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WO2017008165A1 - Méthode de classification et traitement de cancers de l'endomètre - Google Patents

Méthode de classification et traitement de cancers de l'endomètre Download PDF

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WO2017008165A1
WO2017008165A1 PCT/CA2016/050829 CA2016050829W WO2017008165A1 WO 2017008165 A1 WO2017008165 A1 WO 2017008165A1 CA 2016050829 W CA2016050829 W CA 2016050829W WO 2017008165 A1 WO2017008165 A1 WO 2017008165A1
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pole
mmr
sample
abn
endometrial
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Jessica MCALPINE
Aline TALHOUK
David Huntsman
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British Columbia Cancer Agency BCCA
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    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N33/00—Investigating or analysing materials by specific methods not covered by groups G01N1/00 - G01N31/00
    • G01N33/48—Biological material, e.g. blood, urine; Haemocytometers
    • G01N33/50—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing
    • G01N33/53—Immunoassay; Biospecific binding assay; Materials therefor
    • G01N33/574—Immunoassay; Biospecific binding assay; Materials therefor for cancer
    • G01N33/57407—Specifically defined cancers
    • G01N33/57442—Specifically defined cancers of the uterus and endometrial
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61P—SPECIFIC THERAPEUTIC ACTIVITY OF CHEMICAL COMPOUNDS OR MEDICINAL PREPARATIONS
    • A61P35/00—Antineoplastic agents
    • C—CHEMISTRY; METALLURGY
    • C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
    • C12Q—MEASURING OR TESTING PROCESSES INVOLVING ENZYMES, NUCLEIC ACIDS OR MICROORGANISMS; COMPOSITIONS OR TEST PAPERS THEREFOR; PROCESSES OF PREPARING SUCH COMPOSITIONS; CONDITION-RESPONSIVE CONTROL IN MICROBIOLOGICAL OR ENZYMOLOGICAL PROCESSES
    • C12Q1/00—Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions
    • C12Q1/68—Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions involving nucleic acids
    • C12Q1/6876—Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes
    • C12Q1/6883—Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes for diseases caused by alterations of genetic material
    • C12Q1/6886—Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes for diseases caused by alterations of genetic material for cancer
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N33/00—Investigating or analysing materials by specific methods not covered by groups G01N1/00 - G01N31/00
    • G01N33/48—Biological material, e.g. blood, urine; Haemocytometers
    • G01N33/50—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing
    • G01N33/68—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing involving proteins, peptides or amino acids
    • G01N33/6875—Nucleoproteins
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B10/00—Instruments for taking body samples for diagnostic purposes; Other methods or instruments for diagnosis, e.g. for vaccination diagnosis, sex determination or ovulation-period determination; Throat striking implements
    • A61B10/02—Instruments for taking cell samples or for biopsy
    • A61B10/0291—Instruments for taking cell samples or for biopsy for uterus
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61K—PREPARATIONS FOR MEDICAL, DENTAL OR TOILETRY PURPOSES
    • A61K45/00—Medicinal preparations containing active ingredients not provided for in groups A61K31/00 - A61K41/00
    • C—CHEMISTRY; METALLURGY
    • C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
    • C12Q—MEASURING OR TESTING PROCESSES INVOLVING ENZYMES, NUCLEIC ACIDS OR MICROORGANISMS; COMPOSITIONS OR TEST PAPERS THEREFOR; PROCESSES OF PREPARING SUCH COMPOSITIONS; CONDITION-RESPONSIVE CONTROL IN MICROBIOLOGICAL OR ENZYMOLOGICAL PROCESSES
    • C12Q2600/00—Oligonucleotides characterized by their use
    • C12Q2600/112—Disease subtyping, staging or classification
    • C—CHEMISTRY; METALLURGY
    • C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
    • C12Q—MEASURING OR TESTING PROCESSES INVOLVING ENZYMES, NUCLEIC ACIDS OR MICROORGANISMS; COMPOSITIONS OR TEST PAPERS THEREFOR; PROCESSES OF PREPARING SUCH COMPOSITIONS; CONDITION-RESPONSIVE CONTROL IN MICROBIOLOGICAL OR ENZYMOLOGICAL PROCESSES
    • C12Q2600/00—Oligonucleotides characterized by their use
    • C12Q2600/118—Prognosis of disease development
    • C—CHEMISTRY; METALLURGY
    • C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
    • C12Q—MEASURING OR TESTING PROCESSES INVOLVING ENZYMES, NUCLEIC ACIDS OR MICROORGANISMS; COMPOSITIONS OR TEST PAPERS THEREFOR; PROCESSES OF PREPARING SUCH COMPOSITIONS; CONDITION-RESPONSIVE CONTROL IN MICROBIOLOGICAL OR ENZYMOLOGICAL PROCESSES
    • C12Q2600/00—Oligonucleotides characterized by their use
    • C12Q2600/156—Polymorphic or mutational markers
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N2800/00—Detection or diagnosis of diseases
    • G01N2800/52—Predicting or monitoring the response to treatment, e.g. for selection of therapy based on assay results in personalised medicine; Prognosis
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N2800/00—Detection or diagnosis of diseases
    • G01N2800/56—Staging of a disease; Further complications associated with the disease

Definitions

  • the present disclosure relates generally to a method for classifying endometrial cancers. More particularly, the molecular-based method can classify endometrial cancers into distinct and clinically significant sub-groups.
  • Endometrial cancers are the most prevalent gynecologic malignancies in the developed world and are the fourth most common cancer in women overall (Siegel et al, 2015).
