Processing

Please wait...

Settings

Settings

Goto Application

1. WO2020136192 - METHOD FOR MEASURING TELOMERE ASSOCIATED VARIABLES AND USES THEREOF FOR THE DIAGNOSIS AND/OR PROGNOSIS OF TELOMERIC-ASSOCIATED DISEASES

Note: Text based on automatic Optical Character Recognition processes. Please use the PDF version for legal matters

[ EN ]

METHOD FOR MEASURING TELOMERE ASSOCIATED VARIABLES AND USES THEREOF FOR THE DIAGNOSIS AND/OR PROGNOSIS OF TELOMERIC- ASSOCIATED DISEASES.

The invention relates to methods in vitro for the determination of the specific Telomere Associated Variables (TAVs) values which can be correlated with: a measure of health; a risk of a pathological condition; a telomeric disease or drug responsiveness. Particularly, the present invention relates to methods in vitro to diagnose and/or assess telomere-associated diseases occurrence and/or severity. This invention falls within the field of molecular biology, biotechnology and medicine.

BACKGROUND ART

Telomeres, the tips of eukaryotic chromosomes, protect the chromosomes from nucleolytic degradation, end-to-end fusion, and recombination. Telomeres shorten as a consequence of normal cell division and critically short telomeres lead to cellular senescence or apoptosis. This process occurs because the DNA-replication machinery is incapable of fully replicating the ends of linear molecules, and, degradation and oxidative damage of nucleotides in DNA results. Critically short telomeres trigger loss of cell viability in tissues, which has been related to alteration of tissue function and loss of regenerative capabilities in ageing and ageing-related diseases. Hence, telomere length is an important biomarker for aging that can be used in the prognosis of ageing diseases. On the other hand, cancer cells prevent telomere shortening by activating telomere elongation mechanisms, which are important targets for cancer therapies. The former facts highlight the importance of accurately measuring telomere length in cancer and age-related disease studies.

A rich body of epidemiological and clinical studies in humans in the past decade has linked short telomere length to high risks of cancer and aging-related disease and all cause mortality. Genetic, environment, lifestyle, and behavioral factors collectively impact telomere length. Therefore, telomere length has become an index for overall health, disease, and mortality risk. While average telomere length was measured in almost all the clinical studies published and have demonstrated utility in stratifying patient disease and mortality risk, other works has demonstrated that the quantity of short telomeres, and not average telomere length, is one of the critical factors for cell viability and chromosome stability (Hemann, M.T., et al. Cell, 2001 . 107(1 ) 67-77). Therefore, changes in the percentage of short telomere abundance are expected to be a more sensitive measurement of the effects of lifestyle and pharmacological or other interventions on telomeres (Vera E., et al. Cell Rep. 2012 Oct 25;2(4):732-7). In this sense, the telomeric variables such as, the analysis of the telomere length and the percentage of short telomere abundance in a sample, have been used as biomarkers to allow for improvements in risk assessments of diverse health outcomes. However, the utility of the measure depends on valid and reliable techniques to quantify these telomeric variables.

Various methods have been developed for the measurement of telomere length and the percentage of short telomere abundance in genomic DNA, including Southern blotting, qPCR, Q-FISH, flow-FISH and HT Q-FISH. All of these methods can be used in a clinical setting to monitor health status and permit physicians to prescribe prophylactic or therapeutic intervention tailored to the needs of the individual patient.

The qPCR technique uses primers for the telomere region and a single copy gene (may be on the same chromosome or on a different chromosome). PCR-based techniques have become a popular method for estimating telomere length due to relatively low cost, amenability for high-throughput testing, and relative ease of investigators’ access to the necessary equipment used in the assay. While the PCR-based techniques are well suited for large epidemiological studies, the results are difficult to compare between studies. This limitation is due to differences in the DNA quality based on the method used for genomic DNA extraction, as well as differences in sample fixation methods in the case of fixed and paraffin-embedded tissue samples and by their relatively high level of variation among replicate estimates.

Flow-FISH as its name suggests, combines flow cytometry methodology with the hybridization of a pantelomeric (binds to all telomeres) probe to cells in a suspension (rather than hybridizing to cells fixed to slides, as is done for metaphase and interphase Q-FISH). Flow-FISH methodology typically uses the same telomeric (CCCTAA)3 PNA probe used in other Q-FISH approaches to quantify the mean amount of fluorescence present in cells. This value is then used to provide an average telomere length for the cell population being evaluated. A strength of this approach is that it has the capability to sort cells into subpopulations based upon size, granulation, and/or antibody labeling. As a result of this potential, flow-FISH has been widely used for determining mean telomere length in hematopoietic cell subtypes. Flow-FISH is also the first of the telomere assays to be used as a clinical diagnostic tool, with the method being used to assist with the recognition of patients having dyskeratosis congenital -a condition that is associated with shortened telomere lengths. Flow-FISH has limitations that may compromise suitability for use in research/clinical studies. Specifically, unfixed cells can be challenging to process (fragility, clumping, etc.), but the technique is sensitive to fixatives used to preserve cells, with the reliability of the measures reflecting these technical parameters. Also, the PNA probe used in flow-FISH has been shown to demonstrate nonspecific binding to cytoplasmic structures. Thus, it has been suggested that isolated nuclei, rather than intact cells, may be optimal for assessment with this methodology. Due to the above noted technical issues, flow-FISH is not readily adaptable for use in a wide range of cell types, with its application being primarily for use with fresh blood samples. Also, like many of the other techniques, this method provides only a mean value of telomere intensity, and provides no information regarding individual telomeres or a subset of shortened telomeres.

Q-FISH methodologies use a probe specific for the telomeric region of metaphase-spread cells to estimate length based on histograms of telomere signal intensities which represent the length of individual telomeres. While the probe tends to be specific for the telomeric region, it is possible that the probe could bind to a portion of the juxtaposed region (especially the degenerate repeat region). Limitations of this method are that live cells are needed, costs are high, and throughput is low. Thus, this procedure is not well suited for large, epidemiological studies.

To overcome some of the limitations of Q-FISH, adaptations of this procedure have been developed, with these adaptations involving the use of interphase cells rather than metaphase chromosomes. Interphase Q-FISH is amenable for assessing telomere lengths in nuclei from multiple specimen types (blood cells; formalin-fixed; paraffin embedded tissues; frozen tissues). A clear advantage for using interphase Q-FISH methodology is that it allows to concurrently collect information regarding

telomere length and histological information, having the potential to be combined with immunostaining techniques to localize specific cells of interest (sometimes referred to as“telomapping,” (Vera E., et al. Cell Rep. 2012 Oct 25;2(4):732-7). A disadvantage of the interphase Q-FISH method is that it does not allow for the recognition of specific telomeres, and does not allow for the detection of telomere-free ends. Also, the data is typically presented as a mean value, since the overlaying of the 92 telomeres can prohibit one from unequivocally recognizing each individual telomere. However, interphase Q-FISH is less labor intensive than metaphase Q-FISH, with recent adaptations of this technique, called high-throughput (HT) Q-FISH are amenable to automation and for use in larger epidemiological studies.

Although up to now, the main telomeric variables used as useful biomarkers for the diagnosis and/or prognosis of cancer and aging-related diseases are the telomere length and the percentage of short telomere abundance in genomic DNA, which can be determined by the methods mentioned above. There remains a need for a more reliable, accurate and efficient telomeric variables for use as biomarkers for the diagnosis and/or prognosis of cancer and aging-related diseases, which can be analysed by a rapid, reliable, accurate, and efficient high-throughput method.

SUMMARY OF THE INVENTION

The present invention provides an improved in vitro method for measuring specific TAVs. The in vitro methods of the invention can be done rapidly and provide increased sensitivity, efficiency, reliability, and accuracy. Moreover, these in vitro methods are amenable to automation and high through-put formats and provide, in some embodiments, a means to measure the TAVs of an individual chromosome, to compare interchromosomal variance in the specific TAVs, and to measure specific TAVs of a specific cell population within a mixture of cells. The present invention provides numerous advantages over all other methods of telomere length measurement:

It is unique as it allows the determination of a distinctive profile of each of the TAVs and their individual and combined analysis, such as their Principal Component Analysis treatment. The method of the invention is based on the introduction of a set of standard isolated biological samples of known telomere length to assess their intensities fluorescence (by the proceeding mentioned in the examples of the present invention) and then convert that fluorescence intensity to absolute length expressed as base pairs (bp) thanks to the standard curve built pairing the fluorescence intensities of each sample with its true length value. The analysis is later performed on thousands of individual telomere spots derived from the data. The method in vitro of the invention It is the only methodology that can output dispersion (in bp), percentiles (in bp), % of cells with a defined telomere length (in bp), % of telomeres with a defined telomere length (in bp), the interquartile range (in bp), or any other known TAVs.

Unlike flow-FISH, TRF, STELA and qPCR it makes it possible to detect and register the full distribution of the telomere length in a biological sample and evaluate differences in the ratios and proportions of short and the long telomere populations globally and in any fraction of the cells in the sample.

Unlike qPCR that reports a single, average relative telomere length value, the method in vitro of the invention uses an internal standard curve that result in the exact determination of absolute telomere lengths in base pairs of each telomere.

Unlike gel-based methods that measure limited telomere length features such as TRF and STELA the method in vitro of the invention it is a scalable, industrially applied methodology and can be automatized.

Unlike flow-FISH it is not limited to measure the average telomere signal of a cell but of absolute telomere length in bp of each telomere of each cell individually.

Moreover, the method can be applied to any cell type.

