Healthcare analytics, AI solutions for biological big data, providing an AI platform for the biotech, life sciences, medical and pharmaceutical industries, as well as for related technological approaches, i.e., curation and text analysis with machine learning and other activities related to AI applications to these industries.
One blood sample can be tested for a comprehensive array of cancer cell biomarkers: R&D at WPI
Curator: Marzan Khan, B.Sc
A team of mechanical engineers at Worcester Polytechnic Institute (WPI) have developed a fascinating technology – a liquid biopsy chip that captures and detects metastatic cancer cells, just from a small blood sample of cancer patients(1). This device is a recent development in the scientific field and holds tremendous potential that will allow doctors to spot signs of metastasis for a variety of cancers at an early stage and initiate an appropriate course of treatment(1).
Metastasis occurs when cancer cells break away from their site of origin and spread to other parts of the body via the lymph or the bloodstream, where they give rise to secondary tumors(2). By this time, the cancer is at an advanced stage and it becomes increasingly difficult to fight the disease. The cells that are shed by primary and metastatic cancers are called circulating tumor cells (CTCs) and their numbers lie in the range of 1–77,200/m(3). The basis of the liquid biopsy chip test is to capture these circulating tumor cells in the patient’s blood and identify the cell type through specific interaction with antibodies(4).
The chip is comprised of individual test units or small elements, about 3 millimeters wide(4). Each small element contains a network of carbon nanotube sensors in a well which are functionalized with antibodies(4). These antibodies will bind cell-surface antigens or protein markers unique for each type of cancer cell. Specific interaction between a cell surface protein and its corresponding antibody is a thermodynamic event that causes a change in free energy which is transduced into electricity(3). This electrical signature is picked up by the semi-conducting carbon nanotubes and can be seen as electrical spikes(4). Specific interactions create an increase in electrical signal, whereas non-specific interactions cause a decrease in signal or no change at all(4). Capture efficiency of cancer cells with the chip has been reported to range between 62-100%(4).
The liquid biopsy chip is also more advanced than microfluidics for several reasons. Firstly, the nanotube-chip arrays can capture as well as detect cancer cells, while microfluidics can only capture(4). Samples do not need to be processed for labeling or fixation, so the cell structures are preserved(4). Unlike microfluidics, these nanotubes will also capture tiny structures called exosomes spanning the nanometer range that are produced from cancer cells and carry the same biomarkers(4).
Pancreatic cancer is the fourth leading cause of cancer-associated deaths in the United states, with a survival window of 5 years in only 6% of the cases with treatment(5). In most patients, the disease has already metastasized at the time of diagnosis due to the lack of early-diagnostic markers, affecting some of the major organs such as liver, lungs and the peritoneum(5,6). Despite surgical resection of the primary tumor, the recurrence of local and metastatic tumors is rampant(5). Metastasis is the major cause of mortality in cancers(5). The liquid biopsy chip, that identifies CTCs can thus become an effective diagnostic tool in early detection of cancer as well as provide information into the efficacy of treatment(3). At present, ongoing experiments with this device involve testing for breast cancers but Dr. Balaji Panchapakesan and his team of engineers at WPI are optimistic about incorporating pancreatic and lung cancers into their research.
REFERENCES
1.Nanophenotype. Researchers build liquid biopsy chip that detects metastatic cancer cells in blood: One blood sample can be tested for a comprehensive array of cancer cell biomarkers. 27 Dec 2016. Genesis Nanotechnology,Inc
2.Martin TA, Ye L, Sanders AJ, et al. Cancer Invasion and Metastasis: Molecular and Cellular Perspective. In: Madame Curie Bioscience Database [Internet]. Austin (TX): Landes Bioscience; 2000-2013.
3.F Khosravi, B King, S Rai, G Kloecker, E Wickstrom, B Panchapakesan. Nanotube devices for digital profiling of cancer biomarkers and circulating tumor cells. 23 Dec 2013. IEEE Nanotechnology Magazine 7 (4), 20-26
4.Farhad Khosravi, Patrick J Trainor, Christopher Lambert, Goetz Kloecker, Eric Wickstrom, Shesh N Rai and Balaji Panchapakesan. Static micro-array isolation, dynamic time series classification, capture and enumeration of spiked breast cancer cells in blood: the nanotube–CTC chip. 29 Sept 2016. Nanotechnology. Vol 27, No.44. IOP Publishing Ltd
6.Deeb, A., Haque, S.-U., & Olowoure, O. (2015). Pulmonary metastases in pancreatic cancer, is there a survival influence? Journal of Gastrointestinal Oncology, 6(3), E48–E51. http://doi.org/10.3978/j.issn.2078-6891.2014.114
Translation of whole human genome sequencing to clinical practice: The Joint Initiative for Metrology in Biology (JIMB) is a collaboration between the National Institute of Standards & Technology (NIST) and Stanford University, Volume 2 (Volume Two: Latest in Genomics Methodologies for Therapeutics: Gene Editing, NGS and BioInformatics, Simulations and the Genome Ontology), Part 1: Next Generation Sequencing (NGS)
Translation of whole human genome sequencing to clinical practice: The Joint Initiative for Metrology in Biology (JIMB) is a collaboration between the National Institute of Standards & Technology (NIST) and Stanford University.
Reporter: Aviva Lev-Ari, PhD, RN
JIMB’s mission is to advance the science of measuring biology (biometrology). JIMB is pursuing fundamental research, standards development, and the translation of products that support confidence in biological measurements and reliable reuse of materials and results. JIMB is particularly focused on measurements and technologies that impact, are related to, or enabled by ongoing advances in and associated with the reading and writing of DNA.
Stanford innovators and industry entrepreneurs have joined forces with the measurement experts from NIST to create a new engine powering the bioeconomy. It’s called JIMB — “Jim Bee” — the Joint Initiative for Metrology in Biology. JIMB unites people, platforms, and projects to underpin standards-based research and innovation in biometrology.
Genome in a Bottle
Authoritative Characterization of
Benchmark Human Genomes
The Genome in a Bottle Consortium is a public-private-academic consortium hosted by NIST to develop the technical infrastructure (reference standards, reference methods, and reference data) to enable translation of whole human genome sequencing to clinical practice. The priority of GIAB is authoritative characterization of human genomes for use in analytical validation and technology development, optimization, and demonstration. In 2015, NIST released the pilot genome Reference Material 8398, which is genomic DNA (NA12878) derived from a large batch of the Coriell cell line GM12878, characterized for high-confidence SNPs, indel, and homozygous reference regions (Zook, et al., Nature Biotechnology 2014).
There are four new GIAB reference materials available. With the addition of these new reference materials (RMs) to a growing collection of “measuring sticks” for gene sequencing, we can now provide laboratories with even more capability to accurately “map” DNA for genetic testing, medical diagnoses and future customized drug therapies. The new tools feature sequenced genes from individuals in two genetically diverse groups, Asians and Ashkenazic Jews; a father-mother-child trio set from Ashkenazic Jews; and four microbes commonly used in research. For more informationclick here. To purchase them, visit:
Data and analyses are publicly available (GIAB GitHub). A description of data generated by GIAB is published here. To standardize best practices for using GIAB genomes for benchmarking, we are working with the Global Alliance for Genomics and Health Benchmarking Team (benchmarking tools).
High-confidence small variant and homozygous reference calls are available for NA12878, the Ashkenazim trio, and the Chinese son with respect to GRCh37. Preliminary high-confidence calls with respect to GRCh38 are also available for NA12878. The latest version of these calls is under the latest directory for each genome on the GIAB FTP.
The consortium was initiated in a set of meetings in 2011 and 2012, and the consortium holds open, public workshops in January at Stanford University in Palo Alto, CA and in August/September at NIST in Gaithersburg, MD. Slides from workshops and conferences are available online. The consortium is open and welcomes new participants.
Stanford innovators and industry entrepreneurs have joined forces with the measurement experts from NIST to create a new engine powering the bioeconomy. It’s called JIMB — “Jim Bee” — the Joint Initiative for Metrology in Biology. JIMB unites people, platforms, and projects to underpin standards-based research and innovation in biometrology.
JIMB’s mission is to motivate standards-based measurement innovation to facilitate translation of basic science and technology development breakthroughs in genomics and synthetic biology.
By advancing biometrology, JIMB will push the boundaries of discovery science, accelerate technology development and dissemination, and generate reusable resources.
MicroRNAs (miRNAs) are a group of small non-coding RNA molecules that play a major role in posttranscriptional regulation of gene expression and are expressed in an organ-specific manner. One miRNA can potentially regulate the expression of several genes, depending on cell type and differentiation stage. They control every cellular process and their altered regulation is involved in human diseases. miRNAs are differentially expressed in the male and female gonads and have an organ-specific reproductive function. Exerting their affect through germ cells and gonadal somatic cells, miRNAs regulate key proteins necessary for gonad development. The role of miRNAs in the testes is only starting to emerge though they have been shown to be required for adequate spermatogenesis. In the ovary, miRNAs play a fundamental role in follicles’ assembly, growth, differentiation, and ovulation.