  • Endometrial cancers face the challenge of irreproducible pathological categorization, particularly of high grade tumors, which can lead to inaccurate risk category assignment and imprecise estimation of disease metastases, recurrence and death. This can result in the over- and under- treatment of women. Diverse subsets of endometrial cancers may be lumped together in clinical trials, making interpretation of treatment efficacy difficult. It is desirable to provide a method of classification for endometrial cancers.
  • TCGA Cancer Genome Atlas Research et al, 2013
  • TCGA Cancer Genome Atlas Research et al, 2013
  • categorization into subgroups has the potential to provide prognostic and predictive information for individuals.
  • An ability to classify cases into such subgroups might offer an
  • a method of classificatio n is provided to determine meaningful molecular subgroups using data acquisition methods appropriate for use in routine clinical practice.
  • the method permits risk stratifications to direct patient care.
  • the present disclosure provides a method of classifying endometrial cancers comprising: obtaining a sample from a subject having endometrial cancer; evaluating the sample for: (a) a hypermutated mismatch repair (MMR) protein comprising high microsatellite instability (MSI) phenotype to classify the cancer as (i) MMR abnormal (MMR abn) if present or MMR microsatellite stable (MMR MSS) if absent; (b) subsequently evaluating the MMR MSS sample from (a) for a POLE exonuclease domain mutation (EDM) to classify the cancer as (ii) POLE mutated (POLE mut) if present or POLE wild type (POLE wt) if absent; and (c) subsequently evaluating the POLE wt sample from (b) for a p53 copy number abnormality comprising a high copy number or a low copy number to classify the cancer as (iii) p53
  • MMR hypermutated mismatch
  • kits for use in the method described above of classifying endometrial cancers comprising: one or more immunohistochemical reagent for evaluating the sample for the hypermutated mismatch repair protein; one or more PCR reagent for evaluating the sample for POLE exonuclease domain mutation; and one or more immunohistochemical reagent for evaluating the sample for the p53 copy number abnormality.
  • the present disclosure provides a use of (a) a hypermutated mismatch repair (MMR) protein; (b) a POLE exonuclease domain mutation (EDM); and (c) a p53 copy number abnormality for classifying an endometrial cancer in a sample from a subject, wherein the endometrial cancer is classified as: (i) MMR abn; (ii) POLE mut; (iii) p53 wt; or (iv) p53 abn.
  • MMR hypermutated mismatch repair
  • EDM POLE exonuclease domain mutation
  • a method for treating endometrial cancer with a fertility-sparing therapy comprising: obtaining a sample from a subject having endometrial cancer; evaluating the sample for: (a) a hypermutated mismatch repair (MMR) protein comprising high microsatellite instability (MSI) phenotype to determine MMR abnormal (MMR abn) if present or MMR microsatellite stable (MMR MSS) if absent; (b) subsequently evaluating the MMR
  • MMR hypermutated mismatch repair
  • POLE mut if present or POLE wild type (POLE wt) if absent; and (c) subsequently evaluating the POLE wt sample from (b) for a p53 copy number abnormality comprising a high copy number or a low copy number to determine p53 wild type (p53 wt) if the copy number abnormality is absent and p53 abnormal (p53 abn) if present; and classifying the endometrial cancer as: (i) MMR abn; (ii) POLE mut; (iii) p53 wt; or (iv) p53 abn; wherein if the endometrial cancer can be classified as (ii) POLE mut or (iii) p53 wt tumor, the fertility-sparing therapy is used.
  • a system for classifying an endometrial cancer comprising: (a) an immunohistological test for detecting a hypermutated mismatch repair (MMR) protein comprising high microsatellite instability (MSI) phenotype to determine MMR abnormal (MMR abn) if present or MMR microsatellite stable (MMR MSS) if absent; (b) a test for subsequently evaluating the MMR MSS sample from (a) for a POLE exonuclease domain mutation (EDM) to determine POLE mutated (POLE mut) if present or POLE wild type (POLE wt) if absent; and (c) an immunohistological test for subsequently evaluating the POLE wt sample from (b) for a p53 copy number abnormality comprising a high copy number or a low copy number to determine p53 wild type (p53 wt) if absent and p53 abnormal (p53 abn) if present; and where
  • MMR hypermutated mismatch repair
  • Figure 1 is a schematic depiction of a classification method as a decision tree of binary parameters.
  • Figure 2A shows Kaplan-Meier survival analyses and log-rank survival statistics for models 1 , 2, and 3 for pragmatic molecular classification of endometrial cancers applied to the Vancouver cohort.
  • Figure 2B shows Kaplan-Meier survival analyses and log-rank survival statistics for models 4, 5, and 6 for pragmatic molecular classification of endometrial cancers applied to the Vancouver cohort.
  • Figure 2C shows Kaplan-Meier survival analyses and log-rank survival statistics for models 7 and 8 for pragmatic molecular classification of endometrial cancers applied to the Vancouver cohort.
  • Figure 3 depicts Harrell's C-lndex for models 1 to 8, ESMO clinical risk group, and combined molecular and risk groups or pathologic parameters as applied to the Vancouver cohort.
  • Figure 4 is a schematic depiction of the favoured pragmatic model for molecular classification of endometrial cancers, based on MMR IHC / POLE mut / p53 IHC, which corresponds to model 8 of Figure 2C.
  • Figure 5 is a cross-tabulation of clinicopathologic risk groups (ESMO) with molecular classification by the model MMR IHC / POLE mut / p53 IHC, which corresponds to model 8 of Figure 2C.
  • ESMO clinicopathologic risk groups
  • Figure 6 is a schematic representation of the generalized method for testing and categorizing endometrial cancer sample according to the classification method described herein.
