WO2020077552A1 - Procédé et système de prédiction pour un pronostic de tumeur - Google Patents

Procédé et système de prédiction pour un pronostic de tumeur Download PDF

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WO2020077552A1
WO2020077552A1 PCT/CN2018/110565 CN2018110565W WO2020077552A1 WO 2020077552 A1 WO2020077552 A1 WO 2020077552A1 CN 2018110565 W CN2018110565 W CN 2018110565W WO 2020077552 A1 WO2020077552 A1 WO 2020077552A1
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tumor
information
prognosis prediction
model
patient
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PCT/CN2018/110565
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English (en)
Chinese (zh)
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张道允
巩子英
孙永华
叶建伟
王伟
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上海允英医疗科技有限公司
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Priority to CN201880002164.4A priority Critical patent/CN109642258B/zh
Priority to PCT/CN2018/110565 priority patent/WO2020077552A1/fr
Publication of WO2020077552A1 publication Critical patent/WO2020077552A1/fr

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    • C—CHEMISTRY; METALLURGY
    • C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
    • C12Q—MEASURING OR TESTING PROCESSES INVOLVING ENZYMES, NUCLEIC ACIDS OR MICROORGANISMS; COMPOSITIONS OR TEST PAPERS THEREFOR; PROCESSES OF PREPARING SUCH COMPOSITIONS; CONDITION-RESPONSIVE CONTROL IN MICROBIOLOGICAL OR ENZYMOLOGICAL PROCESSES
    • C12Q1/00—Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions
    • C12Q1/68—Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions involving nucleic acids
    • C12Q1/6876—Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes
    • C12Q1/6883—Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes for diseases caused by alterations of genetic material
    • C12Q1/6886—Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes for diseases caused by alterations of genetic material for cancer
    • C—CHEMISTRY; METALLURGY
    • C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
    • C12Q—MEASURING OR TESTING PROCESSES INVOLVING ENZYMES, NUCLEIC ACIDS OR MICROORGANISMS; COMPOSITIONS OR TEST PAPERS THEREFOR; PROCESSES OF PREPARING SUCH COMPOSITIONS; CONDITION-RESPONSIVE CONTROL IN MICROBIOLOGICAL OR ENZYMOLOGICAL PROCESSES
    • C12Q2600/00—Oligonucleotides characterized by their use
    • C12Q2600/118—Prognosis of disease development
    • C—CHEMISTRY; METALLURGY
    • C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
    • C12Q—MEASURING OR TESTING PROCESSES INVOLVING ENZYMES, NUCLEIC ACIDS OR MICROORGANISMS; COMPOSITIONS OR TEST PAPERS THEREFOR; PROCESSES OF PREPARING SUCH COMPOSITIONS; CONDITION-RESPONSIVE CONTROL IN MICROBIOLOGICAL OR ENZYMOLOGICAL PROCESSES
    • C12Q2600/00—Oligonucleotides characterized by their use
    • C12Q2600/156—Polymorphic or mutational markers

Definitions

  • This application relates to the medical field, in particular to a method and system for predicting tumor prognosis.
  • Tumors eg, osteosarcoma, etc.
  • the diagnosis and treatment of tumors continue to improve, the mortality of patients is still not effectively controlled.
  • Recurrence and metastasis are the main causes of death of tumor patients.
  • osteosarcoma can metastasize to various tissues and organs such as lung and spinal cord Threatening the life of the patient.
  • the clinical evaluation of tumors mainly through pathological and imaging morphological changes, to clarify the patient's age, tumor pathology type, surgical stage and residual tumor and other indicators.
  • the screening of tumor-related genes and molecular markers at the molecular level is currently a hot spot in cancer research.
  • Such methods can be used for tumors at the molecular level of tumor cells.
  • the patient provides reference indications for surgery, predicts postoperative recurrence or metastasis, objective indications for radical cure of tumors, and provides targets for anti-metastatic treatment.
  • One of the embodiments of the present application provides a tumor prognosis prediction method, including: obtaining characteristic information of a tumor patient, the characteristic information reflecting at least gene mutation information of the tumor patient; based on the characteristic information of the tumor patient, according to the tumor prognosis The prediction model determines the prognosis prediction result of the tumor patient.
  • the gene mutation information includes genes and mutation abundances that have been mutated on DNA, and / or genes related to tumor prognosis prediction on DNA and their mutation abundances.
  • the obtaining characteristic information of the tumor patient further includes: obtaining a tissue sample of the tumor patient; extracting DNA of the tissue sample; preparing a library of the DNA; performing gene sequencing according to the library to obtain Sequencing results; analyzing the sequencing results to determine gene mutation information of the tumor patient.
  • the characteristic information further includes at least one of the following information of the tumor patient: age, gender, smoking history, years of education, working years, treatment plan, and sample storage time.
  • the tumor prognosis prediction model is a support vector machine model or a neural network model.
  • the tumor prognosis prediction method further includes: training the initial model using the feature information of multiple tumor patients and their prognosis information to obtain the tumor prognosis prediction model.
  • the training of the initial model using the feature information and prognostic information of multiple tumor patients to obtain the tumor prognosis prediction model includes: removing the mutation abundance in the gene mutation information of the multiple tumor patients less than Mutated gene information at a certain threshold.
  • the training the initial model using the feature information of multiple tumor patients and the prognostic information to obtain the tumor prognosis prediction model includes: removing redundant gene mutation information from the gene mutation information of the multiple tumor patients .
  • the tumor prognosis prediction model is a support vector machine model; the training the initial model using the feature information of multiple tumor patients and its prognosis information to obtain the tumor prognosis prediction model includes: The contribution value of each gene mutation information in the feature information to the support vector machine model to determine at least part of the genes as genes related to tumor prognosis prediction; using the gene mutation information of the tumor prognosis prediction related genes of multiple tumor patients and its prognosis information training institute The initial model obtains the tumor prognosis prediction model.
  • the tumor prognosis prediction model is a support vector machine model; the training initial model to obtain the tumor prognosis prediction model further includes: optimizing the support vector machine model using particle swarm optimization or meshing parameter.
  • the prognosis prediction results include: disease progression, stable disease, partial remission, and complete remission; or, the prognosis prediction results include: good treatment effect and bad treatment effect.
  • the tumor is osteosarcoma.
  • the characteristic information at least reflects mutation information of at least one of the following genes in osteosarcoma patients: KMT2C, SOX9, LRP1B, NF-1, PRKDC, FAT1, STAG2, SLIT2, NOTCH1, EPHA7, ATRX, KDM6A APC, RANBP2, RARA.AS1, C11orf30, ROS1, ARID2, TAF1, DICER1, MSH2, MSH6, TP53, KDM5A, JAK2, ALK, RB1, NOTCH2 and RICTOR.
  • the gene mutation information of the tumor patient is gene mutation information of the osteosarcoma lesion site.
  • One of the embodiments of the present application provides a tumor prognosis prediction system, including an acquisition module and a prediction module, wherein the acquisition module is used to acquire characteristic information of a tumor patient, and the characteristic information reflects at least gene mutation information of the tumor patient
  • the prediction module is used to determine the prognosis prediction result of the tumor patient based on the tumor patient's characteristic information and according to the tumor prognosis prediction model.
