Diagnostic method

Abstract

The invention relates to a diagnostic method. The present application provides a method for analyzing a disease state of a subject, the method comprising characterizing genetic information of the subject at two or more points in time or two or more instances with a genetic analyzer, e.g., a deoxyribonucleic acid (DNA) sequencer, and generating an adjusted test result in the characterization of the genetic information of the subject using information from the two or more points in time or the two or more conditions.

Description

[0001] This application is a divisional application of an application filed on April 29, 2016, with application number 202111601219.3 and invention name “Diagnostic Method”.

[0002] The application with the application date of April 29, 2016, application number 202111601219.3, and invention name “Diagnostic Method” is a divisional application of the application with the application date of April 29, 2016, application number 201680039132.2, and invention name “Diagnostic Method”.

[0003] Cross-references

[0004] This application claims the benefit of U.S. Provisional Application No. 62 / 155,755, filed May 1, 2015, which is incorporated herein by reference. Technical Field

[0005] The present application relates to methods and systems for detecting and monitoring disease. Background Art

[0006] Cancer is the leading cause of disease worldwide. Every year, tens of millions of people are diagnosed with cancer around the world, and more than half of those who suffer from it eventually die from it. In many countries, cancer ranks as the second most common cause of death after cardiovascular disease.

[0007] To detect cancer, several screening tests are available. A physical exam and history surveys general health signs, including checking for signs of disease, such as lumps or other unusual physical symptoms. The patient's health habits and history of past illnesses and treatments will also be collected. Laboratory tests are another type of screening test, and before laboratory tests are performed, medical procedures may be required to obtain samples of tissue, blood, urine, or other substances in the body. Imaging procedures screen for cancer by creating visual representations of areas inside the body. Genetic tests detect harmful mutations in certain genes associated with some types of cancer. Genetic tests are particularly useful for many diagnostic methods.

[0008] A method for cancer screening can include monitoring samples derived from cell-free nucleic acids, i.e., polynucleotide populations that can be found in different types of body fluids. In some cases, diseases can be characterized or detected based on detection of genetic variation, such as changes in copy number variation and / or sequence variation of one or more nucleic acid sequences, or the development of certain other rare genetic changes. Cell-free DNA ("cfDNA") can contain genetic variation associated with a specific disease. With the improvement of sequencing and manipulation techniques for nucleic acids, there is a need in the art for improved methods and systems for detecting and monitoring diseases using cell-free DNA. Summary of the invention

[0009] In one aspect, the present disclosure provides a method for analyzing a disease state of a subject, the method comprising (a) using a genetic analyzer to generate genetic data from nucleic acid molecules in a biological sample obtained from the subject at (i) two or more time points or (ii) substantially the same time point, wherein the genetic data is related to the genetic information of the subject and wherein the biological sample comprises a cell-free biological sample; (b) receiving the genetic data from the genetic analyzer; (c) using one or more programmed computer processors to generate an adjusted test result in a representation of the genetic information of the subject; and (d) outputting the adjusted test result to a computer memory.

[0010] In some embodiments, the genetic data comprises a current sequence read and a previous sequence read, and wherein (c) comprises comparing the current sequence read to the previous sequence read and accordingly updating a diagnostic confidence indication of a representation of the genetic information of the subject, the diagnostic confidence indication indicating a probability of identifying one or more genetic variations in a biological sample of the subject.

[0011] In some embodiments, the method further comprises generating a confidence interval for the current sequence read. In some embodiments, the method further comprises comparing the confidence interval to one or more previous confidence intervals and determining disease progression based on overlapping confidence intervals.

[0012] In some embodiments, the biological sample is obtained at two or more time points including a first time point and a second time point, and wherein (c) includes increasing the diagnostic confidence indication in a subsequent characterization or a previous characterization if the information from the first time point confirms the information from the second time point. In some embodiments, the biological sample is obtained at two or more time points including a first time point and a second time point, and wherein (c) includes increasing the diagnostic confidence indication in a subsequent characterization if the information from the first time point confirms the information from the second time point.

[0013] In some embodiments, a first co-variate variation is detected in the genetic data, and wherein (c) includes increasing the diagnostic confidence indication in a subsequent characterization if a second co-variate variation is detected.

[0014] In some embodiments, the biological sample is obtained at two or more time points including a first time point and a second time point, and wherein (c) includes reducing the diagnostic confidence indication in a subsequent characterization if information from the first time point conflicts with information from the second time point.

[0015] In some embodiments, the method also includes obtaining a subsequent characterization and retaining a diagnostic confidence indication unchanged in the subsequent characterization for de novo information. In some embodiments, the method also includes determining the frequency of one or more genetic variants detected in the set of sequence reads included in the genetic data, and at least in part by comparing the frequencies of one or more genetic variants at two or more time points to produce an adjusted test result. In some embodiments, the method also includes determining the amount of copy number variation at one or more genetic loci detected in the set of sequence reads included in the genetic data, and at least in part by comparing the amounts at two or more time points to produce an adjusted test result. In some embodiments, the method also includes using the adjusted test result to provide (i) therapeutic intervention or (ii) diagnosis of health or disease to the subject.

[0016] In some embodiments, the genetic data includes sequence data from a portion of a genome that comprises a disease-associated or cancer-associated genetic variant.

[0017] In some embodiments, the method further comprises increasing the sensitivity of detecting the genetic variant using the adjusted test results by increasing the read depth of the polynucleotides in the sample from the subject.

[0018] In some embodiments, the genetic data includes a first group of genetic data and a second group of genetic data, wherein the first group of genetic data is at or below a detection threshold, and the second group of genetic data is above the detection threshold. In some embodiments, the detection threshold is a noise threshold. In some embodiments, the method also includes, in (c), when the same genetic variant is detected in the first group of genetic data and the second group of genetic data of more than one (a plurality of) sampling situations or time points, the diagnosis of the subject is adjusted from negative or uncertainty to positive. In some embodiments, the method also includes, in (c), when the same genetic variant is detected in the first group of genetic data at an earlier time point and in the second group of genetic data at a subsequent time point, the diagnosis of the subject in the characterization from the earlier time point is adjusted from negative or uncertainty to positive.

[0019] In some embodiments, the disease state is cancer and the genetic analyzer is a nucleic acid sequencer.

[0020] In some embodiments, the biological sample comprises at least two different types of biological samples. In some embodiments, the biological sample comprises biological samples of the same type. In some embodiments, the biological sample is a blood sample. In some embodiments, the nucleic acid molecule is cell-free deoxyribonucleic acid (DNA).

[0021] In another aspect, the present disclosure provides a method for detecting a trend in the amount of a cancer polynucleotide in a biological sample from a subject over time, the method comprising: determining the frequency of the cancer polynucleotide at each of more than one time point using one or more programmed computer processors; determining an error range for the frequency at each of the more than one time point to provide a first error range at at least a first time point and a second error range at a second time point subsequent to the first time point; and determining (1) whether the first error range overlaps with the second error range, the overlap indicating stability of the frequency of the cancer polynucleotide at more than one time point; (2) whether the second error range is greater than the first error range, thereby indicating an increase in the frequency of the cancer polynucleotide at more than one time point, or (3) whether the second error range is less than the first error range, thereby indicating a decrease in the frequency of the cancer polynucleotide at more than one time point.

[0022] In some embodiments, the cancer polynucleotide is a deoxyribonucleic acid (DNA) molecule. In some embodiments, the DNA is cell-free DNA.

[0023] In some embodiments, the frequency of each at more than one time point is determined by sequencing nucleic acid molecules in a biological sample of the subject. In some embodiments, the biological sample is a blood sample. In some embodiments, the nucleic acid molecule is cell-free deoxyribonucleic acid (DNA).

[0024] In another aspect, the present disclosure provides a method for detecting one or more genetic variations and / or the amount of genetic variations in a subject, the method comprising sequencing nucleic acid molecules in a cell-free nucleic acid sample of the subject with a genetic analyzer to generate a first set of sequence reads at a first time point; comparing the first set of sequence reads with at least a second set of sequence reads obtained at a second time point at least prior to the first time point to produce a comparison of the first set of sequence reads and the at least second set of sequence reads; using the comparison to accordingly update a diagnostic confidence indication indicating a probability of identifying one or more genetic variations in the cell-free nucleic acid sample of the subject; and based on the diagnostic confidence indication, detecting the presence or absence of the one or more genetic variations and / or the amount of the genetic variations in the nucleic acid molecules in the cell-free nucleic acid sample of the subject.

[0025] In some embodiments, the method further comprises obtaining the cell-free nucleic acid molecule from the subject.

[0026] In some embodiments, the method further includes sequencing additional cell-free nucleic acid molecules of the subject to generate a third set of sequence reads at a third time point subsequent to the first time point, and based on the diagnostic confidence indication, detecting the presence or absence and / or the amount of one or more genetic variations in the additional cell-free nucleic acid molecules of the subject.

[0027] In some embodiments, the method further includes increasing the diagnostic confidence indication if information obtained from the first set of sequence reads at the first time point confirms information obtained from at least a second set of sequence reads at the second time point.

[0028] In some embodiments, the method further comprises lowering the diagnostic confidence indication if the information obtained from the first set of sequence reads at the first time point does not confirm or conflicts with the information obtained from at least the second set of sequence reads at the second time point. In some embodiments, the method further comprises retaining the diagnostic confidence indication unchanged for the de novo information in a subsequent characterization.

[0029] In another aspect, the present disclosure provides a method for detecting a mutation in a cell-free nucleic acid sample of a subject, the method comprising: (a) determining a consensus sequence by comparing current sequence reads obtained from a genetic analyzer to previous sequence reads from a previous time period to produce a comparison, and updating a diagnostic confidence indication based on the comparison, wherein each consensus sequence corresponds to a unique polynucleotide in a set of tagged parent polynucleotides derived from the cell-free nucleic acid sample, and (b) generating a genetic profile of extracellular polynucleotides in the subject based on the diagnostic confidence, wherein the genetic profile comprises data resulting from copy number variation or mutation analysis.

[0030] In some embodiments, the method further comprises, prior to (a), providing more than one set of tagged parent polynucleotides derived from the cell-free nucleic acid sample, wherein each set is mappable to a different reference sequence.

[0031] In some embodiments, the method further comprises: using a consensus sequence to normalize the ratio or frequency of variation at each mappable base position and determining actual or potential rare variants or mutations; and comparing the resulting number for each region with potential rare variants or mutations with a similarly obtained number from a reference sample.

[0032] In another aspect, the present disclosure provides a method for detecting abnormal cellular activity, the method comprising: providing at least one set of tagged parent polynucleotides derived from a biological sample of a subject; amplifying the tagged parent polynucleotides in the set to produce a corresponding set of amplified progeny polynucleotides; sequencing a subset of the set of amplified progeny polynucleotides using a genetic analyzer to produce a set of sequencing reads; and collapsing the set of sequencing reads by comparing the current sequence read with a previous sequence read from at least one previous time period to generate a set of consensus sequences, and updating a diagnostic confidence indication accordingly, the diagnostic confidence indication indicating the probability of identifying one or more genetic variations in the biological sample of the subject, wherein each consensus sequence corresponds to a unique polynucleotide in the set of tagged parent polynucleotides.

[0033] In some embodiments, the method further comprises increasing the diagnostic confidence indication if the set of sequencing reads was identified in at least one previous time period. In some embodiments, the method further comprises decreasing the diagnostic confidence indication if the set of sequencing reads was not identified in at least one previous time period. In some embodiments, the method further comprises maintaining the diagnostic confidence indication unchanged if the set of sequencing reads was identified in at least one previous time period but was non-deterministic.

[0034] In some embodiments, the set of sequencing reads includes at least one sequencing read.

[0035] In some embodiments, the biological sample is a blood sample.In some embodiments, the biological sample comprises cell-free nucleic acid molecules, and at least one set of tagged parent polynucleotides is generated from the cell-free nucleic acid molecules.

[0036] In some embodiments, the method further comprises generating a genetic profile of the subject's polynucleotides, the genetic profile comprising the results of the analysis of one or more genetic variants of the subject.In some embodiments, the polynucleotides comprise extracellular polynucleotides.

[0037] In another aspect, the present disclosure provides a method for detecting a mutation in a cell-free sample or a substantially cell-free sample from a subject, the method comprising: (a) sequencing extracellular polynucleotides from a body sample from the subject using a genetic analyzer; (b) generating more than one sequencing read for each of the extracellular polynucleotides; (c) filtering out reads that do not meet a set threshold; (d) mapping the sequence reads obtained from the sequencing to a reference sequence; (e) identifying a subset of mapped sequence reads that align with a variant of the reference sequence at each mappable base position; (f) for each mappable base position, calculating the ratio of (i) the number of mapped sequence reads that contain a variant compared to the reference sequence to (ii) the total number of sequence reads for each mappable base position; and (g) using one or more programmed computer processors to compare the sequence reads with other sequence reads from at least one previous time point and update a diagnostic confidence indication accordingly, which indicates the probability of identifying the variant.

[0038] In some embodiments, the body sample is a blood sample.In some embodiments, the extracellular polynucleotides include cell-free deoxyribonucleic acid (DNA) molecules.

[0039] In another aspect, the present disclosure provides a method for operating a genetic testing device, the method comprising: providing initial starting genetic material obtained from a body sample obtained from a subject; converting double-stranded polynucleotide molecules from the initial starting genetic material into at least one set of non-uniquely tagged parent polynucleotides, wherein each polynucleotide in the set can be mapped to a reference sequence; and for each set of tagged parent polynucleotides: (i) amplifying the tagged parent polynucleotides in the set to produce a corresponding set of amplified progeny polynucleotides; (ii) sequencing the set of amplified progeny polynucleotides to produce a set of sequencing reads; and (iii) merging the set of sequencing reads to generate a set of consensus sequences. , wherein sequence information from the tag and at least one of the following: (1) sequence information at a start region of a sequence read, (2) sequence information at an end region of the sequence read, and (3) a length of the sequence read, wherein each consensus sequence of the set of consensus sequences corresponds to a polynucleotide molecule in the set of tagged parent polynucleotides; and (iv) the set of consensus sequences of each set of tagged parent molecules is analyzed; (v) the current sequence read is compared with a previous sequence read from at least one other time point; and (vi) a diagnostic confidence indication is updated accordingly, the diagnostic confidence indication indicating the probability of identifying one or more genetic variations in a body sample of the subject.

[0040] In some embodiments, the body sample is a blood sample.In some embodiments, the initial starting genetic material comprises cell-free deoxyribonucleic acid (DNA).

[0041] In some embodiments, the set of consensus sequences is analyzed separately for each set of tagged parent molecules.

[0042] In some embodiments, the analysis includes detecting mutations, insertions / deletions (indels), copy number variations, transversions, translocations, inversions, deletions, aneuploidy, partial aneuploidy, polyploidy, chromosomal instability, chromosomal structural changes, gene fusions, chromosome fusions, gene truncation, gene amplification, gene duplication, chromosomal damage, DNA damage, abnormal changes in nucleic acid chemical modifications, abnormal changes in epigenetic patterns, abnormal changes in nucleic acid methylation, infection, or cancer.

[0043] In some embodiments, (vi) includes increasing the diagnostic confidence indication in the current sequence read if the information from the previous sequence read confirms the information from the current sequence read. In some embodiments, (vi) includes decreasing the diagnostic confidence indication in the current sequence read if the information from the previous sequence read conflicts with the information from the current sequence read. In some embodiments, (vi) includes maintaining the diagnostic confidence indication unchanged in the current sequence read if the information from the previous sequence read is non-deterministic relative to the information from the current sequence read.

[0044] In some embodiments, (v) comprises comparing one or more current sequence read variations to one or more previous sequence read variations.

[0045] In another aspect, the present disclosure provides a method for detecting one or more genetic variants in a subject, the method comprising: (a) obtaining nucleic acid molecules from one or more cell-free biological samples of the subject; (b) assaying the nucleic acid molecules to generate a first set of genetic data and a second set of genetic data, wherein the first set of genetic data and / or the second set of genetic data are within a detection threshold; (c) comparing the first set of genetic data with the second set of genetic data to identify the one or more genetic variants in the first set of genetic data or the second set of genetic data; and (d) based on the one or more genetic variants identified in (c), using one or more programmed computer processors to update a diagnostic confidence indication of identifying the one or more genetic variants in the cell-free biological sample of the subject.

[0046] In some embodiments, the first set of genetic data and the second set of genetic data are within the detection threshold. In some embodiments, the first set of genetic data is within the detection threshold and the second set of genetic data is above the detection threshold. In some embodiments, the detection threshold is a noise threshold.

[0047] In some embodiments, the method further comprises identifying the one or more genetic variants in the first set of genetic data, and increasing the diagnostic confidence indication.

[0048] In some embodiments, subsets of the nucleic acid molecules are determined at different time points. In some embodiments, the nucleic acid molecules are obtained from more than one cell-free biological sample at the same time point or at different time points.

[0049] In some embodiments, the nucleic acid molecule is deoxyribonucleic acid (DNA). In some embodiments, the DNA is cell-free DNA (cfDNA).

[0050] In some embodiments, the method further comprises generating a genetic profile of the subject, wherein the genetic profile comprises the indication of diagnostic confidence in identifying the one or more genetic variants.

