WO2008045389A2 - An improved molecular diagnostic and computerized decision support system incorporating bioinformatic software for selecting the optimum treatment for human cancer - Google Patents

An improved molecular diagnostic and computerized decision support system incorporating bioinformatic software for selecting the optimum treatment for human cancer Download PDF

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Publication number
WO2008045389A2
WO2008045389A2 PCT/US2007/021500 US2007021500W WO2008045389A2 WO 2008045389 A2 WO2008045389 A2 WO 2008045389A2 US 2007021500 W US2007021500 W US 2007021500W WO 2008045389 A2 WO2008045389 A2 WO 2008045389A2
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patient
cancer
detector
gene
drugs
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PCT/US2007/021500
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French (fr)
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WO2008045389A3 (en
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Kung Ying Chiu
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Genetics Development Corporation
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    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16B—BIOINFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR GENETIC OR PROTEIN-RELATED DATA PROCESSING IN COMPUTATIONAL MOLECULAR BIOLOGY
    • G16B20/00—ICT specially adapted for functional genomics or proteomics, e.g. genotype-phenotype associations
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16B—BIOINFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR GENETIC OR PROTEIN-RELATED DATA PROCESSING IN COMPUTATIONAL MOLECULAR BIOLOGY
    • G16B20/00—ICT specially adapted for functional genomics or proteomics, e.g. genotype-phenotype associations
    • G16B20/20—Allele or variant detection, e.g. single nucleotide polymorphism [SNP] detection
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16B—BIOINFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR GENETIC OR PROTEIN-RELATED DATA PROCESSING IN COMPUTATIONAL MOLECULAR BIOLOGY
    • G16B25/00—ICT specially adapted for hybridisation; ICT specially adapted for gene or protein expression
    • G16B25/10—Gene or protein expression profiling; Expression-ratio estimation or normalisation
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16B—BIOINFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR GENETIC OR PROTEIN-RELATED DATA PROCESSING IN COMPUTATIONAL MOLECULAR BIOLOGY
    • G16B40/00—ICT specially adapted for biostatistics; ICT specially adapted for bioinformatics-related machine learning or data mining, e.g. knowledge discovery or pattern finding
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16B—BIOINFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR GENETIC OR PROTEIN-RELATED DATA PROCESSING IN COMPUTATIONAL MOLECULAR BIOLOGY
    • G16B50/00—ICT programming tools or database systems specially adapted for bioinformatics
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H20/00—ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance
    • G16H20/10—ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance relating to drugs or medications, e.g. for ensuring correct administration to patients
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
    • G16H50/20—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H70/00—ICT specially adapted for the handling or processing of medical references
    • G16H70/40—ICT specially adapted for the handling or processing of medical references relating to drugs, e.g. their side effects or intended usage
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16B—BIOINFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR GENETIC OR PROTEIN-RELATED DATA PROCESSING IN COMPUTATIONAL MOLECULAR BIOLOGY
    • G16B25/00—ICT specially adapted for hybridisation; ICT specially adapted for gene or protein expression

Definitions

  • the present invention relates to the treatment of humans suffering from disease, and in particular, an improved computerized decision support system and method incorporating bioinformatic software for predicting which of one or more drugs suitable to treat a cancerous condition in a patient are the optimum drug(s), where such selection is based upon the particular patient's genotype.
  • the side effects caused by a particular drug are known to also vary from patient to patient, wherein one patient may experience few if any undesired side effects from receiving a particular drug while another patient may suffer a great number of side effects, some potentially lethal.
  • FISH device manufactured and sold by Vysis/Abbott.
  • Vysis/Abbott In such a prior art system only one gene is measured and correlated to one drug. This system only presents the operator with an indication as to the degree to which the particular gene is amplified within the patient sample.
  • a potentially more significant shortcoming is the fact that the system's detector generates an output which has little, if any, direct meaning to the operator, be they a physician or technician.
  • the physician or technician using such a prior art device must interpret the output generated by the detector to determine whether or not a particular drug, among many typically used, might be suitable for treating a given patient.
  • the operator In order to make such a determination, the operator must take the results of the one or more repeated analysis output by the detector and compare those results to the raw database of information assembled from the multiple clinical trials which have been conducted in the medical community summarizing the results of trials and which specifically indicates which drugs have been proven by trial as being useful in treating patients having a particular genotype.
  • the drug's response to an individual's genotype data normally is not available or has not been established.
  • a particular pharmaceutical can be identified as being optimum for treating a cancerous condition based upon the patient's genotyping where a different pharmaceutical would be identified for a patient having a different genotyping.
  • doctors may prescribe a combination of drugs to treat a given condition, such as breast cancer. Such combinations may lead to further or increased side effects. It is therefore desirable to identify which singular drugs or known combinations of drugs are most effective in treating a given condition.
  • the present invention discloses a computerized decision support system and apparatus for selecting the optimum treatment for a cancerous condition in a human patient.
  • the system comprises a PCR kit and/or a gene chip designed to detect multiple genes, expressions and/or mutations associated with a particular cancer using a sample of the patient's tissue or blood; a detector for accepting receipt of the PCR output and/or gene chip toward analyzing the patient's genotype; a database describing the correlation of patient genotypes and the efficacy and toxicity of various anti-cancer drugs used in treating patients with a particular cancerous condition; and a computerized decision support system operably connected to the detector for correlating the output of the detector to the database. The operator is thereby provided with a definitive recommendation as to which drug or drugs are deemed optimum for treating the patient's cancer;
  • a method for selecting the optimum treatment for a cancerous condition in a human patient comprising the steps of isolating mRNA from a patient's tumor or blood sample with an extraction buffer; synthesizing and amplifying cDNA in a patient's tumor or blood sample with primers highly specific for targeted cancer genes; detecting cancer genes, mutations using a kit and/or a gene chip; analyzing and interpreting PCR and/or gene chip results using a detector linked to a computerized decisions support system running a diagnostic software program with accompanying database for providing an indication of the drug which is optimum for treating the patient's cancer with the least likely chance for a drug interaction.