  • the gene mutation information includes genes and mutation abundances that have been mutated on DNA, and / or genes related to tumor prognosis prediction on DNA and their mutation abundances.
  • the characteristic information further includes at least one of the following information of the tumor patient: age, gender, smoking history, years of education, years of work, treatment plan, and sample storage time.
  • the tumor prognosis prediction model is a support vector machine model or a neural network model.
  • the tumor prognosis prediction system further includes a training module for training the initial model to obtain the tumor prognosis prediction model by using feature information of multiple tumor patients and their prognosis information.
  • the training module is further configured to remove the mutation gene information whose mutation abundance is less than a set threshold in the gene mutation information of the multiple tumor patients.
  • the training module is further used to remove redundant gene mutation information from the gene mutation information of the multiple tumor patients.
  • the tumor prognosis prediction model is a support vector machine model; the training module is further configured to: according to the contribution value of each gene mutation information in the feature information of multiple tumor patients to the support vector machine model, determine at least Some genes are genes related to tumor prognosis prediction; the gene mutation information of the tumor prognosis prediction related genes of multiple tumor patients and their prognosis information are used to train the initial model to obtain the tumor prognosis prediction model.
  • the tumor prognosis prediction model is a support vector machine model; the training module is also used to optimize the parameters of the support vector machine model using particle swarm optimization or meshing.
  • the prognosis prediction results include: disease progression, stable disease, partial remission, and complete remission; or, the prognosis prediction results include: good treatment effect and bad treatment effect.
  • the tumor is osteosarcoma.
  • the characteristic information at least reflects mutation information of at least one of the following genes in osteosarcoma patients: KMT2C, SOX9, LRP1B, NF-1, PRKDC, FAT1, STAG2, SLIT2, NOTCH1, EPHA7, ATRX, KDM6A, APC, RANBP2, RARA.AS1, C11orf30, ROS1, ARID2, TAF1, DICER1, MSH2, MSH6, TP53, KDM5A, JAK2, ALK, RB1, NOTCH2 and RICTOR.
  • the gene mutation information of the tumor patient is gene mutation information of the osteosarcoma lesion site.
  • the device includes at least one processor and at least one memory; the at least one memory is used to store computer instructions; and the at least one processor is used to execute the computer instructions At least part of the instructions to implement the tumor prognosis prediction method.
  • One embodiment of the present application provides a computer-readable storage medium that stores computer instructions, and when the computer instructions are executed by a processor, implements the tumor prognosis prediction method.
  • a tumor prognosis prediction system including: at least one computer-readable storage medium, including a set of instructions for tumor prognosis prediction; and at least one processor in communication with the at least one storage medium, When executing the set of instructions, the at least one processor is configured to: obtain characteristic information of a tumor patient, the characteristic information reflects at least gene mutation information of the tumor patient; and based on the characteristic information of the tumor patient, according to The tumor prognosis prediction model determines the prognosis prediction result of the tumor patient.
  • FIG. 1 is a schematic diagram of an application scenario of a tumor prognosis prediction system according to some embodiments of the present application
  • FIG. 2 is a schematic structural diagram of a computing device according to some embodiments of the present application.
  • FIG. 3 is a block diagram of a tumor prognosis prediction system according to some embodiments of the present application.
  • FIG. 4 is an exemplary flowchart of a tumor prognosis prediction method according to some embodiments of the present application.
  • FIG. 5 is an exemplary flowchart for determining gene mutation information of a tumor patient according to some embodiments of the present application.
  • FIG. 6 is an exemplary flowchart of obtaining a tumor prognosis prediction model according to training shown in some embodiments of the present application;
  • FIG. 7 is a heat map of gene mutation in a patient with osteosarcoma according to an exemplary embodiment of the present application.
  • FIG. 10 is a schematic diagram of prediction result verification of a tumor prognosis prediction model according to an exemplary embodiment of the present application.
  • system means for distinguishing different components, elements, parts, parts or assemblies at different levels.
  • the words can be replaced by other expressions.
  • FIG. 1 is a schematic diagram of an application scenario of a tumor prognosis prediction system 100 according to some embodiments of the present application.
  • the tumor prognosis prediction system 100 may include a server 110, a network 120 and a database 130.
  • the database 130 can store the patient's basic information, disease history, treatment plan data, and can also store the patient's genetic information, such as the genetic mutation information of the tumor patient 140 at the tumor site, the genetic information of the normal tissue of the tumor patient, and Reference gene information, etc.
  • the patient's biological tissue sample or fluid sample, such as the tissue sample 145 of the tumor patient 140 can be stored in a special storage device for further processing, such as gene sequencing processing.
  • the tissue sample 145 may include a tumor tissue sample of the patient or tissue samples of other parts of the patient's body.
  • the server 110 may be used to process and analyze relevant information to generate a prognostic prediction result.
  • the server 110 may obtain relevant information and / or data from the database 130 (for example, gene mutation information of the tumor patient at the tumor site, basic information of the tumor patient, reference gene data, etc.), or may directly obtain work Relevant information and / or data obtained by processing the tissue sample 145 of the tumor patient 140 by personnel or other equipment and instruments.
  • the server 110 may be a server or a server group.
  • the server group may be centralized, such as a data center.
  • the server group can also be distributed, such as a distributed system.
  • the server 110 may be local or remote.
  • the server 110 may be implemented on a cloud platform.
  • the cloud platform may include a private cloud, a public cloud, a hybrid cloud, a community cloud, a distributed cloud, an intermediate cloud, a multi-cloud, etc., or any combination thereof.
  • the server 110 may be implemented on the computing device 200 having at least one component shown in FIG. 2.
  • the server 110 may include a processing engine 112.
  • the processing engine 112 can be used to execute instructions (program code) of the server 110.
  • the processing engine 112 can execute an instruction to analyze the characteristic information of the tumor patient 140, and then obtain a tumor prognosis prediction result.
  • the instructions for analyzing the characteristic information of the tumor patient 140 may be stored in a computer-readable storage medium (not shown) in the form of computer instructions.
  • the processing engine 112 may include one or more sub-processing devices (eg, single-core processing devices or multi-core multi-core processing devices).
  • the processing engine 112 may include a central processing unit (CPU), an application specific integrated circuit (ASIC), an application specific instruction processor (ASIP), a graphics processor (GPU), a physical processor (PPU), and a digital signal processor ( DSP), field programmable gate array (FPGA), editable logic circuit (PLD), controller, microcontroller unit, reduced instruction set computer (RISC), microprocessor, etc. or any combination of the above.
  • CPU central processing unit
  • ASIC application specific integrated circuit
  • ASIP application specific instruction processor
  • GPU graphics processor
  • PPU physical processor
  • DSP digital signal processor
  • FPGA field programmable gate array
  • PLD field programmable gate array
  • controller microcontroller unit
  • RISC reduced instruction set computer
  • the network 120 may provide a channel for information exchange.
  • information can be exchanged between the server 110 and the database 130 through the network 120.
  • the server 110 may receive the reference gene data in the database 130 through the network 120.
  • information about tumor patients 140 and / or tissue samples 145 may be transmitted to the server 110 and / or database 130 via the network 120.
