In vitro methods for diagnosing or evaluating the progression or response to treatment of a disorder based on circulatory disease stress parameters.

Abstract

The present invention relates to an in vitro method for diagnosing or evaluating the progression of a disorder in an individual, or for evaluating the responsiveness / response to a treatment in an individual, comprising the steps of: counting the number of target cells and the number of target cell fragments; determining a circulatory disease load (CDL) parameter that is a function of the number of target cells and the number of target cell fragments; and correlating the circulatory disease load (CDL) parameter with the presence or progression of a disorder in the individual or with responsiveness / response to treatment.

Description

[Technical Field]

[0001] Cross-reference of related applications This patent application claims priority to Italian Patent Application No. 102023000019227, filed on 19 September 2023, the entire disclosure of which is incorporated herein by reference.

[0002] The present invention relates to an in vitro method for diagnosing or evaluating the progression of a disorder in an individual, evaluating the response / responsiveness to a treatment in an individual, or evaluating the presence of a treatment target in an organic fluid sample, and for determining a circulatory disease load (CDL) parameter as a function of the amounts of target cells, target cell fragments, circulating proteins, RNA, and DNA (the latter three collectively referred to as circulating biomarkers). [Background technology]

[0003] Carcinoma begins as a primary tumor within the body. These grow locally and, at some point, begin to spread to more distal locations. In this evolutionary process, cancer cells acquire or lose genes that enable migration and proliferation in distal organs. Most patients die due to metastasis, either because metastasis suppresses vital functions or because the growth of carcinoma throughout the body becomes exhausting. At various stages of this process, there are various issues that must be addressed in order to make the best treatment decisions.

[0004] First, cancer must be diagnosed, then a treatment should be selected, and then the success of the selected treatment should be evaluated. In selecting a treatment, it should be considered whether the risk of disease progression is high enough to justify a treatment with serious side effects. Furthermore, an evaluation should be conducted to determine whether targeted therapy is likely to be effective.

[0005] In this decision-making process, information obtained from circulating tumor cells (CTCs) can be extremely valuable. However, CTCs are very rare cells, and their count may be zero, even in patients with advanced disease and ongoing metastasis. In this regard, it has been found that tumor-derived extracellular vesicles (tdEVs) play an important role in homeostatic and oncogenic processes and can be informative in relation to the presence, progression, and prognosis of tumors.

[0006] In principle, CTC counts are considered more reliable due to their very low background counts, but if CTC counts are low, tdEV can supplement the assessment.

[0007] As a practical example, most patients diagnosed with prostate cancer or developing metastatic disease initially respond to androgen deficiency but eventually progress to gonadectomy-resistant prostate cancer (CRPC) and death. In recent years, an excessive number of new drugs have been introduced or developed for patients who develop mCRPC. The main burden in evaluating the effectiveness of these new drugs is to fully assess the response to treatment, as many patients' disease is limited to the bone and does not accept evaluation by RECIST and bone scans, and PSA changes are unreliable prior to 12-16 weeks of treatment. Multivariate analysis has shown that CTC measurement is an excellent biomarker for prognosis and assessment of disease response in mCRPC. CTC burden is strongly associated with overall survival. To simplify matters in the FDA-approved CellSearch CTC assay (Menarini Silicon Biosystems), mCRPC patients were divided into those with 5 or more (undesirable) or fewer than 5 (desirable) CTCs / 7.5 ml of blood. The response to treatment after 3–6 weeks of treatment was assessed by switching from undesirable to desirable. Several methods have been investigated for the best assessment of treatment response based on changes in CTCs. The low frequency of CTCs is an obstacle to accurate assessment of all mCRPC patients. In the initial prospects of the CTC study, 38% of patients had fewer than 5 CTCs in all blood draws, making it impossible to assess the response. The observation that CTCs represented only a small portion of cytokeratin+, DAPI+, CD45- subjects in images created with CellSearch, and that subjects not assigned as CTCs showed similar prognostic values, suggests potential to improve the proportion of patients for which response assessment can be performed. Cytokeratin+, DAPI+, CD45- subjects were identified and classified using the open-source image analysis program ACCEPT, and within these subjects, a certain population was defined as tumor-derived extracellular vesicles (tdEVs). These tdEVs appeared 10 times more frequently than CTCs and showed a similar association with overall survival.Furthermore, in mCRPC patients with five or fewer CTCs, elevated tdEV was associated with a 4.7 times higher risk of death compared to those without elevated tdEV.

[0008] To date, CTC counts and tdEV counts have been studied separately and correlated individually with diseases and patient conditions.

[0009] Furthermore, using plasma from collected blood, other circulating markers, including small tdEVs, circulating tumor DNA, circulating tumor RNA, and circulating proteins, can be detected, all of which may have some correlation with the metastatic activity of the tumor.

[0010] Therefore, there is a need to find ways to support clinicians in treatment decisions by combining information from CTCs and tdEVs, particularly in evaluating the presence and development of tumors in patients. Such combinations may be useful not only in oncology but also in other medical fields. For example, the detection of circulating endothelial cells (CECs) and their fragments (hereinafter also referred to as endothelial extracellular vesicles, edEVs) may be an indicator of cardiovascular disease. [Prior art documents] [Non-patent literature]

[0011] [Non-Patent Document 1] Coumans, FA et al., Challenges in the enumeration and phenotyping of CTC. Clin Cancer Res, 2012. 18(20): pp. 5711-58 [Overview of the project] [Problems that the invention aims to solve]

[0012] Therefore, the object of the present invention is to provide an improved method for supporting clinicians in making treatment decisions. [Means for solving the problem]

