelligence - Breakthroughs in Theories and Technologies, Artificial Intelligence - General, Artificial Intelligence Applications in Health Care, Artificial Intelligence in Health Care - Tools & Innovations, Artificial Intelligence in Medicine - Application for Diagnosis, Artificial Intelligence in Medicine - Applications in Therapeutics, BioIT: BioInformatics, BioIT: BioInformatics, NGS, Clinical & Translational, Pharmaceutical R&D Informatics, Clinical Genomics, Cancer Informatics, Biological Engineering, Biological Networks, Biological Networks, Gene Regulation and Evolution, Biomarkers & Medical Diagnostics, Cell Level, Computational Biology/Systems and Bioinformatics, Computational Histopathology, Deep Learning in Pathology, Developmental biology, Diagnostics and Lab Tests, Digital HealthCare – biotech & internet joint ventures, Disease Biology, Genomic Testing: Methodology for Diagnosis, Genomics Pharmacy, HealthCare IT, Healthcare Reform, Machine Learning, MicroEngineering Cell-Tissue & Systems, Placenta, Preimplantation Genetic Diagnosis and Reproductive Genomics, Reproductive Andrology, Embryology, Genomic Endocrinology, Preimplantation Genetic Diagnosis and Reproductive Genomics, Reproductive Biology & Bio Instrumentation, therapeutics, Tissue Engineering, Translational Research, Women Health, tagged aneuploidy, Diagnosis, Genomic data, IVF, Miscarriage, treatment on June 15, 2022| 1 Comment »

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

Infertility is a major reproductive health issue that affects about 12% of women of reproductive age in the United States. Aneuploidy in eggs accounts for a significant proportion of early miscarriage and in vitro fertilization failure. Recent studies have shown that genetic variants in several genes affect chromosome segregation fidelity and predispose women to a higher incidence of egg aneuploidy. However, the exact genetic causes of aneuploid egg production remain unclear, making it difficult to diagnose infertility based on individual genetic variants in mother’s genome. Although, age is a predictive factor for aneuploidy, it is not a highly accurate gauge because aneuploidy rates within individuals of the same age can vary dramatically.

Researchers described a technique combining genomic sequencing with machine-learning methods to predict the possibility a woman will undergo a miscarriage because of egg aneuploidy—a term describing a human egg with an abnormal number of chromosomes. The scientists were able to examine genetic samples of patients using a technique called “whole exome sequencing,” which allowed researchers to home in on the protein coding sections of the vast human genome. Then they created software using machine learning, an aspect of artificial intelligence in which programs can learn and make predictions without following specific instructions. To do so, the researchers developed algorithms and statistical models that analyzed and drew inferences from patterns in the genetic data.

As a result, the scientists were able to create a specific risk score based on a woman’s genome. The scientists also identified three genes—MCM5, FGGY and DDX60L—that when mutated and are highly associated with a risk of producing eggs with aneuploidy. So, the report demonstrated that sequencing data can be mined to predict patients’ aneuploidy risk thus improving clinical diagnosis. The candidate genes and pathways that were identified in the present study are promising targets for future aneuploidy studies. Identifying genetic variations with more predictive power will serve women and their treating clinicians with better information.

References:

https://medicalxpress-com.cdn.ampproject.org/c/s/medicalxpress.com/news/2022-06-miscarriage-failure-vitro-fertilization-genomic.amp

https://pubmed.ncbi.nlm.nih.gov/35347416/

https://pubmed.ncbi.nlm.nih.gov/31552087/

https://pubmed.ncbi.nlm.nih.gov/33193747/

https://pubmed.ncbi.nlm.nih.gov/33197264/

Read Full Post »

Three Expert Opinions on “The alarming rise of complex genetic testing in human embryo selection”

Posted in Gene Regulation and Evolution, Genetics & Innovations in Treatment, Genome Biology, Genomic Endocrinology, Genomic Testing: Methodology for Diagnosis, Reproductive Andrology, Embryology, Genomic Endocrinology, Preimplantation Genetic Diagnosis and Reproductive Genomics, Reproductive Biology & Bio Instrumentation on March 25, 2022| Leave a Comment »

Three Expert Opinions on “The alarming rise of complex genetic testing in human embryo selection”

Reporter: Aviva Lev-Ari, PhD, RN

Based on this articles three expert opinions where formed by the following domain knowledge experts and are presented, below.

Expert Opinions on rise of complex genetic testing in human embryo selection

ttps://www.nature.com/articles/d41586-022-00787-z

Domain Knowledge Experts:

Prof. Marc Feldman, Genetics, Stanford University

Dr. Shraga Rottem, MD, D.Sc., Fetal OB

Prof. Steven J. Williams, Biological Sciences, Temple University

First expert opinion by Prof. Marcus W. Feldman

The recent publication in Nature Medicine on genetic risk prediction in pre-implementation embryos⁽¹⁾ has already engendered heated discussion.^(2,3) Kumar et al.⁽¹⁾ advocate the integration of polygenic risk scores (PRS) derived from pre-implantation genetic testing (PGT) with standard monogenic prediction. The paper focuses primarily on BRCA1 (and breast cancer) and APC (and colon cancer). Genetic tests for inherited disorders such as Tay-Sachs disease and breast cancers caused by BRCA1 and BRCA2 have been approved, but these are potentially devastating conditions with relatively simple inheritance; in most counseling situations the risks are straightforward to calculate.

The limitation on the amount and quality of DNA available from early embryo biopsies has made it difficult to produce genomic profiles of embryos in the IVF situation. Kumar et al. genotyped more than one-hundred embryos at hundreds of thousands of nucleotide sites and combined these genotype data with whole genome sequences of the prospective parents to produce reconstructed embryo genomes. These genomes were compared with those of ten born siblings and polygenic risk scores (PRS) were calculated for twelve conditions related to diseases. The PRS were claimed to be 97–99 percent accurate.

The primary market for this procedure would be couples seeking IVF, and Kumar and his colleagues, most of whom are employees of biotech companies, show that it is feasible to calculate a PRS for an embryo. The authors do present several caveats for the use of their procedure for PGT. For example, if a couple has a family history of a disease, they “may unintentionally prioritize” a mutant embryo for PGT-based only on PRS. They also acknowledge that results from research cohorts may not generalize to sibling embryos in IVF, which could limit the clinical utility of their approach. Kumar et al. also acknowledge the “portability” problem, namely PRSs have limited predictive accuracy in people with non-European ancestry^(2,3) or of different ages or socioeconomic status.^(4,5) They also mention the issue of unequal access to IVF technology in general.⁽²⁾

It is also important, However, to stress the limited predictive utility of PRS for common traits, not only diseases. There is increasing use of PRS among social scientists for characteristics such as years of education, which have heritabilities in the 10–15 percent range. Such studies, and potentially this one by Kumar et al., can lead to reduced emphasis on environmental and social associations with diseases or other traits. For omnigenic traits, such as height or body mass index (BMI), that have hundreds or thousands of associated nucleotide polymorphisms, and high heritability, the public might receive the mistaken impression that PGT or other genomic interventions can allow parents to choose their offspring’s phenotype.

