LESSONS IN GENETICS

PROBIOTICS AND PREBIOTICS

2015-01-25T03:18:00.002-08:00

Probiotic and prebiotic supplements may be used to restore the stability of the gut microbiome, especially in the elderly or after an antibiotic treatment, an infective bowel disease (gastroenterocolitis) or malnutrition.

A probiotic is a supplement containing living bacteria, whereas a prebiotic is a supplement containing special olygosaccharides in the form of indigestible fibers (e.g. inulin) which works as nutritional substrate for bacteria.

Therefore probiotics and prebiotics can be taken together, since the latter works as energy source for the first ones. Probiotics are for example the Bifidobacterium and the Lactobacillus, two bacteria of which presence is typically reduced in the gut of the elderly. Probiotics available on the market may contain both the Bifidobacterium and the Lactobacillus or only one of the two.

Why restoring the gut microbiome with probiotics and/or prebiotics? Evidences have arisen that increased quantities of Bifidobacterium and Lactobacillus associate with a reduction in the presence of pathogenic bacteria strains. Not only: it has been demonstrated that prebiotics alone may have a bifidogeneic effect, that means they can induce the genesis of new Bifidobacteria.

The real long-term benefits of taking probiotics/prebiotics supplements, especially in the elderly, is still under scrutiny. However, thanks to new techniques of sequencing, which are now more powerful and more cost-effective (high-throughout sequencing) and thanks to the progression of the knowledge in omics in general, the future might bring new evidence about the advantages of taking probiotics and prebiotics.

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THE MICROBIOME

THE GUT MICROBIOME

THE GUT MICROBIOME

2015-01-24T06:10:00.001-08:00

What drives the development on gut microbiome? Which factors mainly influence its composition?

The microbiome is properly defined by all the genomes of the microorganisms which are living within the human body and on its surface. However, the term microbiome is also used to identify all the microorganisms themselves. The gut microbiota (which includes the the microorganisms living in the bowel, in the mouth and other parts of the digestive tract), the bacteria of the skin and the bacteria of the respiratory airways are examples of microorganisms belonging to the human microbiome.

Although it has been estimated that everyone of us harbors about 170 different species, it is known that the human microbiome includes more than 1000 species. It is therefore important to know how the microbiome is composed and how its development of influenced.

The gut microbiome

The microorganisms living within the intestinal tract are constituting the human gut microbiome. Among those factors which are consistently influencing the composition of the human microbiome are the type of delivery (cesarean or natural), the use of antibiotics and the hygienic conditions of the living environment. However, the most important factor is nutrition.

Infancy. The gut microbiome in infacy is very unstable and its composition becomes similar to that one of the adult at around 2-3 years of age. The composition of the human microbiome at this stage is mostly influenced by the post-natal nutrition (maternal feeding vs artificial milk) and by the moment of introduction of solid foods in the daily diet.

Adult. The gut microbiome of the adult is more stable in its composition, although it can rapidly change according to variations in diets, antibiotic therapy or bowel infections. Such changes can be complete even within 24-48 hours, although the microbiome can be very fast also in restoring itself to its standard composition.

In the elderly. In the elderly the gut microbiome becomes tendentially unstable, probably as consequence of the ageing process and of the increased rate of antibiotic treatment administration.

To restore the normal composition of the gut microbiome it is possible to use probiotic and prebiotic integrators, of which effects are now more than ever under scrutiny in order to explore possible long-term benefits.

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MICROBIOME

THREE IMPORTANT GENES IN LUNG CANCER

2015-01-18T06:21:00.003-08:00

Molecular studies of lung cancer cells have been useful in the setting of advanced stage disease, whereas their usefulness in resectable disease is still under investigation. So far, molecular testing has been proved to be useful only for NSCLC (Non Small Cell Lung Cancer). Molecular testing can be done on samples from biopsies or on cytology specimens.

Amongst others, three genes have been frequently reèprted for showing mutations of diagnostic and predictive significance in lung cancer: EGFR, ALK and BRAF.

EGFR

EGFR (7p21) has 28 exons. The two most common mutations are in-frame deletions (15bp) in exon 19 and L858R in exon 21 (these represent about 90% of all EGFR mutations in lung adenocarcinoma). EGFR mutations are typical of lepidic growth pattern with acinar/papillar growth. EGFR exon 19 and exon 21 mutations are associated with EGFR gene amplification. In the past EGFR amplification was also tested, but exon 19/exon 21 testing now shows better predictability.

Response to Gefitinib is seen in 85% of patients with exon 19/exon 21 mutations, although resistance mutations may then arise in exon 18 and exon 20. Somehow paradoxical: patients with exon 18 and exon 20 mutations (resistance mutations) may show better survival than patients without this mutations.

ALK

ALK mutations show first of all a diagnostic value: they are mostly found in adenocarcinomas. A minority is also found in squamous cells cancers. Adenocarcinomas with ALK mutations are poorly differentiated, often with solid or acinar growth, often express mucin and sometimes signet ring differentiation.

The therapeutic value of ALK mutations is in that they may confer sensitivity to crizotinib. Overall, a response rate of 57% in high-stage lung adenocarcinoma after 6 months of therapy has been found. Over 70% of patients showed an estimated 6 months PFS (progression free survival), in contrast with a normal expectation of 10-30%. The gold-standard approach to ALK mutations detection is FISH (Fluorecence In Situ Hybridization).

BRAF

BRAF gene mutations are exclusive of EGFR, ALK and KRAS mutations and are often seen in smokers (in contrast to EGFR and ALK mutations). BRAF mutations are mostly V600E, G469A and G594G. BRAF mutations are more typical of the elderly and show same sex distribution.

The diagnostic value of BRAF mutations is in that they are typical of higher stage adenocarcinoma, poorly differentiated, with acinar and solid, papillary and micropapillary growth, and mucin production.

