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Multiple factors related to initial trial design may predict low patient accrual for cancer clinical trials

Reporter: Stephen J. Williams, Ph.D.

UPDATED 5/15/2019

A recently published paper in JCNI highlights results determining factors which may affect cancer trial patient accrual and the development of a predictive model of accrual issues based on those factors.

To hear a JCNI podcast on the paper click here

but below is a good posting from scienmag.com which describes their findings:

Factors predicting low patient accrual in cancer clinical trials

source: http://scienmag.com/factors-predicting-low-patient-accrual-in-cancer-clinical-trials/

Nearly one in four publicly sponsored cancer clinical trials fail to enroll enough participants to draw valid conclusions about treatments or techniques. Such trials represent a waste of scarce human and economic resources and contribute little to medical knowledge. Although many studies have investigated the perceived barriers to accrual from the patient or provider perspective, very few have taken a trial-level view and asked why certain trials are able to accrue patients faster than expected while others fail to attract even a fraction of the intended number of participants. According to a study published December 29 in the JNCI: Journal of the National Cancer Institute, a number of measurable trial characteristics are predictive of low patient accrual.

Caroline S. Bennette, M.P.H., Ph.D., of the Pharmaceutical Outcomes Research and Policy Program, University of Washington, Seattle, and colleagues from the University of Washington and the Fred Hutchinson Cancer Research Center analyzed information on 787 phase II/III clinical trials sponsored by the National Clinical Trials Network (NCTN; formerly the Cooperative Group Program) launched between 2000 and 2011. After excluding trials that closed because of toxicity or interim results, Bennette et al. found that 145 (18%) of NCTN trials closed with low accrual or were accruing at less than 50% of target accrual 3 years or more after opening.

The authors identified potential risk factors from the literature and interviews with clinical trial experts and found multiple trial-level factors that were associated with poor accrual to NCTN trials, such as increased competition for patients from currently ongoing trials, planning to enroll a higher proportion of the available patient population, and not evaluating a new investigational agent or targeted therapy. Bennette et al. then developed a multivariable prediction model of low accrual using 12 trial-level risk factors, which they reported had good agreement between predicted and observed risks of low accrual in a preliminary validation using 46 trials opened between 2012 and 2013.

The researchers conclude that “Systematically considering the overall influence of these factors could aid in the design and prioritization of future clinical trials…” and that this research provides a response to the recent directive from the Institute of Medicine to “improve selection, support, and completion of publicly funded cancer clinical trials.”

In an accompanying editorial, Derek Raghavan, M.D., Levine Cancer Institute, writes that the focus needs to be on getting more patients involved in trials, saying, “we should strive to improve trial enrollment, giving the associated potential for improved results. Whether the basis is incidental, because of case selection bias, or reflects the support available to trial patients has not been determined, but the fact remains that outcomes are better.”

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Contact info:

Article: Caroline S. Bennette, M.P.H., Ph.D., cb11@u.washington.edu

Editorial: Derek Raghavan, M.D., derek.raghavan@carolinashealthcare.org

Other investigators also feel that initial trial design is of UTMOST importance for other reasons, especially in the era of “precision” or “personalized” medicine and why the “basket trial” or one size fits all trial strategy is not always feasible.

In Why the Cancer Research Paradigm Must Transition to “N-of-1” Approach

Dr. Maurie Markman, MD gives insight into why the inital setup of a trial and the multi-center basket type of  accrual can be a problematic factor in obtaining meaningful cohorts of patients with the correct mutational spectrum.

The anticancer clinical research paradigm has rapidly evolved so that subject selection is increasingly based on the presence or absence of a particular molecular biomarker in the individual patient’s malignancy. Even where eligibility does not mandate the presence of specific biological features, tumor samples are commonly collected and an attempt is subsequently made to relate a particular outcome (eg, complete or partial objective response rate; progression-free or overall survival) to the individual cancer’s molecular characteristics.

One important result of this effort has been the recognition that there are an increasing number of patient subsets within what was previously—and incorrectly—considered a much larger homogenous patient population; for example, non–small cell lung cancer (NSCLC) versus EGFR-mutation–positive NSCLC. And, while it may still be possible to conduct phase III randomized trials involving a relatively limited percentage of patients within a large malignant entity, extensive and quite expensive effort may be required to complete this task. For example, the industry-sponsored phase III trial comparing first-line crizotinib with chemotherapy (pemetrexed plus either carboplatin or cisplatin) in ALK-rearrangement–positive NSCLC, which constitutes 3% to 5% of NSCLCs, required an international multicenter effort lasting 2.5 years to accrue the required number of research subjects.1

But what if an investigator, research team, or biotech company desired to examine the clinical utility of an antineoplastic in a patient population representing an even smaller proportion of patients with NSCLC such as in the 1% of the patient population with ROS1 abnormalities,2 or in a larger percentage of patients representing 4%-6% of patients with a less common tumor type such as ovarian cancer? How realistic is it that such a randomized trial could ever be conducted?

Further, considering the resources required to initiate and successfully conduct a multicenter international phase III registration study, it is more than likely that in the near future only the largest pharmaceutical companies will be in a position to definitively test the clinical utility of an antineoplastic in a given clinical situation.

One proposal to begin to explore the benefits of targeted antineoplastics in the setting of specific molecular abnormalities has been to develop a socalled “basket trial” where patients with different types of cancers with varying treatment histories may be permitted entry, assuming a well-defined molecular target is present within their cancer. Of interest, several pharmaceutical companies have initiated such clinical research efforts.

Yet although basket trials represent an important research advance, they may not provide the answer to the molecular complexities of cancer that many investigators believe they will. The research establishment will have to take another step toward innovation to “N-of-1” designs that truly explore the unique nature of each individual’s cancer.

Trial Illustrates Weaknesses

A recent report of the results of one multicenter basket trial focused on thoracic cancers demonstrates both the strengths but also a major fundamental weakness of the basket trial approach.3

However, the investigators were forced to conclude that despite accrual of more than 600 patients onto a study conducted at two centers over a period of approximately 2 years, “this basket trial design was not feasible for many of the arms with rare mutations.”3

They concluded that they needed a larger number of participating institutions and the ability to adapt the design for different drugs and mutations. So the question to be asked is as follows: Is the basket-type approach the only alternative to evaluate the clinical relevance of a targeted antineoplastic in the presence of a specific molecular abnormality?

