Individualized antitumor drug automatic screening method based on three-dimensional tumor cell simulation body culture and application

Abstract

The invention provides a personalized antitumor drug automatic screening method based on three-dimensional tumor cell simulation body culture and application, and belongs to the technical field of biological medicine. The high-throughput automatic screening platform is integrated by three modules: a mechanical arm liquid treatment system (the liquid separation precision is + / -1 microliter); a non-contact spotting instrument is driven by sound waves (the medicine distribution precision is + / -2.5%); a high content imaging system (AI assisted cell recognition); a double-index interpretation system: PI dyeing dead cell rate (fluorescence imaging); cCK-8 is used for detecting the cytotoxic effect of the medicine (reading of a microplate reader OD450). According to the method, a seamless automatic chain from sample processing to data output can be constructed, the screening period is compressed to be smaller than or equal to 7 days from 6-8 months of a traditional PDX model, the single-sample cost (lt, 5000 yuan) is reduced to 1 / 4 (gt, 20,000) of a traditional scheme, and the personalized medicine screening time is remarkably shortened while the nano-upgrade operation precision is guaranteed.

Description

Technical Field

[0001] This invention relates to the field of biomedical technology, and in particular to a personalized automated screening method and application for anti-tumor drugs based on three-dimensional tumor cell mimicry culture. Background Technology

[0002] Cancer is a disease with extremely complex mechanisms, involving multiple levels such as gene mutations, abnormal signaling pathways, and immune evasion. Its incidence rate is increasing year by year, seriously endangering public health. Therefore, in-depth research into cancer mechanisms is crucial for the development of targeted therapies. Furthermore, cancers are highly heterogeneous; tumors may differ from patient to patient. Therefore, achieving individualized and targeted medication, avoiding ineffective treatment, and prolonging patient survival are of paramount importance.

[0003] However, there are still many problems to be solved in the field of cancer research and drug screening, such as: (1) Traditional two-dimensional culture models have been widely used for a long time, but their drawbacks are becoming increasingly apparent. Cells grow adherently in a two-dimensional plane, lacking sufficient contact with the extracellular matrix, and cannot simulate the complex interactions between cells and between cells and the extracellular matrix in the tumor microenvironment. This makes it easy for dominant clonal selection to occur during cell culture and passage, resulting in the loss of the original tumor characteristics and extremely low correlation between drug response and actual clinical situation. For example, the sensitivity of liver cancer cells to chemotherapy drugs under two-dimensional culture conditions is very different from that in the tumor microenvironment in vivo, and drugs screened based on this often have poor effects in clinical applications.

[0004] (2) Although patient-derived xenograft (PDX) models are considered relatively advanced preclinical oncology models, they have serious limitations. Constructing this model requires transplanting tumor tissue into immunodeficient mice, resulting in a low success rate. Moreover, the modeling cycle is long, usually exceeding 6 months, and the cost is high, making large-scale, high-efficiency drug screening difficult to achieve. At the same time, the tumor microenvironment in immunodeficient mice differs significantly from that in humans, and the transplanted tumor tissue may undergo mouse-like evolution, further reducing the accuracy of predicting human drug responses and failing to meet the clinical need for rapid and accurate drug screening.

[0005] (3) Although traditional 3D culture technology has improved the cell culture environment to some extent, it still suffers from cumbersome operation. Whether it is scaffold-based culture, such as embedding cells in hydrogels to simulate the extracellular matrix in vivo, or scaffold-free cell aggregate culture, the operation process requires precise operation by professionals and has a low throughput. This means that the types of drugs that can be tested are limited, making it difficult to achieve automated, large-scale drug screening, which greatly limits its application in drug development. For example, when screening novel targeted drugs for various cancers, traditional 3D culture technology cannot quickly provide a large amount of data to evaluate drug efficacy.

[0006] Currently, there is a significant gap in the field of personalized drug screening. There is a lack of an integrated platform that can combine primary tumor cell culture, automated screening, and clinical data analysis. Primary tumor cell culture is difficult to maintain its in vivo characteristics, and automated screening technology has not been effectively integrated into the entire process, resulting in low drug screening efficiency. Simultaneously, clinical data analysis is disconnected from preliminary screening, failing to fully utilize clinical data to guide drug screening, and the screening results cannot be efficiently fed back into clinical treatment, making it difficult for personalized treatment to truly achieve precision.