Deciphering the underlying causes of idiopathic male infertility is one of the main challenges in reproductive medicine. This is especially relevant in infertile patients displaying normal seminal parameters and no urogenital or genetic abnormalities. In these cases, the search for additional sperm biomarkers is of high interest. This study was aimed to determine the implications of the sperm miRNA expression profiles in the reproductive capacity of normozoospermic infertile individuals. The expression levels of 736 miRNAs were evaluated in spermatozoa from normozoospermic infertile males and normozoospermic fertile males analyzed under the same conditions. 57 miRNAs were differentially expressed between populations; 20 of them was regulated by a host gene promoter that in three cases comprised genes involved in fertility. The predicted targets of the differentially expressed miRNAs unveiled a significant enrichment of biological processes related to embryonic morphogenesis and chromatin modification. Normozoospermic infertile individuals exhibit a specific sperm miRNA expression profile clearly differentiated from normozoospermic fertile individuals. This miRNA cargo has potential implications in the individuals’ reproductive competence.
Circulating or “extracellular” miRNAs detected in biological fluids, could be used as potential diagnostic and prognostic biomarkers of several disease, such as cancer, gynecological and pregnancy disorders. However, their contributions in female infertility and in vitro fertilization (IVF) remain unknown. Polycystic ovary syndrome (PCOS) is a frequent endocrine disorder in women. PCOS is associated with altered features of androgen metabolism, increased insulin resistance and impaired fertility. Furthermore, PCOS, being a syndrome diagnosis, is heterogeneous and characterized by polycystic ovaries, chronic anovulation and evidence of hyperandrogenism, as well as being associated with chronic low-grade inflammation and an increased life time risk of type 2 diabetes. Altered miRNA levels have been associated with diabetes, insulin resistance, inflammation and various cancers. Studies have shown that circulating miRNAs are present in whole blood, serum, plasma and the follicular fluid of PCOS patients and that these might serve as potential biomarkers and a new approach for the diagnosis of PCOS. Presence of miRNA in mammalian follicular fluid has been demonstrated to be enclosed within microvesicles and exosomes or they can also be associated to protein complexes. The presence of microvesicles and exosomes carrying microRNAs in follicular fluid could represent an alternative mechanism of autocrine and paracrine communication inside the ovarian follicle. The investigation of the expression profiles of five circulating miRNAs (let-7b, miR-29a, miR-30a, miR-140 and miR-320a) in human follicular fluid from women with normal ovarian reserve and with polycystic ovary syndrome (PCOS) and their ability to predict IVF outcomes showed that these miRNAs could provide new helpful biomarkers to facilitate personalized medical care for oocyte quality in ART (Assisted Reproductive Treatment) and during IVF (In Vitro Fertilization).
Immuno-Therapy Strategies on BioMarker’s cutoff value for defining PD-L1 positive/negative patients: First-line and Second-line setting – FDA stand on BMS’s “Test-free Prescribing” in Opdivo (nivolumab) vs Merck’s “Companion Diagnostic” in Keytruda (pembrolizumab) vs Genetech’s “Complementary Diagnostics” and “Companion Diagnostic”?? in Tecentriq (atezolizumab)
Reporter: Aviva Lev-Ari, PhD, RN
UPDATED on 3/11/2019
Roche’s I-O drug Tecentriq picks up key first-in-class breast cancer nod
In a first for the immuno-oncology field, Roche’s Tecentriq has a big new approval in breast cancer. And once again, the Swiss drugmaker has nabbed a piece of the immuno-oncology market all for itself.
On Monday, the FDA greenlighted the immuno-oncology drug, in combination with Celgene chemo drug Abraxane, as a treatment for patients with triple-negative breast cancer (TNBC) whose tumors express the protein PD-L1. The go-ahead makes it the first immunotherapy regimen cleared in TNBC—and in breast cancer in general.
Regulators based their positive decision on data presented last fall that showed that adding Tecentriq to Abraxane in newly diagnosed patients could pare down the risk of disease worsening or death by 20%. The duo staved off progression by a median 7.2 months, compared with 5.5 months for Abraxane on its own.
Now, Roche will lead the way in TNBC without market competition from its PD-1/PD-L1 nemeses—at least for now. Tecentriq’s other two therapy areas, bladder cancer and lung cancer, are ultracrowded, thanks to drugs such as Merck’s Keytruda and Bristol-Myers Squibb’s Opdivo, both of which hit the scene before Tecentriq did.
“The response from the physician community has been phenomenal,” Amreen Husain, M.D., global development team leader for Roche immuno-oncology, said in an October interview.
But when it comes to breast cancer, Roche is used to leading the way, and the company will no doubt lean on its marketing expertise—gleaned through years selling drugs like Herceptin and, more recently, Perjeta and Kadcyla—to get Tecentriq off the ground.
Meanwhile, the approval is an exciting one for doctors and patients who’ve battled the notoriously tough-to-treat disease for years with few good options.
Checkpoint inhibitors like Merck’s Keytruda (pembrolizumab) have changed cancer care across a number of tumor types, but none are currently approved for prostate cancer. Neither are any of three PARP inhibitors currently on the U.S. market for ovarian and breast cancers.
Merck hopes to change that through these late-stage studies, pairing Keytruda with the PARP inhibitor Lynparza (olaparib), chemotherapy and approved prostate cancer agent Xtandi (enzalutamide).
Patients with mCRPC pose a particular challenge to treat. Metastatic cancer has spread to other parts of the body, while castration-resistant tumors have continued to grow despite surgery or treatment to lower the amount of male sex hormones, or androgens, that typically drive prostate cancer.
Current treatment options for this type of prostate cancer are led by Zytiga (abiraterone acetate), a Johnson & Johnson drug now facing generic competition, and Pfizer and Astellas’ Xtandi.
Merck’s Phase 1b/2 study tested four Keytruda combinations, pairing the immunotherapy with both of those current treatment options as well as with chemotherapy and with Lynparza, which is jointly owned by AstraZeneca and Merck.
Results presented Thursday came from a small number of patients, but showed some positive responses in previously treated patients.
While both Keytruda and Opdivo won initial U.S. approval just months apart in 2014, Bristol-Myers’ drug saw much faster sales growth to start. Securing OKs in first-line lung cancer has been Merck’s ticket to catching Bristol-Myers, and Keytruda sales in the first quarter were roughly even with Opdivo’s.
Merck expects the success of its KEYNOTE-189 study will further drive uptake of Keytruda as a first-line treatment for advanced NSCLC. Results, presented last month at the annual meeting of the American Association of Cancer Research, showed a combination of Keytruda and chemotherapy cut the risk of death in half compared to chemotherapy alone.
Importantly, data supported the combination’s benefit across varying levels of PD-L1 expression , a biomarker used to identify patients most likely to respond to immunotherapy. Merck previously won a conditional approval for the combo from a smaller study, but physicians have been hesitant to broadly prescribe without further data.
Now, impressive results in hand, Merck hopes more doctors will prescribe Keytruda plus chemotherapy to NSCLC patients with PD-L1 expression levels below 50% or even below 1% — patient populations currently not served by Keytruda monotherapy.
In a phase 3 study, Roche’s Tecentriq combined with chemo beat out solo chemo at cutting the risk of disease worsening or death in previously untreated patients with the squamous form of the disease. As of now, researchers haven’t seen evidence of a benefit to overall survival, but the trial, dubbed IMpower131, is continuing to collect that data.
The results hand Roche the chance to nab a first-to-market lead in the front-line squamous setting. Squamous NSCLC affects just 25% to 30% of all NSCLC patients, but it’s more complicated, and patients have fewer treatment options than those with non-squamous NSCLC, Jefferies analyst Ian Hilliker wrote in a note to clients. Hilliker predicts $1.1 billion in peak sales for Tecentriq in that set of patients.
Roche’s study had three arms. All patients received carboplatin and paclitaxel, the cancer drug once sold as Taxol. The control group also received Avastin, one of Roche’s best-selling cancer drugs. Then two groups got Tecentriq, one with Avastin and one without. What Roche has announced today is that the Avastin-Tecentriq-chemotherapy combination did better than Avastin and chemotherapy alone, and that the survival results so far are “encouraging.” That leaves a big question: how are the patients who got Tecentriq, but not Avastin, doing?
Close to a year after Merck $MRK won an accelerated FDA OK to use its PD-1 checkpoint star Keytruda for treating second-line cases head and neck squamous cell carcinoma in combination with platinum-containing chemo, the pharma giant announced that its big Phase III study for that indication failed.
The pivotal KEYNOTE-040 trial failed to meet the primary endpoint on overall survival in comparing the blockbuster checkpoint against standard therapies, the pharma giant reported. But the current approval stands nevertheless, Merck said in a statement.
“The company noted that the FDA remains comfortable with the drug’s current accelerated approval in this indication despite the trial results,” observed Leerink’s Seamus Fernandez. “Importantly, Keytruda appears to have another shot on goal for full approval in H&N cancer, as the Keynote-048 study in first-line patients could, if positive, serve as the confirmatory trial.”
Roche’s shocking Tecentriq fail raises red flag for bladder cancer rivals
Roche’s Tecentriq wasn’t supposed to fail its phase 3 trial in second-line bladder cancer. But that’s what it just did—and the data shortfall not only endangers the drug’s conditional FDA approval, but could augur trouble ahead for other checkpoint inhibitors that followed Tecentriq into the field.
