A method for predicting breast cancer treatment outcomes based on cell-free DNA in blood

Abstract

The present invention relates to a method for predicting the prognosis of breast cancer treatment based on cell-free DNA in blood, more specifically, the method includes extracting cell-free DNA (cfDNA) from a biological sample before anti-cancer treatment, obtaining sequence information, obtaining an I score using normalization calibration and regression analysis of chromosomal regions, and analyzing the I score and breast image information after anti-cancer treatment. The method for predicting the prognosis of breast cancer according to the present invention not only improves the accuracy of prognosis prediction for breast cancer patients using next generation base sequence analysis technology (Next Generation Sequencing, NGS), but also improves the accuracy of prognosis prediction based on very low concentrations of cell-free DNA that are difficult to detect, thereby increasing commercial applicability. Therefore, the method of the present invention is useful for determining the prognosis of breast cancer patients.

Description

[Technical field]

[0001] The present invention relates to a method for predicting the prognosis of breast cancer treatment based on cell-free DNA in blood, more specifically, to a method for predicting the prognosis of breast cancer treatment based on cell-free DNA, comprising the steps of extracting cell-free DNA (cfDNA) from a biological sample before anti-cancer treatment, acquiring sequence information, obtaining an I-score using normalization calibration and regression analysis of chromosomal regions, and analyzing the I-score together with breast image information after anti-cancer treatment. [Background technology]

[0002] Breast cancer, a mass of cancer cells that develops in the breast, is the second most common type of cancer worldwide after lung cancer and the fifth most deadly cancer after lung, stomach, liver and colon cancer. Breast cancer is also the most common cancer in women and the second most deadly cancer.

[0003] Risk factors for the development of breast cancer include race, age, and mutations in the tumor suppressor genes BRCA-1, BRCA-2, and p53. Alcohol intake, high-fat diet, lack of exercise, exogenous postmenopausal hormones, and ionizing radiation also increase the risk of developing breast cancer. Breast cancer is classified into four subtypes, namely, luminal A, luminal B, HER2, and triple-negative breast cancer (TNBC), depending on the expression status of hormone receptors (estrogen receptors or progesterone receptors) and HER2 (human epidermal growth factor receptor 2). Each subtype of breast cancer has its own molecular characteristics that distinguish it.

[0004] In the current breast cancer treatment method, after tumor removal surgery, additional supportive treatment such as anticancer chemotherapy, antihormonal therapy, targeted therapy, or radiation therapy may be required to reduce future recurrence. Although 70% to 80% of early breast cancer patients do not require anticancer chemotherapy because the risk of metastasis to other organs is very low, the majority of patients are prescribed anticancer chemotherapy and radiation therapy after surgery because it is difficult to accurately distinguish in the existing breast cancer treatment guidelines. However, continuous administration of anticancer drugs to patients who do not respond well to chemotherapy only increases side effects and may cause unwanted pain to the patient. Therefore, it is necessary to clearly predict the future cancer prognosis of early breast cancer patients, wisely select the most appropriate treatment method at this time, and prepare for poor prognosis such as metastatic recurrence.

[0005] Once breast cancer treatment is started, the progression of the cancer must be monitored periodically, but diagnostic methods are expensive and time-consuming, and it is very difficult to detect and diagnose cancer when the patient's mass is small or the number of cancer cells is small. Some products that can predict prognosis exist, but they are still expensive, cannot check the condition during the treatment process, and can only predict simple prognosis at a single point in time.

[0006] Meanwhile, in the past, the focus has been on proliferation and cell cycle signals as prognostic indicators for breast cancer. Thus, proliferation / cell cycle regulatory genes have been used as markers and applied to gene expression-based analysis methods for prognostic prediction. Representatively, products such as Oncotype DX, MammaPrint, PAM50, and Endopredict are commercial analysis methods based on a complex gene expression profiling technique targeting proliferation genes in frozen or formalin-fixed paraffin-embedded (FFPE) samples. However, these commercial kits have limitations in that the subtypes of breast cancer they target are limited, making them difficult to use universally for each subtype of breast cancer molecules. The Oncotype DX, MammaPrint, PAM50, and Endopredict kits primarily target ER+ breast cancer. As can be seen from these commercial kits, they are only capable of predicting prognosis for hormone receptor-positive breast cancer subtypes, and there are currently no commercial kits for hormone receptor-negative breast cancer subtypes.

[0007] Given the current situation, there is a need to improve existing analytical methods used to predict breast cancer prognosis in order to more accurately predict patient survival outcomes and response to adjuvant chemotherapy, and there is a need for prognostic analytical methods that are universally applicable to various types of breast cancer.

[0008] Recently, research has been conducted to detect chromosomal abnormalities using cell-free DNA (cfDNA) present in plasma due to cell necrosis, apoptosis, and secretion based on liquid biopsy technology. In particular, circulating cfDNA derived from tumor cells contains tumor-specific chromosomal abnormalities and mutations that are not present in normal cells, and has the advantage of reflecting the current state of the tumor because it has a short half-life of about 2 hours. In addition, circulating cfDNA is in the spotlight as a tumor-specific biomarker in various cancer-related fields such as cancer diagnosis, monitoring, and prognosis monitoring because it can be collected non-invasively and repeatedly.