Of course, the correct answer to this question is surely: No!

– See more at: http://www.onclive.com/publications/Oncology-live/2015/July-2015/Why-the-Cancer-Research-Paradigm-Must-Transition-to-N-of-1-Approach#sthash.kLGwNzi3.dpuf

The following is a video on the website ClinicalTrials.gov which is a one-stop service called EveryClinicalTrial to easily register new clinical trials and streamline the process:

 

UPDATED 5/15/2019

Another possible roadblock to patient accrual has always been the fragmentation of information concerning the availability of clinical trails and coordinating access among the various trial centers, as well as performing analytics on trial data to direct new therapeutic directions.  The NIH has attempted to circumvent this problem with the cancer trials webpage trials.gov however going through the vast number of trials, patient accrual requirements, and finding contact information is a daunting task.  However certain clinical trial marketplaces are now being developed which may ease access problems to clinical trials as well as data analytic issues, as highlighted by the Scientist.com article below:

Scientist.com Launches Trial Insights, A Transformative Clinical Trials Data Analytics Solution

The world’s largest online marketplace rolls out first original service, empowering researchers with on demand insights into clinical trials to help drive therapeutic decisions

SAN DIEGO–(BUSINESS WIRE)–Scientist.com, the online marketplace for outsourced research, announced today the launch of Trial Insights, a digital reporting solution that simplifies data produced through clinical trial, biomarker and medical diagnostic studies into an intuitive and user-friendly dashboard. The first of its kind, Trial Insights curates publicly available data nightly from information hubs such as clinicaltrials.gov and customizes it to fit a researcher or research organization’s specific project needs.

Trial Insights, new clinical trial reporting solution, allows researchers to keep track of the evolving landscape of drugs, diseases, sponsors, investigators and medical devices important to their work.

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“Trial Insights offers researchers an easy way to navigate the complexity of clinical trials information,” said Ron Ranauro, Founder of Incite Advisors. “Since Trial Insights’ content is digitally curated, researchers can continuously keep track of the evolving landscape of drugs, diseases, sponsors, investigators and medical devices important to their work.”

As the velocity, variety and veracity of data available on sites like clinicaltrials.gov continues to increase, the ability to curate it becomes more valuable to different audiences. With the advancement of personalized medicine, it is important to make the data accessible to the health care and patient communities. Information found on the Trial Insights platform can help guide decision making across the pharmaceutical, biotechnology and contract research organization industries as clinical trial data is a primary information source for competitive intelligence, research planning and clinical study planning.

“We are extremely excited to launch the first Scientist.com exclusive, original service offering to our clients in the life sciences,” said Mark Herbert, Scientist.com Chief Business Officer. “Our goal at Scientist.com is to help cure all diseases by 2050, and we believe solutions like Trial Insights, which greatly simplifies access to and reporting of clinical trial data, will get us one step closer to reaching that goal.”

source: https://www.businesswire.com/news/home/20190416005362/en/Scientist.com-Launches-Trial-Insights-Transformative-Clinical-Trials?utm_source=TrialIO+List

 

Other article on this Open Access Journal on Cancer Clinical Trial Design include:

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Precision Medicine for Future of Genomics Medicine is The New Era

Demet Sag, PhD, CRA, GCP

 

Are we there yet?  Life is a journey so the science.

Governor Brown announced Precision Medicine initiative for California on April 14, 2015.  UC San Francisco is hosting the two-year initiative, through UC Health, which includes UC’s five medical centers, with $3 million in startup funds from the state. The public-private initiative aims to leverage these funds with contributions from other academic and industry partners.

With so many campuses spread throughout the state and so much scientific, clinical and computational expertise, the UC system has the potential to bring it all together, said Atul Butte, MD, PhD, who is leading the initiative.

At the beginning of 2015 President Obama signed this initiative and assigned people to work on this project.

Previously NCI Director Harold Varmus, MD said that “Precision medicine is really about re-engineering the diagnostic categories for cancer to be consistent with its genomic underpinnings, so we can make better choices about therapy,” and “In that sense, many of the things we’re proposing to do are already under way.”

The proposed initiative has two main components:

  • a near-term focus on cancers and
  • a longer-term aim to generate knowledge applicable to the whole range of health and disease.

Both components are now within our reach because of advances in basic research, including molecular biology, genomics, and bioinformatics. Furthermore, the initiative taps into converging trends of increased connectivity, through social media and mobile devices, and Americans’ growing desire to be active partners in medical research.

Since the human genome is sequenced it became clear that actually there are few genes than expected and shared among organisms to accomplish same or similar core biological functions.  As a result, knowledge of the biological role of such shared proteins in one organism can be transferred to another organism.

I remember when I was screening the X-chromosome by using deletion/duplication mapping and using P elements and bar balancers as a tool to keep the genome stable to identify transregulating elements of ovo gene, female germline specific Drosophila melanogaster germline sex determination gene. At the time for my dissertation, I screened X-chromosome using 45 deficiency strains, I found that these trans-regulating regions were grouped into 12 loci based on overlapping cytology. Five regions were trans-regulating activators, and seven were trans-regulating repressors; extrapolating to the entire genome, this result predicted nearly 85 loci. This one gene may expressed three proteins at different time of development and activate/downregulate various regions to accommadate proper system development in addition to auto-regulate and gene dose responses. Drosophila has only four chromosomes but the cellular interactions and signaling mechanisms are still complicated yet as not complicated as human. I do appreciate the new applications and upcoming changes.

Now, the technology is much better and precision is the key to establish to use in clinics.  However, we have new issues to overcome like computing such a big data, align properly, analyze effectively, compare and contrast the outcomes to identify the variations that may function in on  population, or two etc. At the end of the day collaboration, standardization, and data sharing are few of the key factors.

It is necessary to generate a dynamic yet controlled standardized collection of information with ever changing and accumulating data so  Gene Ontology Consortium is created. Three independent ontologies can be reached at  (http://www.geneontology.org) developed based on :

  1. biological process,
  2. molecular function and
  3. cellular component.