[0007] Personalized screening for hematologic malignancies faces unique bottlenecks. Primary hematologic malignancies are difficult to maintain viability in vitro for extended periods, posing a significant challenge to establishing stable culture systems. Furthermore, the lack of standardized 3D culture systems makes drug delivery to tumor cells susceptible to interference from various factors, leading to high false-positive rates for drug penetration. For example, in leukemia drug screening, the inability to accurately determine whether a drug truly works on tumor cells may result in misjudgments of drug efficacy, delaying timely treatment for patients.

[0008] In summary, the shortcomings of existing technologies in tumor research and drug screening severely restrict the development of precision medicine, and there is an urgent need to develop new technologies and platforms to overcome these obstacles. Summary of the Invention

[0009] The purpose of this invention is to provide a personalized automated screening method and application for anti-tumor drugs based on three-dimensional tumor cell mimicry culture.

[0010] To achieve the above-mentioned objectives, the present invention provides the following technical solution: This invention provides a personalized and automated screening method for antitumor drugs, comprising the following steps: (1) Pretreatment of tumor tissues from different sources; (2) Personalized and automated screening of antitumor drugs, including: S1. Use a robotic arm integrated with a multi-channel microplate washing and dispensing system to plate the tumor cells pretreated in step (1). S2. Use a sound-wave driven non-contact spotting and pipetting system for drug delivery; S3. After culture, the cell status was assessed using a high-content fluorescence imaging system. S4. After the evaluation is completed, the CCK-8 solution is added using a robotic arm integrated with a multi-channel microplate washing and dispensing system. The microplate reader is then used to analyze the readings and obtain potential effective drugs.

[0011] Preferably, in step (2), the robotic arm integrated multi-channel microplate washing and dispensing system of S1 includes the Biotek EL406 microplate washing and dispensing system.

[0012] Preferably, in step (2), the acoustic wave driven non-contact spotting and pipetting system S2 includes the nano-level acoustic wave pipetting system Echo 650, the final drug concentration for drug addition and culture is 50 nM~10 μM, and the culture time is 12-72 h.

[0013] Preferably, in step (2), the high-content fluorescence imaging system of S3 includes the Opera Phenix laser confocal high-content imaging microscope.

[0014] Preferably, in step (2), the screening method for the potential effective drugs in S4 is as follows: Compared with control values ​​of tumors treated with an equal volume of DMSO solvent, drugs with relative activity ≤50% were considered potentially effective, of which drugs with relative activity ≤30% were considered highly effective, and drugs with relative activity >30% and ≤50% were considered effective.

[0015] Preferably, in step (1), the tumor cells from different sources include solid tumors or hematologic malignancies; When the tumor cells are solid tumors, the pretreatment method is as follows: (1) Mechanical / enzymatic synergistic dissociation of tumor cells: Tumor tissue was collected, and non-tumor tissue and necrotic areas were removed to obtain pure tumor tissue. The pure tumor tissue was then cut into small pieces, enzymatically dissociated, and red blood cells were removed to obtain tumor cells. (2) Scaffold-free three-dimensional tumor cell mimicry culture of 3D spherical cell clusters: 3D cell spheres were obtained by culturing tumor cells in microspheres made of ultra-low adsorption material and 3DTS culture medium, and were used as pretreated tumor cells. When the tumor cells are of hematopoietic origin, the pretreatment method is as follows: Mononuclear cells were separated using density gradient centrifugation, and tumor cells were enriched using magnetic beads to obtain pretreated tumor cells.

[0016] Preferably, in step (1), the enzyme is type II collagenase, and the concentration of type II collagenase is 4~6 mg / mL.

[0017] Preferably, in step (2), the 3DTS culture medium comprises the following components: DMEM / F12 basal medium, 1% penicillin-streptomycin-amphoteric acid B, 10%-20% FBS, 10-50 µM β-mercaptoethanol, 5-20 μM ROCK inhibitor, 1× non-essential amino acid solution, 5-20 mM HEPES, 20-100 ng / mLEGF, 10-50 ng / mL bFGF.