  • Figure 7 shows the histopathologic features of endometrial carcinoma with discrepant mismatch repair protein results on endometrial sampling and hysterectomy.
  • Figure 8A shows a breakdown of cases evaluated in both the discovery and confirmation cohorts.
  • Figure 9A shows the molecular subgroup for the full cohort for overall survival time
  • Figure 9B shows the molecular subgroup for the full cohort for disease specific survival (DSS) outcomes in Example 3.
  • Figure 9C shows the molecular subgroup for the full cohort for progression-free survival (PFS) outcomes in Example 3.
  • Figure 10A shows Kaplan-Meier survival analyses for the full cohort in Example 3 according to the European Society for Medical Oncology (ESMO) subtype for overall survival time (OS) outcomes.
  • ESMO European Society for Medical Oncology
  • OS overall survival time
  • Figure 10B shows Kaplan-Meier survival analyses for the full cohort in Example 3 according to the European Society for Medical Oncology (ESMO) subtype for disease specific survival (DSS) outcomes.
  • ESMO European Society for Medical Oncology
  • DSS disease specific survival
  • Figure 10C shows Kaplan-Meier survival analyses for the full cohort in Example 3 according to the European Society for Medical Oncology (ESMO) subtype for progression-free survival (PFS) outcomes.
  • ESMO European Society for Medical Oncology
  • PFS progression-free survival
  • Figure 11 shows C-index data for three end-points for the full cohort based on ESMO risk groups, classifications of the current classification method (alternatively referred to herein as the ProMisETM method) and combined, as well as diagnostic model and post-surgical model.
  • Figure 12 shows the four molecular subgroups determined according to the classification method versus ESMO risk groups.
  • the present disclosure provides a method for classifying endometrial cancers into four different categories associated with different outcomes and modes of treatment.
  • formalin-fixed paraffin embedded samples can be assessed from diagnostic endometrial samples such as from biopsy or curetting specimens, or hysterectomy samples may be used.
  • Techniques that can be done routinely using immunohistochemistry methods are employed, which makes the method economically advantageous.
  • Information gained in the instant classification method can be used to guide treatment decisions, including referring an individual to hereditary testing, such as for Lynch syndrome testing for abnormal MMR IHC, immune blockade therapy, fertility sparing options, or an individual may be found to be a candidate for more extensive and aggressive treatment involving chemotherapy and radiation.
  • Early classification of endometrial cancers can optimize patient treatment outcomes.
  • Figure 1 depicts a classification method as a decision tree (100) of binary parameters, with the result being determined by the presence or absence of a protein or of a mutation. The method helps to ensure consistent subgroup assignment for patients having endometrial cancer. Molecular classification appears to offer prognostic information superior to current standards. Using the ESMO 2013 classifier, purportedly the strongest of the available traditional systems, only two outcomes groups were discernible, with the low- and intermediate-risk group survival curves overlapping. In contrast, molecular classification yielded four distinct subgroups with significantly different survival curves. A new sample (102) which may be from a diagnostic endometrial biopsy is assessed for MMR using immunohistochemistry.
  • MMR Magnetic resonance
  • the sample is deemed unclassifiable (106).
  • MMR-deficient 108
  • the sample is classified into the group shown as MMR-D (1 10), indicating that the individual may best be referred to hereditary testing, and/or may be a good candidate for immune blockade therapy.
  • MMR-D MMR-D
  • an assessment of POLE is made, for example using molecular methods such as PCR.
  • POLE is missing (114) the sample is considered unclassifiable (116).
  • POLE be mutated (1 18) the sample is classified as POLE EDM (120), and the patient from whom the sample is derived may be advised to consider less treatment, and/or a fertility-sparing surgery.
  • a method of classifying endometrial cancers comprises obtaining a sample from a subject having endometrial cancer; evaluating the sample for: (a) a hypermutated mismatch repair (MMR) protein comprising high microsatellite instability (MSI) phenotype to classify the cancer as (i) MMR abnormal (MMR abn) if present or MMR microsatellite stable (MMR MSS) if absent; (b) subsequently evaluating the MMR MSS sample from (a) for a POLE exonuclease domain mutation (EDM) to classify the cancer as (ii) POLE mutated (POLE mut) if present or POLE wild type (POLE wt) if absent; and (c) subsequently evaluating the POLE wt sample from (b) for a p53 copy number abnormality comprising a high copy number or a low copy number to classify the cancer as (iii) p53 wild type (p53 wt
  • the method categorizes (i) MMR abn and (ii) POLE mut and as associated with intermediate clinical risk; (iii) p53 wt as associated with low clinical risk; and (iv) p53 abn as associated with high risk, wherein risk is evaluated by the European Society for Medical Oncology (ESMO) risk groupings.
  • ESMO European Society for Medical Oncology
  • the hypermutated MMR protein and/or the p53 copy number abnormality is evaluated by immunohistochemistry.
  • the POLE exonuclease domain mutation is evaluated by sequencing, for example using digital PCR (dPCR) for focused evaluation in a clinical setting.
  • the sample may be a hysterectomy sample, or a diagnostic endometrial sample, such as derived from an endometrial biopsy or an endometrial curetting specimen.
  • the sample can formalin-fixed and paraffin -em bedded.
  • a kit for use in the method of classifying endometrial cancers as described herein may be provided as having: one or more immunohistochemical reagent for evaluating the sample for the hypermutated mismatch repair protein; one or more PCR reagent for evaluating the sample for POLE exonuclease domain mutation; and one or more immunohistochemical reagent for evaluating the sample for the p53 copy number abnormality.
  • one or more immunohistochemical reagent for evaluating the sample for the hypermutated mismatch repair protein may comprises MSH6 and PMS2 antibodies, for example with a 2-panel model.