Precision-Medicine-Timeline2

We need a common language for annotation for a functional conservation. Genesis of the grand biological unification made it possible to complete the genomic sequences of not only human but also the main model organisms and more. some examples include:

  • the budding yeast, Saccharomyces cerevisiae,
  • the nematode worm Caenorhabditis elegans
  • the fruitfly Drosophila melanogaster,
  • the flowering plant Arabidopsis thaliana
  • fission yeast Schizosaccharomyces pombe
  • the  mouse , Mus musculus

On the other hand, as we know there are allelic variations that underlie common diseases and complete genome sequencing for many individuals with and without disease is required.  However, there are advantages and disadvantages as we can carry out partial surveys of the genome by genotyping large numbers of common SNPs in genome-wide association studies but there are problems such as computing the data efficiently and sharing the information without tempering privacy. Therefore we should be mindful about few main conditions including:

  1. models of the allelic architecture of common diseases,
  2. sample size,
  3. map density and
  4. sample-collection biases.

This will lead into the cost control and efficiency while identifying genuine disease-susceptibility loci. The genome-wide association studies (GWAS) have progressed from assaying fewer than 100,000 SNPs to more than one million, and sample sizes have increased dramatically as the search for variants that explain more of the disease/trait heritability has intensified.

In addition, we must translate this sequence information from genomics locus of the genes to function with related polymorphism of these genes so that possible patterns of the gene expression and disease traits can be matched. Then, we may develop precision technologies for:

  1. Diagnostics
  2. Targeted Drugs and Treatments
  3. Biomarkers to modulate cells for correct functions

With the knowledge of:

  1. gene expression variations
  2. insight in the genetic contribution to clinical endpoints ofcomplex disease and
  3. their biological risk factors,
  4. share etiologic pathways

therefore, requires an understanding of both:

  • the structure and
  • the biology of the genome.

These studies demonstrated hundreds of associations of common genetic variants with over 80 diseases and traits collected under a controlled online resource.  However, identifying published GWAS can be challenging as a simple PubMed search using the words “genome wide association studies”  may be easily populated with unrelevant  GWAS.

National Human Genome Research Institute (NHGRI) Catalog of Published Genome-Wide Association Studies (http://www.genome.gov/gwastudies), an online, regularly updated database of SNP-trait associations extracted from published GWAS was developed.

Therefore, sequencing of a human genome is a quite undertake and requires tools to make it possible:

  • to explore the genetic component in complex diseases and
  • to fully understand the genetic pathways contributing to complex disease

Examples of Gene Ontology

The rapid increase in the number of GWAS provides an unprecedented opportunity to examine the potential impact of common genetic variants on complex diseases by systematically cataloging and summarizing key characteristics of the observed associations and the trait/disease associated SNPs (TASs) underlying them.

 

With this in mind, many forms can be established:

  1. to describe the features of this resource and the methods we have used to produce it,
  2. to provide and examine key descriptive characteristics of reported TASs such as estimated risk allele frequencies and odds ratios,
  3. to examine the underlying functionality of reported risk loci by mapping them to genomic annotation sets and assessing overrepresentation via Monte Carlo simulations and
  4. to investigate the relationship between recent human evolution and human disease phenotypes.

 

This procedure has no clear path so there are several obstacles in the actual functional variant that is often unknown. This may be due to:

  1. trait/disease associated SNPs (TASs),
  2. a well known SNP+ strong linkage disequilibrium (LD) with the TAS,
  3. an unknown common SNP tagged by a haplotype
  4. rare single nucleotide variant tagged by a haplotype on which the TAS occurs, or
  5. Copy Number variation (CNV), a linked copy number variant.

 

There can be other factors such as

  • Evolution,
  • Natural Selection
  • Environment
  • Pedigree
  • Epigenetics

 

Even though heritage is another big factor, the concept of heritability and its definition as an estimable, dimensionless population parameter as introduced by Sewall Wright and Ronald Fisher almost a century ago.

 

As a result, heritability gain interest since it allows us to compare of the relative importance of genes and environment to the variation of traits within and across populations. The heritability is an ongoing mechanism and  remains as a main factor:

 

  • to selection in evolutionary biology and agriculture, and
  • to the prediction of disease risk in medicine.

Reported TASs associated with two or more distinct traits

Chromosomal region Rs number(s) Attributed genes Associated traits reported in catalog
1p13.2 rs2476601, rs6679677 PTPN22 Crohn’s disease, type 1 diabetes, rheumatoid arthritis
1q23.2 rs2251746, rs2494250 FCER1A Serum IgE levels, select biomarker traits (MCP1)
2p15 rs1186868, rs1427407 BCL11A Fetal hemoglobin, F-cell distribution
2p23.3 rs780094 GCKR CRP, lipids, waist circumference
6p21.33 rs3131379, rs3117582 HLA / MHC region Systemic lupus erythematosus, lung cancer, psoriasis, inflammatory bowel disease, ulcerative colitis, celiac disease, rheumatoid arthritis, juvenile idiopathic arthritis, multiple sclerosis, type 1 diabetes
6p22.3 rs6908425, rs7756992, rs7754840, rs10946398, rs6931514 CDKAL1 Crohn’s disease, type 2 diabetes
6p25.3 rs1540771, rs12203592, rs872071 IRF4 Freckles, hair color, chronic lymphocytic leukemia
6q23.3 rs5029939, rs10499194 TNFAIP3 Systemic lupus erythematosus, rheumatoid arthritis
7p15.1 rs1635852, rs864745 JAZF1 Height, type 2 diabetes*
8q24.21 rs6983267 Intergenic Prostate or colorectal cancer, breast cancer
9p21.3 rs10811661, rs1333040, rs10811661, rs10757278, rs1333049 CDKN2A, CDKN2B Type 2 diabetes, intracranial aneurysm, myocardial infarction
9q34.2 rs505922, rs507666, rs657152 ABO Protein quantitative trait loci (TNF-α), soluble ICAM-1, plasma levels of liver enzymes (alkaline phosphatase)
12q24 rs1169313, rs7310409, rs1169310, rs2650000 HNF1A Plasma levels of liver enzyme (GGT), C-reactive protein, LDL cholesterol
16q12.2 rs8050136, rs9930506, rs6499640, rs9939609, rs1121980 FTO Type 2 diabetes, body mass index or weight
17q12 rs7216389, rs2872507 ORMDL3 Asthma, Crohn’s disease
17q12 rs4430796 TCF2 Prostate cancer, type 2 diabetes
18p11.21 rs2542151 PTPN2 Type 1 diabetes, Crohn’s disease
19q13.32 rs4420638 APOE, APOC1, APOC4 Alzheimer’s disease, lipids

* The well known association of JAZF1 with prostate cancer was reported with a p value of 2 × 10−6, which did not meet the threshold of 5 × 10−8 for this analysis.