  • the method or the step of isolating mRNA from a patient's tumor or blood sample comprises the substeps of: homogenizing a sample of the patient's tumor, blood or serum in 1 ml of denaturing solution containing 4M guanidine thiocyanate, 25mM sodium citrate, and 0.1 mM 2-mercaptoethanol; mixing the resultant and homogenizing sequentially with 0.1 ml of 49:1 chloroform/isoamyl alcohol; incubating the resulting mixture for 15 minutes on ice and centrifuging at 10,000 X g for 20 minutes at 4 degrees C; transferring the upper aqueous phase into a new container and mixing with 1 ml of 100% isopropanol; incubating the resulting mixture at -20 degrees C for thirty minutes at 10,000 X g for 10 minutes; washing the resulting pellet with 1 ml of 75% ethanol and redissolving in RNase-free water; and then quantifying the resulting RNA
  • the step of synthesizing and amplifying cDNA in a patient's tumor or blood sample with specific primers for breast cancer genes further comprises the substeps of adding the RNA sample (1 ⁇ g ) into 25 ⁇ g of 2X reaction mix containing 0.4 mM of each dNTP, 2.4mM MgSO 4 , 16 U reverse transcriptase, and 2.5 U Tag DNA polymerase, and 10 ⁇ M cDNA amplification primers for breast cancer genes; adjusting the final solution volume to 50 ⁇ l with autoclaved distilled water; performing cDNA synthesis and amplification using a DNA Thermal Cycler with the following programs, - cDNA synthesis performed at 1 cycle of 45 - 55 degrees C for 20 - 30 minutes, followed by an incubation at 94 degrees C for two minutes, -cDNA amplification performed at 35-40 cycles of 94 degrees C for 15 s (Denature )/55-60 degrees C for 30 s (Anneal)/68-72 degrees C for 1 minute (
  • the breast cancer genes amplified with primers consist of ER Alpha, Her2, ErbB1 , BRCA1 and BRCA2.
  • the step of detecting and analyzing the PCR product further comprises the substeps of resolving the PCR product by electrophoresis in 1.5% agarose gel; visualizing by electrofluorescence; and analyzing the number of PCR fragments using a detector device linked to a computerized decision support system.
  • This system automatically correlates the output of the detector to a database comprising the results of clinical studies testing the efficacy and toxicity of various drugs in treating patients with particular genotypes having breast cancer; and providing an indication of one or more drugs which is optimum for treating the patient's breast cancer with the most effective outcome and the least amount of side effect.
  • the PCR primer pairs comprise:
  • Another embodiment of the invention includes an integrated detector/analyzer which is designed to combine the function of PCR and gene chip reader(s).
  • the output from the integrated detector/analyzer is linked to a computerized decision support system.
  • Fig. 1 of the drawings is a schematic representation of the primary components of the present invention, consisting of a sample preparation buffer, PCR detection kit, SNP gene chip and integrated analyzer;
  • Fig. 2 of the drawings is a schematic representation of the operation of the present invention, including preparing the blood or tissue sample, performing PCR reaction and gene chip hybridization, and detecting and analyzing the results;
  • FIG. 3 of the drawings is a schematic representation of the output of five breast cancer genes and/or mutations amplified on a PCR machine with the specific primers;
  • Fig. 4 of the drawings is a schematic representation of the clinical decision support software used to assist physicians to prescribe the most effective available drugs.
  • FIG. 1 of the drawings illustrates one embodiment of the present invention.
  • the system for selecting the optimum treatment for a cancerous condition in human patient 10 is shown schematically as comprising chemicals and compounds 11 suitable for preparing the patient's blood or tissue sample, a gene chip 12 plated on a glass slide, a PCR kit 13 containing specific primers and reagents for detection of breast cancer genes, and a detector 14 and computer 17 running a bioinformatic software program.
  • a patient's blood or tumor tissue sample is prepared and hybridized with gene chip 12 or amplified with PCR detection kit 13, and then input into receptacle 16 of detector 14.
  • the analysis of the gene chip 12 or PCR reaction is performed by detector 14 and is interpreted by a bioinformatic software package 17 running on computer 18.
  • the output is displayed on monitor 19 that presents the results of the analysis to the doctor in plain language.
  • Fig. 2 of the drawings illustrates the genomic technology used in the present invention.
  • High purity of mRNA 21 is prepared from the patient's blood or tumor tissue 20 with a unified extraction buffer as described by the following example.
  • the tumor tissues or blood cells from a patient are homogenized in 1 ml of denaturing solution containing 4M guanidine thiocyanate, 25mM sodium citrate, and 0.1 mM 2-mercaptoethanol.
  • the momogiate is mixed sequentially with 0.1 ml of 49:1 chloroform/isoamyl alcohol.
  • the resulting mixture is incubated for 15 minutes on ice and centrifuged at 10,000 X g for 20 minutes at 4 0 C.
  • RNA sample 21 is quantified on a spectophotometer at 260 nm and used for the detection of expression or mutation of cancer genes with PCR kit or SNP chip, respectively.
  • the preparation of the blood or tumor tissue samples may be performed manually or alternatively by an automated unit.
  • cDNA is synthesized and amplified in one-step.
  • the RNA sample (1 ⁇ g ) is added into 25 ⁇ g of 2X reaction mix containing 0.4 mM of each dNTP, 2.4mM MgSO 4 , 16 U reverse transcriptase, and 2.5 U Tag DNA polymerase, and 10 ⁇ M cDNA amplification primers for breast cancer genes, Ef? Alpha, Her2, ErbB1, BRCA1 and BRCA2.
  • the final reaction volume is adjusted to 50 ⁇ l with autoclaved distilled water.
  • cDNA synthesis and amplification are performed using a DNA Thermal Cycler with the following programs.
  • A) cDNA synthesis perform 1 cycle of 45 - 55 0 C for 20 - 30 minutes, followed by an incubation at 94 0 C for 2 minutes.
  • the resulting reaction is analyzed with a detector which separates the DNA fragments into different groups according to the size and determines the number of copy in each group.
  • a pre-fabricated disease specific (such as breast cancer, liver cancer or ovarian cancer) SNP gene chip is provided and consists of chemically treated DNA fragments spotted on a plate. These DNA fragments are designed specifically for the detection of gene site mutation related to different cancer development stages and drug response. The preparation of the plate may be significantly different based on commercially available products. A unique technology of the present disclosure relates to specifying the content or what DNAs and/or their fragments are placed on the gene chip.
  • the purified mRNA sample 21 is labeled by direct incorporation of fluorescent Cy3-dUTP (red color) or Cy5 dUTP (green color) in reverse transcription.
  • the sample is hybridized with the oligonucleotides plated on gene chip in a automate hybridization chamber.
  • the chip is then processed into an integrated detector 14 and analyzed for fluorescence intensity which is further converted to the gene mutation pattern and its relevance to drug response using GDC Clinical Decision Support Software (CDSS) 17 (as detailed in Fig. 4).
  • CDSS is running on computer 18.
  • the output is displayed on monitor 19 that presents the results of the analysis to the doctor in plain language.