The data presented herein shows that the specific TAVs, derived from thousands of individual telomere intensities expressed as absolute value in base pair and analysed within cells and group of cells, have a defined distribution that is specific and that the shape of this distribution determines disease status of such cells or group of cells. This is due to the specific TAVs variation associated with both normal and abnormal cell division. The length of telomeres and their ability to protect the genome are modified in tumor cells such that the particular variables associated with the distribution of the TAVs can be determined and analysed in a given biological sample allowing the application of these values to determine the health status of the subjects. Thus, based on the present findings, it is now possible to reliably examine and quantify the presence or absence of individual specific TAVs mentioned herein in interphase nuclei and to monitor them over time. Such new avenues will impact on cancer biology, genetics and diagnostic and/or prognosis innovations in medicine.

The method of the present invention used high resolution fluorescence microscopy and imaging to elucidate the individual length of telomeres and chromosomes in the interphase nuclei of analysed cells measured in absolute units, i.e. base pairs. It has been found that telomeres from test cells have a defined distribution, and that this distribution differs from their normal counterparts.

The results obtained in the present studies show that the specific TAVs of the invention, measured in base pairs, analysed in a cell or in a group of cells are correlated with high accuracy with a measure of health, a risk of a pathological condition, a telomeric disease or responsiveness to a drug.

Also, disclosed herein are methods and systems for determining with a high accuracy a measure of health; a risk of a pathological condition; a telomeric disease or drug responsiveness based on the measurement of individual telomere length in absolute base pairs and the calculation of specific TAVs comprising at least one of the list selected from: telomere length percentiles from 1 th to 99th, percentages of telomeric length values that can be specified to a particular bp range or number, percentages of cells with specific telomere median or average value that can be specified to a particular bp value, median absolute deviation of the intensities of a sample, difference between the 99th and 1 st percentile of the telomere length of the sample, interquartile of the telomere length of a sample, standard deviation of the median telomere length values, or any combinations thereof.

The method disclosed herein allow for; 1 ) distinction of normal and tumor cells, 2) identification of patient subgroups that will allow for new treatment designs, 3) identification of patients who will recur and therefore should obtain different treatments, 4) treatment monitoring, and 5) personalized medical management of

patients (not one treatment for all, but a treatment specifically adapted to each patient).

In one aspect, the present invention provides a method in vitro for measuring TAVs, hereinafter first method in vitro of the invention, in an isolated biological test sample of a subject, wherein the method uses a set of standard isolated biological samples of known telomere length, and comprises the following steps:

a) providing a first set of intensity values of known telomere length from the set of standard isolated biological sample;

b) generating a standard curve correlating the first set of intensity values with the known telomere length from the set of standard isolated biological samples;

c) providing a second set of intensity values from the isolated biological test sample;

d) calculating the telomere lengths within the isolated biological test sample of a subject by interpolating the second set of intensity values into the standard curve; and

e) calculating, in the isolated biological test sample, at least one of the TAVs selecting from the list consisting of: telomere length percentiles from 1 th to 99th, percentages of telomeric length values that can be specified to a particular bp range or number, percentages of cells with specific telomere median or average value that can be specified to a particular bp value, median absolute deviation of the intensities of a sample, difference between the 99th and 1 st percentile of the telomere length of the sample, interquartile of the telomere length of a sample, standard deviation of the median telomere length values, or any combinations thereof.

In a preferred embodiment, the first method in vitro of the present invention it is characterized in that in the step a) of providing the first set of intensity values of known telomere length, in turn, comprises the steps of:

(i). performing high-resolution microscopy, preferably high-resolution fluorescent microscopy, to the set of standard isolated biological sample of known telomere length, obtaining a first set of image data; and

(ii). processing the first set of image data obtained, extracting a first set of intensity values.

In another preferred embodiment, the first method in vitro of the present invention it is characterized in that the step c) of providing the second set of intensity values from the isolated biological test sample, in turn, comprises the steps of:

(i). performing high-resolution microscopy, preferably high-resolution fluorescent microscopy, to the isolated biological test sample of a subject, obtaining a second set of image data; and

(ii). processing the second set image data obtained in step d), extracting a second set of intensity values.

The term“biological sample”“or sample” as used herein, refers to a sample isolated from a subject. Examples of biological samples include but are not limited to cells, tissues and/or biological fluids of an organism obtained by means of any method known by a person skilled in the art. In a particular embodiment, the biological sample is a biological fluid. In a more particular embodiment, the biological fluid is selected from the list consisting of plasma, serum, blood, lymphatic fluid, cerebrospinal fluid, synovial fluid, urine, saliva, mucous, phlegm and sputum. In one embodiment, the biological sample is selected from the group consisting of blood, serum, and plasma. In another embodiment, the isolated biological sample is a human sample. The isolated biological sample of the present invention may be collected by any suitable method. The biological sample may be used immediately or may be stored for later use. Any suitable storage method known in the art may be used to store the biological sample; for example, the sample may be frozen at about -20° C to about -196° C.

As used herein, the term“cell” includes more than one cell or a plurality of cells or portions of cells. An advantage of the method in vitro of the present invention is that any single cell suspension can be used.

As used herein, the term“test cell” or“test sample” or“biological test sample” is a cell or sample from a subject that is suspected of having a cell-proliferative disorder such as cancer or a telomeric-associated disease. In such an embodiment, the test cell includes, but is not limited to, a cancer cell.

The terms “control cell” or “control sample” or “reference sample” is a suitable comparator cell, e.g. a cell that is an age matched control, and preferably a disease-free cell, or a cell.

The term“biological sample” or“sample” as used herein, refers to a sample isolated from a subject. Examples of biological samples include but are not limited to cells, tissues and/or biological fluids of an organism obtained by means of any method known by a person skilled in the art. In a particular embodiment, the biological sample is a biological fluid. In a more particular embodiment, the biological fluid is selected from the list consisting of plasma, serum, blood, lymphatic fluid, cerebrospinal fluid, synovial fluid, urine, saliva, mucous, phlegm and sputum. In one embodiment, the biological sample is selected from the group consisting of blood, serum, and plasma. In another embodiment, the isolated biological sample is a human sample. The isolated biological sample of the present invention may be collected by any suitable method. The biological sample may be used immediately or may be stored for later use. Any suitable storage method known in the art may be used to store the biological sample; for example, the sample may be frozen at about -20° C to about -196° C.

As used herein, the term“cell” includes more than one cell or a plurality of cells or portions of cells. An advantage of the method in vitro of the present invention is that any single cell suspension can be used.

As used herein, the term“test cell” or“test sample” or“biological test sample” is a cell or sample from a subject that is suspected of having a cell-proliferative disorder such as cancer or a telomeric-associated disease. In such an embodiment, the test cell includes, but is not limited to, a cancer cell.

The terms “reference sample”, “control cell” or “control sample” is a suitable comparator cell or sample, e.g. a cell that is an age matched control, and preferably a disease-free cell, or a cell. These terms as used herein refer to an isolated biological sample(s) from a subject(s) that is considered a control. A reference sample could be, for example, an isolated biological sample from healthy subjects or from non-telomeric diseases subjects.

The term“control” as used herein means any tissue, biological sample or cell sample from one or more subjects not having telomeric disease (e.g. control subjects) or also having earlier stage non-telomeric disease, such as an age matched control or can be a reference value derived from a sample from a control subject or group of control subjects.

As used herein the term“standard biological samples of known telomere length” refers to cells or samples, preferably a collection of at least five samples from healthy or control subjects, or alternatively, immortalized cell lines from which the telomere length, in absolute units, is known. For the purpose of the present invention, the intensity values obtained for each of the samples on the set of the standard biological samples, as determined by the method, is paired with their corresponding known telomere length to generate the standard curve and to check the accuracy, reproducibility and robustness of the method in each run.

As used herein the term“standard curve” or“reference standard curve” refers to a curve built from the intensity value of each of the standard isolated biological samples of known telomere length. Pairing the fluorescence intensities of each sample with its true value as calculated using the first method in vitro of the invention renders a set of coordinates pairs that are then adjusted to a logarithmic curve. The standard curve will include the set of standard or control biological samples comprising immortalized cell lines whose median telomere length, as determined by HT-QFISH, are ³ 2,000 base pairs and £ 40,000 base pairs. Any and each of the telomere intensity spots measured in a cell or in a standard or control sample can be interpolated in the standard curve to obtain the corresponding value in base pairs as absolute length of that telomere for that specific intensity.

For the purposes of the present invention, each of the telomere signal intensity obtained from the biological test sample by the first method in vitro of the present

invention is interpolated into the standard curve and an absolute telomere length value is obtained for each of the individual telomere spot (signal intensity) detected in the biological test sample. Consequently, the standard curve of the present invention allows to calculate the TAVs in absolute units, according to the intensity value of each individual telomere. Thus, all the data obtained for all the telomeres determined by the method allow the calculation of the TAVs of the biological test samples of interest that can be determined with high precision by reference to the standard curve of the invention.

As used herein, the terms "subject" or“patient” refers to any animal, preferably a mammal and includes, but is not limited to, domestic and farm animals, primates, and humans, for example, human beings, non-human primates, cows, horses, pigs, sheep, goats, dogs, cats, or rodents like rats and mice. In a preferred embodiment, the subject is a human being of any age, sex or race. Thus, adult and newborn subjects, as well as fetuses, whether male or female, are intended to be covered.

In another preferred embodiment of the first method in vitro of the invention, the TVAs are determined, for example, by using standard hybridization techniques, such as fluorescence in situ hybridization (FISH), Quantitative Fluorescent in situ hybridization (Q-FISH), or High Throughput Quantitative Fluorescent in situ hybridization (HT Q-FISH). In a more preferred embodiment, the TVAs are determined by HT Q-FISH.