PMC full text: Proc Natl Acad Sci U S A. 2009 Jun 9; 106(23): 9362–9367.Published online 2009 May 27. doi:  10.1073/pnas.0903103106

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Allele-Frequency Data for Nine Reproducible Associations

frequency
gene diseasea SNP associated alleleb Europeand Africane δf FST reference(s)c
CTLA4 T1DM Thr17Ala Ala .38 (1,670) .209 (402) .171 .06 Osei-Hyiaman et al. 2001; Lohmueller et al. 2003
DRD3 Schizophrenia Ser9Gly Ser/Ser .67 (202) .116 (112) .554 .458 Crocq et al. 1996; Lohmueller et al.2003
AGT Hypertension Thr235Met Thr .42 (3,034) .91 (658) .49 .358 Rotimi et al. 1996; Nakajima et al.2002
PRNP CJD Met129Val Met .72 (138) .556 (72) .164 .049 Hirschhorn et al. 2002; Soldevila et al. 2003
F5 DVT Arg506Gln Gln .044 (1,236) .00 (251) .044 .03 Rees et al. 1995; Hirschhorn et al.2002
HFE HFE Cys382Tyr Tyr .038 (2,900) .00 (806) .038 .024 Feder et al. 1996; Merryweather-Clarke et al. 1997
MTHFR DVT C677T T .3 (188) .066 (468) .234 .205 Schneider et al. 1998; Ray et al.2002
PPARG T2DM Pro12Ala Pro .925 (120) 1.0 (120) .075 .067 Altshuler et al. 2000HapMap Project
KCNJ11 T2DM Asp23Lys Lys .36 (96) .09 (98) .27 .182 Florez et al. 2004

aCJD = Creutzfeldt-Jacob disease; DVT = deep venous thrombosis; HFE = hemochromatosis; T1DM = type I diabetes; T2DM = type II diabetes.

bThe associated allele is the SNP associated with disease, regardless of whether it is the derived or the ancestral allele. The frequencies for this allele are given.

cThe reference that claims this to be a reproducible association, as well as the reference from which the allele frequencies were taken. For allele frequencies obtained from a meta-analysis, only the reference claiming reproducible association is given.

dAllele frequency obtained from the literature involving a European population. Either the general population frequency or the frequency in control groups in an association study was used. To reduce bias, when a control frequency was used for Europeans, a control frequency was also used for Africans. The total number of chromosomes surveyed is given in parentheses after each frequency.

eAllele frequency obtained from the literature involving a West African population. The total number of chromosomes surveyed is given in parentheses after each frequency.

fδ = The difference in the allele frequency between Europeans and Africans.

 

PMC full text:

Am J Hum Genet. 2006 Jan; 78(1): 130–136.Published online 2005 Nov 16. doi:  10.1086/499287Copyright/License ►Request permission to reuse

Allele-Frequency Data for 39 Reported Associations

frequency
gene disease/phenotypea SNP associated alleleb Europeand Africane δf FST referencec
ADRB1 MI Arg389Gly Arg .717 (46) .467 (30) .251 .1 Iwai et al. 2003
ALOX5AP MI, stroke rs10507391 T .682 (44) .159 (44) .523 .425 Helgadottir et al. 2004
CAT Hypertension −844 (C/T) Tg .714 (42) .659 (44) .055 0 Jiang et al. 2001
CCR2 AIDS susceptibility Ile64Val Val .87 (46) .813 (48) .057 0 Smith et al. 1997
CD36 Malaria Y to stop Stop 0 (46) .083 (48) .083 .062 Aitman et al. 2000
F13 MI Val34Leu Val .762 (42) .795 (44) .033 0 Kohler et al. 1999
FGA Pulmonary embolism Thr312Ala Ala .2 (40) .5 (42) .3 .159 Carter et al. 2000
GP1BA CAD Thr145Met Met .022 (46) .167 (48) .145 .095 Gonzalez-Conejero et al.1998
ICAM1 MS Lys469Glu Lys .643 (42) .875 (48) .232 .12 Nejentsev et al. 2003
ICAM1 Malaria Lys29Met Met 0 (46) .354 (48) .354 .335 Fernandez-Reyes et al.1997
IFNGR1 Hp infection −56 (C/T) T .455 (44) .604 (48) .15 .023 Thye et al. 2003
IL13 Asthma −1055 (C/T) T .196 (46) .25 (44) .054 0 van der Pouw Kraan et al. 1999
IL13 Bronchial asthma Arg110Gln Gln .273 (44) .119 (42) .154 .05 Heinzmann et al. 2003
IL1A AD −889 (C/T) T .295 (44) .391 (46) .096 0 Nicoll et al. 2000
IL1B Gastric cancer −31 (C/T) T .826 (46) .375 (48) .451 .335 El-Omar et al. 2000
IL3 RA −16 (C/T) C .739 (46) .875 (48) .136 .037 Yamada et al. 2001
IL4 Asthma −590 (T/C) T .174 (46) .708 (48) .534 .436 Noguchi et al. 1998
IL4R Asthma Gln576Arg Arg .295 (44) .565 (46) .27 .118 Hershey et al. 1997
IL6 Juvenile arthritis −174 (C/G) G .5 (44) 1 (46) .5 .494 Fishman et al. 1998
IL8 RSV bronchiolitis −251 (T/A) Th .659 (44) .229 (48) .43 .301 Hull et al. 2000
ITGA2 MI 807 (C/T) T .316 (38) .25 (48) .066 0 Moshfegh et al. 1999
LTA MI Thr26Asn Asn .357 (42) .5 (44) .143 .018 Ozaki et al. 2002
MC1R Fair skin Val92Met Met .068 (44) 0 (44) .068 .047 Valverde et al. 1995
NOS3 MI Glu298Asp Asp .5 (44) .136 (44) .364 .247 Shimasaki et al. 1998
PLAU AD Pro141Leu Pro .659 (44) .979 (48) .32 .287 Finckh et al. 2003
PON1 CAD Arg192Gln Arg .174 (46) .727 (44) .553 .461 Serrato and Marian 1995
PON2 CAD Cys311Ser Ser .826 (46) .762 (42) .064 0 Sanghera et al. 1998
PTGS2 Colon cancer −765 (G/C) C .238 (42) .292 (48) .054 0 Koh et al. 2004
PTPN22i RA Arg620Trp Trp .084 (1,120) .024 (818) .059 .03 Begovich et al. 2004
SELE CAD Ser128Arg Arg .091 (44) .021 (48) .07 .025 Wenzel et al. 1994
SELL IgA nephropathy Pro238Ser Ser .065 (46) .333 (48) .268 .183 Takei et al. 2002
SELP MI Thr715Pro Thr .864 (44) .977 (44) .114 .063 Herrmann et al. 1998
SFTPB ARDS Ile131Thr Thr .5 (44) .348 (46) .152 .025 Lin et al. 2000
SPD RSV infection Met11Thr Met .568 (44) .478 (46) .09 0 Lahti et al. 2002
TF AD Pro570Ser Pro .957 (46) .935 (46) .022 0 Zhang et al. 2003
THBD MI Ala455Val Ala .87 (46) .848 (46) .022 0 Norlund et al. 1997
THBS4 MI Ala387Pro Pro .341 (44) .083 (48) .258 .166 Topol et al. 2001
TNFA Infectious disease −308 (A/G) A .182 (44) .205 (44) .023 0 Bayley et al. 2004
VCAM1 Stroke in SCD Gly413Ala Gly 1 (46) .938 (48) .063 .041 Taylor et al. 2002