As for what concerns the therapeutic value of BRAF mutations: PLX4032 is drug in early stage trails (phase I) for the treatment of high stage lung adenocarcinoma.

In summary. Molecular testing is useful for less than 50% patients with lung cancer, even if currently ongoing trials are promising. Lung carcinomas seem to be “genetically simple”, as they are tend to be dominated by a single molecular alteration.

THE MICROBIOME

2015-01-17T15:38:00.001-08:00

The microbiome is going to be the next most important thing in genetics. Already decades ago Joshua Lederberg pointed out the importance of characterizing the human microbiome, arguing that the Human Genome Project should have included microbiome sequencing since its very beginning.

What's the microbiome? Microbiome is a term by which scientists identify the totality of the genetic sequences of the microorganisms harbored in the human body. So, the microbiome is made up by the genomes of all bacteria, viruses and other microorganisms which are hosted within the human body and on its skin. For the importance of its interactions with the human body, some are considering the microbiome like an additional organ of the body.

How many genes does the microbiome include? It has been estimated that the total number of genes of all the microorganisms harbored in the human body is about 3 millions (the human genes are about 20.000). Overall, about 1000 different species of microorganisms have been identified to compose the microbiome and it has been calculated that every persons harbors at least 170 species.

The hygiene hypothesis. Some authors are convinced that the microbiome can explain several human diseases, also beyond the chapter of infectivology, i.e. from autoimmunity to cancer. According to the hygiene hypothesis, many human disorders are caused by insufficient contact with microorganisms.

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THE GUT MICROBIOME

NLG: NATURAL LANGUAGE GENERATION

2015-01-16T12:22:00.001-08:00

Natural Language Generation (NLG) is an informatic process based on algorithms that try to transform text data into sentences which can be understandable by a human. The ultimate application of NLG is scientific curation. A computer can be taught to read a scientific article, extract important data from it (e.g. evidences on genotype-phenotype correlations) and re-elabororate them in condensed statements that can be fast and easily understood by a scientist without him reading the original article. In theory.

For instance, a computer could 'read' an article on EGFR gene mutations in lung cancer, extract the association between the L858R mutation and the sensitivity to the treatment with gefitinib and finally output a statement like 'the somatic mutation L858R in lung cancer cells increases the sensitivity to the treatment with gefitinib'. In theory.

Although it is said that machines are doubling their memory and their computational capacity every year, we are still at a stage where algorithms may find very difficult to understand, interpret, and synthesize complex scientific concepts (and to distill them in a correct and understandable human language). As consequence, probably most outputs of NLG from complex scientific data remains today odd, contradictory and cumbersome. To use another word, wrong.

The way to artificial intelligence is certainly exciting and we all hope in future clever (yet friendly) machines which may be able to help humans as never before. However, data coming from scientific curation by NLG is still to be used very, very cautiously.

NLG has been used so far mostly to curate scientific sources on somatic mutations in cancer. There is an incredible amount of data on cancer genetics out there, and the race to decode all published evidences into clinical practice guidelines to realize the dream of personalized medicine has already started. Some companies and institutions are already using NLG for this purpose. However, it is just the amount of data and the often arbitrary selection of publications (NLG is often working on articles which have been preselected by a human) that makes difficult to check the consistency of NLG statements and that puts the reliability of NLG (and databases build with it) in doubt. Considering that at the end of the process there is always a human patient, maximum caution must be certainly warranted before physicians may confidently rely on NLG based datasets.

WORKFLOW IN IDENTIFYING DRIVER MUTATIONS IN CANCER

2015-01-12T12:56:00.001-08:00

As said in the article STRATEGIES IN IDENTIFYING DRIVER MUTATIONS IN CANCER, Whole-Exome-Sequencing (WES) is probably the most widespread technique used to identify driver mutations in cancer. Here below some steps to be undertaken in identifying driver mutations:

1. SIMULTANEOUS SEQUENCING OF TUMOR AND A MATCHED NORMAL SAMPLE

Identifying driver mutations is first of all a matter of identifying and differentiating cancer somatic mutations from patient's germline mutations. Therefore, in the effort of identifying driver mutations it is always necessary to do simultaneous sequencing of both the tumor sample and a matched control of normal tissue from the same patient.

2. ASSESSMENT OF TUMOR PURITY

Each tumor sample is always an admixture of normal tissue sample and different cancer cell lines. Tumor purity is defined by the fraction of cancerous cells in the sample. Therefore to identify putative driver mutations it is necessary to proceed to an estimate of tumor purity. Such an estimate can be done by special algorithms which can be included in a variant calling software. For instance, with VarScan2 the user can enter an estimate manually, whereas software like MuTect and Strelka calculate this automatically based on allele frequencies observed. There are also software which are solely dedicated to the estimation of tumor purity: ABSOLUTE and ASCAT can infer tumor purity and tumor ploidy from SNP array data.

3. ASSESSMENT OF INTRATUMOR HETEROGENEITY

Although cancer development is a clonal process, not all cancer cells are identical. In any cancer it is actually possible to identify different subtypes of cancer cells. Also in this case bioinformatics comes to help. The following methods can be used to infer intratumor heterogeneity: (1) with a software (e.g. THetA: Tumor Heterogeneity Algorithm), (2) by next generation sequencing at ultra high coverage to identify different SNVs (i.e. differentiating clonal mutations, arisen at the beginning, from subclonal mutations, arisen later in tumor development) (3) by sequencing different tumor regions after multiple biopsies.

4. EVALUATION OF ALLELE FREQUENCY

As said in STRATEGIES IN IDENTIFYING DRIVER MUTATIONS IN CANCER, assessing the allele frequency is probably the most accepted strategy in identifying driver mutations, following the concept that the higher the allele frequency of a mutation, the higher the possibility it is a driver mutation.

To do so, it is important to determine if the putative driver mutation has an allele frequency which exceeds the background mutation rate (BMR), i.e. the probability that a given locus a passenger mutation arises (a passenger mutation is a mutation with no significance for cancer development). BMR depends on genomic context, mutation type, gene transcription rate and gene replication timing.