[0018] Preferably, in step (2), the density of the 3D spherical cell clusters is 50 to 100 3D spherical cell clusters per pore.

[0019] The present invention also provides an application of the above-mentioned personalized and automated screening method for antitumor drugs in screening tumor treatment drugs.

[0020] The beneficial effects of this invention compared to the prior art are as follows: (1) The high-throughput automated screening platform of the present invention is integrated from three modules: a robotic arm liquid handling system (dispensing accuracy ±1μl); an acoustically driven non-contact spotting instrument (drug dispensing accuracy ±2.5%); a high-content imaging system (AI-assisted cell identification); and a dual-index interpretation system: PI staining dead cell rate (fluorescence imaging); and CCK-8 detection of drug cytotoxicity (ELISA reader OD450 number). The present invention can construct a seamless automated chain from sample processing to data output, reducing the screening cycle from 6-8 months in the traditional PDX model to ≤7 days, and reducing the cost per sample (<5000 yuan) to 1 / 4 of the traditional solution (>20,000 yuan). While ensuring nano-level operational accuracy, it also significantly shortens the time for personalized drug screening.

[0021] (2) For hematologic malignancies, this invention uses immunomagnetic beads such as CD45 / CD3 / CD19 / CD138 (magnetic beads: cells = 1:5) to separate tumor cells from peripheral blood / bone marrow samples (sorting purity > 95%). For solid tumors, a scaffold-free 3D biomimetic culture system is used, relying on ultra-low adsorption materials to promote cell self-aggregation, combined with mechanical / enzymatic dissociation technology and a 3D culture system; suspension microsphere culture is adopted to sort tumor cells in solid tumors, and after sorting, the cells are directly seeded onto ultra-low adsorption plates, and special culture medium (containing IL-7 / FLT3 ligands) is added to promote the formation of B / T cell tumor spheroids; the seeding density is adjusted to 2×10 6By controlling the size of microspheres (80±20μm in diameter) in a cell / dish configuration, 3D spherical cell clusters are obtained, representing a paradigm shift from "planar cell culture" to "three-dimensional microecological replication" compared to traditional 2D models. This invention provides a near-in vivo testing environment for drug screening, filling a technological gap in personalized treatment for hematologic malignancies and solid tumors. Attached Figure Description

[0022] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0023] Figure 1 This is a personalized and automated screening method for antitumor drugs in the embodiments of the present invention; Figure 2 This is a 3D cultured tumor sphere of osteosarcoma in Example 1 of the present invention; Figure 3 This is a diagram showing the results of PI red fluorescence staining in Example 1 of the present invention; Figure 4 This is a visualization heatmap of live and dead cells from Embodiment 1 of the present invention; Figure 5 The image shows the results of magnetic bead sorting of blood tumors in Embodiment 2 of the present invention, where A represents tumor cells after magnetic bead sorting and B represents the results of flow cytometry analysis of tumor cell purity after magnetic bead sorting. Detailed Implementation

[0024] Various exemplary embodiments of the present invention will now be described in detail. This detailed description should not be considered as a limitation of the present invention, but rather as a more detailed description of certain aspects, features, and embodiments of the present invention.

[0025] It should be understood that the terminology used in this invention is merely for describing particular embodiments and is not intended to limit the invention. Furthermore, with respect to numerical ranges in this invention, it should be understood that each intermediate value between the upper and lower limits of the range is also specifically disclosed. Every smaller range between any stated value or intermediate value within a stated range, and any other stated value or intermediate value within said range, is also included in this invention. The upper and lower limits of these smaller ranges may be independently included or excluded from the range.

[0026] Unless otherwise stated, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art. While only preferred methods and materials have been described herein, any methods and materials similar or equivalent to those described herein may be used in the implementation or testing of this invention. All references to this specification are incorporated by way of citation to disclose and describe methods and / or materials associated with those references. In the event of any conflict with any incorporated reference, the content of this specification shall prevail.

[0027] Various modifications and variations can be made to the specific embodiments described in this specification without departing from the scope or spirit of the invention, as will be apparent to those skilled in the art. Other embodiments derived from this specification will also be apparent to those skilled in the art. This specification and embodiments are merely exemplary.

[0028] The terms “include,” “including,” “have,” “contain,” etc., used in this article are all open-ended terms, meaning that they include but are not limited to.