Tecentriq, approved last year on the basis of phase 2 data showing a durable response to the drug, failed to prove it could actually prolong patients’ lives, the company said Wednesday. The bladder cancer indication, Tecentriq’s first, accounts for about 70% of the med’s current sales, analysts say, and the FDA could well decide to strike that approval off the drug’s label.
“[W]e assume that this will put this indication at risk of being removed from the label,” Leerink analyst Seamus Fernandez wrote Wednesday morning, noting that the results were unexpected. “This comes as a surprise to us, considering Merck’s Keytruda showed an overall survival benefit.”
Opdivo(nivolumab) Shows Durable Response in Longest Follow-up for a PD-1 Inhibitor in Previously Treated Advanced Non-Small Cell Lung Cancer
Updated data from CheckMate -057 and -017 showOpdivo-treated patients had tripled the duration of response compared to those treated with docetaxel, with a minimum follow-up of two years
In CheckMate -057, durable responses and complete responses were observed withOpdivo in both PD-L1 expressors and non-expressors
Patient-reported outcomes from CheckMate -057 show favorable overall health status withOpdivo versus docetaxel in previously treated advanced non-small cell lung cancer patients
Bristol-Myers Squibb Company (NYSE: BMY) announced today updated results from two pivotal Phase 3 studies, CheckMate -057 and CheckMate -017, which showed more than one-third of previously treated metastatic non-small cell lung cancer (NSCLC) patients in both trials experienced ongoing responses with Opdivo, compared to no ongoing responses in the docetaxel arm. The median duration of response (DOR) with Opdivo versus docetaxel in CheckMate -057 was 17.2 months (95% CI: 8.4, NE) and 5.6 months (95% CI: 4.4, 6.9), respectively, and in CheckMate -017 it was 25.2 months (95% CI: 9.8, 30.4) and 8.4 months (95% CI: 8.4, NE), respectively. In CheckMate -057, patients with PD-L1 ≥1% had a median DOR of 17.2 months (95% CI: 8.4, NE) and in patients with PD-L1 <1%, it was 18.3 months (95% CI: 5.5, NE). In both studies, durability of response was observed in both PD-L1 expressors and non-expressors, and in CheckMate -057, one out of the four complete responses occurred in a patient with <1% PD-L1 expression.
There were no new safety signals identified for Opdivo in the pooled safety analysis from both studies. No new treatment-related deaths occurred between one and two years’ minimum follow-up despite the longer treatment exposure, and new events were observed in 11/418 patients with an additional one year of follow up.
These findings were presented today, October 9, during a poster discussion session at the 2016 European Society for Medical Oncology Congress from 3:46-4:06 p.m. CEST (Abstract #1215PD).
“Further evaluation of Opdivo in previously treated non-small cell lung cancer showed continued superior survival and the potential for durable responses compared to docetaxel across histologies in this patient population,” said Martin Reck, M.D., Ph.D., head of thoracic oncology at the Hospital Grosshansdorf. “Notably, the median duration of response with Opdivo was more than three times that observed with docetaxel.”
PD-L1 testing as part of the tumor profiling workup for patients. Diaceutics’ surveys show a sharp uptick in the number of labs offering PD-L1 testing over the past year-and-a-half and 52 labs in the US offer at least one PD-L1 test. The company also reviewed biomarkers being studied in 95 Phase II/III NSCLC, and found that approximately half are incorporating patients’ PD-L1 status either alone or in combination with other markers, such as EGFR and ALK mutations.
At Cancer Genetics over the past year, there has also been a notable ramp up in orders for PD-L1 testing for lung cancer patients, but also for melanoma and head and neck cancer patients.
Labs are also challenged by having to decide whether to invest in validating and offering all four FDA-approved PD-L1 tests. “If you look from a laboratory perspective, in the ideal world, you need one test, and clear instructions about the algorithm and cutoff values to assign patients to treatment,” Braendle said. “Four different tests creates quite a confusing situation for the labs and the physicians.”
SOURCES
Diaceutics Group Report Reveals Significant Real-Time PD-L1 Testing Gaps in the US
Chapter 1: Evolution of the Foundation for Diagnostics and Pharmaceuticals Industries
1.1 Outline of Medical Discoveries between 1880 and 1980
1.2 The History of Infectious Diseases and Epidemiology in the late 19th and 20th Century
1.3 The Classification of Microbiota
1.4 Selected Contributions to Chemistry from 1880 to 1980
1.5 The Evolution of Clinical Chemistry in the 20th Century
1.6 Milestones in the Evolution of Diagnostics in the US HealthCare System: 1920s to Pre-Genomics
Chapter 2. The search for the evolution of function of proteins, enzymes and metal catalysts in life processes
2.1 The life and work of Allan Wilson
2.2 The evolution of myoglobin and hemoglobin
2.3 More complexity in proteins evolution
2.4 Life on earth is traced to oxygen binding
2.5 The colors of life function
2.6 The colors of respiration and electron transport
2.7 Highlights of a green evolution
Chapter 3. Evolution of New Relationships in Neuroendocrine States
3.1 Pituitary endocrine axis
3.2 Thyroid function
3.3 Sex hormones
3.4 Adrenal Cortex
3.5 Pancreatic Islets
3.6 Parathyroids
3.7 Gastointestinal hormones
3.8 Endocrine action on midbrain
3.9 Neural activity regulating endocrine response
3.10 Genomic Promise for Neurodegenerative Diseases, Dementias, Autism Spectrum, Schizophrenia, and Serious Depression
Chapter 4. Problems of the Circulation, Altitude, and Immunity
4.1 Innervation of Heart and Heart Rate
4.2 Action of hormones on the circulation
4.3 Allogeneic Transfusion Reactions
4.4 Graft-versus Host reaction
4.5 Unique problems of perinatal period
4.6. High altitude sickness
4.7 Deep water adaptation
4.8 Heart-Lung-and Kidney
4.9 Acute Lung Injury
4.10 Reconstruction of Life Processes requires both Genomics and Metabolomics to explain Phenotypes and Phylogenetics
Chapter 5. Problems of Diets and Lifestyle Changes
5.1 Anorexia nervosa
5.2 Voluntary and Involuntary S-insufficiency
5.3 Diarrheas – bacterial and nonbacterial
5.4 Gluten-free diets
5.5 Diet and cholesterol
5.6 Diet and Type 2 diabetes mellitus
5.7 Diet and exercise
5.8 Anxiety and quality of Life
5.9 Nutritional Supplements
Chapter 6. Advances in Genomics, Therapeutics and Pharmacogenomics
6.1 Natural Products Chemistry
6.2 The Challenge of Antimicrobial Resistance
6.3 Viruses, Vaccines and immunotherapy
6.4 Genomics and Metabolomics Advances in Cancer
6.5 Proteomics – Protein Interaction
6.6 Pharmacogenomics
6.7 Biomarker Guided Therapy
6.8 The Emergence of a Pharmaceutical Industry in the 20th Century: Diagnostics Industry and Drug Development in the Genomics Era: Mid 80s to Present
6.09 The Union of Biomarkers and Drug Development
6.10 Proteomics and Biomarker Discovery
6.11 Epigenomics and Companion Diagnostics
Chapter 7
Integration of Physiology, Genomics and Pharmacotherapy
7.1 Richard Lifton, MD, PhD of Yale University and Howard Hughes Medical Institute: Recipient of 2014 Breakthrough Prizes Awarded in Life Sciences for the Discovery of Genes and Biochemical Mechanisms that cause Hypertension
7.2 Calcium Cycling (ATPase Pump) in Cardiac Gene Therapy: Inhalable Gene Therapy for Pulmonary Arterial Hypertension and Percutaneous Intra-coronary Artery Infusion for Heart Failure: Contributions by Roger J. Hajjar, MD
7.3 Diagnostics and Biomarkers: Novel Genomics Industry Trends vs Present Market Conditions and Historical Scientific Leaders Memoirs
7.4 Synthetic Biology: On Advanced Genome Interpretation for Gene Variants and Pathways: What is the Genetic Base of Atherosclerosis and Loss of Arterial Elasticity with Aging
Crowdsourcing Genetic Data Yields Discovery of DNA loci associated with Major Depressive Disorder (MDD) in European Descendants, Volume 2 (Volume Two: Latest in Genomics Methodologies for Therapeutics: Gene Editing, NGS and BioInformatics, Simulations and the Genome Ontology), Part 1: Next Generation Sequencing (NGS)
Crowdsourcing Genetic Data Yields Discovery of DNA loci associated with Major Depressive Disorder (MDD) in European Descendants
Reporter: Kelly Perlman, Life Sciences Student and Research Assistant, McGill University
UPDATED on 11/24/2019
Can AI help diagnose depression? It’s a long shot
At the moment, machine intelligence is just as subjective as human intelligence
Researchers from Pfizer Global Research and Development, 23andMe, and the Massachusetts General Hospital have published a study in Nature Genetics, pinpointing 15 genetic loci associated with the risk of developing major depressive disorder (MDD) in individuals of European ancestry. Evidence from previous research suggests that MDD is heritable, but the details of the specific gene correlates are unclear. The identification of loci where single nucleotide polymorphisms (SNPs) related to MDD exist could provide better insight into the neurobiology of depression, and therefore better treatment options.