  • One unique aspect of the present invention is to define patient's genotype by measuring both gene expression and mutation in a combined procedure, and to convert these data to the most appropriate drug therapy.
  • FIG. 3 illustrates the specificity of PCR primers used for the detection of breast cancer genes ERa, Her2, ErbB1 , BRCA1 and BRCA2 by gel electrofluorescence. As illustrated in lanes 1 - 5, numbered 31 , 32, 33, 34 and 35, each contain PCR reactions using individual gene primer pairs. Lane 6 , numbered 36, as illustrated contains PCR reaction using a mixture of all 5 gene primer pairs. The mRNA sample used for PCR reactions is isolated from MCF7, a breast cancer cell line.
  • the detector 14 used in an embodiment has the key components which include more than one sensor, such as immunohistochemistry, fluorescent etc., an interface chip linking the biological genotyping, interface circuit board connecting the detector 14 to computer 18 running a data and bioinformatic software package, a gene chip reader and holder and sample holder(s).
  • the detector 14 is equipped with an interface board (not shown) which serves to electronically connect detector 14 to personal computer 18 which runs a bioinformatic software program.
  • a bioinformatic software package consisting of the correlation, calculation, criterion, and interpretation features which serve to correlate genetic data output from the detector 14 with a database of data toward providing the physician with a recommendation into plain English in order to assist doctors to select the most effective medicine with the least amount of side effect for patients.
  • the interaction or correlation between individual genotyping and medicines is developed from clinical and/or published peer reviewed publications. This software may be further customized for a single disease or multiple diseases.
  • Fig. 4 of the drawings illustrates a flow chart which further describes the bioinformatic software program, including an improved method to determine which from among multiple drugs is best suited to treat a specific cancer - all in a single computerized operation.
  • a physician can no longer depend on the conventional method to prescribe an anticancer drug, which is basically a mere trial and error approach to prescribing a drug for a patient.
  • the clinical decision support system and bioinformatic software of the present invention is designed to aid the physician in making decisions based on the available and affordable information regarding patient diagnosis, a clinical knowledge database, analytical biology models and physicians' empirical experience.
  • Step 40 illustrates data output from the detector 14 provided in terms of gene expression level and gene mutation type. This data is output from the gene detector in terms of gene expression level and gene mutation count for tumor samples (and normal samples, if available). This data output is supplied to a pre-processor 41 which is a module which maps the gene detector results into an algorithm that can be processed with system biology models and gene and drug database, 43 and 42. This module functions to normalize an individual patient's gene expression level by his or her total mRNA from the sample.
  • Gene and drug database 42 is a module which stores the statistical association tables based on public domain or privately conducted clinical trial results.
  • the basic data variables consist of patient genotype and patient drug responses determined over time.
  • the database is of course expected to grow over time with the addition of new patient derived data.
  • the data may be stored by genetic demographics and by reagent batches used for data mapping and data adjustment.
  • the system biology model 43 is the module that stores the multiple genes and multiple drug pathway analytical models at a molecular biology level based on public domain or privately conducted research. This module also stores disease development analytical process at cellular biology and molecular biology level.
  • the analytical processor 45 functions to perform advanced analysis in quantifying gene expression and determine the gene expression cutoff point (GECOP), in the event it has not already been established and stored in the database.
  • the GECOP is established based on the outcome of the clinical significance analysis which is used in separating the positive or effective from the negative or non-effective in the clinical practice.
  • the steps of establishing the GECOP are as follows:
  • Step 1 Test the normality of the normalized tumor sample data, transform data (i.e., Log Normal) if the data is found to be skewed to the right or left.
  • transform data i.e., Log Normal
  • Step 2 Pre-determine a confidence level (i.e., at 90% for over- expression and 10% for under-expression) and compute Z-value to set the GECOP.
  • the percentile method may be applied with a chosen percentile level.
  • the GECOP cutoff value is preferably established near the deflection point (i.e., drastic slope change) of the data curve to maximize sensitivity.
  • Step 3 perform the Statistical Concordance Test (SCT) for the selected gene with an existing "Gold Standard” or method with high certainty, for example, VYSIS/ABBOTTS Her2 FISH method in the clinical test. Accept the GECOP, if the concordance test result is in strong agreement (i.e., correlation coefficient is at 0.75 or higher).
  • SCT Statistical Concordance Test
  • Table 1 illustrates the Normalized Tumor Tissue Gene Expression
  • Table 2 illustrates an implementation of the improved method that takes place in software and specifically showing, in column 4, the ranking of each Sample Number by Normalized Tumor Tissue Gene Expression (NTT) level in increasing order.
  • NTT Normalized Tumor Tissue Gene Expression
  • Optimization processor 46 consists of a number of search algorithms that find the best fit results for the patient using the knowledge contained in the system biology models and gene and drug database or even physician's feedback, if desirable.
  • Report processor 47 provides the computer analysis from the optimization processor 46 in a printout form 49 or on a computer screen 19.
  • Physician interface module 48 provides a physician an opportunity to do 'what-if analysis using the optimization processor 46 based on the physician's empirical experience with his or her medical practices and the patient.
  • Computer 18 and monitor 19 present recommendations as to the optimum drugs based upon a patient genotype to the doctor in an understandable manner, for example, listing the benefits of the drug, the efficacy for the patient's particular genotype, the drug's side effects based upon the patient's genotype and other relevant information.
  • the system isolates mRNA from patient tumor for blood sample with an improved extraction buffer.
  • the system synthesizes and amplifies cDNA in with highly specific primers for five breast cancer genes.
  • the system detects and analyzes PCR product with a detector apparatus linked to a PC running a diagnostic software program with accompanying database for prediction of gene and drug interaction.
  • the PCR Detection of breast cancer genes is accomplished using the following specific gene primer pairs referred to above.
  • a patient's breast tumor sample and/or blood sample is prepared using a test kit according to the present invention, depending upon the assay or detector mode of the sensor used in the detector module.
  • a patient's breast tumor sample is prepared using a one step detection method designed to detect multiple cancer genes to extract the predetermined mRMAs or genes related to the targeted drugs such as Herception, Tamoxifen and Fermera.
  • the prepared test sample with the selected mRMAs is applied to a gene chip or slide.
  • the chip or slide is then placed in the detector sample holder which is, in turn, inserted into the detector apparatus.
  • a bioinformatic software program serves to correlate and calculate the raw signals/data provided by the detector apparatus and will interpret the raw signals/data according to criteria and drug information stored in the system database.
  • the bioinformatic software serves to translate genetic and drug data into plain spoken language (be it in English, Chinese, etc.) in order to assist doctors to select the most effective drug for treating the particular patient's breast cancer.