5. VERIFICATION BY FUNCTIONAL STUDIES

Allele frequency and in silico analysis may help the initial phase, but the final evidence that a mutation is a driver mutation must be obtained by functional assays in vitro, which may demonstrate that the mutation has the power to influence cell division, cell dedifferentiation, ecc.

Related subjects:

DRIVER MUTATIONS AND PASSENGER MUTATIONS IN CANCER

STRATEGIES IN IDENTIFYING DRIVER MUTATIONS IN CANCER

PRECISION ONCOLOGY

STRATEGIES IN IDENTIFYING DRIVER MUTATIONS IN CANCER

2015-01-12T12:49:00.001-08:00

Driver mutations are somatically acquired changes in the DNA of cancer cells which are driving the development of cancer. Identifying driver mutations is not an easy task, but it is necessary to better understand cancer biology and to design new potential treatments.

There are some strategies which have been used so far to identify driver mutations. The first one is based on allele frequency, i.e.: the higher the mutation frequency across different samples of the same tumor, the higher the probability that that is a driver mutation. In fact, if a mutation confers growth advantage to cancer cells, this will be perpetuated in the clonal proliferation and therefore will be present in all cells of the cancer. However, it should be mentioned that some driver mutations can be rare mutations (precision oncology is just based just on this: identifying rare driver mutations alongside with more common driver mutations).

Another strategy is to look first at known cancer genes, where driver mutations are more likely to arise, whereas passenger mutations (i.e. mutations with no particular effect on cancer development) are usually randomly distributed in the rest of the genome.

Actually, any putative driver mutation should be finally proved to be a real driver mutation by functional assays in vitro, which can demonstrate the power of that mutation on cell growth, cell dedifferentiation, etc.

Whole-Genome-Sequencing (WGS) and Whole-Exome-Sequencing (WES) are the main techniques used in identifying driver mutations. WES is probably the most widespread because of its reduced costs and reduced amount of data to be elaborated in comparison to WGS.

The detection of somatic mutation in cancer is also the aim of some collaborative projects like The Cancer Genome Atlas (TCGA) and the International Cancer Genome Consortium (ICGC).

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DRIVER MUTATIONS AND PASSENGER MUTATIONS IN CANCER

WORKFLOW IN IDENTIFYING DRIVER MUTATIONS IN CANCER

PRECISION ONCOLOGY

DRIVER MUTATIONS AND PASSENGER MUTATIONS IN CANCER

2015-01-12T12:42:00.001-08:00

Driver mutations are those mutations which have a fundamental role in cancer genesis and development. It is because of these driver mutations that a cancer can grow and invade the human body. Cancer cells also bear several other genetic mutations which are actually not important as driver mutations and which are called passenger mutations.

Driver mutations are typically somatic mutations, i.e. mutations which arise de novo in cancer cells. As such, driver mutations are not present in all other cells of the patient, i.e. they are not germline mutations (even if we know that some germline mutations may predispose to certain types of cancer).

A major challenge in cancer genetics is identifying these driver mutations. Identifying driver mutations is important (1) to better understand the subverted processes of cancer biology and (2) because such mutations might be "actionable", i.e. targeted in new treatments (for example as mutations giving resistance or sensitivity to certain drugs or as mutations lockable by gene therapy). This would be the promise of personalized medicine and precision oncology, which are actually aimed to tailor each patient's treatment based on the cancer genetic profile. In order to identify driver mutations some strategies have been invented in the recent years, which can be explored here.

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STRATEGIES IN IDENTIFYING DRIVER MUTATIONS IN CANCER

WORKFLOW IN IDENTIFYING DRIVER MUTATIONS IN CANCER

PRECISION ONCOLOGY

PERSONALIZED MEDICINE

PERSONALIZED MEDICINE

2015-01-10T03:54:00.003-08:00

Personalized medicine is a term used to identify a personalized approach in diagnosis and treatment based on the individual genetic profile of a patient. In oncology, personalized medicine is essentially based on the analysis of the genetic profile of a tumor.

It is actually proved that some somatic mutations in cancer can confer susceptibility or resistance to certain drugs and that as consequence the patient's treatment can be tailored in order to obtain a better response (see also PHARMACOGENOMICS). Not only: some of these somatic cancer mutations are of help in narrowing the histological diagnosis as they can be typical of certain cellular types and/or cancer growth patterns (histomolecular correlations). As a collateral effect of their therapeutic and diagnostic relevance, some somatic mutations may also show a prognostic significance in terms of association with (1) progression-free survival (PFS) and/or (2) longer/shorter overall survival (OS). Some EGFR gene mutations, for instance, are known to increase sensitivity to the treatment with gefinitinb, whereas some other mutations in the same gene are known to give resistance to that same drug.

These cancer somatic mutations of crucial therapeutic, diagnostic and/or prognostic value are sometimes belonging to the pool of the so-called driver mutations. Properly, driver mutations are mutations which have a fundamental role in cancer genesis and development. It is because of these driver mutations that a cancer can thrive and invade the human body. Cancer cells also bear several other genetic mutations which are thought not to be as important as driver mutations and which are called passenger mutations. Not all driver mutations can be targetable by a specific drug and, on the other side, not all somatic mutations with a relevant diagnostic or prognostic significance can be proved to be driver mutations, i.e. mutations which drive the growth of the cancer.

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PRECISION ONCOLOGY

2015-01-09T08:05:00.003-08:00

Precision oncology (or precision cancer medicine) can be considered a chapter of personalized medicine. The aim of precision oncology would actually be to tailor patient handling and treatment at a precision that goes beyond the few pharmacogenomic correlations known to date. The existing knowledge on the genetic associations of cancer drugs and biomarkers is still very limited. Only few mutations in few genes (pharmacogenomic markers) have been confirmed to always give resistance or sensitivity to a certain drug. However, according to several authors, there is a huge (and unfortunately disorganized) amount of data regarding patient treatments and tumor genetic variants which could be analyzed and possibly decoded into complex interaction principles to help personalized medicine to become even more powerful. This would mean identifying additional common driver mutations, rarer driver mutations, epigenetic and environmental factor and characterizing cancer mutations activity on different germline substrata.