  • the test may additionally comprise MLH 1 or MSH2 antibodies, in a 4-panel model.
  • the one or more immunohistochemical reagent for evaluating the sample for the p53 copy number abnormality may comprises an anti-p53 monoclonal antibody.
  • a hypermutated mismatch repair (MMR) protein (b) a POLE exonuclease domain mutation (EDM); and (c) a p53 copy number abnormality for classifying an endometrial cancer in a sample from a subject is described.
  • MMR hypermutated mismatch repair
  • EDM POLE exonuclease domain mutation
  • p53 copy number abnormality for classifying an endometrial cancer in a sample from a subject is described.
  • the endometrial cancer is classified as: (i) MMR abn; (ii) POLE mut; (iii) p53 wt; or (iv) p53 abn.
  • a surgery may be delayed when the endometrial cancer is classified as (ii) POLE mut or (iii) p53 wt tumor.
  • a surgery may be pursued when the endometrial cancer is classified as (i) MMR abn or (Iv) p53 abn.
  • a fertility-sparing therapy may be pursued when the endometrial cancer is classified as (ii) POLE mut or (iii) p53 wt.
  • the method comprises: obtaining a sample from a subject having endometrial cancer; evaluating the sample for: (a) a hypermutated mismatch repair (MMR) protein comprising high microsatellite instability (MSI) phenotype to determine MMR abnormal (MMR abn) if present or MMR microsatellite stable (MMR MSS) if absent; (b) subsequently evaluating the MMR MSS sample from (a) for a POLE exonuclease domain mutation (EDM) to determine POLE mutated (POLE mut) if present or POLE wild type (POLE wt) if absent; and (c) subsequently evaluating the POLE wt sample from (b) for a p53 copy number abnormality comprising a high copy number or a low copy number to determine p53 wild type (p53 wt) if the copy number abnormality is absent and p53 abnormal (p53 abn) if present; and classifying the endometrial cancer as: (i) M
  • the endometrial cancer can be classified as (ii) POLE mut or (iii) p53 wt tumor, the fertility- sparing therapy is used.
  • a method of treating an endometrial cancer comprising: classifying the endometrial cancer according to the methods described above.
  • the cancer is classified as MMR MSS; the subject may be subsequently treated by referral to hereditary testing, or use of immune blockade therapy.
  • the cancer is classified as POLE MUT; the subject may be subsequently treated with less aggressive treatment, and/or fertility sparing surgery.
  • the cancer is classified as p53 wt; the subject may be subsequently treated with less aggressive treatment, and/or fertility sparing surgery; and when the cancer is classified as p53 abn; the subject may be
  • a system for classifying an endometrial cancer comprising: (a) an immunohistological test for detecting a hypermutated mismatch repair (MMR) protein comprising high microsatellite instability (MSI) phenotype to determine MMR abnormal (MMR abn) if present or MMR microsatellite stable (MMR MSS) if absent; (b) a test for subsequently evaluating the MMR MSS sample from (a) for a POLE exonuclease domain mutation (EDM) to determine POLE mutated (POLE mut) if present or POLE wild type (POLE wt) if absent; and (c) an immunohistological test for subsequently evaluating the POLE wt sample from (b) for a p53 copy number abnormality comprising a high copy number or a low copy number to determine p53 wild type (p53 wt) if absent and p53 abnormal (p53 abn) if present; and wherein results obtained from the tests
  • the POLE exonuclease domain mutation is evaluated by molecular evaluation, such as by sequencing, for example by PCR such as digital PCR.
  • the sample for use in the system may be a hysterectomy sample, or a diagnostic endometrial sample comprising an endometrial biopsy or an endometrial curetting specimen.
  • a two-year clinical follow-up period may be utilized as a minimal clinical follow-up time for patients with endometrial cancer. Patients with potential follow-up less than two years were not considered in the Examples below.
  • a "cohort wise" follow-up approach is used in the Examples below, censoring observations on December 31 st of the fifth year following the year in which a patient had her surgery (year of diagnosis). This ensures random censoring and minimize ascertainment bias.
  • Time to death or overall survival time (OS) from surgery until death of any cause is a parameter evaluated in the Examples below. Patients who are still alive prior to the censoring date are censored at the time of last follow up, or on December 31 st of the fifth year post diagnosis, whichever was sooner. If the event status or the date of last follow-up is unknown, data is considered missing.
  • Time to death from disease or disease specific survival (DSS), time from surgery until death due to endometrial cancer is evaluated in the Examples below. Patients who are alive prior to the censoring date or if they died of an unrelated cause, are censored at the time of last follow-up, or on December 31 st of the fifth year post diagnosis, whichever was sooner. If the event status or the date of last follow-up is unknown data is considered missing. The event status is considered unknown if patients die of an intercurrent disease or if the reason for death is unspecified. These patients would not be considered in the disease specific survival analyses.
  • Time to progression or progression-free survival is the time from surgery until there is evidence of recurrent or progressive disease (this is based on either clinical evidence of recurrence, or imaging confirmation of recurrence) or if the patient died of the disease prior to the censoring date.
  • Patients who are alive and disease-free prior to the censoring date or if they died of an unrelated cause, are censored at the time of last follow-up, or on December 31 st of the fifth year post diagnosis, whichever was sooner. If the event status or the date of last follow-up is unknown data is considered missing.
  • Endometrial cancers are the most prevalent gynecologic malignancies in the developed world and are the fourth most common cancer in women overall (Siegel et al, 2015).