  • a sample of the raw signal or data generated by the system detector is as follows:
  • the foregoing raw data typically generated by the conventional detector unit is not particularly intuitive and does not readily convey to the physician or technician any direct indication of the drug most appropriate for treating the patient. Accordingly, the present invention preferably provides an output to the user which may consist of a plain language message which reads:
  • Tamoxifen may have resistant if BCL-2 gene is up regulated (use additional kit guide for BCL-2 testing)".
  • a further example of the raw signal or data output by the system detector may be: Mutation Type Expression Level
  • the system according to the present invention preferably generates an output to the user which reads:
  • PCR Products are resolved by electrophoresis.
  • the number of PCR fragments are analyzed with the detector 14 equipped with a fluorescent sensor and which is electronically linked 17 to the system PC.
  • Step 1 calculate the computed copy number of cDNA (data generated with the old batch of reagents) using the new batch standard formulation (i.e., the sDNA Standard Curve in case of Real-Time PCR).
  • the new batch standard formulation i.e., the sDNA Standard Curve in case of Real-Time PCR.
  • Step 2 calculate GECAF by dividing the computed copy number of cDNA with the measured or old copy number with the old batch of reagents.
  • GECAF Expression Compatibility Adjustment Factor
  • the present automated system can be used to identify an optimum drug for treating virtually any disease for which there exists an established correlation between a patient genotype and the efficacy and toxicity of each of a group of drugs developed to treat the general condition.

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Abstract

A computerized decision support system and method for predicting which of one or more drugs suitable to treat a cancerous condition in a patient are the optimum drug(s), where such selection is based upon the particular patient' s genotype. A PCR kit and/or a gene chip detects multiple genes, expressions and/or mutations associated with a particular cancer using a sample of the patient's tissue or blood. A detector accepts the gene chip and analyzes the patient's genotype; and a computerized system utilizing a gene expression cutoff point uses a database which associates patient genotypes and the efficacy and toxicity of various anti-cancer drugs used in treating patients with a particular cancerous condition connected to the detector to correlate the output of the detector to the database to provide a definitive recommendation as to which drug or drugs are optimum for treating the patient's cancer.

Description

TITLE OF THE INVENTION
An Improved Molecular Diagnostic and Computerized Decision Support System Incorporating Bioinformatic Software for Selecting the Optimum Treatment for Human Cancer
BACKGROUND OF THE INVENTION
1. Field of the Invention
[0001] The present invention relates to the treatment of humans suffering from disease, and in particular, an improved computerized decision support system and method incorporating bioinformatic software for predicting which of one or more drugs suitable to treat a cancerous condition in a patient are the optimum drug(s), where such selection is based upon the particular patient's genotype.
2. Prior Art
[0002] It is well known in the medical community that many cancerous conditions suffered by human patients may be treated by a single drug or a combination of drugs. In certain circumstances a medical professional may simply elect to treat a particular patient with one or more drugs selected from the multiple available drugs developed to treat the particular cancerous condition where the selection is made by the physician based upon the best available clinical data. This data may in some cases merely comprise the reported results of clinical trials which suggests that a particular drug has been demonstrated by trial as being effective in treating cancer in a percentage of trial patients who received the drug. This decision making process is consistent with the physician's desire to treat the patient with a regiment of drugs by trial and error which have the best chance of curing the cancerous conditions.
[0003] It is further known that the effectiveness of a particular drug in treating a medical condition, and in particular a cancerous condition, is not necessarily consistent from patient to patient. A drug that works well to treat a condition in one patient may not work at all to treat the very same condition suffered by another patient. In some cases the effectiveness of a particular drug may range from 30 to 70 percent across a patient group. Moreover one or more specific groups of people within the overall population may not even be viable candidates for a particular drug. At the same time, a particular drug if used to treat a cancer may produce unwanted side effects in a patient or may even be toxic to that patient. As with the effectiveness of a drug, the side effects caused by a particular drug are known to also vary from patient to patient, wherein one patient may experience few if any undesired side effects from receiving a particular drug while another patient may suffer a great number of side effects, some potentially lethal.
[0004] Variability relating to both the efficacy and toxicity of a particular cancer drug among the given population is well known to the medical community. Indeed, it is known that patients with a particular genotype are likely to react similarly to particular given drug while patients with a different genotype may react differently to that very same drug. Accordingly, there exists a known database of information which documents these findings - developed typically through clinical trial. Indeed, as new drugs are developed and as existing drugs are used more and more, the database grows.
[0005] There does exist prior art systems which are designed to identify a particular gene as existing within a given patient towards determining the genotype of that patient. In certain prior art systems, a process is used to determine whether a particular patient possesses a particular genotype wherein such analysis is accomplished through the use of detector devices such as the
FISH device manufactured and sold by Vysis/Abbott. In such a prior art system only one gene is measured and correlated to one drug. This system only presents the operator with an indication as to the degree to which the particular gene is amplified within the patient sample.
[0006] In the case of using PCR technique including RT - PCR, there is no known prior art using multiple genes for the determination of the effectiveness of the marketed cancer drugs. [0007] The foregoing described prior art system unfortunately suffers from several potential shortcomings. In particular, such a prior art system may often operate ineffectively given that only one gene's measurement is detected - one drug at a time. In order to detect the presence of multiple gene types within a patient tissue sample that relates to multiple drugs, the process must be repeated again and again for each of the genes and drug combinations. In most cases these combinations are unknown.
[0008] Moreover, a potentially more significant shortcoming is the fact that the system's detector generates an output which has little, if any, direct meaning to the operator, be they a physician or technician. In practice, the physician or technician using such a prior art device must interpret the output generated by the detector to determine whether or not a particular drug, among many typically used, might be suitable for treating a given patient. In order to make such a determination, the operator must take the results of the one or more repeated analysis output by the detector and compare those results to the raw database of information assembled from the multiple clinical trials which have been conducted in the medical community summarizing the results of trials and which specifically indicates which drugs have been proven by trial as being useful in treating patients having a particular genotype. Furthermore, the drug's response to an individual's genotype data normally is not available or has not been established.
[0009] One further shortcoming of the foregoing prior art example is the fact that the system may be subject to error introduced by the need to repeat the process multiple times in order to identify whether or not genes/mutations do or do not exist or are up or down regulated within a patient's sample. It is entirely possible that a technician may contaminate or otherwise mishandle a single tissue sample of among a series of multiple tissue samples where that one defective sample if properly prepared would potentially have indicated and produced the most desirable result. Furthermore, there may not be sufficient tumor samples from a patient to conduct many tests. In such a case, the physician may proceed to prescribe a less than optimum drug for treating the patient all the while being completely unaware that one pass through the detector among the many used to reach the result was defective. [0010] Accordingly, it is an object of the present invention to provide a system which can be used by doctors to identify which pharmaceutical drugs from among several potential choices is indeed the most appropriate to treat a patient's particular medical condition.