[0029] Example 1 Embodiment 1 of the present invention according to Figure 1 The flowchart shown provides a personalized and automated screening method for anti-tumor drugs derived from solid tumors. The specific steps are as follows: (1) Preparation of three-dimensional cell pseudomorphic culture medium (3DTS medium): The following ingredients were added to DMEM / F12 basal medium: 1% penicillin-streptomycin-amphoteric acid B, 10% FBS, 50 µM β-mercaptoethanol, 5 μM ROCK inhibitor, 1× non-essential amino acid solution, 5 mM HEPES, 20 ng / mL EGF, and 10 ng / mL bFGF.

[0030] (2) Mechanical / enzymatic synergistic dissociation Osteosarcoma tissue samples were obtained through surgery or biopsy. The samples were washed three times with 4°C pre-cooled PBS containing 1% penicillin-streptomycin-amphotericidal. After washing, the tumor tissue was immersed in 4°C DMEM containing 5% fetal bovine serum. Sterile tissue scissors and surgical forceps were used to remove any remaining connective tissue, muscle tissue, any scabs or fragments, or other non-tumor tissue, as well as necrotic areas of the tumor tissue, to ensure that pure and cell-rich tumor tissue was obtained.

[0031] The cleaned tumor tissue was transferred to a culture dish containing a preheated (37°C) 5 mg / mL type II collagenase solution (type II collagenase was prepared using DMEM containing 5% fetal bovine serum). The selected tumor tissue was then minced into 1-2 mm pieces using a scalpel-assisted cutting technique. 3 Small pieces or minced meat.

[0032] Trim the sterile pipette tip with ophthalmic scissors, transfer the tumor tissue to a tissue dissociation tube, add type II collagenase to a final concentration of 5 mg / mL to 10 mL, and place the tube on an automated tissue dissociator (such as GentleMACS) to run a preset program to dissociate the tumor tissue—keep the solution agitated and dissociate at 37°C for 30 min until the tissue becomes viscous. If there are still relatively large tissue fragments, continue digestion for another 30 min. If the solution is too viscous, consider using an appropriate amount of trypsin and DNase I to assist in dissociation.

[0033] After dissociation, place a 70μm sterile cell filter on a 50mL centrifuge tube and filter the cell suspension into the 50mL centrifuge tube. Centrifuge the filtered cell suspension at 500g for 5 min at room temperature. Filter the supernatant once more, centrifuge at 500g for 5 min at room temperature, and discard the supernatant. If the cell pellet is red, it indicates an excess of red blood cells. In this case, lyse the red blood cells using red blood cell lysis buffer, incubate on ice for 10 min, terminate the red blood cell lysis process with 5 times the volume of room temperature PBS, centrifuge at 500g for 5 min at room temperature, discard the supernatant, resuspend the cell pellet in PBS, centrifuge at 500g for 5 min at room temperature, discard the supernatant, resuspend the cell pellet in 3DTS medium, add 10μL of single-cell suspension to a clean EP tube, add 10μL of 0.4% trypan blue solution, mix well, stain for 3 min, and aspirate 10μL of stained cells. Count the cells and determine the viability using a Countstar automated cell counter. Dead cells turn blue, swell, and become dull; living cells remain unstained and retain their normal shape. This process needs to be completed within 24 hours.

[0034] (3) Scaffold-free three-dimensional tumor cell mimicry culture 3D spherical cell clusters After accurate counting, the cell suspension was diluted with 3DTS medium at a concentration of 5 × 10⁻⁶. 6 Cells were placed in microspheres made of ultra-low adsorption material and cultured as scaffold-free three-dimensional tumor cell mimics. On the second day of culture, the culture supernatant was discarded (to remove cell debris and dead cells), and the cells were washed with cold PBS and replaced with fresh culture medium. Culture continued, with the culture medium changed every 3-5 days, allowing the cells to gradually aggregate in suspension. After several days of culture, a compact, somewhat irregular multicellular spheroid several hundred micrometers in diameter was formed. When the number of cells in the multicellular spheroid reached 500, it was passaged, the culture medium was removed, and the cells were washed with cold PBS.