The term HT-Q-FISH as used herein refers to any technique that allows the processing of tens or hundreds of samples in a high-capacity multiwell plate, using high resolution fluorescence microscopy. The image data obtained by the high-resolution fluorescence microscopy can include the intensities of one or more colours at pixels that comprise an image of a nucleus or nuclei. The image data can also be grey level image data of a nucleus that has been appropriately stained to highlight telomeres and/or chromosomes. Several images, preferably at least 75 are obtained corresponding to slices along a particular axis.

To obtain an image of telomeres stains with different fluorescent dyes (fluorophores) are performed. As used herein, “fluorescent dyes” or“fluorochrome” refers to a particular fluorescent dye molecule or conjugated moiety, suitable for use as labels in the present method. These fluorochromes can be selected from any of the many dyes suitable for use in imaging applications. A large number of dyes are commercially available from a variety of sources that provide great flexibility in selecting a set of dyes having the desired spectral properties. Examples of fluorophores include, but are not limited to, 4-acetamido-4'-isothiocyanatostilbene-2,2' disulfonic acid; acridine and derivatives such as acridine, acridine orange, acridine yellow, acridine red, and acridine isothiocyanate; cyanine and derivatives such as cyanosine, Cy3, Cy5, Cy5.5, and Cy7; 4',6-diaminidino-2-phenylindole (DAPI); 5',5"-dibromopyrogallol-sulfonephthalein (Bromopyrogallol Red); eosin and derivatives such as eosin and eosin isothiocyanate; erythrosin and derivatives such as erythrosin B and erythrosin isothiocyanate; ethidium; fluorescein and derivatives such as 5-carboxyfluorescein (FAM), 5-(4,6-dichlorotriazin-2-yl)aminofluorescein (DTAF), pyrene and derivatives such as pyrene, pyrene butyrate and succinimidyl 1 -pyrene butyrate; Reactive Red 4 (Cibacron™ Brilliant Red 3B-A); rhodamine and derivatives such as 6-carboxy-X-rhodamine (ROX), 6-carboxyrhodamine (R6G), tetramethyl rhodamine, and tetramethyl rhodamine isothiocyanate (TRITC); riboflavin; rosolic acid and terbium chelate derivatives; xanthene; Alexa-Fluor dyes (e.g., Alexa Fluor 350, Alexa Fluor 430, Alexa Fluor 488, Alexa Fluor 546, Alexa Fluor 555, Alexa Fluor 568, Alexa Fluor 594, Alexa Fluor 633, Alexa Fluor 647, Alexa Fluor 660, Alexa Fluor 680, Alexa Fluor 700, Alexa Fluor 750), Pacific Blue, Pacific Orange, Cascade Blue, Cascade Yellow; Quantum Dot dyes (Quantum Dot Corporation); Dylight dyes from Pierce (Rockford, III.), including Dylight 800, Dylight 680, Dylight 649, Dylight 633, Dylight 549, Dylight 488, Dylight 405; or combinations thereof. Other fluorophores or combinations thereof known to those skilled in the art may also be used.

As has been mentioned previously, the standard curve of the invention is built using the intensity data from an isolated biological sample of known telomere length. The standard curve will include immortalized cell lines whose median telomere length as determined by HT -QFISH are ³ 2,000 base pairs and < 40,000 base pairs. In this standard curve each of the telomere signals of measurable intensities obtained from the isolated test sample is interpolated into the standard curve and an absolute telomere length value is given to each of the telomere spots detected. TAVs are calculated involving the intensity of each individual telomere.

The terms“Telomeric Associated Variables”,“TAVs”,“Telomeric Related Variables” and/or“TRVs” as used herein refer to the individual measurements of telomeres lengths in absolute base pairs units and their distribution during any phase of a cell cycle and includes such parameters as telomere length percentiles from 1th to 99th, percentages of telomeric length values that can be specified to a particular bp range or number, percentages of cells with specific telomere median or average value that can be specified to a particular bp value, median absolute deviation of the intensities of a sample, difference between the 99th and 1 st percentile of the telomere length of the sample, interquartile of the telomere length of a sample, standard deviation of the median telomere length values, or any combinations thereof. These terms also refer to the combination of several TAVs.

In another particular embodiment of the first method in vitro of the invention, the percentage of telomeric length value, preferably the percentage of long telomeres TLU, in the isolated biological sample Mt, is determined by the following formula:

TLU = 100— TSU

wherein the percentage of short telomeres TSU in the isolated biological test sample Mt , for a threshold length u is determined as:

TSU = 100


wherein

Mt = {Ti, T2 ... Tn} is the complete set of the telomeres measured in the isolated biological sample Mt ,

n is the number of telomeres in Mt, and

ti ({tlr t2, ... , tn}) is the telomeric length of the telomer e Ti ({T1, T2,

Moreover, the threshold length u ranges from 500 to 40,000 pb in increments of 100 pb.

In another particular embodiment of the first method in vitro of the invention, the percentage of cells with specific telomere length value, preferably the percentage of cells having short telomeres ( CSU ) in the isolated biological test sample M for a threshold length u is determined as:


wherein

cr is the length of Cr e Mc calculated as: cr = avg(tri) ,

n is the number of cells in Mc,

tri is the telomeric length of the telomere rri;

Kr = {Trl, Tr2, , Trn} is the set of telomeres analyzed in a cell Cr and

Mc = [Cri> cn> > crn] is the complete set of cells in the isolated biological sample M.

Moreover, the threshold length u ranges from 500 to 40,000 pb in increments of 100 pb.

In another preferred embodiment of the first method in vitro of the invention, further comprises in step e) the analysis of at least one TAV selected from the list consisting of: the differences in base pair units between percentile values 1 th and 99th of the telomere distribution of the sample (P1 -99), the difference in base pairs units between percentile values 25th and 75th of the telomere distribution of the sample (P75-25), also known as interquartile (Q3-Q1 ), the nuclear size distribution of the cells in the sample, or any combinations thereof.

The term“telomere length” as used herein refers to the absolute size of telomeres as determined by interpolation onto the standard curve of the in vitro method of the invention expressed in base pairs. For example, telomeres with a length in base pairs (x-axis) ranging from 500 to 3,000 bp are classified as very short, with a length ranging from >3,000 to 5,000 bp as short, with a length ranging from >5,000 to 10,000 bp as mid-sized, with length from >10,000 to 15,000 bp as large and with lengths >15,000 bp as very large.

In a preferred embodiment of the first method in vitro of the invention, the long telomeres are preferably telomeres having at least 13,000 bp and more preferably more than 15,000 bp. In another preferred embodiment of the first method in vitro of the invention, the short telomeres are preferably telomeres having less than 5,000 bp.

In a more preferred embodiment of the first method in vitro of the invention, the cells having short telomeres are preferably, cells having an average telomere length below 5,000 bp.

In a more particular embodiment of the first method in vitro of the invention, further comprises the measurement of the MAD I2 of the telomeric length in the isolated biological sample.

In a more preferred embodiment, the MAD I2 of the telomeric length in the isolated biological sample Mt is determined by the following formula:

MAD = median(\nii— fn|)

wherein

m is the set of fluorescence intensities corresponding to the telomeres in the sample Mt and m is the median of m,


Mt = {Ti, T2, - , Tn} is the complete set of telomeres measured in the isolate biological sample; and

ti {{tlr t2, ... , tn}) is the fluorescence intensity of the telomere Tt ({'G1, 'G2, ... , Tn }).

In another preferred embodiment, the first method in vitro of the invention, further comprises an additional step wherein the measurement of the telomerase activity is performed in the isolated biological sample.

In another preferred embodiment of the first method in vitro of the invention, the telomerase activity is measurement by Q-TRAP.

The method in vitro of the invention can be performed rapidly and provide increased sensitivity, efficiency, reliability and accuracy. Moreover, this method for measurement the TAVs mentioned herein can be employed in a high through-put and/or automated process format.

All the definitions, terms and particular embodiments mentioned for the first method in vitro of the present invention, apply for all the aspects mentioned in the present document.

In another aspect, the present invention relates to a method in vitro for the diagnosis, prognosis, identification of a subgroups of subject who may benefit for a treatment, and/or monitoring of the evolution of a telomeric-associated disease, hereinafter second method in vitro of the invention, in an isolated biological test sample of a subject, wherein the method comprising:

a) measuring and/or quantifying at least one of the TAVs in the isolated biological test sample by the first method in vitro of the present invention;

b) measuring and/or quantifying at least one of the same TAVs of the step a) in an isolated biological reference or control sample, by the steps c) to e) of the first method in vitro of the invention;

c) comparing the value of the at least one of the TAVs detected in step (a) with the value of at least one of the same TAVs obtained in a reference or control sample,

d) identifying a significant difference between the TAVs comparison of step b); and

e) correlating the significant difference of step (c) with the diagnosis, prognosis identification of a subgroups of subject who may benefit for a treatment, and/or the evolution of a telomeric-associated disease in the subject.

In another aspect, the present invention relates to a method in vitro of screening, testing or validating drug candidates useful for the treatment of telomeric-associated diseases, hereinafter third method in vitro of the invention, wherein the method comprises:

a) contacting the drug candidate with an isolated biological test sample of a subject suffering from a telomeric-associated disease, according to the present invention,

b) measuring and/or quantified at least one of the TAVs by the first method in vitro of the invention, and

c) select the drug candidate of step (a) having the ability to maintain or increase the telomere length regarding to the value of the telomere length obtained in a reference or control sample.