For example, a recent study⁽⁶⁾ of BMI in 881 subjects from Quebec found that PRS could explain only between 1.2 percent and 7.5 percent of the variance in BMI of these participants. Even when PRSs are statistically significant, their predictive value is too weak to be applied. The use of polygenic risk scores to select embryos, abbreviated ESPS for embryo selection based on polygenic scores, has been criticized before.⁽⁷⁾ One of the important points raised by Turley et al.⁽⁷⁾ concerns the environmental context of the children of IVG customers, which may be quite different from that of the sample of people from which the PRS was calculated. Because of gene-environment interactions, the predictive power of PRS for any complex trait is limited. As pointed out by Turley et al. (p. 79), “the predictive power of a polygenic score is maximized when the person is from the same environment as the research participants from whom the polygenic scores were derived. But this will never be the case in ESPS.”

PGT and ESPS raise ethical issues beyond IVG that more generally concern designer babies.^(7,8) PRSs have been calculated for non-disease related traits such as educational attainment, income, or IQ, and it is conceivable that some prospective parents might regard these as important enough for intervention. There are also traits related to social constructs of race including skin pigmentation or facial features, and parental choice based on these phenotypes could enhance racial prejudices.

References

Kumar, A., K. Im, M. Banjevic, P.C. Ng, T. Tunstall, G. Garcia, L. Galhardo, J. Sun,O.N. Schaedel, B. Levy, D. Hongo, D. Kijacic, M. Kiehl, N.D. Tran, P.C. Klatsky, and M. Rabinowitz. 2022. Whole-genome risk prediction of common diseases in human preimplantation embryos. Nature Medicine 28: 514–516. doi: 10.1038/s41591-022-01735-0.
Johnston, J., and L.J. Matthews. 2022. Polygenic embryo testing: understated ethics, unclear utility. Nature Medicine 28: 445–451. doi: 10.1038/s41591-022-01743-0.
Nature editorial. 2022. The alarming rise of complex genetic testing in human embryo testing. Nature 603: 549–550. doi: 10.1038/d41586-022-00787-z.
Rosenberg, N., M. Edge, J. Pritchard, and M. Feldman. 2019. Interpreting polygenic scores, polygenic adaptation, and human phenotypic differences. Evol. Med. Public Health 2019: 26–34. doi: 10.1093/emph/eoy036.
Duncan, L.E., H. Shen, B. Gelaye, J. Meijsen, K.J. Ressler, M.W. Feldman, R.E. Peterson, and B.W. Domingue. 2019. Analysis of polygenic score usage and performance in diverse human populations. Nat. Comm. 10: 3328. doi: 10.1038/s41467-019-11112-0.
De Toro-Martin, J.E., F. Guenard, C. Bouchard, A. Tremblay, L. Perusse, and M.-C. Vohl. 2019. The challenge of stratifying obesity: attempts in the Quebec family study. Front. Genet. 10:994. doi: 10.3389/fgene.2019.00994.
Turley, P., M.N. Meyer, N. Wang, D. Cesarini, E. Hammonds, A.R. Martin, B.M. Neale, H.L. Rehm, L. Wilkins-Haug, D.J. Benjamin, S. Hyman, D. Laibson, and P.M. Visscher. 2021. Problems with using polygenic scores to select embryos. N. Engl. J. Med 385(1): 78–86.
Forzano, F., O. Antonova, A. Clarke, G. de Wert, S. Hentze, Y. Jamshidi, Y. Moreau, M. Perola, I. Prokopenko, A. Read, A. Reymond, V. Stefansdottir, C. van El, and M. Genuardi. 2021. The use of polygenic risk scores in pre-implantation genetic testing: an unproven, unethical practice. European Journal of Human Genetics. doi: 10.1038/s41431-021-01000-x.

Second expert opinion by Dr. Shraga Rottem, MD, D.Sc., Fetal OB

PENDING

Third expert opinion by Prof. Steven J. Williams, Biological Sciences, Temple University

There has been much opinion, either as commentary in literature, meeting proceedings, or communiques from professional societies warning that this type of “high-impact” genetic information should not be given directly to the consumer as consumers will not fully understand the information presented to them, be unable to make proper risk-based decisions, results could cause panic and inappropriate action such as prophylactic oophorectomy or unwarranted risk-reduction mastectomy, or false reassurance in case of negative result and reduced future cancer screening measures taken by the consumer. However, there have been few studies to investigate these concerns.

The article by Kumar The alarming rise of complex genetic testing in human embryo selection

discusses the common trend of DTC (direct to consumer) and other genetic consutancy groups to offer disease risk assesment based on genetic predispostion genetic information in preimplantation embryos upon in vitro fertilization. Although this editorial discusses some caveats and potential ethical issues the opinion of this reviewer feels a certain number of key issues points have not been addressed (which will be discussed below) including:

the underlying risk of disclosure of all parties involved in decision making based on genetic testing including other family members
complicating ethical issues not addressed through proper guideline establishment and regulation as seen in countries that allow such advances to go without proper review board
a lack of discussion of the health disparities which may result of this type of genetic information or “selection” where groups of people would be shut out of such services due to socioeconomic status

Although the editorial highlights the issue that most genome wide association studies, on which most of the genetic counseling is based upon is from cohorts of European descent (and misses a large cohort which is Asian or African descent), there is little attention given to the issue that most panels of these agreed upon risk associated variants have not been validated in larger GWAS studies or that these panels only focus on the most common variants. An example of this would be BRCA1/2 and assumed future breast cancer risk.

Gleicher al state

PGS and PGT-A
diagnoses have been built on biologically
incorrect assumptions and on unvalidated
guidelines dating back to 2016. These
guidelines, which remain influential to this
day, were published without a description
of methods, without peer review, with no
author identification, and without any
references1
. The guidelines changed the
binary diagnosis of euploid and aneuploid
to normal, mosaic and aneuploid.

In fact most family risk assesment programs are more effective upon counseling of young women, not at the embryonic stage where genetic risk factors may not be evident or resulting from epigenetic changes or accumulated somatic mutation.

Lack of communication to all related and involved parties

Many times it is women, who having undergone these testings, have problems in communicating these risk findings to their children and family members, resulting in familial strains.

For instance, some women who discover they have the BRCA gene mutation, which puts them at higher risk for breast cancer, choose to tell their children about it before the children are old enough to understand the significance or deal with it, a new study found.

“Parents with the BRCA mutation are discussing their genetic test results with their offspring often many years before the offspring would need to do anything,” said study author Dr. Angela Bradbury, director of the Fox Chase Cancer Center’s Family Risk Assessment Program, in Philadelphia.

According to Bradbury, more than half of parents she surveyed told their children about genetic test results. Some parents reported that their children didn’t seem to understand the significance of the information, and some had initial negative reactions to the news.

“A lot of genetic information is being shared within families and there hasn’t been a lot of guidance from health-care professionals,” Bradbury said. “While this genetic risk may be shared accurately, there is risk of inaccurate sharing.”

In the study, Bradbury’s team interviewed 42 women who had the BRCA mutation. The researchers found that 55 percent of parents discussed the finding and the risk of breast cancer with at least one of their children who was under 25.