[0035] Dilute 5 U / mL Dispase to 1 U / mL in serum-free medium, and add 2 mL of diluted Dispase to each 6-well plate. Incubate at 37°C for 10 min, observing under a microscope in real time. If the edges of the multicellular spheroids become loose or the structure dissociates (avoid over-digestion), stop immediately. Collect the digested cell suspension in a centrifuge tube, centrifuge at 250g for 3 min at room temperature, and discard the supernatant. Add an appropriate amount of cold PBS, centrifuge at 250g for 3 min at 4°C, and discard the supernatant. The cell pellet obtained at this time (mainly small cell clusters, such as...) Figure 2 (As shown) can be used for re-inoculation and amplification or for direct drug screening.

[0036] The scaffold-free three-dimensional tumor cell mimicry culture system of this invention inhibits cell adhesion and growth through an ultra-low adsorption material, which facilitates spontaneous cell aggregation into spheres and allows direct proliferation in culture plates without passage. The size and number of the final cell microspheres can be controlled by adjusting the number of cells inoculated. The 3D cell model obtained through this method has a complete spherical structure, can spontaneously form an extracellular matrix, and effectively simulates the characteristics of the tumor microenvironment.

[0037] (4) Personalized and automated screening of antitumor drugs The Selleck FDA-Approved Drug Library-II-3113 cmpds database was used for screening, containing 3113 FDA-approved drugs. Each drug was used in three replicates, requiring 34 384-well plates (with the first and last two wells of each plate blank, and additional consideration given to DMSO control, culture medium blank control, and layout redundancy).

[0038] The plating density is determined based on the growth rate of the tumor microspheres during the initial culture process. When the average size of the tumor microspheres is around 50 cells (at this stage, uniformity is good and drug response is sensitive), 50 tumor microspheres per well are typically selected for plating. 50 μL of culture medium is used per well of a 384-well plate. The tumor microspheres are diluted to a concentration of 1000 microspheres / mL, i.e., 50 microspheres per 50 μL well. A multi-channel microplate washing and dispensing system with a robotic arm (automated liquid handling workstation, Biotek EL406 microplate washing and dispensing system) is used to dispense and plate the microspheres in the 384-well plates. After plating, the 384-well plates are centrifuged at 100g for 1 min at room temperature to allow the microspheres to gently settle to the center of the bottom of the wells, improving uniformity between wells. The plates are then incubated statically in a cell culture incubator for 6 hours to allow the microspheres to stabilize and regain activity.

[0039] Nano-level drug dispensing was achieved using an acoustically driven non-contact pipetting system (Echo650 nano-level acoustic pipetting system). The drug dispensing accuracy reached ±2.5%, the single-well processing time was less than 50 ms, and the final drug concentration was 2 μM. At the same time, a DMSO control (to eliminate the influence of drug solvent on cell growth, usually with a final concentration ≤0.1%) and a blank culture medium control containing only the same volume of culture medium and CCK8 solution were set up (to eliminate background interference and standardize the calculation basis). After drug addition, the cells were cultured for 36 h.

[0040] Cell status was assessed using a high-content fluorescence imaging system (OperaPhenix laser confocal high-content imaging microscope) after 36 hours of culture. First, using a robotic arm integrated with a multi-channel microplate washing and dispensing system, 5 μL of PI dye diluted 100-fold (with PBS) was added to each well of a 384-well plate. After incubation at room temperature in the dark for 10 min, high-content fluorescence imaging was used to perform time-series cyclic imaging of the cells in each well. After imaging, AI-assisted image recognition was used. Researchers manually delineated PI-positive and PI-negative cells and trained the AI ​​to recognize PI-positive cells as dead cells and unstained cells as normal cells. The total number of cells recognized by the AI ​​was confirmed to be approximately consistent with the number of cells plated. The ratio of PI-positive cells to the total number of cells was the output. Figure 3 As shown.

