阅读说明：本技术 预测患有肝细胞癌(hcc)或胆管癌(cca)的受试者的生存结果的方法 (Method of predicting survival outcome in a subject with hepatocellular carcinoma (HCC) or cholangiocarcinoma (CCA) ) 是由 朱拉蓬·玛希敦公主殿下 马图罗斯·鲁奇拉瓦特 吉蒂珀恩·沙伊桑蒙空 斯蒂芬·梅里尔·休伊特 于 2017-05-29 设计创作，主要内容包括：本公开提供用于产生受肝癌影响的受试者的预测性生存结果的方法。该方法优选地包括提供受试者的组织样本,该组织样本为肝细胞癌或胆管癌中的一种；进行基于核酸和/或基于蛋白质组学的试验中的至少一种,以量化所提供的样本中的polo样激酶1(PLK1)和上皮细胞转化2(ECT2)的转录组和/或蛋白质表达；推导出所提供的样本上限定的区域内PLK1表达与ECT2表达的比率；和根据所推导出的比率产生受试者的预测性生存结果,当推导出的比率小于预定值时,认为受试者具有第一预测性生存结果,或当推导出的比率等于或大于预定值时,认为受试者具有第二预测性生存结果。第一预测性生存结果对应于生存分析中预定时间段内大于40％-60％的总生存百分比,并且第二预测性生存结果对应于生存分析中预定时间段内40％-60％或更低的总生存百分比。(The present disclosure preferably includes providing a tissue sample of the subject, the tissue sample being of hepatocellular carcinoma or cholangiocarcinoma, performing at least of nucleic acid-based and/or proteomic-based assays to quantify transcriptome and/or protein expression of polo-like kinase 1(PLK1) and epithelial cell transformation 2(ECT2) in the provided sample, deriving a ratio of PLK1 expression to ECT2 expression within a defined region on the provided sample, and generating a predictive survival result for the subject according to the derived ratio, the subject being considered to have a predictive survival result when the derived ratio is less than a predetermined value, or a second predictive survival result when the derived ratio is equal to or greater than the predetermined value, the predictive survival result corresponding to a total survival percentage greater than 40% -60% within a predetermined time period in the analysis, and the second survival result corresponding to a total survival percentage greater than 40% -60% or less within the predetermined time period in the analysis.)

1, a method for producing a predictive survival outcome for a subject affected by liver cancer, the method comprising:

providing tissue samples of the subject, the tissue samples being of hepatocellular carcinoma or cholangiocarcinoma;

performing at least of nucleic acid-based and/or proteomic-based assays to quantify transcriptome and/or protein expression of polo-like kinase 1(PLK1) and epithelial cell transformation 2(ECT2) in the provided sample;

deriving the ratio of expression of PLK1 to expression of ECT2 within the defined region on the provided sample; and

generating the predictive survival outcome for the subject from the derived ratio, the subject being considered to have a th predictive survival outcome when the derived ratio is less than a predetermined value or a second predictive survival outcome when the derived ratio is equal to or greater than a predetermined value,

wherein said th predictive survival outcome corresponds to a percentage of total survival in said survival assay that is greater than 40% -60% over a predetermined time period, and said second predictive survival outcome corresponds to a percentage of total survival in said survival assay that is 40% -60% or less over said predetermined time period.

2. The method of claim 1, wherein the predetermined period of time is from 12 to 36 months.

3. The method of claim 1, wherein the nucleic acid-based assay is any or a combination of microarray-based and polymerase chain reaction-based techniques.