[0011] Specifically, it is desirable to determine which pharmaceutical will have the greatest effectiveness with the least potential for causing toxic reaction or other side effects based upon the patient's genotyping. For example, using the present system, a particular pharmaceutical can be identified as being optimum for treating a cancerous condition based upon the patient's genotyping where a different pharmaceutical would be identified for a patient having a different genotyping.
[0012] To address the potential ineffectiveness of a particular pharmaceutical, doctors may prescribe a combination of drugs to treat a given condition, such as breast cancer. Such combinations may lead to further or increased side effects. It is therefore desirable to identify which singular drugs or known combinations of drugs are most effective in treating a given condition.
[0013] These and other desirable characteristics of the invention will become apparent in light of the present specification, including claims, and drawings.
SUMMARY OF THE INVENTION
[0014] The present invention discloses a computerized decision support system and apparatus for selecting the optimum treatment for a cancerous condition in a human patient. The system comprises a PCR kit and/or a gene chip designed to detect multiple genes, expressions and/or mutations associated with a particular cancer using a sample of the patient's tissue or blood; a detector for accepting receipt of the PCR output and/or gene chip toward analyzing the patient's genotype; a database describing the correlation of patient genotypes and the efficacy and toxicity of various anti-cancer drugs used in treating patients with a particular cancerous condition; and a computerized decision support system operably connected to the detector for correlating the output of the detector to the database. The operator is thereby provided with a definitive recommendation as to which drug or drugs are deemed optimum for treating the patient's cancer;
[0015] A method for selecting the optimum treatment for a cancerous condition in a human patient, is also disclosed comprising the steps of isolating mRNA from a patient's tumor or blood sample with an extraction buffer; synthesizing and amplifying cDNA in a patient's tumor or blood sample with primers highly specific for targeted cancer genes; detecting cancer genes, mutations using a kit and/or a gene chip; analyzing and interpreting PCR and/or gene chip results using a detector linked to a computerized decisions support system running a diagnostic software program with accompanying database for providing an indication of the drug which is optimum for treating the patient's cancer with the least likely chance for a drug interaction.
[0016] In one embodiment of the present invention, the method or the step of isolating mRNA from a patient's tumor or blood sample comprises the substeps of: homogenizing a sample of the patient's tumor, blood or serum in 1 ml of denaturing solution containing 4M guanidine thiocyanate, 25mM sodium citrate, and 0.1 mM 2-mercaptoethanol; mixing the resultant and homogenizing sequentially with 0.1 ml of 49:1 chloroform/isoamyl alcohol; incubating the resulting mixture for 15 minutes on ice and centrifuging at 10,000 X g for 20 minutes at 4 degrees C; transferring the upper aqueous phase into a new container and mixing with 1 ml of 100% isopropanol; incubating the resulting mixture at -20 degrees C for thirty minutes at 10,000 X g for 10 minutes; washing the resulting pellet with 1 ml of 75% ethanol and redissolving in RNase-free water; and then quantifying the resulting RNA sample on a spectophotometer at 260 nm and stored at -70 degrees C.
[0017] The step of synthesizing and amplifying cDNA in a patient's tumor or blood sample with specific primers for breast cancer genes further comprises the substeps of adding the RNA sample (1 μg ) into 25 μg of 2X reaction mix containing 0.4 mM of each dNTP, 2.4mM MgSO4, 16 U reverse transcriptase, and 2.5 U Tag DNA polymerase, and 10 μM cDNA amplification primers for breast cancer genes; adjusting the final solution volume to 50 μl with autoclaved distilled water; performing cDNA synthesis and amplification using a DNA Thermal Cycler with the following programs, - cDNA synthesis performed at 1 cycle of 45 - 55 degrees C for 20 - 30 minutes, followed by an incubation at 94 degrees C for two minutes, -cDNA amplification performed at 35-40 cycles of 94 degrees C for 15 s (Denature )/55-60 degrees C for 30 s (Anneal)/68-72 degrees C for 1 minute (Extend), and -final extension performed at 1 cycle of 72 degrees C for 5-10 minutes.
[0018] As disclosed, the breast cancer genes amplified with primers consist of ER Alpha, Her2, ErbB1 , BRCA1 and BRCA2.
[0019] In an embodiment, the step of detecting and analyzing the PCR product further comprises the substeps of resolving the PCR product by electrophoresis in 1.5% agarose gel; visualizing by electrofluorescence; and analyzing the number of PCR fragments using a detector device linked to a computerized decision support system. This system automatically correlates the output of the detector to a database comprising the results of clinical studies testing the efficacy and toxicity of various drugs in treating patients with particular genotypes having breast cancer; and providing an indication of one or more drugs which is optimum for treating the patient's breast cancer with the most effective outcome and the least amount of side effect. [0020] For example, in such embodiment, the PCR primer pairs comprise:
ERa 5'-gctactgtgcagtgtgcaat (F), 5'-tcgtatcccacctttcatca (B); Her2 5'- aggatatccaggaggtgcag (F), 5'-actgctcatggcagcagtca (B); ErbB1 5'- gtggagaactctgagtgcat (F), 5'-cgaggatttccttgttggct (B); BRCA2 5'- ctgtccaggtatcagatgct (F), 5'-atgtgtggcatgacttggca (B); and BRCA1 5'- tagctgatgtattggacgtt (F), 5'-gagatctttggggtcttcag (B).
[0021] Another embodiment of the invention includes an integrated detector/analyzer which is designed to combine the function of PCR and gene chip reader(s). The output from the integrated detector/analyzer is linked to a computerized decision support system.
BRIEF DESCRIPTION OF THE DRAWINGS
[0022] Fig. 1 of the drawings is a schematic representation of the primary components of the present invention, consisting of a sample preparation buffer, PCR detection kit, SNP gene chip and integrated analyzer;
[0023] Fig. 2 of the drawings is a schematic representation of the operation of the present invention, including preparing the blood or tissue sample, performing PCR reaction and gene chip hybridization, and detecting and analyzing the results;
[0024] Fig. 3 of the drawings is a schematic representation of the output of five breast cancer genes and/or mutations amplified on a PCR machine with the specific primers; and
[0025] Fig. 4 of the drawings is a schematic representation of the clinical decision support software used to assist physicians to prescribe the most effective available drugs.