23andMe, a private biotechnology company situated in California, offers a DNA sequencing service in which consumers send in a saliva swab for testing, and later receive a report listing the findings of the analysis related to ancestry, physical and behavioral traits, along with risk of inheriting certain diseases. The participants of this study had agreed to provide the results of their genetic testing for scientific research.
The results of 75,607 participants with self-reported diagnoses of depression were compared to the results of 231,747 participants reporting having never experienced depression. This data was combined with the results of previously published MDD genome-wide association studies (GWAS). To test the whether these results could be replicated, another set of results from 23andMe was analyzed, in which there were 45,773 MDD subjects, and 106,354 controls.
After the joint analysis, 17 SNPs were identified at 15 different loci. Tissue and gene enrichment assays showed that the genes that were over-expressed in the CNS were related to functions including neurodevelopment, histone methylation, neurogenesis and synaptic modification.
The team then created a weighted genetic risk score (GRS) in which they compared the 17 SNPs with factors including medication use, comorbid diseases and behavioral phenotypes, all of which were correlated with the GRS. Of note, the GRS was very highly correlated with age of onset of MDD.
The crowdsourcing of genetic data proves to be an efficient and powerful tool for large-scale MDD studies. Pooling large subject databases together is essential in order to account for the heterogeneous nature of the disease. Despite not being able to precisely assess each subject’s disease phenotype, scientists can make more rapid headway by collaborating with biotechnology companies in the quest to better understand the biological mechanisms of depression. Ron Perlis, M.D., M.Sc., of the Massachusetts General Hospital and co-author of this paper explained that “finding genes associated with depression should help make clear that this is a brain disease, which we hope will decrease the stigma still associated with these kinds of illnesses”.
Hyde, C. L., Nagle, M. W., Tian, C., Chen, X., Paciga, S. A., Wendland, J. R., . . . Winslow, A. R. (2016). Identification of 15 genetic loci associated with risk of major depression in individuals of European descent. Nature Genetics Nat Genet. doi:10.1038/ng.3623
MGH & BWH Researchers: Brains of cognitively normal older individuals carrying the APOE4 gene variant – association between lower weight and more extensive deposits of the Alzheimer’s-associated protein beta-amyloid
Reporter: Aviva Lev-Ari, PhD, RN
While the concept of a preclinical version of Alzheimer’s disease is theoretical and not yet being used to guide clinical diagnosis or treatment, the current hypothesis involves three stages. Individuals at stage 1 are cognitively normal but have elevated amyloid deposits; stage 2 adds evidence of neurodegeneration, such as elevated tau deposits or characteristic loss of certain brain tissues, with no cognitive symptoms; and stage 3 adds cognitive changes that, while still in a normal range, indicate a decline for that individual. The current study is part of the MGH-based Harvard Aging Brain Study (HABS), designed to identify markers that predict who is likely to develop Alzheimer’s disease and how soon symptoms are likely to develop.
This investigation explored the relationship between body mass index (BMI) and beta amyloid levels in the brains of the first 280 participants to enroll in HABS, who were ages 62 to 90, cognitively normal and in good general health. Participants’ initial enrollment data included medical histories; physical exams; testing for the presence of APOE4, the major genetic risk factor for late-onset Alzheimer’s; and PET imaging with Pittsburgh compound B (PiB), which can visualize amyloid plaques in the brain.
After adjusting for factors including age, sex, education and APOE4 status, researchers found that having a lower BMI was associated with greater retention of PiB, indicating more extensive amyloid deposits in the brain. The association was most pronounced in normal-weight participants, who were the group with the lowest BMI in the study. Analysis focused on APOE status revealed that the association between lower BMI and greater PiB retention was particularly significant for individuals with the APOE4 gene variant, which is associated with increased Alzheimer’s disease risk.
SOURCE
MGH News Release
Tuesday, August 2, 2016
Lower weight in late life may increase risk of Alzheimer’s Disease
Supreme Court declining to hear an appeal from Sequenom, that markets a Prenatal Test based on Screening Fetal DNA – A Natural Biological Process
Reporter: Aviva Lev-Ari, PhD, RN
Supreme Court on Monday left a whole lot of biotech entrepreneurs fearful that their inventions may not be worth all that much after all.
The justices spooked the industry by declining to hear an appeal from Sequenom, a California company that markets a prenatal test based on screening fetal DNA. A lower court had ruled that Sequenom couldn’t patent the test because it was based on a natural biological process.
“This is a huge ruling,” said David O. Taylor, an assistant professor at Southern Methodist University’s Dedman School of Law.
Justices in lower courts were effectively pleading for the Supreme Court to provide some guidance on the issue of patent eligibility in biotech, Taylor said. The top court’s silence leaves an entire industry guessing.
Taylor predicted that companies and investors will be less likely to fund expensive research for fear of having patents invalidated by the courts. Sequenom CEO Dirk van den Boom made a similar argument.
“Their refusal to clarify this area of the law would seem to point in the direction of reduced investment in diagnostic invention, and that is obviously problematic from a public health concern,” Taylor said.
Sequenom’s opponent in the legal case, Ariosa Diagnostics, said the fetal test was not novel enough to deserve a patent. Asking the justices to intervene “is inviting the court to revisit and rewrite decades of jurisprudence covering patent-eligible subject matter,” the company wrote in a brief. “In the future, there may be a case that tests the boundaries” of what’s appropriate to patent, Ariosa wrote, “but this is not that case.”
The tell-tale DNA
Sequenom’s story begins in 1996, when a pair of doctors noted that there were trace amounts of tell-tale fetal DNA in the plasma of expectant mothers. So they created a test that could extract that DNA and determine a baby’s sex and risk of genetic disorders like Down syndrome, without the need for the invasive procedures that were then the norm. They patented the idea the following year, and their test is now sold by Sequenom as MaterniT21. But a lower court invalidated that patent in 2013.
He and others fear that the Supreme Court’s ruling in Mayo was so broad that judges will be able to invalidate a slew of life science patents by picking apart the components of an invention to find the naturally occurring biological process that inspired it.
Even Noonan, however, said courts were unlikely to invalidate patents based on truly novel discoveries, such as CRISPR-Cas9 gene editing, which is about to be tested in humans.
The CRISPR technology harnesses a naturally occurring process, but it’s “so revolutionary,” Noonan said, “that even the Supreme Court wouldn’t overrule it.”
Damian Garde can be reached at damian.garde@statnews.com
Follow Damian on Twitter @damiangarde
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mRNA Data Survival Analysis, Volume 2 (Volume Two: Latest in Genomics Methodologies for Therapeutics: Gene Editing, NGS and BioInformatics, Simulations and the Genome Ontology), Part 1: Next Generation Sequencing (NGS)
mRNA Data Survival Analysis
Curators: Larry H. Bernstein, MD, FCAP and Aviva Lev-Ari, PhD, RN
SURVIV for survival analysis of mRNA isoform variation
The rapid accumulation of clinical RNA-seq data sets has provided the opportunity to associate mRNA isoform variations to clinical outcomes. Here we report a statistical method SURVIV (Survival analysis of mRNA Isoform Variation), designed for identifying mRNA isoform variation associated with patient survival time. A unique feature and major strength of SURVIV is that it models the measurement uncertainty of mRNA isoform ratio in RNA-seq data. Simulation studies suggest that SURVIV outperforms the conventional Cox regression survival analysis, especially for data sets with modest sequencing depth. We applied SURVIV to TCGA RNA-seq data of invasive ductal carcinoma as well as five additional cancer types. Alternative splicing-based survival predictors consistently outperform gene expression-based survival predictors, and the integration of clinical, gene expression and alternative splicing profiles leads to the best survival prediction. We anticipate that SURVIV will have broad utilities for analysing diverse types of mRNA isoform variation in large-scale clinical RNA-seq projects.
Eukaryotic cells generate remarkable regulatory and functional complexity from a finite set of genes. Production of mRNA isoforms through alternative processing and modification of RNA is essential for generating this complexity. A prevalent mechanism for producing mRNA isoforms is the alternative splicing of precursor mRNA1. Over 95% of the multi-exon human genes undergo alternative splicing2, 3, resulting in an enormous level of plasticity in the regulation of gene function and protein diversity. In the last decade, extensive genomic and functional studies have firmly established the critical role of alternative splicing in cancer4, 5, 6. Alternative splicing is involved in a full spectrum of oncogenic processes including cell proliferation, apoptosis, hypoxia, angiogenesis, immune escape and metastasis7, 8. These cancer-associated alternative splicing patterns are not merely the consequences of disrupted gene regulation in cancer but in numerous instances actively contribute to cancer development and progression. For example, alternative splicing of genes encoding the Bcl-2 family of apoptosis regulators generates both anti-apoptotic and pro-apoptotic protein isoforms9. Alternative splicing of the pyruvate kinase M (PKM) gene has a significant impact on cancer cell metabolism and tumour growth10. A transcriptome-wide switch of the alternative splicing programme during the epithelial–mesenchymal transition plays an important role in cancer cell invasion and metastasis11, 12.