4. The method of claim 1, wherein the proteomics-based assay is tissue microarray and/or immunohistochemical staining.

5. The method of claim 1, wherein the subject is of Asian descent.

A device for producing a predictive survival outcome for a subject affected by liver cancer, the device comprising:

module , the module being capable of detecting transcriptome and/or protein expression of polo-like kinase 1(PLK1) and epithelial cell transformation 2(ECT2) in a tissue sample of the subject, and

a second module configured to derive a ratio of PLK1 expression to ECT2 expression within a defined region on a provided sample and to generate the predictive survival outcome for the subject from the derived ratio, the second module configured to consider the subject as having a th predictive survival outcome when the derived ratio is less than a predetermined value or a second predictive survival outcome when the derived ratio is equal to or greater than the predetermined value,

wherein said th predictive survival outcome corresponds to a percentage of total survival in said survival assay that is greater than 40% to 60% over a predetermined time period, and said second predictive survival outcome corresponds to a percentage of total survival in said survival assay that is 40% -60% or less over said predetermined time period.

7. The device of claim 6, further comprising a test module capable of performing at least of a nucleic acid-based assay or proteomics-based assay to couple the expressed transcriptome or protein of PLK1 and ECT2 with a signaling moiety formed to continuously or periodically emit a signal detectable by the module.

8. The device of claim 6, wherein the predetermined period of time is 12 to 36 months.

9. The device of claim 7, wherein the nucleic acid based assay is any or a combination of microarray based and polymerase chain reaction based techniques.

10. The device of claim 7, wherein the proteomics-based assay is a tissue microarray, and/or immunohistochemical staining.

11. The device of claim 6, wherein the subject is of Asian descent.

12. The device of claim 6, wherein the tissue sample of the subject is cancer and is of hepatocellular carcinoma or cholangiocarcinoma.

Technical Field

The present disclosure relates to methods for generating a predictive survival outcome for a subject having liver cancer. More specifically, predictive survival outcomes were generated based on the expression profiles of two genes or proteins, polo-like kinase 1(PLK1) and epithelial cell transformation 2(ECT2) exhibited in cancer tissue samples obtained from the subjects. The present disclosure also provides devices equipped for generating predictive survival outcomes by the disclosed methods.

Background

Primary liver cancer consists of two major histologically distinct subtypes, hepatocellular carcinoma (HCC) and intrahepatic cholangiocarcinoma (CCA), the diagnosis and treatment decision of which is only based on their baseline clinical characteristics. -generalized tumor heterogeneity of HCC or CCA is attributed to the presence of complex, multi-factorial causes, including environmental factors such as Hepatitis B Virus (HBV), Hepatitis C Virus (HCV), parasitic infections, and chemical carcinogens³Among them, liver cancer mainly affects men and is very common in Asian population (http:// globocan. iarc. fr. /). HBV and HCV are the major causative factors of HCC, accounting for up to 90% of liver cancers worldwide, while CCA is not common except in southeast asia, such as northern eastern thailand, liver fluke (o.viverrini) infection is endemic and about 60% of liver cancers are CCA⁴ hypotheses that various pathogenic factors can induce different molecular mechanisms to independently initiate malignant transformation, which leads to intratumoral genomic heterogeneity, like many other solid cancers , each type of biological and genetic heterogeneity of the noted HCC and CCA means that they are highly resistant to treatment, listing them as the second most lethal malignancy worldwide^1,2. Therefore, efforts have been made to identify potential genes or proteins expressed in driving cancer progression so that patients can better stratify according to the identified cancer drivers, thus facilitating treatment by precise medicine.

For example, Chen et al⁴⁵ECT2 expression was found to be closely associated with promoting activation of the Rho/ERK signaling axis in the recurrence of early HCC. Sun et al⁴⁶Another genes PLK1 were also shown to be significantly more highly expressed in HCC samples and could be independent prognostic factors for HCC suitable for diagnosis and treatment⁴⁷Similar conclusions were drawn in their publications, supporting the discovery of Sun et al 46. researchers have taken advantage of the association established between liver cancer and PLK1 or ECT2 expression, and have further thought of several diagnostic or prognostic approaches.A DNA biochip for HCC diagnosis by scanning multiple gene markers including PLK1 is disclosed, for example, in U.S. Pat. publication No. 2011/03119280. most of the foregoing methods have focused on the correlation of cancer prognosis with individual expression of candidate genes or proteins, rather than the in-depth information generated from the correlation between candidate genes or proteins, although deriving diagnostic results based on the correlation of multiple expressed candidate genes has been proposed in some publications related to other cancer types. in their U.S. patent publication No. 2012/0197540 Somma et al have provided a method for determining the presence of a marker in a tissue sample from a cancer patient by measuring PLK1 and 2 in their tissue sampleMethods of predicting the risk of mortality in breast cancer patients by the expression levels of a plurality of genes; the method then calculates a prognostic score indicative of the risk of mortality of the subject by a predetermined algorithm using each measured gene expression level. However, no similar method or platform has been disclosed for the prognosis or diagnosis of liver cancer using the correlation of co-expressed genes or proteins. Prognostic outcomes derived from such methods or platforms should be suitable for promoting higher therapeutic outcomes or assigning medical resources.