[0051] In some embodiments, in (c), a covariate variant is identified in the first set of genetic data, and further comprising updating the diagnostic confidence indication of a second covariate variant identified in the cell-free biological sample of the subject. In some embodiments, the method further comprises increasing the diagnostic confidence indication in (c) if the first set of genetic data is observed in the second set of genetic data. In some embodiments, the method further comprises reducing the diagnostic confidence indication in (c) if the first set of genetic data is different from the second set of genetic data.

[0052] In some embodiments, the detection threshold includes errors introduced by sequencing or amplification.

[0053] In some embodiments, the detection threshold comprises a per-base error rate of 0.5% to 5%. In some embodiments, the detection threshold comprises a per-base error rate of 0.5% to 1%.

[0054] In some embodiments, the nucleic acid molecule is obtained from a second cell-free biological sample of the subject. In some embodiments, the second cell-free biological sample is obtained after obtaining the cell-free biological sample of (a). In some embodiments, the second cell-free biological sample is obtained before obtaining the cell-free biological sample of (a). In some embodiments, the second cell-free biological sample is obtained simultaneously with the cell-free biological sample of (a). In some embodiments, the first set of genetic data corresponds to the cell-free biological sample of (a), and the second set of genetic data corresponds to the second cell-free biological sample.

[0055] In some embodiments, the method further comprises: attaching a tag to the nucleic acid molecule to generate a tagged parent polynucleotide; amplifying the tagged parent polynucleotide to produce a tagged progeny polynucleotide; and sequencing the tagged progeny polynucleotide to produce sequencing reads.

[0056] In some embodiments, attaching comprises uniquely tagging the nucleic acid molecules. In some embodiments, attaching comprises non-uniquely tagging the nucleic acid molecules such that no more than 5% of the nucleic acid molecules are uniquely tagged.

[0057] In some embodiments, the method further comprises selectively enriching the sequence of interest prior to sequencing.

[0058] In some embodiments, the method further comprises grouping the sequence reads into families based at least on the sequence tags. In some embodiments, grouping the sequence reads is further based on one or more of: sequence information at the beginning of the sequence reads derived from the nucleic acid molecule, sequence information at the end of the sequence derived from the nucleic acid molecule, and the length of the sequence reads.

[0059] In some embodiments, the method further comprises comparing the sequence reads grouped within each family to determine a consensus sequence for each family, wherein each consensus sequence corresponds to a unique polynucleotide among the tagged parent polynucleotides.

[0060] In some embodiments, the method further comprises obtaining less than 100 ng of the nucleic acid molecule.

[0061] In another aspect, the present disclosure provides a method for calling genetic variants in cell-free deoxyribonucleic acid (cfDNA) from a subject, the method comprising: (a) sequencing cfDNA from a sample obtained from the subject at a first time point using a DNA sequencing system; (b) detecting a genetic variant in the sequenced cfDNA from the first time point, wherein the genetic variant is detected at a level below the diagnostic limit; (c) sequencing cfDNA from a sample obtained from the subject at one or more subsequent time points using a DNA sequencing system; (d) detecting a genetic variant in the sequenced cfDNA from one or more subsequent time points, wherein the genetic variant is detected at a level below the diagnostic limit; (e) calling the sample as positive for the genetic variant based on detecting a genetic variant below the diagnostic limit in samples obtained at more than one time point.

[0062] In some embodiments, the method further includes (f) detecting a trend wherein, at a first time point, a genetic variant is detected below a diagnostic limit and is determined to be positive, and at one or more subsequent time points, the genetic variant is detected above a diagnostic limit, such that the genetic variant increases.

[0063] In some embodiments, the diagnostic limit is less than or equal to about 1.0%.

[0064] In another aspect, the present disclosure provides a method for determining genetic variants in cell-free deoxyribonucleic acid (cfDNA) from a subject, the method comprising: (a) sequencing cfDNA from a sample from the subject using a deoxyribonucleic acid (DNA) sequencing system; (b) detecting genetic variants in the sequenced cfDNA, wherein the genetic variants are detected at a level below the diagnostic limit; (c) sequencing cfDNA from a sample obtained from the subject using a DNA sequencing system, wherein the sample is resequenced one or more times; (d) detecting genetic variants in the sequenced cfDNA from one or more resequenced samples, wherein the genetic variants are detected at a level below the diagnostic limit; and (e) determining the sample to be positive for the genetic variant based on detecting the genetic variant below the diagnostic limit in the resequenced sample.

[0065] In another aspect, the present disclosure provides a method for detecting one or more genetic variants in a subject, the method comprising:

[0066] (a) obtaining nucleic acid molecules from one or more cell-free biological samples of the subject;

[0067] (b) assaying the nucleic acid molecule to generate a set of genetic data, wherein the genetic variant is within a detection threshold in the set of genetic data;

[0068] (c) identifying one or more covariate genetic variations associated with the genetic variation by querying the set of genetic data for the one or more covariate genetic variations associated with the genetic variation within a detection threshold; and

[0069] (d) updating, using one or more programmed computer processors, an indication of diagnostic confidence that the one or more genetic variants were identified in the cell-free biological sample of the subject based on the one or more genetic variants identified in (c).

[0070] In another aspect, the present disclosure provides a method for processing genetic data, the method comprising:

[0071] (a) receiving genetic data from a genetic analyzer, the genetic data generated from a cell-free nucleic acid sample of a subject;

[0072] (b) analyzing the genetic data using a programmed computer processor to identify one or more genetic variants in the genetic data; and

[0073] (c) providing an output on an electronic portal corresponding to the one or more genetic variants identified in (b).

[0074] In some embodiments, one or more results are output on a web portal or graphical user interface.

[0075] In another aspect, the present disclosure provides a method comprising:

[0076] (a) sequencing cfDNA from a subject using a gene sequencing system and generating sequencing data;

[0077] (b) receiving the sequence data into a computer memory;

[0078] (c) using a processor to execute logic to:

[0079] (i) determining the frequency of a genetic variant at one or more genetic loci;

[0080] (ii) determining whether the frequency of the genetic variant is above or below a predetermined diagnostic limit;

[0081] (iii) if the frequency is above the diagnostic limit, determining that the genetic variant is present in the sample;

[0082] (iv) alternatively, if the frequency is below the diagnostic limit, accessing sequence data from the subject taken at another time, and calling the genetic variant as present if the variant is detected at another time point;

[0083] (v) generating a report identifying a genetic variant that is determined to be present;

[0084] (d) sending the report to a website wherein the report is viewable on a graphical user interface.

[0085] In another aspect, the present disclosure provides a non-transitory computer readable medium containing machine executable code that, when executed by one or more computer processors, implements any of the methods described above or elsewhere herein.

[0086] In another aspect, the present disclosure provides a computer system comprising one or more computer processors and a memory coupled thereto. The memory comprises a non-transitory computer-readable medium comprising machine executable code that, when executed by the one or more computer processors, implements any of the methods described above or elsewhere herein.

[0087] From the following detailed description, other aspects and advantages of the present disclosure will become apparent to those skilled in the art, and only illustrative embodiments of the present disclosure are shown and described in the detailed description. As will be appreciated, the present disclosure can have other and different embodiments, and its several details can be modified in a variety of obvious aspects, all of which do not depart from the present disclosure. Accordingly, the drawings and descriptions are considered to be illustrative rather than restrictive in nature.

[0088] Incorporated by Reference

[0089] All publications, patents, and patent applications mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent, or patent application was specifically and individually indicated to be incorporated by reference. BRIEF DESCRIPTION OF THE DRAWINGS

[0090] The novel features of the present disclosure are particularly set forth in the appended claims. A better understanding of the features and advantages of the present disclosure will be obtained by referring to the following detailed description of the invention and the accompanying drawings which set forth illustrative embodiments in which the principles of the present disclosure are utilized, in which:

[0091] Figures 1A-1DExemplary systems for reducing error rates and bias in DNA sequence reading are illustrated.

[0092] Figure 2 An exemplary process for analyzing polynucleotides in a sample of initial genetic material is illustrated.

[0093] Figure 3 Another exemplary process for analyzing polynucleotides in a sample of initial genetic material is illustrated.

[0094] Figure 4 Another exemplary process for analyzing polynucleotides in a sample of initial genetic material is illustrated.

[0095] Figure 5A and 5B A schematic diagram showing a report generated by copy number variation analysis of a subject with cancer accessed via the internet.

[0096] Figure 6 A schematic diagram showing reports of subjects with cancer accessed via the Internet.

[0097] Figure 7 A computer system programmed or otherwise configured to analyze genetic data is described.

[0098] Figure 8 Detection of sequences in samples spiked with nucleic acids carrying cancer mutants is shown.

[0099] Fig. 9 A gene panel is shown that can be used with the methods and systems of the present disclosure. DETAILED DESCRIPTION

[0100] Although various embodiments of the present invention have been shown and described herein, it will be apparent to those skilled in the art that such embodiments are provided by way of example only. Many variations, changes and substitutions may occur to those skilled in the art without departing from the present invention. It should be understood that a variety of alternatives to the embodiments of the present invention described herein may be employed.

[0101] The terms used herein are for the purpose of describing specific embodiments only and are not intended to limit the present invention. As used herein, the singular forms "a", "an", and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise. In addition, where the terms "including", "includes", "having", "has", "with", or variations thereof are used in the detailed description and / or claims, such terms are intended to be inclusive in a manner similar to the term "comprising".

[0102] The term "about" or "approximately" means within an acceptable error range for a particular value as determined by one of ordinary skill in the art, which will depend in part on how the value is measured or determined, i.e., the limitations of the measurement system. For example, "about" may mean within 1 or more than 1 standard deviation according to practice in the art. Alternatively, "about" may mean a range of up to 20%, up to 10%, up to 5%, or up to 1% of a given value. Alternatively, particularly with respect to biological systems or processes, the term may mean within the order of magnitude of a value, such as within 5 times of a value or within 2 times of a value. Unless otherwise noted, when describing a particular value in the present application and claims, it should be assumed that the term "about" refers to within an acceptable error range for a particular value.

[0103] In certain embodiments, diagnosis involves detecting (e.g., measuring) a signal indicating a disease, such as a biomarker, and detecting or measuring is associated with a disease state. However, due to low sample concentrations, the signal may be weak, or the signal may be obscured by noise. If the signal is weak, so that the signal is at or below the noise threshold or detection limit, it may be difficult to distinguish the signal from the noise generated by the detection system or it may be difficult to detect the signal. In such cases, people may not have the confidence to make a diagnosis. By viewing genetic data or variations detected from more than one time point, more than one test as a confirmatory signal or more than one covariate genetic variant detected together, diagnostic confidence can be enhanced.

[0104] As used herein, the term detection limit and diagnostic limit generally refer to the ability (capability) of detecting the presence or absence or amount of a given gene or variant with a predetermined confidence level. As generally used herein, detection threshold refers to the scope at or below the detection limit, in which some genetic variants can not be detected or can not be distinguished from noise. In some cases, "detection limit" can be the minimum frequency or concentration at which a variant is detected in a variant positive sample in the case of 95%. The diagnostic limit can be the minimum frequency for making a positive judgment. The diagnostic limit can be from about 0.01% to about 1%. The diagnostic limit can be less than or equal to about 5%, about 1.0%, about 0.8%, about 0.5%, about 0.25%, about 0.1%, about 0.08%, about 0.05%, about 0.03%, about 0.01% or less. In some cases, the detection limit can be identical with the diagnostic limit. The detection limit or diagnostic limit can be a noise limit or a noise threshold. In such cases, the detection limit or diagnostic limit are the limits that can not distinguish signal from noise.

[0105] In some cases, the diagnostic limit can be lower than the detection limit. Using the methods and systems described herein, a genetic variant present in an amount at or below the detection limit can be determined as positive with a predetermined confidence level (e.g., at least 80%, 90%, or 95% confidence), even when the genetic variant is present at or below the detection limit.

[0106] Therefore, for example, sequence analysis of a sample can reveal many different genetic variants and multiple frequencies or concentrations in the sample. The diagnostic limit can be set by a clinician at, for example, 1%, that is, unless the variant is present at a concentration of at least 1%, no variant is reported as "present" in the sample or "determined" in the report. If the first variant is detected at 5%, the variant is "determined" to be present in the sample and reported. Another variant is detected at 0.5%. This is lower than the diagnostic limit and may be lower than the detection limit of the sequencing system. In this case, the clinician has several options. First, the same sample can be retested. If the variant is detected again below or above the detection limit, it is now "determined" to be present in the sample. Secondly, the sequence data can be checked for covariate variation. For example, the variant can be a known resistance mutation. If a driver mutation of the same gene is detected from the sequence data, this also indicates that the resistance mutant may not be a "noise" detection, and a positive determination can be made again. Third, the subject can be tested again at a subsequent time point. If the variant is detected in a subsequent sample, the first sample can be determined to be a "presence" variant. Alternatively, if a subsequent test reveals an amount of a variant with a confidence score that does not overlap with the first test, the variant can be judged to be increased or decreased in the subject, as appropriate.

[0107] Several factors may affect the ability to detect genes or variants at or near the detection limit or diagnostic limit. The detected gene or variant may exist in low amounts or low concentrations, causing the sequence analyzer to be unable to detect the gene or variant. For example, in one million analyzed cell-free nucleic acid molecules, a genetic mutation may be present in a cell-free nucleic acid molecule analyzed, so the variant base is judged to exist at a frequency of one in a million. The sequencing analyzer may erroneously characterize the genetic mutation as a non-variant base determination, because the genetic mutation occurs at a lower frequency relative to all other base determinations at the same site. In such cases, the detection limit may generally refer to the ability of a genetic analyzer or sequencer to detect genetic variations present at very low frequencies. In addition, sequence errors or artifacts introduced by sequencing or amplification may make it difficult to distinguish or impossible to distinguish errors and / or artifacts from detected genes or genetic variations. In such cases, the detection limit may refer to the ability to distinguish variant base determinations from erroneous determinations with confidence. The present disclosure provides a technique for detecting genetic variations at or below the detection limit and / or within the detection threshold.

[0108] As used herein, the term "diagnosis confidence indication" generally refers to the expression, number, ranking, scoring, degree or value designated as indicating the presence of one or more genetic variants and the presence of how much is trusted. The diagnosis confidence indication can indicate the probability of identifying one or more genetic variants in the biological sample of the subject. For example, among others, the representation can be a binary value or an alphanumeric number sorted from A to Z. In another example, among others, the diagnosis confidence indication can have any value from 0 to 100. In another example, the diagnosis confidence indication can be expressed as a range or degree, for example, "low" or "high", "more" or "less", "increased" or "reduced". Low diagnosis confidence indication indicates that the detected genetic variant may be noise (for example, the presence of the detected genetic variant cannot be too trusted). High diagnosis confidence indication means that for the detected genetic variant, the genetic variant may exist. In some cases, if the diagnosis confidence indication of the result is lower than 25-30 in 100, the result may be unreliable.

[0109] The diagnostic confidence indication of each variant can be adjusted to indicate the confidence of the predicted genetic variation. The confidence can be increased or decreased by using the measurement at more than one time point or from the measurement at the same time point or more than one sample at different time points. The diagnostic confidence can be further adjusted based on the detection of covariate variation. The diagnostic confidence indication can be specified by any of many statistical methods, and can be based at least in part on the frequency of the measurements observed over a period of time.

[0110] As used herein, the term "covariate variation" or "covariate variant" generally refers to genetic variations that tend to change together, for example, the presence of one variation is associated with the presence of a covariate variation. Accordingly, if a variant is observed to be below the diagnostic limit or detection limit, and a covariate variant (whether above or below the detection limit) is detected at the same time, it is more likely that the sample is positive for both variants, and both variants can be "judged" to be present in the sample. An example of a covariate variation is a driver mutation and a resistance mutation or a mutation of unknown significance. That is, after the driver mutation exists, other mutations in the same gene, such as resistance mutations, may occur, particularly after treatment and recurrence of cancer. As a non-limiting example, a driver mutation can be detected with a high diagnostic confidence above the detection limit. However, due to insufficient sampling or noise, it may be difficult to confidently evaluate whether another genetic variation exists. If a genetic variation is usually present with a driver mutation, so that the variant is a covariate variant (such as a passenger mutation or a resistance mutation), the diagnostic confidence indication of the genetic variant will increase. The strength of the association between certain variants detected together can increase the probability, likelihood, and / or confidence that the genetic data detected below the detection limit is a genetic variation.

[0111] As used herein, the term "DNA sequencing system" generally refers to a DNA sample preparation scheme used in conjunction with a sequencing instrument. The DNA sample preparation scheme may involve library preparation, amplification, adapter connection, and single-strand extension, among other molecular biological methods. A sequencing instrument may be any instrument capable of automating a variety of sequencing methods or processes. Non-limiting examples of a variety of sequencing methods or processes include: Sanger sequencing, high-throughput sequencing, pyrophosphate sequencing, sequencing-by-synthesis, single-molecule sequencing, nanopore sequencing, semiconductor sequencing, sequencing-by-ligation, sequencing-by-hybridization, RNA-Seq (Illumina), digital gene expression (Digital Gene Expression) (Helicos), next-generation sequencing, single-molecule sequencing (SMSS) (Helicos), large-scale parallel sequencing, cloned single-molecule arrays (Solexa), shotgun sequencing, Maxim-Gilbert sequencing, primer walking, and any other sequencing method known in the art. A DNA sequencing system may contain all protocols for preparing samples for sequencing in a specific sequencing instrument.