Some (big) efforts along the way to build the knowledge foundation necessary to develop precision oncology have been already initiated. See for instance the open-access knowledge base for precision medicine PMC Wiki and Knowledgebase (PMID: 25563458). Both public and private organizations have started to build their own databases and some of their projects are also going beyond genetics, involving the collection of many additional biochemical and clinical data (see for instance Flatiron Health).

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INCOMPLETE PENETRANCE: A TYPICAL FEATURE OF AUTOSOMAL DOMINANT INHERITANCE

2015-01-02T02:16:00.001-08:00

What's incomplete penetrance? In which diseases incomplete penetrance is more frequent?

Sometimes a genetic disease does not become clinically evident (i.e. the patient remains totally asymptomatic) even when the patient has a disease-causing mutation. This phenomenon is called incomplete penetrance and it is not rarely observed in some autosomal dominantly inherited genetic disorders. Penetrance is a term that defines the percentage of subjects with a disease-causing mutation who really shows clinical signs of the disease (i.e. are clinically affected).

Sometimes it is the mutation type to cause incomplete penetrance. This can be the case of several disorders caused by repeat expansion. In Huntington disease, for instance, alleles with 36-39 repeats are classified as "incomplete penetrance alleles", whereas alleles with 40 repeats or more are classified as alleles with full penetrance (Warby SC et al, PMID: 20301482).

Sometimes penetrance is linked to the age of onset of the disease. In autosomal dominant disorders with typical onset in the adult age the penetrance rate tends to increase as the patient ages (i.e. the more the patient gets older, the higher the chance that clinical signs and symptoms are showing up). Conversely, for an old patient without clinical disease it is easier to think that he is not carrying the disease-causing mutation (especially if he's already gone beyond the typical range of clinical onset).

Incomplete penetrance is typical of autosomal dominant diseases, which are often caused by genetic mutations with particular biological characteristics. Mutations that cause dominant traits are often gain-of-function mutations or mutations that cause haploinsufficiency or affect proteins which are crucial for the proper functioning of some tissues/organs, but not others. Reason, the latter, which is also the basis of variable expressivity (in fact, those factors that can drive variable expressivity in the clinical manifestation of the disease could also be the ones that can lead to the extreme pole of complete absence of signs and symptoms, i.e. incomplete penetrance). Such factors are likely to be genetic, epigenetic and/or environmental.

However, it must be mentioned that there are also autosomal dominant diseases where penetrance is complete or almost complete. Let's consider, for example, tuberous sclerosis, in which penetrance is estimated to be 100% (Northrup H, 201, PMID: 20301399).

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HAPLOINSUFFICIENCY, DOMINANT NEGATIVE EFFECT AND AUTOSOMAL DOMINANT INHERITANCE

HAPLOINSUFFICIENCY, DOMINANT NEGATIVE EFFECT AND AUTOSOMAL DOMINANT INHERITANCE

2015-01-01T10:03:00.001-08:00

We know that most human apparatuses operate under conditions of functional redundancy. Many biological systems have a functional reserve to offer a better chance of survival in case of illness or accident. In fact, men have two lungs, two kidneys, two eyes (but can survive even with only one), a liver function which can be satisfactorily maintained even after the removal of 75% of the liver parenchyma, a quantity of blood greater than needed, and so on. At the genetic level, the presence of two copies of the same gene, in addition to being the basis of interindividual differences, could be somehow considered a functional reserve.

In fact, for many proteins, we can survive even when the protein production is just 50% of the normal quantity. This also explains why each of us is a healthy carrier of various autosomal recessive diseases (i.e. for a certain gene we have just one normal copy whereas the other one is pathogenically mutated). Therefore, it can be surprising that for some genes (not many, indeed) the loss of function of just one copy turns out to be pathogenic and results eventually in a clinically evident disease. This phenomenon is known under the name of haploinsufficiency: because of a pathogenic mutation in one of the two copy of a gene, the protein production suffers a 50% reduction and this turns out to be sufficient to cause a disease. Genetic diseases caused by haploinsufficiency are typically due to mutations in genes of which proteins interact in fixed quantitative proportions with other proteins (i.e. it is not important the absolute quantity of the protein, but the ratio of all the proteins involved in that metabolic process). In other cases, a certain protein can be less important for the structure and function of some tissues, whereas it is absolutely crucial for other tissues/organs (where it must be 100% normally functional). This also explains why haploinsufficiency is sometimes associated to the so-called phenomenon of variable expressivity. Some typical diseases caused by haploinsufficiency are Alagylle syndrome, tricho-rhino-phalangeal syndrome and multiple exostosis.

Totally different from haploinsufficiency is autosomal dominant inheritance caused by gain-of-function mutations. In this case, the disease is not caused by the loss of protein function, yet by a change in protein function (usually as a result of a missense mutation). Actually gain-of-function mutations can sometimes achieve an even worse effect than non-functional mutations. This happens especially in those cases where the protein contributes to the formation of dimers or multimers. This is called dominant negative effect. See for example the case of the KCNQ1 gene, in which heterozygous nonsense mutations are asymptomatic, whereas heterozygous missense mutations cause long QT syndrome (and homozygous nonsense mutations cause Jervell and Lange-Nielsen syndrome).

MULTIPLEXING: A FORMIDABLE APPLICATION OF NEXT GENERATION SEQUENCING

2014-12-23T15:11:00.000-08:00

The modern Next Generation Sequencing (NGS) machinery allows to process a large number of sequences in parallel. This has opened the way to the fast and cost-effective analysis of the entire genome of an individual (whole-genome sequencing). Similarly, it is also possible to analyze large parts of the genome of many individuals in parallel.