Disclosure of Invention

The present disclosure relates to methods for measuring or quantifying expression of PLK1 and ECT2 in a tissue sample or specimen obtained from a subject.

aims of the present disclosure are to provide methods of using PLK1 and ECT2 expression measured or quantified in tissue samples to produce a prognostic or diagnostic result for a subject affected by HCC or CCA. the prognostic or diagnostic result generally relates to a predictive survival or risk of death of the subject.

It is another objects of the present disclosure to provide devices configured to quantify the expression of PLK1 and ECT2, preferably in a fully or semi-automated manner, and then produce a prognostic or diagnostic result relative to the medical condition of a subject affected by liver cancer.

At least of the foregoing objects are met in whole or in part by the present invention, wherein of the embodiments include a method for generating a predictive survival outcome for a subject affected by liver cancer, the method comprising providing a tissue sample of the subject, the tissue sample comprising or being of hepatocellular carcinoma or cholangiocarcinoma, performing at least of nucleic acid-based and/or proteomics-based assays to quantify the transcriptome and/or protein expression of polo-like kinase 1(PLK1) and epithelial cell transformation 2(ECT2) in the provided sample, deriving a ratio of PLK1 expression to ECT2 expression over a defined region on the provided sample, and generating a predictive survival outcome for the subject according to the derived ratio, the subject being considered to have a predictive survival outcome when the derived ratio is less than a predetermined value, or the subject being considered to have a second survival outcome when the derived ratio is equal to or greater than a predetermined value, more specifically corresponds to a 40% -60% predictive survival outcome in a survival analysis and a total survival time period of 40% -60% to a predetermined month or less general survival period, but not limited to 12% total survival period.

In many embodiments, the nucleic acid-based assay is any or a combination of microarray-based techniques and polymerase chain reaction-based techniques including digital PCR.

In several embodiments, the proteomics-based assays include a set of tissue microarrays and immunohistochemical staining of human tissue.

In another aspect of the disclosure, an apparatus for generating a predictive survival result for a subject having liver cancer is disclosed, the apparatus comprising a th module capable of identifying, detecting, or determining transcriptome and/or protein expression of polo-like kinase 1(PLK1) and epithelial cell transformation 2(ECT2) in a tissue sample of the subject, and a second module configured to derive a ratio of PLK1 expression to ECT 563 expression in a defined region on the provided sample and generate a predictive survival result for the subject according to the derived ratio, the second module configured to consider the subject to have a th predictive survival result when the derived ratio is less than a predetermined value, or to consider the subject to have a second predictive survival result when the derived ratio is equal to or greater than a predetermined value, more particularly, the th predictive survival result corresponds to a total survival percentage greater than 40% for a predetermined time period in a predictive time analysis, and the second survival result corresponds to a total survival percentage of 40% or less for a predetermined time period in a survival analysis or preferably a total survival percentage based on a combination of nucleic acid based on a PCR technique such as a PCR and a PCR based on a PCR.

For the various embodiments, the disclosed device further includes a test module capable of performing at least of a nucleic acid-based assay or proteomics-based assay to couple the expressed transcriptome or protein of PLK1 and ECT2 with a signaling moiety formed to continuously or periodically emit a signal detectable by the module.