In another aspect, the present invention also relates to a method in vitro of response to a drug treatment, hereinafter fourth method in vitro of the invention, in an isolated biological test sample of a subject with a telomeric-associated disease, wherein the method comprising:

a) measure and/or quantified at least one of the TAVs by the first method in vitro disclosed; and

b) compare the value obtained in step a) with the value of at least one of the same TAVs obtained from responsive and non-responsive patients who showed, respectively, responsiveness to the treatment and those who showed non-responsiveness thereto, alternatively, the present step b) may be performed by the comparison of the value obtained in step a) with the value of at least one of the same TAVs obtained from a reference sample, wherein the maintenance of the telomere length or a longer telomere length correlates with better drug efficacy.

The term“reference value” as used herein relates to a predetermined criterion used as a reference for evaluating the values or data obtained from the samples collected from a subject. Particularly, the reference value or reference level is a suitable comparator such as a percentage, any value having significant difference regarding test sample, threshold or cut off value, for example corresponding to the TAVs mentioned in the present invention, which is associated with a telomeric-disease. The reference value or reference level can be an absolute value, a relative value, a value that has an upper or a lower limit, a range of values, an average value, a median value, a mean value, or a value as compared to a particular control or baseline value. In embodiments where the severity of the telomeric-disease is being compared, the reference value can be a disease reference value for example mild telomeric-disease reference value, moderate telomeric-disease reference value or severe telomeric-disease reference value, wherein a value such as a threshold value is determined for a population of subjects having similar disease e.g. mild telomeric-disease, moderate telomeric-disease or severe telomeric-disease. The reference value can be a value

arising from population studies, theoretical models, or the characterization of control cells. The reference value can be based on a large number of samples, such as from population of subjects of the chronological age matched group, or based on a pool of samples including or excluding the sample to be tested.

In particular, the value of the TAVs, as determined using the first in vitro method of the present invention, determine, as mentioned above, the diagnostic, prognostic or response to treatment of a disease. Therefore, in an embodiment of the present invention, the in vitro method to determine the presence of abnormal values of the TAVs in a test sample versus the TAVs values obtained in a reference or control sample, is indicative of cancer and/or telomeric disease.

As mentioned previously, The TAVs measurement method of the present invention can be used for diagnostic, prognostic, and research applications. Such applications include, but are not limited to: (i) determination of the proliferative lifespan of cells; (ii) identification and analysis of the effectiveness of agents capable of extending, maintaining, or reducing telomere length; (iii) diagnosis of disease or medical conditions characterized by a different telomere length in a patient relative to an individual not having the disease or particular medical condition; (iv) prognosis of disease or medical conditions as correlated to telomere length; (iv) monitoring and evaluating the efficacy of a therapy specific for the telomeric-associated disease; and (v) screening for drug candidates affecting TAVs. In general, measurement of the specific TAVs disclosed herein provides a powerful means to assess and monitor cellular lifespan for a variety of useful purposes.

In general, conditions associated with telomere length have typically been derived using a measure of average telomere length. However, the present invention discloses the use of the TAVs in absolute units of length which are useful since are more sensitive and potentially more accurate predictors of clinically meaningful outcomes (e.g. disease or mortality risk) than the measurement of average telomere length or the rate of change in average telomere length.

TAVs, as determined by the first in vitro method of the present invention, show a degree of correlation that suggests that the predictive value of these variables may

be potentiated by using a Principal Component Analysis (PCA), particularly the PCA can be used to generate new variables with minimal loss of information, including predictive capabilities. Accordingly, the results from PCA with each block or set of TAVs, can be used to discern particular conditions. Thus, if the test biological sample has a percentage of long telomeres, as measured by the accurate and precise first in vitro method of the invention, that is significantly different from the percentage of long telomeres in the reference biological sample, then the test biological sample will be more prone to disease or mortality risk compared to biological samples with a non-significantly different percentage of long telomeres. Quantitative assessment of the percentage of long telomeres will have significant diagnostic utility in the health monitoring and also in disease diagnostics, prognosis, and companion diagnostics. In the same sense, a test biological sample with a value of average abundance of long telomeres significantly different than the average abundance of long telomeres compared to a reference biological sample (age-matched reference population of normal individuals) have an increased risk of morbidity or mortality. Such a person (test biological sample) would be motivated to change their behaviour towards a better lifestyle and increase the abundance of long telomeres, while an individual (test biological sample) with a greater average abundance of long telomeres than the average abundance of long telomeres in the reference population, would know that their cellular health is likely better than average, and hence would be motivated to at least to maintain, if not further improve, their lifestyle habits to maintain their good health. Moreover, as mentioned above, the first in vitro method disclosed herein, for the determination of the TAVs of the invention, can be also used to monitor the effectiveness of a specific treatment, preferably the effectiveness of cancer chemotherapeutics during treatment.

As it is mentioned above, the first method in vitro of the invention, using the TAVs mentioned herein, determined from isolated biological test samples from subjects, can be correlated with measures of health. Of particular interest are measures of health involving perceived stress. Telomere shortening can be accelerated by genetic and environmental factors, including multiple forms of stress such as oxidative damage, psychological stress, post-traumatic stress disorder, biochemical stressors, chronic inflammation and viral infections. The most frequently studied diseases and conditions include arthritis, back pain, cancer, cardiovascular disease, chronic obstructive

pulmonary disease, depression, diabetes, gastro-intestinal disease, migraine headache, HIV/aids, hypertension, irritable bowel syndrome, kidney disease, low back pain, multiple sclerosis, musculoskeletal conditions, neuromuscular conditions, osteoarthritis, psychiatric diagnoses, rheumatoid arthritis, sleep disorders, spinal injuries, stroke, substance abuse, surgical procedures, transplantation and trauma.

In a particular aspect of the in vitro methods of the invention, the isolated biological sample from the subjects, are collected over time and measurements of TAVs by the first method in vitro of the invention, are determined from the samples. Appropriate time periods for collection of a plurality of samples include, but are not limited to, 1 month, 3 months, 6 months, 1 year, 2 years, 5 years and 10 years (for example, the time between the earliest and the last sample can be about these time periods). The in vitro methods of the invention allows for monitoring of patient efforts to improve their general health status and/or to monitor their health status and/or disease risk. Since short telomeres trigger cell death, a finding that the percentage of short telomere length is lowered or maintained with time within an individual indicates a health improvement, while increase of percentage of short telomeres overtime represents a decrease or worsening in health.

Fig. 1 A shows that MAD-I2 diminished with age. In this sense, if MAD-I2 is increased or maintained overtime in an isolated biological sample from a subject, this is indicative of an improvement in the health status of this subject. Moreover, if MAD-I2 is decreased overtime in an isolated biological sample from a subject, this is indicative of a worsening in the health status of this subject. In the same sense, the percentage of long telomeres in an isolated biological sample diminished with age (Fig. 1 B). The maintenance of the percentage of long telomeres contributes to cellular health. Thus, if the percentage of long telomeres, preferably having at least 15,000 pb, is increased or maintained overtime in an isolated biological sample from a subject, this is indicative of an improvement in the health status of this subject. Moreover, if the percentage of long telomeres, preferably having at least 15,000 pb is decreased overtime in an isolated biological sample from a subject, this is indicative of a worsening in the health status of this subject. Additionally, the percentage of cells having short telomeres, preferably having less than 5,000 pb, increases with age (Fig. 1 C). Thus, if the percentage of cells having short telomeres, preferably having less than 5,000 pb, is decreased or maintained overtime in an isolated biological sample from a subject, this is indicative of an improvement in the health status of this subject. Moreover, if the percentage of cells having short telomeres, preferably having less than 5,000 pb, is increased overtime in an isolated biological sample from a subject, this is indicative of a worsening in the health status of this subject.

In one aspect of the in vitro methods of the invention, the pathological condition is a telomeric-associated disease. Telomeric-associated diseases are disorders associated with an abnormal or premature shortening of telomeres, which can, for example, additionally result from defects in telomerase activity. These diseases may be inherited and include certain forms of congenital aplastic anaemia, certain inherited diseases of the skin and the lungs, such as dyskeratosis congenita, idiopathic interstitial pneumonias and idiopathic pulmonary fibrosis. Additionally, telomeric-associated diseases also comprise, and are not limited to, multiple forms of stress such as oxidative damage, psychological stress, post-traumatic stress disorder, biochemical stressors; infertility; chronic inflammation, arthritis, chronic obstructive pulmonary disease, depression, diabetes, haematological diseases, gastro-intestinal diseases, migraine headache, HIV/aids, hypertension, irritable bowel syndrome, kidney disease, low back pain, multiple sclerosis, musculoskeletal conditions, neuromuscular conditions, osteoarthritis, psychiatric diagnoses, rheumatoid arthritis, sleep disorders, spinal injuries, stroke, substance abuse, surgical procedures, transplantation and trauma, viral infections, cardiovascular disease, diabetes, cancer, liver fibrosis, and depression. Moreover, in another aspect of the in vitro methods of the invention, the TAVs mentioned herein can be used as indicator of prognosis, disease progression and treatment outcome.

In one aspect of the in vitro methods of the invention, both the presence and the progress of telomeric-associated diseases may be determined using isolated biological test samples as mentioned above.