[0112] The term "subject" as used herein generally refers to any organism used in the methods of the present disclosure. In some instances, the subject is a human, a mammal, a vertebrate, an invertebrate, a eukaryote, an archaea, a fungus or a prokaryote. In some cases, the subject may be a human. The subject may be living or dead. The subject may be a patient. For example, the subject may suffer from a disease (or may be suspected of suffering from a disease) and / or may be under the care of a medical practitioner. The subject may be an individual who is undergoing treatment and / or a diagnosis of a health or medical condition. The subject and / or family member may be a relative (e.g., sister, brother, mother, father, nephew / nephew, niece / niece, aunt / auntie, uncle / uncle, grandparent, great-grandparent, cousin) with another subject used in the methods of the present disclosure.

[0113] As used herein, the term "nucleic acid" generally refers to a molecule comprising one or more nucleic acid subunits. Nucleic acid can include one or more subunits selected from adenosine (A), cytosine (C), guanine (G), thymine (T) and uracil (U) or its variants. Nucleotide can include A, C, G, T or U or its variants. Nucleotide can include any subunit that can be incorporated into the growing nucleic acid chain. Such subunits can be A, C, G, T or U, or any other subunit that is specific to one or more complementary A, C, G, T or U or complementary to purine (i.e., A or G or its variants) or pyrimidine (i.e., C, T or U or its variants). Subunits can enable individual nucleic acid bases or base groups (e.g., AA, TA, AT, GC, CG, CT, TC, GT, TG, AC, CA or uracil-its counterparts) to be resolved. In some instances, nucleic acid is deoxyribonucleic acid (DNA) or ribonucleic acid (RNA) or its derivatives. Nucleic acid can be single-stranded or double-stranded.

[0114] The term "genome" generally refers to the entirety of an organism's genetic information. A genome can be encoded in DNA or in RNA. A genome can contain coding regions and non-coding regions that encode proteins. A genome can contain a collection of sequences of all chromosomes in an organism. For example, the human genome has a total of 46 chromosomes. All of these sequences together make up the human genome.

[0115] As used herein, the term "sample" generally refers to a biological sample. A sample can be or include blood, serum, plasma, vitreous, sputum, urine, tears, sweat, saliva, semen, mucosal secretions, mucus, spinal fluid, amniotic fluid, lymph, etc. A sample can be a cell-free sample. A sample can include nucleic acid molecules, such as polynucleotides. A polynucleotide can be deoxyribonucleic acid (DNA) or ribonucleic acid (RNA). Cell-free polynucleotides can be fetal-derived (via fluid obtained from a pregnant subject), or can be derived from the subject's own tissue.

[0116] Detection limit / noise range

[0117] Polynucleotide sequencing can be compared to problems in communication theory. The initial individual polynucleotide or the ensemble of polynucleotides is conceptualized as the original message. Labeling and / or amplification can be considered as encoding the original message into a signal. Sequencing can be considered as a communication channel. The output of the sequencer, e.g., sequence reads, can be considered as a received signal. Bioinformatics processing can be considered as a receiver that decodes the received signal to produce the transmitted information (e.g., one nucleotide sequence or more nucleotide sequences). The received signal may include artifacts, such as noise and distortion. Noise can be considered as an unwanted random increase in the signal. Distortion can be considered as a change in the amplitude of the signal or a portion of the signal.

[0118] Noise can be introduced by errors in copying and / or reading polynucleotides.For example, in the sequencing process, a single polynucleotide may first undergo amplification.Amplification may introduce errors, causing the subset of amplified polynucleotides to include bases different from the original bases at the locus at a specific locus.In addition, in the reading process, the base at any specific locus may be incorrectly read.Therefore, the set of sequence reads may include a certain percentage of base determinations different from the original bases at the locus.In common sequencing techniques, this error rate can be a single digit, for example, 2%-3%.In some cases, the error rate can be up to about 10%, up to about 9%, up to about 8%, up to about 7%, up to about 6%, up to about 5%, up to about 4%, up to about 3%, up to about 2%, or up to about 1%.When sequencing is performed on the set of all molecules assumed to have the same sequence, such noise can be small enough so that people can identify the original bases with high reliability.

[0119] However, if the collection of parental polynucleotides includes a subset of polynucleotides with variations at a specific locus, noise may be a significant problem. For example, when cell-free DNA includes not only germline DNA but also DNA from another source such as fetal DNA or DNA from cancer cells, this may be the case. In this case, if the frequency of molecules with sequence variants may be in the same range as the frequency of errors introduced by the sequencing process, true sequence variants may not be distinguished from noise. This may interfere with, for example, the detection of sequence variants in a sample. For example, a sequence may have a per-base error rate of 0.5%-1%. Amplification bias and sequencing errors introduce noise into the final sequencing product. This noise can reduce the sensitivity of detection. As a non-limiting example, sequence variants whose frequency is lower than the sequencing error rate can be mistaken for noise.

[0120] Noise range or detection limit refers to the situation where the frequency of molecules with sequence variants is in the same range as the frequency of errors introduced by the sequencing process." Detection limit" may also refer to the situation where too few molecules carrying the variant are sequenced for the variant to be detected. The frequency of molecules with sequence variants may be in the same range as the frequency of errors due to the small amount of nucleic acid molecules. As a non-limiting example, the sampling amount of nucleic acid, for example, 100ng, may contain a relatively small amount of cell-free nucleic acid molecules, for example, circulating tumor DNA molecules, causing the frequency of sequence variants to be low, although the variant may be present in most circulating tumor DNA molecules. Alternatively, sequence variants may be rare, or occur only in very small amounts of sampled nucleic acids, making the detected variants indistinguishable from noise and / or sequencing errors. As a non-limiting example, genetic variants at a specific locus may be detected in only 0.1% to 5% of all reads at the locus.

[0121] Distortion can be manifested in the sequencing process as a difference in signal intensity generated by molecules of the same frequency in the parental population, for example, the total number of sequence reads. For example, distortion can be introduced by amplification bias, GC bias or sequencing bias. This may interfere with the detection of copy number variation in the sample. GC bias results in an uneven presentation of GC-rich or poor regions in the sequence reads. In addition, amplification bias can distort the measurement of copy number variation by providing sequence reads in an amount larger or smaller than their actual number in the population.

[0122] In the polynucleotide sequencing process, sequencing and / or amplification artifacts or errors, such as noise and / or distortion can be reduced. Sequencing and / or amplification artifacts or errors can be reduced using a variety of techniques for sequencing and sequence analysis. A variety of techniques can include sequencing methods and / or statistical methods.

[0123] One way to reduce noise and / or distortion is to filter the sequence reads. As a non-limiting example, the sequence reads can be filtered by requiring the sequence reads to meet a quality threshold or by reducing GC bias. Such methods are typically performed on a collection of sequence reads that are the output of a sequencer, and can be performed in a sequence read-by-sequence read manner without regard to family structure (a subset of sequences derived from a single original parent molecule).

[0124] Another way to reduce noise and / or distortion from a single individual molecule or from the entire molecule is to group sequence reads into families derived from the original individual molecule to reduce noise and / or distortion from a single individual molecule or from the entire molecule. The individual polynucleotides in the sample of the initial genetic material are effectively converted into the parental polynucleotides of the tagging ready for sequencing, which can increase the probability that the individual polynucleotides in the sample of the initial genetic material will be presented in the sample ready for sequencing. This can generate sequence information about more polynucleotides in the initial sample. In addition, by sampling the progeny polynucleotides amplified from the parental polynucleotides of the tagging, and stacking the generated sequence reads into the consensus sequence of the sequence of the polynucleotides of the parental tagging, the consensus sequence of the parental polynucleotides of the tagging can be generated in high yield to reduce the noise introduced by amplification bias and / or sequencing errors and can increase the sensitivity of detection. Stacking the sequence reads into a consensus sequence is a way to reduce the noise in the information received from a molecule. Using the probability function of the received frequency of the conversion is another way to reduce noise and / or distortion. With respect to the molecule as a whole, grouping reads into families and determining quantitative measures of the families reduces distortion of, for example, the amount of the molecule at each locus in more than one different locus. Furthermore, merging sequence reads from different families into a common sequence eliminates errors introduced by amplification and / or sequencing errors. In addition, determining the frequency of base calls based on probabilities derived from family information also reduces noise in the information received from the molecule as a whole.

[0125] Noise and / or distortion can be further reduced by comparing genetic variations in sequence reads with genetic variations in other sequence reads. Genetic variations observed in one sequence read and again in other sequence reads increase the probability that the detected variant is actually a genetic variant and not just a sequencing error or noise. As a non-limiting example, if a genetic variation is observed in a first sequence read and is also observed in a second sequence read, a Bayesian inference can be made about whether the variation is actually a genetic variation and not a sequencing error.

[0126] The present disclosure provides methods for detecting variations in nucleic acid molecules, particularly those variations that are within the noise range or below the detection limit. Variants initially detected in nucleic acid molecules can be compared with other variants (such as, for example, variants at the same locus or covariate genetic variants) to determine whether the variant is more likely or less likely to be accurately detected. Variants can be detected in amplified nucleic acid molecules, in sequence reads or superimposed sequence reads.

[0127] Repeated detection of variants can increase the probability, likelihood and / or confidence that the variant is accurately detected. Variants can be repeatedly detected by comparing two or more groups of genetic data or genetic variation. Two or more groups of genetic variation can be samples at multiple time points and different samples (such as re-analyzed blood samples) at the same time point. When a variant is detected to be within the noise range or below the noise threshold, resampling or repeated detection of low-frequency variants makes it more likely that it is actually a variant rather than a sequencing error. Resampling can be from the same sample, such as a sample reanalyzed or rerun, or from samples at different time points.

[0128] As a non-limiting example, the genetic variant with low confidence score can be detected with a frequency or amount lower than detection limit or noise range. However, if the genetic variant is observed again, such as for example at a subsequent time point, in a previous sample or when reanalyzing the sample, the confidence score can increase. Therefore, although there is a frequency or amount lower than detection limit or noise range, variant can be detected with greater confidence. In other cases, for example, when resampling, the genetic variant is not observed again, the confidence score can remain unchanged or reduce. Alternatively, if the genetic variant observed at a specific locus conflicts with the resampling result, the confidence score can reduce.

[0129] Covariant detection can increase the probability, likelihood and / or confidence that variants are accurately detected.For covariant genetic variants, the presence of a genetic variant is related to the presence of one or more other genetic variants.Based on the detection of covariant genetic variation, even when the relevant genetic variation exists below the detection limit, it is possible to infer the presence of the relevant covariant genetic variation.Alternately, based on the detection of covariant genetic variation, the diagnostic confidence indication of the relevant genetic variation can be increased.In addition, in some cases where covariant variants are detected, the detection threshold of the covariant variant detected below the detection limit can be reduced.Non-limiting examples of covariant variation or gene include: drive mutation and resistance mutation, drive mutation and passenger mutation.As a specific example of covariant or gene is EGFRL858R activating mutation and EGFR T790M resistance mutation found in lung cancer.Many other covariant variants and genes are related to a variety of resistance mutations, and will be identified by those skilled in the art.

[0130] The present disclosure provides a method for detecting genetic variants, wherein at least some variants are within a noise range or threshold. Within a noise threshold or range, it may be difficult or impossible or difficult to detect genetic variation with confidence. In some cases, the noise threshold provides a limit for detecting genetic variation with statistical confidence. The noise threshold or range may overlap with the sequencing error rate. The noise threshold may be the same as the sequencing error rate. The noise threshold may be lower than the sequencing error rate. The noise threshold may be up to about 10%, up to about 9%, up to about 8%, up to about 7%, up to about 6%, up to about 5%, up to about 4%, up to about 3%, up to about 2%, or up to about 1%. In some cases, the noise range is about 0.5% to 10% of each base error. In some cases, the noise threshold is about 0.5% to 5% of each base error. In some cases, the noise threshold is about 0.5% to 1% of each base error. The terms noise and threshold may be used interchangeably.

[0131] Several types of genetic variants can be detected in nucleic acid molecules. Genetic variation can be interchangeably referred to as genetic variants or genetic aberrations. Genetic variation can include single base substitutions, copy number variation, gain and loss positions, and gene fusions. The combination of these genetic variants can be detected. Non-limiting examples of other genetic variants can also include: transversions, translocations, inversions, deletions, aneuploidy, partial aneuploidy, polyploidy, chromosome instability, chromosome structural changes, chromosome fusions, gene truncation, gene amplification, gene duplication, chromosome damage, DNA damage, abnormal changes in nucleic acid chemical modifications, abnormal changes in epigenetic patterns, and abnormal changes in nucleic acid methylation.

[0132] In one embodiment, using measurements from more than one sample collected substantially simultaneously or at more than one time point, the diagnostic confidence indication of each variant can be adjusted to indicate the confidence of the observation of predicted copy number variation (CNV) or mutation. Confidence can be increased by using measurements at more than one time point to determine whether cancer progresses, alleviates or stabilizes. Diagnostic confidence indications can be specified by any of many statistical methods, and can be based at least in part on the frequency of observing measurements over a period of time. For example, a statistical correlation of current and previous results can be made. Optionally, for each diagnosis, a hidden Markov model can be established so that maximum likelihood or maximum a posteriori probability decisions can be made based on the frequency of occurrence of specific test events from more than one measurement or time point. As part of the model, the error probability of a specific decision and the resulting diagnostic confidence indication can also be output. In this way, the measurement of parameters, whether they are within the noise range, can be provided with confidence intervals. By testing over time, one can increase the prediction confidence of whether the cancer progresses, stabilizes or alleviates by comparing the confidence intervals over time. Two sampling time points can be separated by at least about 1 microsecond, 1 millisecond, 1 second, 10 seconds, 30 seconds, 1 minute, 10 minutes, 30 minutes, 1 hour, 12 hours, 1 day, 1 week, 2 weeks, 3 weeks, 1 month or 1 year. Two time points can be separated by about one month to about one year, about one year to about 5 years, or no more than about three months, two months, one month, three weeks, two weeks, one week, one day or 12 hours.

[0133] Figure 1A A first exemplary system is shown that reduces error rates and biases that may be several orders of magnitude higher than required to reliably detect de novo genomic alterations associated with cancer. The process first captures genetic information by collecting a sample of bodily fluid as a source of genetic material (blood, saliva, sweat, among others), and then the process sequences the material (1). For example, polynucleotides in a sample can be sequenced, producing more than one sequence read. Tumor burden in a sample containing polynucleotides can be estimated as the ratio of the relative number of sequence reads carrying the variant to the total number of sequence reads generated from the sample. Moreover, in the case of copy number variants, tumor burden can be estimated as the relative excess (in the case of gene duplication) or relative lack (in the case of gene elimination) of the total number of sequence reads at the test locus compared to the control locus. Thus, for example, a run can produce 1000 reads mapped to an oncogene locus, of which 900 correspond to wild type and 100 correspond to cancer mutants, indicating a tumor burden of 10%. hereinafter described in detail. Figure 2-4 Further details regarding exemplary collection and sequencing of genetic material are discussed in.

[0134] Next, the genetic information is processed (2). Genetic variants are then identified. For example, if it is a common genetic variant, the variant can be a single nucleotide polymorphism (SNP); in the case of a rare genetic variant, the variant can be a mutation or copy number variation. The process then determines the frequency of the genetic variant in the sample containing the genetic material. Since the process is noisy, the process separates the information from the noise (3).

[0135] Sequencing methods have error rates. For example, Illumina's mySeq system can produce low single-digit error rate percentages. Therefore, for 1000 sequence reads mapped to a locus, one can expect about 50 reads (about 5%) to include errors. Certain methods, such as those described in WO 2014 / 149134 (Talasaz and Eltoukhy), which are incorporated herein by reference in their entirety, can significantly reduce error rates. Errors generate noise, obscuring signals from cancers present at low levels in the sample. Therefore, if a sample has a tumor burden at a level near the sequencing system error rate (e.g., about 0.1%-5%), it may be difficult to distinguish signals corresponding to genetic variants due to cancer from signals caused by noise.

[0136] Cancer diagnoses can be made by analyzing genetic variants even in the presence of noise. The analysis can be based on the frequency of sequence variants or the level of CNVs (4), and an indication or level of diagnostic confidence for detecting genetic variants within the noise range can be established (5).

[0137] Next, the process increases the confidence in the diagnosis. This can be done by using more than one measurement to increase the confidence in the diagnosis (6), or alternatively, using measurements at more than one time point to determine whether the cancer is progressing, in remission, or stable (7).

[0138] Diagnostic confidence can be used to identify disease states. For example, cell-free polynucleotides obtained from a subject can include polynucleotides derived from normal cells, and polynucleotides derived from diseased cells such as cancer cells. Polynucleotides from cancer cells can carry genetic variants, such as somatic mutations and copy number variants. When the cell-free polynucleotides from a sample of a subject are sequenced, these cancer polynucleotides are detected as sequence variants or as copy number variants. The relative amount of tumor polynucleotides in a sample of cell-free polynucleotides is referred to as "tumor burden".

[0139] Measurements of parameters, whether they are within the noise range or not, can provide confidence intervals. Testing over time allows one to determine if the cancer is progressing, stable, or in remission by comparing confidence intervals over time. In cases where confidence intervals do not overlap, this indicates the direction of the disease.