In some embodiments, the subject is preferably of Asian descent for greater accuracy of the results, hi addition, the tissue sample of the subject includes or is of hepatocellular carcinoma or cholangiocarcinoma.

Drawings

FIG. 1 shows the results of consistency clustering using a hierarchical clustering algorithm, left graph of empirical Cumulative Distribution (CDF) plots of consistency matrices corresponding to a cluster number (K) in the range of 2-8, middle graph of corresponding CDF lower area variation, right graph of consistency matrices corresponding to a cluster number K in the range of 2 and 5.

FIG. 2(a) is a heat map of CCA subtypes based on causal and hierarchical clustering of the most variable genes, the x-axis representing CCA subtype causal cluster, CCA samples represented in columns, grouped into 4 main clusters by dendrograms, and genes represented in rows, identified by t-test as the expression of 1189 genes differentially expressed between C1 and C2 (FDR)<0.05, fold change>2) Log shown at-3 to 3₂Performing the following steps; (b) is based on 1020 differentially expressed genes (FDR) as shown in (a)<0.05, fold change>2) And (c) is a subclass mapping of CCA and HCC subtypes, the significant relationship between clusters is represented by Bonferroni adjusted p-values;

figure 3 shows a VENN plot indicating the basis factors for differential expression (FDR <0.05, fold change >2) between the CCA and HCC C1 or C2 samples defined by student T-test, the left plot showing the number of gene upregulations in C1 of CCA (n-578) and HCC (n-656) and the overlapping gene (n-218), where the probability of overlapping genes is statistically significant (p-3.20 x 10-97; hyper-geometric distribution test), and the right plot showing similar results for C2 upregulated genes;

FIG. 4 shows a Kaplan-Meier survival analysis of a CCA subtype (top panel) or an HCC subtype (bottom panel);

figure 5 shows significant pathways identified by GSEA analysis for HCC or CCA C1 or C2 subtypes, represented by log10 p-values from 2 to 0 (p-values from 0.01 to 1);

figure 6 shows a comparison between (a) thailand CCA subtype and published signatures indicated on the y-axis, each column representing a tumor sample, the positives of each signature represented by gray bars, negatives represented by black bars, and cases with FDR >0.05 represented in white, and (b) thailand HCC subtype and published signatures indicated on the y-axis;

figure 7 shows (a) the frequency of chromosomal aberrations for HCC (upper panel) or HCC with frequency > 30% (lower panel), and (b) similar results for CCA;

FIG. 8 shows (a) the frequency of chromosomal aberrations for CCA C1 subtype (top panel) or CCA C2 subtype (bottom panel), the copy number gain or loss being shown in light gray or black, respectively, and (b) the frequency of chromosomal aberrations for HCC C1 subtype (top panel) or HCC C2 subtype (bottom panel);

FIG. 9 shows the relationship between high sex genes and tumor subtypes and the frequency of samples with Copy Number Variation (CNV);

FIG. 10 is a Kaplan-Meier plot of all 378 HCC cases;

figure 11 shows representative images of CCA and HCC cases at 200x magnification based on immunohistochemical staining of PLK1 (left panel) or ECT2 (right panel);

figure 12 reveals the correlation of (a) PLK1 (left panel) or ECT2 (right panel) array expression and TMA score in CCA cases (top panel) or HCC cases (bottom panel), and (b) PLK1 and ECT2 array expression or TMA score in CCA or HCC cases;

figure 13 shows the results of Kaplan-Meier survival analysis of CCA cases (left panel) or HCC cases (right panel) based on median cut-off of PLK or ECT 2;