[0013] This object is achieved by the method as defined in claim 1. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] [Figure 1] This is a diagram showing a flowchart of a method according to an embodiment of the present invention. [Figure 2] This is a diagram showing a graph illustrating the combination of CTC count and tdEV count in BTL parameters. [Figure 3] This is a diagram showing a block diagram of a specific embodiment according to the present invention. [Figure 4] This is a diagram showing a block diagram of a specific embodiment according to the present invention. [Figure 5] This is a diagram showing a block diagram of a specific embodiment according to the present invention. [Figure 6] This is a diagram showing a graph of the hazard ratio for overall survival evaluated using BTL as a continuous variable, obtained using three different BTL equations respectively. [Figure 7] This is a diagram showing a graph of the continuous hazard ratio for the optimal form of various BTL equations and the hazard ratio (HR) for operator CTC. [Figure 8] This is a diagram showing graphs of the fraction and mean intensity of the HER2 marker expression of CTC alone as well as the biomarker of tumor marker expression (TME combining CTC and tdEV) in 98 breast cancer patient samples. The upper left panel shows the fraction of CTC that are positive (more than 25 above control staining) or weakly positive (more than 1 above control staining). Samples are sorted by fraction positivity, and results are not available when there are 2 or fewer CTC. The upper right panel shows the TME parameters determined using CTC and tdEV. Samples are sorted by fraction positivity, and results are not available when the total number of CTC and / or tdEV is 2 or less. The lower panel shows the mean intensity for the same samples. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0015] The method according to the present invention is for diagnosing or evaluating the progression of a disorder in an individual, or for evaluating the responsiveness / response to treatment in an individual (patient).

[0016] In detail, the following description focuses on applying this method to circulating tumor cells (CTCs) and circulating cell fragments (hereinafter also referred to herein as tumor-derived extracellular vesicles, tdEVs) in organic fluid samples from patients, as well as other prognostic biomarkers. CTCs and tdEVs are indicators of metastatic tumors, particularly carcinomas or melanomas. However, this method can also be applied to detect other types of target cells.

[0017] The expression “cells of interest” is intended to refer to cells whose presence and number in a patient’s organic fluid sample may be indicators of the presence of a disorder in the patient. In preferred embodiments, cells of interest are rare cells with low density in the patient’s organic fluid. The density ranges for various types of cells are listed below.

[0018] For example, the method according to the present invention can also be applied to the detection of circulating endothelial cells (CECs) and circulating cell fragments (hereinafter also referred to herein as endothelial extracellular vesicles, edEVs), which are indicators of cardiovascular disease, infectious disease, or cancer. The density of CECs in blood ranges from 0 to 200 / mL of whole blood, particularly 0 to 50 / mL of whole blood, and more specifically, about 5 / mL of whole blood.

[0019] This method can also be applied to the detection of circulating multiple myeloma cells (CMMCs) and circulating cell fragments (hereinafter also referred to as extracellular vesicles, mmEVs, derived from multiple myeloma). The presence and number of CMMCs in a patient's blood can be an indicator of their presence in the bone marrow at the time of diagnosis. Furthermore, monitoring their number can be used to evaluate the effectiveness of myeloma and various treatments for this tumor. The density of CMMCs in blood ranges from 0 to 20,000 / mL of whole blood, more specifically from 0 to 2,000 / mL of whole blood. Typically, the median is about 1,000 before treatment, decreases to about 5 during remission in response to effective treatment, and increases back to about 100 during relapse.

[0020] In some diseases, the counting of multiple types of target cells and target cell fragments is particularly informative, such as in cases of CTCs, CECs, and tdEVs and edEVs in colorectal cancer. Additional circulating markers that have independent prognostic values ​​and can originate from the same sample may exist, including circulating tumor DNA (ctDNA), various types of RNA, and protein markers.

[0021] Other types of rare cells to which this method may be applicable include fetal cells, respiratory virus cells, and cells collected from cerebrospinal fluid aspiration.

[0022] The expression “target cell fragment” is intended to refer to phospholipid-encapsulated structures, particularly cell particles including, for example, exosomes, microvesicles, and apoptotic bodies, whose presence and number in a patient’s organic fluid sample may be indicators of the presence of a disorder in the patient.

[0023] The expression “target component” is intended to generally refer to “target cell,” “target cell fragment,” and, optionally, one or more of the target circulating biomarkers derived from the pathological condition (disorder), such as circulating tumor DNA, circulating RNA, or circulating proteins.

[0024] The expression "responsiveness" is intended to indicate an individual's suitability for a particular treatment. The expression "response" is intended to mean the actual effect of a treatment on an individual.

[0025] The method according to the present invention includes the following steps.

[0026] First, the number of target cells and target cell fragments is counted in the organic fluid sample of the individual. Optionally, the number of target cell clusters can also be counted. For example, sometimes CTCs in whole blood samples are found not isolated, but instead clustered. Counting these clusters, along with this additional information regarding the number of target cells and target cell fragments, may further aid in assessing the disease or disease progression. Optionally, plasma can also be analyzed for the presence of circulating tumor DNA (ctDNA), disease-specific RNA, and disease-specific proteins.

[0027] In the next step, the circulatory disease load (CDL) parameter is determined as a function of the number of target cells and target cell fragments, and optionally, the number of target cell clusters, and the concentrations of target ctDNA, RNA, and / or proteins.

[0028] Next, circulatory disease load (CDL) parameters are correlated with the presence or progression of the disorder in the individual, or with the response / response to treatment.

[0029] The CDL parameter in patients is a function of the number of target cells and target cell fragments, and optionally the number of target cell clusters, relative to the background number of target cells, target cell fragments, and optionally the background number of target cell clusters in normal individuals.

[0030] Figure 1 shows a flowchart of one embodiment of the method according to the present invention.

[0031] In step S01, a sample of the patient's organic fluid, particularly a blood sample, is provided.

[0032] Subsequently, in step S02, the organic fluid sample is processed to enable selective imaging of the sample and detection of the presence of target cells and target cell fragments (and optionally clusters) in the sample.

[0033] In detail, in step S02, the organic fluid sample is concentrated and labeled with respect to the target component COI (e.g., CTC and tdEV).

[0034] For example, blood samples can be subjected to immunomagnetically concentrated targeted epithelial cell adhesion molecules (EpCAM) for the detection of CTCs and tdEVs.

[0035] The concentrated organic fluid sample is labeled with at least one marker specific to rare cells and their fragments, such as CTCs and tdEVs, and / or at least one marker specific to cells and / or their fragments other than rare cells and / or their fragments, which are detected at an optional rate.

[0036] The markers can be identified by selective imaging.