In other words, depending on the sequence size and the power of the machine available, it is possible to perform the analysis of more than one sample at a time. It is not uncommon, for example, to perform the exome sequencing for two individuals in a single run. In microbiology for instance because the genomes of microorganisms are very small, it is possible to sequence the genomes of many viruses and bacteria simultaneously.

The analysis of multiple samples in parallel is called multiplexing. In order to do a multiplexing analysis, it is necessary to add to each library fragment a short, patient-specific, synthetic sequence, also known as barcode sequence. The barcode sequence works as a label to uniquely identify all the DNA fragments belonging to the same individual (or to the same microorganism). Once the DNA library fragments of every sample have been labelled with the barcode sequence, they can be put all together into the same tube to start the sequencing reaction. All the reads produced will also contain the barcode sequence. Thanks to the barcode sequences it will be then possible to separate the reads belonging to each sample (de-multiplexing) before proceeding to alignment and variant calling.

Multiplexing became a reality just thanks to NGS, since costs and times have always made such analysis prohibitive by means of Sanger capillary electrophoresis. NGS machines also minimize the human intervention in the analysis, since there's no electropherogram to interpret and alignment and variant calling are fully automated. Not only that: the amount of DNA required for an NGS analysis (even in multiplexing) is much lower than what is needed for a Sanger analysis: only 30 ng of DNA may be enough to get an entire exome!

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WHAT IS A REFERENCE SEQUENCE?

2014-12-23T13:38:00.000-08:00

Of many organisms, including humans, we know the entire coding and non-coding genetic sequence. Coding and non-coding sequences of each gene are annotated in database and are commonly known as reference sequences. In some cases there may be several reference sequences for the same gene, each referring to a diverse transcript (i.e. to a diverse isoform of the protein). The reference sequences are regularly utilized in research and routine diagnostics to compare patient sequences. This comparison is done with the help of bioinformatic tools through a process that is known as alignment: the sequence of the patient is aligned to the reference sequence to see if there are differences (i.e. mutations) that could explain the patient's phenotype. During an alignment process several variants can be identified, of which of course not all are pathogenic. The phase of variant identification during the alignment process is called variant calling and can be done manually by the operator (usually for small DNA fragments) or automatically by a software. In analyses where huge amounts of data are produced (like, for instance, in an exome sequencing or a genome sequencing), the variant calling process is always done automatically.

From a practical point of view it is very important to insist on the concept of alternative isoforms generated by alternative transcripts. In fact, if a same variant can be clinically irrelevant in one transcript (because, for instance, it falls in a deep intronic region with no effect on the coding sequence or on the splicing process), it could be harmful in another transcript (because it falls, for example, within the coding region creating a stop mutation). These various possibilities must be taken into account when interpreting the final results of a genetic test.

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NEXT GENERATION SEQUENCING FOR DUMMIES
EXOME SEQUENCING

HIGH-THROUGHPUT SEQUENCING

2014-12-23T07:43:00.000-08:00

Very often we hear about high-throughput sequencing. "High-throughput sequencing" is pretty commonly used to identify all modern sequencing techniques (sometimes referred to also as second or third generation sequencing). High-throughput sequencing is typically Next Generation Sequencing (NGS). Thanks to NGS, the sequencing of huge amounts of sequences (also from many different subjects in parallel) has become cost-effective and achievable in a relatively short time. High-throughput sequencing is the evolution from Sanger sequencing, which can still be more precise, yet definitively slower and much more expensive. If the daily output of a capillary sequencer based on Sanger technique is of thousands of bases, the output of an NGS sequencer is in terms of gigabases per day.

By NGS it is possible to analyse either DNA or RNA. Typical high-throughput sequencing analyses are exome sequencing and genome sequencing. After having conquered the research, thanks to the support of continuously improving bioinformatic analysis tools, high-throughput sequencing is about to take over also in the context of routine diagnostics.

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THE STAR ALLELE NOMENCLATURE: HOW IT WORKS

2014-12-23T07:10:00.000-08:00

The variations in the cytochrome P450 genes (CYP) lead to four different metabolizer phenotypes: ultrarapid, extensive, intermediate and poor.

A patient with a ultrarapid drug metabolism bears double or multiple copies of an allele with normal or increased functionality, whereas patients with intermediate or poor drug metabolism bear one or more alleles with reduced functionality (these alleles are typically consistent with inactivating mutations or large gene deletions). The term extensive metabolizer is used instead to describe those individuals with two standard copies of the normally functional allele. Extensive metabolizers are therefore carrying the wild-type allele, also called consensus allele, which corresponds to the allele *1 in the star allele nomenclature. The numbers *2, *3, *4 and so on represent alleles with altered functionality which may lead to profiles of increased or reduced drug metabolism.

By using one single star allele one can identify not just a single variant, but even a group of variants. Rules are as follows:

A) to distinguish an allele of proved pharmacogenomic relevance from a group of alleles where this allele is segregating together with other neutral alleles, numbers and letters are used.

Example: the symbol CYP2B6*4A identifies the pharmacogenomic relevant allele K262R in the CYP2B6 gene, whereas the symbol CYP2B6*4B identifies the combination of the K262R allele with one or more of the polymorphisms which can be easily detected in association with it: -2320T>C; -1778A>G; -1186C>G; -750T>C; 18053A>G. In both cases (CYP2B6*4A and CYP2B6*4B) the pharmacogenomic effect remains the one of K262R.

B) similarly, to identify group of functional alleles, the number of the most "powerful" allele is always used, followed by a letter.