Also, most of the women didn’t avail themselves of the services of a doctor or genetic counselor in helping to tell their children, Bradbury’s group found.

The identification of familial risk factors can have very stressful impacts on the affected and their family however an IVF selection might even augment that familial stress. More research is needed on the psychological impact of such testing and a patient’s choice.

2. Lack of health disparity considerations in IVF selection research or guidelines

Another major concern, which has been highlighted in multiple articles on this site, is the growing health disparities between those who can obtain access to quality health care and those who are left out in the void of the medical system, either for economic or sociological reasons. This has been very apparent in the cancer treatment and personalized medicine world (for example the disparities of health care access for cancer treatment in the southern poorer rural parts of the US versus metropolitan areas and the gaping disparities seen between rich and poor countries in Africa). These health disparities have been also apparant in the genetic testing market, and although the DTC market meant to make genetic testing more affordable, interestingly these disparities still exist in this niche market.

3. Lack of proper establishment of Institutional Review Board oversight in countries allowing this technique have been problematic with regard to addressing bioethical concerns

The third concern is, of course, a bioethical concern on the use of advanced genetic technologies in the human and clinical setting. It has come to many people’s attention at the speed at which countries that do not seem to have strong bioethical review boards readily allow this type of research to be carried out without regulatory oversight or consequence. A prime example of this included the shunned Chinese research carried out to produce cloned humans, which was rapidly condemmed in the biomedical world however this research was conducted nonetheless. This lack of attention is addressed in Kumar’s article yet little guidance is given as to best practices to establish review boards overseeing such work and or research.

SOURCE

https://www.nature.com/articles/d41586-022-00787-z

Read Full Post »

@MIT Artificial intelligence system rapidly predicts how two proteins will attach: The model called Equidock

Posted in Academic Publishing, Amino acids, Artificial Intelligence - Breakthroughs in Theories and Technologies, Artificial Intelligence - General, Artificial Intelligence Applications in Health Care, Artificial Intelligence in Health Care - Tools & Innovations, Artificial Intelligence in Medicine - Applications in Therapeutics, Genome Biology, Genomic Testing: Methodology for Diagnosis, Machine Learning, Proteins, Proteomics, tagged graph neural networks, protein complexes, protein structure, rigid body docking, SE(3) equivariance on February 28, 2022| Leave a Comment »

@MIT Artificial intelligence system rapidly predicts how two proteins will attach: The model called Equidock, focuses on rigid body docking — which occurs when two proteins attach by rotating or translating in 3D space, but their shapes don’t squeeze or bend

Reporter: Aviva Lev-Ari, PhD, RN

This paper introduces a novel SE(3) equivariant graph matching network, along with a keypoint discovery and alignment approach, for the problem of protein-protein docking, with a novel loss based on optimal transport. The overall consensus is that this is an impactful solution to an important problem, whereby competitive results are achieved without the need for templates, refinement, and are achieved with substantially faster run times.

Independent SE(3)-Equivariant Models for End-to-End Rigid Protein Docking

Octavian-Eugen Ganea, Xinyuan Huang, Charlotte Bunne, Yatao Bian, Regina Barzilay, Tommi S. Jaakkola, Andreas Krause

28 Sept 2021 (modified: 18 Nov 2021)ICLR 2022 SpotlightReaders: Everyone Show BibtexShow Revisions

Keywords:protein complexes, protein structure, rigid body docking, SE(3) equivariance, graph neural networks

Abstract: Protein complex formation is a central problem in biology, being involved in most of the cell’s processes, and essential for applications such as drug design or protein engineering. We tackle rigid body protein-protein docking, i.e., computationally predicting the 3D structure of a protein-protein complex from the individual unbound structures, assuming no three-dimensional flexibility during binding. We design a novel pairwise-independent SE(3)-equivariant graph matching network to predict the rotation and translation to place one of the proteins at the right location and the right orientation relative to the second protein. We mathematically guarantee that the predicted complex is always identical regardless of the initial placements of the two structures, avoiding expensive data augmentation. Our model approximates the binding pocket and predicts the docking pose using keypoint matching and alignment through optimal transport and a differentiable Kabsch algorithm. Empirically, we achieve significant running time improvements over existing protein docking software and predict qualitatively plausible protein complex structures despite not using heavy sampling, structure refinement, or templates.

One-sentence Summary: We perform rigid protein docking using a novel independent SE(3)-equivariant message passing mechanism that guarantees the same resulting protein complex independent of the initial placement of the two 3D structures.

SOURCE

https://openreview.net/forum?id=GQjaI9mLet

MIT researchers created a machine-learning model that can directly predict the complex that will form when two proteins bind together. Their technique is between 80 and 500 times faster than state-of-the-art software methods, and often predicts protein structures that are closer to actual structures that have been observed experimentally.

This technique could help scientists better understand some biological processes that involve protein interactions, like DNA replication and repair; it could also speed up the process of developing new medicines.

“Deep learning is very good at capturing interactions between different proteins that are otherwise difficult for chemists or biologists to write experimentally. Some of these interactions are very complicated, and people haven’t found good ways to express them. This deep-learning model can learn these types of interactions from data,” says Octavian-Eugen Ganea, a postdoc in the MIT Computer Science and Artificial Intelligence Laboratory (CSAIL) and co-lead author of the paper.

Ganea’s co-lead author is Xinyuan Huang, a graduate student at ETH Zurich. MIT co-authors include Regina Barzilay, the School of Engineering Distinguished Professor for AI and Health in CSAIL, and Tommi Jaakkola, the Thomas Siebel Professor of Electrical Engineering in CSAIL and a member of the Institute for Data, Systems, and Society. The research will be presented at the International Conference on Learning Representations.

SOURCE

https://news.mit.edu/2022/ai-predicts-protein-docking-0201

Significance of the Scientific Development by the @MIT Team

EquiDock wide applicability:

Our method can be integrated end-to-end to boost the quality of other models (see above discussion on runtime importance). Examples are predicting functions of protein complexes [3] or their binding affinity [5], de novo generation of proteins binding to specific targets (e.g., antibodies [6]), modeling back-bone and side-chain flexibility [4], or devising methods for non-binary multimers. See the updated discussion in the “Conclusion” section of our paper.

Advantages over previous methods:

Our method does not rely on templates or heavy candidate sampling [7], aiming at the ambitious goal of predicting the complex pose directly. This should be interpreted in terms of generalization (to unseen structures) and scalability capabilities of docking models, as well as their applicability to various other tasks (discussed above).

Our method obtains a competitive quality without explicitly using previous geometric (e.g., 3D Zernike descriptors [8]) or chemical (e.g., hydrophilic information) features [3]. Future EquiDock extensions would find creative ways to leverage these different signals and, thus, obtain more improvements.

Novelty of theory:

Our work is the first to formalize the notion of pairwise independent SE(3)-equivariance. Previous work (e.g., [9,10]) has incorporated only single object Euclidean-equivariances into deep learning models. For tasks such as docking and binding of biological objects, it is crucial that models understand the concept of multi-independent Euclidean equivariances.

All propositions in Section 3 are our novel theoretical contributions.

We have rewritten the Contribution and Related Work sections to clarify this aspect.