[0037] According to a preferred embodiment, the marker is a fluorescent marker that can be imaged by fluorescence imaging. For example, for the detection of CTCs and their fragments, a concentrated sample may be stained with one or more fluorescent markers, in particular DAPI, CD45-APC, and CK-PE.

[0038] However, the processing of the sample may vary depending on the specific application, for example, to enable the detection of the presence or absence of the component of interest using scattering markers, temporal behavior in response to stimuli, or any other known method.

[0039] In step S03, an image of the labeled sample is captured using a method known in itself. Specifically, in this embodiment, the image is a fluorescence image.

[0040] For example, the marked samples may be arranged in a cartridge, and a digital image of the surface of each fluorophore may be recorded.

[0041] According to a preferred embodiment, one or more of steps S02 and S03, particularly both steps S02 and S03, can be performed by the CellSearch System®. Step S03 can be performed by another imaging system such as DEPArray®.

[0042] Subsequently, in step S04, the captured image is processed by a control unit to determine the presence of target cells and target cell fragments in the image and, therefore, in the patient's organic fluid sample. The control unit is configured to detect and enumerate the target components in the received image.

[0043] In particular, the control unit may be configured to detect and enumerate target cells and target cell fragments by using one or more machine learning algorithms that have been trained to identify whether objects stained with markers in an image are target cells or target cell fragments.

[0044] In the embodiment illustrated in Figure 1, the method further includes step S05, in which a control unit provides a plurality of counters, i.e., one counter for each of the target component COIs detected and enumerated in step S04. For example, the counters may be a CTC counter CTC-CNT and a tdEV counter tdEV-CNT.

[0045] For example, various counters can be provided to the user, for instance, through visual signals on a graphical user interface.

[0046] In step S06, a combination of rare counters is used to establish the value of circulatory disease burden CDL, and the patient's diagnosis is evaluated as a function of the CDL value (step S07). Steps S06 and S07 are described in detail later with reference to Figure 3.

[0047] Optionally, according to the embodiment of Figure 1, after the detection and enumeration of the target components in step S04, the method may further include the following: - A step (step S08) to calculate the marker expression of each target component detected in step S04. Marker expression for each COI can be calculated by a method known to be true. For example, by separately calculating the average marker expression for CTCs and tdEVs, or by determining the number of CTCs and tdEVs that are negative, weakly positive, and strongly positive. The threshold for the transition from negative to weakly positive to strongly positive depends on the marker and the disease. For example, Her2 expression in breast cancer is negative if it is at the same level as the control cells, weakly positive if it is up to 20 more than the control cells, and strongly positive if it is more than 20, and - Step (S09) of calculating a tumor marker expression (TME) value, which is a function of all single marker expressions calculated for each COI and counters for CTCs and tdEVs. For example, weighting the mean marker values ​​of CTCs and tdEVs together in a manner that depends on their relative frequencies, or weighting the proportion of negative CTCs and tdEVs, the proportion of weakly stained CTCs and tdEVs, and the proportion of strongly positive CTCs and tdEVs in a manner that depends on their relative frequencies.

[0048] Further details regarding steps S08 and S09 are provided herein with reference to the embodiment shown in Figure 4.

[0049] TME may be useful in assessing whether a patient is eligible for targeted therapy.

[0050] According to one embodiment, the method of the present invention may include additional steps for the analysis of ctDNA, RNA, and / or proteins. For example, in step S50, plasma may be separated from the cell fraction in the fluid obtained in step S01. However, in addition to the one collected for CTC analysis, another tube of blood may be collected.

[0051] In step S51, ctDNA, RNA, and / or proteins are analyzed using assays optimized for each target. For example, the plasma sample may be centrifuged at a high g force (20,000 × g) to remove any cellular material, and next-generation sequencing may be applied to the supernatant of this treatment to identify circulating DNA with mutations typical of tumors. Subsequently, to provide CDL, the extracted concentrates of circulating proteins, DNA, and / or RNA may be used in combination with a cell counter in step S06.

[0052] The method according to the present invention is - A step of marking a sample with at least one marker specific to target cells and target cell fragments, and / or optionally at least one marker specific to cells and / or cell fragments other than target cells and / or target cell fragments, wherein one or more markers are identifiable by selective imaging. - A step of obtaining at least one image of a marked sample, wherein the image includes an object stained with at least one marker to be classified. - A process of classifying objects stained with markers, - A step of determining whether the object stained with the marker is the target cell or target cell fragment, based on the results of classifying the object stained with the marker. Includes.

[0053] According to this method, the step of counting the number of target cells and / or target cell fragments and / or target cell clusters in a sample is: A step of increasing the target cell counter (CTC-CNT) in response to subjects stained with each marker determined to be the target cell, and / or A step of increasing the target cell fragment counter (tdEV-CNT) in response to a target stained with each marker determined to be the target cell fragment, and / or A step of increasing the target cell cluster counter (CTCcl-CNT) in response to subjects stained with each marker determined to be the target cell cluster, at the discretion of the user. Includes.

[0054] The classification process is classification-based and can be performed by running one or more machine learning algorithms trained to identify whether objects stained with markers in an image contain target cells or target cell fragments.

[0055] Preferably, the CDL parameter is a function of the difference in the value of the CTC counter CTC-CNT with respect to its relative background level in a normal individual.

[0056] In a preferred embodiment, the cells of interest are CTCs, the fragments are tdEVs, and the clusters are CTC clusters. In this example, these elements can be marked by cytokeratin and CD45 antibodies and DAPI staining. CTCs and clusters are positive for cytokeratin and DAPI and negative for CD45. tdEVs are positive for cytokeratin and negative for CD45.

[0057] Target cells, fragments, and clusters can be counted, for example, by using the Cellsearch system (Menarini Silicon Biosystems), which detects, concentrates, and enumerates circulating tumor cells or other rare cell types from whole blood in a standardized and reproducible workflow. Enumeration of target cells and fragments can also be performed using other imaging systems such as DEPArray® (Menarini Silicon Biosystems).

[0058] The value of the circulatory disease burden parameter CDL may be a linear combination of the values ​​of the CTC counter CTC-CNT and the tdEV counter tdEV-CNT.