[0140] Figure 1B A second exemplary system is shown that reduces error rates and biases that may be several orders of magnitude higher than required to reliably detect de novo genomic alterations associated with cancer. This is done by generating sequence reads from a sample by a genetic analyzer, such as a DNA sequencer (10). The system then characterizes the genetic information of the subject at two or more samples or time points (12). Next, the system uses information from the two or more sampling points or time points to generate an adjusted test result in the genetic information characterizing the subject (14).

[0141] The test results can be adjusted by enhancing or eliminating confidence indications. For example, the process includes increasing the diagnostic confidence indication in a subsequent characterization or a previous characterization if the information from the first time point confirms the information from the second time point. Optionally, if the information from the first time point confirms the information from the second time point, the process can increase the diagnostic confidence indication in the subsequent characterization. If the information from the first time point conflicts with the information from the second time point, the diagnostic confidence indication in the subsequent characterization can be reduced. Optionally, the process can retain the diagnostic confidence indication unchanged in the subsequent characterization for the de novo information.

[0142] exist Figure 1B In one embodiment, the system compares the current sequence reads by a genetic analyzer (e.g., a DNA sequencer) with the previous sequence reads and updates the diagnostic confidence indication accordingly. Based on the enhanced confidence signal, the system accurately generates a genetic profile of extracellular polynucleotides in the subject, wherein the genetic profile comprises more than one data obtained by copy number variation and / or mutation analysis.

[0143] Figure 1C A third exemplary system is shown that reduces error rates and biases that may be several orders of magnitude higher than required to reliably detect de novo genomic alterations associated with cancer. As a non-limiting example, the system performs cancer detection by sequencing cell-free nucleic acid, wherein at least a portion of each gene in any of a set of at least 10, 25, 50, or 100 genes is sequenced (20); comparing a current sequence read to a previous sequence read and updating a diagnostic confidence indication accordingly (22). The system then detects the presence or absence of a genetic alteration and / or the amount of genetic variation in an individual based on the diagnostic confidence indication of the current sequence read (24).

[0144] Figure 1DAnother exemplary system is shown that reduces error rates and biases that may be several orders of magnitude higher than required to reliably detect de novo genomic alterations associated with cancer. The system detects cancer, for example, by sequencing cell-free nucleic acid (30); comparing current sequence reads to previous sequence reads by a DNA sequencer and updating diagnostic confidence accordingly, each consensus sequence corresponding to a unique polynucleotide in a set of tagged parental polynucleotides (32); and generating a genetic profile of extracellular polynucleotides in a subject, wherein the genetic profile includes more than one data obtained from copy number variation or rare mutation analysis (34).

[0145] Figures 1A-1D The system detects genetic variation in a sample of initial genetic material with high sensitivity. The method involves using one to three of the following tools: first, effectively converting individual polynucleotides in the sample of initial genetic material into sequencing-ready tagged parental polynucleotides so as to increase the probability that individual polynucleotides in the sample of initial genetic material will be presented in the sequencing-ready sample. This can generate sequence information about more polynucleotides in the initial sample. Second, by high-rate sampling of progeny polynucleotides amplified from tagged parental polynucleotides, and stacking the generated sequence reads into a consensus sequence presenting the sequence of the parental tagged polynucleotide, a consensus sequence of the tagged parental polynucleotide is generated at high yield. This can reduce the noise introduced by amplification bias and / or sequencing errors and can increase the sensitivity of detection. Third, the noise in the detection of mutations and copy number variations is reduced by comparing the previous sample analysis with the current sample, and increasing the diagnostic confidence indication if the same mutation and copy number variation have appeared in the previous analysis, and otherwise reducing the diagnostic confidence indication if this is the first time the sequence has been observed.

[0146] The system detects genetic variation in a sample of initial genetic material with high sensitivity. In a specific embodiment, the system operation includes sample preparation or extraction and separation of cell-free polynucleotide sequences from body fluids; the cell-free polynucleotides are then sequenced by techniques utilized in the art; and bioinformatics tools are applied to detect mutations and copy number variations compared to a reference. The detection of mutations and copy number variations is enhanced by comparing the previous sample analysis with the current sample, and increasing the diagnostic confidence indication if the same mutation and copy number variation have already appeared in the previous analysis, otherwise if this is the first time the sequence has been observed, the diagnostic confidence indication is reduced or the diagnostic confidence indication is kept unchanged. The system and method may also include a database or collection of different mutations or copy number variation spectra for different diseases, so as to be used as an additional reference for mutation detection, copy number variation spectrum analysis, or general genetic spectrum analysis of auxiliary diseases.

[0147] After collecting sequencing data of cell-free polynucleotide sequences, one or more bioinformatics processes can be applied to the sequence data to detect genetic features or variations, such as copy number variation, mutation, or changes in epigenetic markers, including but not limited to methylation profiles. In some cases where copy number variation analysis is desired, the sequence data can be: 1) aligned with a reference genome; 2) filtered and mapped; 3) divided into sequence windows or bins; 4) count the coverage reads of each window; 5) then the coverage reads can be normalized using a random or statistical modeling algorithm; 6) and an output file can be generated, which reflects the discrete copy number states at multiple positions in the genome. In other cases where mutation analysis is desired, the sequence data can be 1) aligned with a reference genome; 2) filtered and mapped; 3) the frequency of variant bases is calculated based on the coverage reads of the specific base; 4) the variant base frequencies are normalized using a random, statistical, or probabilistic modeling algorithm; and 5) an output file can be generated, which reflects the mutation states at multiple positions in the genome. Temporal information from current and previous analyses of a patient or subject is used to enhance the analysis and determination.

[0148] A variety of different reactions and / or operations can occur in the systems and methods disclosed herein, including but not limited to: nucleic acid sequencing, nucleic acid quantification, sequencing optimization, detection of gene expression, quantitative gene expression, genome spectrum analysis, cancer spectrum analysis or analysis of expressed markers. In addition, the systems and methods have many medical applications. For example, it can be used for the identification, detection, diagnosis, treatment, monitoring, staging or risk prediction of a variety of genetic and non-genetic diseases and disorders (including cancer). It can be used to evaluate the response of a subject to different treatments for genetic and non-genetic diseases, or provide information about disease progression and prognosis.

[0149] Polynucleotide isolation and extraction

[0150] The systems and methods of the present disclosure can have a wide range of uses in the manipulation, preparation, identification and / or quantification of nucleic acids (including cell-free polynucleotides). Examples of nucleic acids or polynucleotides include, but are not limited to, DNA, RNA, amplicons, cDNA, dsDNA, ssDNA, plasmid DNA, cosmid DNA, high molecular weight (MW) DNA, chromosomal DNA, genomic DNA, viral DNA, bacterial DNA, mtDNA (mitochondrial DNA), mRNA, rRNA, tRNA, nRNA, siRNA, snRNA, snoRNA, scaRNA, microRNA, dsRNA, ribozymes, riboswitches, and viral RNA (e.g., retroviral RNA).

[0151] Cell-free polynucleotides can be derived from a variety of sources, including human, mammalian, non-human mammalian, ape, monkey, chimpanzee, reptilian, amphibian, or avian sources. In addition, samples can be extracted from a variety of animal fluids containing cell-free sequences, including but not limited to blood, serum, plasma, vitreous, sputum, urine, tears, sweat, saliva, semen, mucosal secretions, mucus, spinal fluid, amniotic fluid, lymph fluid, etc. Cell-free polynucleotides can be of fetal origin (via fluids obtained from a pregnant subject), or can be derived from the subject's own tissues.

[0152] Isolation and extraction of cell-free polynucleotides can be performed by collecting body fluids using a variety of techniques. In some cases, collection can include aspirating body fluids from a subject using a syringe. In other cases, collection can include pipetting or directly collecting fluids into a collection container.

[0153] After the body fluid is collected, the cell-free polynucleotides can be isolated and extracted using a variety of techniques used in the art. In some cases, commercially available kits such as Qiagen Circulating Nucleic Acid Kit Protocol for Isolation, Extraction, and Preparation of Cell-Free DNA. In other instances, Qiagen Qubit TM dsDNA HS Assay Kit Protocol, Agilent TM DNA 1000 Kit or TruSeq TM Sequencing Library Preparation; Low-Throughput (LT) solution.

[0154] Typically, cell-free polynucleotides are extracted and separated from body fluids by a partitioning step, in which the cell-free DNA found in the solution is separated from other insoluble components of cells and body fluids. Segmentation may include, but is not limited to, techniques such as centrifugation or filtration. In other cases, cells are not first segmented with cell-free DNA, but are lysed. In this example, the genomic DNA of intact cells is segmented by selective precipitation. Cell-free polynucleotides (including DNA) can remain soluble and can be separated from insoluble genomic DNA and extracted. Typically, after adding different kit-specific buffers and other washing steps, isopropanol precipitation can be used to precipitate DNA. Further cleaning steps such as silica-based columns can be used to remove contaminants or salts. General steps can be optimized for specific applications. For example, nonspecific bulk carrier polynucleotides can be added throughout the reaction to optimize certain aspects of the program such as yield.

[0155] Isolation and purification of cell-free DNA can be accomplished using any method, including but not limited to the use of commercial kits and protocols provided by companies such as Sigma Aldrich, Life Technologies, Promega, Affymetrix, IBI, etc. Kits and protocols may also be non-commercially available.

[0156] Following isolation, in some cases, the cell-free polynucleotides are premixed with one or more additional substances such as one or more reagents (eg, ligase, protease, polymerase) prior to sequencing.

[0157] One method to increase conversion efficiency involves using a ligase engineered for optimal reactivity on single-stranded DNA, such as a ThermoPhage ssDNA ligase derivative. Such ligases bypass the traditional steps of end repair and A-tailing in library preparation, which may have poor efficiency and / or cumulative losses due to intermediate cleaning steps, and allow the probability of sense or antisense starting polynucleotides being converted into appropriately tagged polynucleotides to be doubled. It also converts double-stranded polynucleotides that may have overhangs that may not be sufficiently blunted by conventional end repair reactions. The optimal reaction conditions for this ssDNA reaction are: 1x reaction buffer (50mM MOPS (pH 7.5), 1mM DTT, 5mMMgCl2, 10mM KCl). At 65°C, 200pmol 85nt ssDNA oligomers and 5U ssDNA ligase were incubated with 50mM ATP, 25mg / ml BSA, 2.5mM MnCl2 for 1 hour. Subsequent use of PCR amplification can further convert the tagged single-stranded library into a double-stranded library and yield an overall conversion efficiency far above 20%. Other methods to increase the conversion rate to, for example, above 10% include, for example, any of the following methods alone or in combination: annealing-optimized molecular inversion probes, blunt-end ligation with a well-controlled polynucleotide size range, sticky-end ligation, or an upfront multiplex amplification step with or without fusion primers.

[0158] Molecular barcoding of cell-free polynucleotides

[0159] The systems and methods of the present disclosure can also enable cell-free polynucleotides to be tagged or traced to allow subsequent identification and origin determination of specific polynucleotides. This feature is different from other methods that use pooled or multiple reactants and only provide measurements or analyses as average values ​​of multiple samples. Here, assigning identifiers to individual polynucleotides or subgroups of polynucleotides can allow unique identities to be assigned to individual sequences or fragments of sequences. This can allow data to be obtained from individual samples without being limited to the average value of the sample.

[0160] In some instances, nucleic acid or other molecules derived from a single strand can share a common tag or identifier and therefore can be subsequently identified as being derived from the strand. Similarly, all fragments from a single strand of nucleic acid can be tagged with the same identifier or tag, thereby allowing subsequent identification of fragments from the parental strand. In other cases, gene expression products (e.g., mRNA) can be tagged to quantify expression, whereby a barcode or a combination of a barcode and its attached sequence can be counted. Still in other cases, the system and method can be used as a PCR amplification control. In such cases, multiple amplified products from PCR reactions can be tagged with the same tag or identifier. If the product is subsequently sequenced and shows sequence differences, the difference between products with the same identifier can be attributed to PCR errors.

[0161] In addition, individual sequences can be identified based on the characteristics of the sequence data of the reads themselves. For example, the detection of unique sequence data at the beginning (start) and end (termination) parts of individual sequencing reads can be used alone, or in combination with the length or base pair number of each sequence read unique sequence, to assign unique identities to individual molecules. Fragments from single strands of nucleic acids to which unique identities have been assigned can thereby allow subsequent identification of fragments from the parental strand. This can be used in conjunction with bottlenecking the initial starting genetic material to limit diversity.

[0162] In addition, the unique sequence data and sequencing read length used at the beginning (start) and end (termination) parts of individual sequencing reads can be used alone or in combination with the use of barcodes. In some cases, the barcodes can be unique as described herein. In other cases, the barcodes themselves may not be unique. In this case, the use of non-unique barcodes combined with sequence data and sequencing read lengths at the beginning (start) and end (termination) parts of individual sequencing reads can allow unique identities to be assigned to individual sequences. Similarly, fragments from nucleic acid single strands to which unique identities have been assigned can thereby allow subsequent identification of fragments from the parental strand.

[0163] Typically, the methods and systems provided herein are useful for preparing cell-free polynucleotide sequences for downstream application sequencing reactions. The sequencing method can be a classical Sanger sequencing. The sequencing method can include, but is not limited to: high-throughput sequencing, pyrophosphate sequencing, sequencing by synthesis, single-molecule sequencing, nanopore sequencing, semiconductor sequencing, sequencing by connection, sequencing by hybridization, RNA-Seq (Illumina), digital gene expression (Helicos), next-generation sequencing, single-molecule sequencing by synthesis (SMSS) (Helicos), massive parallel sequencing, clonal single-molecule arrays (Solexa), shotgun sequencing, Maxim-Gilbert sequencing, primer walking, and any other sequencing method known in the art. Assign barcodes to cell-free Nucleotide sequence

[0164] The systems and methods disclosed herein can be used for applications involving assigning unique or non-unique identifiers or molecular barcodes to cell-free polynucleotides. The identifier can be a barcode oligonucleotide used to tag the polynucleotide; but in some cases, different unique identifiers are used. For example, in some cases, the unique identifier is a hybridization probe. In other cases, the unique identifier is a dye, in which case attachment can include embedding the dye into the analyte molecule (such as into DNA or RNA) or binding to a probe labeled with a dye. Still in other cases, the unique identifier can be a nucleic acid oligonucleotide, in which case attachment to the polynucleotide sequence can include a ligation reaction between the oligonucleotide and the sequence or incorporation by PCR. In other cases, the reaction can include adding a metal isotope directly to the analyte or adding it by a probe labeled with an isotope. Generally, the assignment of unique or non-unique identifiers or molecular barcodes in reactants of the present disclosure can follow the methods and systems described, for example, in U.S. Patent Publication Nos. 2001 / 0053519, 2003 / 0152490, 2011 / 0160078, and U.S. Patent No. 6,582,908, each of which is incorporated herein by reference in its entirety.

[0165] The method may include attaching the oligonucleotide barcode to the nucleic acid analyte by an enzymatic reaction (including but not limited to a ligation reaction). For example, a ligase can covalently attach a DNA barcode to fragmented DNA (e.g., high molecular weight DNA). After the barcode is attached, the molecule can undergo a sequencing reaction.

[0166] However, other reactions may also be used. For example, oligonucleotide primers comprising a barcode sequence may be used in an amplification reaction (e.g., PCR, qPCR, reverse transcriptase PCR, digital PCR, etc.) of a DNA template analyte to produce a tagged analyte. After assigning barcodes to individual cell-free polynucleotide sequences, the molecular pool may be sequenced.

[0167] In some cases, PCR can be used for global amplification of cell-free polynucleotide sequences. This can include the use of adapter sequences that can first be connected to different molecules and then PCR amplified using universal primers. PCR for sequencing can be performed using any method, including but not limited to the use of commercial kits provided by Nugen (WGA kit), Life Technologies, Affymetrix, Promega, Qiagen, etc. In other cases, only certain target molecules in the cell-free polynucleotide molecule population can be amplified. Specific primers, which can be connected in conjunction with adapters, can be used to selectively amplify certain targets for downstream sequencing.

[0168] Unique identifiers (e.g., oligonucleotide barcodes, antibodies, probes, etc.) can be introduced randomly or non-randomly into the cell-free polynucleotide sequence. In some cases, they are introduced with the expected ratio of unique identifiers to micropores. For example, unique identifiers can be loaded so that each genome sample is loaded with more than about 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 20, 50, 100, 500, 1000, 5000, 10000, 50,000, 100,000, 500,000, 1,000,000, 10,000,000, 50,000,000 or 1,000,000,000 unique identifiers. In some cases, unique identifiers can be loaded such that less than about 2, 3, 4, 5, 6, 7, 8, 9, 10, 20, 50, 100, 500, 1000, 5000, 10000, 50,000, 100,000, 500,000, 1,000,000, 10,000,000, 50,000,000, or 1,000,000,000 unique identifiers are loaded per genomic sample. In some cases, the average number of unique identifiers loaded per sample genome is less than or greater than about 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 20, 50, 100, 500, 1000, 5000, 10000, 50,000, 100,000, 500,000, 1,000,000, 10,000,000, 50,000,000, or 1,000,000,000 unique identifiers per genomic sample.