DETAILED DESCRIPTION OF THE DRAWINGS
[0026] While this invention is susceptible of embodiment in many different forms, there is shown in the drawings and will be described herein in detail, a specific embodiment, with the understanding that the present invention is to be considered as an exemplification of the principles of the invention and is not intended to limit the invention to the embodiment illustrated.
[0027] Fig. 1 of the drawings illustrates one embodiment of the present invention. The system for selecting the optimum treatment for a cancerous condition in human patient 10 is shown schematically as comprising chemicals and compounds 11 suitable for preparing the patient's blood or tissue sample, a gene chip 12 plated on a glass slide, a PCR kit 13 containing specific primers and reagents for detection of breast cancer genes, and a detector 14 and computer 17 running a bioinformatic software program.
[0028] A patient's blood or tumor tissue sample is prepared and hybridized with gene chip 12 or amplified with PCR detection kit 13, and then input into receptacle 16 of detector 14. The analysis of the gene chip 12 or PCR reaction is performed by detector 14 and is interpreted by a bioinformatic software package 17 running on computer 18. The output is displayed on monitor 19 that presents the results of the analysis to the doctor in plain language.
[0029] Fig. 2 of the drawings illustrates the genomic technology used in the present invention. High purity of mRNA 21 is prepared from the patient's blood or tumor tissue 20 with a unified extraction buffer as described by the following example. The tumor tissues or blood cells from a patient are homogenized in 1 ml of denaturing solution containing 4M guanidine thiocyanate, 25mM sodium citrate, and 0.1 mM 2-mercaptoethanol. The momogiate is mixed sequentially with 0.1 ml of 49:1 chloroform/isoamyl alcohol. The resulting mixture is incubated for 15 minutes on ice and centrifuged at 10,000 X g for 20 minutes at 4 0C. The upper aqueous phase is transferred into a new tube and mixed with 1 ml of 100% isopropanol. The mixture is incubated at -20 0C for 30 minutes and centrifuged at 10,000 X g for 10 minutes. The resulting pellet is washed with 1 ml of 75% ethanol and redissolved in RNase-free water. The RNA sample 21 is quantified on a spectophotometer at 260 nm and used for the detection of expression or mutation of cancer genes with PCR kit or SNP chip, respectively. The preparation of the blood or tumor tissue samples may be performed manually or alternatively by an automated unit.
[0030] For detection of gene expression using PCR kit 22, cDNA is synthesized and amplified in one-step. The RNA sample (1 μg ) is added into 25 μg of 2X reaction mix containing 0.4 mM of each dNTP, 2.4mM MgSO4, 16 U reverse transcriptase, and 2.5 U Tag DNA polymerase, and 10 μM cDNA amplification primers for breast cancer genes, Ef? Alpha, Her2, ErbB1, BRCA1 and BRCA2. The final reaction volume is adjusted to 50 μl with autoclaved distilled water. cDNA synthesis and amplification are performed using a DNA Thermal Cycler with the following programs.
A) cDNA synthesis: perform 1 cycle of 45 - 55 0C for 20 - 30 minutes, followed by an incubation at 94 0C for 2 minutes.
B) cDNA amplification: perform 35-40 cycles of 94 0C for 15 s (Denature )/55- 60 0C for 30 s (Anneal)/68-72 0C for 1 minute (Extend).
C) Final extension: perform 1 cycle of 72 0C for 5-10 minutes.
[0031] The resulting reaction is analyzed with a detector which separates the DNA fragments into different groups according to the size and determines the number of copy in each group.
[0032] In Figure 1 , for detection of gene mutation 12, a pre-fabricated disease specific (such as breast cancer, liver cancer or ovarian cancer) SNP gene chip is provided and consists of chemically treated DNA fragments spotted on a plate. These DNA fragments are designed specifically for the detection of gene site mutation related to different cancer development stages and drug response. The preparation of the plate may be significantly different based on commercially available products. A unique technology of the present disclosure relates to specifying the content or what DNAs and/or their fragments are placed on the gene chip. [0033] The purified mRNA sample 21 is labeled by direct incorporation of fluorescent Cy3-dUTP (red color) or Cy5 dUTP (green color) in reverse transcription. After labeling, the sample is hybridized with the oligonucleotides plated on gene chip in a automate hybridization chamber. The chip is then processed into an integrated detector 14 and analyzed for fluorescence intensity which is further converted to the gene mutation pattern and its relevance to drug response using GDC Clinical Decision Support Software (CDSS) 17 (as detailed in Fig. 4). CDSS is running on computer 18. The output is displayed on monitor 19 that presents the results of the analysis to the doctor in plain language.
[0034] One unique aspect of the present invention is to define patient's genotype by measuring both gene expression and mutation in a combined procedure, and to convert these data to the most appropriate drug therapy.
[0035] The Fig. 3 illustrates the specificity of PCR primers used for the detection of breast cancer genes ERa, Her2, ErbB1 , BRCA1 and BRCA2 by gel electrofluorescence. As illustrated in lanes 1 - 5, numbered 31 , 32, 33, 34 and 35, each contain PCR reactions using individual gene primer pairs. Lane 6 , numbered 36, as illustrated contains PCR reaction using a mixture of all 5 gene primer pairs. The mRNA sample used for PCR reactions is isolated from MCF7, a breast cancer cell line.
[0036] In Fig. 2 the detector 14 used in an embodiment has the key components which include more than one sensor, such as immunohistochemistry, fluorescent etc., an interface chip linking the biological genotyping, interface circuit board connecting the detector 14 to computer 18 running a data and bioinformatic software package, a gene chip reader and holder and sample holder(s).
[0037] The detector 14 is equipped with an interface board (not shown) which serves to electronically connect detector 14 to personal computer 18 which runs a bioinformatic software program.
[0038] A bioinformatic software package is provided consisting of the correlation, calculation, criterion, and interpretation features which serve to correlate genetic data output from the detector 14 with a database of data toward providing the physician with a recommendation into plain English in order to assist doctors to select the most effective medicine with the least amount of side effect for patients. The interaction or correlation between individual genotyping and medicines is developed from clinical and/or published peer reviewed publications. This software may be further customized for a single disease or multiple diseases.
[0039] Fig. 4 of the drawings illustrates a flow chart which further describes the bioinformatic software program, including an improved method to determine which from among multiple drugs is best suited to treat a specific cancer - all in a single computerized operation. With the increasing technological ability in providing patient diagnostics information at the molecular biology level and the increasing numbers of available patient treatment drugs in the market, a physician can no longer depend on the conventional method to prescribe an anticancer drug, which is basically a mere trial and error approach to prescribing a drug for a patient. The clinical decision support system and bioinformatic software of the present invention is designed to aid the physician in making decisions based on the available and affordable information regarding patient diagnosis, a clinical knowledge database, analytical biology models and physicians' empirical experience.