  • Incidence rates have increased dramatically over the last decades attributable at least in part to the global epidemic of obesity. Although the majority of women with EC have good outcomes, women with advanced disease or more aggressive subtypes may not be curable with adjuvant therapy. In Canada over the last decade, the annual percentage increase in age-standardized mortality rate for EC is greater than any other cancer in women, and society is in desperate need of new approaches, including diagnostic tools, to manage this cancer.
  • pathologists are unable to reproducibly diagnose histotype and grade of EC due to lack of consensus between expert pathologists has been demonstrated, even with the addition of immunohistochemistry (Gilks et al, 2013; Guan et al, 2011 ; Han et al, 2013; Hoang et al, 2013). This lack of reproducibility is a major barrier to improving care for women with this disease, and treatments vary within and between cancer centers globally.
  • the other distinct subgroups identified from the TCGA data included microsatellite instability (MSI), copy number low (CN low) and copy number high (CN high), the latter consisting mostly of cases diagnosed by referring center pathologists as high-grade serous cancers.
  • MSI microsatellite instability
  • CN low copy number low
  • CN high copy number high
  • MSI microsatellite instability
  • CN low copy number low
  • CN high copy number high
  • (a) POLE In TCGA, the POLE ultramutated cluster was identified based on a POLE mutation, a high percent of C to A transversions and low percent of C to G transversions, as well as more than 500 SNVs.
  • PTEN was initially included in the assessment of the models along with POLE mutations because in the TCGA mutational analysis, although PTEN mutations were seen to some degree across MSI and CN low subgroups, PTEN and POLE mutations were noted to co-occur almost all of the "ultramutated" subgroup and seemed to better define this category.
  • two methods were initially proposed to identify the ultramutated subgroup; one using the POLE mutation status alone, and one that uses both the POLE and PTEN mutation status.
  • MSI The MSI group in the TCGA analysis was based on results from the MSI assay using 7 markers (Cancer Genome Atlas Research et al, 2013). These models identified the MSI phenotype subsequent to POLE ultramutated, as done in TCGA and also considered switching the order to identify the MSI cluster first. In practice this is practical as it would be useful to have this information as early as possible to enable referral to hereditary cancer programs for Lynch syndrome testing. Using the MSI assay was changed to using MMR IHC testing (MLH1 , MSH2, MSH6, PMS2), which is highly concordant with MSI assay (data not shown) and more cost effective and practical.
  • MMR IHC testing MMR IHC testing
  • TP53 was noted to be mutated in most of the copy number high cases in the TCGA cohort and in silico analysis demonstrated that p53 status was able to reproduce the CN high/low survival curves. TP53 mutation status was not equivalent to CN high subgroup in TCGA but identified a subgroup of EC cases with distinctly worse outcomes.
  • CN status was determined as follows: i) FISH determination of copy number status at 3 loci most associated with CN high subgroup in TCGA (FGFR (4p16.3), SOX17 (8q1 1.23), and MYC (8q24.12), scored on two thresholds, and ii) p53 status determined by IHC or TP53 sequencing, yielding four possible ways to classify CN high following determination of MSI and POLE groups.
  • Multivariate cox proportional hazard regression model analysis was performed to assess any additional prognostic information that would be added by this classifier beyond the clinical risk group classification and the standard prognostic factors (age, BMI, grade, stage, histology, LVSI, and treatment).
  • two sets of multivariable analyses were performed: (i) multivariable analyses with ESMO clinical risk groups, (ii) multivariable analyses with individual clinicopathological parameters: age, BMI, grade, stage, histology, LVSI.
  • the performance was quantified by computing accuracy measures (in the TCGA cohort where true labels are available) and Harrell's C-index in the TCGA and in the Vancouver cohort when considering survival outcome.
  • the C-index is a measure of discriminative ability of the model.
  • a C- index of 0.5 indicates that the model has no discriminative ability and a C-index of 1 indicates that a model perfectly distinguishes between those who have an event and those who do not.
  • Bootstrapping techniques were used for internal validation in both the TCGA data and the this cohort. Validation using bootstrap re-sampling would estimate the likely performance of the model on a new sample of patients from a same clinical setting. One thousand bootstrap samples are used; in each bootstrap iteration, a sample of size equal to the original cohort is drawn with replacement from the original cohort. Models assessed with the C-index were developed in the bootstrap samples were tested in those subjects not included in the bootstrap sample. In comparing the TCGA predicted subtypes to the actual labels, the sensitivity and the specificity is obtained on the bootstrap samples alone.
  • Oncologists (ESMO) criteria (Colombo et al, 2013) and compared to molecular subgroups in both the TCGA (data not shown) and new endometrial carcinoma cohorts .
  • POLE exonuclease domain (EDM) mutations were found in 13 cases in the total cohort.
  • VOA 843 data not shown
  • a low level (5%) validated POLE mutation was found in exon 12 that is not a known hot spot mutation, and this tumour also demonstrated isolated MSH6 loss with IHC.
  • MMR IHC abnormal not grouped with 'POLE'
  • 'POLE' mutant 12 of 143 (8.4%) cases
  • TP53 mutations were identified in 3 of 12 POLE mutated cases by sequencing and 1 of 12 cases by abnormal p53 IHC (score 0 or 2+).
  • Full details of the subset of cases in the Vancouver cohort with POLE mutations, including chromosome, genomic position, and amino acid change as well as TP53 and PTEN mutation details and the status of MMR IHC (MLH1 , MSH2, MSH6, PMS2) and p53 IHC for these cases were obtained, but are not shown here. There were no recurrences or deaths in the cases with POLE EDM mutations, with an observation time of over five years for this subgroup.