aAD = Alzheimer disease; AIDS = acquired immunodeficiency syndrome; ARDS = acute respiratory distress syndrome; CAD = coronary artery disease; Hp = Helicobacter pylori; MI = myocardial infarction; MS = multiple sclerosis; RA = rheumatoid arthritis; RSV = respiratory syncytial virus; SCD = sickle cell disease.

bThe associated allele is the SNP associated with disease, regardless of whether it is the derived or the ancestral allele. The frequencies for this allele are given.

cThe reference that reported association with the listed disease/phenotype.

dFrequency obtained from the Seattle SNPs database for the European sample. The total number of chromosomes surveyed is given in parentheses after each frequency.

eFrequency obtained from the Seattle SNPs database for the African American sample. The total number of chromosomes surveyed is given in parentheses after each frequency.

fδ = The difference in the allele frequency between African Americans and Europeans.

gAssociated allele in database is A.

hAssociated allele in reference is A.

iThis SNP was not from the Seattle SNPs database; instead, allele frequencies from Begovich et al. (2004) were used.

 

They reported that “The SNPs associated with common disease that we investigated do not show much higher levels of differentiation than those of random SNPs. Thus, in these cases, ethnicity is a poor predictor of an individual’s genotype, which is also the pattern for random variants in the genome. This lends support to the hypothesis that many population differences in disease risk are environmental, rather than genetic, in origin. However, some exceptional SNPs associated with common disease are highly differentiated in frequency across populations, because of either a history of random drift or natural selection. The exceptional SNPs given  are located in AGT, DRD3, ALOX5AP, ICAM1, IL1B, IL4, IL6, IL8, and PON1.

Of note, evidence of selection has been observed for AGT (Nakajima et al. 2004), IL4(Rockman et al. 2003), IL8 (Hull et al. 2001), and PON1 (Allebrandt et al. 2002). Yet, for the vast majority of the common-disease–associated polymorphisms we examined, ethnicity is likely to be a poor predictor of an individual’s genotype.”

 

In 2002 the International HapMap Project was launched:

  • to provide a public resource
  • to accelerate medical genetic research.

Two Hapmap projects were completed.

In phase I the objective was to genotype at least one common SNP every 5 kilobases (kb) across the euchromatic portion of the genome in 270 individuals from four geographically diverse population.

In Phase II of the HapMap Project, a further 2.1 million SNPs were successfully genotyped on the same individuals.

The re-mapping of SNPs from Phase I of the project identified 21,177 SNPs that had an ambiguous position or some other feature indicative of low reliability. These are not included in the filtered Phase II data release. All genotype data are available from the HapMap Data Coordination Center located at (http://www.hapmap.org) and dbSNP (http://www.ncbi.nlm.nih.gov/SNP).

In the Phase II HapMap we identified 32,996 recombination hotspots (an increase of over 50% from Phase I) of which 68% localized to a region of≤5 kb. The median map distance induced by a hotspot is 0.043 cM (or one crossover per 2,300 meioses) and the hottest identified, on chromosome 20, is 1.2 cM (one crossover per 80 meioses). Hotspots account for approximately 60% of recombination in the human genome and about 6% of sequence.

In addition to many previously identified regions in HapMap Phase I including LARGESYT1 andSULT1C2 (previously called SULT1C1), about  200 regions identified from the Phase II HapMap that include many established cases of selection, such as the genes HBB andLCT, the HLA region, and an inversion on chromosome 17. Finally, in the future, whole-genome sequencing will provide a natural convergence of technologies to type both SNP and structural variation. Nevertheless, until that point, and even after, the HapMap Project data will provide an invaluable resource for understanding the structure of human genetic variation and its link to phenotype.

HMM libraries, such as PANTHER, Pfam, and SMART, are used primarily

  • to recognize and
  • to annotate conserved motifs in protein sequences.

In the genomic era, one of the fundamental goals is to characterize the function of proteins on a large scale.

PANTHER, for relating protein sequence relationships to function relationships in a robust and accurate way under two main parts:

  • the PANTHER library (PANTHER/LIB)- collection of “books,” each representing a protein family as a multiple sequence alignment, a Hidden Markov Model (HMM), and a family tree.
  • the PANTHER index (PANTHER/X)- ontology for summarizing and navigating molecular functions and biological processes associated with the families and subfamilies.