[0041] Then, using a robotic arm integrated multi-channel microplate washing and dispensing system, 10 μL of CCK-8 solution diluted 1:1 (with PBS) was added to each well of a 384-well plate. The plates were incubated at 37°C in the dark for 3 h to allow intracellular dehydrogenases to reduce WST-8 and generate formazan dye. Readings were performed using a microplate reader (OD450 set). Data analysis was performed by subtracting the blank control readings from all data. The average value of three replicates was taken and compared with the control readings of wells containing DMSO (normally growing tumors). A reading 0.3 times lower than the normal growth reading (i.e., relative viability ≤30%) was defined as highly effective, and a reading 0.5 times lower than the normal growth reading (i.e., relative viability >30% and ≤50%) was defined as effective. A visual heatmap showed the ratio of dead to live cells; darker colors represented a higher proportion of dead cells to live cells, reflecting a higher tumor-killing effect of the drug (e.g., ...). Figure 4 (As shown). To prevent low values ​​from being caused by abnormal cell plating or CCK-8 solution loading, the wells of potential effective drugs were examined under a microscope (such as an automated imaging microscope) to confirm significant changes in cell morphology (shrinkage, fragmentation), and the list of effective drugs was finally confirmed.

[0042] All screened effective drugs were ranked according to their relative readings (from low to high) compared to the control group to reflect drug efficacy. The targets of all screened effective drugs were statistically analyzed and scored according to a drug quantitative scoring system (as shown in Table 1, the scoring formula is ((B+C+0.25×D+E+0.25×G+I / H+0.5×J)×F) / (1+A)). Enrichment analysis of target signals was performed, and pathways were ranked based on enrichment significance (P / Q value) and enrichment score. Furthermore, for drugs analyzed in conjunction with clinical trials, priority was given to those approved by the FDA for solid tumor indications (especially for this type of cancer); drugs in Phase 3 clinical trials or already marketed were also given priority.

[0043] Consider drug targeting based on the patient's tumor molecular characteristics (such as gene mutations and protein expression) (prioritizing matching driver mutations); broad-spectrum chemotherapy drugs: suitable for patients lacking molecular targets; safety: avoid the superposition of serious side effects (such as hematological, cardiac, hepatotoxic and nephrotoxicity), and consider the patient's tolerance and physical condition; medication cycle and convenience (oral administration preferred); cost-effectiveness.

[0044] Based on the above factors such as drug efficacy, pathway enrichment, clinical status (indications, stage), targeting, safety, side effects, treatment cycle, and cost, a quantitative scoring system (e.g., 1-10 points) is established, and weights are assigned to each dimension (e.g., clinical indications and efficacy have higher weights). Effective drugs are scored using a weighted average (overall score = Σ(dimension weight × dimension score)) (see attached table). Based on the overall scores, drugs are sorted from high to low to obtain a final personalized list of recommended anti-solid tumor drugs (usually including the top 5-10 preferred drugs and key supporting evidence).

[0045] Table 1. Scoring Table for Drug Quantitative Scoring System Drug efficacy (A) Relative CCK-8 readings Pathway enrichment (B) The ratio of effective drugs enriched in the same pathway to all effective drugs. Indications (C) The ratio of effective drugs enriched in the same pathway to all effective drugs. Includes application to a certain type of cancer: 1 point Applicable to tumors in the same organ / tissue / system as the patient: 2 points Clinical trial phase (D) No clinical trials conducted: 0 points Through Phase I and Phase II experiments: 1 point Phase III experiment passed: 2 points Having passed Phase III trials and currently submitting applications to the FDA, EMA, HMA, CFDA, NDC, and PMDA. Available for purchase: 4 points Targeting (E) Non-targeted drugs: 0 points Targeted therapy: 1 point Safety (F) Toxins that are clearly defined as lethal or carcinogenic in my country / the United States / Europe, or WHO Group 1 carcinogens: 0 points Potential carcinogens, such as WHO Group 2B carcinogens: 0.5 points Not the first two categories: 1 point Side effects (refer to drug package insert / Phase III clinical trial, prioritizing clinical trials conducted in Chinese populations) (G) No clinical trials conducted and no corresponding data available, or more than 50% of cases: 0 points Grade 3 / 4 adverse reaction incidence ≤50%: 1 point Grade 3 / 4 adverse reaction incidence ≤40%: 2 points Grade 3 / 4 adverse reaction incidence ≤30%: 3 points Grade 3 / 4 adverse reaction incidence ≤20%: 4 points Grade 3 / 4 adverse reaction incidence ≤10%: 5 points Cost (RMB / month, if not listed, I / H is recorded as 0) (H) Renminbi Acquisition Difficulty (I) Production halted: 0 points Available for purchase abroad or through a proxy: 1 point Available for purchase in China: 2 cents Method of medication (J) Other (more complicated than injection, such as MMAE requiring connection to a suitable monoclonal antibody before infusion): 0 points Injection: 1 minute Oral or topical application: 2 points Example 2