  • the kappa statistic for level of agreement between testing methods for T1 is 0.66(95%CI 0.42-0.84) with a sensitivity, specificity, positive (PPV) and negative predictive value (NPV) of 1 , 0.56, 0.88, and 1 respectively.
  • the kappa statistic is 0.49 (95%CI 0.24-0.72) with corresponding accuracy calculations 1 , 0.39, 0.85, and 1.
  • the Vancouver cohort included the major histological subtypes (83% endometrioid histotype, the remainder being of serous or mixed histotypes, with the exception of a single undifferentiated carcinoma), all stages, and grades (Table 1 ) similar in distribution to both TCGA (data not shown) and the general population.
  • the estimated median follow-up time in the Vancouver cohort, as calculated by the reverse Kaplan-Meir method is 5 years.
  • the median observation time is 4.67 years.
  • OS Overall survival
  • DSS disease-specific survival
  • RFS recurrence-free survival
  • Model numbers 1 through 8 are shown in each row, with models 1 to 3 on Figure 2A, models 4 to 6 shown on Figure 2B, and models 7 and 8 shown on Figure 2C.
  • Model 8 is outlined in Figure 2C, as it is the model that was used for subsequent univariate and multivariate analysis. Model 8 was combined with either European Society of Medical Oncologists clinical risk groups or pathological parameters. [0091] Statistical significance of the log-rank statistic is noted in the majority of these models, with the exception of FISH (T2 or threshold 2), shown in Models 3 and 6 illustrated in Figures 2A and 2B, respectively.
  • T1 FISH testing of three loci (MYC, SOX17, FGFR3) segregated by the first threshold (T1 ) may act as a surrogate test for copy number, it is suboptimal for clinical use because 1 ) results were only achieved in subset of cases, 2) there was a high level of subjectivity in scoring, and 3) the log-rank test for T1 did not reach statistical significance for all outcome parameters in Models 2 and 5 of Figures 2A and 2B, respectively.
  • Addition of PTEN mutation status to POLE mutation categorization although seemingly helpful in the initial discovery phase with the TCGA data (not shown) did not add apparent benefit to the models based on the Vancouver data set (Models 4, 5, 6,7 of Figures 2B and 2C, respectively). Of note, 1 1 of 12 cases with POLE mutations also harbour PTEN mutations in this data set.
  • a C-index of 0.5 indicates that the model has no discriminative ability and a C-index of 1 indicates that a model perfectly distinguishes between those who have an event and those who do not.
  • the model parameters used in the pragmatic model chosen to move forward (column 8) is outlined in Figure 3. Also outlined are the indices for the molecular classifier combined with clinical risk groups (column 10) or pathological parameters (column 1 1 ), suggesting an improved ability to discriminate outcomes when taken together.
  • Figure 4 shows the favoured pragmatic model for molecular classification of endometrial cancers (Model 8 of Figure 2C). Selection was based on survival analyses, C-index, anticipated clinical benefit in order of testing, and cost and accessibility of methods.
  • this model option (Model 8 of Figure 2C, and see column 8 in Figure 3), based on pragmatic surrogate molecular assays inclusive of: 1 ) MMR IHC abnormalities ('MMR IHC abn'), 2) 'POLE' EDM mutations, and 3) p53 status determined by IHC, as a surrogate to delineate 'p53 wt' and 'p53 abn' groups.
  • MMR MSS status was evaluated for abn (MMR MSI- high) was first determined, and found in 41 subjects.
  • MMR-MSS MMR-MSS
  • POLE mutation was assessed, and found in 12 subjects.
  • LRT likelihood ratio test
  • OS Overall Survival
  • RSF Recurrence-Free Survival.
  • Molecular classifier subgroups MMR IHC abn, POLE EDM mutated and p53 abn are considered.
  • the HRs reflect a relative increase in risk associated with unit change, for example, HR for each additional year of age.
  • the reference for comparison is indicated.
  • F indicates that the Firth's penalized maximum likelihood bias reduction method was used.
  • Figure 4 shows the design of the study and the number of individuals classified into
  • MMR IHC abn (41 of 143, or 28.7%); POLE EDM (12 of 143, or 8.4%); p53 wt (63 of 143; or 44%), and p53 abn (25 or 143, or 17.5%). Two subjects were in the unclassifiable categories.
  • Figure 5 shows the cross-tabulation of clinicopathologic risk groups (ESMO) with molecular classification by proposed model: MMR IHC / POLE / p53 IHC. Approximately half of the POLE and MMR IHC abn molecular subgroups are noted to include cases that would be designated as 'high risk' by traditional clinical risk group stratification. The p53 abn molecular subgroup includes about 25% 'low' and 'intermediate' risk cases who would usually be designated to receive minimal (e.g., vaginal brachytherapy) or no therapy. Although both molecular subgroups and clinical risk groups were associated with outcomes, they may identify different women with endometrial cancers.
  • ESMO clinicopathologic risk groups
  • Endometrial carcinomas in particular high-grade cancers, cannot be reliably classified by histomorphologic criteria, even by expert pathologists and with the addition of
  • MMR IHC for MSI phenotype
  • POLE mutation status for MSI phenotype
  • p53 status The order of the determination of these three major components is also worth consideration. Initially it was considered to pull out MMR IHC abnormal cases first, prompting hereditary cancer referral and yielding information that could be important for both patients and physicians to learn of early. A young woman diagnosed with EC may be considering conservative management (e.g.
  • MMR IHC results can be available at the same time as initial pathologic diagnosis of malignancy; POLE sequencing is not widely available and thus takes time to receive results. However improved turnaround time for POLE testing is expected in the future.