[0059] The circulatory disease load parameter CDL is a function of the difference in the value of the CTC counter CTC-CNT relative to its relative background level in healthy patients, as follows: CDL=f(CTC-CTC BG , tdEV-tdEV BG ), where in the formula, CTC is the number of target cells in the individual, CTC BG is the number of target cells in a normal individual, tdEV is the number of target cell fragments in the individual, tdEV BG is the number of target cell fragments in a normal individual.

[0060] Optionally, CDL=f(CTC-CTC BG , tdEV-tdEV BG , CTC クラスター -CTC クラスター、BG ), where in the formula, CTC クラスター is the number of target cell clusters in the individual, CTC クラスター、BG is the number of target cell clusters in a normal individual.

[0061] In a preferred embodiment, the circulatory disease load (CDL) parameter ranges from a minimum value, for example 0, to a maximum value, for example 1, as a function of the values of the CTC counter CTC-CNT and / or the tdEV counter tdEV-CNT, and the disease risk increases monotonically with the value of CDL.

[0062] Figure 2 shows a graph illustrating a possible calculation of this CDL, taking into account the combination of CTC counting and tdEV counting. Accordingly, the CDL parameter is newly named "Blood Tumor Load" (BTL), and ranges between a value of 0 (low disease risk) and a value of 1 (high disease risk).

[0063] Low concentrations of CTCs limit the reliability of CTC-based diagnostics, and in patient groups where CTCs were not detected before treatment, treatment selection and efficacy determination are impossible. Metastatic patients almost certainly have CTCs, but their concentrations may be too low to detect in a single blood tube. However, the majority of CTCs fail to extravasate and are therefore degraded at the metastatic site or in circulation, potentially leading to tdEV. While enhancing CTC-based diagnostics with tdEV may expand the number of patients for whom a diagnosis can be made, providing two indicators for the same treatment means that interpreting the results becomes more difficult. The goal of BTL is to consolidate CTCs and tdEV (and possibly others) that are higher than background levels in a higher proportion of patients into a single measure.

[0064] BTL can be applied, for example, to application a) assess the risk or probability that a patient is metastatic, application b) assist in the selection of targeted therapies, and application c) monitor the effect of treatment on metastatic activity.

[0065] The following table summarizes clinical questions, current standard practices, and potential ways in which CTC and tdEV may improve diagnosis.

[0066] [Table 1]

[0067] Referring to the applications mentioned above, in order to provide a higher probability of patients having a higher background CDL (Application a), as shown in Figure 3), it is necessary to model the background BTL or the background of CTC and tdEV. This means representing the background BTL as a random variable, or the CTC and tdEV as random variables.

[0068] In detail, according to the embodiment in Figure 3, the multiple counters determined in step S05 of Figure 1 are collected, for example, by a processing unit (steps S10_1, ..., S10_N). Each counter is an index of the number of each COI.

[0069] Subsequently, in step S11, the CDL value is determined as a function of the collected counters. The CDL value can be calculated using any of the equations reported below. In this example, the CDL value may be called the circulatory disease burden CDL.

[0070] Subsequently, in step S12, the probability of finding the CDL value thus obtained in a patient that is normal or benign is calculated from the reference value of benign and / or normal donors using an applicable statistical distribution, such as a simultaneous Poisson distribution or a normal distribution. The complement of this probability is taken as the probability that the patient has material of circulating tumor origin, and therefore has a metastatic tumor. For example, a probability greater than 0.8 warrants additional diagnostics, such as larger imaging, tissue biopsy, or similar methods.

[0071] In step S13, the calculated probability signal index is provided in the output. For example, the signal may be provided to the user through a graphical user interface or any other known interface.

[0072] To determine marker expression (application b), in addition to the CDL parameter, tumor marker expression (TME) parameters can be used, as shown in Figure 4.

[0073] In this particular embodiment, the method according to the present invention - A step of evaluating the expression of at least one biomarker that is a therapeutic target for the disorder in the target cells and / or the target cell fragments, - The process of determining tumor marker expression (TME) parameters as a function of biomarker expression, - A step of correlating TME parameters with the response to treatment in an individual. It also includes.

[0074] In detail, according to the embodiment in Figure 4, multiple marker expressions from step S08 in Figure 1 are recovered (steps S14_1, ..., S14N).

[0075] Subsequently, in step S15, tumor marker expression is determined as a function of all collected marker expressions for COI. Tumor marker expression can be calculated for TME using the equation described below in this specification.

[0076] In step S16, a signal indicator of tumor marker expression is provided in the output. For example, the signal may be provided to the user through a graphical user interface or any other known interface.

[0077] To determine the treatment response (application c, Figure 5, response, stable disease, disease progression), it is necessary to model the previous CTC and tdEV counts as random variables and calculate the probability that the posterior counts were drawn from the same distribution.

[0078] In detail, according to the embodiment in Figure 5, organic fluid samples from a solid are processed at different time points (for example, steps S01 to S04 in Figure 1). For example, a first sample is processed at time point T1 (step S17), and a second sample is processed at time point T2, which is greater than T1 (step S20), to evaluate the patient's progression.

[0079] In response to the processing of both the first and second samples, a COI counter (e.g., CTC and tdEV, and optionally a counter for CTC clusters) is provided (steps S18_1, ... S18_N for the first sample and steps S21_1, ... S21_N for the second sample).

[0080] In step S19, as described for step S11 in Figure 3, the circulatory disease load CDL[1] of the first sample is determined as a function of each COI counter.

[0081] In step S22, as described for step S11 in Figure 3, the circulatory disease load CDL[2] of the second sample is determined as a function of each COI counter.

[0082] Subsequently, in step S23, the first probability P 低下 We calculate the probability that the second measurement was drawn from a shifted distribution rather than the same distribution, i.e., the probability that the true posterior CDL[2] is lower than the previous CDL[1]. For example, assuming that the CDL follows a joint Poisson distribution, and using the previous CDL as a parameter of this distribution, we can calculate the probability that a posterior CDL or any possible lower value of CDL is found.

[0083] Subsequently, in step S24, the first probability P 低下 This is compared to a threshold, for example, 0.8. The threshold can be selected depending on the specific application.