[0009] With the development of molecular diagnostic technology, it has been reported that it is possible to detect tumor-specific chromosomal abnormalities in cell-free DNA from the blood of cancer patients through digital karyotyping, PARE analysis, NGS, etc., and research results that clinically confirmed this have been published (Leary RJ et al., Sci Transl Med. Vol. 4, Issue 162. 2012). Daniel G. Stover analyzed tissue-specific CNAs through cfDNA in 164 metastatic TNBC (Triple-Negative Breast Cancer) patients (Stover DG. et al., J Clin Oncol. Vol. 36 (6): 543-553). As a result, it was confirmed that copy number gain of specific genes such as NOTCH2, AKT2, and AKT3 was higher in metastatic TNBC than in primary TNBC, and that the survival rate of metastatic TNBC patients with duplication of chromosomes 18q11 and 19p13 was statistically significantly lower.

[0010] Under these technical background, the inventors have made intensive efforts to develop a method for predicting the prognosis of breast cancer based on cell-free DNA in blood. As a result, they have confirmed that when normalization calibration and regression analysis of chromosomal regions are performed on cell-free DNA in blood samples obtained before anti-cancer treatment, and the results are integrated and analyzed with image interpretation information after cancer treatment, the prognosis of breast cancer patients can be predicted with high sensitivity, thereby completing the present invention. Summary of the Invention [Problem to be solved by the invention]

[0011] An object of the present invention is to provide a cell-free DNA (cfDNA)-based method for predicting the prognosis of breast cancer.

[0012] Another object of the present invention is to provide an apparatus for predicting the prognosis of breast cancer.

[0013] It is yet another object of the present invention to provide a computer readable medium comprising instructions adapted to be executed by a processor for predicting breast cancer prognosis according to said method.

[0014] It is still another object of the present invention to provide a method for providing information for determining the prognosis of breast cancer, comprising the above-mentioned method.

[0015] It is still another object of the present invention to provide a method for determining the prognosis of breast cancer, comprising the above-mentioned method. [Means for solving the problem]

[0016] In order to achieve the above object, the present invention provides a method for obtaining a sequence of cell-free DNA isolated from a biological sample before anti-cancer treatment; b) aligning the sequence reads to a reference genome database of a reference population; c) checking the quality of the aligned sequence reads and selecting only sequence reads that are equal to or above a cut-off value; d) dividing the standard chromosome into certain bins, and normalizing the amount of each bin for the selected sequence reads; e) calculating the average and standard deviation of reads matched to each normalized bin of the reference population, and then calculating a Z-score between the normalized values ​​in step d); f) classifying chromosomes using the Z-score and calculating an I-score; g) obtaining breast tissue image interpretation information after anti-cancer treatment; and h) determining whether the calculated I-score is equal to or above a cut-off value. determining that the prognosis of breast cancer is poor when the breast tissue image interpretation information is positive and the breast tissue image interpretation information is equal to or greater than the threshold value;

[0017] The present invention also provides a cfDNA-based breast cancer prognosis prediction device, including a decoding unit that decodes sequence information of cell-free DNA isolated from a biological sample before anti-cancer treatment; an alignment unit that aligns the decoded sequence to a standard chromosome sequence database of a reference population; a quality control unit that selects only sequence information of samples whose aligned sequence information (reads) is equal to or greater than a cut-off value; an I-score calculation unit that compares the selected sequence information (reads) with a reference population sample to calculate a Z-score and then calculates an I-score based on the Z-score; an image interpretation information receiving unit that acquires breast tissue image interpretation information after anti-cancer treatment; and a determination unit that determines that the prognosis of breast cancer is poor if the I-score is equal to or greater than the cut-off value and the image interpretation information is positive.

[0018] The present invention also provides a computer-readable medium that includes instructions configured to be executed by a processor for predicting the prognosis of breast cancer, the computer-readable medium comprising: a) obtaining sequence information of cell-free DNA isolated from a biological sample before anti-cancer treatment; b) aligning the sequence information (reads) to a reference genome database of a reference population; c) checking the quality of the aligned sequence information (reads) and selecting only sequence information that is equal to or greater than a cut-off value; d) dividing the standard chromosome into certain bins, checking the amount of each bin for the selected sequence information (reads) and normalizing it; e) calculating the average and standard deviation of reads matched to each normalized bin of the reference population, and then calculating a Z-score between the values ​​normalized in step d); f) classifying chromosomes using the Z-score and calculating an I-score; g) obtaining breast tissue image reading information after anti-cancer treatment; and h) determining whether the calculated I-score is equal to or greater than a cut-off value. determining that the prognosis of breast cancer is poor when the breast tissue image interpretation information is positive and the breast tissue image interpretation information is equal to or greater than the threshold value; A computer-readable medium is provided that includes instructions configured to be executed by a processor including:

[0019] The present invention also provides a method for providing information for determining the prognosis of breast cancer, comprising the above-mentioned method. The present invention also provides a method for determining the prognosis of breast cancer, comprising the above-mentioned method. [Brief description of the drawings]