 

PANTHER can be applied on three areas of active research:

  • to report the size and sequence diversity of the families and subfamilies, characterizing the relationship between sequence divergence and functional divergence across a wide range of protein families.
  • use the PANTHER/X ontology to give a high-level representation of gene function across the human and mouse genomes.
  • to rank missense single nucleotide polymorphisms (SNPs), on a database-wide scale, according to their likelihood of affecting protein function.

PRINTS is ” a compendium of protein motif ‘fingerprints’. A fingerprint is defined as a group of motifs excised from conserved regions of a sequence alignment, whose diagnostic power or potency is refined by iterative databasescanning (in this case the OWL composite sequence database)”.

The information contained within PRINTS is distinct from, but complementary to the consensus expressions stored in the widely-used PROSITE dictionary of patterns.

However, the position-specific amino acid probabilities in an HMM can also be used to annotate individual positions in a protein as being conserved (or conserving a property such as hydrophobicity) and therefore likely to be required for molecular function. For example, a mutation (or variant) at a conserved position is more likely to impact the function of that protein.

In addition, HMMs from different subfamilies of the same family can be compared with each other, to provide hypotheses about which residues may mediate the differences in function or specificity between the subfamilies.

Several computational algorithms and databases for comparing protein sequences developed and matured profile methods (Gribskov et al. 1987;Henikoff and Henikoff 1991Attwood et al. 1994):

  1. particularly Hidden Markov Models (HMM;Krogh et al. 1994Eddy 1996) and
  2. PSI-BLAST (Altschul et al. 1997),

The profile has a different amino acid substitution vector at each position in the profile, based on the pattern of amino acids observed in a multiple alignment of related sequences.

Profile methods combine algorithms with databases:

A group of related sequences is used to build a statistical representation of corresponding positions in the related proteins. The power of these methods therefore increases as new sequences are added to the database of known proteins.

Multiple sequence alignments (Dayhoff et al. 1974) and profiles have allowed a systematic study of related sequences. One of the key observations is that some positions are “conserved,” that is, the amino acid is invariant or restricted to a particular property (such as hydrophobicity), across an entire group of related sequences.

The dependence of profile and pattern-matching approaches (Jongeneel et al. 1989) on sequence databases led to the development of databases of profiles

  1. BLOCKS,Henikoff and Henikoff 1991;
  2. PRINTS,Attwood et al. 1994) and
  3. patterns (Prosite,Bairoch 1991) that could be searched in much the same way as sequence databases.

 

Among the most widely used protein family databases are

  1. Pfam (Sonnhammer et al. 1997;Bateman et al. 2002) and
  2. SMART (Schultz et al. 1998;Letunic et al. 2002), which combine expert analysis with the well-developed HMM formalism for statistical modeling of protein families (mostly families of related protein domains).

Either knowing its family membership to predict its function, or subfamily within that family is enough (Hannenhalli and Russell 2000).

  • Phylogenetic trees (representing the evolutionary relationships between sequences) and
  • dendrograms (tree structures representing the similarity between sequences) (e.g.,Chiu et al. 1985Rollins et al. 1991).

 

The PANTHER/LIB HMMs can be viewed as a statistical method for scoring the “functional likelihood” of different amino acid substitutions on a wide variety of proteins. Because it uses evolutionarily related sequences to estimate the probability of a given amino acid at a particular position in a protein, the method can be referred to as generating position-specific evolutionary conservation” (PSEC) scores.

 

The process for building PANTHER families include:

  1. Family clustering.
  2. Multiple sequence alignment (MSA), family HMM, and family tree building.
  3. Family/subfamily definition and naming.
  4. Subfamily HMM building.
  5. Molecular function and biological process association.

Of these, steps 1, 2, and 4 are computational, and steps 3 and 5 are human-curated (with the extensive aid of software tools).

 

Conclusion:

Precision medicine effort is the beginning of a new journey to provide better health solutions.

 

Further Reading and References:

Human Phenome Project:

Freimer N., Sabatti C. The human phenome project. Nat. Genet. 2003;34:15–21.

Jones R., Pembrey M., Golding J., Herrick D. The search for genenotype/phenotype associations and the phenome scan. Paediatr. Perinat. Epidemiol. 2005;19:264–275.

Stearns F.W. One hundred years of pleiotropy: A retrospective. Genetics.2010;186:767–773.

Welch J.J., Waxman D. Modularity and the cost of complexity. Evolution.2003;57:1723–1734.

Albert A.Y., Sawaya S., Vines T.H., Knecht A.K., Miller C.T., Summers B.R., Balabhadra S., Kingsley D.M., Schluter D. The genetics of adaptive shape shift in stickleback: Pleiotropy and effect size. Evolution. 2008;62:76–85.

Brem R.B., Yvert G., Clinton R., Kruglyak L. Genetic dissection of transcriptional regulation in budding yeast. Science. 2002;296:752–755.

Morley M., Molony C.M., Weber T.M., Devlin J.L., Ewens K.G., Spielman R.S., Cheung V.G. Genetic analysis of genome-wide variation in human gene expression. Nature. 2004;430:743–747. [PMC free article] [PubMed]

Wagner G.P., Zhang J. The pleiotropic structure of the genotype-phenotype map: The evolvability of complex organisms. Nat. Rev. Genet. 2011;12:204–213.

Cooper Z.N., Nelson R.M., Ross L.F. Informed consent for genetic research involving pleiotropic genes: An empirical study of ApoE research. IRB. 2006;28:1–11.

Model Organisms:

Worm Sequencing Consortium. The C. elegans Sequencing Consortium Genome sequence of the nematode C. elegans: a platform for investigating biology. Science.1998;282:2012–2018.

Adams MD, et al. The genome sequence of Drosophila melanogasterScience.2000;287:2185–2195.

Meinke DW, et al. Arabidopsis thaliana: a model plant for genome analysis. Science. 1998;282:662–682. [PubMed]

Chervitz SA, et al. Using the Saccharomyces Genome Database (SGD) for analysis of protein similarities and structure. Nucleic Acids Res. 1999;27:74–78.

The FlyBase Consortium The FlyBase database of the Drosophila Genome Projects and community literature. Nucleic Acids Res. 1999;27:85–88.