Footnote [a]: We have fixed an important bug in the cross-attention code. We have done a more extensive hyperparameter search and understood that layer normalization is crucial in layers used in Eqs. 5 and 9, but not on the h embeddings as it was originally shown in Eq. 10. We have seen benefits from training our models with a longer patience in the early stopping criteria (30 epochs for DIPS and 150 epochs for DB5). Increasing the learning rate to 2e-4 is important to speed-up training. Using an intersection loss weight of 10 leads to improved results compared to the default of 1.

Bibliography:

[1] Protein-ligand blind docking using QuickVina-W with inter-process spatio-temporal integration, Hassan et al., 2017

[2] GNINA 1.0: molecular docking with deep learning, McNutt et al., 2021

[3] Protein-protein and domain-domain interactions, Kangueane and Nilofer, 2018

[4] Side-chain Packing Using SE(3)-Transformer, Jindal et al., 2022

[5] Contacts-based prediction of binding affinity in protein–protein complexes, Vangone et al., 2015

[6] Iterative refinement graph neural network for antibody sequence-structure co-design, Jin et al., 2021

[7] Hierarchical, rotation-equivariant neural networks to select structural models of protein complexes, Eismann et al, 2020

[8] Protein-protein docking using region-based 3D Zernike descriptors, Venkatraman et al., 2009

[9] SE(3)-transformers: 3D roto-translation equivariant attention networks, Fuchs et al, 2020

[10] E(n) equivariant graph neural networks, Satorras et al., 2021

[11] Fast end-to-end learning on protein surfaces, Sverrisson et al., 2020

SOURCE

https://openreview.net/forum?id=GQjaI9mLet

Read Full Post »

The Wide Variability in Reported COVID-19 Epidemiologic Data May Suggest That Personalized Omic Testing May Be Needed to Identify At-Risk Populations

Posted in COVID-19, Evolution of Biology Through Culture, Genomic Testing: Methodology for Diagnosis, Health Care System by Country, number of asymptomatic infections, Personalized and Precision Medicine & Genomic Research, Population genetics, Population Health Management, Population Health Management, Genetics & Pharmaceutical, SARS-CoV-2, tagged #COVID-19, biomarker, covid19, Epidemiology, Molecular Epidemiology, SARS-CoV-2, SARS-CoV-2 Entry Factors, SARS-CoV2 on June 9, 2020| Leave a Comment »

The Wide Variability in Reported COVID-19 Epidemiologic Data May Suggest That Personalized Omic Testing May Be Needed to Identify At-Risk Populations

Curator: Stephen J. Williams, PhD

I constantly check the Youtube uploads from Dr. John Campbell, who is a wonderful immunologist and gives daily reports on new findings on COVID-19 from the scientific literature. His reporting is extremely insightful and easily understandable. This is quite a feat as it seems the scientific field has been inundated with a plethora of papers, mostly reported clinical data from small retrospective studies, and many which are being put on preprint servers, and not peer reviewed.

It has become a challenge for many scientists, already inundated with expanding peer reviewed literature in their own fields, as well as the many requests to review papers, to keep up with all these COVID related literature. Especially when it is up to the reader to do their own detailed peer review. So many thanks to people like Dr. Campbell who is an expert in his field for doing this.

However the other day he had posted a video which I found a bit disturbing, as a central theme of the video was that many expert committee could not find any reliable epidemiologic study concerning transmission or even incidence of the disease. In all studies, as Dr. Campell alluded to, there is such a tremendous variability in the reported statistics, whether one is looking at percentage of people testing positive who are symptomatic, the percentage of asymptomatic which may be carriers, the transmission of the disease, and even the percentage of people who recover.

With all the studies being done it would appear that, even if a careful meta analysis were done using all available studies, and assuming their validity before peer review, that there would be a tighter consensus on some of these metrics of disease spread, incidence and prevalence.

Below is the video from Dr. Campbell and the topic is on percentage of asymptomatic carriers of the COVID-19 virus. This was posted last week but later in this post there will be updated information and views by the WHO on this matter as well as other literature (which still shows to my point that this wide variability in reported data may be adding to the policy confusion with respect to asymptomatic versus symptomatic people and why genetic testing might be needed to further discriminate these cohorts of people.

Below is the video:

From the Oxford Center for Evidence Based Medicine: COVID-19 Portal at https://www.cebm.net/oxford-covid-19-evidence-service/

“There is not a single reliable study to determine the number of asymptomatic infections”

And this is very troubling as this means there is no reliable testing resulting in any meaningful data.

As Dr. Campell says

” This is not good enough. There needs to be some sort of coordinated research program it seems all ad hoc”

A few other notes from post and Oxford Center for Evidence Based Medicine:

Symptom based screening will miss a lot of asymptomatic and presymptomatic cases
Some asymptomatic cases will become symptomatic over next week (these people were technically presymptomatic but do we know the %?)
We need a population based antibody screening program
An Italian study of all 3,000 people in city of Vo’Euganeo revealed that 50-75% of those who tested positive were asymptomatic and authors concluded that asymptomatic represents “a formidable source of infection”; Dr. Campbell feels this was a reliable study
Another study from a Washington state nursing facility showed while 56% of positive cases were asymptomatic, 75% of these asymptomatic developed symptoms within a week. Symptom based screening missed half of cases.
Other studies do not follow-up on the positive cases to determine in presymptomatic
It also appears discrepancies between data from different agencies (like CDC, WHO) on who is shedding virus as different tests used (PCR vs antibody)

Recent Studies Conflict Concerning Asymptomatic, Presymtomatic and Viral Transmission

‘We don’t actually have that answer yet’: WHO clarifies comments on asymptomatic spread of Covid-19

From StatNews

A top World Health Organization official clarified on Tuesday that scientists have not determined yet how frequently people with asymptomatic cases of Covid-19 pass the disease on to others, a day after suggesting that such spread is “very rare.”

The clarification comes after the WHO’s original comments incited strong pushback from outside public health experts, who suggested the agency had erred, or at least miscommunicated, when it said people who didn’t show symptoms were unlikely to spread the virus.

Maria Van Kerkhove, the WHO’s technical lead on the Covid-19 pandemic, made it very clear Tuesday that the actual rates of asymptomatic transmission aren’t yet known.

Some of the confusion boiled down to the details of what an asymptomatic infection actually is, and the different ways the term is used. While some cases of Covid-19 are fully asymptomatic, sometimes the word is also used to describe people who haven’t started showing symptoms yet, when they are presymptomatic. Research has shown that people become infectious before they start feeling sick, during that presymptomatic period.

At one of the WHO’s thrice-weekly press briefings Monday, Van Kerkhove noted that when health officials review cases that are initially reported to be asymptomatic, “we find out that many have really mild disease.” There are some infected people who are “truly asymptomatic,” she said, but countries that are doing detailed contact tracing are “not finding secondary transmission onward” from those cases. “It’s very rare,” she said.

Source: https://www.statnews.com/2020/06/09/who-comments-asymptomatic-spread-covid-19/

Therefore the problems have been in coordinating the testing results, which types of tests conducted, and the symptomology results. As Dr. Campbell previously stated it appears more ‘ad hoc’ than coordinated research program. In addition, defining the presymptomatic and measuring this group have been challenging.