Example: the symbol CYP2C19*2 identifies the splice mutation 681G>A, which reduces the protein functionality. Other variants which also show some functional effect can often segregate in association with this splice mutation. However, the effect of the associated variants is always less relevant than the effect exerted by the splice mutation 681G>A. So different groups of associated alleles can be represented as follows:

CYP2C19*2A: 99C>T; 681G>A; 990C>T; 991A>G

CYP2C192*B: 99C>T; 276G>C; 681G>A; 990C>T; 991A>G

CYP2C19*C: 99C>T; 481G>C; 681G>A; 990C>T; 991A>G

In all cases, the final pharmacogenomic effect which can be observed at the clinical level is always the one given by the splice mutation 681G>A.

Of note, the first alleles described in the CYP genes family did not follow these rules exactly, nevertheless their original nomenclature is still used for simplicity.

STAR ALLELE NOMENCLATURE: INTRODUCTION
PHARMACOGENOMICS: MAIN PAGE

THE STAR ALLELE NOMENCLATURE IN PHARMACOGENOMICS

2014-12-22T01:52:00.001-08:00

In pharmacogenomics it is very common to identify genetic variants with a special nomenclature, which is not elsewhere used in genetics. It is the so-called star allele nomenclature. In this nomenclature, alleles with a pharmacogenomic relevance aren't identified with their cDNA or genomic position as usual (HGVS nomenclature), but through the means of numbers and letters separated from the gene name by a star (that's why this is called star allele nomenclature). For example: CYP3A5*2 identifies the genetic variant in genomic position g.27289C>A leading to the amino acid substitution p.T398N in the protein chain. The star allele nomenclature is therefore useful to identify a certain allele in a fast and easy way, whereas the standard HGVS nomenclature could be less comprehensible (and more subject to transcription mistakes) by not specialized personnel.

The star allele nomenclature has been firstly used to identify alleles within the cytochrome P450 (CYP) gene family. From that it spread to all genes that have been studied in pharmacogenomics. Of note, the star allele nomenclature is used only do identify pharmacogenomic markers and it is not elsewhere used in genetics.

The list of the star alleles of the cytochrome P450 gene family and of the POR gene is available at http://www.cypalleles.ki.se.

However the star allele nomenclature has some defects. First of all it does not indicate the mutation type, i.e. if it is a missense, nonsense or splice mutation or again a large deletion/duplication. Moreover, despite the possibility it has to identify even a group of alleles under one single number (to know how the star allele nomenclature works you can read here), it is not flexible enough to express the complexity and the enormous amount of data as produced by high-throughput sequencing (e.g exome sequencing and genome sequencing). Nevertheless the star allele nomenclature remains the gold-standard in the identification of pharmacogenomic markers in the scientific literature and in the official documents of the drug agencies.

STAR ALLELE NOMENCLATURE: HOW IT WORKS

PHARMACOGENOMICS: INTRODUCTION

COMMERCIAL KITS FOR PHARMACOGENOMIC TESTING

2014-12-10T06:20:00.000-08:00

Some commercial kits are already existing which can help any lab to do the screening of relevant pharmacogenetic variants known today. One of these is for instance the DMET™ Plus Solution kit by Affimetrix, which covers 1,936 genetic variants across 231 relevant genes.

Another commercial kit available for pharmacogenomic testing is the VeraCode ADME Core Panel by Illumina, which interrogates variants in 34 genes. This panel is based on the recommendation of PharmaADME working group, consisting of industry and academic experts who has developed a list of pharmacogenetic markers.

Another kit offering the screening of the ADME genes as proposed by the PharmaADME working group is the iPLEX® ADME PGx Pro Panel provided by Agena Bioscience.

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PHARMACOGENOMIC DATABASES

PHARMACOGENOMIC DATABASES

2014-12-10T06:18:00.002-08:00

The best pharmacogenomic database today available is PharmGKB. At PharmGKB website it is also possible to download the current guidelines on selected gene variants as edited by the Clinical Pharmacogenetics Implementation Consortium (CPIC).

It’s not a database, but it’s worth to be highlighted for its activity of research and collaboration funding: it’s the PharmacoGenomics Reasearch Network (PGRN), supported by the National Institute of Health (NIH) since 2000.

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PHARMACOGENOMICS AND POPULATION DIVERSITY

2014-12-10T06:16:00.002-08:00

Because of population genetic diversity, pharmacogenomics correlations are significantly varying from an ethnicity to another one. It happens so that a pharmacogenetic test shows to be of great epidemiological relevance within a certain geographical area, whereas it is of lower impact in some other ones. For instance, the variant 3673A of the VKORC1 gene, an allele to be considered in warfarin dosage, is rare in sub-Saharian Africans (less than 10%) but it is found at an extremely higher frequency in Southeast Asian populations (more than 90%). The Pharamcogenomics for Every Nation Initiative (PGENI) has exactly the aim of characterizing such pharmacogenomics differences among various populations. An online resource dedicated to multi-ethnic frequency data for pharmacogenetically relevant single nucleotide polymorphisms is FINDbase-PGx. Such data are also summarized at PharmaGKB website.

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GENETIC VARIANTS OF PHARMACOGENOMIC RELEVANCE

2014-12-10T06:15:00.001-08:00

Many pharmacogenetically important genes belong to the family of the cytochrome P450, solute carrier (SLC), ATP-binding cassette (ABC), aldehyde dehydrogenase (ALDH) and UDP-glucoronyltransferase (UGT) families.

The first (and one of the more important) gene involved in pharmacogenomic studies is CYP2D6. It is believed that the enzyme encoded by this gene (cytochrome P450-2D6) is involved in the metabolism of about 25% of drugs available today. So far, more than 100 CYP2D6 alleles have been reported to be of pharmacogenomic relevance. In the last years CYP2D6 genotyping has been elaborated to classify patients into four metabolizer phenotypes: ultrarapid, extensive, intermediate and poor.