[0040] The schematic of Fig. 4 illustrates the process and components of the software used to provide the physician with the plain language recommendation as to which drugs to use for a particular patient. Step 40 illustrates data output from the detector 14 provided in terms of gene expression level and gene mutation type. This data is output from the gene detector in terms of gene expression level and gene mutation count for tumor samples (and normal samples, if available). This data output is supplied to a pre-processor 41 which is a module which maps the gene detector results into an algorithm that can be processed with system biology models and gene and drug database, 43 and 42. This module functions to normalize an individual patient's gene expression level by his or her total mRNA from the sample.
[0041] Gene and drug database 42 is a module which stores the statistical association tables based on public domain or privately conducted clinical trial results. The basic data variables consist of patient genotype and patient drug responses determined over time. The database is of course expected to grow over time with the addition of new patient derived data. The data may be stored by genetic demographics and by reagent batches used for data mapping and data adjustment. The system biology model 43 is the module that stores the multiple genes and multiple drug pathway analytical models at a molecular biology level based on public domain or privately conducted research. This module also stores disease development analytical process at cellular biology and molecular biology level.
[0042] The analytical processor 45 functions to perform advanced analysis in quantifying gene expression and determine the gene expression cutoff point (GECOP), in the event it has not already been established and stored in the database. The GECOP is established based on the outcome of the clinical significance analysis which is used in separating the positive or effective from the negative or non-effective in the clinical practice. The steps of establishing the GECOP are as follows:
[0043] Step 1 , Test the normality of the normalized tumor sample data, transform data (i.e., Log Normal) if the data is found to be skewed to the right or left.
[0044] Step 2, Pre-determine a confidence level (i.e., at 90% for over- expression and 10% for under-expression) and compute Z-value to set the GECOP. For non-parametric data, the percentile method may be applied with a chosen percentile level. The GECOP cutoff value is preferably established near the deflection point (i.e., drastic slope change) of the data curve to maximize sensitivity.
[0045] Step 3, perform the Statistical Concordance Test (SCT) for the selected gene with an existing "Gold Standard" or method with high certainty, for example, VYSIS/ABBOTTS Her2 FISH method in the clinical test. Accept the GECOP, if the concordance test result is in strong agreement (i.e., correlation coefficient is at 0.75 or higher). There are a number of statistical correlation analysis methods such as Chi-square, Spearman, Pearson and Contingency Table etc. that can be applied for qualitative data analysis. However, for quantitative data, Lin's Test is a preferred method.
[0046] Table 1 illustrates the Normalized Tumor Tissue Gene Expression
(NTT) levels as measured for twenty (20) tissue samples.
Figure imgf000015_0001
Table 1
[0047] Table 2 illustrates an implementation of the improved method that takes place in software and specifically showing, in column 4, the ranking of each Sample Number by Normalized Tumor Tissue Gene Expression (NTT) level in increasing order. The Log value of each ranked NTT value is shown in column 5 where the break or transition can be clearly seen.
GECOP
Figure imgf000016_0001
Table 2
[0048] Accordingly, with a P- Value of 90% the Z- Value (Log10 NTT) equates to 3.38659196, as shown in Table 3. Thus, samples returning a positive value, namely greater than the GECOP value, are identified by the software implemented the foregoing method as being suitable for treating a particular patent's cancerous condition. Conversely, samples returning a negative or noneffective value, less than the GECOP, are identified by the software implemented the foregoing method as not being suitable for treating a particular patent's cancerous condition.
Figure imgf000016_0002
Table 3 [0049] In the event if there is no existing standard available for validating the clinical significance of the GECOP, a standard is hereby proposed based on the results of association studies between the chosen gene and the patient response to the gene targeted therapies. GECOP has utility in clinical practice, if and only if, it can pass the concordance test with an existing standards or a standards.
[0050] Optimization processor 46 consists of a number of search algorithms that find the best fit results for the patient using the knowledge contained in the system biology models and gene and drug database or even physician's feedback, if desirable. Report processor 47 provides the computer analysis from the optimization processor 46 in a printout form 49 or on a computer screen 19.
[0051] Physician interface module 48 provides a physician an opportunity to do 'what-if analysis using the optimization processor 46 based on the physician's empirical experience with his or her medical practices and the patient. Computer 18 and monitor 19 present recommendations as to the optimum drugs based upon a patient genotype to the doctor in an understandable manner, for example, listing the benefits of the drug, the efficacy for the patient's particular genotype, the drug's side effects based upon the patient's genotype and other relevant information.
Detailed description of the operation of the present system and method.
[0052] In the first step, the system isolates mRNA from patient tumor for blood sample with an improved extraction buffer. In the second step, the system synthesizes and amplifies cDNA in with highly specific primers for five breast cancer genes. Lastly, the system detects and analyzes PCR product with a detector apparatus linked to a PC running a diagnostic software program with accompanying database for prediction of gene and drug interaction.
[0053] In the embodiment of the present invention directed to selecting drugs to treat breast cancer, the PCR Detection of breast cancer genes is accomplished using the following specific gene primer pairs referred to above.
Gene Primer Predicted Size
ERa 5'-gctactgtgcagtgtgcaat (F) 202 bp
5'-tcgtatcccacctttcatca (B)
Her2 5'-aggatatccaggaggtgcag (F) 416 bp 5'-actgctcatggcagcagtca (B)
ErbB1 5'-gtggagaactctgagtgcat (F) 603 bp
5'-cgaggatttccttgttggct (B)
BRCA2 5'-ctgtccaggtatcagatgct (F) 799 bp
5'-atgtgtggcatgacttggca (B) BRCA1 5'-tagctgatgtattggacgtt (F) 1024 bp
5'-gagatctttggggtcttcag (B)
[0054] A patient's breast tumor sample and/or blood sample is prepared using a test kit according to the present invention, depending upon the assay or detector mode of the sensor used in the detector module. A patient's breast tumor sample is prepared using a one step detection method designed to detect multiple cancer genes to extract the predetermined mRMAs or genes related to the targeted drugs such as Herception, Tamoxifen and Fermera.