  • POLE mutation status identifies women with the most favourable outcomes (Cancer Genome Atlas Research et al, 2013; Church et al, 2015), seeming to supersede other prognostic factors such as high grade disease. In a cohort of the size used in this Example, and given that all cases classified as 'POLE' had normal MMR I HC, it was not determined whether changing the order of molecular assessments such that POLE EDM mutations were detected first, would be more informative.
  • the described method of classification can improve upon the current system of clinicopathologic risk group stratification that is based on stage and the irreproducible variables of grade and histotype assignment (Colombo et al, 2013; Kong et al, 2015; Kwon et al, 2009; Mariani et al, 2008), which is not highly predictive of outcomes (Bendifallah et al, 2015).
  • Using the ESMO criteria it was demonstrated both in the TCGA data set and in the training set of cases, that the
  • clinicopathologic risk groups are not equivalent to the molecular subgroups identified. What is evident is the number of cases that would be considered 'undertreated Or 'overtreated' depending on categorization. For example, over one quarter of the CN high molecular subgroups were designated as low or intermediate risk and may have been undertreated, with subsequent recurrence and death. Half of the POLE molecular subgroup and MSI subgroups were identified as 'high-risk' based on grade, stage and/or histotype. These women would have received chemotherapy and radiation based on the clinical centers' and consensus guidelines. The prognosis for women with POLE mutations is excellent. Whether that is because they received aggressive treatment or is independent of this remains to be determined.
  • the POLE ultramutated phenotype is extremely sensitive to therapy or, as has been suggested previously, has a higher immune infiltrate (Hussein et al, 2015) that may be further stimulated by the introduction of treatment(s).
  • these women had an excellent prognosis, independent of treatment, and received toxic therapies with long term treatment side effects with no survival benefit.
  • Clinical risk groups were associated with outcomes in both TCGA and the cohorts of this Example, and should not be abandoned. But in terms of managing an individual the inconsistency of histotype and grade classification, it means that the same woman may be recommended to receive vastly different treatments depending on where her pathology is read. For example a woman with a pathology report indicating an endometrial high-grade serous cancer invading less than half her myometrial wall, with all other sites negative for disease (stage IA) receives systemic chemotherapy and pelvic radiation based on being HIGH risk. That same woman whose pathology is interpreted at another center as high-grade endometrioid EC would receive vaginal vault radiation only (associated with intermediate risk).
  • molecular classification Independent of any prognostic ability, molecular classification has the ability to direct clinical care, such as by referral to hereditary cancer programs for Lynch syndrome testing for abnormal MMR IHC. Molecular classification in ECs will allow stratification of cases for clinical trials and assessment of treatment efficacy within specific molecular subgroups. This has been a game-changing approach in ovarian cancers (Despierre et al, 2014) and has the potential to greatly advance progress in EC research.
  • EDMs in POLE and immunohistochemistry for mismatch repair (MMR) proteins and p53 were applied to both pre- and post-staging archival specimens from 60 individuals to identify four molecular subgroups: MMR-D, POLE EDM, p53 wild type, p53 abn(abnormal).
  • MMR-D MMR-D
  • POLE EDM POLE EDM
  • p53 wild type p53 abn(abnormal).
  • Three gynecologic subspecialty pathologists assigned histotype and grade to a subset of samples. Concordance of molecular and clinicopathologic subgroup assignments were determined, comparing biopsy/curetting to hysterectomy specimens.
  • Example 2 the classifier method described in Example 1 , and referred to as the proactive molecular risk classification tool for endometrial cancers (or as the instant "method” or “system") was evaluated to determine if it could be applied to endometrial biopsy or curetting specimens containing EC that were obtained for diagnostic purposes (e.g., to evaluate postmenopausal uterine bleeding), and if classification of these samples was concordant with final hysterectomy endometrial samples obtained at definitive surgical staging.
  • the proactive molecular risk classification tool for endometrial cancers or as the instant "method” or "system” was evaluated to determine if it could be applied to endometrial biopsy or curetting specimens containing EC that were obtained for diagnostic purposes (e.g., to evaluate postmenopausal uterine bleeding), and if classification of these samples was concordant with final hysterectomy endometrial samples obtained at definitive surgical staging.
  • the molecular classification method described herein was used to assign EC specimens (both diagnostic and final hysterectomy within the same individual) to one of four molecular subgroups using methodologies described in Example 1. Testing involved sequential assessment of i) IHC for MMR proteins MLH1 , MSH2, MSH6 and PMS2 ii) sequencing for polymerase epsilon (POLE) exonuclease domain mutations (EDMs), and iii) p53 IHC ( Figure 6). Agreement of the molecular classification method was then compared between pre- and post-surgical staging specimens.
  • Figure 6 shows how new EC samples are tested and categorized according to the above steps.
  • IHC immunohistochemistry
  • MSH1 , MSH2, MSH6, PMS2 mismatch repair proteins
  • MMR- D MMR deficient
  • POLE EDM sequencing for the presence of POLE exonuclease domain mutations
  • FFPE formalin fixed paraffin embedded
  • Targeted primers were designed to cover the POLE EDM exons 9-14.
  • PCR products 150-200bp were amplified using the Fluidigm 48X48 Access Arrays, as per manufacturer's protocol, with input of 100ng FFPE derived DNA, and 50ng high-quality DNA from buffy coat or frozen tumour DNA.
  • DNA barcodes (10bp) with lllumina cluster-generating adapters were added to the libraries, and 96 samples pooled.
  • the library pools were sequenced using the lllumina MiSeq for ultra-deep sequencing. All validated POLE mutations were bi-directionally sequenced twice at minimum using tumor DNA, and once in the normal to validate somatic or germline status using either ultra-deep MiSeq sequencing or Sanger sequencing.