Many other cytochrome P450 genes like CYP2C9 and CYP2C19 have been pharmacogenetically characterized. CYP2C9 alleles, along with VKORC1 and CYP4F2 alleles, are important in warfarin efficacy and dosing. The CYP2C19*2 allele has been associated with a lower production of active metabolites of clopidrogel, higher platelet aggregation ratio and adverse clinical outcome. Other notable associations of these two genes are with phenytoin, acenocoumarol, glibenclamide, gliclazide, glimepiride, phenprocoumon, and tolbutamide (CYP2C9) and with carisoprodol, citalopram, clobazam, clopidogrel, dexlansoprazole, diazepam, esomeprazole, lansoprazole, nelfinavir, omeprazole, pantoprazole, prasugrel, rabeprazole, drospirenone ethinyl estradiol, atazanavir, axitinib, ticagrelor and voriconazole (CYP2C19).

Other important pharmacogenomic markers have been identified in the following gene/drug associations: ABCB1/aliskiren, CYP2B6/efavirenz, CYP34A/ticagrelor, aripiprazole, cabazitaxel, darunavir, dronedarone, fosamprenavir, gefitinib, indinavir, ivabradine, nelfinavir, posaconazole, ritonavir, ruxolitinib, sirolimus, sunitinib, telithromycin, tipranavir, voriconazole, zonisamide, CYP3A5/tacrolimus, DPYD/tegafur, capecitabine, fluorouracil, NAT2/isosorbide dinitrate and hydralazine hydrochloride, rifampin-isoniazid-pyrazinamid, SLC22A2/fampridine, SLCO1B1/simvastatin, TPMT/azathioprine, mercaptopurine, thioguanine, azathioprine, UGT1A1/irinotecan, IFNL3/PEG-interpheron-α (PEG-IFN alpha).

A working group of experts from the academy and the industry has actually collected the information on the most important genes so far recognized to be of relevance for their pharmacogenomic correlation. This is the PharmaADME working group, whose list have been used also to design commercial kits for genetic testing (the ADME gene list).

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CLINICAL VALIDITY OF PHARMACOGENOMIC TESTING

2014-12-10T06:13:00.000-08:00

In genetic testing, clinical validity can be defined as the ability to predict a phenotype associated with a genotype. For typical genetic diseases (i.e. chromosomal aberrations and Mendelian disorders) clinical validity is approaching 100%, since there is a direct cause-effect relation between a certain genetic mutation and the presence of the disease.

Much different are things for what concerns pharmacogenomics since the scientific evidence of the association between certain genetic variant(s) and the response to a drug can be sometime controversial. It can happen so that a genetic variant found to be significantly associated with high sensitivity to a certain treatment in one study, is found to be of no pharmacogenomic relevance in another reseach. In extreme cases a certain genetic variant is found to exert a total opposite effect from another author. This issue of controversial evidence is still a major obstacle to the diffusion of pharmacogenetic testing in daily routine since it mostly affects the trust of health professionals in pharmacogenomic solutions.

However, it must be said that in certain cases pharmacogenomic correlations have been well characterized and replicated by several independent studies. In such cases the pharmacogenomic evidence has been coded into clinical practice guidelines by known working groups of specialists (see for instance the guidelines from the Clinical Pharmacogenetics Implementation Consortium - CPIC, www.pharmagkb.org/page/cipc, the Royal Dutch Association for the Advancement of Pharmacy-Pharmacogenetics Working Group – KNMP-PWG, the EGAPP working group or the ACMG). By following these guidelines, health care operators can confidently take decisions in drug selection and dose adjustment.

An example of pharmacogenomic evidence successfully confirmed and applied in daily clinical practice is the one between the allele B*5701 in the HLA-B gene and the susceptibility to severe hypersensitivity reaction to abacavir treatment in HIV-positive patients. This pharmacogenetic test is now routinely performed to help infectivologists in selecting the most appropriate treatment combination for HIV-positive patients.

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PHARMACOGENOMICS

2014-12-06T07:41:00.004-08:00

Pharmacogenomics (sometimes referred to also as pharmacogenetics) is a discipline aimed to identify human genetic traits which can be associated either to drug response (sensitivity/resistance) or to adverse drug reactions. Some individuals have certain genetic variants (also called pharmacogenetic or pharmacogenomic markers) which can affect the way a drug is metabolized and therefore influence the drug power and/or the appearance of side-effects. Therefore pharmacogenomics is essentially relevant for (1) dose adjustment and (2) drug selection.

Until yesterday, the costs and turn-around time of testing, a lack of skilled counselling staff and regulatory and reimbursement issues curbed the spreading of pharmacogenetic testing. Today, thanks to time-effective analyses and preventive genotyping programs, pharmacogenomics is about to become a reality in the context of the so-called personalized medicine.

Personalized medicine is not something new. Already in ancient Greece Hippocrates used to measure the balance of blood, phlegm, yellow bile, and black bile to select the best therapy for every patient. Today we prefer to look at genetics rather than to these four fanciful humors, but the point is the same: the “one-fit-for-all” model is, at least for some available drug, not even desirable, as considerable advantages in treatment outcome (and side-effects avoidance) can be achieved through a personalized approach.

Just to explain the concept with a few numbers: only 30-60 % of patients respond properly to beta-blockers, anti-depressants, statins and antipsycothic agents. Adverse drug reactions can cause significant prolongation of hospital visits and, only in the USA, they are estimated to cause approximately 100,000 deaths every year. To learn more about pharmacogenomics:

CLINICAL VALIDITY OF PHARMACOGENOMIC TESTING

GENETIC VARIANTS OF PHARMACOGENOMIC RELEVANCE
PHARMACOGENOMICS AND POPULATION DIVERSITY

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COMMERCIAL KITS FOR PHARMACOGENOMIC TESTING

EXOME SEQUENCING

2014-11-28T07:33:00.000-08:00

Exome sequencing (or whole-exome sequencing/WES) is a term that identifies the sequencing of the entire coding region of the genome of an individual. Until a few years ago, genetic testing for excellence consisted of the sequencing of a single gene or a small group of genes using the method of capillary electrophoresis, more commonly known as Sanger sequencing. The advent of new sequencing technologies (Next Generation Sequencing - NGS and Third Generation Sequencing) has instead turned to the study of the genetics of human disease, leading to an era of unprecedented productivity. Due to the costs and reduced times, via NGS it is now possible to analyze a large number of DNA fragments in parallel to obtain the sequence of many genes simultaneously or even the entire coding region of an individual. This is of great interest for both routine diagnostics and scientific research. In fact, even if the coding region represents only 1% of the entire genome, it is estimated that up to 85% of all pathogenic mutations are contained in this region. Exome sequencing is useful for example in those cases where a a precise clinical diagnosis is lacking or in genetically heterogeneous disorders, for which exome sequencing can help identifying yet unknown causative mutations.