FIG. 14 shows (a) Kaplan-Meier survival analysis of all CCA and HCC cases based on the ratio combination of protein expression (ECT2/PLK1), low and high cutoffs defined by ECT2/PLK1 ratio ≦ 1 (for the low group) and >1 (for the high group), and (b) Kaplan-Meier survival analysis of HCC cases as in (a);

figure 15 shows (a) thailand HCC with chinese HCC subtype, (b) asian american (AsA) subtype, (c) thailand CCA with japanese CCA subtype, (d) subclass mapping of thailand HCC with European American (EA) HCC subtype and related Kaplan-Meier survival analysis, and (e) subclass mapping of thailand CCA with caucasian CCA subtype, the significant relationship between clusters is represented by Bonferroni-adjusted p values of 0 to 1, and the subtype is represented by group (cohort), no matching subtypes in the thailand group are represented by X, and the significant association between clusters is represented in light gray, p < 0.05;

fig. 16 shows boxplots of BMI with median and standard deviation for subtypes C1 and C2 of cases of thailand HCC, asian american HCC, and thailand CCA;

fig. 17 shows (a) a heat map representing the correlation of metabolite abundance and gene expression in CCA samples, and (b) a heat map showing the overall correlation of metabolite abundance and gene expression in HCC samples, with light gray and black bars indicating positive or negative correlations based on pearson R values of-1 to 1, respectively;

FIG. 18 shows (a) hierarchical clustering of 81 metabolites to separate HCC-C1 (light gray bars) and C2 cases (black bars), and (b) hierarchical clustering of 77 metabolites to separate CCA-C1 (light gray bars) and C2 cases (black bars), with samples represented by columns, metabolites represented by rows, and metabolite abundances represented by log 2;

FIG. 19 shows (a) an innovative pathway analysis of a metabolite/gene network resulting from height , and (b) an innovative pathway analysis of a metabolite/gene network resulting from height , with up-regulated metabolites of subtype C1 or subtype C2 being shown in light and dark grey, respectively;

fig. 20 shows a boxplot of the abundance of three representative bile acid-related metabolites in (a) C1 (n-15) and C2 (n-14) HCC samples with student T-test p-values, and (b) C1 (n-33) and C2 (n-18) CCA samples, shown with student T-test p-values;

figure 21 shows (a) a CIBERSORT analysis of HCC C1 with HCC C2 subtype, and (b) a CIBERSORT analysis of CCA C1 with CCA C2 subtype, with high to low associations between cell types ranked 1 to-1, and the size of the circles indicating significance of the association, with larger circles indicating higher significance;

fig. 22 shows a boxplot of the abundances of three leukocyte types in (a) C1 and C2 HCC samples with standard deviation and student T-test p-values, and (b) C1 and C2 CCA samples with standard deviation and student T-test p-values.

Detailed Description

Hereinafter, representative or preferred embodiments according to the present invention will be described and the present invention will be described by referring to the attached specification and drawings. It should be understood, however, that the description and drawings corresponding to such embodiments are for clarity and understanding, and it is contemplated that modifications may be devised by those having ordinary skill in the relevant art without departing from the scope of the present invention as defined by the appended claims.

The term "gene" as used herein may refer to a DNA sequence having a functional meaning. It may be a natural nucleic acid sequence, or a recombinant nucleic acid sequence derived from natural sources or synthetic structures. The term "gene" may also be used to refer to, for example, but not limited to, cDNA and/or mRNA encoded directly or indirectly by or derived from a genomic DNA sequence.

The term "transcriptome" as used herein means a collection of RNA transcripts transcribed in a specific tissue, whether coding or non-coding, and preferably comprising all or substantially all of the RNA transcripts produced in that tissue. These transcripts include, among a wide range of other transcripts, messenger RNA (mRNA) that is not translated into protein, alternatively spliced mRNA, ribosomal RNA (rrna), transfer RNA (trna), such as small nuclear RNA (snrna), antisense molecules such as short interfering RNA (sirna), and microrna, as well as other RNA transcripts of unknown function. Transcriptome may also refer to proteins translated from an RNA transcript as an extension of gene transcription.

Basically, the method includes the steps of providing a tissue sample from a subject affected by of hepatocellular carcinoma or cholangiocarcinoma, conducting at least of nucleic acid-based and/or proteomics-based assays to quantify transcriptome and/or protein expression of polo-like kinase 1(PLK1) and epithelial cell transformation 2(ECT2) in the provided sample, deriving a ratio of PLK1 expression to ECT2 expression within a defined region on the provided sample, and generating a predictive survival result for the subject according to the derived ratio, the subject being considered to have a predictive survival result of when the derived ratio is less than a predetermined value, or the subject being considered to have a second predictive survival result when the derived ratio is equal to or greater than a predetermined value.