Blake JA, et al. The Mouse Genome Database (MGD): expanding genetic and genomic resources for the laboratory mouse. Nucleic Acids Res. 2000;28:108–111.

Ball CA, et al. Integrating functional genomic information into the Saccharomyces Genome Database. Nucleic Acids Res. 2000;28:77–80.

Venter, J.C., Adams, M.D., Myers, E.W., Li, P.W., Mural, R.J., Sutton, G.G., Smith, H.O., Yandell, M., Evans, C.A., Holt, R.A., et al. 2001. The sequence of the human genome. Science 291: 1304–1351.

Lander, E.S., Linton, L.M., Birren, B., Nusbaum, C., Zody, M.C., Baldwin, J., Devon, K., Dewar, K., Doyle, M., FitzHugh, W., et al. 2001. Initial sequencing and analysis of the human genome. Nature 409: 860–921.

Mi, H., Vandergriff, J., Campbell, M., Narechania, A., Lewis, S., Thomas, P.D., and Ashburner, M. 2003. Assessment of genome-wide protein function classification for Drosophila melanogaster. Genome Res.

Ashburner, M., Ball, C.A., Blake, J.A., Botstein, D., Butler, H., Cherry, J.M., Davis, A.P., Dolinski, K., Dwight, S.S., Eppig, J.T., et al. The Gene Ontology Consortium. 2000. Gene ontology: Tool for the unification of biology. Nat. Genet. 25: 25–29.

Computational Biology

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Obenauer JC, Yaffe MB. Computational prediction of protein-protein interactions.

Methods Mol Biol. 2004;261:445-68. Review.

Aitken A. Protein consensus sequence motifs. Mol Biotechnol. 1999 Oct;12(3):241-53. Review.

Bork P, Koonin EV. Protein sequence motifs. Curr Opin Struct Biol. 1996 Jun;6(3):366-76. Review.

Hodgman TC. The elucidation of protein function by sequence motif analysis.  Comput Appl Biosci. 1989 Feb;5(1):1-13. Review.

Altschul, S.F., Madden, T.L., Schäffer, A.A., Zhang, J., Zhang, Z., Miller, W., and Lipman, D.J. 1997. Gapped BLAST and PSI-BLAST: A new generation of protein database search programs. Nucleic Acids Res. 25: 3389–3402.

Spencer CC, et al. The influence of recombination on human genetic diversity.PLoS Genet. 2006;2:e148.

Petes TD. Meiotic recombination hot spots and cold spots. Nature Rev. Genet.2001;2:360–369.

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Attwood, T.K., Beck, M.E., Bleasby, A.J., and Parry-Smith, D.J. 1994. PRINTS—A database of protein motif fingerprints. Nucleic Acids Res. 22: 3590–3596.

Bairoch, A. 1991. PROSITE: A dictionary of sites and patterns in proteins. Nucleic Acids Res. 19 Suppl: 2241–2245.

Bairoch, A. and Apweiler, R. 2000. The SWISS-PROT protein sequence database and its supplement TrEMBL in 2000. Nucleic Acids Res. 28: 45–48.

Bateman, A., Birney, E., Cerruti, L., Durbin, R., Etwiller, L., Eddy, S.R., Griffiths-Jones, S., Howe, K.L., Marshall, M., and Sonnhammer, E.L. 2002. The Pfam protein families database. Nucleic Acids Res. 30: 276–280.

Sonnhammer, E.L., Eddy, S.R., and Durbin, R. 1997. Pfam: A comprehensive database of protein domain families based on seed alignments. Proteins 28:405–420.

Swets, J.A. 1988. Measuring the accuracy of diagnostic systems. Science 240:1285–1293. [PubMed]

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HUGO Gene Nomenclature Committee (2011). HGNC Database.http://www.genenames.org/.

Population Genomics, GWAS, Inheritance, Heritability, Migration, Selection  an Evolution:

Dayhoff, M.O., Barker, W.C., and McLaughlin, P.J. 1974. Inferences from protein and nucleic acid sequences: Early molecular evolution, divergence of kingdoms and rates of change. Orig. Life 5: 311–330.

Joseph Lachance Disease-associated alleles in genome-wide association studies are enriched for derived low frequency alleles relative to HapMap and neutral expectations BMC Med Genomics. 2010; 3: 57.

Joseph Lachance, Sarah A. Tishkoff  Biased Gene Conversion Skews Allele Frequencies in Human Populations, Increasing the Disease Burden of Recessive Alleles  Am J Hum Genet. 2014 October 2; 95(4): 408-420.

Hemalatha Kuppusamy, Helga M. Ogmundsdottir, Eva Baigorri, Amanda Warkentin, Hlif Steingrimsdottir, Vilhelmina Haraldsdottir, Michael J. Mant, John Mackey, James B. Johnston, Sophia Adamia, Andrew R. Belch, Linda M. Pilarski Inherited Polymorphisms in Hyaluronan Synthase 1 Predict Risk of Systemic B-Cell Malignancies but Not of Breast Cancer  PLoS One. 2014; 9(6): e100691.

Joseph Lachance, Sarah A. Tishkoff  Population Genomics of Human Adaptation

Annu Rev Ecol Evol Syst. Author manuscript; available in PMC 2014 November 5.

Published in final edited form as: Annu Rev Ecol Evol Syst. 2013 November; 44: 123–143

Joseph Lachance, Sarah A. Tishkoff SNP ascertainment bias in population genetic analyses: Why it is important, and how to correct it  Bioessays.

Erik Corona, Rong Chen, Martin Sikora, Alexander A. Morgan, Chirag J. Patel, Aditya Ramesh, Carlos D. Bustamante, Atul J. Butte Analysis of the Genetic Basis of Disease in the Context of Worldwide Human Relationships and Migration PLoS Genet. 2013 May; 9(5): e1003447.

Olga Y. Gorlova, Jun Ying, Christopher I. Amos, Margaret R. Spitz, Bo Peng, Ivan P. Gorlov J Derived SNP Alleles Are Used More Frequently Than Ancestral Alleles As Risk-Associated Variants In Common Human Diseases Bioinform Comput Biol.