[0046] Embodiment 2 of the present invention according to Figure 1 The procedure shown in Example 2 employs the method of Example 1 for personalized automated screening of antitumor drugs for hematological malignancies. The difference from Example 1 is that Example 2 enriches cells derived from hematological malignancies. The specific steps for enriching cells derived from hematological malignancies are as follows: For example, CD138 magnetic beads are used to sort multiple myeloma cells; for other hematologic malignancies, different cell surface molecular markers can be used to sort tumor cells.

[0047] 1. First, check the blood sample for blood clots. If blood clots are present, remove them with a pipette tip.

[0048] 2. After allowing the blood sample to stand at room temperature for 30 minutes, mix the blood sample by pipetting and aliquot it into 15 mL EP tubes at 2 mL each. Add an equal volume of room temperature serum-free 1640 medium to each tube and mix gently by pipetting. Add the diluted blood sample dropwise to a 15 mL tube containing 3 mL of Ficoll solution (tilt the tube, add dropwise, and do not point the pipette tip directly at the Ficoll solution to prevent a large amount of blood sample from settling instantly). Centrifuge at 400 RCF for 40 minutes at 20°C with an ascending speed of 2 and a descending speed of 0.

[0049] 3. After centrifugation, carefully remove the centrifuge tube, avoiding shaking. The upper red layer after centrifugation is the culture medium, the bottom layer is the red blood cell pellet, and the middle layer is a thin, pale yellow mononuclear cell layer. Use a 1mL pipette tip to transfer the middle layer into a 15mL tube, combine the two tubes into one, and add pre-cooled sterile PBS to a final volume of 14mL. Centrifuge at 500rcf, 20℃, for 5 minutes. The resulting cell pellet is the bone marrow mononuclear cells.

[0050] 4. Discard the supernatant and resuspend the pellet in 1 mL of BD erythrocyte lysis buffer (pre-diluted). Incubate on ice at 4°C for 10 min. Add PBS to 14 mL and gently mix. Centrifuge at 500 rcf, 20°C for 5 min (if there are still erythrocytes, lyse once more). Discard the supernatant, add pre-chilled sterile PBS to 14 mL, centrifuge at 500 rcf, 20°C for 5 min, discard the supernatant, and resuspend the pellet in 1 mL of PBS into a 1.5 mL EP tube. Count the cells. Centrifuge at 500 rcf, 20°C for 5 min, and discard the supernatant.

[0051] 5. Cell pellet was prepared using 80 µL (2 × 10⁻⁶) 7 For internal use, 80 µL, 3 × 10 7 Cells were resuspended in 160 µL of MACS buffer (40 mL autoMACS Rinsing Solution + 2 mL MACS BSA Stock Solution), and 20 µL (or 40 µL) of CD138 magnetic beads (Miltenyi Biotec CD138 MicroBeads, shaken well in the palm of the hand after being taken out of the 4°C freezer before use) were added to a 1.5 mL EP tube. After mixing by pipetting, the tube was incubated at 4°C for 15 min.

[0052] 6. After removing the sample, add 1 mL of MACS buffer and gently pipette to mix and terminate the reaction. Centrifuge at 500 rcf, 20 °C for 5 min, discard the supernatant, and resuspend the precipitate in 500 µL of MACS buffer.

[0053] 7. Before washing the MS column, rinse once with 500 µL MACS buffer, add cell suspension, and the first cells to flow out are CD138 negative cells. Pass the cell suspension through a new MS column. Then wash both MS columns three times each with 500 µL MACS buffer. Centrifuge the CD138 negative cells at 500 rcf for 5 min at 20°C, discard the supernatant, and flash freeze in liquid nitrogen at -80°C.