However, an alternative explanation to the wide variability in the data may be we need to redefine the cohorts of patients we are evaluating and the retrospective data we are collecting. It is feasible that sub groups, potentially defined by genetic background may be identified and data re-evaluated based on personalized omic data, in essence creating new cohorts based on biomarker data.

From a Perspective in The Lancet about a worldwide proteomic effort (COVID-19 MS Coalition) to discover biomarkers related to COVID19 infection risk, by identifying COVID-related antigens.

The COVID-19 MS Coalition is a collective mass spectrometry effort that will provide molecular level information on SARS-CoV-2 in the human host and reveal pathophysiological and structural information to treat and minimise COVID-19 infection. Collaboration with colleagues at pace involves sharing of optimised methods for sample collection and data generation, processing and formatting for maximal information gain. Open datasets will enable ready access to this valuable information by the computational community to help understand antigen response mechanisms, inform vaccine development, and enable antiviral drug design. As countries across the world increase widespread testing to confirm SARS-CoV-2 exposure and assess immunity, mass spectrometry has a significant role in fighting the disease. Through collaborative actions, and the collective efforts of the COVID-19 MS Coalition, a molecular level quantitative understanding of SARS-CoV-2 and its effect will benefit all.

In an ACS Perspective below, Morteza Mahmoudi suggests a few possible nanobased technologies (i.e., protein corona sensor array and magnetic levitation) that could discriminate COVID-19-infected people at high risk of death while still in the early stages of infection.

Emerging Biomolecular Testing to Assess the Risk of Mortality from COVID-19 Infection

Morteza Mahmoudi*

Publication Date:May 7, 2020

https://doi.org/10.1021/acs.molpharmaceut.0c00371

Please see other articles on COVID-19 on our Coronavirus Portal at

An Epidemiological Approach Stephen J. Williams, PhD and Aviva Lev-Ari, PhD, RN Lead Curators – e–mail Contacts: sjwilliamspa@comcast.net and avivalev-ari@alum.berkeley.edu

https://pharmaceuticalintelligence.com/coronavirus-portal/an-epidemiological-approach-stephen-j-williams-phd-and-aviva-lev-ari-phd-rn-lead-curators-e-mail-contacts-sjwilliamspacomcast-net-and-avivalev-arialum-berkeley-edu/

and

https://pharmaceuticalintelligence.com/coronavirus-portal/

Read Full Post »

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

Posted in BioIT: BioInformatics, BioIT: BioInformatics, NGS, Clinical & Translational, Pharmaceutical R&D Informatics, Clinical Genomics, Cancer Informatics, Biological Networks, Biological Networks, Gene Regulation and Evolution, Cancer - General, Cancer and Current Therapeutics, CANCER BIOLOGY & Innovations in Cancer Therapy, Cancer Genomics, Cancer Screening, Gene Regulation and Evolution, Genetics & Innovations in Treatment, Genome Biology, Genomic Expression, Genomic Testing: Methodology for Diagnosis, interventional oncology, Variation in human protein-coding regions, tagged AACR, American Association for Cancer Research, breast cancer, Cancer Genomics, cancer genomics and therapy, cancer metastasis, Clonal evolution, Curation, curation communities, Glioma, gliomas, mutational burden, mutational spectrum, myeloid dysplatic syndrome, myeloma, spliceosome, tumor heterogeneity on April 28, 2020| Leave a Comment »

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

Reporter: Stephen J. Williams, PhD

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

Oncologic therapy shapes the fitness landscape of clonal hematopoiesis

April 28, 2020, 4:10 PM – 4:20 PM

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

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

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

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

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

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

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

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

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

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

Follow on Twitter at:

Opinion on the use of Germline Genome Editing in IVF

Posted in Gene Regulation, Gene Therapy & Gene Editing Development, Genetics & Innovations in Treatment, Genome Biology, Genomic Testing: Methodology for Diagnosis, Regenerative Biology and Medicine, Reproductive Andrology, Embryology, Genomic Endocrinology, Preimplantation Genetic Diagnosis and Reproductive Genomics, Reproductive Biology & Bio Instrumentation on February 23, 2020| Leave a Comment »

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

In-vitro fertilisation (IVF) is now regarded as a huge clinical success which has benefitted an estimated 16 million parents, at the time the development not only sparked moral outrage but led to political and legislative constraints. Patients undergoing IVF may be presented with numerous assisted reproductive treatments purportedly increasing the chances of pregnancy. Such commercialised “IVF add-ons” often come at high costs without clinical evidence of validity. Additionally, long-term studies of children born through IVF have historically been scarce and inconsistent in their data collection. This has meant that potential genetic predispositions, such as increased body fat composition and blood pressure, as well as congenital abnormalities long associated with IVF births, lack proof of causality.

With Preimplantation genetic testing mutated embryos are automatically discarded, whereas CRISPR could correct mutations to increase the number of viable embryos for implantation. Moreover, in instances where all embryos in a given cycle are destined to develop with severe or lethal mutations, CRISPR could bring success for otherwise doomed IVF treatments. Genetic screening programs offered to couples in hot-spot areas of carrier frequency of monogenic disorders have had huge success in alleviating regional disease burdens. Carried out since the 1970s these programs have altered the course of natural evolution, but few would dispute their benefits in preventing heritable disease transmission.

Mutations are as inevitable as death and taxes. Whilst age is considered one of the largest factors in de-novo mutation generation, it appears that these are inherited primarily from the paternal line. Thus, the paternal age of conception predominantly determines the mutation frequency inherited by children. Whereas advanced maternal age is not associated with mutagenic allele frequency but chromosomal abnormalities. The risk of aneuploidy rises steadily in mothers over the age of 26. Although embryos are screened for aneuploidy prior to implantation, with so many other factors simultaneously being screened the probability of having enough embryos remaining to allow for 50% rate of blastocyte development in-vitro are often fairly low.

Despite IVF being used routinely for over 40 years now, it’s not abundantly clear if, or how often, IVF may introduce genomic alternations or off-target affects in embryos. Likewise, scientists and clinicians are often unable to scrutinise changes produced through natural cellular processes including recombination and aging. So, it may be OK to do controlled experiments on using CRISPR to try and prevent multi-generational suffering. But, there has to be a long term investigation on the side effects of germline genome editing. Science has advanced a lot but still there are lot of things that are yet to be described or discovered by science. Trying to reduce human suffering should not give rise to new bigger sufferings and care must be taken not to create a Frankenstein.

References:

http://www.frontlinegenomics.com/news/29321/opinion-piece-morally-is-germline-genome-editing-all-that-different-to-ivf/

Read Full Post »

Genetic Testing in CVD and Precision Medicine

Posted in Congenital Heart Disease, Frontiers in Cardiology and Cardiovascular Disorders, Genomic Testing: Methodology for Diagnosis, Origins of Cardiovascular Disease on December 16, 2019| Leave a Comment »

Genetic Testing in CVD and Precision Medicine

Reporter: Aviva Lev-Ari, PhD, RN

See

Series A: e-Books on Cardiovascular Diseases

Series A Content Consultant: Justin D Pearlman, MD, PhD, FACC

VOLUME THREE

Etiologies of Cardiovascular Diseases:

Epigenetics, Genetics and Genomics

http://www.amazon.com/dp/B018PNHJ84

Larry H Bernstein, MD, FCAP, Senior Editor, Author and Curator

and

Aviva Lev-Ari, PhD, RN, Editor and Curator

Genetic Testing in CVD and Precision Medicine

Based on

. 2018 Apr; 3(2): 313–326.