  • the characteristic information of the tumor patient 140 (such as gene mutation information, basic information, etc.) may be transmitted to the server 110 through the network 120.
  • the network 120 may be any type of wired or wireless network.
  • the network 120 may include a cable network, a wired network, a fiber optic network, a telecommunications network, an internal network, an internet network, a regional network (LAN), a wide area network (WAN), a wireless regional network (WLAN), and a metropolitan area network (MAN ), Public switched telephone network (PSTN), Bluetooth network, ZigBee network, near field communication (NFC) network, etc. or any combination of the above.
  • LAN regional network
  • WAN wide area network
  • WLAN wireless regional network
  • MAN metropolitan area network
  • PSTN Public switched telephone network
  • Bluetooth network ZigBee network
  • NFC near field communication
  • the database 130 may be used to store data and / or instruction sets. In some embodiments, the database 130 may store data obtained from the server 110. In some embodiments, the database 130 may store information and / or instructions for execution or use by the server 110 to perform the exemplary methods described in this application. In some embodiments, the reference gene data may be stored in the database 130. Specifically, the database 130 may store genetic data in various types of genomic databases and / or genetic data that has an impact (or significant impact) on tumorigenesis reported in existing literature.
  • the genome database may include, but is not limited to, COSMIC database, ClinVar database, HGMD database, OMIM database, TCGA database, GeneCards database, and so on.
  • the database 130 may include mass storage, removable storage, volatile read-write memory, read-only memory (ROM), etc., or any combination thereof.
  • the database 130 may be implemented on a cloud platform.
  • the cloud platform may include a private cloud, a public cloud, a hybrid cloud, a community cloud, a distributed cloud, an intermediate cloud, a multi-cloud, etc., or any combination thereof.
  • the database 130 may be part of the server 110.
  • the tumor patient 140 may be a patient with one or more tumor diseases.
  • tumor diseases may include cancer, sarcoma, benign tumor, etc. or any combination thereof.
  • the cancer may include squamous cell carcinoma, adenocarcinoma, undifferentiated carcinoma, and the like.
  • squamous cell carcinoma may include cancers that occur in the skin, esophagus, lungs, cervix, vagina, vulva, penis, and the like.
  • Adenocarcinoma can include cancers that occur in the digestive tract, lungs, uterus, breast, ovary, prostate, thyroid, liver, kidney, pancreas, gallbladder, and other parts.
  • Sarcomas can include, but are not limited to: soft tissue sarcoma, osteosarcoma, malignant fibrous histiocytoma, bilateral sarcoma, rhabdomyosarcoma, lymphosarcoma, synovial sarcoma, leiomyoma, and the like.
  • Benign tumors may include, but are not limited to, hamartomas, benign pancreatic tumors, thyroid adenoma, breast fibroids, uterine tumors, gastrointestinal plain osteomyomas, soft tissue fibroids, synovial tumors, ligament fibroma, and the like.
  • the tumor patient 140 may be an osteosarcoma patient.
  • the tumor patient 140 may be a patient whose tumor is at various stages (eg, early, middle, late, etc.).
  • the tumor patient 140 may also be a patient at various stages of treatment (eg, before treatment, during treatment, after treatment, etc.).
  • the tissue sample 145 may be used to reflect tumor patient 140 tumor related information.
  • the tissue sample 145 may be a biological tissue or fluid sample extracted from a tumor site (such as a target lesion) and / or a non-tumor site (such as a site other than the lesion) of the tumor patient 140.
  • tissue samples may include, but are not limited to: sputum, blood samples, fresh tissue (such as surgical tissue, puncture tissue, etc.), paraffin-embedded tissue, urine, serous cavity fluid (such as ascites, pleural effusion, Pericardial effusion, etc.), or tissues, cells, etc. extracted from the tumor site, or any combination of the above.
  • the tissue sample 145 may include the tissues and cells of the tumor patient 140 at the tumor site and at sites other than the tumor.
  • the tissue sample 145 may include only the tissues and cells of the tumor patient 140 at the tumor site.
  • the relevant information of the tumor patient 140 and / or the tissue sample 145 may be transmitted to one or more components (such as a server) of the tumor prognosis prediction system 100 by humans (such as staff) or machines (such as equipment, etc.) 110, database 130).
  • humans such as staff
  • machines such as equipment, etc.
  • FIG. 2 is a schematic diagram of the architecture of a computing device 200 according to some embodiments of the present application.
  • the computing device 200 may include a processor 210, a memory 220, an input / output interface 230 and a communication port 240.
  • the server 110 and / or the database 130 may be implemented on the computing device 200.
  • the processing engine 112 may be implemented on the computing device 200 and configured to perform the functions of the processing engine 112 in this application.
  • the processor 210 may execute calculation instructions (program code) and perform the functions of the server 110 described in this application.
  • Computing instructions may include programs, objects, components, data structures, processes, modules, and functions (functions refer to specific functions described in this application).
  • the processor 210 may process instructions in the tumor prognosis prediction system 100 to predict the effect of tumor prognosis.
  • the processor 210 may include a microcontroller, a microprocessor, a reduced instruction set computer (RISC), an application specific integrated circuit (ASIC), an application specific instruction set processor (ASIP), and a central processing unit (CPU) , Graphics processing unit (GPU), physical processing unit (PPU), microcontroller unit, digital signal processor (DSP), field programmable gate array (FPGA), advanced RISC machine (ARM), programmable logic device and capable Any circuit, processor, etc. that performs one or more functions, or any combination thereof.
  • RISC reduced instruction set computer
  • ASIC application specific integrated circuit
  • ASIP application specific instruction set processor
  • CPU central processing unit
  • GPU Graphics processing unit
  • PPU physical processing unit
  • DSP digital signal processor
  • FPGA field programmable gate array
  • ARM advanced RISC machine
  • the memory 220 may store data / information obtained from any component in the tumor prognosis prediction system 100.
  • the memory 220 may include mass storage, removable memory, volatile read and write memory, read-only memory (ROM), etc., or any combination thereof.
  • Exemplary mass storage may include magnetic disks, optical disks, solid-state drives, and the like.
  • Removable memory can include flash drives, floppy disks, optical disks, memory cards, U disks, compact disks, and mobile hard disks.
  • Volatile read and write memory can include random access memory (RAM).
  • RAM may include dynamic RAM (DRAM), double-rate synchronous dynamic RAM (DDRSDRAM), static RAM (SRAM), thyristor RAM (T-RAM), zero capacitance (Z-RAM), and so on.
  • DRAM dynamic RAM
  • DDRSDRAM double-rate synchronous dynamic RAM
  • SRAM static RAM
  • T-RAM thyristor RAM
  • Z-RAM zero capacitance
  • ROM may include mask ROM (MROM), programmable ROM (PROM), erasable programmable ROM (PEROM), electrically erasable programmable ROM (EEPROM), compact disk ROM (CD-ROM) and digital universal disk ROM Wait.
  • MROM mask ROM
  • PROM programmable ROM
  • PEROM erasable programmable ROM
  • EEPROM electrically erasable programmable ROM
  • CD-ROM compact disk ROM
  • digital universal disk ROM Wait digital universal disk ROM Wait.
  • the input / output interface 230 may be used to input or output signals, data, or information.