RNA sequencing (RNA-seq) has become a popular and cost-effective technology to study transcriptome regulation and mRNA isoform variation13, 14. As the cost of RNA-seq continues to decline, it has been widely adopted in large-scale clinical transcriptome projects, especially for profiling transcriptome changes in cancer. For example, as of April 2015 The Cancer Genome Atlas (TCGA) consortium had generated RNA-seq data on over 11,000 cancer patient specimens from 34 different cancer types. Within the TCGA data, breast invasive carcinoma (BRCA) has the largest sample size of RNA-seq data covering over 1,000 patients, and clinical information such as survival times, tumour stages and histological subtypes is available for the majority of the BRCA patients15. Moreover, the median follow-up time of BRCA patients is ~400 days, and 25% of the patients have more than 1,200 days of follow-up. Collectively, the large sample size and long follow-up time of the TCGA BRCA data set allow us to correlate genomic and transcriptomic profiles to clinical outcomes and patient survival times.
To date, systematic analyses have been performed to reveal the association between copy number variation, DNA methylation, gene expression and microRNA expression profiles with cancer patient survival16, 17. By contrast, despite the importance of mRNA isoform variation and alternative splicing, there have been limited efforts in transcriptome-wide survival analysis of alternative splicing in cancer patients. Most RNA-seq studies of alternative splicing in cancer transcriptomes focus on identifying ‘cancer-specific’ alternative splicing events by comparing cancer tissues with normal controls (see refs 18, 19, 20, 21, 22, 23 for examples). A recent analysis of TCGA RNA-seq data identified 163 recurrent differential alternative splicing events between cancer and normal tissues of three cancer types, among which five were found to have suggestive survival signals for breast cancer at a nominal P-value cutoff of 0.05 (ref. 21). Some other studies reported a significant survival difference between cancer patient subgroups after stratifying patients with overall mRNA isoform expression profiles24, 25. However, systematic cancer survival analyses of alternative splicing at the individual exon resolution have been lacking. Two main challenges exist for survival analyses of mRNA isoform variation and alternative splicing using RNA-seq data. The first challenge is to account for the estimation uncertainty of mRNA isoform ratios inferred from RNA-seq read counts. The statistical confidence of mRNA isoform ratio estimation depends on the RNA-seq read coverage for the events of interest, with larger read coverage leading to a more reliable estimation14. Modelling the estimation uncertainty of mRNA isoform ratio is an essential component of RNA-seq analyses of alternative splicing, as shown by various statistical algorithms developed for detecting differential alternative splicing from multi-group RNA-seq data14, 26, 27, 28,29. The second challenge, which is a general issue in survival analysis, is to properly model the association of mRNA isoform ratio with survival time, while accounting for missing data in survival time because of censoring, that is, patients still alive at the end of the survival study, whose precise survival time would be uncertain. To date, no algorithm has been developed for survival analyses of mRNA isoform variation that accounts for these sources of uncertainty simultaneously.
Here we introduce SURVIV (Survival analysis of mRNA Isoform Variation), a statistical model for identifying mRNA isoform ratios associated with patient survival times in large-scale cancer RNA-seq data sets. SURVIV models the estimation uncertainty of mRNA isoform ratios in RNA-seq data and tests the survival effects of isoform variation in both censored and uncensored survival data. In simulation studies, SURVIV consistently outperforms the conventional Cox regression survival analysis that ignores the measurement uncertainty of mRNA isoform ratio. We used SURVIV to identify alternatively spliced exons whose exon-inclusion levels significantly correlated with the survival times of invasive ductal carcinoma (IDC) patients from the TCGA breast cancer cohort. Survival-associated alternative splicing events are identified in gene pathways associated with apoptosis, oxidative stress and DNA damage repair. Importantly, we show that alternative splicing-based survival predictors outperform gene expression-based survival predictors in the TCGA IDC RNA-seq data set, as well as in TCGA data of five additional cancer types. Moreover, the integration of clinical information, gene expression and alternative splicing profiles leads to the best prediction of survival time.
SURVIV statistical model
The statistical model of SURVIV assesses the association between mRNA isoform ratio and patient survival time. While the model is generic for many types of alternative isoform variation, here we use the exon-skipping type of alternative splicing to illustrate the model (Fig. 1a). For each alternative exon involved in exon-skipping, we can use the RNA-seq reads mapping to its exon-inclusion or -skipping isoform to estimate its exon-inclusion level (denoted as ψ, or PSI that is Per cent Spliced In14). A key feature of SURVIV is that it models the RNA-seq estimation uncertainty of exon-inclusion level as influenced by the sequencing coverage for the alternative splicing event of interest. This is a critical issue in accurate quantitative analyses of mRNA isoform ratio in large-scale RNA-seq data sets14, 26, 27, 28, 29. Therefore, SURVIV contains two major components: the first to model the association of mRNA isoform ratio with patient survival time across all patients, and the second to model the estimation uncertainty of mRNA isoform ratio in each individual patient (Fig. 1a).
Figure 1: The statistical framework of the SURVIV model.
(a) For each patient k, the patient’s hazard rate λk(t) is associated with the baseline hazard rate λ0(t) and this patient’s exon-inclusion level ψk. The association of exon-inclusion level with patient survival is estimated by the survival coefficient β. The exon-inclusion level ψk is estimated from the read counts for the exon-inclusion isoform ICk and the exon-skipping isoform SCk. The proportion of the inclusion and skipping reads is adjusted by a normalization function f that considers the lengths of the exon-inclusion and -skipping isoforms (see details in Results and Supplementary Methods). (b) A hypothetical example to illustrate the association of exon-inclusion level with patient survival probability over time Sk(t), with the survival coefficient β=−1 and a constant baseline hazard rate λ0(t)=1. In this example, patients with higher exon-inclusion levels have lower hazard rates and higher survival probabilities. (c) The schematic diagram of an exon-skipping event. The exon-inclusion reads ICk are the reads from the upstream splice junction, the alternative exon itself and the downstream splice junction. The exon-skipping reads SCk are the reads from the skipping splice junction that directly connects the upstream exon to the downstream exon.
Briefly, for any individual exon-skipping event, the first component of SURVIV uses a proportional hazards model to establish the relationship between patient k’s exon-inclusion level ψk and hazard rate λk(t).
For each exon, the association between the exon-inclusion level and patient survival time is reflected by the survival coefficient β. A positive β means increased exon inclusion is associated with higher hazard rate and poorer survival, while a negative β means increased exon inclusion is associated with lower hazard rate and better survival. λ0(t) is the baseline hazard rate estimated from the survival data of all patients (see Supplementary Methods for the detailed estimation procedure). A particular patient’s survival probability over time Sk(t) can be calculated from the patient-specific hazard rate λk(t) as . Figure 1b illustrates a simple example with a negative β=−1 and a constant baseline hazard rate λ0(t)=1, where higher exon-inclusion levels are associated with lower hazard rates and higher survival probabilities.
The second component of SURVIV models the exon-inclusion level and its estimation uncertainty in individual patient samples. As illustrated in Fig. 1c, the exon-inclusion level ψk of a given exon in a particular sample can be estimated by the RNA-seq read count specific to the exon inclusion isoform (ICk) and the exon-skipping isoform (SCk). Other types of alternative splicing and mRNA isoform variation can be similarly modelled by this framework29. Given the effective lengths (that is, the number of unique isoform-specific read positions) of the exon-inclusion isoform (lI) and the exon-skipping isoform (lS), the exon-inclusion level ψk can be estimated as . Assuming that the exon-inclusion read count ICk follows a binomial distribution with the total read count nk=ICk+SCk, we have:
The binomial distribution models the estimation uncertainty of ψk as influenced by the total read count nk, in which the parameter pk represents the proportion of reads from the exon-inclusion isoform, given the exon-inclusion level ψk adjusted by a length normalization function f(ψk) based on the effective lengths of the isoforms. The definitions of effective lengths for all basic types of alternative splicing patterns are described in ref. 29.
Distinct from conventional survival analyses in which predictors do not have estimation uncertainty, the predictors in SURVIV are exon-inclusion levels ψk estimated from RNA-seq count data, and the confidence of ψk estimate for a given exon in a particular sample depends on the RNA-seq read coverage. We use the statistical framework of survival measurement error model30 to incorporate the estimation uncertainty of isoform ratio in the proportional hazards model. Using a likelihood ratio test, we test whether the exon-inclusion levels have a significant association with patient survival over the null hypothesis H0:β=0. The false discovery rate (FDR) is estimated using the Benjamini and Hochberg approach31. Details of the parameter estimation and likelihood ratio test in SURVIV are described in Supplementary Methods.
Figure 2: Simulation studies to assess the performance of SURVIV and the importance of modelling the estimation uncertainty of mRNA isoform ratio.