[0020] [Figure 1] FIG. 1 is an overall flow chart for the cfDNA-based prognosis prediction of breast cancer according to the present invention. [Diagram 2] This is a diagram showing the results of correcting the number of sequencing reads before and after GC calibration using the LOESS algorithm during the QC (quality control) process of read data. [Diagram 3]1 shows the results of Kaplan Meier analysis of prediction of breast cancer progression and survival using the method of the present invention, where (A) is the result for the exploratory group and (B) is the result for the validation group. [Figure 4] 1 shows the results of risk analysis of breast cancer progression and survival using the method of the present invention, where (A) is the result for the exploration group and (B) is the result for the validation group. [Diagram 5] The relationship between the I score of the present invention and pathological complete response (pCR) was confirmed by Kaplan Meier analysis, where (A) is the result for the exploration group and (B) is the result for the validation group. [Figure 6] 1 shows the results of prognostic prediction for the survival of breast cancer patients divided into groups based on the I score and pCR of the present invention. [Figure 7] 1 shows the results of risk assessment in predicting the prognosis of breast cancer patients for survival according to the I score and pCR of the present invention. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0021] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one skilled in the art to which this invention belongs. In general, the nomenclature used herein and the experimental procedures described below are those well known and commonly used in the art.

[0022] Terms such as "first," "second," "A," and "B" may be used to describe various components, but each component is not limited by the terms and are used only to distinguish one component from another. For example, the first component may be named the second component, and similarly, the second component may be named the first component, without departing from the scope of the technology described below. The term "and / or" includes a combination of multiple associated listed items or any item of multiple associated listed items.

[0023] In the terms used in this specification, singular expressions should be understood to include plural expressions unless the context clearly indicates otherwise, and terms such as "comprise" should be understood to mean the presence of a stated feature, number, step, operation, component, part, or combination thereof, but not to exclude the presence or additional possibility of one or more other features, numbers, steps, operations, components, parts, or combinations thereof.

[0024] Before describing the drawings in detail, it should be made clear that the classification of each component in this specification is merely a classification according to the main function of each component. That is, two or more components described below may be combined into one component, or one component may be divided into two or more components according to more detailed functions. Furthermore, each of the components described below may additionally perform some or all of the functions of the other components in addition to its own main function, and some of the main functions of each component may be exclusively performed by the other components.

[0025] Furthermore, in performing a method or method of operation, the steps making up the method may occur in an order other than that stated, unless the context clearly dictates a particular order, i.e., the steps may occur in the same order as stated, may occur substantially simultaneously, or may occur in the opposite order.

[0026] In the present invention, sequence analysis data obtained from breast cancer patient samples is normalized and organized based on a reference value, then divided into certain intervals (bins), the amount of reads for each interval (bin) is normalized, and a Z score with a reference population sample is calculated. Chromosomes are re-segmented based on the derived Z score, and an I-score is calculated based on the segmentation. It has been confirmed that an I-score equal to or greater than the reference value indicates a poor prognosis, and an I-score less than the reference value indicates a good prognosis. Specifically, risk groups for death or progression due to breast cancer can be classified and confirmed according to the reference value of the I-score and the presence or absence of pathological complete response (pCR). More specifically, it has been confirmed that when the I-score is equal to or greater than the reference value and the image interpretation information is negative, the patient is classified as a moderate risk group, when the I-score is less than the reference value and the image interpretation information is positive, the patient is classified as a high risk group, and when the I-score is equal to or greater than the reference value and the image interpretation information is positive, the patient is classified as an ultra-high risk group.

[0027] That is, in one embodiment of the present invention, DNA extracted from blood of 20 normal subjects and 456 breast cancer patients before anti-cancer treatment was sequenced, quality was controlled using the LOESS algorithm, chromosomes were divided into certain intervals (bins), the amount of reads matched for each interval was normalized by GC ratio, the average and standard deviation of the reads matched to each interval (bin) in the normal subject sample were obtained, and the Z score was calculated with the normalized value. Based on this, chromosomal regions where the Z score suddenly changes were re-segmented, and an I-score was calculated using this. The pCR information of breast cancer patients after anti-cancer treatment was also analyzed, and a method was developed to determine that the prognosis of breast cancer patients is poor if the I-score is 7.81 or more without pCR (Figure 1).

[0028] In the present invention, the term "reads" refers to a single nucleic acid fragment whose sequence information has been analyzed using various methods known in the art. Thus, in this specification, the terms "sequence information" and "read" have the same meaning in that they are the results of obtaining sequence information through a sequencing process.

[0029] In the present invention, the term "prognosis prediction", which is used in the same sense as "prognosis", refers to the act of predicting the course and outcome of a disease in advance. More specifically, prognosis prediction can be interpreted as any act of predicting the course of a disease after treatment, taking into consideration the physiological or environmental conditions of a patient as a whole, since the course of a disease after treatment may vary depending on the physiological or environmental conditions of the patient.

[0030] For the purposes of the present invention, the prognosis prediction can be interpreted as the act of predicting the course of the disease and predicting the risk of cancer progression, cancer recurrence and / or cancer metastasis after breast cancer treatment. For example, the term "good prognosis" means that after breast cancer treatment, the patient's risk of cancer progression, cancer recurrence and / or cancer metastasis is lower than 1, and the breast cancer patient is more likely to survive, which is also referred to as "positive prognosis". The term "bad prognosis" means that after breast cancer treatment, the patient's risk of cancer progression, cancer recurrence and / or cancer metastasis is higher than 1, and the breast cancer patient is more likely to die, which is also referred to as "negative prognosis".