[0084] If the first probability is higher than the threshold, a (partial) response is provided in the output of the patient assessment method (step S25). For example, the (partial) response may be provided to the user through a graphical user interface or any other known interface. The partial response output is an indicator of decreased CDL and is interpreted as the effect of treatment, and therefore may support the maintenance of such treatment, where appropriate.

[0085] The first probability P 低下 If it is not higher than the threshold, then the second probability P 成長 The probability that the second measurement was drawn from a shifted-up distribution rather than the same distribution, i.e., the probability that the true posterior CDL2 is increased compared to the previous CDL1, is calculated (step S26). For example, assuming that the CDL follows a joint Poisson distribution, the probability that the posterior CDL or any possible higher value of CDL is found can be calculated using the previous CDL as a parameter of this distribution.

[0086] The second probability P 成長This is compared to a threshold value (step S27), for example, 0.8. The threshold value can be selected depending on the specific application.

[0087] The second probability P 成長 If the threshold is higher, progression is provided in the output of the patient assessment method (step S28). The progression output indicates that the patient's disease has worsened between time T1, when the first sample was taken, and time T2, when the second sample was taken. Progression may be interpreted as an indication that the treatment has not yet had any effect, or, depending on the treatment, as support for modification to the current treatment.

[0088] If the second probability is not higher than the threshold, stable disease is provided in the output of the patient assessment method (step S28). Stable disease may be a signal of a known kind provided to the user through a graphical user interface or any other known interface. Stable disease indicates that the patient's disease has not substantially changed between time T1, when the first sample was taken, and time T2, when the second sample was taken. Depending on the goal of the current treatment, this may be interpreted as a success or failure of treatment, as interpreted by the oncologist performing the treatment.

[0089] All of the above applications are based on the most common concept that BTL is a measure of the metastatic burden of a tumor, which should preferably be reduced or stably maintained as a therapeutic effect. BTL is a function of the enumeration of CTCs, tdEVs, and CTC clusters found in a blood sample, as well as other features belonging to them, against their relative background levels in a normal / benign donor (BG). BTL = f(CTC, CTC) BG ,tdEV,tdEV BG , CTC cluster, CTC cluster BG )

[0090] BTL has many applications. Without intending to limit the present invention in any way, some of these are described below.

[0091] The first application of the BTL concept is to estimate the expression of tumor antigen markers, which are potential therapeutic targets. Some examples of targets are listed in the table below.

[0092] [Table 2]

[0093] The table above follows the American Society of Clinical Oncology (ASCO, August 2023), where (+) indicates cancer has higher-than-normal expression of the target, (-) indicates normal expression, ER is an endogenous receptor, PR is a progesterone receptor, EGFR is an epidermal growth factor receptor, PARP is a poly-ADP-ribose polymerase, and gene mutations are indicated by -gm after the gene name or by an explanation of the abnormality. In multiple myeloma, CD38, CD47, CD138, SLAMF7, GPRC5D, FcRH5, and BCMA are highly expressed by malignant plasma cells.

[0094] Tumor antigen expression can be measured on circulating tumor cells (CTCs) and tdEVs (tdEVs). However, false positives are most likely to be negative for the antigen and therefore artificially lower the determined expression. Due to the more desirable false-positive rate of CTCs compared to tdEVs, and because some of the aforementioned treatment targets may be expressed at very low levels, detection of treatment targets on CTCs should be considered more reliable than detection on tdEVs. Nevertheless, expression can still be detected on both CTCs and tdEVs and combined to obtain a combined value for fractions of circulating tumor-derived material expressing the marker of interest. The form of the equation required to match CTC and tdEV expression to marker expression is similar to that of the BTL equation. As with the BTL, whenever the information from CTCs alone is not sufficiently informative, the diagnostic information obtained from CTCs is combined with the information from tdEVs. This marker expression is a gauge of the tumor's sensitivity to a particular treatment. The accuracy of tumor marker expression is a function of the number of events evaluated and can be estimated using a probabilistic noise model. This probabilistic noise model can also be used to estimate the amount of blood that needs to be evaluated for some desired accuracy.

[0095] A second application is the use of blood transfusion logs (BTLs) to determine the sample volume required to obtain a sufficient number of CTCs for molecular characterization. Molecular characterization of CTCs, from which the molecular profile of the tumor and heterogeneity within the tumor can be assessed, is strongly related to the number of CTCs from which a molecular profile can be obtained. Based on morphological features and nuclear staining, a likelihood of the number of CTCs from which a molecular profile can be obtained can be provided. The success rate of single-cell isolation procedures can be used to estimate the number of CTCs from which a molecular profile can be obtained, and it can be determined whether this is sufficient for the desired molecular profile. For example, one molecular profile obtained from a CTC can be used to determine whether cellular abnormalities and treatment targets are present, but this is clearly not sufficient to assess tumor heterogeneity or to determine whether a patient is a candidate for a particular treatment. In contrast, if 10 molecular CTC profiles are all obtained with similar profiles, the reliability of the assessment is fairly high, but if they are all abnormal and different, 10 may not be sufficient to obtain a clear picture of the tumor. Since tdEV correlates with CTCs, it can be used to estimate how much blood needs to be examined to obtain a sufficient number of CTCs for molecular characterization. Depending on the required blood volume, this can be achieved using routine blood sampling or diagnostic leukocytosis (DLA). Such extrapolation requires an estimate of the true CTC concentration with a confidence interval. When the CTC count is low (0, 1, 2, etc.), a relationship for estimating the true CTC concentration from CTC and tdEV concentrations can be derived from BTL. Molecular characterization requires isolating cells and amplifying nucleic acids, and the better the appearance of the cells, the higher the likelihood of successful isolation and characterization. Such features can be extracted from images and morphological features can be derived so that a likelihood of success can be provided. The distribution of very good and very poor-looking cells may differ between cancers and treatments. Using this collected knowledge of distribution, an equation can be added to provide an estimate of how much blood is needed for successful molecular characterization.

[0096] Another application is hematopoietic myeloma burden (BML) based on the identification and enumeration of CMMC and mmEV.