We compared our SURVIV model with Cox regression using point estimates of exon-inclusion levels, which does not consider the estimation uncertainty of the mRNA isoform ratio. (a) To study the effect of RNA-seq depth, we simulated the mean total splice junction read counts equal to 5, 10, 20, 50, 80 and 100 reads. We generated two sets of simulations with and without data-censoring. For each simulation, the true-positive rate (TPR) at 5% false-positive rate is plotted. The inset figure shows the empirical distribution of the mean total splice junction read counts in the TCGA IDC RNA-seq data (x axis in the log10 scale). (b) To faithfully represent the read count distribution in a real data set, we performed another simulation with read counts directly sampled from the TCGA IDC data. Sampled read counts were then multiplied by different factors ranging from 10 to 300% to simulate data sets with different RNA-seq read depth. Continuous and dashed lines represent the performance of SURVIV and Cox regression, respectively. Red lines represent the area under curve (AUC) of the ROC curve (TPR versus false-positive rate plot). Black lines represent the TPR at 5% false-positive rate.
Using these simulated data, we compared SURVIV with Cox regression in two settings, without or with censoring of the survival time. In the setting without censoring, the death and survival time of each individual is known. In the setting with censoring, certain individuals are still alive at the end of the survival study. Consequently, these patients have unknown death and survival time. Here, in the simulation with censoring, we assumed that 85% of the patients were still alive at the end of the study, similar to the censoring rate of the TCGA IDC data set. In both settings and with different depths of RNA-seq coverage, SURVIV consistently outperformed Cox regression in the true-positive rate at the same false-positive rate of 5% (Fig. 2a). As expected, we observed a more significant improvement in SURVIV over Cox regression when the RNA-seq read coverage was low (Fig. 2a).
To more faithfully recapitulate the read count distribution in a real cancer RNA-seq data set, we performed another simulation study with read counts directly sampled from the TCGA IDC data. To assess the influence of RNA-seq read depth on the performance of SURVIV and Cox regression, sampled read counts were then multiplied by different factors ranging from 10 to 300% to simulate data sets with different RNA-seq read depths (Fig. 2b). The TCGA IDC data set has an average RNA-seq depth of ~60 million paired-end reads per patient. Thus, the read depth of these simulated RNA-seq data sets ranged from ~6 million reads to 180 million reads per patient, representing low-coverage RNA-seq studies designed primarily for gene expression analysis32 up to high-coverage RNA-seq studies designed primarily for alternative isoform analysis29. At all levels of RNA-seq depth, SURVIV consistently outperformed Cox regression, as reflected by the area under curve of the receiver operating characteristic (ROC) curve as well as the true-positive rate at 5% false-positive rate (Fig. 2b). The improvement of SURVIV over Cox regression was particularly prominent when the read depth was low. For example, at 10% read depth, SURVIV had 7% improvement in area under curve (68% versus 61%) and 8% improvement in the true-positive rate at 5% false-positive rate (46% versus 38%). Collectively, these simulation results suggest that SURVIV achieves a higher accuracy by accounting for the estimation uncertainty of mRNA isoform ratio in RNA-seq data.
SURVIV analysis of TCGA IDC breast cancer data
To illustrate the practical utility of SURVIV, we used it to analyse the overall survival time of 682 IDC patients from the TCGA breast cancer (BRCA) RNA-seq data set (see Methods for details of the data source and processing pipeline). We chose to analyse IDC because it is the most frequent type of breast cancer33, comprising ~70% of patients in the TCGA breast cancer data set. To control for the effects of significant clinical parameters such as tumour stage and subtype and identify alternative splicing events associated with patient outcomes across multiple molecular and clinical subtypes, we followed the procedure of Croce and colleagues in analysing mRNA and microRNA prognostic signature of IDC33 and stratified the patients according to their clinical parameters. We then conducted SURVIV analysis in 26 clinical subgroups with at least 50 patients in each subgroup. We identified 229 exon-skipping events associated with patient survival in multiple clinical subgroups that met the criteria of SURVIV P-value≤0.01 in at least two subgroups of the same clinical parameter (cancer subtype, stage, lymph node, metastasis, tumour size, oestrogen receptor status, progesterone receptor status, HER2 status and age as shown in Fig. 3). DAVID (Database for Annotation, Visualization and Integrated Discovery) Gene Ontology analyses34 of the 229 alternative splicing events suggest an enrichment of genes in cancer-related functional categories such as intracellular signalling, apoptosis, oxidative stress and response to DNA damage (Supplementary Fig. 1). Table 1 shows a few selected examples of survival-associated alternative splicing events in cancer-related genes. Using two-means clustering of each individual exon’s inclusion levels, the 682 IDC patients can be segregated into two subgroups with significantly different survival times as illustrated by the Kaplan–Meier survival plot (Fig. 4). We also carried out hierarchical clustering of IDC patients using 176 survival-associated alternative exons (P≤0.01; SURVIV analysis of all IDC patients). Using the exon-inclusion levels of these 176 exons, we clustered IDC patients into three major subgroups, with 95, 194 and 389 patients, respectively. As illustrated by the Kaplan–Meier survival plots, the three subgroups had significantly different survival times (Supplementary Fig. 2).
Figure 3: SURVIV analysis of exon-skipping events in the TCGA IDC RNA-seq data set.
IDC patients are stratified into multiple clinical subgroups based on clinical parameters including cancer subtype, stage, lymph node status, metastasis, tumour size, oestrogen receptor status, progesterone receptor status, HER2 status and age. Only clinical subgroups with at least 50 patients are included in further analyses. Numbers of patients in the subgroups are indicated next to the names of the subgroups. Shown in the heatmap are the log10 SURVIV P-values of the 229 exons associated with patient survival (P≤0.01) in at least two subgroups of the same class of clinical parameters. Turquoise colour indicates positive correlation that higher exon-inclusion levels are associated with higher survival probabilities. Magenta colour indicates negative correlation that lower exon-inclusion levels are associated with higher survival probabilities.
Figure 4: Kaplan–Meier survival plots of IDC patients stratified by two-means clustering of the exon-inclusion levels of four survival-associated alternative splicing events.
Clustering was generated for each of the four exons separately. Black lines represent patients with high exon-inclusion levels. Red lines represent patients with low exon-inclusion levels. The P-values are from SURVIV analysis of the TCGA IDC RNA-seq data. (a) ATRIP. (b) BCL2L11. (c) CD74. (d) PCBP4.
Figure 5: Alternative splicing of STAT5A exon 5 is significantly associated with IDC patient survival.
(a) The gene structure of the STAT5A full-length isoform compared to the ΔEx5 isoform skipping the 5th exon. (b) Kaplan–Meier survival plot of IDC patients stratified by two-means clustering using exon-inclusion levels of STAT5A exon 5. The 420 patients in Group 1 (average exon 5 inclusion level=95%) have significantly higher survival probabilities than the 262 patients in Group 2 (average exon 5 inclusion level=85%) (SURVIV P=6.8e−4). (c) Exon 5 inclusion levels of IDC patients stratified by two-means clustering using exon 5 inclusion levels. Group 1 has 420 patients with average exon-inclusion level at 95%. Group 2 has 262 patients with average exon-inclusion level at 85%. (d) STAT5A exon 5 inclusion levels in normal breast tissues versus breast cancer tumour samples. Exon-inclusion levels are extracted from 86 TCGA breast cancer patients with matched normal and tumour samples. Normal breast tissues have average exon 5 inclusion level at 95%, compared to 91% average exon-inclusion level in tumour samples. Error bars represent 95% confidence interval of the mean.
Figure 6: Splicing factor regulatory network of survival-associated alternative splicing events in IDC.
(a–c) Kaplan–Meier survival plots of IDC patients stratified by the gene expression levels of three splicing factors: TRA2B (a, Cox regression P=1.8e−4), HNRNPH1 (b, P=3.4e−4) and SFRS3 (c, P=2.8e−3). Black lines represent patients with high gene expression levels. Red lines represent patients with low gene expression levels. (d) The exon-inclusion levels of a DHX30 alternative exon are negatively correlated with TRA2B gene expression levels (robust correlation coefficient r=−0.26, correlation P=1.2e−17). (e) The exon-inclusion levels of a MAP3K4 alternative exon are positively correlated withHNRNPH1 gene expression levels (robust correlation coefficient r=0.16, correlation P=2.6e−06). (f) A splicing co-expression network of the three splicing factors and their correlated survival-associated alternative exons. In total, 84 survival-associated alternative exons are significantly correlated with the three splicing factors. The positive/negative correlation between splicing factors and alternative exons is represented by blue/red lines, respectively. Exons whose inclusion levels are positively/negatively correlated with survival times are represented by blue/red dots, respectively. The size of the splicing factor circles is proportional to the number of correlated exons within the network.
Figure 7: Cross-validation of different classes of IDC survival predictors measured by the C-index
A C-index of 1 indicates perfect prediction accuracy and a C-index of 0.5 indicates random guess. The plots indicate the distribution of C-indexes from 100 rounds of cross-validation. The centre value of the box plot is the median C-index from 100 rounds of cross-validation. The notch represents the 95%confidence interval of the median. The box represents the 25 and 75% quantiles. The whiskers extended out from the box represent the 5 and 95% quantiles. Two-sided Wilcoxon test was used to compare different survival predictors. The different classes of predictors are: (a) clinical information (median C-index 0.67). (b) Gene expression (median C-index 0.68). (c) Alternative splicing (median C-index 0.71). (d) Clinical information+gene expression (median C-index 0.69). (e) Clinical information+alternative splicing (median C-index 0.73). (f) Clinical information+gene expression+alternative splicing (median C-index 0.74). Note that ‘Gene’ refers to ‘Gene-level expression’ in these plots.