  • the input / output interface 230 may be used for user (eg, tumor patient 140, user of the tumor prognosis prediction system 100, etc.) to contact the server 110.
  • the user can input the characteristic information of the tumor patient through the input / output interface 230.
  • the input / output interface 230 may include an input device and an output device.
  • Exemplary input devices may include a keyboard, mouse, touch screen, microphone, etc., or any combination thereof.
  • Exemplary output devices may include display devices, speakers, printers, projectors, etc., or any combination thereof.
  • Exemplary display devices may include a liquid crystal display (LCD), a light-emitting diode (LED) based display, a flat panel display, a curved display, a television device, a cathode ray tube (CRT), etc., or any combination thereof.
  • LCD liquid crystal display
  • LED light-emitting diode
  • flat panel display a flat panel display
  • curved display a television device
  • cathode ray tube (CRT) cathode ray tube
  • the communication port 240 may be connected to the network 120 for data communication.
  • the connection may be a wired connection, a wireless connection, or a combination of both.
  • Wired connections may include cables, fiber optic cables, telephone lines, etc., or any combination thereof.
  • the wireless connection may include Bluetooth, WiFi, WiMax, WLAN, ZigBee, mobile network (e.g., 3G, 4G, or 5G, etc.), etc., or any combination thereof.
  • the communication port 240 may be a standardized port, such as RS232, RS485, and so on.
  • the communication port 240 may be a specially designed port.
  • FIG. 3 is a block diagram of a tumor prognosis prediction system according to some embodiments of the present application.
  • the tumor prognosis prediction system may include an acquisition module 310, a prediction module 320, and a training module 330.
  • the obtaining module 310 can be used to obtain the characteristic information of the tumor patient 140.
  • the feature information may reflect at least the gene mutation information of the tumor patient.
  • the characteristic information of the tumor patient 140 may include any combination of one or more of gene mutation information of the tumor patient, basic information of the tumor patient, and the like.
  • the prediction module 320 may be used to predict the prognosis prediction result of the tumor patient. For example, the prediction module 320 may determine the prognosis prediction result of the tumor patient based on the tumor patient's feature information and according to the tumor prognosis prediction model.
  • the training module 330 may be used for training to obtain a tumor prognosis prediction model. Specifically, the training module 330 may obtain the characteristic information and prognostic information of multiple tumor patients. The training module 330 may use the feature information and prognostic information of multiple tumor patients to train the initial model to obtain a tumor prognosis prediction model. In some embodiments, the training module 330 can remove the mutation gene information whose mutation abundance is less than a set threshold in the gene mutation information. In some embodiments, the training module 330 may remove redundant gene mutation information from the gene mutation information. In some embodiments, the training module 330 may determine that at least part of the genes are related genes for tumor prognosis prediction according to the contribution value of each gene mutation information in the feature information of multiple tumor patients to the support vector machine model.
  • the training module 330 may use the gene mutation information of the related genes of the tumor prognosis prediction genes of multiple tumor patients and the prognosis information to train the initial model to obtain the tumor prognosis prediction model. In some embodiments, the training module 330 may also use particle swarm optimization or meshing to optimize the parameters of the support vector machine model.
  • system and its modules shown in FIG. 3 can be implemented in various ways.
  • the system and its modules may be implemented by hardware, software, or a combination of software and hardware.
  • the hardware part can be implemented with dedicated logic;
  • the software part can be stored in the memory and executed by an appropriate instruction execution system, such as a microprocessor or dedicated design hardware.
  • processor control code for example, on a carrier medium such as a magnetic disk, CD, or DVD-ROM, such as a read-only memory (firmware Such codes are provided on programmable memories or data carriers such as optical or electronic signal carriers.
  • the system and its modules of the present application can be implemented by not only hardware circuits such as very large scale integrated circuits or gate arrays, semiconductors such as logic chips, transistors, or programmable hardware devices such as field programmable gate arrays, programmable logic devices, etc. It can also be implemented by, for example, software executed by various types of processors, or by a combination of the above hardware circuits and software (for example, firmware).
  • the acquisition module 310, the prediction module 320, and the training module 330 may be different modules in a system, or may be a module that implements the functions of the above two or more modules.
  • the acquisition module 310 and the prediction module 320 may also be a module having both acquisition and prediction functions.
  • each module may share a storage module, or each module may have its own storage module.
  • FIG. 4 is an exemplary flowchart of a tumor prognosis prediction method according to some embodiments of the present application. As shown in FIG. 4, the tumor prognosis prediction method may include:
  • Step 410 Obtain characteristic information of a tumor patient, and the characteristic information can at least reflect gene mutation information of the tumor patient. Specifically, step 410 may be performed by the obtaining module 310.
  • the characteristic information of the tumor patient 140 may include any combination of one or more of gene mutation information of the tumor patient, basic information of the tumor patient, and the like. In some embodiments, the characteristic information of the tumor patient may only include the genetic mutation information of the tumor patient. Specifically, the gene mutation information of the tumor patient may include genes and mutation abundances that have been mutated on DNA, and / or genes related to tumor prognosis prediction on DNA and their mutation abundances. The basic information of the tumor patient can reflect other information related to the tumor patient except the gene mutation information.
  • the basic information of cancer patients can include the age, sex, smoking history, years of education, working years, sample storage time (such as blood storage time, tumor tissue storage time, and other normal tissue storage time) of the cancer patient, treatment plan Etc., or any combination thereof.
  • the treatment regimen may include the type of treatment regimen (eg, radiation therapy, chemotherapy, immunotherapy, etc.), duration of treatment, dose of radiation used, dose of medication, name or type of medication, and so on.
  • the gene mutation information of the tumor patient may be the gene mutation information of the tumor patient at the tumor site (such as the target lesion).
  • the gene mutation information of the osteosarcoma patient may be the gene mutation information of the osteosarcoma lesion.
  • the tumor patient 140 may be a patient at various stages of the tumor (eg, early, middle, late, etc.), and / or at various stages of treatment (eg, before treatment, during treatment, after treatment, etc.).
  • stages of the tumor eg, early, middle, late, etc.
  • treatment e.g., before treatment, during treatment, after treatment, etc.
  • characteristic information of patients with osteosarcoma before treatment can be obtained for predicting the prognostic effect of treatment, and can further provide reference for the formulation and selection of treatment plans.
  • acquiring / determining the gene mutation information of the tumor patient 140 may include: obtaining a tissue sample 145 of the tumor patient 140, extracting the DNA of the tissue sample, preparing a library of the DNA, and performing gene sequencing based on the library to obtain sequencing results , Analyze the sequencing results to determine the genetic mutation information of tumor patients and other steps.
  • determining mutation information of 140 genes in tumor patients please refer to FIG. 5 and related descriptions.
  • Step 420 based on the characteristic information of the tumor patient, according to the tumor prognosis prediction model, determine the prognosis prediction result of the tumor patient. Specifically, this step 420 may be performed by the prediction module 320.
  • the characteristic information of the tumor patient may be input into the trained tumor prognosis prediction model to obtain the prognosis prediction result of the tumor patient.
  • the tumor prognosis prediction model may be a supervised learning model.
  • the supervised learning model may include one or a combination of one of support vector machine model, decision tree model, neural network model, nearest neighbor classifier, and so on.