[0031] In the present invention, the term "risk" refers to an odds ratio, a risk ratio, etc., for the probability that a patient will develop cancer progression, recurrence, and / or metastasis after treatment for breast cancer.

[0032] Thus, in one aspect, the present invention provides a method for producing a method for treating a pulmonary artery disease comprising: a) obtaining sequence information of cell-free DNA isolated from a biological sample prior to anti-cancer treatment; b) aligning the sequence reads to a reference genome database of a reference population; c) checking the quality of the aligned sequence reads and selecting only those sequence reads that are equal to or above a cut-off value; d) dividing the standard chromosome into certain intervals (bins), and confirming and normalizing the amount of each interval with respect to the selected sequence information (reads); e) calculating the mean and standard deviation of the reads matched to each normalized bin of the reference population, and then calculating the Z-score between the normalized values ​​in step d); f) classifying chromosomes using the Z scores and calculating I scores; g) obtaining breast tissue image interpretation information after anti-cancer treatment; and h) determining that the prognosis of breast cancer is poor if the calculated I-score is equal to or greater than a cut-off value and the breast tissue image interpretation information is positive.

[0033] In the present invention, the anti-cancer treatment may be any method capable of treating cancer, and may be preferably selected from the group consisting of neoadjuvant therapy, neoadjuvant chemotherapy, adjuvant anti-cancer chemotherapy, surgical therapy, and radiation therapy, but is not limited thereto.

[0034] In the present invention, The step a) comprises: (ai) removing proteins, fats, and other residues from the collected cell-free DNA using a salting-out method, a column chromatography method, or a beads method to obtain purified nucleic acids; (a-ii) preparing a single-end sequencing or pair-end sequencing library from the purified nucleic acid; (a-iii) subjecting the prepared library to a next-generation sequencer; and (a-iv) acquiring sequence information (reads) of the nucleic acid using the next-generation gene sequence testing machine.

[0035] In the present invention, the method may further include a step between steps (ai) and (a-ii) of randomly fragmenting the nucleic acid purified in step (ai) by enzymatic cleavage, crushing or a hydroshear method to prepare a single-end sequencing or paired-end sequencing library.

[0036] In the present invention, the step of obtaining sequence information in step a) may be characterized by obtaining the sequence information through full-length genome sequencing of the isolated cell-free DNA at a depth of 0.01 to 100 reads.

[0037] In the present invention, the next-generation sequencer may be, but is not limited to, Illumina Inc.'s Hiseq system, Illumina Inc.'s Miseq system, Illumina Inc.'s Genome Analyzer (GA) system, Roche Company's 454 FLX, Applied Biosystems Inc.'s SOLiD system, and Life Technologies Inc.'s Ion Torrent system.

[0038] In the present invention, the biological sample means any substance, biological fluid, tissue, or cell obtained or derived from an individual, and includes, for example, whole blood, leukocytes, peripheral blood mononuclear cells, buffy coat, blood including plasma and serum, sputum, tears, mucus, nasal washes, nasal aspirate, breath, urine, semen, saliva, peritoneal washings, pelvic fluids, cystic fluid, meningeal fluid, amniotic fluid, glandular fluid, pancreatic juice, and the like. The fluid may include, but is not limited to, tissue, lymph fluid, pleural fluid, nipple aspirate, bronchial aspirate, synovial fluid, joint aspirate, organ secretions, cells, cell extracts, semen, hair, saliva, urine, buccal cells, placental cells, cerebrospinal fluid, and mixtures thereof.

[0039] In the present invention, the term "reference population" refers to a reference population that can be compared, such as a standard base sequence database, and means a group of people who are currently free of a specific disease or illness. In the present invention, the standard base sequence in the standard chromosome sequence database of the reference population may be a reference chromosome registered in a public health institution such as NCBI.

[0040] In the present invention, the alignment step may be performed using, but is not limited to, the BWA algorithm and the Hg19 sequence.

[0041] In the present invention, the BWA algorithm may include, but is not limited to, BWA-ALN, BWA-SW, Bowtie2, and the like.

[0042] In the present invention, in step c), checking the quality of the aligned sequence information means checking how well the actual sequencing reads match the reference chromosome sequence using a Mapping Quality Score index.

[0043] In the present invention, the step c) comprises: (ci) identifying regions of each aligned nucleic acid sequence; and (c-ii) selecting sequences that satisfy criteria for a mapping quality score and a GC ratio within the region;

[0044] In the present invention, in the step (ci) of identifying the region of the nucleic acid sequence, the region of the nucleic acid sequence may be, but is not limited to, 20 kb to 1 MB.

[0045] In the present invention, in step (c-ii), the reference value may be a mapping quality score that may vary according to a desired criterion, specifically, 15 to 70, more specifically, 30 to 65, and most specifically, 60. In step (c-ii), the GC ratio may vary according to a desired criterion, specifically, 20% to 70%, more specifically, 30% to 60%.