Next, we carried out the SURVIV analysis in five additional cancer types in TCGA, including GBM (glioblastoma multiforme), KIRC (kidney renal clear cell carcinoma), LGG (lower grade glioma), LUSC (lung squamous cell carcinoma) and OV (ovarian serous cystadenocarcinoma). As expected, the number of significant events at different FDR or P-value significance cutoffs varied across cancer types, with LGG having the strongest survival-associated alternative splicing signals with 660 significant exon-skipping events at FDR≤5% (Supplementary Data 3 and 4). Strikingly, regardless of the number of significant events, alternative splicing-based survival predictors outperformed gene expression-based survival predictors across all cancer types (Supplementary Fig. 3), consistent with our initial observation on the IDC data set.
Alternative processing and modification of mRNA, such as alternative splicing, allow cells to generate a large number of mRNA and protein isoforms with diverse regulatory and functional properties. The plasticity of alternative splicing is often exploited by cancer cells to produce isoform switches that promote cancer cell survival, proliferation and metastasis7, 8. The widespread use of RNA-seq in cancer transcriptome studies15, 47, 48 has provided the opportunity to comprehensively elucidate the landscape of alternative splicing in cancer tissues. While existing studies of alternative splicing in large-scale cancer transcriptome data largely focused on the comparison of splicing patterns between cancer and normal tissues or between different subtypes of cancer18, 21, 49, additional computational tools are needed to characterize the clinical relevance of alternative splicing using massive RNA-seq data sets, including the association of alternative splicing with phenotypes and patient outcomes.
We have developed SURVIV, a novel statistical model for survival analysis of alternative isoform variation using cancer RNA-seq data. SURVIV uses a survival measurement error model to simultaneously model the estimation uncertainty of mRNA isoform ratio in individual patients and the association of mRNA isoform ratio with survival time across patients. Compared with the conventional Cox regression model that uses each patient’s mRNA isoform ratio as a point estimate, SURVIV achieves a higher accuracy as indicated by simulation studies under a variety of settings. Of note, we observed a particularly marked improvement of SURVIV over Cox regression for low- and moderate-depth RNA-seq data (Fig. 2b). This has important practical value because many clinical RNA-seq data sets have large sample size but relatively modest sequencing depth.
Using the TCGA IDC breast cancer RNA-seq data of 682 patients, SURVIV identified 229 alternative splicing events associated with patient survival time, which met the criteria of SURVIVP-values≤0.01 in multiple clinical subgroups. While the statistical threshold seemed loose, several lines of evidence suggest the functional and clinical relevance of these survival-associated alternative splicing events. These alternative splicing events were frequently identified and enriched in the gene functional groups important for cancer development and progression, including apoptosis, DNA damage response and oxidative stress. While some of these events may simply reflect correlation but not causal effect on cancer patient survival, other events may play an active role in regulating cancer cell phenotypes. For example, a survival-associated alternative splicing event involving exon 5 of STAT5A is known to regulate the activity of this transcription factor with important roles in epithelial cell growth and apoptosis37. Using a co-expression network analysis of splicing factor to exon correlation across all patients, we identified three splicing factors (TRA2B, HNRNPH1 and SFRS3) as potential hubs of the survival-associated alternative splicing network of IDC. The expression levels of all three splicing factors were negatively associated with patient survival times (Fig. 6a–c), and both TRA2B and HNRNPH1 were previously reported to have an impact on cancer-related molecular pathways40, 41, 42, 43, 44, 45. Finally, despite the limited power in detecting individual events, we show that the survival-associated alternative splicing events can be used to construct a predictor for patient survival, with an accuracy higher than predictors based on clinical parameters or gene expression profiles (Fig. 7). This further demonstrates the potential biological relevance and clinical utility of the identified alternative splicing events.
We performed cross-validation analyses to evaluate and compare the prognostic value of alternative splicing, gene expression and clinical information for predicting patient survival, either independently or in combination. As expected, the combined use of all three types of information led to the best prediction accuracy. Because we used penalized regression to build the prediction model, combining information from multiple layers of data did not necessarily increase the number of predictors in the model. The perhaps more surprising and intriguing result is that alternative splicing-based predictors appear to outperform gene expression-based predictors when used alone and when either type of data was combined with clinical information (Fig. 7). We observed the same trend in five additional cancer types (Supplementary Fig. 3). We note that this finding was consistent with a previous report that cancer subtype classification based on splicing isoform expression performed better than gene expression-based classification25. While this trend seems counterintuitive because accurate estimation of gene expression requires much lower RNA-seq depth than accurate estimation of alternative splicing29, one possible explanation may be the inherent characteristic of isoform ratio data. By definition, mRNA isoform ratio is estimated as the ratio of multiple mRNA isoforms from a single gene. Therefore, mRNA isoform ratio data have a ‘built-in’ internal control that could be more robust against certain artefacts and confounding issues that influence gene expression estimates across large clinical RNA-seq data sets, such as poor sample quality and RNA degradation12. Regardless of the reasons, our data call for further studies to fully explore the utility of mRNA isoform ratio data for various clinical research applications.
The SURVIV source code is available for download at https://github.com/Xinglab/SURVIV. SURVIV is a general statistical model for survival analysis of mRNA isoform ratio using RNA-seq data. The current statistical framework of SURVIV is applicable to RNA-seq based count data for all basic types of alternative splicing patterns involving two isoform choices from an alternatively spliced region, such as exon-skipping, alternative 5′ splice sites, alternative 3′ splice sites, mutually exclusive exons and retained introns, as well as other forms of alternative isoform variation such as RNA editing. With the rapid accumulation of clinical RNA-seq data sets, SURVIV will be a useful tool for elucidating the clinical relevance and potential functional significance of alternative isoform variation in cancer and other diseases.
New Studies toward Understanding Alzheimer Disease
Curators: Larry H. Bernstein, MD, FCAP and Aviva Lev-Ari, PhD, RN
There is no unifying concept of Alzheimer Disease beyond the Tau and beta amyloid roles. Recently, Ingenbleek and Bernstein (journal AD) made the connection between the age related decline of liver synthesis of plasma transthyretin and the more dramatic decline of transthyretin at the blood brain barrier, and the relationship to inability to transfer vitamin A via retinol binding protein to the brain. Related metabolic events are reported by several groups.
They show that Aβ oligomerization, a behavior traditionally viewed as intrinsically pathological, may be necessary for the antimicrobial activities of the peptide. Collectively, our data are consistent with a model in which soluble Aβ oligomers first bind to microbial cell wall carbohydrates via a heparin-binding domain. Developing protofibrils inhibited pathogen adhesion to host cells. Propagating β-amyloid fibrils mediate agglutination and eventual entrapment of unatttached microbes….Salmonella Typhimurium bacterial infection of the brains of transgenic 5XFAD mice resulted in rapid seeding and accelerated β-amyloid deposition, which closely colocalized with the invading bacteria.
This is quite interesting in that infection drives the production of acute phase reactants resulting in decreased production of transthyretin. Whether this also has ties to chronic disease in the elderly and risk of AD is not known.
Through whole-genome sequencing of 1345 individuals from 410 families with late-onset AD (LOAD), they identified three highly penetrant variants in PRKCA, the gene that encodes protein kinase Cα (PKCα), in five of the families. All three variants linked with LOAD displayed increased catalytic activity relative to wild-type PKCα as assessed in live-cell imaging experiments using a genetically encoded PKC activity reporter. Deleting PRKCA in mice or adding PKC antagonists to mouse hippocampal slices infected with a virus expressing the Aβ precursor CT100 revealed that PKCα was required for the reduced synaptic activity caused by Aβ. In PRKCA(-/-) neurons expressing CT100, introduction of PKCα, but not PKCα lacking a PDZ interaction moiety, rescued synaptic depression, suggesting that a scaffolding interaction bringing PKCα to the synapse is required for its mediation of the effects of Aβ. Thus, enhanced PKCα activity may contribute to AD, possibly by mediating the actions of Aβ on synapses.
Kim BM, You MH, Chen CH, Suh J, Tanzi RE, Ho Lee T.
Hum Mol Genet. 2016 Apr 19. pii: ddw114.
Extracellular deposition of amyloid-beta (Aβ) peptide, a metabolite of sequential cleavage of amyloid precursor protein (APP), is a critical step in the pathogenesis of Alzheimer’s disease (AD). While death-associated protein kinase 1 (DAPK1) is highly expressed in AD brains and its genetic variants are linked to AD risk, little is known about the impact of DAPK1 on APP metabolism and Aβ generation. This study demonstrated a novel effect of DAPK1 in the regulation of APP processing using cell culture and mouse models. DAPK1, but not its kinase deficient mutant (K42A), significantly increased human Aβ secretion in neuronal cell culture models. Moreover, knockdown of DAPK1 expression or inhibition of DAPK1 catalytic activity significantly decreased Aβ secretion. Furthermore, DAPK1, but not K42A, triggered Thr668 phosphorylation of APP, which may initiate and facilitate amyloidogenic APP processing leading to the generation of Aβ. In Tg2576 APPswe-overexpressing mice, knockout of DAPK1 shifted APP processing toward non-amyloidogenic pathway and decreased Aβ generation. Finally, in AD brains, elevated DAPK1 levels showed co-relation with the increase of APP phosphorylation. Combined together, these results suggest that DAPK1 promotes the phosphorylation and amyloidogenic processing of APP, and that may serve a potential therapeutic target for AD.