Ani Manichaikul, Wei-Min Chen, Kayleen Williams, Quenna Wong, Michèle M. Sale, James S. Pankow, Michael Y. Tsai, Jerome I. Rotter, Stephen S. Rich, Josyf C. Mychaleckyj  Analysis of Family- and Population-Based Samples in Cohort Genome-Wide Association Studies Hum Genet.

Altshuler D, Daly MJ, Lander ES. Genetic mapping in human disease. Science. 2008; 322(5903):881–888. doi: 10.1126/science.1156409.

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Tomlinson I, Webb E, Carvajal-Carmona L, Broderick P, Kemp Z, Spain S, Penegar S, Chandler I, Gorman M, Wood W. et al. A genome-wide association scan of tag SNPs identifies a susceptibility variant for colorectal cancer at 8q24.21. Nature Genetics. 2007;39(8):984–988. doi: 10.1038/ng2085.

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Personalized Medicine in NSCLC larryhbern
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Understanding the Role of Personalized Medicine larryhbern
Directions for Genomics in Personalized Medicine larryhbern
Personalized Medicine: An Institute Profile – Coriell Institute for Medical Research: Part 3 2012pharmaceutical
Paradigm Shift in Human Genomics – Predictive Biomarkers and Personalized Medicine – Part 1 2012pharmaceutical
Harnessing Personalized Medicine for Cancer Management, Prospects of Prevention and Cure: Opinions of Cancer Scientific Leaders @ http://pharmaceuticalintelligence.com 2012pharmaceutical
Nanotechnology, personalized medicine and DNA sequencing tildabarliya
Personalized medicine gearing up to tackle cancer ritusaxena
Personalized Medicine Company Genection launched ritusaxena
Personalized Medicine: Cancer Cell Biology and Minimally Invasive Surgery (MIS) 2012pharmaceutical
The Way With Personalized Medicine: Reporters’ Voice at the 8th Annual Personalized Medicine Conference,11/28-29, 2012, Harvard Medical School, Boston, MA 2012pharmaceutical
Personalized Medicine Coalition: Upcoming Events 2012pharmaceutical
Highlights from 8th Annual Personalized Medicine Conference, November 28-29, 2012, Harvard Medical School, Boston, MA 2012pharmaceutical
Personalized medicine-based cure for cancer might not be far away ritusaxena
GSK for Personalized Medicine using Cancer Drugs needs Alacris systems biology model to determine the in silico effect of the inhibitor in its “virtual clinical trial” 2012pharmaceutical
Congestive Heart Failure & Personalized Medicine: Two-gene Test predicts response to Beta Blocker Bucindolol 2012pharmaceutical
Personalized Medicine as Key Area for Future Pharmaceutical Growth 2012pharmaceutical
Clinical Genetics, Personalized Medicine, Molecular Diagnostics, Consumer-targeted DNA – Consumer Genetics Conference (CGC) – October 3-5, 2012, Seaport Hotel, Boston, MA 2012pharmaceutical
AGENDA – Personalized Diagnostics, February 16-18, 2015 | Moscone North Convention Center | San Francisco, CA Part of the 22nd Annual Molecular Medicine Tri-Conference 2012pharmaceutical
Arrowhead’s 6th Annual Personalized & Precision Medicine Conference is coming to San Francisco, October 29-30, 2014 2012pharmaceutical
Personalized Cardiovascular Genetic Medicine at Partners HealthCare and Harvard Medical School 2012pharmaceutical
Precision Medicine for Future of Genomics Medicine is The New Era Demet Sag, Ph.D., CRA, GCP
Precision Medicine Initiative: Now is a State Initiative in California 2012pharmaceutical
1:30 pm – 2:20 pm 3/26/2015, LIVE Precision Medicine: Who’s Paying? @ MassBio Annual Meeting 2015, Cambridge, MA, Sonesta Hotel, 3/26 – 3/27, 2015 2012pharmaceutical
We Celebrate >600,000 Views for our 2,830 Scientific Articles in Life Sciences and Medicine 2012pharmaceutical
attn #3: Investors in HealthCare — Platforms in the Ecosystem of Regulatory & Reimbursement – Integrated Informational Platforms in Orthopedic Medical Devices, and Global Peer-Reviewed Scientific Curations: Bone Disease and Orthopedic Medicine – Draft 2012pharmaceutical
Foundation Medicine: Roche has Taken Over at $1.2B and 52.4 percent to 56.3 percent of Foundation Medicine on a fully diluted basis 2012pharmaceutical
Bridging the Gap in Precision Medicine @UCSF 2012pharmaceutical
Germline Genes and Drug Targets: Medicine more Proactive and Disease Prevention more Effective. 2012pharmaceutical
Proteomics – The Pathway to Understanding and Decision-making in Medicine larryhbern
Multi-drug, Multi-arm, Biomarker-driven Clinical Trial for patients with Squamous Cell Carcinoma called the Lung Cancer Master Protocol, or Lung-MAP launched by NCI, Foundation Medicine, and Five Pharma Firms 2012pharmaceutical
Preventive Care: Anticipated Changes caused by Genomics in the Clinic and Personalised Medicine 2012pharmaceutical
Cancer Labs at School of Medicine @ Technion: Janet and David Polak Cancer and Vascular Biology Research Center 2012pharmaceutical
Reprogramming Adult Patient Cells into Stem Cells: the Promise of Personalized Genetic Therapy 2012pharmaceutical
US Personalized Cancer Genome Sequencing Market Outlook 2018 – 2012pharmaceutical
Summary of Translational Medicine – e-Series A: Cardiovascular Diseases, Volume Four – Part 1 larryhbern
Introduction to Translational Medicine (TM) – Part 1: Translational Medicine larryhbern
Cancer Diagnosis at the Crossroads: Precision Medicine Driving Change, 9/14 – 9/17/2014, Sheraton Seattle Hotel, Seattle WA 2012pharmaceutical
Genomic Medicine and the Bioeconomy: Innovation for a Better World May 12–16, 2014 • Boston, MA 2012pharmaceutical
Institute of Medicine (IOM) Report on Genome-based Therapeutics and Companion Diagnostics 2012pharmaceutical
“Medicine Meets Virtual Reality” – NextMed-MMVR21 Conference 2/19 – 2/22/2014, Manhattan Beach Marriott, Manhattan Beach, CAView 2012pharmaceutical

 

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