  • FIG. 6 and related descriptions For the training process of the tumor prognosis prediction model, please refer to FIG. 6 and related descriptions.
  • the prognostic prediction result may be the prognosis of a period of time (eg, 5 years) after treatment.
  • the prognostic prediction results can be divided into four categories according to changes in target lesions: PD (progressive disease), stable disease (SD), partial response (PR), partial response (PR), and complete response (CR). .
  • PD can mean that the sum of the largest diameters of target lesions increases by 20% or more, or new lesions appear (such as new lesions due to tumor metastasis); SD can mean that the sum of the largest diameters of target lesions does not reach PR, or increases Not reaching PD; PR can refer to the reduction of the sum of the maximum diameter of the target lesions by 30% or more, which should be maintained for at least 4 weeks; CR can refer to the disappearance of all target lesions, no new lesions appear, and the normal tumor markers should be maintained for at least 4 weeks.
  • the prognostic prediction result may include two types: good treatment effect and bad treatment effect. Specifically, whether the treatment effect is good or not can be determined according to clinical standards.
  • the tumor patient relapses within 5 years after treatment, it means that the treatment effect is not good, and if the tumor patient does not relapse within 5 years after treatment, it means that the treatment effect is good.
  • PD and SD can be classified as having a poor therapeutic effect
  • PR and CR can be classified as having a good therapeutic effect.
  • the survival time of the patient exceeds 5 years after the first treatment, it means that the treatment effect is good; if the survival time of the patient after the first treatment is less than 5 years, it means that the treatment effect is not good.
  • the prognostic prediction results may also be classified into other categories, which are not limited in the embodiments of the present application.
  • the prognosis prediction results can be divided into three categories: good treatment effect, general treatment effect and poor treatment effect.
  • the prognostic prediction result may also be the prediction value of a specific indicator.
  • the prognostic prediction results may include, but are not limited to, disease remission rate, disease recurrence rate, disease recurrence within a few years, disease survival rate, survival time, recent mortality, long-term mortality, hospital mortality, out-of-hospital mortality, surgical death Rate etc.
  • FIG. 5 is an exemplary flowchart for determining gene mutation information of a tumor patient according to some embodiments of the present application. Specifically, the steps shown in FIG. 5 may be performed by staff (such as doctors, laboratory personnel, operators, etc.) and / or instruments (such as detectors, analyzers, etc.). As shown in FIG. 5, the process of determining gene mutation information of a tumor patient may include:
  • Step 510 Obtain a tissue sample of a tumor patient.
  • the tissue sample 145 can be used to reflect tumor-related information.
  • the tissue sample 145 may be a biological tissue or fluid sample extracted from a tumor site (such as a target lesion) and / or a non-tumor site (such as a site other than the lesion) of the tumor patient 140.
  • tissue samples may include, but are not limited to: sputum, blood samples, fresh tissue (such as surgical tissue, puncture tissue, etc.), paraffin-embedded tissue, urine, serous cavity fluid (such as ascites, pleural effusion, Pericardial effusion, etc.), or tissues, cells, etc. extracted from the tumor site, or any combination of the above.
  • the tissue sample 145 may include the tissues and cells of the tumor patient 140 at the tumor site or at a site other than the tumor. In some embodiments, the tissue sample 145 may include only the tissues and cells of the tumor patient 140 at the tumor site. In some embodiments, inclusion criteria may be established for the tissue sample 145. For example, the requirements for collecting tissue samples can be formulated. The requirements are surgical tissue, fresh tissue, puncture tissue, 10% neutral formalin, and paraffin-embedded tissue.
  • the paraffin white film can be 10 (5 micrometer) or 5 (10 micrometer) white films, and to ensure that the sliced tissue contains a sufficient proportion of tumor cells (such as> 70%), the same HE stain can be added Film (or email to inform the number of tumor cells after HE staining of the specimen sent for examination).
  • tumor cells such as> 70%
  • the same HE stain can be added Film (or email to inform the number of tumor cells after HE staining of the specimen sent for examination).
  • the size of the sample collected is> 0.3 cm 3 , and quickly placed in the EP tube.
  • the sample transportation standard can be formulated: the paraffin white tablets can be sent for inspection at room temperature within 2 weeks after cutting, such as using an EP tube, and the mouth of the tube is sealed with a sealing film to prevent leakage during transportation and needs to be sent The pathology number of the test sample is written on the application form.
  • criteria for screening tissue samples can be formulated, such as sample rejection criteria: tissues other than 10% neutral formalin fixatives, sample information submitted for inspection does not match the application form, tissue autolysis or degeneration, etc.
  • step 520 the DNA of the tissue sample is extracted.
  • the method of extracting the DNA of the tissue sample may include cetyltrimethylammonium bromide method (CTAB method), glass bead method, ultrasonic method, grinding method, freeze-thaw method, guanidine isothiocyanate Method, alkaline lysis method, enzymatic method, etc. or any combination of the above.
  • CTAB method cetyltrimethylammonium bromide method
  • glass bead method ultrasonic method
  • grinding method freeze-thaw method
  • guanidine isothiocyanate Method alkaline lysis method
  • enzymatic method etc. or any combination of the above.
  • any known method may also be used to extract the DNA of the tissue sample, which is not limited in the embodiments of the present application.
  • Step 530 Prepare the DNA library.
  • the library preparation process may include some or all of the steps of DNA disruption, end repair, magnetic bead fragment screening, end tailing, adaptor ligation, PCR enrichment, hybrid sequencing library, and the like.
  • any known method can also be used to prepare a DNA library of tissue samples, which is not limited in the embodiments of the present application.
  • Step 540 Perform gene sequencing according to the library to obtain sequencing results.
  • the prepared library may be subjected to gene sequencing to obtain sequencing data.
  • the gene sequencing technology may be a high-throughput sequencing technology.
  • High-throughput sequencing technology (“Next-generation" sequencing technology, NGS) may include: single-molecule real-time sequencing ( Pacific Bio), ion semiconductor (Ion Torrent sequencing), pyrophosphate sequencing (454), sequencing by synthesis (Illumina) , Sequencing by connection (SOLiD sequencing), chain termination method (Sanger sequencing) and any combination of one or more.
  • any known method can also be used for gene sequencing, which is not limited in the embodiments of the present application.
  • Step 550 Analyze the sequencing results to determine the genetic mutation information of the tumor patient.
  • the acquired sequencing data can be analyzed to obtain gene mutation information of the tumor patient (including the gene and mutation abundance on the DNA, and / or genes related to tumor prognosis prediction on the DNA, Mutation site mutation abundance, gene mutation abundance, etc.).
  • the gene mutation abundance may be the cumulative sum of the mutation abundances at the positions where the non-synonymous single nucleotide variation (Single Nucleotide Variation, SNV) in the statistical sequencing result is greater than a certain set value.
  • the set value may be 0.05%, 0.1%, 0.2%, 1%, 2%, 3% or 10%, and so on.
  • Mutation site abundance can refer to the proportion of a base mutation.
  • mutation abundance at the mutation site number of mutant reads / (number of mutant reads + number of wild-type reads), where reads represents a short sequence of sequencing fragments.
  • the mutant gene KMT2C of a patient obtained by sequencing has 5 mutation sites.