[0097] In healthy individuals, epithelial cells do not circulate in the bloodstream. The presence of epithelial cells in the blood indicates a pathological condition, and in most cases, these epithelial cells originate from tumors (carcinomas) that release tumor cells into the bloodstream, which can lead to extravascular migration and distant metastasis. In contrast, plasma cells are present in the bloodstream, albeit in small numbers, which makes the detection of malignant plasma cells (CMMCs) more difficult. Differences in antigen expression between normal plasma cells and CMMCs, such as the absence of CD27 and CD19 expression and the monoclonality (lambda or kappa) of CMMCs, can be used to help distinguish between normal and malignant plasma cells. Morphological differences can also help distinguish between the two. The latter can be utilized using the methods described for CTCs and tdEVs. After training deep learning convolutional networks (DLCNs) for CMMC classification and identification by experts, a second classification can be applied by presenting DLCN subjects detected in normal individuals or patients with infectious diseases where a higher number of normal plasma cells are present, and subjects detected in CMMCs. Furthermore, the difference in the number of extracellular viable cells (EVs) derived from normal plasma cells and myeloma cells (mmEVs) can help increase the sensitivity and specificity of CMMC detection. Moreover, similar to BTL, BML can be derived from the counting of CMMC and mmEVs.

[0098] Another application is blood endothelial load (BEL) based on the identification and enumeration of CECs and edEVs. Endothelial cells and extracellular vesicles derived from endothelium can be detected in the blood of healthy individuals and are frequently detected at high levels in patients with cardiovascular disease (heart attack), infectious diseases, and cancer. Endothelial cells underpin the complete vascular system and can be released into circulation as CECs and edEVs from any location. The origin and cause of CEC and edEV release can vary considerably in cancer, cardiovascular disease, infectious diseases, and healthy donors. While the morphology of CECs is highly heterogeneous, a classification that identifies the origin of CECs and the cause of their release has not yet been established. DLCN can be trained on subjects classified as CECs and edEVs by experts, but this does not identify their origin. In a second classification, where CECs and edEVs are identified in healthy donors, patients with cardiovascular disease, infectious diseases, and cancer in the first DLCN, a classification can be given that identifies the origin of CECs and edEVs. As with other examples, the obtained values ​​can be converted to a single blood endothelial load, from which the severity of cancer, cardiovascular disease, and / or infection can be inferred. [Examples]

[0099] (Example 1) BTL equation Several equations were tested. Three of them are described below.

[0100] The main considerations underlying the equation are as follows: 1) While CTC and tdEV are related to the same pathology, CTC is more important than tdEV. 2) CTC and tdEV are correlated, but at low CTC concentrations, tdEV is relatively more accurate, while at high CTC concentrations, tdEV is relatively less accurate. 3) Estimating confidence intervals for CTC concentrations is fundamental to any extrapolation with respect to the sample volume required for the molecular analysis. BTL is the relevance that enables the use of tdEV for estimating such confidence intervals.

[0101] A simpler form of BTL is a linear combination of the CTC and tdEV counts shown in Equation 1. Here, both counts are logarithmically transformed to reduce the impact of large outliers, setting log 0 to -1. Furthermore, the coefficient "a" balances the contribution of tdEV compared to CTC. BTL 一次 =log 10 CTC+a log 10 tdEV (Equation 1)

[0102] A more advanced form of the BTL equation is built on the observation that, since CTC has a high correlation with tdEV, a high tdEV count means a higher probability that the CTC count is underestimated. This is expressed in equation 2, and the sigmoid function in parentheses can be simplified if desired.

[0103]

number

[0104] Another advanced form of this equation clearly shows a gradual decrease in the effect of the tdEV factor when the CTC factor is higher.

[0105]

number

[0106] Another advanced form of the BTL equation utilizes the log-logistic function optimized in previous studies, and the shape and shift parameters for CTC and tdEV are obtained separately by fitting the distribution as described in [Coumans, FA et al., Challenges in the enumeration and phenotyping of CTC. Clin Cancer Res, 2012. 18(20): pp. 5711-58].

[0107]

number

[0108] During the ceremony,

[0109]

number

[0110] Therefore, α = 1 / (1+e シフト )

[0111] For all of these equations, the CTC / tdEV count can be an absolute count, or an absolute count with some background count levels.

[0112] As can be seen, the CDL parameter can be a function of the number of target cells and / or the number of target cell fragments, including a transformation of the number of target cells and / or the number of target cell fragments. This transformation can be any nonlinear function suitable for a right-skewed distribution, including square root, cube root, logarithmic, or power transformations, or a combination thereof.

[0113] Other transformations of equations 1-3 are not limited to these, but include:

[0114]

number

[0115] These are some examples.

[0116] As can be noted, part of the above equation is a mutation in circulating tumor DNA (ctDNA). 突然変異 ), circulating RNA (exRNA pCA3 ), and other circulating biomarkers correlated with pathological conditions, such as protein (PSA), should also be considered.

[0117] Many more variations are possible for equations 1-3, but in this specification, equations 1-3 are applied to illustrate the latent nature of BTL.

[0118] Any of these functions can be scaled and centered to achieve the range that is easiest for clinicians to interpret, for example, 0 to 1 or 0 to 100.

[0119] (Example 2) Evaluation of the BTL equation in cancer research BTL was applied to research archives from studies on metastatic prostate (IMMC38), metastatic colorectal cancer (CAIRO-2), and metastatic and non-metastatic breast cancer (IMMC01 and IMMC26, respectively). In this assessment, background was not subtracted from the tdEV count. The value of "a" was evaluated in the range of 0.1 to 10, representing the range from underweight to overweight of tdEV in BTL. The risk of overall survival was evaluated using BTL as a continuous variable, but the risk was scaled to the interquartile range to account for differences in scale. The results are shown in Figure 6 in comparison to the risk of operator / manual CTC.

[0120] The following points should be noted. - For all equations, there exists a range of values ​​for the coefficient "a" in which the prognosis of BTL is higher than the prognosis of operator-CTC (OP-CTC). - If the contribution of tdEV is overweight, BTL has a lower prognosis. This was predicted from the assumption that CTC is more important than tdEV. - Studies with generally lower CTC concentrations (non-metastatic breast and metastatic colorectal) benefit more from BTLs.