[0054] 8. CD138-positive cells were attached to an MS column. The MS column was removed from the magnetic rack and placed on a 15ml tube. 1ml of MACS buffer was added, and the cells were flushed off using the stopcock. The cells were centrifuged at 500rcf, 20°C, for 5 min. The supernatant was discarded, and the pellet was washed once with PBS. Finally, the cells were resuspended in 1ml of PBS and counted. The purity of tumor cells after magnetic bead sorting was detected by flow cytometry. The results are shown below. Figure 5 As shown.

[0055] Figure 5 The results showed that the purity of tumor cells after magnetic bead sorting could reach 98.9%.

[0056] The above description is only a preferred embodiment of the present invention. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the principle of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention.

Claims

1. An anti-tumor drug individualization, automation screening method, characterized in that, Comprising the following steps: (1) Pretreatment of tumor tissues from different sources; (2) Individualized automatic screening of anti-tumor drugs, comprising: S1, using a mechanical arm integrated multi-channel microplate washing and dispensing system to plate the tumor cells pretreated in step (1); S2, using a non-contact dispensing system driven by sound waves to add drugs; S3, after incubation, using a high-content fluorescence imaging system to evaluate the cell state; S4, after evaluation, using a mechanical arm integrated multi-channel microplate washing and dispensing system to add CCK-8 solution, and using an enzyme marker to read and analyze to obtain potential effective drugs.

2. The method according to claim 1, wherein the method is characterized by, In step (2), the mechanical arm integrated multi-channel microplate washing and dispensing system described in S1 comprises a microplate washing and dispensing system Biotek EL406.

3. The personalized and automated screening method for antitumor drugs according to claim 1, characterized in that, In step (2), the non-contact dispensing system driven by sound waves described in S2 comprises a nanoliter sound wave dispensing system Echo 650, the final concentration of the drug for incubation is 50 nM-10 μM, and the incubation time is 12-72 h.

4. The personalized and automated screening method for antitumor drugs according to claim 1, characterized in that, In step (2), the high-content fluorescence imaging system described in S3 comprises a laser confocal high-content imaging microscope Opera Phenix.

5. The personalized and automated screening method for antitumor drugs according to claim 1, characterized in that, In step (2), the screening method of the potential effective drugs described in S4 is: Compared with the control value of the tumor treated with an equal volume of DMSO solvent, drugs with a relative viability ≦50% are potential effective drugs, drugs with a relative viability ≦30% are extremely effective drugs, and drugs with a relative viability >30% and ≦50% are effective drugs.

6. The method according to claim 1, wherein the method is characterized by, In step (1), the tumor cells from different sources include solid tumors or blood tumors; When the tumor cells are solid tumors, the pretreatment method is: (1) Mechanical / enzymatic dissociation of tumor cells: Take the tumor tissue, remove the non-tumor tissue part and necrotic area to obtain pure tumor tissue; cut the pure tumor tissue, add enzyme to dissociate, and remove red blood cells to obtain tumor cells; (2) Three-dimensional tumor cell spheroid culture 3D spheroid cell clusters: Use ultra-low adsorption material microspheres and 3DTS medium to culture tumor cells to obtain 3D cell spheroids as pretreated tumor cells; When the tumor cells are from blood tumor sources, the pretreatment method is: Use density gradient centrifugation to separate mononuclear cells, and use magnetic beads to enrich tumor cells to obtain pretreated tumor cells.

7. The personalized and automated screening method for antitumor drugs according to claim 6, characterized in that, In step (1), the enzyme is type II collagenase, and the concentration of the type II collagenase is 4-6 mg / mL.

8. The personalized and automated screening method for antitumor drugs according to claim 6, characterized in that, In step (2), the 3DTS medium comprises the following components: DMEM / F12 basal medium, 1% penicillin-streptomycin-amphotericin B, 10%-20% FBS, 10-50 µM β-mercaptoethanol, 5-20 μM ROCK inhibitor, 1× non-essential amino acid solution, 5-20 mM HEPES, 20-100 ng / mL EGF, 10-50 ng / mL bFGF.

9. The personalized and automated screening method for antitumor drugs according to claim 6, characterized in that, In step (2), the density of the 3D spheroid cell clusters is 50-100 3D spheroid cell clusters / well.

10. The use of the method for individualized and automated screening of antitumor drugs according to any one of claims 1 to 9 for screening of drugs for the treatment of tumors.

Images