As said, by definition, exome sequencing refers to the sequencing of the coding region of the genome. It is true that some enrichment kits are still incomplete and lack coverage of some areas, however it is also true that other kits containing primers for some non-coding regions, such as the exon/intron boundaries (where lots of mutations can be found), are already available.

EXOME SEQUENCING APPLICATIONS

1. STUDY OF MENDELIAN DISORDERS

The traditional approach in Mendelian pathology research has always been the linkage study followed by positional cloning. This approach, however, presents considerable difficulties, mainly related to the need of having to test more than one family (not an easy undertaking for very rare diseases) and genetic heterogeneity that sometimes characterizes certain Mendelian diseases. Think, for example, of syndromes such as Bardet-Biedl, Joubert or Kallmann, which can be caused by mutation in one of several different genes. Exome sequencing, however, screening thoroughly the coding sequence, allows you to quickly identify pathogenic or likely pathogenic variants also in genetically heterogeneous disorders. The genes involved in Kabuki and Miller syndromes pathogenesis, for example, have recently been identified thanks to exome sequencing studies. Suffice it to say that, only in the last three years, the genes associated to about 100 Mendelian diseases have been discovered through exome sequencing!

2. MULTIFACTORIAL DISEASES STUDIES

In recent years, the traditional approach to the genetic study of multifactorial diseases (i.e. those diseases that are supposed to arise as a result of the interaction between environmental factors and genetic predisposition) was mainly based on genome array, i.e. screening of a selected frequent variants scattered throughout the genome. These studies, also known as GWAS (Genome Wide Association Study), are based on the assumption that genetic susceptibility is caused by the interaction of certain common genetic variants. In fact, being multifactorial diseases frequent diseases (just think of diabetes mellitus type II or ischemic heart disease), the underlying assumption is: frequent disease, FREQUENT variant. NGS has revolutionized this research field too, with genome/exome sequencing having already replaced genome/exome array, which allow the screening of just a few variants (exome sequencing, because of its affordability compared to genome sequencing, is actually the most used one). Of note, the philosophy underlying the use of genome/exome sequencing in multiple disease research studies, is exactly the opposite of genome/exome array. It is: frequent disease, RARE variant (i.e. that susceptibility to multifactorial disease is more about the interaction of rare rather than frequent variants).

3. ROUTINE DIAGNOSIS

Exome sequencing is certainly becoming very useful in routine diagnosis too. Sequencing the entire coding region can be economically advantageous (especially in respect to certain Sanger sequencing analyses) and offers the chance to identify the genetic cause even when clinical confirmation is poor or not specific enough to limit the diagnostic suspicion to a number of possible disorders.

NEXT GENERATION SEQUENCING APPLICATIONS

2014-11-28T07:22:00.002-08:00

Next Generation Sequencing (NGS) represents the latest evolution of sequencing. For many years, sequencing was done by capillary electrophoresis (Sanger sequencing). Capillary electrophoresis sequencing allows us to reconstitute the sequence of a single DNA fragment. Sanger sequencing is done by recording the signals of incorporation of fluorochromes-labeled nucleotides, which are used to synthesize the strand complementary to the original DNA fragment. Even NGS is based on a similar principle; however, the reaction may be done for many fragments of DNA in parallel, not just one. Through NGS it is therefore possible to obtain an enormous amount of sequences (in a single stroke you can get gigabases or terabases of information) more quickly and at a much lower cost. For this reason, NGS is also known as high-throughput sequencing.

In NGS, the DNA of an individual is broken into many small fragments (for example, through the use of ultrasound) to constitute the so-called sequencing library. These small fragments serve as templates for the synthesis of numerous, complementary fragments called reads. Each small fragment of DNA is copied many times in a variable number of reads. Depending on the desired level of precision, it is possible to set the system to achieve a certain level of coverage, i.e., a certain number of reads for each fragment of the library. For example, 30 reads per fragment, which would have been defined in jargon as 'coverage 30x', are already sufficient for routine diagnostics of Mendelian diseases, while the diagnosis of somatic mutations typical of tumors may require coverage up to 1000x. A computer then collects all the reads and aligns them with the reference sequence of the human genome annotated in the databases. By this way the reads can be reassembled like in a puzzle to obtain the sequence of the gene or of the entire genome.

NGS machines available today are produced by many different brands and they are very flexible devices. A NGS sequencer can actually be used for different types of applications:

1. Whole-genome sequencing (WGS) - also known as whole-genome shotgun, this is the analysis of the entire genome of an individual.

2. Whole-exome sequencing (WES) - analysis of the entire coding region of all the genes of an individual.

3. Targeted sequencing - analysis of a group of genes (panel) or of a single gene.

4. Transcriptome analysis - analysis of all RNA produced by a cell (transcriptome).

Programs 2 and 3 require an additional step (target enrichment) and can also be performed for many samples simultaneously through the so-called technique of multiplexing. The DNA of each individual can, in fact, be distinguished by attaching a specific sequence (barcode sequence) to it. Since barcode sequences are also sequenced, the reads belonging to each individual can be recognized and sorted before alignment thanks to the barcodes.