Published online 2018 May 30. doi: 10.1016/j.jacbts.2018.01.003

PMCID: PMC6059349

PMID: 30062216

Cardiovascular Precision Medicine in the Genomics Era

Alexandra M. Dainis, BS^a and Euan A. Ashley, BSc, MB ChB, DPhil^a,^b,^c,^∗

Author information

In 2010, we introduced an approach to the evaluation of a personal genome in a clinical context (7). A patient with a family history of coronary artery disease (CAD) and sudden death was evaluated by a cardiac clinical team in conjunction with whole genome sequencing and interpretation. The genomic analysis revealed an increased genetic risk for myocardial infarction and type 2 diabetes. In addition, a pharmacogenomics analysis was performed to assess how the genetics of the patient might influence response to certain drugs, including lipid-lowering therapies and warfarin (7). This clinical assessment, which focused heavily on cardiovascular risk, suggested that whole genome sequencing might provide clinically relevant information for patients.

A 2011 joint statement from the Heart Rhythm Society and the European Heart Rhythm association recommended genetic testing as a class I indication for patients with a number of channelopathies and cardiomyopathies, including long QT syndrome (LQTS), arrhythmogenic right ventricular cardiomyopathy, familial dilated cardiomyopathy (DCM), and hypertrophic cardiomyopathy (HCM) (8). Similarly, a statement from the American Heart Association and the American College of Cardiology recommended genetic testing for HCM, DCM, and thoracic aortic aneurysms to facilitate familial cascade screening and deduce causative mutations 9, 10.

The diagnostic power of genetic testing is significant across the spectrum of CVDs, ranging from cardiomyopathies to life-threatening arrhythmias 10, 11, 12. In the clinic, genetic testing can:

1.

clarify disease diagnoses: genetic testing can help to clarify the diagnosis of diseases that cause similar clinical presentation (e.g., cardiac hypertrophy could be TTR amyloidosis, Fabry disease, or sarcomeric HCM);

2.

facilitate cascade screening: genetic testing can help to identify relatives at risk for CVD before disease symptoms manifest if a disease-associated variant is found in a proband and then screened for in relatives;

3.

direct more precise therapy: genetic testing can help physicians choose appropriate treatments and plan appropriate timing of those treatments. For example, inherited connective tissue disease due to variants in ACTA2, MYH11, or TGFBR2 might prompt consideration of surgical intervention at a smaller aortic aneurysm diameter (13); and

4.

identify patients for targeted therapies: targeted medical therapies, including antibody-based therapeutics, gene editing, and silencing technologies, are available or under development for several genetic diseases, including LQTS, Duchenne muscular dystrophy (DMD), TTR cardiac amyloidosis (14), and Fabry disease 13, 15.

REFERENCES

7. Ashley E.A., Butte A.J., Wheeler M.T. Clinical assessment incorporating a personal genome. Lancet. 2010;375:1525–1535. [PMC free article] [PubMed] [Google Scholar]

8. Ackerman M.J., Priori S.G., Willems S. HRS/EHRA expert consensus statement on the state of genetic testing for the channelopathies and cardiomyopathies: this document was developed as a partnership between the Heart Rhythm Society (HRS) and the European Heart Rhythm Association (EHRA) Europace. 2011;13:1077–1109. [PubMed] [Google Scholar]

9. Gersh B.J., Maron B.J., Bonow R.O. 2011 ACCF/AHA guideline for the diagnosis and treatment of hypertrophic cardiomyopathy: executive summary: a report of the American College of Cardiology Foundation/American Heart Association Task Force on Practice Guidelines. J Am Coll Cardiol. 2011;58:2703–2738. [PubMed] [Google Scholar]

10. Harper A.R., Parikh V.N., Goldfeder R.L., Caleshu C., Ashley E.A. Delivering clinical grade sequencing and genetic test interpretation for cardiovascular medicine. Circ Cardiovasc Genet. 2017;10(2) [PubMed] [Google Scholar]

11. Walsh R., Thomson K.L., Ware J.S. Reassessment of Mendelian gene pathogenicity using 7,855 cardiomyopathy cases and 60,706 reference samples. Genet Med. 2017;19:192–203. [PMC free article] [PubMed] [Google Scholar]

12. Sturm A.C., Hershberger R.E. Genetic testing in cardiovascular medicine: current landscape and future horizons. Curr Opin Cardiol. 2013;28:317–325. [PubMed] [Google Scholar]

13. Caleshu C., Ashley E. Genetic testing for cardiovascular conditions predisposing to sudden death. In: Wilson M.G., Drezner J., editors. IOC Manual of Sports Cardiology. Wiley & Sons, Ltd; Hoboken, NJ: 2016. pp. 175–186. [Google Scholar]

14. Benson M.D., Dasgupta N.R., Rissing S.M., Smith J., Feigenbaum H. Safety and efficacy of a TTR specific antisense oligonucleotide in patients with transthyretin amyloid cardiomyopathy. Amyloid. 2017;24:217–223. [PubMed] [Google Scholar]

15. Parikh V.N., Ashley E.A. Next-generation sequencing in cardiovascular disease: present clinical applications and the horizon of precision medicine. Circulation. 2017;135:406–409. [PMC free article] [PubMed] [Google Scholar]

SOURCE

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6059349/

Read Full Post »

Medicine in 2045 – Perspectives by World Thought Leaders in the Life Sciences & Medicine

Posted in Advanced Computing Platform, Artificial Intelligence - General, Big Data, BioBanking, BioIT: BioInformatics, NGS, Clinical & Translational, Pharmaceutical R&D Informatics, Clinical Genomics, Cancer Informatics, Cancer and Current Therapeutics, CAST - Alternative to CRISPR/Cas9, Computational Biology/Systems and Bioinformatics, CRISPR/Cas9 & Gene Editing, Diagnostic Immunology, Digital HealthCare – biotech & internet joint ventures, Disease Biology, DNA repair, Drug Delivery Platform Technology, Gene Editing Impact on Longevity, Genetics & Innovations in Treatment, Genome Biology, Genomic Testing: Methodology for Diagnosis, Health Care System by Country, Health Economics and Outcomes Research, HealthCare IT, Human Immune System in Health and in Disease, Immuno-Oncology & Genomics, Immunodiagnostics, Liquid Biopsy: Circulating Tumor Cells in Urine and Blood, Microfuidics, Single Cell Genomics, Variation in human protein-coding regions on December 9, 2019| Leave a Comment »

Medicine in 2045 – Perspectives by World Thought Leaders in the Life Sciences & Medicine

Reporter: Aviva Lev-Ari, PhD, RN

This report is based on an article in Nature Medicine | VOL 25 | December 2019 | 1800–1809 | http://www.nature.com/naturemedicine

Looking forward 25 years: the future of medicine.