[0121] Figure 7 shows the continuous risk (HR) in the optimal form of equations 1-3 and the risk (HR) of the operator CTC. Vertical markers represent HR, and horizontal bars represent the 95% confidence interval (CI) for the HR. It should be noted that the 95% CI is influenced by the study data (individual patient survival and censorship) and the characteristics of the BTL. It is reasonable to expect that the study data will dominate the confidence interval.

[0122] HR is the change in risk resulting from a 1-point increase in the predictor variable. For this reason, the equation BTL = 0.5 * CTC would have a higher HR, even though a simple scale change does not alter the prognostic value of the CTC coefficient. To prevent such misinterpretation, the HRs presented here are continuous risk rates scaled to the interquartile range in transferable studies and to the 10th to 90th percentiles in non-transferable studies. With this scaling, the scale-change equation described above has the same HR as the unscaled version.

[0123] The primary effect of BTL is to expand coverage for patients with low CTC counts. When making decisions on data with bifurcated significance (i.e., split around the median), BTL is expected to have a slightly lower significance because the noise on BTL is higher than the noise on CTC alone.

[0124] The above results demonstrate the potential of CDL parameters when applied to tumors.

[0125] (Example 3) TME equation Tumor marker expression (TME) may also show tumor marker expression fraction, TMEF, and tumor marker expression fraction TMEF is

[0126]

number

[0127] Calculated as, in the formula, MEF + RCThis is the fraction of target cells (RC) that express a positive marker, and MEF + FR is the fraction of the target cell fragment (FR) that expresses a positive marker, and a is a coefficient that balances the contribution of the target cell fragment compared to the target cell.

[0128] For example, referring to the Her2+ biomarker, the tumor marker expression fraction TMEF is,

[0129]

number

[0130] Even if that is the case,

[0131]

number

[0132] The Her2+ fraction is a fraction of CTC / tdEVs that have expression levels exceeding the threshold applicable to Her2 marker expression, and ctDNA TOP2A This represents the change in copy number within TOP2A determined on circulating tumor DNA.

[0133] Tumor marker expression (TME) may show tumor marker expression levels, TMEL, in addition to or instead of tumor marker expression fractions. Tumor marker expression levels are

[0134]

number

[0135] It may also be calculated as follows, and in the formula, MEL RC This is the marker expression intensity level on target cells (RC), and MEL FR This is the marker expression intensity level on the target cell fragment (FR), and N RC is the number of target cells, N FRis the number of target cell fragments, and a is a coefficient that balances the contribution of the target cell fragments compared to the target cells. The expression of coefficient a is only one example and may differ from the value shown.

[0136] In other words, TMEL can be considered as a weighted average expression of biomarkers.

[0137] For example, referring to the Her2+ biomarker, the tumor marker expression level TMEL is,

[0138]

number

[0139] It is possible.

[0140] Combining information from multiple sources can fill in gaps in information. Tumor marker expression is increasingly used to guide treatment. However, in most cases, the only available tissue material to determine expression is taken at the time of diagnosis, sometimes more than 10 years prior. Tumors have evolved since then and no longer express the same markers as before. Liquid biopsies in the form of circulating material can provide a current assessment of marker expression. Circulating tumor cells are particularly interesting because they allow for the determination of proteins, DNA, and RNA. However, this determination is clearly hindered if even just a few CTCs are found in a single tube of blood. As with CDL, combining information from multiple sources into a single parameter can fill in gaps where needed. Here, we demonstrate this for HER2 expression on CTCs and tdEVs. Other targets and other circulating markers are possible. Figure 8 shows HER2 marker expression for CTCs only, and fractions and mean intensities of tumor marker expression (TME) parameters in 98 breast cancer patient samples.

[0141] Patients in whom the majority of CTCs are HER2-positive may be sensitive to trastuzumab, and patients in whom the majority of CTCs are at least weakly positive may be sensitive to other HER2-targeted therapies. The left panel in Figure 8 shows that, when evaluated only for CTCs, HER2 marker expression (both fractional and mean intensity) is unavailable in 40 out of 98 patient samples due to the presence of fewer than three CTCs (i.e., insufficient CTCs for reliable measurement). On the other hand, as shown in the right panel in Figure 8, when HER2 expression measurements on CTCs and tdEVs are combined as a single TME parameter, HER2 marker expression is unavailable in only 11 out of 98 samples, where fewer than three CTCs and / or tdEVs are present in total.

[0142] The method of the present invention may also include a sorting step that enables the isolation of cells from an organic fluid sample, identified as target cells (e.g., rare cells, particularly CTCs or embryonic cells) or target cell fragments (e.g., circulating cell fragments, particularly tdEVs), for downstream processing, including molecular analysis of target cells (e.g., whole-genome amplification, low-pass sequencing, next-generation sequencing, copy number variation, FISH, RT-PCR, qPCR, dPCR, STR analysis, mutation detection, gene expression profiling, etc.), or culture and proliferation of target cells. The sorting step may be performed by DEPArray® or by other known image-based cell sorters.

Claims

1. - A step of marking a sample of organic fluid of a solid with at least one marker specific to the target cell and the target cell fragment, wherein one or more markers are identifiable by selective imaging. - A step of obtaining at least one image of a marked sample, wherein the image includes an object stained with at least one marker to be classified, - A process of classifying objects stained with markers, - A step of determining whether the object stained with the marker is the target cell or target cell fragment, based on the results of classifying the object stained with the marker, - A step of counting the number of target cells and the number of target cell fragments, wherein the target cells are circulating tumor cells (CTCs) and the target cell fragments are tumor-derived extracellular vesicles (tdEVs), or the target cells are circulating endothelial cells (CECs) and the target cell fragments are endothelial-derived extracellular vesicles (edEVs), or the target cells are circulating multiple myeloma cells (CMMCs) and the target cell fragments are multiple myeloma extracellular vesicles (mmEVs), The process involves counting the number of target cells and the number of target cell fragments. - A step of increasing the target cell counter (CTC-CNT) in response to subjects stained with each marker determined to be the target cell, and / or - A step of increasing the target cell fragment counter (tdEV-CNT) in response to a target stained with each marker determined to be the target cell fragment. Processes and - A step of determining a circulatory disease load (CDL) parameter which is a function of the difference between the values ​​of a target cell counter (CTC-CNT) and / or a target cell fragment counter (tdEV-CNT) and / or a relative background level in a normal individual, wherein the function is CDL=f(CTC-CTC BG tdEV-tdEV BG ) and in the formula, CTCs are the number of target cells in an individual. CTC BG However, this is the number of target cells in a normal individual. tdEV is the number of target cell fragments in an individual. tdEV BG However, the number of target cell fragments in a normal individual is The process, - A process of correlating circulatory disease load (CDL) parameters with the presence or progression of a disorder in an individual or with responsiveness / response to treatment, An in vitro method, including, for diagnosing or evaluating the progression of a disorder in an individual, or for evaluating the responsiveness / response to treatment in an individual.