[0169] In some cases, the unique identifiers can be of various lengths, such that each barcode is at least about 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 20, 50, 100, 500, 1000 base pairs. In other cases, the barcodes can contain less than 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 20, 50, 100, 500, 1000 base pairs.

[0170] In some cases, the unique identifier can be a predetermined or random or semi-random sequence oligonucleotide. In other cases, more than one barcode can be used so that the barcodes do not have to be unique relative to each other in the more than one barcode. In this example, the barcode can be connected to an individual molecule so that the combination of the barcode and the sequence that can be connected thereto produces a unique sequence that can be traced back individually. As described herein, the detection of non-unique barcodes combined with the sequence data at the beginning (start) and end (termination) parts of the sequence reads can allow a unique identity to be assigned to a specific molecule. The length or base pair number of individual sequence reads can also be used to assign a unique identity to such molecules. As described herein, fragments from a single strand of nucleic acid to which a unique identity has been assigned can thereby allow subsequent identification of fragments from the parental chain. In this way, the polynucleotides in a sample can be uniquely or substantially uniquely labeled.

[0171] Unique identifiers can be used to label a wide range of analytes, including but not limited to RNA or DNA molecules. For example, a unique identifier (e.g., a barcode oligonucleotide) can be attached to an entire strand of nucleic acid or to a fragment of nucleic acid (e.g., fragmented genomic DNA, fragmented RNA). Unique identifiers (e.g., oligonucleotides) can also be combined with gene expression products, genomic DNA, mitochondrial DNA, RNA, mRNA, etc.

[0172] In many applications, it may be important to determine whether individual cell-free polynucleotide sequences each receive a different unique identifier (e.g., an oligonucleotide barcode). If the population of unique identifiers introduced into the system and method is not significantly diverse, different analytes may be tagged with the same identifier. The systems and methods disclosed herein can enable the detection of cell-free polynucleotide sequences tagged with the same identifier. In some cases, a reference sequence can be included with the population of cell-free polynucleotide sequences to be analyzed. The reference sequence can be, for example, a nucleic acid with a known sequence and a known amount. If the unique identifier is an oligonucleotide barcode and the analyte is a nucleic acid, the tagged analyte can then be sequenced and quantified. These methods can indicate whether one or more fragments and / or analytes have been assigned the same barcode.

[0173] The methods disclosed herein may include utilizing reagents necessary for assigning a barcode to an analyte. In the case of a ligation reaction, reagents may be loaded into the system and method, including but not limited to a ligase, a buffer, an adapter oligonucleotide, more than one unique identifier DNA barcode, and the like. In the case of enrichment, reagents may be used to prepare for sequencing, including but not limited to more than one PCR primer, an oligonucleotide containing a unique identification sequence, or a barcode sequence, a DNA polymerase, a DNTP, and a buffer, and the like.

[0174] In general, the methods and systems of the present disclosure can utilize the methods of U.S. Pat. No. 7,537,897, which is incorporated herein by reference in its entirety, to count molecules or analytes using molecular barcodes.

[0175] In samples containing fragmented genomic DNA from more than one genome, such as cell-free DNA (cfDNA), there is a certain likelihood that more than one polynucleotide from different genomes will have the same start and end positions ("duplicates" or "cognates"). The possible number of duplicates starting at any position is a function of the number of haploid genome equivalents in the sample and the distribution of fragment sizes. For example, cfDNA has a fragmentation peak at about 160 nucleotides, and most of the fragments in this peak range from about 140 nucleotides to 180 nucleotides. Accordingly, cfDNA from a genome with about 3 billion bases (e.g., the human genome) can contain almost 20 million (2x10 7 ) polynucleotide fragments. A sample with about 30 ng of DNA may contain about 10,000 haploid human genome equivalents. (Similarly, a sample with about 100 ng of DNA may contain about 30,000 haploid human genome equivalents.) 4 ) haploid genome equivalents of this DNA can have approximately 200 billion (2x10 11 ) individual polynucleotide molecules. It has been empirically determined that in a sample of human DNA having about 10,000 haploid genome equivalents, there are about 3 replica polynucleotides starting at any given position. Thus, such a collection may contain about 6x10 10 Up to 8x10 10 (about 60 billion to 80 billion, for example, about 70 billion (7x10 10 ))The diversity of differently sequenced polynucleotide molecules.

[0176] The probability of correctly identifying a molecule depends on the initial number of genome equivalents, the length distribution of the sequenced molecules, the sequence uniformity, and the number of tags. When the tag count is equal to 1, it is equivalent to having no unique tags or being untagged. The following table lists the probability of correctly identifying a molecule as unique, assuming a typical cell-free size distribution as above.

[0177] Tag Count % of tags correctly and uniquely identified 1000 human haploid genome equivalents 1 96.9643 4 99.2290 9 99.6539 16 99.8064 25 99.8741 100 99.9685 3000 human haploid genome equivalents 1 91.7233 4 97.8178 9 99.0198 16 99.4424 25 99.6412 100 99.9107

[0178] In this case, after sequencing the genomic DNA, it may not be possible to determine which sequence reads come from which parental molecules. This problem can be reduced by labeling the parental molecules with a sufficient number of unique identifiers (e.g., tag counting) so that there is a likelihood that two replica molecules (i.e., molecules with the same start and end positions) carry different unique identifiers so that the sequence reads can be traced back to a specific parental molecule. One solution to this problem is to uniquely label each or nearly each different parental molecule in the sample. However, depending on the number of haploid gene equivalents and the distribution of fragment sizes in the sample, this may require billions of different unique identifiers.

[0179] The above method may be cumbersome and expensive. The individual polynucleotide fragments in a genomic nucleic acid sample (e.g., a genomic DNA sample) can be uniquely identified by labeling with a non-unique identifier, for example, non-uniquely labeling the individual polynucleotide fragments. As used herein, if each of at least 95% of the molecules in the set of molecules carries an identification tag ("identifier") that is not shared by any other molecule in the set, the set can be considered to be "uniquely labeled" ("unique tag" or "unique identifier"). For unique tags, the number of tags can be less than the number of unique molecules in the sample. For unique tags, the number of tags can be less than 10% of the number of molecules in the sample. For unique tags, the number of tags can be less than 1% of the number of molecules in the sample. A collection of molecules may be considered "non-uniquely tagged" ("non-unique tags" or "non-unique identifiers") if at least 1%, at least 5%, at least 10%, at least 15%, at least 20%, at least 25%, at least 30%, at least 35%, at least 40%, at least 45%, or at least 50%, or about 50% of each of the molecules in the collection carry an identifying tag that is shared by at least one other molecule in the collection. In some embodiments, for a population that is non-uniquely tagged, no more than 1%, 5%, 10%, 15%, 20%, 25%, 30%, 35%, 40%, 45%, or 50% of the molecules are uniquely tagged. In some embodiments, for unique tagging, at least twice as many different tags are used as compared to the estimated number of molecules in the sample. The number of different identification tags used to tag the molecules in the collection can range, for example, between any of 2, 4, 8, 16, or 32 at the low end of the range and any of 50, 100, 500, 1000, 5000, and 10,000 at the high end of the range. Thus, for example, a collection of between 100 million and 1 trillion molecules can be tagged with between 4 and 100 different identification tags.

[0180] The present disclosure provides methods and compositions in which a population of polynucleotides in a sample of fragmented genomic DNA is tagged with n different unique identifiers. In some embodiments, n is at least 2 and no more than 100,000*z, where z is a centered trend measure (e.g., mean, median, mode) of the expected number of replicate molecules with the same start and end positions. In some embodiments, z is 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, or more than 10. In some embodiments, z is less than 10, less than 9, less than 8, less than 7, less than 6, less than 5, less than 4, less than 3. In certain embodiments, n is at least any one of 2*z, 3*z, 4*z, 5*z, 6*z, 7*z, 8*z, ​​9*z, 10*z, 11*z, 12*z, 13*z, 14*z, 15*z, 16*z, 17*z, 18*z, 19*z, or 20*z (e.g., a lower limit). In other embodiments, n is not greater than 100,000*z, 10,000*z, 1000*z, or 100*z (e.g., an upper limit). Therefore, the scope of n can be between any combination of these lower and upper limits. In certain embodiments, n is between 5*z and 15*z, between 8*z and 12*z, or about 10*z. For example, a haploid human genome equivalent has about 3 picograms of DNA. A sample having about 1 microgram of DNA contains about 300,000 haploid human genome equivalents. In some embodiments, the number n can be between 5 and 95, 6 and 80, 8 and 75, 10 and 70, 15 and 45, between 24 and 36, or about 30. In some embodiments, the number n is less than 96. For example, the number n can be greater than or equal to 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81 93, 94, or 95. In some instances, the number n can be greater than 0 but less than 100, 99, 98, 97, 96, 95, 94, 93, 92, 91, or 90. In some instances, the number n is 64. The number n can be less than 75, less than 50, less than 40, less than 30, less than 20, less than 10, or less than 5.As long as at least part of the replica or homolog polynucleotides carry a unique identifier, i.e., carry different tags, an improvement in sequencing can be achieved. However, in certain embodiments, the number of tags used is selected so that the chance that all replica molecules containing the same start and stop sequences carry a unique identifier is at least 95%.

[0181] Some embodiments provide a method for performing a ligation reaction in which a parent polynucleotide in a sample is mixed with a reaction mixture comprising y different barcode oligonucleotides, where y = the square root of n. The ligation can result in the barcode oligonucleotide being randomly attached to the parent polynucleotide in the sample. The reaction mixture can then be incubated under ligation conditions sufficient to achieve ligation of the barcode oligonucleotide to the parent polynucleotide of the sample. In some embodiments, a random barcode selected from y different barcode oligonucleotides is connected to both ends of the parent polynucleotide. The random connection of y barcodes to one or both ends of the parent polynucleotide can result in the generation of y 2 Unique identifiers. For example, a sample containing about 10,000 haploid human genome equivalents of cfDNA can be tagged with about 36 unique identifiers. The unique identifiers can include 6 unique DNA barcodes. The connection of the 6 unique barcodes to the two ends of the polynucleotide can result in 36 possible unique identifiers.

[0182] In some embodiments, a sample containing about 10,000 haploid human genome equivalents of DNA is tagged with 64 unique identifiers, wherein the 64 unique identifiers are generated by connecting 8 unique barcodes to both ends of the parent polynucleotide. The connection efficiency of the reaction can be more than 10%, more than 20%, more than 30%, more than 40%, more than 50%, more than 60%, more than 70%, more than 80%, or more than 90%. The connection conditions can include the use of a bidirectional adapter that can bind to either end of the fragment and still be amplified. The connection conditions can include blunt end connection, which is different from tailing with a forked adapter. The connection conditions can include carefully titrating the amount of adapters and / or barcode oligonucleotides. The connection conditions can include using more than 2X, more than 5X, more than 10X, more than 20X, more than 40X, more than 60X, more than 80X (e.g., ~100X) molar excess of adapters and / or barcode oligonucleotides compared to the amount of parent polynucleotide fragments in the reaction mixture. The connection conditions may include the use of T4 DNA ligase (e.g., NEBNExt Ultra Ligation Module). In one example, 18 microliters of ligase master mix are used with 90 microliters of connection (18 of 90) and connection enhancers. Accordingly, labeling the parental polynucleotides with n unique identifiers may include using different barcodes with a number of y, where y=the square root of n. The sample labeled in this manner may be a sample having a fragmented polynucleotide ranging from about 10 ng to about 100 ng, about 1 μg, about 10 μg, for example, genomic DNA, for example, cfDNA. The number y of the barcodes for identifying the parental polynucleotides in the sample may depend on the amount of nucleic acid in the sample.

[0183] The present disclosure also provides a composition of a polynucleotide of labeling. The polynucleotide may include fragmented DNA, such as cfDNA. A group of polynucleotides mapped to a mappable base position in a composition may be non-uniquely labeled, i.e., the number of different identifiers may be at least 2 and less than the number of polynucleotides mapped to a mappable base position. A composition of about 10 ng to about 10 μg (e.g., about 10 ng-1 μg, about 10 ng-100 ng, about 100 ng-10 μg, about 100 ng-1 μg, about 1 μg-10 μg) may carry 2, 5, 10, 50 or 100 different identifiers between any one of 100, 1000, 10,000 or 100,000. For example, different identifiers between 5 and 100 may be used to label the polynucleotides in such compositions.

[0184] Figure 2An exemplary process for analyzing polynucleotides in a sample of initial genetic material is shown. First, a sample comprising initial genetic material is provided, and cell-free DNA (50) can be extracted. The sample can contain target nucleic acids in low abundance. For example, nucleic acids from a normal or wild-type genome (e.g., a germline genome) can account for the vast majority of the sample, and the sample also includes no more than 20%, no more than 10%, no more than 5%, no more than 1%, no more than 0.5% or no more than 0.1% of nucleic acids from at least one other genome (e.g., a cancer genome or a fetal genome or a genome from another individual or species) comprising a genetic variation. The sample can include, for example, cell-free nucleic acids or cells comprising nucleic acids, wherein the original polynucleotides are appropriately oversampled by sequencing or a genetic analysis process.

[0185] Next, the initial genetic material is converted into a set of tagged parental polynucleotides and sequenced to produce sequence reads (52). This step generates more than one genomic fragment sequence read. In some cases, these sequence reads may contain barcode information. In other examples, barcodes are not utilized. Tagging may include attaching sequencing tags to molecules in the initial genetic material. Sequencing tags may be selected so that all unique polynucleotides mapped to the same reference sequence have unique identification tags. The conversion may be performed with high efficiency, such as at least 50%. The set of tagged parental polynucleotides may be amplified to produce a set of amplified progeny polynucleotides. The amplification may be, for example, a 1000-fold amplification. The set of amplified progeny polynucleotides is sampled at a sampling rate for sequencing so that the generated sequence reads both (1) cover the target number of unique molecules in the set of tagged parental polynucleotides and (2) cover the unique molecules in the set of tagged parental polynucleotides at a target coverage multiple (e.g., 5 to 10 times the coverage of the parental polynucleotides). The set of sequence reads is overlapped to generate a set of consensus sequences corresponding to the unique tagged parental polynucleotides. The qualifications of the sequence reads included in the analysis can be reviewed. For example, sequence reads that do not meet the quality control score can be removed from the collection. The sequence reads can be classified into families representing reads of progeny molecules derived from specific unique parental molecules. For example, a family of amplified progeny polynucleotides can constitute molecules that are amplified from a single parental polynucleotide. By comparing the sequences of the progeny in the family, the consensus sequence of the original parental polynucleotide can be inferred. This generates a set of consensus sequences representing the unique parental polynucleotides in the collection of tags.

[0186] Next, the process assigns a confidence score to the sequence (54). After sequencing, a quality score is assigned to the read. The quality score can be a representation of the read that indicates whether these reads can be used for subsequent analysis based on a threshold. In some cases, some reads do not have sufficient quality or length to perform a subsequent mapping step. Sequence reads with a predetermined quality score (e.g., greater than 90%) can be filtered out from the data. Genomic fragment reads that meet a specified quality score threshold are mapped to a reference genome or a template sequence known to not contain copy number variation. After mapping alignment, a mapping score is assigned to the sequence read. The mapping score can be a representation or read mapped back to the reference sequence, indicating that each position is or is not uniquely mappable. In some cases, the read can be a sequence that is unrelated to copy number variation analysis. For example, some sequence reads can be derived from contaminant polynucleotides. Sequencing reads with a mapping score of at least 90%, 95%, 99%, 99.9%, 99.99% or 99.999% can be filtered out from the data set. In other cases, sequencing reads that are assigned less than a predetermined percentage of mapping scores may be filtered out of the data set.

[0187] The genomic fragment reads that meet the quality score threshold of the regulation are mapped to the reference genome or the template sequence known to not contain copy number variation.After mapping alignment, the sequence reads are assigned a mapping score.In some cases, the reads can be sequences unrelated to copy number variation analysis.After data filtering and mapping, more than one sequence read generates chromosome region coverage (chromosomal region of coverage).These chromosome regions can be divided into windows or boxes of variable length.Window or box can be at least 5kb, 10kb, 25kb, 30kb, 35kb, 40kb, 50kb, 60kb, 75kb, 100kb, 150kb, 200kb, 500kb or 1000kb. The windows or bins may also have bases of up to 5 kb, 10 kb, 25 kb, 30 kb, 35 kb, 40 kb, 50 kb, 60 kb, 75 kb, 100 kb, 150 kb, 200 kb, 500 kb, or 1000 kb. The windows or bins may also be about 5 kb, 10 kb, 25 kb, 30 kb, 35 kb, 40 kb, 50 kb, 60 kb, 75 kb, 100 kb, 150 kb, 200 kb, 500 kb, or 1000 kb.

[0188] For coverage normalization, each window or bin is selected to include about the same number of mappable bases. In some cases, each window or bin in the chromosome region can include an exact number of mappable bases. In other cases, each window or bin can include different numbers of mappable bases. In addition, each window or bin can not overlap with adjacent windows or bins. In other cases, a window or bin can overlap with another adjacent window or bin. In some cases, a window or bin can overlap at least 1bp, 2bp, 3bp, 4bp, 5bp, 10bp, 20bp, 25bp, 50bp, 100bp, 200bp, 250bp, 500bp or 1000bp.