For example, a tissue sample obtained by laparoscopy is more suitable for proteomics-based assays, such as tissue microarrays, which require sufficient area to be prepared in the sample for immunochemical staining and signal detection.

For many embodiments, the nucleic acid-based assays are microarray-based techniques and polymerase chain reaction-based techniques, including any or combination of digital PCR nucleic acid-based assays generally involve extracting total mRNA or transcriptome from a tissue sample of predetermined size, followed by selective quantification of mRNA or transcriptome associated with expression of PLK1 and/or ECT2 with or without selective amplification of mRNA or transcriptome of PLK 3578 and/or ECT2 preferably the transcriptome of PLK1 and/or ECT2 hybridizes on a substantially complementary probe anchored on a platform.

Similarly, for various embodiments, proteomic-based assays are tissue microarrays of human tissue and immunohistochemical staining through which expressed proteins or peptides of PLK1 and/or ECT2 are selectively coupled to or more signal transduction moieties, followed by detection of signal transduction released from the coupled signal transduction moieties such that quantitative PLK1 and/or ECT2 expression is achieved, for example, in Tissue Microarrays (TMAs), the obtained tissue samples are used to prepare formalin-fixed and paraffin-embedded tissue samples of 0.5-2.0mm nuclear in the form of slides for immunohistochemical processing of 1 to 10 μm TMA sections, further, slides containing trimmed sections of tissue samples are paraffin removed in xylene and rehydrated in graded alcohol, then antigen retrieval is performed with acid buffer under pressure for 5 to 60 minutes with anti-PLK 2 (mouse monoclonal) to 1:1000 degree in 2% tissue treated solution, or more specifically with a dilution of a staining system to generate a quantitative marker score corresponding to the marker score of tumor cells or a dilution of a mouse stain equivalent to a standard curve representing the tumor score of the tumor expression curve of the curve of curve of curve.

For example, when the expression level of PLK1 is numerically represented as 12 and the calculated expression level of ECT2 is 8, the derived ratio is 1.5. for several embodiments, the ratio may be calculated or operated by inverting the numerator and denominator of the aforementioned ratio using ECT2 and PLK1 expression levels, respectively, as numerator and denominator, the disclosed method considers the subject to have, but is not limited to, a survival result of when the derived ratio is less than a predetermined value or a predictive ratio equal to or greater than a predetermined value and to have a predictive result of 1mm to 4 mm thickness to simplify the process of deriving the predictive survival result of the subject, or to have a predictive result of a second to 4 mm thickness when the derived ratio is equal to or greater than a predetermined value, or to have a predictive score of 0.001 to 0.68, or to be associated with the number of genes, or to be used in a predictive scale analysis, or other ways that can be used in order to generate a predictive value, or to be correlated with the number of genes, whether the subject is 0.1 to when the derived survival result is less than the predetermined value or equal to greater than the predictive value.

As noted above, according to embodiments, the th predictive survival result corresponds to a total percent survival greater than 40% -60% within a predetermined time period in the survival analysis, and the second predictive survival result corresponds to a total percent survival of 40% -60% or less within a predetermined time period in the survival analysis.

In , the predictive survival outcome depicts or merely depicts the percentage of survival of the subject over a predetermined period of time, preferably spanning 6 to 48 months, or more preferably 12 to 36 months.

In accordance with another aspect of the disclosure, an apparatus for generating a predictive survival outcome for a subject affected by liver cancer is disclosed that includes, inter alia, a module that is capable of identifying or detecting transcriptome and/or protein expression of PLK1 and ECT2 in a tissue sample of the subject, and a second module configured to derive a ratio of expression of PLK1 to expression of ECT2 within a defined region on the provided sample and generate a predictive survival outcome for the subject based on the derived ratio, the second module configured to consider the subject to have the th predictive survival outcome when the derived ratio is less than a predetermined value or to have a second predictive survival outcome when the derived ratio is equal to or greater than a predetermined value.