Nat Med 25, 1804–1807 (2019) doi:10.1038/s41591-019-0693-y

Aviv Regev, PhD

Core member and chair of the faculty, Broad Institute of MIT and Harvard; director, Klarman Cell Observatory, Broad Institute of MIT and Harvard; professor of biology, MIT; investigator, Howard Hughes Medical Institute; founding co-chair, Human Cell Atlas.

millions of genome variants, tens of thousands of disease-associated genes, thousands of cell types and an almost unimaginable number of ways they can combine, we had to approximate a best starting point—choose one target, guess the cell, simplify the experiment.
In 2020, advances in polygenic risk scores, in understanding the cell and modules of action of genes through genome-wide association studies (GWAS), and in predicting the impact of combinations of interventions.
we need algorithms to make better computational predictions of experiments we have never performed in the lab or in clinical trials.
Human Cell Atlas and the International Common Disease Alliance—and in new experimental platforms: data platforms and algorithms. But we also need a broader ecosystem of partnerships in medicine that engages interaction between clinical experts and mathematicians, computer scientists and engineers

Feng Zhang, PhD

investigator, Howard Hughes Medical Institute; core member, Broad Institute of MIT and Harvard; James and Patricia Poitras Professor of Neuroscience, McGovern Institute for Brain Research, MIT.

fundamental shift in medicine away from treating symptoms of disease and toward treating disease at its genetic roots.
Gene therapy with clinical feasibility, improved delivery methods and the development of robust molecular technologies for gene editing in human cells, affordable genome sequencing has accelerated our ability to identify the genetic causes of disease.
1,000 clinical trials testing gene therapies are ongoing, and the pace of clinical development is likely to accelerate.
refine molecular technologies for gene editing, to push our understanding of gene function in health and disease forward, and to engage with all members of society

Elizabeth Jaffee, PhD

Dana and Albert “Cubby” Broccoli Professor of Oncology, Johns Hopkins School of Medicine; deputy director, Sidney Kimmel Comprehensive Cancer Center at Johns Hopkins.

a single blood test could inform individuals of the diseases they are at risk of (diabetes, cancer, heart disease, etc.) and that safe interventions will be available.
developing cancer vaccines. Vaccines targeting the causative agents of cervical and hepatocellular cancers have already proven to be effective. With these technologies and the wealth of data that will become available as precision medicine becomes more routine, new discoveries identifying the earliest genetic and inflammatory changes occurring within a cell as it transitions into a pre-cancer can be expected. With these discoveries, the opportunities to develop vaccine approaches preventing cancers development will grow.

Jeremy Farrar, OBE FRCP FRS FMedSci

Director, Wellcome Trust.

shape how the culture of research will develop over the next 25 years, a culture that cares more about what is achieved than how it is achieved.
building a creative, inclusive and open research culture will unleash greater discoveries with greater impact.

John Nkengasong, PhD

Director, Africa Centres for Disease Control and Prevention.

To meet its health challenges by 2050, the continent will have to be innovative in order to leapfrog toward solutions in public health.
Precision medicine will need to take center stage in a new public health order— whereby a more precise and targeted approach to screening, diagnosis, treatment and, potentially, cure is based on each patient’s unique genetic and biologic make-up.

Eric Topol, MD

Executive vice-president, Scripps Research Institute; founder and director, Scripps Research Translational Institute.

In 2045, a planetary health infrastructure based on deep, longitudinal, multimodal human data, ideally collected from and accessible to as many as possible of the 9+ billion people projected to then inhabit the Earth.
enhanced capabilities to perform functions that are not feasible now.
AI machines’ ability to ingest and process biomedical text at scale—such as the corpus of the up-to-date medical literature—will be used routinely by physicians and patients.
the concept of a learning health system will be redefined by AI.

Linda Partridge, PhD

Professor, Max Planck Institute for Biology of Ageing.

Geroprotective drugs, which target the underlying molecular mechanisms of ageing, are coming over the scientific and clinical horizons, and may help to prevent the most intractable age-related disease, dementia.

Trevor Mundel, MD

President of Global Health, Bill & Melinda Gates Foundation.

finding new ways to share clinical data that are as open as possible and as closed as necessary.
moving beyond drug donations toward a new era of corporate social responsibility that encourages biotechnology and pharmaceutical companies to offer their best minds and their most promising platforms.
working with governments and multilateral organizations much earlier in the product life cycle to finance the introduction of new interventions and to ensure the sustainable development of the health systems that will deliver them.
deliver on the promise of global health equity.

Josep Tabernero, MD, PhD

Vall d’Hebron Institute of Oncology (VHIO); president, European Society for Medical Oncology (2018–2019).

genomic-driven analysis will continue to broaden the impact of personalized medicine in healthcare globally.
Precision medicine will continue to deliver its new paradigm in cancer care and reach more patients.
Immunotherapy will deliver on its promise to dismantle cancer’s armory across tumor types.
AI will help guide the development of individually matched
genetic patient screenings
the promise of liquid biopsy policing of disease?

Pardis Sabeti, PhD

Professor, Harvard University & Harvard T.H. Chan School of Public Health and Broad Institute of MIT and Harvard; investigator, Howard Hughes Medical Institute.

the development and integration of tools into an early-warning system embedded into healthcare systems around the world could revolutionize infectious disease detection and response.
But this will only happen with a commitment from the global community.

Els Toreele, PhD

Executive director, Médecins Sans Frontières Access Campaign

we need a paradigm shift such that medicines are no longer lucrative market commodities but are global public health goods—available to all those who need them.
This will require members of the scientific community to go beyond their role as researchers and actively engage in R&D policy reform mandating health research in the public interest and ensuring that the results of their work benefit many more people.
The global research community can lead the way toward public-interest driven health innovation, by undertaking collaborative open science and piloting not-for-profit R&D strategies that positively impact people’s lives globally.

Read Full Post »

Complex rearrangements and oncogene amplification revealed by long-read DNA and RNA sequencing of a breast cancer cell line

Posted in Cancer - General, CANCER BIOLOGY & Innovations in Cancer Therapy, Cancer Genomics, Genomic Testing: Methodology for Diagnosis, Next Generation Sequencing (NGS), Single Cell Genomics, tagged 10X Genomics Pacific Biosciences, breast cancer, cancer variants, chromosomal abberation, disease variants, fusion genes, gene fusions, long read sequencing, mutational spectrum, next gen sequencing (NGS), oncogenes, sequencing methodology, Whole genome sequencing on August 14, 2019| Leave a Comment »

Complex rearrangements and oncogene amplification revealed by long-read DNA and RNA sequencing of a breast cancer cell line

Reporter: Stephen J. Williams, PhD

In a Genome Research report by Marie Nattestad et al. [1], the SK-BR-3 breast cancer cell line was sequenced using a long read single molecule sequencing protocol in order to develop one of the most detailed maps of structural variations in a cancer genome to date. The authors detected over 20,000 variants with this new sequencing modality, whereas most of these variants would have been missed by short read sequencing. In addition, a complex sequence of nested duplications and translocations occurred surrounding the ERBB2 (HER2) while full-length transcriptomic analysis revealed novel gene fusions within the nested genomic variants. The authors suggest that combining this long-read genome and transcriptome sequencing results in a more comprehensive coverage of tumor gene variants and “sheds new light on the complex mechanisms involved in cancer genome evolution.”