[0189] In some cases, the size of each of the window regions can be set so that they contain approximately the same number of unique mappable bases. The mappability of each base constituting the window region is determined and used to generate a mappability file containing representations of reads from a reference that are mapped back to the reference of each file. The mappability file contains a row per position indicating that each position is or is not uniquely mappable.

[0190] In addition, the predetermined window that is known to be difficult to order-checking or comprises high GC bias basically can be filtered out from data set in whole genome.For example, the known region (i.e. centromeric DNA) that falls near the chromosomal centromere is known to comprise the high repetitive sequence that can produce false positive result.These regions can be filtered out.Other regions of genome, such as the region that comprises other high repetitive sequences such as microsatellite DNA of abnormal high concentration, can be filtered out from data set.

[0191] The number of windows analyzed can also be different. In some cases, at least 10, 20, 30, 40, 50, 100, 200, 500, 1000, 2000, 5,000, 10,000, 20,000, 50,000 or 100,000 windows are analyzed. In other cases, the number of windows analyzed is up to 10, 20, 30, 40, 50, 100, 200, 500, 1000, 2000, 5,000, 10,000, 20,000, 50,000 or 100,000 windows.

[0192] For the exemplary genome derived from the cell-free polynucleotide sequence, the next step includes determining the coverage of the reads in each window region. This can be carried out using reads with or without a barcode. In the absence of a barcode, the previous mapping step will provide the coverage of different base positions. Sequence reads with enough mappings and quality scores and falling into the chromosome window that is not filtered out can be counted. Scoring can be specified for the number of coverage reads according to each mappable position. In the case of barcodes, all sequences with the same barcode, physical property or a combination of the two can be overlapped and merged into one read, because they are all derived from the sample parent molecule. This step reduces the bias that may have been introduced during any previous step, such as during the step involving amplification. For example, if a molecule is amplified 10 times but another is amplified 1000 times, each molecule is only presented once after overlapping, thereby eliminating the effect of uneven amplification. To each mappable position, only the reads with unique barcodes can be counted and only these reads affect the specified score. For this reason, it is important that the barcode ligation step be performed in a manner optimized to produce the lowest amount of bias. The sequence for each base is aligned to the most dominant nucleotide read for that particular position. In addition, the number of unique molecules can be counted at each position to obtain simultaneous quantification at each position. This step reduces bias that may have been introduced during any previous steps, such as those involving amplification.

[0193] The discrete copy number state of each window region can be used to identify the copy number variation in the chromosome region. In some cases, all adjacent window regions with the same copy number can be merged into a section to report the presence or absence of the copy number variation state. In some cases, multiple windows can be filtered before they merge with other sections.

[0194] When determining the nucleic acid read coverage of each window, the coverage of each window can be normalized by the average coverage of the sample. Using such methods, it is expected that both the test subject and the control are sequenced under similar conditions. The read coverage for each window can then be expressed as a ratio in similar windows.

[0195] The nucleic acid read coverage ratio for each window of the test subject can be determined by dividing the read coverage of each window region of the test sample by the read coverage of the corresponding window region of the control sample.

[0196] Next, the process looks up the previous confidence scores for each read family for the patient (58). This information is stored in a database. Previous analysis of the patient's test results can be used to improve the confidence scores, such as Figure 2This information is used to infer the frequency of each sequence read at the locus in the set of tagged parental polynucleotides based on the confidence scores between the families of sequence reads (60). The historical database is then updated with the current confidence scores for future use (62). In this way, a consensus sequence can be generated from the families of sequence reads to improve noise removal.

[0197] Now turn to Figure 3 , the process receives genetic material from a blood sample or other body sample (102). The process converts polynucleotides from the genetic material into tagged parental nucleotides (104). The tagged parental nucleotides are amplified to produce amplified progeny polynucleotides (106). A subset of the amplified polynucleotides is sequenced to produce sequence reads (108), which are grouped into families, each of which is generated by a unique tagged parental nucleotide (110). At the selected locus, the process assigns a confidence score for each family to each family (112). Next, previous reads are used to determine consensus. This is accomplished by reviewing the previous confidence scores for each family, and if there are consistent previous confidence scores, the current confidence score is increased (114). In one embodiment, if there are previous confidence scores, but they are inconsistent, the current confidence score is not modified (116). In other embodiments, the confidence score is adjusted in a predetermined manner for inconsistent previous confidence scores. If this is the first time the family has been detected, the current confidence score can be lowered because this is likely a false read (118).The process can infer the frequency of the family at the locus in the set of tagged parental polynucleotides based on the confidence score (120).

[0198] Despite Figures 1A-1D and Figure 2In the present invention, time information has been used to enhance the information for mutation or copy number variation detection, but other consistent methods can be applied.In other embodiments, historical comparison can be used in combination with other consensus sequences mapped to specific reference sequences to detect the situation of genetic variation.The consensus sequence mapped to the specific reference sequence can be measured and normalized relative to the control sample.The metric of the molecule mapped to the reference sequence can be compared on the whole genome to identify the region of copy number variation or loss of heterozygosity in the genome.Consistent methods include, for example, linear or nonlinear methods (such as election, average, statistics, maximum a posteriori probability or maximum likelihood detection, dynamic programming, Bayesian, hidden Markov or support vector machine methods, etc.) of the construction consensus sequence obtained by digital communication theory, information theory or bioinformatics.After the sequence read coverage has been determined, the normalized nucleic acid sequence read coverage of each window region is converted into a discrete copy number state using a random modeling algorithm.In some cases, the algorithm can include one or more of the following: hidden Markov model, dynamic programming, support vector machine, Bayesian network, grid decoding, Viterbi decoding, expectation maximization, Kalman filtering method and neural network.

[0199] After this, a report can be generated. For example, the copy number variation can be reported as a graph indicating different positions in the genome and the corresponding increase or decrease or maintenance of the copy number variation at each corresponding position. In addition, the copy number variation can be used to report a percentage score indicating how much disease material (or nucleic acid with copy number variation) is present in the cell-free polynucleotide sample.

[0200] In one embodiment, the report includes annotations to assist the physician. Annotations may include Oncology TM The annotations may include a report of the status of the NCCN Clinical Practice Guidelines or the American Society of Clinical Oncology (ASCO) Clinical Practice Guidelines. In the report, the annotations may include a list of one or more FDA-approved drugs for off-label uses, one or more drugs listed in the Centers for Medicare and Medicaid Services (CMS) Anticancer Treatment Compendium, and / or one or more experimental drugs found in the scientific literature. The annotations may include linking the listed drug treatment options to references containing scientific information about the drug treatment options. The scientific information may come from peer-reviewed articles in medical journals. The annotations may include the use of Information provided by the system. In a report, an annotation may include a link to provide clinical trial information about a drug treatment option. In an electronic-based report, an annotation may include presenting information in a pop-up box or fly-over box near the provided drug treatment options. An annotation may include adding to the report information selected from the group consisting of: one or more drug treatment options, scientific information about one or more drug treatment options, one or more links to scientific information about one or more drug treatment options, and one or more links to references to scientific information about one or more drug treatment options, and clinical trial information about one or more drug treatment options.

[0201] As in Figure 4 As depicted in , comparison of sequence coverage to a control sample or reference sequence can aid in normalization over the entire window. In this embodiment, cell-free DNA is extracted and isolated from readily available body fluids such as blood. For example, cell-free DNA can be extracted using a variety of methods known in the art, including but not limited to isopropanol precipitation and / or silica-based purification. Cell-free DNA can be extracted from any number of subjects, such as subjects without cancer, subjects at risk of cancer, or subjects known to have cancer.

[0202] After the separation / extraction step, the cell-free polynucleotide sample can be subjected to any of a number of different sequencing operations. The sample can be treated with one or more reagents (e.g., enzymes, unique identifiers (e.g., barcodes), probes, etc.) prior to sequencing. In some cases, if the sample is treated with a unique identifier such as a barcode, the sample or fragments of the sample can be tagged with the unique identifier individually or in subsets. The tagged sample can then be used in downstream applications, such as sequencing reactions, whereby individual molecules can be traced back to parent molecules.

[0203] Usually, as in Figure 4As shown in , mutation detection can be performed on selectively enriched regions of the purified and isolated genome or transcriptome (302). As described herein, specific regions can be selectively amplified from the total population of cell-free polynucleotides, and the specific regions can include, but are not limited to, genes, oncogenes, tumor suppressor genes, promoters, regulatory sequence elements, non-coding regions, miRNAs, snRNAs, etc. This can be performed as described herein. In one example, multiple sequencing can be used with or without barcode markers for individual polynucleotide sequences. In other examples, sequencing can be performed using any nucleic acid sequencing platform known in the art. This step generates more than one genomic fragment sequence read (304). In addition, the reference sequence is obtained from a control sample obtained from another subject. In some cases, the control subject can be a subject known not to have a known genetic variation or disease. In some cases, these sequence reads can contain barcode information. In other examples, barcodes are not used. In other examples, non-unique sequence tags are used.

[0204] After sequencing, the reads are assigned a quality score. The quality score can be a representation of the reads, which indicates whether these reads can be used for subsequent analysis based on a threshold value. In some cases, some reads do not have enough quality or length to carry out subsequent mapping steps. In step 306, the genomic fragment reads that meet the specified quality score threshold are mapped to a reference genome or reference sequence known to not contain mutations. After mapping alignment, the sequence reads are assigned a mapping score. The mapping score can be a representation or read mapped back to the reference sequence, indicating that each position is or is not uniquely mappable. In some cases, the reads may be sequences unrelated to mutation analysis. For example, some sequence reads may originate from contaminant polynucleotides. Sequencing reads with a mapping score of at least 90%, 95%, 99%, 99.9%, 99.99% or 99.999% may be filtered out from the data set. In other cases, sequencing reads assigned a mapping score of less than 90%, 95%, 99%, 99.9%, 99.99%, or 99.999% may be filtered out of the data set.

[0205] For each mappable base, bases that do not meet a minimum threshold for mappability or low-quality bases can be replaced with the corresponding base as found in the reference sequence.

[0206] After the read coverage can be determined and the variant bases in each read relative to the reference sequence are identified, the frequency of the variant base can be calculated as the number of reads containing the variant divided by the total number of reads (308). This can be expressed as a ratio for each mappable position in the genome.

[0207] For each base position, the frequencies of all four nucleotides, i.e., cytosine, guanine, thymine, and adenine, are analyzed compared to the reference sequence (310). A stochastic or statistical modeling algorithm is used to convert the normalized ratio of each mappable position into a frequency state reflecting each base variant. In some cases, the algorithm may include one or more of the following: hidden Markov models, dynamic programming, support vector machines, Bayesian or probabilistic modeling, trellis decoding, Viterbi decoding, expectation maximization, Kalman filtering methods, and neural networks.

[0208] The discrete mutation state of each base position can be used to identify base variants with high variation frequency compared to the baseline of the reference sequence. In some cases, the baseline may represent a frequency of at least 0.0001%, 0.001%, 0.01%, 0.1%, 1.0%, 2.0%, 3.0%, 4.0%, 5.0%, 10% or 25%. In other cases, the baseline may represent a frequency of at least 0.0001%, 0.001%, 0.01%, 0.1%, 1.0%, 2.0%, 3.0%, 4.0%, 5.0%, 10% or 25%. In some cases, all adjacent base positions with base variants or mutations can be merged into one segment to report the presence or absence of mutations. In some cases, multiple positions can be filtered before they merge with other segments.

[0209] After calculating the variation frequency of each base position, the variant with the largest deviation compared to the reference sequence at a specific position in the sequence derived from the subject is identified as a mutation. In some cases, the mutation can be a cancer mutation. In other cases, the mutation can be associated with a disease state.

[0210] Mutation or variant can include genetic aberration, described genetic aberration includes but is not limited to single base substitution, transversion, translocation, inversion, deletion, aneuploidy, partial aneuploidy, polyploidy, chromosome instability, chromosome structure change, chromosome fusion, gene truncation, gene amplification, gene duplication, chromosome damage, DNA damage, abnormal change of nucleic acid chemical modification, abnormal change of epigenetic pattern and abnormal change of nucleic acid methylation.In some cases, mutation can be at most 1,2,3,4,5,6,7,8,9,10,15 or 20 nucleotides in length.In other cases, mutation can be at least 1,2,3,4,5,6,7,8,9,10,15 or 20 nucleotides in length.

[0211] Next, the previous reads are used to determine consistency. This is accomplished by reviewing the previous confidence scores for the corresponding bases, and if there are consistent previous confidence scores, the current confidence score is increased (314). In one embodiment, if there are previous confidence scores, but they are inconsistent, the current confidence score is not modified (316). In other embodiments, the confidence score is adjusted in a predetermined manner for inconsistent previous confidence scores. If this is the first time a family has been detected, the current confidence score can be reduced because this may be a false read (318). The process then converts the variation frequency / each base into a discrete variant state for each base position (320).

[0212] The presence or absence of a mutation can be reflected in a graphical form, indicating different positions in the genome and the corresponding increase or decrease or maintenance of the mutation frequency at each corresponding position. In addition, the mutation can be used to report a percentage score, indicating how much disease material is present in the cell-free polynucleotide sample. In view of the statistics of the usual variations at the reported positions in the known non-disease reference sequences, a confidence score can accompany each detected mutation. Mutations can also be ranked in order of abundance in the subject or in order of clinically actionable importance.

[0213] Next, the application of this technology is described in detail. One application is the detection of cancer. Many cancers can be detected using the methods and systems described herein. Cancer cells, like most cells, can be characterized by a renewal rate, in which old cells die and are replaced by newer cells. Typically, dead cells that come into contact with the vascular system in a given subject can release DNA or DNA fragments into the bloodstream. This is also true for cancer cells in different stages of the disease. Depending on the stage of the disease, cancer cells can also be characterized by a variety of genetic variations, such as copy number variations and mutations. This phenomenon can be used to detect the presence or absence of cancer individuals using the methods and systems described herein.

[0214] For example, blood can be drawn from a subject at risk for cancer and prepared as described herein to generate a cell-free polynucleotide population. In one example, this can be cell-free DNA. The systems and methods of the present disclosure can be used to detect mutations or copy number variations that may be present in certain existing cancers. The method can help detect the presence of cancer cells in the body even if there are no symptoms or other hallmarks of the disease.

[0215] The types and numbers of cancers that can be detected may include, but are not limited to, blood cancer, brain cancer, lung cancer, skin cancer, nose cancer, throat cancer, liver cancer, bone cancer, lymphoma, pancreatic cancer, skin cancer, intestinal cancer, rectal cancer, thyroid cancer, bladder cancer, kidney cancer, oral cancer, gastric cancer, solid state tumors, heterogeneous tumors, homogeneous tumors, etc.

[0216] In the early detection of cancer, any system or method described herein (including mutation detection or copy number variation detection) can be used to detect cancer.These systems and methods can be used to detect any number of genetic variations that may cause or result from cancer.These can include point not limited to mutation, gain and loss, copy number variation, transversion, translocation, inversion, deletion, aneuploidy, partial aneuploidy, polyploidy, chromosome instability, chromosome structural changes, gene fusion, chromosome fusion, gene truncation, gene amplification, gene replication, chromosome damage, DNA damage, abnormal changes of nucleic acid chemical modification, abnormal changes of epigenetic pattern, abnormal changes of nucleic acid methylation, infection and cancer.

[0217] In addition, the systems and methods described herein can also be used to help characterize certain cancers. The genetic data generated by the systems and methods of the present disclosure can allow for helping practitioners better characterize specific forms of cancer. Cancer can be heterogeneous in both composition and staging. Genetic profile data can allow for characterization of specific subtypes of cancer, which may be important in the diagnosis or treatment of the specific subtype. This information can also provide clues to the prognosis of a specific type of cancer for a subject or practitioner.

[0218] The system and method provided herein can be used to monitor cancer or other diseases in a particular subject. This can allow a subject or practitioner to adjust treatment options according to the progression of the disease. In this example, the system and method described herein can be used to construct the genetic profile of a particular subject in the course of the disease. In some cases, cancer can progress and become more aggressive and genetically unstable. In other examples, cancer can remain benign, inactive, or dormant. The system and method of the present disclosure can be used to determine the progression of the disease.

[0219] In addition, the system and method described herein can be used to determine the efficacy of a specific treatment option. In one example, if the treatment is successful, the successful treatment option can actually increase the amount of copy number variation or mutation detected in the subject's blood, because more cancers may die and cause DNA to fall off. In other examples, this may not happen. In another example, perhaps some treatment options may be associated with the genetic spectrum of cancer over time. This correlation can be used to select therapy. In addition, if it is observed that cancer subsides after treatment, the system and method described herein can be used to monitor the remaining disease or the recurrence of the disease.

[0220] The methods and systems described herein may not be limited to the detection of mutations and copy number variations associated only with cancer. A variety of other diseases and infections may lead to other types of conditions, which may be suitable for early detection and monitoring. For example, in some cases, genetic disorders or infectious diseases may cause certain genetic mosaicism in subjects. This genetic mosaicism can cause observable copy number variations and mutations. In another example, the systems and methods of the present disclosure may also be used to monitor the genomes of immune cells in the body. After the presence of certain diseases, immune cells, such as B cells, may undergo rapid clonal expansion. Clonal expansion can be monitored using copy number variation detection and certain immune states can be monitored. In this example, copy number variation analysis can be performed over time to generate a spectrum of how a particular disease may progress.