The term "cancer", as used herein, refers to a disease characterized by uncontrolled cell division (or by an increase of survival or apoptosis resistance) and by the ability of such cells to invade other neighbouring tissues (invasion) and spread to other areas of the body where the cells are not normally located (metastasis) through the

lymphatic and blood vessels, circulate through the bloodstream, and then invade normal tissues elsewhere in the body. Depending on whether or not they can spread by invasion and metastasis, tumors are classified as being either benign or malignant: benign tumors are tumors that cannot spread by invasion or metastasis, i.e., they only grow locally; whereas malignant tumors are tumors that are capable of spreading by invasion and metastasis. Biological processes known to be related to cancer include angiogenesis, immune cell infiltration, cell migration and metastasis. As used herein, the term cancer includes, but is not limited to, the following types of cancer: breast cancer; biliary tract cancer; bladder cancer; brain cancer including glioblastomas and medulloblastomas; cervical cancer; choriocarcinoma; colon cancer; endometrial cancer; esophageal cancer; gastric cancer; hematological neoplasms including acute lymphocytic and myelogenous leukemia; T-cell acute lymphoblastic leukemia/lymphoma; hairy cell leukemia; chronic myelogenous leukemia, multiple myeloma; AIDS-associated leukemias and adult T-cell leukemia/lymphoma; intraepithelial neoplasms including Bowen's disease and Paget's disease; liver cancer; lung cancer; lymphomas including Hodgkin's disease and lymphocytic lymphomas; neuroblastomas; oral cancer including squamous cell carcinoma; ovarian cancer including those arising from epithelial cells, stromal cells, germ cells and mesenchymal cells; pancreatic cancer; prostate cancer; rectal cancer; sarcomas including leiomyosarcoma, rhabdomyosarcoma, liposarcoma, fibrosarcoma, and osteosarcoma; skin cancer including melanoma, Merkel cell carcinoma, Kaposi's sarcoma, basal cell carcinoma, and squamous cell cancer; testicular cancer including germinal tumors such as seminoma, non-seminoma (teratomas, choriocarcinomas), stromal tumors, and germ cell tumors; thyroid cancer including thyroid adenocarcinoma and medullar carcinoma; and renal cancer including adenocarcinoma and Wilms tumor.

The term “metastatic progression”, as used herein, refers to the process through which a tumor spreads to body tissues different than the primary site of tumor origin.

In another aspect, the in vitro methods of the present invention are useful in monitoring effectiveness of therapeutics or in screening for drug candidates affecting telomere length and/or telomerase activity. The ability to monitor telomere characteristics can provide a window for examining the effectiveness of particular therapies and

pharmacological agents. The drug responsiveness of a disease state to a particular therapy in an individual may be determined by the in vitro methods of the present disclosure, wherein shorter telomere length correlates with better drug efficacy. For example, the present disclosure also relate to the monitoring of the effectiveness of cancer therapy since the proliferative potential of cells is related to the maintenance of telomere integrity.

The term“treatment”, as used herein, refers to the medical management of a patient with the intent to cure, ameliorate, stabilize, or prevent a disease, pathological condition, or disorder. This term includes active treatment, that is, treatment directed specifically toward the improvement of a disease, pathological condition, or disorder, and also includes causal treatment, that is, treatment directed toward removal of the cause of the associated disease, pathological condition, or disorder. In addition, this term includes palliative treatment, that is, treatment designed for the relief of symptoms rather than the curing of the disease, pathological condition, or disorder; preventative treatment, that is, treatment directed to minimizing or partially or completely inhibiting the development of the associated disease, pathological condition, or disorder; and supportive treatment, that is, treatment employed to supplement another specific therapy directed toward the improvement of the associated disease, pathological condition, or disorder. In various aspects, the term covers any treatment of a subject, including a mammal (e.g., a human), and includes: (i) preventing the disease from occurring in a subject that can be predisposed to the disease but has not yet been diagnosed as having it; (ii) inhibiting the disease, i.e., arresting its development; or (iii) relieving the disease, i.e., causing regression of the disease.

As used herein, the term "prevent" or "preventing" refers to precluding, averting, obviating, forestalling, stopping, or hindering something from happening, especially by advance action. It is understood that where reduce, inhibit or prevent are used herein, unless specifically indicated otherwise, the use of the other two words is also expressly disclosed.

As used herein, the term“prognosis” or“prediction” or“monitoring” refers, but is not limited, to an expected course of clinical disease. The prognosis provides an

indication of disease progression and includes for example, an indication of likelihood of recurrence, metastasis, death due to disease, tumor subtype or tumor type. Moreover, these terms also refer to the probability that a patient, such as a patient suffering from a telomeric-associated disease according to the present invention, will respond in a favorable or unfavorable way to a specific treatment, and to the extension of said responses, or that the patient will survive, after the surgical removal of a primary tumor and/or chemotherapy for a period of time without an associated disease, preferably cancer. The prognosis can comprise a good prognosis which corresponds to a good clinical outcome relative to the spectrum of possible clinical outcomes for the specific cancer or syndrome, and a poor prognosis, which corresponds to a poor clinical outcome relative to the spectrum of possible clinical outcomes for the specific cancer or syndrome. As used herein, “good prognosis” means a probable course of disease or disease outcome that has increased quality-adjusted life-year (QALY), reduced morbidity and/or reduced mortality compared to the average for the disease or condition. As used herein,“poor prognosis” means a probable course of disease or disease outcome that has increased morbidity and/or increased mortality compared to the average for the disease or condition.

Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skilled in the art to which this invention belongs. Methods and materials similar or equivalent to those described herein can be used in the practice of the present invention. Throughout the description and claims the word "comprise" and its variations are not intended to exclude other technical features, additives, components, or steps. Additional objects, advantages and features of the invention will become apparent to those skilled in the art upon examination of the description or may be learned by practice of the invention. The following examples and drawings are provided by way of illustration and are not intended to be limiting of the present invention.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1. Scatter plots for the TAVs of the invention. All the subjects from zero to 93 years of age were analyzed following HT-Q-FISH to obtain the TAVs of the invention MAD-I2 (A), percentage of long telomeres (LongTel15000) (B) and percentage of

cells having short telomeres (ShortCell 5000 (C). In the x axis the individual age in years is correlated to the TAVs values in the y axis.

FIG. 2. The TAVs of the invention, such as (A) MAD-I2, (B) long telomeres > 15, 000 pb (LongTel15000) and (C) cells having short telomeres < 5,000 pb (ShortCell 5000), analyzed in biological sample from subjects having prostate cancer (positive biopsy) and from healthy control subject (negative biopsy) across the different age-ranges. Diamond shaped marked line represents patients diagnosed with prostate cancer. Square shaped marked line represents healthy controls matched by age.

FIG. 3. The TAVs of the invention, such as (A) MAD-I2, (B) long telomeres > 15,000 pb (LongTel15000) and (C) cells having short telomeres < 5,000 pb (ShortCell5000), analyzed in biological sample from subjects having CLL cancer (positive biopsy) and from healthy control subject across the different age-ranges. Diamond shaped marked line represents patients diagnosed with Lung cancer. Square shaped marked line represents healthy controls matched by age.

FIG. 4. The TAVs of the invention, such as (A) MAD-I2, (B) long telomeres > 15,000 pb (LongTel15000) and (C) cells having short telomeres < 5,000 pb (ShortCell5000), analyzed in biological sample from subjects having lung cancer and from healthy control subjects across the different age-ranges. Diamond shaped marked line represents patients diagnosed with lung cancer. Square shaped marked line represents healthy controls matched by age.

FIG. 5. The TAVs of the invention, such as (A) MAD-I2 and (B) the percentage of cell having short telomeres (<5 Kbp), analyzed in biological sample from subjects having cancer and from healthy control.

FIG. 6. Principal Component Analysis comprising data from the patients (n=401 ) using the TAVs of the invention. X- axis shows the PCA of the telomeres (Tel_PCA dimension 0); Y-axis shows the PCA of cellular data (Cell_PCA dimension 1 ) and the Z-axis shows the MAD-I2.

FIG. 7. PCA using TAVs of the invention (n= 401 ). White dots representing patients that need to have a confirmatory biopsy and black dots representing patients that do not require a confirmatory biopsy. X- axis shows the Tel_PCA dimension 0; Y-axis shows the Cell_PCA dimension 1 and the Z-axis shows the MAD-I2.

FIG. 8. PCA using TAVs of the invention (n= 401 ). Black dots represent patients with no significant cancer found after biopsy and white dots represent patients with significant cancer diagnosed after biopsy. X- axis shows the Tel_PCA dimension 0; Y-axis shows the Cell_PCA dimension 1 and the Z-axis shows the MAD-I2

FIG. 9. PCA using TAVs of the invention (n= 401 ). White dots represent patients with significant cancer found after biopsy by the method of the invention based on the TAVs of the invention, wherein the data obtained indicates that they should have a biopsy (true positives); light grey dots represent patients that the TAVs obtained by the method of the invention indicate that they should have a biopsy yet it was not necessary (false positives); dark grey dots represent patients that the TAVs obtained by the method of the invention indicate that they did not have a biopsy and indeed they did not need one (true negatives) and black dots represents patients with cancer diagnosed after biopsy wherein the TAVs obtained by the method of the invention indicate that they should not have a biopsy (false negatives). X- axis shows the Tel_PCA dimension 0; Y-axis shows the Cell_PCA dimension 1 and the Z-axis shows the MAD-I2.

FIG. 10. Scatter plots shown the telomere length obtained by qPCR (A), Flow-FISH (B) and by the in vitro method of the invention which show the data obtained by the analysis of the TAV: median telomere length (Kb) (C).

FIG. 11. Comparison of the values and distributions obtained for control and prostate cancer subjects -matched by age and gender, across the calculated values for (A) percentages of telomeres with a defined length value (%ShortTEL), (B) percentages of cells with a defined average telomere length value (%ShortCELL) and (C) distribution of percentiles (1 st to 99th ) with their corresponding telomere length in bp. FIG. 12 Box plot comparing control and prostate cancer patients -matched by age and gender, for their values on the percentage of telomeres shorter than 2,500 bp (A) and for their MAD- 12 (B).