2. - The step of counting the number of target cells and the number of target cell fragments includes a step of counting the number of target cell clusters, wherein the target cell cluster counter (CTCcl-CNT) increases in response to objects stained with each marker determined to be a target cell cluster. - The circulatory disease load (CDL) parameter is also a function of the number of target cell clusters. The method according to claim 1.

3. - The step of counting the number of target cells and the number of target cell fragments also includes the step of counting at least one circulating biomarker correlated with the disorder. - The circulatory disease load (CDL) parameter is also a function of at least one circulatory biomarker correlated with disability. The method according to claim 1 or 2.

4. The method according to any one of claims 1 to 3, wherein the step of marking a sample also includes the step of marking the sample with at least one marker specific to cells and / or cell fragments other than the target cells and / or the target cell fragments.

5. The method according to any one of claims 1 to 4, wherein the circulatory disease load (CDL) parameter is in the range of a minimum value, e.g., 0, to a maximum value, e.g., 1, and the circulatory disease load (CDL) parameter is a monotonic function of the number of cells of interest, e.g., CTCs, and the number of cell fragments of interest, e.g., tdEVs, and optionally the number of clusters of cells of interest.

6. The method according to any one of claims 1 to 5, wherein the CDL parameter depends on a transformation of the number of cells of interest and / or the number of cell fragments of interest, and optionally on a transformation of the number of clusters of cells of interest, the transformation being, for example, a root function, a logarithmic function, a power function, or a combination thereof.

7. CDL=log 10 N RC +a log 10 N FR wherein, in the formula, N RC is the number of target cells, and N FR is the number of target cell fragments, and a is a coefficient that balances the contribution of rare cell fragments relative to the target cells, the method according to any one of claims 1 to 6.

8. The method according to any one of claims 1 to 6, wherein the CDL parameter depends on the sigmoid function of the number of rare cell fragments. [Request Item 9] [Number 1] And N RC This is the number of target cells, N FR The method according to any one of claims 1 to 6, wherein is the number of target cell fragments, and a is a coefficient that balances the contribution of the target cell fragments compared to the target cell. [Request Item 10] [Number 2] And N RC This is the number of target cells, N FR The method according to any one of claims 1 to 6, wherein is the number of target cell fragments, and a is a coefficient that balances the contribution of the target cell fragments compared to the target cell. [Request Item 11] [Number 3] And in the formula, [Math 4] Therefore, α = 1 / (1+e シフト ) and Here, N RC This is the number of target cells, N FR is the number of target cell fragments, and llg is the log-logistic function obtained separately by fitting the corresponding distributions, using the shape and shift parameters of CTCs and tdEVs. The method according to any one of claims 1 to 6.

12. The method according to any one of claims 1 to 11, wherein the CDL parameter depends on the number of target cell fragments scaled by the number of target cells.

13. The method according to any one of claims 1 to 12, wherein the disorder is a tumor.

14. - Whether the target cells are circulating tumor cells (CTCs), the fragments are tumor-derived extracellular vesicles (tdEVs), and the lesion is carcinoma or melanoma, - Whether the target cells are circulating multiple myeloma cells (CMMCs), the fragments are multiple myeloma extracellular vesicles (mmEVs), and the disorder is myeloma, or - The target cells are circulating endothelial cells (CECs), the fragments are extracellular vesicles (edEVs) derived from endothelium, and the disorder is cardiovascular disease. The method according to any one of claims 1 to 13.

15. The method according to any one of claims 1 to 13, wherein the target cells are CTCs and CECs, the fragments are tdEVs and edEVs, and the lesion is colorectal cancer.

16. A step of evaluating the expression of at least one biomarker that is a therapeutic target for the disorder in target cells and / or target cell fragments, The process involves evaluating tumor marker expression (TME) parameters and determining them as a function of biomarker expression, A process of correlating TME parameters with the response of an individual to treatment. The method according to claim 1, further comprising:

17. The method according to claim 16, wherein the biomarker is selected from the group consisting of Her2, PSMA, MUC1, EGFR, ER, PR, KI67, PDL1, RAS, RAF, ROS1, KRAS, BRAF, MET, RET, and PARP.

18. Tumor marker expression (TME) shows the tumor marker expression fraction, TMEF, and the tumor marker expression fraction is [Math 5] And in the formula, MEF + RC However, this is the fraction of target cells (RC) that express a positive marker, and MEF + FR The method according to claim 16 or 17, wherein is a fraction of a target cell fragment (FR) having positive marker expression, and a is a coefficient that balances the contribution of the target cell fragment compared to the target cell.

19. Tumor marker expression (TME) indicates tumor marker expression level, TMEL, and tumor marker expression level, [Math 6] And in the formula, MEL RC However, this is the marker expression intensity level on the target cell (RC), and MEL FR However, this is the marker expression intensity level on the target cell fragment (FR), and N RC This is the number of target cells, N FR The method according to any one of claims 15 to 17, wherein is the number of target cell fragments, and a is a coefficient that balances the contribution of the target cell fragments compared to the target cell.

20. The method according to any one of claims 3 to 19, wherein the circulating biomarker correlated with the disorder is at least one circulating protein, RNA, DNA, or a combination thereof.

21. The method according to any one of claims 1 to 20, further comprising the step of selecting at least one of cells determined to be a target cell or a target cell fragment by an image-based cell sorting machine.

22. The method according to claim 21, wherein the sorting step is for downstream molecular analysis or for culture and proliferation of the sorted cells.

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