[0221] In addition, the system and method of the present disclosure can also be used to monitor systemic infection itself, which can be caused by pathogens such as bacteria or viruses. Copy number variation or even mutation detection can be used to determine how the pathogen population changes during the infection process. This may be particularly important during chronic infections such as HIV / AIDS or hepatitis infections, where the virus can change life cycle states and / or mutate into a more virulent form during the infection process.

[0222] Another example of the system and method of the present disclosure can be used to monitor transplant subjects. Typically, transplanted tissue undergoes a certain degree of body rejection after transplantation. When immune cells attempt to destroy transplanted tissue, the method of the present disclosure can be used to determine or spectrally analyze the rejection activity of the host body. This can be used to monitor the state of transplanted tissue and change the course of treatment or prevent rejection.

[0223] In addition, the method of the present disclosure can be used to characterize the heterogeneity of abnormal conditions in a subject, the method comprising generating a genetic profile of extracellular polynucleotides in a subject, wherein the genetic profile comprises more than one data obtained by copy number variation and mutation analysis. In some cases, including but not limited to cancer, the disease can be heterogeneous. The disease cells may not be the same. In the example of cancer, some tumors contain different types of tumor cells, some cells at different stages of cancer. In other examples, heterogeneity can include multiple lesions of the disease. Again, in the example of cancer, there may be multiple tumor lesions, perhaps one or more of which are the result of metastasis that has spread from the primary site.

[0224] The methods of the present disclosure can be used to generate or profile a fingerprint map or data set, which is the sum of genetic information obtained from different cells in a heterogeneous disease. The data set can contain copy number variation and mutation analysis alone or in combination.

[0225] In addition, the systems and methods of the present disclosure can be used to diagnose, prognose, monitor or observe cancer or other diseases of fetal origin. That is, these methods can be used in pregnant subjects to diagnose, prognose, monitor or observe cancer or other diseases in unborn subjects, whose DNA and other polynucleotides can co-circulate with maternal molecules.

[0226] In addition, these reports are submitted and accessed electronically via the Internet. Analysis of the sequence data is performed at a location other than the subject's location. Reports are generated and sent to the subject's location. Via an Internet enabled computer, the subject accesses a report reflecting their tumor burden.

[0227] The annotated information can be used by a medical care provider to select other drug treatment options and / or provide information about drug treatment options to insurance companies. The method can include, for example, Oncology (“NCCN ”) or the American Society of Clinical Oncology (ASCO) Clinical Practice Guidelines explain drug treatment options for the condition.

[0228] Drug therapy options that are ranked in the report can be annotated in the report by listing additional drug therapy options. The additional drug therapy can be an FDA-approved drug for an informally approved use. A provision in the Omnibus Budget Reconciliation Act of 1993 (OBRA) requires Medicare to cover informally approved uses of anticancer drugs included in standard medical compendia. Drugs for annotated listings can be found in CMS-approved compendia, including the National Comprehensive Cancer Network (NCCN) drugs and the Biologics Compendium TM 、ThomsonMicromedex Elsevier Gold Standard's Clinical Pharmacology compendium, and American Hospital Formulary Service—Drug Information

[0229]

[0230] Drug treatment options can be annotated by listing experimental drugs that can be used to treat cancer with one or more molecular markers of a particular state. Experimental drugs can be drugs for which in vitro data, in vivo data, animal model data, preclinical trial data, or clinical trial data are available. Data can be published in the peer-reviewed medical literature in journals listed in the CMS Medicare Benefit Policy Manual, including, for example, American Journal of Medicine, Annals of Internal Medicine, Annals of Oncology, Annals of Surgical Oncology, Biology of Blood and Marrow Transplantation, Blood, Bone Marrow Transplantation, British Journal of Cancer, British Journal of Hematology, British Medical Journal, Cancer, Clinical Cancer Research, Drugs, European Journal of Cancer (formerly European Journal of Cancer and Clinical Oncology), Gynecologic Oncology, International Journal of Radiation, Oncology, Biology, and Physics, The Journal of the American Medical Association, Journal of Clinical Oncology, Journal of the National Cancer Institute, Journal of the National Comprehensive Cancer Network (NCCN), Journal of Urology, Lancet, Lancet Oncology, Leukemia, The New England Journal of Medicine, and Radiation Oncology.

[0231] The drug treatment options can be annotated by providing a link based on the electronic report to connect the listed drugs with scientific information about the drug. For example, a link to information about drug clinical trials (clinicaltrials.gov) can be provided. If the report is provided via a computer or computer website, the link can be a footnote, a hyperlink to the website, a pop-up box or floating box with information, etc. The report and annotation information can be provided in printed form, and the annotation can be, for example, a footnote of reference.

[0232] In the report, the information used to annotate one or more drug treatment options may be provided by a business entity that stores scientific information, e.g. The system can be provided. A health care provider can treat a subject, such as a cancer patient, with an experimental drug listed in the annotated information, and the health care provider can access the annotated drug treatment options, retrieve scientific information (e.g., print medical journal articles) and submit the scientific information (e.g., print medical journal articles) to an insurance company while submitting a claim for reimbursement for providing the drug treatment. The physician can use any of a variety of Diagnosis-related group (DRG) codes to achieve reimbursement.

[0233] The drug treatment options in the report can also be annotated with information about other molecular components in the pathway affected by the drug (e.g., information about a drug that targets a kinase downstream of a cell surface receptor that is a drug target). The drug treatment options can be annotated with information about drugs that target one or more other molecular pathway components. The identification and / or annotation of pathway-related information can be outsourced or subcontracted to another company.

[0234] The annotated information can be, for example, a drug name (e.g., an FDA-approved drug for an informally approved use; a drug found in a CMS-approved compendium, and / or a drug described in a scientific (medical) journal article), scientific information about one or more drug treatment options, one or more links to scientific information about one or more drugs, clinical trial information about one or more drugs (e.g., information from clinicaltrials.gov / ), one or more links to citations to scientific information about a drug, and the like.

[0235] The annotated information may be inserted into any location in the report. The annotated information may be inserted into multiple locations in the report. The annotated information may be inserted into the report near the section regarding the hierarchical medication therapy options. The annotated information may be inserted into the report on a separate page from the hierarchical medication therapy options. A report that does not contain hierarchical medication therapy options may be annotated with the information.

[0236] The provided methods can also be used to study the effects of drugs on samples (eg, tumor cells) isolated from subjects (eg, cancer patients). In vitro cultures using tumors from cancer patients can be established using techniques known to those skilled in the art.

[0237] The provided methods can also include high throughput screening of FDA approved, informally approved drugs or experimental drugs using in vitro culture and / or xenograft models.

[0238] The provided methods can also include monitoring tumor antigens for recurrence detection.

[0239] Reports mapping genomic locations and copy number variations can be generated for subjects with cancer, such as Figure 5A and 5B . Compared with other spectra of subjects with known results, these reports can indicate that a specific cancer is aggressive and resistant to treatment. The subject is monitored for a period of time and retested. If at the end of this period of time, the copy number variation spectrum begins to increase sharply, this can indicate that the current treatment does not work. Compare with the genetic spectrum of other prostate subjects. For example, if it is determined that this increase in copy number variation indicates that cancer is progressing, the cancer is no longer treated according to the original treatment regimen prescribed, and a new treatment method is prescribed.

[0240] In one embodiment, the system supports Fig. 9 The groups of genes shown in . Fig. 9 Groups of genes can be used with the methods and systems of the present disclosure.

[0241] These reports can be submitted and accessed electronically via the Internet. Analysis of the sequence data is performed at a location other than the subject's location. Reports are generated and sent to the subject's location. Via an Internet-enabled computer, the subject accesses a report reflecting their tumor burden ( Figure 5A and 5B ).

[0242] Figure 6 Schematic representation of reports of subjects with cancer accessed via the Internet. Figure 6 The system can use a handheld DNA sequencer or a desktop DNA sequencer. A DNA sequencer is a scientific instrument used to automate the DNA sequencing process. Given a DNA sample, a DNA sequencer is used to determine the order of four bases: adenine, guanine, cytosine, and thymine. The order of the DNA bases is reported as a text string, called a read. Some DNA sequencers can also be considered optical instruments because they analyze light signals from fluorescent dyes attached to nucleotides.

[0243] DNA sequencer can apply the Gilbert sequencing method of chemical modification based on DNA and then cracking at specific base, or DNA sequencer can apply Sanger technology based on dideoxynucleotide chain termination.Sanger method becomes popular due to its increased efficiency and low radioactivity.DNA sequencer can use the technology that does not need DNA amplification (polymerase chain reaction-PCR), which speeds up the sample preparation before sequencing and reduces errors.In addition, the reaction caused by the real-time addition of nucleotides in complementary chain collects sequencing data.For example, DNA sequence analyzer can utilize the method called single-molecule real-time (Single-molecule real-time) (SMRT), wherein sequencing data is produced by the light (captured by camera) emitted when nucleotides are added to complementary chain by enzymes containing fluorescent dyes.Alternatively, DNA sequencer can use the electronic system based on nanopore sensing technology.

[0244] Data is sent by the DNA sequencer to a computer for processing, either by direct connection or via the Internet. The data processing aspects of the system may be implemented in digital electronic circuits or in computer hardware, firmware, software, or a combination thereof. The data processing apparatus of the present invention may be tangibly embodied in a machine-readable storage device for implementation by a computer program product executed by a programmable processor; and the data processing method steps of the present invention may be performed by a programmable processor executing an instruction program to perform the functions of the present invention by operating input data and generating output. The data processing aspects of the present invention may advantageously be implemented in one or more computer programs, which may be executed in a programmable system, the programmable system comprising at least one programmable processor coupled to receive data and instructions from a data storage system and transmit data and instructions to a data storage system, at least one input device, and at least one output device. If desired, each computer program may be implemented in a high-level procedural or object-oriented programming language or in an assembly or machine language; and, in any case, the language may be a compiled or interpreted language. Suitable processors include, for example, both general-purpose and special-purpose microprocessors. Typically, the processor will receive instructions and data from a read-only memory and / or a random access memory. Storage devices suitable for tangibly embodying computer program instructions and data include all forms of non-volatile memory, including, for example, semiconductor memory devices such as EPROM, EEPROM and flash memory devices; magnetic disks such as internal hard disks and removable magnetic disks; magneto-optical magnetic disks; CD-ROM disks. Any of the foregoing may be supplemented by or incorporated in an ASIC (application specific integrated circuit).

[0245] To provide interaction with a user, the present invention may be implemented using a computer system having a display device, such as a monitor or LCD (liquid crystal display) screen, for displaying information to the user, and an input device, whereby the user can provide input to the computer system, such as a keyboard, a two-dimensional pointing device, such as a mouse or trackball, or a three-dimensional pointing device, such as a data glove or a gyroscopic mouse. The computer system may be programmed to provide a graphical user interface through which the computer program interacts with the user. The computer system may be programmed to provide a three-dimensional display interface for virtual reality.

[0246] Computer control system

[0247] The present disclosure provides a computer controlled system programmed to implement the method of the present disclosure. Figure 7 A computer system 701 programmed or otherwise configured to analyze genetic data is shown. The methods described herein for detecting genetic variations below a detection limit can provide more efficient processing of genetic data, thereby improving the functionality of a computer system. For example, a computer system may be able to more quickly or more efficiently process genetic data and identify genetic variants (e.g., if a computer system is able to identify genetic variants below a detection limit, then there may be no need to reprocess genetic data or process additional genetic data).

[0248] Computer system 701 can regulate multiple aspects of detecting genetic variation below the noise range or detection limit of the present disclosure, such as, for example, detecting genetic variation in nucleic acid molecules, comparing multiple groups of genetic variation, determining diagnostic confidence indications, determining confidence intervals, sequencing nucleic acids (including massively parallel sequencing), grouping sequence reads into families, overlapping grouped sequence reads, determining consensus sequences. Computer system 701 can be an electronic device of a user or a computer system remotely located relative to the electronic device. The electronic device can be a mobile electronic device.

[0249] The computer system 701 includes a central processing unit (CPU, also referred to herein as a "processor" and "computer processor") 705, which may be a single-core or multi-core processor or more than one processor for parallel processing. The computer system 701 also includes a memory or memory location 710 (e.g., random access memory, read-only memory, flash memory), an electronic storage unit 715 (e.g., a hard disk), a communication interface 720 (e.g., a network adapter) for communicating with one or more other systems, and peripherals 725, such as cache memory, other memory, data storage, and / or an electronic display adapter. The memory 710, storage unit 715, interface 720, and peripherals 725 communicate with the CPU 705 via a communication bus (solid lines), such as a motherboard. The storage unit 715 may be a data storage unit (or data repository) for storing data. The computer system 701 may be operably coupled to a computer network ("network") 730 by means of the communication interface 720. The network 730 can be the Internet, the internet and / or an extranet, or an intranet and / or an extranet communicating with the internet. In some cases, the network 730 is a telecommunications and / or data network. The network 730 may include one or more computer servers, which may support distributed computing, such as cloud computing. In some cases, with the aid of the computer system 701, the network 730 may implement a peer-to-peer network, which may enable devices coupled to the computer system 701 to operate as clients or servers.

[0250] The CPU 705 may execute a series of machine-readable instructions, which may be embodied in a program or software. The instructions may be stored in a memory location, such as the memory 710. The instructions may be directed to the CPU 705, which may then program or otherwise configure the CPU 705 to implement the methods of the present disclosure. Examples of operations performed by the CPU 705 may include reading, decoding, executing, and writing back.

[0251] CPU 705 may be part of a circuit such as an integrated circuit. One or more other components of system 701 may be included in the circuit. In some cases, the circuit is an application specific integrated circuit (ASIC).

[0252] The storage unit 715 can store files, such as drivers, libraries, and saved programs. The storage unit 715 can store user data, such as user preferences and user programs. In some cases, the computer system 701 may include one or more additional data storage units that are external to the computer system 701, such as located on a remote server that communicates with the computer system 701 via an intranet or the Internet.

[0253] Computer system 701 can communicate with one or more remote computer systems via network 730. For example, computer system 701 can communicate with remote computer systems of users (e.g., physicians, laboratory technicians, genetic counselors, scientists, and others). Examples of remote computer systems include personal computers (e.g., portable PCs), tablet or tablet PCs (e.g., iPad, Galaxy Tab), phones, smartphones (such as iPhone, Android supported devices, ) or a personal digital assistant. A user can access the computer system 701 via a network 730.

[0254] The methods as described herein may be implemented by means of machine (e.g., computer processor) executable code stored in an electronic storage location of the computer system 701, such as, for example, the memory 710 or the electronic storage unit 715. The machine executable code or machine readable code may be provided in the form of software. During use, the code may be executed by the processor 705. In some cases, the code may be retrieved from the storage unit 715 and stored on the memory 710 for rapid access by the processor 705. In some cases, the electronic storage unit 715 may be excluded and the machine executable instructions may be stored in the memory 710.

[0255] The code may be precompiled and configured for use with a machine having a processor suitable for executing the code, or may be compiled during runtime. The code may be provided in a programming language that may be selected so that the code can be executed in a precompiled or as-compiled manner.

[0256] Aspects of the systems and methods provided herein, such as computer system 701, can be embodied in programming. Aspects of the technology can be considered as "products" or "articles of manufacture" in the form of machine (or processor) executable code and / or related data, which are usually performed or embodied in a machine-readable medium. The machine executable code can be stored in an electronic storage unit such as a memory (e.g., read-only memory, random access memory, flash memory) or a hard disk. "Storage" type media can include any or all tangible memories of a computer, processor, etc., or its related modules, such as various semiconductor memories, tape drives, disk drives, etc., which can provide non-temporary storage for software programming at any time. All or part of the software can sometimes communicate through the Internet or various other telecommunications networks. For example, such communication can enable software to be loaded from one computer or processor to another computer or processor, for example, from a management server or host to a computer platform of an application server. Therefore, another type of medium capable of carrying software elements includes optical waves, radio waves, and electromagnetic waves such as physical interfaces between local devices, through wired and optical fiber landline networks, and used on various air links. Physical elements that carry such waves, such as wired or wireless links, optical links, etc., can also be considered media that carry software. As used herein, unless restricted to non-transitory, tangible "storage" media, terms such as computer or machine "readable media" refer to any medium that participates in providing instructions to a processor for execution.

[0257] Thus, machine-readable media, such as computer executable code, may take a variety of forms, including but not limited to tangible storage media, carrier media, or physical transmission media. Non-volatile storage media include, for example, optical or magnetic disks, such as any storage device in any computer, etc., such as may be used to implement a database, etc., as shown in the accompanying drawings. Volatile storage media include dynamic memory, such as the main memory of such a computer platform. Tangible transmission media include coaxial cables; copper wire and optical fiber, including the wires that make up a bus within a computer system. Carrier transmission media may take the form of electrical or electromagnetic signals, or acoustic or light waves, such as those generated during radio frequency (RF) and infrared (IR) data communications. Thus, common forms of computer-readable media include, for example: a floppy disk, a flexible disk, a hard disk, a magnetic tape, any other magnetic medium, a CD-ROM, a DVD or DVD-ROM, any other optical medium, a punched card paper tape, any other physical storage medium with a punched pattern, a RAM, a ROM, a PROM and an EPROM, a FLASH-EPROM, any other memory chip or cartridge, a carrier wave that transmits data or instructions, a cable or link that transmits such a carrier wave, or any other medium from which a computer can read programming code and / or data. Many of these forms of computer-readable media can participate in transmitting one or more sequences of one or more instructions to a processor for execution.