FIG. 13. Comparison of the values and distributions obtained for control and lung cancer subjects -matched by age and gender, across the calculated values for (A) percentages of telomeres with a defined length value (%ShortTEL), (B) percentages of cells with a defined average telomere length value (%ShortCELL) and (C) distribution of percentiles (1 st to 99th ) with their corresponding telomere length in bp. FIG. 14. Box plot comparing control and lung cancer patients -matched by age and gender, for their calculated interquartile values (p75-25 in Kbp) (A) and for their MADI2 values (B).FIG. 15. Comparison of the values and distributions obtained for control and colon cancer subjects -matched by age and gender, across the calculated

values for (A) percentages of telomeres with a defined length value (%ShortTEL), (B) percentages of cells with a defined average telomere length value (%ShortCELL) and (C) distribution of percentiles (1 st to 99th ) with their corresponding telomere length in bp.

FIG. 16. Box plot comparing control and colon cancer patients -matched by age and gender, for their calculated interquartile values (p75-25 in Kbp) (A) and for their MADI2 values (B).

FIG. 17. Comparison of the values and distributions obtained for control and gastric cancer subjects -matched by age and gender, across the calculated values for (A) percentages of telomeres with a defined length value (%ShortTEL), (B) percentages of cells with a defined average telomere length value (%ShortCELL) and (C) distribution of percentiles (1 st to 99th ) with their corresponding telomere length in bp. FIG. 18. Box plot comparing control and gastric cancer patients -matched by age and gender, for their calculated interquartile values (p75-25 in Kbp) (A) and for their MADI2 values (B).

FIG. 19. Comparison of the values and distributions obtained for control and melanoma subjects -matched by age and gender, across the calculated values for (A) percentages of telomeres with a defined length value (%ShortTEL), (B) percentages of cells with a defined average telomere length value (%ShortCELL) and (C) distribution of percentiles (1 st to 99th ) with their corresponding telomere length in bp. FIG. 20. Box plot comparing control and melanoma patients -matched by age and gender, for their calculated 20th percentile (A) and for their MADI2 values (B).

FIG. 21. Comparison of the values and distributions obtained for control and ovarian cancer subjects -matched by age and gender, across the calculated values for (A) percentages of telomeres with a defined length value (%ShortTEL), (B) percentages of cells with a defined average telomere length value (%ShortCELL) and (C) distribution of percentiles (1 st to 99th ) with their corresponding telomere length in bp. FIG. 22. Box plot comparing control and ovarian cancer patients -matched by age and gender, for their calculated interquartile values (p75-25 in Kbp) (A) and for their MADI2 values (B).

FIG. 23. Comparison of the values and distributions obtained for control and breast cancer subjects -matched by age and gender, across the calculated values for (A) percentages of telomeres with a defined length value (%ShortTEL), (B) percentages

of cells with a defined average telomere length value (%ShortCELL) and (C) distribution of percentiles (1 st to 99th ) with their corresponding telomere length in bp. FIG. 24. Box plot comparing control and breast cancer patients -matched by age and gender, for their calculated 80th percentile telomere length value in bp (A) and for their MADI2 values (B).

EXAMPLES

All references cited herein are incorporated by reference in their entirety. The following examples are provided for reference and are not limiting . The present invention is also described and demonstrated by way of the following examples. However, the use of these and other examples anywhere in the specification is illustrative only and in no way limits the scope and meaning of the invention or of any exemplified term. Likewise, the invention is not limited to any particular preferred embodiments described here. Indeed, many modifications and variations of the invention may be apparent to those skilled in the art upon reading this specification, and such variations can be made without departing from the invention in spirit or in scope. The invention is therefore to be limited only by the terms of the appended claims along with the full scope of equivalents to which those claims are entitled.

Example 1. TAVs constructed from population studies of healthy individuals.

Method

Blood samples from healthy individuals were collected voluntarily under ethically approved clinical study protocols and health-related data were also gathered to ensure absence of disease. The peripheral blood mononuclear cells were isolated from the blood samples, counted and stored frozen until used. To perform the method cells were plated on 384-well microtiters plates to perform HT-Q-FISH (Canela A. et at., Proc Natl Acad Sci U S A. 2007 Mar 27;104(13) :5300-5).

Immortalized, human cell lines were used to stablish a standard curve. The standard curve is the logarithmic regression curve defined by the coordinates derived from pairing the fluorescence intensities of each sample with its true value in base pairs as calculated using the method. These pairs of coordinates are then adjusted to a

logarithmic curve. The standard curve will include the set of biological samples comprising immortalized cell lines whose median telomere length, as determined by HT-QFISH, are ³ 2,000 base pairs and £ 40,000 base pairs. Any and each of the telomere intensity spots measured in a cell or in a sample can be interpolated in the standard curve to obtain the corresponding value in base pairs for that intensity. The corresponding absolute value in base pairs of each telomere in the test sample is use to derive the value of all of the TAVs of the invention in healthy individuals from 0 to 93 years of age. The immortalized human lymphoblast cell lines used included commercially available lines from the American Tissue Culture Collection (ATCC #CCL-159, ATCC #CCL-1 19, ATCC #ccl-86 and ATCC #crl-8286) and from the European Collection of Authenticated Cell Cultures (ECACC #91071212 and ECACC #93012805) as well as from the Life Length collection (LL #125).

Image Acquisition and Analysis. Quantitative image acquisition and analysis is performed on a High Content Screening Opera System (Perkin Elmer), using the Acapella software, Version 2.0 (Perkin Elmer). Images are captured, using a 40x 0.95 NA water immersion objective. UV and 488 nm excitation wavelengths are used to detect the DAPI and A488 signals respectively. With constant exposure settings, at least 15 independent images are captured at different positions for each well. Next, the nuclei images are used to define the region of interest for each cell, measuring telomere fluorescence intensity of the A488 image in all of them. The results of intensity for each foci are exported to the Columbus 2.4 software (Perkin Elmer).

Telomere length distribution, median telomere length and other TAVs such as MAD-I2, LongTel15000 and ShortCell5000 are calculated using the Plate Analyzer program of Life Length. For quantitative analysis to generate the TAVs and obtain the distribution of these TAVs in a biological sample, Plate Analyzer analysis software developed by Life Length SL (Madrid, Spain) is used. In order to facilitate processing, the analysis can be configured with filters that enable threshold configuration at multiple points during the data acquisition and data integration that determine the quality of the data and whether they must be kept, discarded, or marked as invalid.

The software works on two different dimensions - both intensity and length, with length being derivable at any point from intensity through a configurable interpolation. Both these dimensions can be analysed at the individual telomere level as well as at other levels - including but not limited to a cell, group of cells, well or aliquot. Statistical information about the distribution is obtained at any of these levels.

Results

Blood samples from healthy subjects from zero to 93 years of age were analysed by the in vitro method of the invention to obtain the specific three TAVs. The data was plotted such that the age of the subject was paired with the 50th percentile of the telomere length of the sample. The 50th percentile of the distribution corresponds with the median telomere length of the sample.

A total of 2,652 samples were used to obtain the scatter pot of the population for the TAVs of the invention, this is MAD-I2 of the telomeric length, percentage of long telomeres (LongTel15000) and percentage of cells having short telomeres (ShortCell5000). For each TAVs resulting from the in vitro method of the invention the different percentiles curves were also calculated.

TAVs analysed are age dependent and the method allows the generation of a database to stablish the control or references values to compare test samples.

Fig. 1 A shows that the TAV MAD-I2 is a variable that decreases with age. In the same sense, Fig. 1 B shows that the percentage of cell having long telomeres (at least 15,000 pb) (LongTel15000) is a TAV that decreases with age and moreover, Fig. 1 C shows that the percentage of cells with short telomeres (less than 5,000 pb) (ShortCell5000) increases with age.

Example 2. The value of the TAVs of the invention are significantly different in solid tumors and haematological cancer when compared to the TAVs value from non-cancer subjects.

Method

Blood samples from healthy subjects as well as from individuals in the early stages of prostate cancer (PC), lung cancer (LC) and chronic lymphocytic leukaemia (CLL) were collected voluntarily under ethically approved clinical study protocols and clinical-related data were also gathered. The peripheral blood mononuclear cells were isolated from the blood samples, counted and stored frozen until used. To perform the method cells were plated on 384- well microtiters plates to perform HT-Q-FISH (Canela A. et al., Proc Natl Acad Sci U S A. 2007 Mar 27;104(13):5300-5).

Human cell lines selected from IM9, CEM, C0106, COJ, C0154 RAJI, REH and 1301 are used to stablish a standard curve. Any and each of the telomere intensity spots measured in the healthy and cancer subjects are interpolated in the standard curve to obtain the corresponding value in base pairs for those intensities. The corresponding absolute value in base pairs of each telomere in the test sample is use to derive the value of all of the TAVs of the invention from healthy subjects and subjects in the early stage of diagnostic for prostate cancer, lung cancer and CLL.

Image Acquisition and Analysis was performed as disclosed in Example 1.

Results

Prostate cancer.

TAVs data results to study individuals selected from a group of male subjects that have a prostate specific antigen blood test (PSA) greater than 3 ng/ml were tested for telomere associated variables according to the in vitro method of the invention. Other clinical variables recorded were age, digital rectal examination and free-PSA.

All patients with PSA above 3 ng/ml underwent a prostate biopsy to diagnose if the elevated PSA levels were caused by the presence of prostate cancer.