Genomic instability is a hallmark of cancer [2], which lead to numerous genetic variations such as:

Copy number variations
Chromosomal alterations
Gene fusions
Deletions
Gene duplications
Insertions
Translocations

Efforts such as the Cancer Genome Atlas [3], and the International Genome Consortium (2010) use short-read sequencing technology to detect and analyze thousands of commonly occurring mutations however short-read technology has a high false positive and negative rate for detecting less common genetic structural variations {as high as 50% [4]}. In addition, short reads cannot detect variations in close proximity to each other or on the same molecule, therefore underestimating the variation number.

Methods: The authors used a long-read sequencing technology from Pacific Biosciences (SMRT) to analyze the mutational and structural variation in the SK-BR-3 breast cancer cell line. A split read and within-read mapping approach was used to detect variants of different types and sizes. In general, long-reads have better alignment qualities than short reads, resulting in higher quality mapping. Transcriptomic analysis was performed using Iso-Seq.

Results: Using the SMRT long-read sequencing technology from Pacific Biosciences, the authors were able to obtain 71.9% sequencing coverage with average read length of 9.8 kb for the SK-BR-3 genome.

A few notes:

Most amplified regions (33.6 copies) around the locus spanning the ERBB2 oncogene and around MYC locus (38 copies), EGFR locus (7 copies) and BCAS1 (16.8 copies)
The locus 8q24.12 had the most amplifications (this locus contains the SNTB1 gene) at 69.2 copies
Long-read sequencing showed more insertions than deletions and suggests an underestimate of the lengths of low complexity regions in the human reference genome
Found 1,493 long read variants, 603 of which were between different chromosomes
Using Iso-Seq in conjunction with the long-read platform, they detected 1,692,379 isoforms (93%) mapping to the reference genome and 53 putative gene fusions (39 of which they found genomic evidence)

A table modified from the paper on the gene fusions is given below:

Table 1. Gene fusions with RNA evidence from Iso-Seq and DNA evidence from SMRT DNA sequencing where the genomic path is found using SplitThreader from Sniffles variant calls. Note link in table is GeneCard for each gene.

SplitThreader path

#	Genes		Distance (bp)	Number of variants	Chromosomes in path	Previously observed in references
1	KLHDC2	SNTB1	9837	3	14\|17\|8	Asmann et al. (2011) as only a 2-hop fusion
2	CYTH1	EIF3H	8654	2	17\|8	Edgren et al. (2011); Kim and Salzberg
						(2011); RNA only, not observed as 2-hop
3	CPNE1	PREX1	1777	2	20	Found and validated as 2-hop by Chen et al. 2013
4	GSDMB	TATDN1	0	1	17\|8	Edgren et al. (2011); Kim and Salzberg
						(2011); Chen et al. (2013); validated by
						Edgren et al. (2011)
5	LINC00536	PVT1	0	1	8	No
6	MTBP	SAMD12	0	1	8	Validated by Edgren et al. (2011)
7	LRRFIP2	SUMF1	0	1	3	Edgren et al. (2011); Kim and Salzberg
						(2011); Chen et al. (2013); validated by
						Edgren et al. (2011)
8	FBXL7	TRIO	0	1	5	No
9	ATAD5	TLK2	0	1	17	No
10	DHX35	ITCH	0	1	20	Validated by Edgren et al. (2011)
11	LMCD1-AS1	MECOM	0	1	3	No
12	PHF20	RP4-723E3.1	0	1	20	No
13	RAD51B	SEMA6D	0	1	14\|15	No
14	STAU1	TOX2	0	1	20	No
15	TBC1D31	ZNF704	0	1	8	Edgren et al. (2011); Kim and Salzberg
						(2011); Chen et al. (2013); validated by
						Edgren et al. (2011); Chen et al. (2013)

SplitThreader found two different paths for the RAD51B-SEMA6D gene fusion and for the LINC00536-PVT1 gene fusion. Number of Iso-Seq reads refers to full-length HQ-filtered reads. Alignments of SMRT DNA sequence reads supporting each of these gene fusions are shown in Supplemental Note S2.

References

Nattestad M, Goodwin S, Ng K, Baslan T, Sedlazeck FJ, Rescheneder P, Garvin T, Fang H, Gurtowski J, Hutton E et al: Complex rearrangements and oncogene amplifications revealed by long-read DNA and RNA sequencing of a breast cancer cell line. Genome research 2018, 28(8):1126-1135.
Hanahan D, Weinberg RA: The hallmarks of cancer. Cell 2000, 100(1):57-70.
Kandoth C, McLellan MD, Vandin F, Ye K, Niu B, Lu C, Xie M, Zhang Q, McMichael JF, Wyczalkowski MA et al: Mutational landscape and significance across 12 major cancer types. Nature 2013, 502(7471):333-339.
Sudmant PH, Rausch T, Gardner EJ, Handsaker RE, Abyzov A, Huddleston J, Zhang Y, Ye K, Jun G, Fritz MH et al: An integrated map of structural variation in 2,504 human genomes. Nature 2015, 526(7571):75-81.

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

Posted in Advanced Computing Platform, Artificial Intelligence Applications in Health Care, Artificial Intelligence in Health Care - Tools & Innovations, Artificial Intelligence in Medicine - Applications in Therapeutics, Big Data & Analytics, BioIT: BioInformatics, BioIT: BioInformatics, NGS, Clinical & Translational, Pharmaceutical R&D Informatics, Clinical Genomics, Cancer Informatics, Biotechnology, Cancer Genomics, Cardiovascular Pharmacogenomics, Cell Biology, Clinical Genomics, Computational Biology/Systems and Bioinformatics, Data Science, Genome Biology, Genomic Analysis of EKG Electrophysiology Genomics, Genomic Endocrinology, Genomic Expression, Genomic Testing: Methodology for Diagnosis, Genomics Pharmacy, Immuno-Oncology & Genomics, Nutrigenomics, Personalized and Precision Medicine & Genomic Research, Pharmacogenomics, Preimplantation Genetic Diagnosis and Reproductive Genomics, Reproductive Andrology, Embryology, Genomic Endocrinology, Preimplantation Genetic Diagnosis and Reproductive Genomics, RNA Biology, RNA Biology, Cancer and Therapeutics, Single Cell Genomics, Single-cell sequencing, Uncategorized, tagged comparative analysis, Computational Biology/Systems and Bioinformatics, computational genomics, scPopCorn, single-cell RNA sequencing, subpopulation on July 16, 2019| Leave a Comment »

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

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

4.2.5

4.2.5 scPopCorn: A New Computational Method for Subpopulation Detection and their Comparative Analysis Across Single-Cell Experiments, Volume 2 (Volume Two: Latest in Genomics Methodologies for Therapeutics: Gene Editing, NGS and BioInformatics, Simulations and the Genome Ontology), Part 4: Single Cell Genomics

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

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

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

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

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

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

References:

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

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

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

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

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