  • the mutation abundances of the 5 mutation sites are: 1%, 3%, 4%, 6%, 8%, and the threshold is set to 2% .
  • the mutation abundance of the mutant gene KMT2C is the cumulative sum of the mutation abundances of 4 mutation sites greater than 2%.
  • data analysis may include (1) removing linker sequences in sequencing data; (2) performing quality control and removing low-quality sequencing data (eg, low-quality bases, too short sequencing data, etc.); 3) Compare the processed sequencing data with the reference gene data to identify the mutant gene; (4) remove the normal mutations of the gene (such as polymorphic mutation, synonymous mutation, etc.); (5) obtain the tumor patient ’s Gene mutation information and some or all of the above steps.
  • the reference gene data may be normal gene data (for example, gene data in normal cells of a non-tumor site of a tumor patient, gene data of a non-tumor patient, etc.), gene data of a corresponding tumor disease (for example, each tumor Prognosis prediction related genes) and so on.
  • the reference gene data may be stored in the database 130, and may be retrieved from the database 130 in use.
  • any known method can also be used to determine the mutation abundance of a gene. For example, second-generation sequencing, BEAMING, PARE and other technologies.
  • FIG. 7 are heat maps of gene mutations in all osteosarcoma patients according to exemplary embodiments of the application; The heat map of gene mutation in patients with osteosarcoma with good treatment effect according to the exemplary embodiment of the present application;
  • FIG. 9 is the heat map of gene mutation in patients with osteosarcoma with poor treatment effect according to the exemplary embodiment of the present application.
  • the corresponding tissues and cells can be extracted from the target lesion (osteosarcoma lesion site) of osteosarcoma patients (93 samples of osteosarcoma patients as shown in FIG. 7), and the genes of osteosarcoma patients can be determined therefrom Mutation information.
  • the gene mutation information of the osteosarcoma patient can be determined through the foregoing process steps of determining the gene mutation information of the tumor patient.
  • the mutation status (eg, gene mutation abundance) of 315 genes in the sample (according to the genes reported in the literature that have a more significant effect on cancer) is mainly detected.
  • the number of genes detected may increase or decrease as appropriate.
  • Figure 7-9 lists the gene mutation heat maps of the top 29 mutation ratios in all patients with osteosarcoma, patients with good prognosis and patients with poor prognosis. Among them, the left ordinate of Figure 7-9 represents a certain mutation The ratio of mutations in genes in 93 samples. The right ordinate represents the mutant gene and the abscissa represents the sample.
  • the mutation gene information with a higher proportion of gene mutations in the sample includes: Lysine N-methyltransferase 2C (KMT2C), SRY- box 9 (SOX9), LDL receptor related protein 1B (LRP1B), Neurofibromatosis type I (NF-1), protein Kinase (PRKDC), FAT typical cadherin 1 (FAT1), slit Guidance ligand 2 (SLIT2), Notch1, EPHreceptor A7 (EPHA7), ATRX, Lysine demethylase 6A (KDM6A), APC, RAN binding protein 2 (RANBP2), ROS proto-oncogene 1 (ROS1), EMSY (C11orf30), AT-rich interactive domain-containing protein 2 (ARID2), RARA antisense RNA 1 (RARA.AS1), TATA-box binding protein protein associated 1 factor (TAF1), mutS homolog 2 (MS
  • Table 1 lists the high-abundance mutant gene information corresponding to each patient (only 10 patients with good prognosis and 10 patients with poor prognosis are shown as examples).
  • FIG. 6 is an exemplary flowchart of obtaining a tumor prognosis prediction model according to training shown in some embodiments of the present application. Specifically, the process shown in FIG. 6 (such as step 610, step 620, etc.) may be performed by the training module 330. As shown in FIG. 6, an exemplary process of training to obtain a tumor prognosis prediction model may include:
  • Step 610 Acquire feature information and prognostic information of multiple tumor patients.
  • the characteristic information of multiple tumor patients may include: any combination of one or more of gene mutation information of tumor patients, basic information of tumor patients, and the like.
  • the gene mutation information of multiple tumor patients may include genes and mutation abundances of mutations in the DNA of each tumor patient.
  • the genetic mutation information of the multiple tumor patients may be the genetic mutation information of the tumor patient at the tumor site (such as a target lesion).
  • the basic information of the tumor patient can reflect other information related to the tumor patient except the gene mutation information.
  • the basic information of the cancer patient may include the age, gender, smoking history, years of education, working years, treatment plan, sample storage time, medication type, etc. of the cancer patient, or any combination thereof.
  • the prognostic information of multiple tumor patients can be divided into disease progression (PD, progressive disease), stable disease (SD, stable disease), partial response (PR, partial response), and complete response according to the changes of target lesions (CR, complete, response) Four categories.
  • the prognosis may include two types: good treatment effect and bad treatment effect.
  • the prognosis may also be the value of a specific indicator.
  • the prognosis may include but is not limited to disease remission rate, disease recurrence rate, disease recurrence within a few years, disease survival rate, survival time, recent mortality, long-term mortality, hospital mortality, out-of-hospital mortality, surgical mortality Wait.
  • the prognosis situation described herein may correspond to the prognosis prediction result determined in step 420.
  • Step 620 Using the feature information and prognostic information of multiple tumor patients, train an initial model to obtain a tumor prognosis prediction model.
  • the tumor prognosis prediction model may be a supervised learning model.
  • the supervised learning model may include one or a combination of one of support vector machine model, decision tree model, neural network model, nearest neighbor classifier, and so on.
  • the support vector machine model will be used as an example to illustrate the training process of the tumor prognosis prediction model.
  • initial model parameters may be set to establish an initial support vector machine model. It can also use the meshing method to search for the optimal model parameters (eg, parameter c (cost), parameter g (gamma), etc.) based on the feature information and prognostic information of multiple tumor patients to update and optimize the model.
  • the kernel function of the support vector machine model (such as linear kernel function, polynomial kernel function, Gaussian (RBF) kernel function, sigmoid kernel function) can be selected, and based on the characteristic information of multiple tumor patients and their prognosis Information training obtains the support vector machine model.
  • the optimal model parameters can also be found by combining the grid division method and the verification method.
  • the model parameters eg, parameter c (cost), parameter g (gamma), etc.
  • the optimal model parameter is selected according to the verification result.
  • the particle swarm optimization algorithm may be used to optimize the parameters of the support vector machine model. Specifically, you can first initialize the parameters of the particle swarm optimization algorithm, and then use the particle swarm optimization algorithm to find the best parameters to update the model (eg, paired parameters c, g, etc.), and use the best parameters as optimization After the model parameters.
  • the particle swarm optimization algorithm may include but is not limited to a basic particle swarm optimization algorithm, an adaptive mutation particle swarm optimization algorithm, and the like.
  • the parameters of the particle swarm optimization algorithm can include local search capability parameters, global search capability parameters, elastic coefficients for speed updates, maximum number of evolutions, maximum number of populations, number of cross-validation folds, change range of parameter C, change range of parameter g, etc. , Or any combination thereof.
  • the parameters of the particle swarm optimization algorithm can be initialized manually or non-manually.