The TAVs analysed by the in vitro method of the invention from patients that have a positive biopsy (which have prostate cancer) or have a negative biopsy (which do not have prostate cancer) were observed to be significantly different across the different age-ranges as depicted in Table 1 and FIG. 2A-C.

Table 1. Shows the TAVs of the invention for biopsy positive (prostate cancer) and biopsy negative (non-prostate cancer) patients matched by age.

TAVs


The results showed in Table 1 and FIG. 2A-C point out that the percentage of telomeres longer than 15,000 bp is greater in the patients without a cancer (FIG. 2B). Moreover, the MAD-I2 of the telomere distribution on each of the samples is lower in cancer patients (FIG. 2A) and the percentage of cells having a median telomere below 5,000 bp is greater in subjects with positive prostate cancer biopsies (FIG. 2C).

Chronic Lymphocytic leukaemia


Individuals selected from a group of subjects recently diagnosed with CLL were tested for the TAVs of the invention. Other clinical variables recorded were age, gender, disease's stage and p53 mutation.

All patients diagnosed with CLL were paired with healthy controls matched by age.

As shown in Table 2 and FIG. 3A-C, significant differences were observed for each of the TAVs of the invention in healthy control (no cancer) versus CLL cancer patients. The significant differences are age-related such that for elderly patients (>75 years of age) as compared with younger individuals (>65 years of age), the TAV MAD-I2 is significantly reduced while the TAV Shortcell5000 (percentage of cell having short telomeres with a length less than 5, 000 pb) is significantly increased.

Table 2. Shows the TAVs obtained by the in vitro method of the invention for CCL patients and healthy control subject matched by age.

TAVs


The results show that the percentage of telomeres longer than 15,000 bp is significantly greater in the no-cancer patients (FIG. 3B), the MAD-I2 is significantly lower in cancer patients (FIG. 3A) and the percentage of cells with median telomere length below 5,000 bp is significantly greater in subjects that have been diagnosed with CLL (FIG. 3C).

Lung cancer.

Individuals selected from a group of subjects with suspected lung cancer were tested for telomere associated variables. Other clinical variables recorded were age, gender, disease stage and tobacco consumption. All patients diagnosed with lung cancer were compared with healthy controls matched by age. Differences were observed for the TAVs of in the invention examined for all ages.

Table 3. The TAVs obtained by the in vitro method of the invention for lung cancer patients and healthy controls matched by age.

TAVs


TAVs


The results obtained show that the percentage of telomeres longer than 15,000 bp is significantly greater in the no-cancer patients (healthy controls) versus lung cancer patients (FIG. 4B). Moreover, the MAD-I2 on each of the samples is significantly lower in lung cancer patients versus non-cancer patients (FIG. 4A) and the percentage of cells with median telomere length below 5,000 bp is significantly greater in subjects that have been diagnosed with lung cancer versus no-cancer patients (healthy controls) (FIG. 4C).

Example 3. TAVs obtained by the in vitro method of the invention for patient triage into confirmatory biopsy following PSA determination >3 ng/ml.

Method

Blood samples from healthy subjects as well as from individuals in the early stage of diagnostic for prostate cancer (PC) were collected voluntarily under ethically approved clinical study protocols and clinical-related data were also gathered. The peripheral blood mononuclear cells were isolated from the blood samples, counted and stored frozen until used. To perform the method cells were plated on 384-well microtiters plates to perform HT-Q-FISH (Canela A. et a!., Proc Natl Acad Sci U S A. 2007 Mar 27;104(13):5300-5). Image Acquisition and Analysis of the blood samples was performed as in Example 1 .

The TAVs data results from a group of 150 male subjects that have a prostate specific antigen blood test (PSA) greater than 3 ng/ml and lower than 10 ng/ml were used in combination with the in vitro method of the invention to validate the TAVs obtained by this method. To do this, the clinical data mentioned herein with the TAVs obtained by the in vitro method of the invention were used in combination as part of a predictor algorithm (Life Length S.L. Madrid, Spain) to determine if a subject that is suspected of prostate cancer has to be biopsied or not.

To integrate the TAVs of the method of the invention a Principal Component Analysis (PCA) was performed. The variable set to test includes a combination of Principal Component Analysis (PCAs). A total of six combinations were done each of them combining percentile variables, LongTel (percentage of telomeres longer than 15,000 bp) variables, and ShortCell (cells with short telomeres less than 5,000pb) variables - along with the variables MAD-I2, P75-25 (Q3-Q1 ) and P1 -99. A total of five PCA were generated. The principal components for each group are numbered from 0 to 5.

Table 4. Shows the distribution of the 150 patients included in this analysis divided in those that were diagnosed with cancer, those deemed cancer negative (no cancer) and within the cancer group those that have a significant cancer (Gleason) and those that had a non-significant cancer (No Gleason).


Results

When patients were grouped into those diagnosed with cancer and cancer free (no cancer), the TAVs MAD-I2 and cell having short telomeres less than 5, 000 pb (ShortCell5000) analysed by the in vitro method of the invention showed differences in their distribution (FIG. 5A-B). The data showed in FIG. 5 are not segmented by age.

The accuracy for the TAVs of the invention, alone (MAD-I2 or LongTel15000 or ShortCell5000) or in any combinations thereof (MAD-I2 or MAD-I2 and LongTel15000 or MAD-I2 and LongTel15000 and ShortCell5000) was calculated by the in vitro method of the invention and the results obtained show an increase in the percentage for cancer accuracy prediction as more TAVs of the invention are taken into account (Table 5).

Table 5. Accuracy for the TAVs of the invention alone or in combinations thereof.


Once PCA were created a two-tailed t-test assuming unequal variances is performed on each of the aforementioned variables.

Table 6. List of variables analysed by Principal Component Analysis of the data to establish whether they were significantly different (p< 0.05) between cancer and no cancer groups of patients.



The result shows in Table 6 indicates that, MAD-I2 is an order of magnitude above the percentiles P1 -99 and the percentile P25-75 is not statistically significant. Each of the PCAs dimension refers to dimension reduction or the process of reducing the number of random variables under consideration. Dimensions zero are statistically significant while mostly the other dimensions are not, with the only exception of Percentile PCA for the dimension 4, which is statistically significant. This data points towards an uneven distribution of information among the multiple PCs so that the vast majority of the information for each group of variables is contained in the initial dimension, which shows significant differences between cancer/no cancer groups. This is in line with the high amount of statistically significant variables that are processed to generate the PCAs, a total of 209 out of which 186 are significant.

The FIG. 6 shows all the patients included in the Principal Component Analysis using TAVs.

FIG. 7 shows all patients included in the PCA using TAVs colour-coded according to their classification by the model into white dots representing patients that need to have a confirmatory biopsy and black dots representing patients that do not require a confirmatory biopsy.

FIG. 8 shows all patients included in the PCA using TAVs colour-coded according to their biopsy result as well as the significance or not of the diagnosed cancer. Black dots represent patients with no significant cancer found after biopsy, white dots represent patients with significant cancer diagnosed after biopsy.

FIG. 9 shows all patients included in the PCA using TAVs colour-coded according to the prediction of the model generated using the clinical data of their biopsy result. White dots represent patients with significant cancer found after biopsy that the model based on TAVs indicates they should have a biopsy (true positives), light grey dots

represent patients that the model found should have a biopsy yet it was not necessary (false positives), dark grey dots represent patients that the model indicates did not have a biopsy and indeed they did not needed (true negatives) and black dots represents patients with cancer diagnosed after biopsy that the model indicates they should not have a biopsy (false negatives).

Example 4. The method to determine TAVs and not other telomere length determination methods can inform of median average deviation, percentage of long telomeres and percentages of cells with short telomeres.

Method

In order to compare the in vitro method of the invention versus different methods known in the art for the assaying of TAVs (i.e. qPCR and Flow FISH) a biological sample from the same subject was analysed by qPCR, Flow FISH and by the in vitro method of the invention. qPCR was performed in a commercial laboratory (Spectra Cell Laboratories, Houston, Texas). Flow-FISH was performed in a commercial laboratory (Repeat Diagnostic, Vancouver, Canada) and HT-QFISH was performed at Life Length SL. (Madrid, Spain).

Results

The data related to telomeres such as the average length of the telomeres in the biological sample or the length of particular cell type was obtained. The same biological sample was analysed by the in vitro method of the invention to determined TAVs and it is the only method that can determine variables associated with health and disease status such as MAD-I2, LongTel15000 and ShortCell5000.

Table 7. Shows data from a blood sample processed by Flow-FISH. MTL: Patient Median Telomer Length; MTLN: Normal MTL at age (50th percentile) and INT: Telomere length interpretation.



The associated results (Table 7) can determine the sample median telomere length -median distribution of the telomere length calculated per cell and can specify if the cell are lymphocytes or granulocytes. However, the method cannot calculate the MAD-12 or the percentage of long telomeres in the sample.

Table 8. Shows data from a blood sample processed by qPCR.


The associated results (Table 8) can determine the sample average telomere length relative to a standard. qPCR cannot generate values on the TAVs of the invention, such as MAD-I2, percentage of long telomeres and percentage of cells having short telomeres.

Table 9. Shows data from a blood sample processed by the in vitro method of the invention (HT-Q-FISH).


The associated results obtained by the in vitro method of the invention (Table 9) can determine the sample median and average telomere length in absolute base pair units. In addition, data on the TAVs such as MAD-I2, LongteM 5000 and Shortcell5000 can also be obtained.