[0055] The prepared test sample with the selected mRMAs is applied to a gene chip or slide. The chip or slide is then placed in the detector sample holder which is, in turn, inserted into the detector apparatus. A bioinformatic software program serves to correlate and calculate the raw signals/data provided by the detector apparatus and will interpret the raw signals/data according to criteria and drug information stored in the system database. The bioinformatic software serves to translate genetic and drug data into plain spoken language (be it in English, Chinese, etc.) in order to assist doctors to select the most effective drug for treating the particular patient's breast cancer.
[0056] A sample of the raw signal or data generated by the system detector is as follows:
Gene Type Expression Level ErbB2 Up regulated (1.5)
ErbB1 Up regulated (1.6)
Er Alpha Down regulated (0.8)
BRCA1 Up regulated (2.0)
BRCA2 Down regulated (0.5)
[0057] As illustrated, the foregoing raw data typically generated by the conventional detector unit is not particularly intuitive and does not readily convey to the physician or technician any direct indication of the drug most appropriate for treating the patient. Accordingly, the present invention preferably provides an output to the user which may consist of a plain language message which reads:
"Recommendation: May use Fermera and/or Herceptin. Tamoxifen may have resistant if BCL-2 gene is up regulated (use additional kit guide for BCL-2 testing)".
[0058] A further example of the raw signal or data output by the system detector may be: Mutation Type Expression Level
Val335Leu Up regulated
Glu386Ter Up regulated
[0059] The system according to the present invention preferably generates an output to the user which reads:
"Recommendation: May not use 5-FU due to DPD protein related toxicity."
[0060] To that end, the detection and analysis of PCR Products is accomplished whereby the PCR products are resolved by electrophoresis. The number of PCR fragments are analyzed with the detector 14 equipped with a fluorescent sensor and which is electronically linked 17 to the system PC.
[0061] It is known and common best practice within modern laboratory environments to compensate for reagent batch variances. One embodiment of the present invention addresses such compensation in noting that it is essential that the value pertaining to the new gene expression level data to be examined for compatibility with the existing gene expression data in the database.
[0062] One method for calculating a Gene Expression Compatibility
Adjustment Factor (GECAF) to compensate for reagent batch variance is illustrated as follows:
[0063] Step 1 , calculate the computed copy number of cDNA (data generated with the old batch of reagents) using the new batch standard formulation (i.e., the sDNA Standard Curve in case of Real-Time PCR).
[0064] Step 2, calculate GECAF by dividing the computed copy number of cDNA with the measured or old copy number with the old batch of reagents.
[0065] Perform a pair-t test between the computed and measured copy numbers at 95% confidence level, if the null hypothesis is rejected, the measured or old data must be adjusted by the average GECAF. [0066] The following table illustrates an example of the foregoing Gene
Expression Compatibility Adjustment Factor (GECAF).
Figure imgf000021_0001
[0067] It is a further aspect of the present invention to use the present method and apparatus to predict or identify the optimum drug for treating cancers other than breast cancer. Indeed, the present automated system can be used to identify an optimum drug for treating virtually any disease for which there exists an established correlation between a patient genotype and the efficacy and toxicity of each of a group of drugs developed to treat the general condition.
[0068] Accordingly, the foregoing description and drawings merely explain and illustrate the invention, and the invention is not limited except insofar as the appended claims are so limited, as those skilled in the art who have the disclosure before them will be able to make modifications and variations therein without departing from the scope of the invention.

Claims

We Claim:
-1-
An improved computerized decision support system and apparatus incorporating bioinformatic software for selecting the optimum treatment for a cancerous condition in a human patient, the apparatus comprising:
- a PCR kit and/or a gene chip designed to detect expressions and/or mutations of multiple genes associated with a particular cancer using patient's tissue or blood samples;
- an integrated detector for analyzing both PCR and gene chip results.
- a detector for accepting receipt of the gene chip toward analyzing the patient's genotype;
- a database describing the correlation of patient genotypes and the efficacy and toxicity of various anti-cancer drugs used in treating patients with a particular cancerous condition; and
- a computerized decision support system operably connected to the detector for correlating the output of the detector to the database, said decision support system including bioinformatic software programming for establishing a gene expression cutoff point used to identify a drug best suited to treat a specific cancer from among multiple drugs;
- whereby the operator is provided with a definitive recommendation as to which drug or drugs are deemed optimum for treating the patient's cancer.
-2-
A method for screening a drug response genotype database toward determining the optimum medication for a particular genotype, wherein multiple gene types are detected within a single patient sample and a medication best suited to treat a specific cancer is identified from among multiple medications in a single computerized operation, the method comprising:
- preparing PCR kit and gene chip;
- isolating mRNA from a patient's tumor or blood sample with an extraction buffer; - synthesizing and amplifying cDNA in a patient's tumor or blood sample with primers highly specific for targeted cancer genes;
- detecting cancer genes and mutations using a gene chip and/or PCR;
- analyzing and interpreting PCR and/or gene chip results using a detector linked to a computerized decision support system running a diagnostic software program with accompanying database for providing an indication of the drug which is optimum for treating the patient's cancer with the least likely chance for a drug side effect, and
- the step of interpreting the PCR and/or gene chip results including establishing a gene expression cutoff point to identify one or more medications best suited to treat a specific cancer is identified from among multiple medications, the steps including:
- testing the normality of the normalized tumor tissue gene expression level sample data;
- transforming the sample data (i.e., Log Normal) if the data is found to be skewed;
- predetermining a confidence level and computing a Z-value to establish the gene expression cutoff point .
- performing a statistical concordance test for the detected gene with an existing standard in the clinical test.
- accepting the gene expression cutoff point if the concordance test result is in strong agreement; and
- providing the operator with a definitive recommendation as to which drug or drugs are optimum for treating the patient's cancer based upon the gene expression cutoff point as applied to the normalized tumor tissue gene expression levels.
-3-
A method using a clinical computerized decision support system for selecting the optimum medication to prescribe to a patient suffering from breast cancer, the method comprising: - combining a gene chip with PCR measurements for the detection of particular breast cancer genes in a sample of the patient's tissue or blood;
- optically inspecting the resulting chemical and biological reaction using a automated detector;
- establishing a gene expression cutoff point to identify drugs best suited to treat a specific cancer from among multiple drugs
- correlating using the software output from the integrated detector with a disease database comprising the results of clinical studies testing the efficacy and toxicity of various drugs in treating patients with particular genotypes having breast cancer; and
- providing an indication of one or more drugs which are deemed optimum for treating the patient's breast cancer with the most effective outcome and the least amount likelihood of undesirable side effect based upon the gene expression cutoff point as established.
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CN115295116B (en) * 2022-08-04 2023-09-19 上海康黎医学检验所有限公司 Medication review method, system and electronic device

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