  • grid search and particle swarm optimization algorithms can also be used in combination to optimize the parameters of the support vector machine model. For example, you can first use grid search to optimize the parameters of the support vector machine model, and then use particle swarm optimization to optimize it again.
  • the feature information of multiple tumor patients can be further screened, and the filtered feature information can be used for model training.
  • the mutation gene information whose mutation abundance is less than a set threshold can be removed from the gene mutation information of the multiple tumor patients.
  • the gene mutation abundance can be the cumulative sum of the mutation abundances of multiple different mutation sites in the gene, and the threshold of the gene mutation abundance at the mutation site can be artificially set (such as 0.05%, 0.1%, 0.2%, 1%, 2%, 3%, etc.), remove the mutation gene information whose mutation abundance is less than the set threshold. For example, some mutation sites with abundances of mutations less than a certain value (such as 0.05%, 0.1%, 0.2%, etc.) may not be counted in their gene mutation abundances.
  • redundant gene mutation information in the gene mutation information of the multiple tumor patients may be removed.
  • the gene mutation information there may be two or more genes, and the correlation between them is relatively high.
  • the two genes when the mutations of the two genes are the same or similar, or the expressions of the mutation abundances of the two genes are similar, the two genes are considered to be highly correlated. For such highly correlated genes, one or more of them may be considered redundant genes.
  • At least part of the genes may be related genes for tumor prognosis prediction according to the contribution value of the mutation information of each gene in the feature information of multiple tumor patients to the support vector machine model.
  • the mutation information of each gene in the characteristic information of multiple tumor patients may be further screened.
  • the recursive feature elimination method can be used to screen the mutation information of each gene in the feature information of multiple tumor patients. Taking the prediction accuracy of the model as the evaluation standard, the mutation information of each gene in the characteristic information of multiple tumor patients is selectively eliminated to obtain multiple training sets, and a model is trained on each training set. Based on the prediction accuracy The gene mutation information eliminated during the training of each model is sorted by contribution value. It can be understood that the eliminated gene mutation information corresponding to the model with lower prediction accuracy is greater than the eliminated gene mutation information corresponding to the model with higher prediction accuracy.
  • the mutation information of each gene can be screened according to the contribution value to obtain at least part of genes as genes related to tumor prognosis prediction.
  • the random forest algorithm can also be used to screen the mutation information of each gene in the characteristic information of multiple tumor patients. Specifically, (1) First build a decision tree: you can define that there are P trees in the forest (such as 20, 40, etc.); you can use the bootstrap sampling method to extract multiple sample sets from 93 samples as each decision tree. Training sample set, repeating P round sampling can get the training sample set of each decision tree.
  • Each round of sampling can sample 93 times from 93 samples with replacement sampling to get the training set of a decision tree; At each node of the tree, assuming a total of 315 feature variables, m feature variables are randomly selected from it, and a feature is selected from the m feature variables for branch growth. The pruning operation is not performed during the growth process, and the best is calculated.
  • the mutation information of the gene with the most reduced impurity can be used as the feature with the largest contribution value, and so on, to determine the contribution value of different mutant genes to the model (as shown in Table 2), so as to screen out at least some genes for tumor prognosis prediction Related genes.
  • n mutant genes with the largest contribution to the tumor prognosis prediction model can be selected from the mutant genes that have a significant impact on tumorigenesis as tumor prognosis prediction correlation gene.
  • the tumor prognosis prediction model obtained by training can be verified.
  • the cross-validation method may include: Hold-Out Method, K-fold Cross-Validation (K-CV) and Leave-One-Out Cross-Validation, LOO-CV).
  • the training sample can be divided into the total number of samples (for example, 93), one of which is used as a verification sample, and the remaining 92 are used as training samples to input the initial support vector machine model for training, and the cross-validation process is repeated 93 times, 93 verification results were obtained, and the 93 verification results were combined to determine the final verification result of the tumor prognosis prediction model obtained by training.
  • the receiver operating characteristic curve (ROC curve) can be drawn according to the verification result and has been visually represented (as shown in FIG. 10). As shown in FIG.
  • the points on the ROC curve represent the sensitivity and specificity of the osteosarcoma prognosis prediction model under different truncation conditions (such as prognostic effect classification criteria).
  • the upper left corner of the ROC curve is close to the upper left corner, which can reflect the higher prediction accuracy of the osteosarcoma prognosis prediction model obtained in this example; the area under the ROC curve is 0.988, very close to 1, which can reflect The osteosarcoma prognosis prediction model obtained in this example has a good classification effect; in addition, the osteosarcoma prognosis prediction model of the present application has higher sensitivity average value (0.95) and specificity average value (0.97) under different truncation conditions.
  • 6 additional patients with osteosarcoma were selected (4 of which are known to have a poor prognostic effect and the other 2 have a good prognostic effect).
  • Obtain the genetic mutation information of the osteosarcoma lesion site and based on this information, determine the prognostic prediction results of the 6 osteosarcoma patients according to the osteosarcoma prognosis prediction model trained in this embodiment (as shown in Table 3, where the predicted values The threshold is set to 0.5, less than 0.5 is a good prognosis, and greater than 0.5 is a poor prognosis), the obtained prediction results are completely consistent with the known prognostic effect.
  • Sample name Predictive value Predicted performance Actual prognosis effect Patient 1 0.335717 Good prognosis Good prognosis Patient 2 0.44896 Good prognosis Good prognosis Patient 3 0.67417 Poor prognosis Poor prognosis Patient 4 0.735268 Poor prognosis Poor prognosis Patient 5 0.756405 Poor prognosis Poor prognosis Patient 6 0.930926 Poor prognosis Poor prognosis
  • the possible benefits brought by the embodiments of the present application include, but are not limited to: (1) the prognostic effect of tumor patients based on gene mutation information can be realized; (2) the accuracy of tumor prognosis prediction is improved; (3) the tumor prognosis prediction process is implemented Convenient; (4) Provide reference for the formulation and selection of treatment plan. It should be noted that different embodiments may have different beneficial effects. In different embodiments, the possible beneficial effects may be any one or a combination of the above, or any other beneficial effects that may be obtained.
  • the computer storage medium may contain a propagated data signal containing a computer program code, for example, on baseband or as part of a carrier wave.
  • the propagated signal may have multiple manifestations, including electromagnetic form, optical form, etc., or a suitable combination form.
  • the computer storage medium may be any computer-readable medium except the computer-readable storage medium, and the medium may be connected to an instruction execution system, apparatus, or device to communicate, propagate, or transmit a program for use.
  • Program code located on a computer storage medium may be propagated through any suitable medium, including radio, cable, fiber optic cable, RF, or similar media, or any combination of the foregoing.
  • the computer program code required for the operation of each part of this application can be written in any one or more programming languages, including object-oriented programming languages such as Java, Scala, Smalltalk, Eiffel, JADE, Emerald, C ++, C #, VB.NET, Python Etc., conventional programming languages such as C, Visual Basic, Fortran 2003, Perl, COBOL 2002, PHP, ABAP, dynamic programming languages such as Python, Ruby and Groovy, or other programming languages.
  • the program code may run entirely on the user's computer, or as an independent software package on the user's computer, or partly on the user's computer, partly on a remote computer, or entirely on the remote computer or server.