[0046] In the present invention, the step c) may be characterized in that it is carried out excluding data on centrosomes or acrosomes of chromosomes.

[0047] In the present invention, the term "centrosome" may be characterized as being within or beyond 1 Mb from the start of each chromosome long arm (q arm), but is not limited thereto.

[0048] In the present invention, the term "end body" may be characterized as being within 1 Mb of the start of each chromosome's short arm (p arm) or within 1 Mb of the end of the long arm (q arm), but is not limited thereto.

[0049] In the present invention, the step d) is (di) dividing the standard chromosome into certain intervals (bins); (d-ii) calculating the number of aligned reads for each section and the GC content of each read; (d-iii) performing a regression analysis based on the number of reads and the GC content, and calculating a regression coefficient; and (d-iv) normalizing the number of reads using the regression coefficient;

[0050] In the present invention, the certain interval (bin) in (di) may specifically be 100 kb to 2000 kb.

[0051] In the present invention, in the step (di) of identifying a region of a nucleic acid sequence, a certain interval (bin) may be, but is not limited to, 100 kb to 2 MB, specifically 500 kb to 1500 kb, more specifically 600 kb to 1600 kb, even more specifically 800 kb to 1200 kb, and most specifically 900 kb to 1100 kb.

[0052] In the present invention, the regression analysis in step (iii) can be any regression analysis method capable of calculating regression coefficients, and specifically, can be characterized as being LOESS analysis, but is not limited thereto.

[0053] In the present invention, the step of calculating the Z score in step e) may be characterized by normalizing the sequencing read value for each specific region (bin), and more specifically, may be characterized by calculating the Z score according to the following Equation 1:

number

[0054] In the present invention, the step (f) comprises: (fi) dividing the chromosomal regions by a circular binary segmentation method based on the Z score for each section; (f-ii) calculating a Z score for each segmented chromosomal region (segment) as the average of the Z scores calculated for each bin included in the region; (f-iii) performing local regression analysis (LOESS) for each bin and calculating a smoothed Z score (Zn); (In this case, n∈{1,…,N}, N=the total number of bins.) (f-iv) calculating n_score associated with noise according to Equation 2; and

number

number

number

number

[0055] In the present invention, the CBS algorithm means a method for detecting a point at which a change in the number of Z points calculated at the above stage occurs.

[0056] That is, assuming that the start point of the change in the number of Z points on the chromosome is i, the end point is j, the length of the entire region is N, r is the bin value of each nucleic acid sequence (specific bin interval), and s is the standard deviation of each bin value, the following formula is satisfied under the condition of 1 <= i < j <= N.

Equation

Equation

Equation

Equation

Equation

[0057] Here, (i c , j c ) means the position where the change in the number of Z points actually occurred, max means the maximum value, and arg means the argument.

[0058] In the present invention, the reference value of the number of I points can be used without limitation as long as it is a value that can perform prognosis prediction, preferably 5 to 10, and most preferably 7.81, but is not limited thereto.

[0059] In the present invention, the breast tissue image may be any image that can confirm the presence or absence of cancer cells after anti-cancer treatment, and preferably, the breast tissue image may be a magnetic resonance imaging (MRI) image, a histochemically stained image of a breast tissue sample, an ultrasound image, an X-ray image, or a histofluorescently stained image of a breast tissue sample, and more preferably, may be selected from the group consisting of a histochemically stained image of a breast tissue sample and a histofluorescently stained image of a breast tissue sample, but is not limited thereto.

[0060] In the present invention, the breast tissue image interpretation information being positive means that cancer cells are identified in the image, and being negative means that cancer cells are not identified in the image.

[0061] In the present invention, the breast tissue image interpretation information can be used as an index for determining pathological complete response, which is defined as a state in which a breast cancer patient is free of invasive breast cancer when subjected to prior therapy and surgery.

[0062] In the present invention, the method may further include a step of classifying a patient into a moderate risk group when the I score is equal to or greater than a reference value and the image interpretation information is negative, classifying a patient into a high risk group when the I score is less than a reference value and the image interpretation information is positive, and classifying a patient into an extremely high risk group when the I score is equal to or greater than a reference value and the image interpretation information is positive.

[0063] In another aspect, the present invention includes a decoding unit for decoding sequence information of cell-free DNA isolated from a biological sample before anti-cancer treatment; an alignment section that aligns the decoded sequences to a standard chromosome sequence database of a reference population; A quality control section that selects only the sequence reads of samples that meet or exceed a cut-off value; an I-score calculation section that calculates a Z-score for the selected sequence information (reads) by comparing them with a reference population sample, and then calculates an I-score based on the Z-score; an image interpretation information receiving unit for obtaining breast tissue image interpretation information after anti-cancer treatment; and The present invention relates to a cfDNA-based breast cancer prognosis prediction device that includes a decision unit that determines that the prognosis of breast cancer is poor if the I score is above a cut-off value and the image interpretation information is positive.

[0064] In the present invention, the decoding unit may include a nucleic acid injection unit that injects nucleic acid extracted from an independent device; and a sequence information analysis unit that analyzes sequence information of the injected nucleic acid; and may be preferably an NGS analysis device, but is not limited thereto.