[0258] The computer system 701 may include or communicate with an electronic display 735 including a user interface (UI) 740 for providing, for example, a personal or individualized patient report identifying genomic variations or alterations, which may include tumor-specific genomic alterations and related treatment options. Examples of UIs include, but are not limited to, graphical user interfaces (GUIs) and web-based user interfaces. Data generated and displayed using the user interface (740) may be accessed over a network by a user, such as a healthcare professional, laboratory technician, genetic counselor, or scientist.

[0259] The methods and systems of the present disclosure may be implemented by one or more algorithms. The algorithm may be implemented by software after being executed by the central processor 705. The algorithm may, for example, sequence nucleic acids (e.g., massively parallel sequencing), group nucleic acid sequences, overlap grouped nucleic acid sequences, generate consensus sequences, detect genetic variations, update diagnostic confidence intervals, annotate sequences, generate reports, and perform other processes, and the algorithm may include one or more of the following: hidden Markov models, dynamic programming, Bayesian networks, trellis decoding, Viterbi decoding, expectation maximization, Kalman filtering methods, and neural networks.

[0260] The following examples are offered by way of illustration and not by way of limitation.

[0261] Example

[0262] Figure 8 A graph showing the frequency of base changes (compared to the reference genome) detected in a DNA sample along a 70kb sequence of more than one cancer gene amplified and sequenced using a protocol suitable for Illumine sequencing. The sample is spiked with a low percentage of control DNA that carries sequence variants at known positions. These variants are represented by black circles. Variants that occur with log equal to 0 (100%) or log equal to -0.3 (0.5 or 50%) represent homozygous or heterozygous loci. Variants occur with less than log equal to -2 (less than 1%), which is within the noise range of the system and may represent sequencing errors (noise) or actual variants (information). For any variant detected within the noise range, it may not be possible to determine whether the variant represents noise or information. Among the "noise", people have reduced the confidence that the base determination at the mutant position represents information (actual mutants) rather than noise. However, if the control DNA is spiked into the second sample, it should appear again at a similar frequency. In contrast, the probability of detecting an error again at the same locus is a function of the error rate and is unlikely to be observed. Independent detection of the same variant increases the probability that information (rather than noise) is detected and provides increased confidence that the cancer diagnosis is the correct diagnosis.

[0263] In the case where sequencing errors are the result of chance, the probability of detecting the same sequencing error multiple times can be exponentially less than the probability of detecting it once. Therefore, if a particular signal is detected multiple times, the signal is more likely to be information rather than noise. This feature can be used to increase the probability that genetic variants detected at low levels represent actual polynucleotides or groups of polynucleotides rather than sequencing artifacts.

[0264] In one example, a signal indicating a pathology is detected in more than one case. In certain embodiments, the signal is a polynucleotide carrying a somatic mutation associated with cancer or a copy number variation associated with cancer. Repeated detection of a signal increases the probability that the signal represents information rather than noise. Repeated situations include, but are not limited to, (1) repeated testing of the same sample, (2) testing two samples obtained from a subject at the same time, or (3) testing two samples obtained from a subject at different times. When the signal detected for the first time is at a level that cannot be reliably distinguished from noise, it is particularly useful to determine that the increased probability is. Among other things, the methods of the present disclosure can be used to monitor subjects over time for early detection of pathology, for example, when repeated testing detects a pathology at a level that is too low to reliably make a pathological diagnosis in a single test.

[0265] In another example describing covariate variants associated with lung cancer, the signal associated with a detected high confidence variant is detected below the detection limit. If the EGFR L858R activating mutation is detected, the detection threshold for the covariate resistance mutation EGFR T790M resistance mutation is relaxed. Independent detection of activating or driver mutations increases the confidence that covariate variants within the detection threshold are also detected.

[0266] The methods and systems of the present disclosure may be combined with other methods and systems, such as, for example, those described in Patent Cooperation Treaty (PCT) Patent Publication Nos. WO / 2014 / 039556, WO / 2014 / 149134, WO / 2015 / 100427, and WO / 2015 / 175705, each of which is incorporated herein by reference in its entirety.

[0267] Although preferred embodiments of the present invention have been shown and described herein, it will be apparent to those skilled in the art that such embodiments are provided by way of example only. It is not intended that the present invention be limited to the specific examples provided in this specification. Although the present invention has been described with reference to the above-mentioned specification, the description and illustration of the embodiments herein are not intended to be interpreted in a restrictive sense. Without departing from the present invention, those skilled in the art will now think of many variations, changes and replacements. In addition, it should be understood that all aspects of the present invention are not limited to the specific descriptions, configurations or relative proportions depending on various conditions and variables set forth herein. It should be understood that various alternatives of the embodiments of the present invention described herein can be adopted when practicing the present invention. It is therefore contemplated that the present invention should also encompass any such alternatives, modifications, variations or equivalents. The appended claims are intended to define the scope of the present invention, and thus encompass methods and structures and their equivalents within the scope of these claims.

Claims

1. A system for detecting a trend in the amount of a cancer polynucleotide in a biological sample from a subject over time, the system comprising: One or more computer processors and a memory coupled thereto, wherein the memory comprises a non-transitory computer-readable medium, the non-transitory computer-readable medium comprising machine-executable code, which when executed by the one or more computer processors implements a method of detecting a trend in the amount of a cancer polynucleotide in the biological sample from the subject over time, the method comprising: determining the frequency of the cancer polynucleotide at each of more than one time point; determining an error range for the frequency at each of the more than one time points to provide at least a first error range at a first time point and a second error range at a second time point subsequent to the first time point; and Determine whether (1) the first error range overlaps with the second error range, the overlap indicating stability of the frequency of the cancer polynucleotide at more than one time point, (2) the second error range is greater than the first error range, thereby indicating an increase in the frequency of the cancer polynucleotide at more than one time point, or (3) the second error range is less than the first error range, thereby indicating a decrease in the frequency of the cancer polynucleotide at more than one time point.

2. The system of claim 1, wherein the cancer polynucleotide is a deoxyribonucleic acid (DNA) molecule.

3. The system of claim 2, wherein the DNA molecule is cell-free DNA.

4. The system of claim 3, wherein the cell-free DNA is derived from a blood sample, a plasma sample, or a serum sample.

5. The system of claim 1, wherein the cancer polynucleotide comprises one or more genetic variations.

6. The system of claim 5, wherein each of the one or more genetic variations is selected from the group consisting of single base substitutions, copy number variations, gain / loss, gene fusions, transversions, translocations, inversions, deletions, aneuploidy, partial aneuploidy, polyploidy, chromosomal instability, chromosomal structural changes, chromosomal fusions, gene truncation, gene amplification, gene duplication, chromosomal damage, DNA damage, abnormal changes in nucleic acid chemical modifications, abnormal changes in epigenetic patterns, and abnormal changes in nucleic acid methylation.

7. The system of claim 1, wherein the frequency at each of the more than one time points is determined by sequencing nucleic acid molecules in a biological sample of the subject.

8. The system of claim 7, comprising generating a set of sequence reads at each of the more than one time points.

9. The system of claim 8, wherein the sequence reads are filtered by requiring the sequence reads to meet a quality threshold.

10. The system of claim 1, wherein the biological sample is a blood sample, a plasma sample, or a serum sample.

11. A method for detecting a trend in the amount of a cancer polynucleotide in a biological sample from a subject over time, the method comprising: using one or more programmed computer processors, determining the frequency of the cancer polynucleotide at each of more than one time point; determining an error range for the frequency at each of the more than one time points to provide at least a first error range at a first time point and a second error range at a second time point subsequent to the first time point; as well as Determine whether (1) the first error range overlaps with the second error range, the overlap indicating stability of the frequency of the cancer polynucleotide at more than one time point, (2) the second error range is greater than the first error range, thereby indicating an increase in the frequency of the cancer polynucleotide at more than one time point, or (3) the second error range is less than the first error range, thereby indicating a decrease in the frequency of the cancer polynucleotide at more than one time point.

12. The method of claim 11, wherein the cancer polynucleotide is a deoxyribonucleic acid (DNA) molecule.

13. The method of claim 12, wherein the DNA molecule is cell-free DNA.

14. The method of claim 13, wherein the cell-free DNA is derived from a blood sample, a plasma sample or a serum sample.

15. The method of claim 11, wherein the cancer polynucleotide comprises one or more genetic variations.

16. The method of claim 15, wherein each of the one or more genetic variations is selected from the group consisting of single base substitutions, copy number variations, gain and loss of sites, gene fusions, transversions, translocations, inversions, deletions, aneuploidy, partial aneuploidy, polyploidy, chromosomal instability, chromosomal structural changes, chromosomal fusions, gene truncation, gene amplification, gene duplication, chromosomal damage, DNA damage, abnormal changes in nucleic acid chemical modifications, abnormal changes in epigenetic patterns, and abnormal changes in nucleic acid methylation.

17. The method of claim 11, wherein the frequency at each of the more than one time points is determined by sequencing nucleic acid molecules in a biological sample of the subject.

18. The method of claim 17, comprising generating a set of sequence reads at each of the more than one time points.

19. The method of claim 18, wherein the sequence reads are filtered by requiring the sequence reads to meet a quality threshold.

20. The method of claim 11, wherein the biological sample is a blood sample, a plasma sample, or a serum sample.

21. A system for genetic testing, the system comprising: a genetic analyzer configured to generate more than one genetic data set from nucleic acid samples obtained from a subject at (i) two or more time points or (ii) substantially the same time point, wherein the genetic data is associated with genetic information of the subject, and wherein the nucleic acid samples comprise cell-free biological samples; and One or more computer processors and a memory coupled thereto, the memory comprising a non-transitory computer-readable medium, the non-transitory computer-readable medium comprising machine executable code, the machine executable code when executed by the one or more computer processors implementing a method comprising: (a) receiving a plurality of genetic data sets from the genetic analyzer; (b) generating an adjusted test result in a representation of the genetic information of the subject using the plurality of genetic data sets; as well as (c) outputting the adjusted test result to the memory.

22. A system for detecting a mutation in a cell-free nucleic acid sample of a subject, the system comprising one or more computer processors and a memory coupled thereto, the memory comprising a non-transitory computer-readable medium, the non-transitory computer-readable medium comprising machine-executable code, the machine-executable code when executed by the one or more computer processors implementing a method comprising: (a) determining a consensus sequence by comparing a current sequence read obtained from a genetic analyzer to a previous sequence read from a previous time period to generate a comparison; (b) updating a confidence indication based on the comparison, wherein each consensus sequence corresponds to a unique polynucleotide in the set of tagged parent polynucleotides derived from the cell-free nucleic acid sample; and (c) generating a genetic profile of the extracellular polynucleotides in the subject based on the confidence, wherein the genetic profile comprises data obtained from copy number variation or mutation analysis.

23. A system for detecting abnormal cell activity, the system comprising: an amplification instrument configured to amplify at least one set of tagged parent polynucleotides derived from a biological sample of a subject to produce a corresponding set of amplified progeny polynucleotides; a genetic analyzer configured to sequence a subset of the set of amplified progeny polynucleotides to generate a set of sequenced reads; as well as One or more computer processors and a memory coupled thereto, the memory comprising a non-transitory computer-readable medium, the non-transitory computer-readable medium comprising machine executable code, the machine executable code when executed by the one or more computer processors implementing a method comprising: (a) stacking the set of sequencing reads to generate a set of consensus sequences by comparing the current sequence read to previous sequence reads from at least one previous time period; and (b) updating a confidence indicator accordingly, the confidence indicator indicating a probability of identifying one or more genetic variations in the biological sample of the subject, wherein each consensus sequence corresponds to a unique polynucleotide in the set of tagged parent polynucleotides.

24. A system for detecting a mutation in a cell-free or substantially cell-free sample from a subject, the system comprising: a genetic analyzer configured to sequence extracellular polynucleotides from a body sample from the subject; as well as One or more computer processors and a memory coupled thereto, the memory comprising a non-transitory computer-readable medium, the non-transitory computer-readable medium comprising machine executable code, the machine executable code when executed by the one or more computer processors implementing a method comprising: (a) generating more than one sequencing read for each of the extracellular polynucleotides; (b) filtering out reads that do not meet the set threshold; (c) mapping the sequence reads obtained from the sequencing to a reference sequence; (d) identifying a subset of mapped sequence reads that align to a variant of the reference sequence at each mappable base position; (e) for each mappable base position, calculating the ratio of (i) the number of mapped sequence reads that contain a variant compared to the reference sequence to (ii) the number of total sequence reads for each mappable base position; (f) comparing the sequence reads to other sequence reads from at least one previous time point; and (g) updating a confidence indicator accordingly, the confidence indicator indicating the probability of identifying the variant.

25. A system for genetic testing, the system comprising: an amplification instrument configured to amplify a tagged parent polynucleotide of a set of non-uniquely tagged parent polynucleotides to produce a corresponding set of amplified progeny polynucleotides, wherein the set of non-uniquely tagged parent polynucleotides is obtained by converting double-stranded polynucleotide molecules from initial starting genetic material obtained from a bodily sample obtained from a subject, wherein each polynucleotide in the set is mappable to a reference sequence; a genetic analyzer configured to sequence the set of amplified progeny polynucleotides to generate a set of sequenced reads; as well as One or more computer processors and a memory coupled thereto, the memory comprising a non-transitory computer-readable medium, the non-transitory computer-readable medium comprising machine executable code, the machine executable code when executed by the one or more computer processors implementing a method comprising: (a) merging the set of sequencing reads to generate a set of consensus sequences, wherein sequence information from the tags and at least one of: (1) sequence information at a start region of the sequence read, (2) sequence information at an end region of the sequence read, and (3) a length of the sequence read, wherein each consensus sequence of the set of consensus sequences corresponds to a polynucleotide molecule in the set of tagged parent polynucleotides; (b) analyzing the set of consensus sequences for each set of tagged parent molecules; (c) comparing the current sequence read to a previous sequence read from at least one other time point; as well as (d) updating a confidence indicator accordingly, the confidence indicator indicating the probability of identifying one or more genetic variations in the body sample of the subject.

26. A system for determining genetic variants in cell-free deoxyribonucleic acid (cfDNA) from a subject, the system comprising: a deoxyribonucleic acid (DNA) sequencing system configured to sequence cfDNA from a sample taken from a subject at a first time point and a sample taken from the subject at one or more subsequent time points; and One or more computer processors and a memory coupled thereto, the memory comprising a non-transitory computer-readable medium, the non-transitory computer-readable medium comprising machine executable code, the machine executable code when executed by the one or more computer processors implementing a method comprising: (a) detecting a genetic variant in the sequenced cfDNA from the first time point, wherein the genetic variant is detected at a level below a detection limit; (b) detecting the genetic variant in the sequenced cfDNA from the one or more subsequent time points, wherein the genetic variant is detected at a level below a detection limit; and (c) calling the genetic variant present based on detecting the genetic variant below the detection limit in samples taken at more than one time point.

27. A system for determining genetic variants in cell-free deoxyribonucleic acid (cfDNA) from a subject, the system comprising: A deoxyribonucleic acid (DNA) sequencing system configured to sequence cfDNA from a sample from a subject, wherein the sample is resequenced one or more times; and One or more computer processors and a memory coupled thereto, the memory comprising a non-transitory computer-readable medium, the non-transitory computer-readable medium comprising machine executable code, the machine executable code when executed by the one or more computer processors implementing a method comprising: (a) detecting a genetic variant in the sequenced cfDNA, wherein the genetic variant is detected at a level below a detection limit; (b) detecting the genetic variant in sequenced cfDNA from one or more resequenced samples, wherein the genetic variant is detected at a level below a detection limit; and (c) calling the genetic variant as present based on detecting the genetic variant below the detection limit in the resequenced sample.

28. A system for processing genetic data, the system comprising: a genetic analyzer configured to generate genetic data from a cell-free nucleic acid sample of a subject; and One or more computer processors and a memory coupled thereto, the memory comprising a non-transitory computer-readable medium, the non-transitory computer-readable medium comprising machine executable code, the machine executable code when executed by the one or more computer processors implementing a method comprising: (a) analyzing the genetic data to identify one or more genetic variants in the genetic data; and (b) providing an output on an electronic portal corresponding to the one or more genetic variants identified in (a).

29. A system for identifying a genetic variant, the system comprising: A gene sequencing system configured to sequence cell-free deoxyribonucleic acid (cfDNA) from a subject to generate sequencing data; and One or more computer processors and a memory coupled thereto, the memory comprising a non-transitory computer-readable medium, the non-transitory computer-readable medium comprising machine executable code, the machine executable code when executed by the one or more computer processors implementing a method comprising: (a) receiving the sequencing data into the memory; (b) determining the frequency of a genetic variant at one or more genetic loci; (c) determining whether the frequency of the genetic variant is above or below a predetermined detection limit; (d) if the frequency is above the detection limit, calling the genetic variant as present in the sample, or, if the frequency is below the detection limit, accessing sequence data from the subject taken at another time and calling the genetic variant as present if the variant is detected at another time point; (e) generating a report identifying a genetic variant that is determined to be present; as well as (f) sending the report to a website, wherein the report is viewable on a graphical user interface (GUI).

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