FIG. 10 shows different graphs for telomere analysis obtained by qPCR (FIG. 10A), Flow-FISH (FIG. 10B) and HT-Q-FISH (FIG. 10C) based on average or median telomere length calculated over a group of healthy subjects of different ages. The slopes and the distribution of the data is different for each method.

Example 5. The TAVs obtained by the first method of the invention is useful to evaluate cancer risk

Method

Blood samples from healthy subjects as well as from individuals having prostate cancer (n= 336), lung cancer (n= 166), breast cancer (n= 30), ovarian cancer (n= 13), colon cancer (n= 27), gastric cancer (n= 10) and melanoma cancer (n= 15) were collected voluntarily under ethically approved clinical study protocols and clinical-related data were also gathered. The peripheral blood mononuclear cells were isolated from the blood samples, counted and stored frozen until used.

All data was recorded electronically and monitored to guarantee the quality of the results.

Control patients were selected randomly form a cohort that was matched by gender and age to those of the cancer patients.

To perform the method cells were plated on 384-well microtiters plates to perform HT -Q-FISH (Canela A. et al., Proc Natl Acad Sci U S A. 2007 Mar 27;104(13):5300-5).

Statistical analyses were conducted using t-student and odds ratio analysis.

Results

The unexpected results in solid tumours analysed herein, relate to the data obtained using two types of analysis:

1. Based on calculating significant differences of particular TAVs obtained by the method of the invention between controls and cancer patient.

- Several variables have been found to be significantly different in control and cancer patients.

- Each cancer type has a different set of TAVs values with different degrees of significance.

- One of the most consistent TAV found different between all the cancer type is the TAV p25-75 (interquartile).

2. Calculating the odds ratio (OR) in subjects having cancer:

- Different TAVs that allow us to assess the odds ratio of an individual to have cancer have been identified by the method of the invention.

- The odds ratio and the significance (p-values) of the obtained data are specific for each type of cancer.

- Longer telomeres are significantly shorter in the case of lung, Breast and colon cancer patients

- Shorter telomeres are significantly longer in the case of melanoma and ovary cancer patients.

Results in prostate cancer subject vs control subjects.

TAVs obtained by the method in vitro of the present invention are different in control subjects to the prostate cancer subjects (Table 10).

Table 10. The table shows the results of the t-test used to calculate the p-value of different TV As between control and prostate cancer subjects p- values < 0.05 are considered significant.



The comparison of the TAVs as calculated by the method in vitro of the invention, obtained from control and prostate cancer patients, shows slightly different distribution curves for the percentages of telomeres of certain length values (% of ShortTEL) (FIG 11 A) in both groups of subjects. The curves are significantly different between 500 and 2500 pb (Table 10) being lower for the healthy, control samples. The curves for the percentages of cells with a defined average telomere length (% of ShortCELL) (FIG. 11 B) is also slightly different. Significant differences are observed in the bp absolute values for the percentiles in prostate cancer patients compared to controls (FIG 11 C) in particular for the percentiles 4 to 1 1 (Table 10).

Specific TAVs were compared in the control and cancer group by box plots (FIG 12A and 12B) showing the different for interquartile values (p75-25) and MADI2 for prostate cancer patients compared with control, healthy subjects.

Results in lung cancer subject vs control subjects.

TAVs obtained by the method in vitro of the present invention are different in control subjects to the lung cancer subjects (Table 11 ).

Table 11. The table shows the results of the t-test used to calculate the p-value of different TAVs between control and lung cancer subjects p- values < 0.05 are considered significant.



The comparison of the TAVs, as calculated by the method in vitro of the invention, from control and lung cancer patients shows different curves for the percentages of telomeres of certain values (% of ShortTEL) (FIG 13A), percentages of cells with a defined average telomere length (% of ShortCELL) (FIG. 13B) and for the percentile ranges from 1 st to 99th. (FIG. 13C). The most significant differences for all the TAVs analysed are listed in Table 11.

Specific TAVs were compared in the control and cancer group by box plots (FIG 14A and 14B) showing the different for interquartile values (p75-25) and MADI2 for lung cancer patients compared with control, healthy subjects.

Results in colon cancer subject vs control subjects.

TAVs obtained by the method in vitro of the present invention are different in control subjects to the colon cancer subjects (Table 12).

Table 12. The table shows the results of the t-test used to calculate the p-vaiue of different TAVs between control and colon cancer subjects p- values < 0.05 are considered significant.


The comparison of the TAVs, as calculated by the method in vitro of the present invention, from control and colon cancer patients shows different curves for the percentages of telomeres of certain values (% of ShortTEL) (FIG 15A), percentages of cells with a defined average telomere length (% of ShortCELL) (FIG. 15B) and for the percentile ranges from 1 st to 99th. (FIG. 15C). The most significant differences for all the TAVs analysed correspond with long telomere values and are listed in Table 12.

Specific TAVs were compared in the control and colon cancer group by box plots (FIG 16A and 16B) showing the different distribution for p75-25 and MADI2 such that both

TAVs are lower in the colon cancer patients compared with control, healthy subjects.

Results in qastric cancer subject vs control subjects.

TAVs obtained by the in vitro method of the present invention are different in control subjects to the gastric cancer subjects (Table 13).

Table 13. The table show the results of the t-test used to calculate the p-vaiue of the difference of TAV features between control and gastric cancer subjects p- values < 0.05 are considered significant.


The comparison of the TAVs, as calculated by the method in vitro of the invention, from control and gastric cancer patients shows different curves for the percentages of telomeres of certain values (% of ShortTEL) (FIG 17A), percentages of cells with a defined average telomere length (% of ShortCELL) (FIG. 17B) and for the percentile ranges from 1 st to 99th. (FIG. 17C). The most significant differences for all the TAVs analysed correspond with the TAVs listed in Table 13.

Specific TAVs were compared in the control and gastric cancer group by box plots (FIG 18A and 18B) showing the different distribution for p75-25 and MADI2 in the gastric cancer patients compared with control, healthy subjects.

Results in melanoma cancer subject vs control subjects.

TAVs obtained by the method in vitro of the present invention are different in control subjects to the melanoma subjects (Table 14).

Table 14. The table shows the results of the t-test used to calculate the p-vaiue of different TAVs between control and melanoma subjects p- values < 0.05 are considered significant.


The comparison of the TAVs, as calculated by the method in vitro of the invention, from control and melanoma patients shows different curves for the percentages of telomeres of certain values (% of ShortTEL) (FIG 19A), percentages of cells with a defined average telomere length (% of ShortCELL) (FIG. 19B) and for the percentile ranges from 1 st to 99th. (FIG. 19C). The most significant differences for all the TAVs analysed correspond with the TAVs listed in Table 14.

Specific TAVs were compared in the control and gastric cancer group by box plots (FIG 20A and 20B) showing the different distribution for the 20th percentile and MADI2 in the melanoma patients compared with control, healthy subjects.

Results in ovarian cancer subject vs control subjects.

TAVs obtained by the method in vitro of the present invention are different in control subjects to the ovarian cancer subjects (Table 15).

Table 15. The table shows the results of the t-test used to calculate the p-value of different TAVs between control and ovarian cancer subjects p- values < 0.05 are considered significant.


The comparison of the TAVs, as calculated by the method in vitro of the present invention, from control and ovarian cancer patients shows different curves for the percentages of telomeres of certain values (% of ShortTEL) (FIG 21 A), percentages of cells with a defined average telomere length (% of ShortCELL) (FIG. 21 B) and for the percentile ranges from 1 st to 99th. (FIG. 21 C). The most significant differences for all the TAVs analysed correspond with the TAVs listed in Table 15.

Specific TAVs were compared in the control and ovarian cancer group by box plots (FIG 22A and 22B) showing the different distribution for p75-25 and MADI2 in the ovarian cancer patients compared with control, healthy subjects

Results in breast cancer subject vs control subjects.

TAVs obtained by the in vitro method of the present invention are different in control subjects to the breast cancer subjects (Table 16).

Table 16. The table shows the results of the t-test used to calculate the p-value of different TAVs between control and breast cancer subjects p- values < 0.05 are considered significant.


The comparison of the TAVs, as calculated by the method, from control and breast cancer patients shows different curves for the percentages of telomeres of certain values (% of ShortTEL) (FIG 23A), percentages of cells with a defined average telomere length (% of ShortCELL) (FIG. 23B) and for the percentile ranges from 1 st to 99th. (FIG. 23C). The most significant differences for all the TAVs analysed correspond with the TAVs listed in Table 16.

Specific TAVs were compared in the control and ovarian cancer group by box plots (FIG 24A and 24B) showing the different distribution for the 80th percentile and MADI2 in the breast cancer patients compared with control, healthy subjects.

Furthermore, Odds ratio (OR) for the different groups of cancer patients was calculated. OR is the measure of association between the TAVs and the different type of cancers in the analyzed population showed in the present example. Specifically, it was assessed if the value of the TAVs have an effect on the likelihood of the presence of cancer or its absence (no cancer).

Therefore, the OR can be used to figure out if a particular TAV, like p75-25 (in Kpb), is a risk factor for a particular outcome of each specific cancer analyzed, and to compare the various risk factors for that outcome.

Odds ratio calculation was done using standard methodology (Table 17):

Table 17. Odd ratios calculated for the first Quartile (Q1 ) values and their corresponding significance t-test result ( p-value ) of TAV features. Median Telomere Length (MTL) and interquartile p75-25 for different types of cancer.


Measuring telomere length in each telomere individually, their conversion to absolute base pair values and their analysis to calculate TAV allows to use unique TAV, derived from of the method, such as MTL and p75-25 as biomarkers for different cancer types. Each cancer type has a different TAV signature.