The fourth module described in this disclosure may be any known platform operable under the principles of genomics, proteomics, immunochemistry, and/or histochemistry to selectively enable PLK1 and/or ECT2 expression to be detectable by human or machine reading so, in embodiments , the disclosed apparatus may further step include a testing module capable of performing at least of nucleic acid-based or protein-based assays to couple expressed transcriptomes or proteins of PLK1 and ECT2 with a signal conductor formed to continuously or periodically emit signals detectable by the th module, in particular, the th module is configured to express PLK1 and/or ECT2 transcriptomes and/or proteins with a signal conductor capable of releasing detectable signals to highlight the presence of expressed transcriptomes or proteins coupled with the signal conductor in a way to highlight the expressed transcriptomes or proteins coupled with each other, for example, a second 588624 th working station may be composed of a plurality of working stations running a plurality of processing stations to prepare and process images for subsequent processing to create a final highlighted tissue sample, or visualized image analysis by a second lighting module for example, a diagnostic imaging module for further development by a diagnostic imaging system for further development of a biliary cancer cell model 36597, preferably a biliary tree, a third known imaging system operable under the principles of the third known imaging system operable under the principles of genetic, a third known imaging system operable under the principles of genetic.

According to many embodiments, the second module is adapted to derive a ratio of PLK1 expression to ECT2 expression within a defined area on the provided sample, or vice versa, and to generate a predictive survival result for the subject in relation to the derived ratio the second module may be a computing device equipped with a set of commands in the form of a computing program to run an analysis on the captured image, the attributes of the captured image may be manipulated accordingly depending on the analysis performed the second module preferably analyzes the processed tissue sample, or more preferably the image of the processed tissue sample, and provides a score for various image parameters or attributes (such as percentage of tumor cells and intensity of the indicator signal or stain displayed) the second module may further manipulate the resulting scores of different image attributes such as multiplying the scores with each other or inputting the scores into a or more algorithms to produce a more informed finding about subjects affected by hepatocellular carcinoma or cholangiocarcinoma or more preferably a score for each of these image attributes or for a range of quantitative expression of tumor expression of the captured image obtained by the second module particularly preferably calculating a score for multiple levels of expression of PLK 365 or more representative tumor expression of the score for each of the tumor expression of the tumor cells expressed in a range of the image score obtained by the second module (-35-3) or score calculated by the computing module.

In addition, a second module calculates or derives a ratio of PLK1 expression to ECT2 expression, or vice versa, within a defined area of the provided sample, preferably the expectation has dimensions of 0.1 to 1.0mm in length, 0.1 to 1.0mm and 1 to 4um in thickness to simplify the process of deriving the predictive survival outcome for the subject, the disclosed second module considers the subject to have a th predictive survival outcome when the derived ratio is configured to consider the subject to have a th predictive survival outcome when the derived ratio is less than a predetermined value, considers the subject to have a th predictive survival outcome, or considers the subject to have a second predictive survival outcome when the derived ratio is equal to or greater than a predetermined value, the predetermined value may be any number but is not limited within the range of 0 to 16 depending on the level of predictive score implemented, as mentioned above, the predictive outcome corresponds to a total survival percentage greater than 40% -60% within a predetermined time period in the survival analysis, and the second survival outcome corresponds to a total survival percentage of 40% -60% or less within a predetermined time period in the survival analysis, preferably spanning a month to 36 months, preferably to 36 months.

For the embodiments, the nucleic acid-based assay is any or a combination of microarray-based techniques for DNA and/or RNA polynucleotide detection, and polymerase chain reaction-based techniques (including digital PCR).

In other embodiments, the proteomics-based assay is tissue microarray and/or immunohistochemical staining of human tissue.

The following examples are intended to illustrate the invention further without any intention to limit the invention to the specific embodiments described therein.