[0065] In the present invention, the decoding unit may receive and decode sequence information data generated by an independent device.

[0066] In the present invention, the image interpretation information receiving unit may receive image interpretation information generated by an independent device.

[0067] In yet another aspect, the invention includes instructions configured to be executed by a processor for predicting a prognosis of breast cancer, comprising: a) obtaining sequence information of cell-free DNA isolated from a biological sample before anti-cancer treatment; b) aligning the sequence information (reads) to a reference genome database of a reference population; c) checking the quality of the aligned sequence information (reads) and selecting only sequence information above a cut-off value; d) dividing the standard chromosome into certain bins, checking the amount of each bin for the selected sequence information (reads) and normalizing it; e) calculating the average and standard deviation of the reads matched to each normalized bin of the reference population and calculating the Z-score between the normalized values ​​in step d); f) classifying chromosomes using the Z-score and calculating the I-score; g) obtaining breast tissue image reading information after anti-cancer treatment; and h) determining that the prognosis of breast cancer is poor if the calculated I-score is above the cut-off value and the breast tissue image reading information is positive; The present invention relates to a computer-readable medium comprising instructions configured to be executed by a processor including:

[0068] In still another aspect, the present invention relates to a method for providing information for determining the prognosis of breast cancer, comprising the above-mentioned method.

[0069] In the present invention, the breast cancer includes, without limitation, all types of cancer occurring in the breast, and more specifically, includes, but is not limited to, ductal carcinoma in situ, anti-inflammatory carcinoma in situ, invasive ductal carcinoma, invasive lobular carcinoma, ductal carcinoma in situ, and lobular carcinoma in situ.

[0070] The term "prognosis" in the present invention means prediction of the likelihood of cancer progression, cancer recurrence and / or cancer metastasis. The prediction method of the present invention can be used to make clinical treatment decisions, i.e., to select the most appropriate treatment method for any particular patient. The prediction method of the present invention is a valuable diagnostic and / or diagnostic aid for determining whether a patient is likely to develop cancer progression, cancer recurrence and / or cancer metastasis.

[0071] In another aspect, the method of the present application may be implemented using a computer. In one embodiment, the computer includes one or more processors coupled to a chipset. The chipset may also be coupled to a memory, a storage device, a keyboard, a graphics adapter, a pointing device, and a network adapter. In one embodiment, the capabilities of the chipset may be provided by a memory controller hub and an I / O controller hub. In another embodiment, the memory may be directly coupled to the processor instead of the chipset. The storage device may be any device capable of maintaining data, including a hard drive, a compact disk read-only memory (CD-ROM), a DVD, or other memory device. The memory is responsible for the data and instructions used by the processor. The pointing device may be a mouse, a track ball, or other type of pointing device, and may be used in combination with a keyboard to transmit input data through the computer system. The graphics adapter may display images and other information on a display. The network adapter is coupled to a computer system via a local or long distance communication network. However, the computer used in the present application is not limited to the above configuration and may include none or additional components and may be part of a storage area network (SAN). The computer of the present application may be configured to execute modules in a program for performing the method according to the present application.

[0072] In the present application, a module may refer to a functional and structural combination of hardware for carrying out the technical idea of ​​the present application and software for driving the hardware. For example, the module may refer to a logical unit of a certain code and a hardware resource for executing the certain code, and it is obvious to those skilled in the art that the module does not necessarily refer to physically connected code or to one type of hardware. EXAMPLES

[0073] The present invention will be described in more detail with reference to the following examples. It will be obvious to those skilled in the art that these examples are merely for the purpose of illustrating the present invention and should not be construed as limiting the scope of the present invention thereto.

[0074] Example 1. Calculation of I-score in breast cancer patients and normal subjects

[0075] The I score was calculated using the method described in Korean Patent No. 10-2019-0019315.

[0076] More specifically, cel-free DNA was extracted from plasma samples of 456 breast cancer patients who participated in the PEARLY clinical trial (NCT02441933), received neoadjuvant therapy, and underwent surgery, and from plasma samples of 20 normal subjects, and a library for full-length chromosomes was prepared. The cel-free DNA was extracted in the following order: 1) After blood was collected in an EDTA tube, it was centrifuged at 1600g for 10 minutes and 3000g for 10 minutes within 4 hours to separate the supernatant (plasma); 2) cel-free DNA was extracted using a plasma circulating DNA kit (Tiangen, China) using 0.6 ml of the separated plasma; 3) The finally extracted cel-free DNA was reacted with a Qubit 2.0 Fluorometer to measure the concentration (ng / μl). Library preparation was based on the MGI Asey Cell-free DNA Library Prep Kit (MGI, China), and a total of 2 ng to 6 ng of cell-free DNA was used in the reaction.

[0077] The completed libraries were sequenced using a DNBSEQ-G400 sequencing device (MGI), producing an average of 17 million reads of sequence information per sample.

[0078] The Bcl file (including sequence information) was converted to fastq format using next-generation sequencing (NGS) equipment, and the library sequences in the fastq file were aligned to the reference chromosome Hg19 sequence using the BWA-mem algorithm. It was confirmed that the mapping quality score was 60.