  • the remote computer can be connected to the user's computer through any network, such as a local area network (LAN) or a wide area network (WAN), or connected to an external computer (eg, via the Internet), or in a cloud computing environment, or as a service Use as software as a service (SaaS).
  • LAN local area network
  • WAN wide area network
  • SaaS software as a service
  • Some embodiments use numbers describing the number of components and attributes. It should be understood that such numbers used in embodiment descriptions use the modifiers "about”, “approximately”, or “generally” in some examples. Grooming. Unless otherwise stated, “approximately”, “approximately” or “substantially” indicates that the figures allow a variation of ⁇ 20%.
  • the numerical parameters used in the specification and claims are approximate values, and the approximate value may be changed according to the characteristics required by individual embodiments. In some embodiments, the numerical parameters should consider the specified significant digits and adopt the method of general digit retention. Although the numerical fields and parameters used to confirm the breadth of the ranges in some embodiments of the present application are approximate values, in specific embodiments, the setting of such numerical values is as accurate as possible within the feasible range.

Abstract

La présente invention concerne un procédé et un système de prédiction pour un pronostic de tumeur. Le procédé de prédiction pour un pronostic de tumeur consiste : à acquérir des informations de caractéristique concernant un patient atteint de tumeur, les informations de caractéristique reflétant au moins des informations de mutation de gène concernant le patient atteint de tumeur (410) ; et, sur la base des informations de caractéristique concernant le patient atteint de tumeur et en fonction d'un modèle de prédiction pour un pronostic de tumeur, à déterminer un résultat de prédiction pour un pronostic du patient atteint de tumeur (420). Ledit procédé établit un modèle de prédiction pour un pronostic de tumeur sur la base de données de patient atteint de tumeur, permettant d'améliorer la précision de prédiction pour un pronostic de tumeur.
PCT/CN2018/110565 2018-10-17 2018-10-17 Procédé et système de prédiction pour un pronostic de tumeur WO2020077552A1 (fr)

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Families Citing this family (7)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN110675956A (zh) * 2019-08-26 2020-01-10 南京医渡云医学技术有限公司 一种面瘫治疗方案的确定方法、装置、可读介质及电子设备
CN110993106A (zh) * 2019-12-11 2020-04-10 深圳市华嘉生物智能科技有限公司 一种结合病理图像和临床信息的肝癌术后复发风险预测方法
CN111528918B (zh) * 2020-04-30 2023-02-21 深圳开立生物医疗科技股份有限公司 消融术后肿瘤体积变化趋势图生成装置、设备及存储介质
CN111784637A (zh) * 2020-06-04 2020-10-16 复旦大学附属中山医院 一种预后特征可视化方法，系统、设备和存储介质
CN112397172A (zh) * 2020-12-24 2021-02-23 上海墩庐生物医学科技有限公司 一种乳腺癌生存智能顾问互联网应用系统
CN113345564B (zh) * 2021-05-31 2022-08-05 电子科技大学 一种基于图神经网络的患者住院时长早期预测方法及装置
CN114627969A (zh) * 2022-03-23 2022-06-14 中国医学科学院肿瘤医院 基于补体相关基因的肉瘤患者预后预测模型和试剂盒中的应用

Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN106202969A (zh) * 2016-08-01 2016-12-07 东北大学 一种肿瘤分子分型预测系统
CN106960122A (zh) * 2017-03-17 2017-07-18 晶能生物技术（上海）有限公司 基因突变引起的遗传疾病预测方法及装置
CN107169264A (zh) * 2017-04-14 2017-09-15 广东药科大学 一种复杂疾病诊断方法及系统
CN107341366A (zh) * 2017-07-19 2017-11-10 西安交通大学 一种利用机器学习预测复杂疾病易感位点的方法
CN107833636A (zh) * 2017-12-04 2018-03-23 浙江鸿赋堂健康管理有限公司 一种肿瘤预测方法
CN108416190A (zh) * 2018-02-11 2018-08-17 广州市碳码科技有限责任公司 基于深度学习的肿瘤早期筛查方法、装置、设备及介质

Family Cites Families (13)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US9342657B2 (en) * 2003-03-24 2016-05-17 Nien-Chih Wei Methods for predicting an individual's clinical treatment outcome from sampling a group of patient's biological profiles
EP2499486A4 (fr) * 2009-11-13 2013-11-27 Infinity Pharmaceuticals Inc Compositions, kits, et procédés pour l'identification, l'évaluation, la prévention, et la thérapie d'un cancer
WO2012166700A2 (fr) * 2011-05-29 2012-12-06 Lisanti Michael P Profilage moléculaire d'un microenvironnement de tumeur létale
US20150152474A1 (en) * 2012-03-09 2015-06-04 Caris Life Sciences Switzerland Holdings Gmbh Biomarker compositions and methods
CA2885518A1 (fr) * 2012-09-21 2014-03-27 Integragen Procede pour pronostiquer une survie globale et une survie sans rechute dans un carcinome hepatocellulaire
US20180087114A1 (en) * 2015-03-05 2018-03-29 Trovagene, Inc. Early assessment of mechanism of action and efficacy of anti-cancer therapies using molecular markers in bodily fluid
US20160273049A1 (en) * 2015-03-16 2016-09-22 Personal Genome Diagnostics, Inc. Systems and methods for analyzing nucleic acid
WO2016189525A1 (fr) * 2015-05-27 2016-12-01 Cannabics Pharmaceuticals Inc. Système et procédé de dépistage à haut débit de cellules cancéreuses
US20180246104A1 (en) * 2015-08-18 2018-08-30 Agency For Science, Technology And Research Method for detecting circulating tumor cells and uses thereof
CN115418401A (zh) * 2015-10-08 2022-12-02 会聚基因学有限公司 用于膀胱癌的尿监测的诊断测定
WO2017096248A1 (fr) * 2015-12-02 2017-06-08 Clearlight Diagnostics Llc Procédés de préparation et d'analyse d'échantillons de tissu tumoral pour la détection et la surveillance de cancers
JP2017216882A (ja) * 2016-06-02 2017-12-14 国立大学法人金沢大学 骨障害の存在の検出方法、骨障害治療剤及び骨障害治療剤のスクリーニング方法
CN107545144B (zh) * 2017-09-05 2020-12-29 上海市内分泌代谢病研究所 基于分子标志物的嗜铬细胞瘤转移预测系统

Patent Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN106202969A (zh) * 2016-08-01 2016-12-07 东北大学 一种肿瘤分子分型预测系统
CN106960122A (zh) * 2017-03-17 2017-07-18 晶能生物技术（上海）有限公司 基因突变引起的遗传疾病预测方法及装置
CN107169264A (zh) * 2017-04-14 2017-09-15 广东药科大学 一种复杂疾病诊断方法及系统
CN107341366A (zh) * 2017-07-19 2017-11-10 西安交通大学 一种利用机器学习预测复杂疾病易感位点的方法
CN107833636A (zh) * 2017-12-04 2018-03-23 浙江鸿赋堂健康管理有限公司 一种肿瘤预测方法
CN108416190A (zh) * 2018-02-11 2018-08-17 广州市碳码科技有限责任公司 基于深度学习的肿瘤早期筛查方法、装置、设备及介质

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