The “amyloid β hypothesis” of Alzheimer’s disease (AD) has been the reigning hypothesis explaining pathogenic mechanisms of AD over the last two decades. However, this hypothesis has not been fully validated in animal models, and several major unresolved issues remain. Our 3D human neural cell culture model system provides a premise for a new generation of cellular AD models that can serve as a novel platform for studying pathogenic mechanisms and for high-throughput drug screening in a human brain-like environment.
The two key pathological hallmarks of AD are senile plaques (amyloid plaques) and neurofibrillary tangles (NFTs), which develop in brain regions responsible for memory and cognitive functions (i.e. cerebral cortex and limbic system) 3. Senile plaques are extracellular deposits of amyloid-β (Aβ) peptides, while NFTs are intracellular, filamentous aggregates of hyperphosphorylated tau protein 4.
The identification of Aβ as the main component of senile plaques by Drs. Glenner and Wong in 1984 5 resulted in the original formation of the “amyloid hypothesis.” According to this hypothesis, which was later renamed the “amyloid-β cascade hypothesis” by Drs. Hardy and Higgins 6, the accumulation of Aβ is the initial pathological trigger in the disease, subsequently leading to hyperphosphorylation of tau, causing NFTs, and ultimately, neuronal death and dementia 4,7–10. Although the details have been modified to reflect new findings, the core elements of this hypothesis remain unchanged: excess accumulation of the pathogenic forms of Aβ, by altered Aβ production and/or clearance, triggers the vicious pathogenic cascades that eventually lead to NFTs and neuronal death.
Over the last two decades, the Aβ hypothesis of AD has reigned, providing the foundation for numerous basic studies and clinical trials 4,7,10,11. According to this hypothesis, the accumulation of Aβ, either by altered Aβ production and/or clearance, is the initial pathological trigger in the disease. The excess accumulation of Aβ then elicits a pathogenic cascade including synaptic deficits, altered neuronal activity, inflammation, oxidative stress, neuronal injury, hyperphosphorylation of tau causing NFTs and ultimately, neuronal death and dementia 4,7–10.
One of the major unresolved issues of the Aβ hypothesis is to show a direct causal link between Aβ and NFTs 12–14. Studies have demonstrated that treatments with various forms of soluble Aβ oligomers induced synaptic deficits and neuronal injury, as well as hyperphosphorylation of tau proteins, in mouse and rat neurons, which could lead to NFTs and neurodegeneration in vivo18–21. However, transgenic AD mouse models carrying single or multiple human familial AD (FAD) mutations in amyloid precursor protein (APP) and/or presenilin 1 (PS1) do not develop NFTs or robust neurodegeneration as observed in human patients, despite robust Aβ deposition 13,22,23. Double and triple transgenic mouse models, harboring both FAD and tau mutations linked with frontotemporal dementia (FTD), are the only rodent models to date displaying both amyloid plaques and NFTs. However, the NFT pathology in these models stems mainly from the overexpression of human tau as a result of the FTD, rather than the FAD mutations24,25.
Human neurons carrying FAD mutations are an optimal model to test whether elevated levels of pathogenic Aβ trigger pathogenic cascades including NFTs, since those cells truly share the same genetic background that induces FAD in humans. Indeed, Israel et al., observed elevated tau phosphorylation in neurons with an APP duplication FAD mutation 33. Blocking Aβ generation by β-secretase inhibitors significantly decreased tau phosphorylation in the same model, but γ-secretase inhibitor, another Aβ blocker, did not affect tau phosphorylation 33. Neurons with the APP V717I FAD mutation also showed an increase in levels of phospho tau and total tau levels 28. More importantly, Muratore and colleagues showed that treatments with Aβ-neutralizing antibodies in those cells significantly reduced the elevated total and phospho tau levels at the early stages of differentiation, suggesting that blocking pathogenic Aβ can reverse the abnormal tau accumulation in APP V717I neurons 28.
Recently, Moore et al. also reported that neurons harboring the APP V717I or the APP duplication FAD mutation showed increases in both total and phospho tau levels 27. Interestingly, altered tau levels were not detected in human neurons carrying PS1 FAD mutations, which significantly increased pathogenic Aβ42 species in the same cells 27. These data suggest that elevated tau levels in these models were not due to extracellular Aβ accumulation but may possibly represent a very early stage of tauopathy. It may also be due to developmental alterations induced by the APP FAD mutations.
As summarized, most human FAD neurons showed significant increases in pathogenic Aβ species, while only APP FAD neurons showed altered tau metabolism that may represent very early stages of tauopathy. However, all of these human FAD neurons failed to recapitulate robust extracellular amyloid plaques, NFTs, or any signs of neuronal death, as predicted in the amyloid hypothesis.
In our recent study, we moved one step closer to proving the amyloid hypothesis. By generating human neural stem cell lines carrying multiple mutations in APP together with PS1, we achieved high levels of pathogenic Aβ42 comparable to those in brains of AD patients 44–46.
Platform for AD drug screening in human neural progenitor cells with FAD mutations in a 3D culture system, which successfully reproduce human AD pathogenesis (amyloid plaques-driven tauopathy).
In addition to the impact on toxic Aβ species, our 3D culture model can test if these antibodies can block tau pathologies in 3D human neural cell culture systems 44–46. Human cellular AD models can also be used to determine optimal doses of candidate AD drugs to block Aβ and/or tau pathology without affecting neuronal survival (Fig. 1).
While much progress has been made, many challenges still lie on the path to creating human neural cell culture models that comprehensively recapitulate pathogenic cascades of AD. A major difficulty lies in reconstituting the brain regions most affected in AD: the hippocampus and specific cortical layers. Recent progress in 3D culture technology, such as “cerebral organoids,” may also be helpful in rebuilding the brain structures that are affected by AD in a dish 52,53. These “cerebral organoids” were able to model various discrete brain regions including human cortical areas 52, which enabled them to reproduce microcephaly, a brain developmental disorder. Similarly, pathogenic cascades of AD may be recapitulated in cortex-like structures using this model. Adding neuroinflammatory components, such as microglial cells, which are critical in AD pathogenesis, will illuminate the validity of the amyloid β hypothesis. Reconstitution of robust neuronal death stemming from Aβ and tau pathologies will be the next major step in comprehensively recapitulating AD in a cellular model.
Natunen T, Takalo M, Kemppainen S, Leskelä S, Marttinen M, Kurkinen KM, Pursiheimo JP, Sarajärvi T, Viswanathan J, Gabbouj S, Solje E, Tahvanainen E, Pirttimäki T, Kurki M, Paananen J, Rauramaa T, Miettinen P, Mäkinen P, Leinonen V, Soininen H, Airenne K, Tanzi RE, Tanila H, Haapasalo A, Hiltunen M.
Accumulation of β-amyloid (Aβ) and phosphorylated tau in the brain are central events underlying Alzheimer’s disease (AD) pathogenesis. Aβ is generated from amyloid precursor protein (APP) by β-site APP-cleaving enzyme 1 (BACE1) and γ-secretase-mediated cleavages. Ubiquilin-1, a ubiquitin-like protein, genetically associates with AD and affects APP trafficking, processing and degradation. Here, we have investigated ubiquilin-1 expression in human brain in relation to AD-related neurofibrillary pathology and the effects of ubiquilin-1 overexpression on BACE1, tau, neuroinflammation, and neuronal viability in vitro in co-cultures of mouse embryonic primary cortical neurons and microglial cells under acute neuroinflammation as well as neuronal cell lines, and in vivo in the brain of APdE9 transgenic mice at the early phase of the development of Aβ pathology. Ubiquilin-1 expression was decreased in human temporal cortex in relation to the early stages of AD-related neurofibrillary pathology (Braak stages 0-II vs. III-IV). There was a trend towards a positive correlation between ubiquilin-1 and BACE1 protein levels. Consistent with this, ubiquilin-1 overexpression in the neuron-microglia co-cultures with or without the induction of neuroinflammation resulted in a significant increase in endogenously expressed BACE1 levels. Sustained ubiquilin-1 overexpression in the brain of APdE9 mice resulted in a moderate, but insignificant increase in endogenous BACE1 levels and activity, coinciding with increased levels of soluble Aβ40 and Aβ42. BACE1 levels were also significantly increased in neuronal cells co-overexpressing ubiquilin-1 and BACE1. Ubiquilin-1 overexpression led to the stabilization of BACE1 protein levels, potentially through a mechanism involving decreased degradation in the lysosomal compartment. Ubiquilin-1 overexpression did not significantly affect the neuroinflammation response, but decreased neuronal viability in the neuron-microglia co-cultures under neuroinflammation. Taken together, these results suggest that ubiquilin-1 may mechanistically participate in AD molecular pathogenesis by affecting BACE1 and thereby APP processing and Aβ accumulation.
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. Assuming that the exon-inclusion read count ICk follows a binomial distribution with the total read count nk
2012pharmaceutical
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