[0079] We confirmed that the distribution of sequencing read numbers for each chromosomal locus interval (bin) was biased by GC content (Figure 2), and used regression analysis to calibrate the numbers of aligned library sequences by GC ratios by chromosome.

[0080] Then, the Z score was calculated using the following formula 1.

number

[0081] To calculate the I score, the calculated Z score for each bin was used as data, and a process of segmenting the chromosomes using the CBS algorithm was performed.

[0082] The I score was then calculated through the following steps:

[0083] i) calculating a Z score for each divided chromosome segment as an average of the Z scores calculated for each bin included in the segment; ii) A step of performing local regression analysis (LOESS) for each interval (bin) and calculating the smoothed Z score (Zn); where n ∈ {1, ..., N}, N = the total number of bins. iii) calculating n_score associated with noise according to Equation 2; and

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[0084] Example 2. Confirmation of the effect of I score on breast cancer progression and survival

[0085] In Example 1, breast cancer patients were divided into an exploratory group of 232 patients and a validation group of 233 patients. The correlation between I score and disease-free survival (DFS) in the exploratory group was analyzed using univariate Cox regression and maximal log-rank test. As a result, it was confirmed that DFS was significantly decreased in the group with an I score of 7.81 or more, and the hazard ratio (HR) for the time to disease progression was increased (Figures 3A and 4A). The same results were also confirmed in the validation group (Figures 3B and 4B).

[0086] [Table 1] Abbreviations:IQR, interquartile range;SD, standard deviation;pCR, pathologic complete response;CNA, copy number aberration *Calculated by t-test **calculated Fisher's exact test

[0087] Example 3. Confirmation of the relationship between I score and pCR

[0088] Multivariate Cox analysis was used to confirm the relationship between the presence or absence of pathological complete response, which is a powerful factor for predicting the prognosis of breast cancer, and the I-score. As a result, as shown in Figure 5, it was confirmed that in the exploration group, DFS was reduced when the I-score was above the standard value, regardless of the presence or absence of pCR.

[0089] In addition, because pCR and I score each act as independent prognostic predictors, it was confirmed that by combining these two and separating them into four groups, more detailed prognostic predictions were possible (Figures 6 and 7).

[0090] The four groups are (1) Group with I score above the reference value and no pCR (2) Group with I score above the reference value and pCR (3) I score below the reference value and no pCR, and (4) Patients with I score below the reference value and pCR As shown in Figures 6 and 7, it was confirmed that group 1 had the poorest prognosis and group 4 had the best prognosis.

[0091] Although the specific parts of the present invention have been described in detail above, it is clear to those skilled in the art that such specific descriptions are merely preferred embodiments and do not limit the scope of the present invention. Therefore, the substantial scope of the present invention is defined by the appended claims and their equivalents. [Industrial Applicability]

[0092] The method for predicting the prognosis of breast cancer according to the present invention not only improves the accuracy of predicting the prognosis of breast cancer patients using Next Generation Sequencing (NGS), but also improves the accuracy of predicting the prognosis based on very low concentrations of cell-free DNA that have been difficult to detect, thereby increasing commercial applicability. Therefore, the method of the present invention is useful for determining the prognosis of breast cancer patients.

Claims

1. A cell-free DNA (cfDNA)-based breast cancer prognosis method comprising the steps of: a) obtaining sequence information of cell-free DNA isolated from a pre-anticancer treatment biological sample; b) aligning the sequence reads to a reference genome database of a reference population; c) checking the quality of the aligned sequence information (reads) and selecting only sequence information having a quality equal to or higher than a cut-off value; d) dividing the standard chromosome into certain intervals (bins), and confirming and normalizing the amount of each interval with respect to the selected sequence information (reads); e) calculating the mean and standard deviation of the reads matched to each normalized bin of the reference population, and then calculating the Z-score between the normalized values ​​in step d); f) classifying chromosomes using the Z scores and calculating I scores; g) obtaining breast tissue image interpretation information after anti-cancer treatment; and h) determining that the prognosis of breast cancer is poor if the calculated I-score is equal to or greater than a cut-off value and the breast tissue image interpretation information is positive;

2. The cfDNA-based method for predicting breast cancer prognosis according to claim 1, wherein the step a) is carried out by a method comprising the steps of: (a-i) removing proteins, fats, and other residues from the collected cell-free DNA using a salting-out method, a column chromatography method, or a beads method to obtain purified nucleic acids; (a-ii) preparing a single-end sequencing or pair-end sequencing library from the purified nucleic acid; (a-iii) reacting the prepared library with a next-generation gene sequencer; and (a-iv) obtaining nucleic acid sequence information (reads) using the next-generation gene sequencer;

3. The cfDNA-based method for predicting breast cancer prognosis according to claim 2, further comprising a step between steps (a-i) and (a-ii) of randomly fragmenting the nucleic acid purified in step (a-i) by enzymatic cleavage, crushing, or a hydroshear method to prepare a single-end sequencing or paired-end sequencing library.

4. The cfDNA-based method for predicting breast cancer prognosis according to claim 1, wherein the step of acquiring sequence information in step a) is performed by performing full-length genome sequencing of the isolated cell-free DNA at a depth of 0.01 to 100 reads.

5. The cfDNA-based method for predicting breast cancer prognosis according to claim 1, wherein the step c) is carried out by a method comprising the steps of: (ci) identifying a region of each aligned nucleic acid sequence; and (c-ii) selecting sequences that satisfy criteria of mapping quality score and GC ratio within the region.

6. The cfDNA-based method for predicting breast cancer prognosis according to claim 5, wherein the reference value is a mapping quality score of 15 to 70 and a GC ratio of 30 to 60%.

7. The cfDNA-based method for predicting breast cancer prognosis according to claim 5, wherein step c) is performed excluding data from chromosome centrosomes or acrosomes.

8. The cfDNA-based method for predicting breast cancer prognosis according to claim 1, wherein step d) is carried out by a method comprising the following steps: (di) dividing the standard chromosome into certain intervals (bins); (d-ii) calculating the number of aligned reads for each section and the GC content of each read; (d-iii) performing a regression analysis based on the number of reads and the GC amount, and calculating a regression coefficient; and (d-iv) Normalizing the number of reads using the regression coefficients.

9. The cfDNA-based method for predicting breast cancer prognosis according to claim 8, wherein the certain interval (bin) in (di) is 100 kb to 2 Mb.

10. The cfDNA-based method for predicting breast cancer prognosis according to claim 1, wherein step e) is performed by calculating using the following Equation 1: [0010]

11. The cfDNA-based method for predicting breast cancer prognosis according to claim 1, wherein step (f) is carried out by a method comprising the following steps: (f-i) dividing the chromosomal regions by a Circular Binary Segmentation (CBS) method based on the Z-scores of each section; (f-ii) calculating a Z score for each segmented chromosome region (segment) as an average of the Z scores calculated for each bin included in the region; (f-iii) performing a local regression analysis (LOESS) for each interval (bin) and calculating a smoothed Z score (Zn); (In this case, n ∈ {1, ..., N}, N = the total number of bins.) (f-iv) calculating n_score associated with noise according to Equation 2; and [0025] (At this time, [0030] This means the Z score for each bin calculated in step i). (fv) calculating an I score using Equation 3 below: [0045] (At this time, [0050] This means the Z score for each segment calculated in step i).

12. The cfDNA-based method for predicting breast cancer prognosis according to claim 1 , wherein the breast tissue image is selected from the group consisting of a histochemically stained image of a breast tissue sample and a histofluorescently stained image of a breast tissue sample.

13. The cfDNA-based method for predicting breast cancer prognosis according to claim 1, wherein the breast tissue image interpretation information being positive means that cancer cells are identified in the image.

14. The cfDNA-based method for predicting breast cancer prognosis according to claim 1, wherein the reference value of the I score is 5 to 10.

15. The cfDNA-based method for predicting breast cancer prognosis according to claim 1, further comprising the steps of: classifying the subject into a moderate risk group when the I score is equal to or greater than the reference value and the image interpretation information is negative; classifying the subject into a high risk group when the I score is less than the reference value and the image interpretation information is positive; and classifying the subject into an extremely high risk group when the I score is equal to or greater than the reference value and the image interpretation information is positive.

16. A method for providing information for determining the prognosis of breast cancer, comprising a step of predicting the prognosis of breast cancer by the method according to any one of claims 1 to 15.

17. A method for determining the prognosis of breast cancer, comprising a step of predicting the prognosis of breast cancer by the method according to any one of claims 1 to 15.

18. a decoding unit for decoding sequence information of the cell-free DNA isolated from the biological sample before anti-cancer treatment; an alignment section that aligns the decoded sequence to a standard chromosome sequence database of a reference population; A quality control unit that selects only sample sequence information that is equal to or greater than a cut-off value from the aligned sequence information (reads); an I-score calculation unit for calculating a Z-score for the selected sequence information (reads) by comparing it with a reference population sample, and then calculating an I-score based on the Z-score; An image interpretation information receiving unit for obtaining breast tissue image interpretation information after anti-cancer treatment; and A cfDNA-based breast cancer prognosis prediction device comprising: a decision unit for determining that the prognosis of breast cancer is poor when the I score is equal to or greater than a cut-off value and the image interpretation information is positive.

19. The computer-readable medium includes instructions configured to be executed by a processor to predict a prognosis of breast cancer, a) obtaining sequence information of cell-free DNA isolated from a pre-anticancer treatment biological sample; b) aligning the sequence reads to a reference genome database of a reference population; c) checking the quality of the aligned sequence information (reads) and selecting only sequence information having a quality equal to or higher than a cut-off value; d) dividing the standard chromosome into certain intervals (bins), and confirming and normalizing the amount of each interval with respect to the selected sequence information (reads); e) calculating the mean and standard deviation of the reads matched to each normalized bin of the reference population, and then calculating the Z-score between the normalized values ​​in step d); f) classifying chromosomes using the Z scores and calculating I scores; g) obtaining breast tissue image interpretation information after anti-cancer treatment; and h) determining that the prognosis of breast cancer is poor when the calculated I-score is equal to or greater than a cut-off value and the breast tissue image interpretation information is positive; A computer-readable medium comprising instructions configured to be executed by a processor including:

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