  • pathologists were asked to render a diagnosis in one of the following categories: endometrioid, mucinous, serous, clear cell, dedifferentiated, carcinosarcoma, mixed and other. These pathologists were blinded to the original pathology reports.
  • This cohort was enriched for p53 abn and POLE EDM cases to ensure that these lower frequency subgroups could be adequately evaluated.
  • Table 4A shows the overall concordance and the concordance metrics are shown in Table 4B, including average per molecular subgroup sensitivity, specificity, PPV, NPV and kappa statistic for the method of molecular classification described herein comparing diagnostic and final surgical samples, with the latter held to be the "gold standard”.
  • Kappa statistic of 0.86 (95%CI) was consistent with a "near perfect" level of agreement, fulfilling the goal of improvement over previously published data showing poor concordance between pre- and postoperative samples, when assessed for the conventional histopathological parameters of grade and histotype.
  • Case 5 remained discordant after re-review and repeat whole section MMR testing, and the discordant results were due to tissue sampling.
  • endometrial sampling there was only a low-grade endometrioid adenocarcinoma which had retention of MLH1 and PMS2.
  • the hysterectomy however, had a low-grade endometrioid adenocarcinoma as well as dedifferentiated carcinoma, and this latter component, which was not sampled in the endometrial biopsy, showed loss of MLH1 and PMS2, as is commonly seen in dedifferentiated carcinoma of the endometrium ( Figure 7).
  • Figure 7 shows the histopathologic features of endometrial carcinoma with discrepant mismatch repair protein results on endometrial sampling and hysterectomy.
  • the endometrial sampling consists of only low-grade endometrioid adenocarcinoma (Panel A).
  • the superficial portion of the tumor contains the low-grade endometrioid adenocarcinoma while the deeper portion is higher grade with solid architecture (Panel B).
  • the nuclei are enlarged, irregular and the cells are mildly discohesive; peritumoral lymphocytes are also present at the leading edge of the tumor (Panel C).
  • Immunohistochemical staining for MLH1 shows retained staining in the low-grade glandular component and loss of staining in the high-grade solid component (Panel D).
  • Case 6 shows discordance in POLE EDM results, with mutations found in the diagnostic biopsy sample at 18% frequency (23% on retesting) but no POLE EDMs found in the final hysterectomy sample. Both diagnostic and hysterectomy samples were grade 1 endometrioid tumors, with minimal myometrial invasion in the hysterectomy specimen.
  • Table 6A shows a comparison of histology assessment (using simplified categories) of diagnostic samples (rows) and post-staging hysterectomy samples (columns) from original pathology reports.
  • Table 6B shows a Comparison of overall concordance statistics (with 95% confidence intervals) based on histotype assessment (using simplified categories) of diagnostic samples and post- staging samples from original pathology reports. Please note that " ⁇ " is used in the table to indicate that kappa must be interpreted with caution due to symmetrical imbalance of row and column marginals, in Table 6A.
  • Table 6C shows a comparison of grade assessment of diagnostic samples (rows) and post-staging samples (columns) from original pathology reports.
  • Table 6D shows a comparison of overall concordance statistics (with 95% confidence intervals) based on assessment of grade of diagnostic samples and post-staging samples from original pathology reports.
  • Table 7A shows interobserver agreement between three subspecialty pathologists, computed using Fleiss's kappa. Table 7A illustrates that the average concordance metrics for grade and histotype between the three gynecologic pathologists as evaluated within the 48 diagnostic (pre- surgical staging) and final hysterectomy (post-surgical staging) samples available for review. Concordance remains low; kappa for grouped grade (grade1/2 vs. grade 3) (0.74) and simplified histotype (0.51 ), even when assigned by experts.
  • Table 7B shows concordance between diagnostic samples and post-staging hysterectomy specimen pathologic assignment computed using Cohen's kappa. Level of agreement for each pathologist is shown as well as averages. Finally comparing each subspecialty pathologists diagnoses for grade and histotype in diagnostic vs. final hysterectomy specimens i.e., WITHIN an individual patient there was on average kappa of 0.56 and 0.57 respectively (Table 7B).
  • TCGA represented a positive step towards informative classification
  • the methods used were impractical.
  • Lower cost methods applicable to formalin-fixed paraffin-embedded (FFPE) specimens are desirable to identify four prognostically distinct molecular subgroups of EC.
  • FFPE formalin-fixed paraffin-embedded
  • the Institute of Medicine guidelines for the development of 'omics based testing are followed.
  • Reproducibility of any classifier is of critical importance, and one aspect of reproducibility is the potential to give a definitive classification based on diagnostic specimens e.g. biopsy or curetting.
  • Endometrial biopsy or curettage specimens are routinely obtained during the work up and evaluation of endometrial cancers and if/when cancer is diagnosed the herein- described method can be applied. Processing of the sample is done with well-known methods, requiring no special handling, as steps can be performed on FFPE material.
  • Endometrial biopsies or curetting specimens for MMR assessment as they are promptly fixed, with better antigen preservation than the corresponding hysterectomy specimen.
  • Immunohistochemistry for presence or absence of mismatch repair (MMR) proteins (identifies MMR-deficient or 'MMR-D'), sequencing for POLE exonuclease domain mutations ('POLE EDM') and IHC for p53 (wild type vs. null or missense mutations; 'p53 wt' and 'p53 abn', respectively) were performed on 460 EC samples from patients in British Columbia.
  • Molecular subgroups were derived according to a decision tree classification system and characterized by demographic and clinicopathologic features. Association between molecular risk groups and survival outcomes were examined and the prognostic ability was compared to current risk stratification methods.
  • the classification method identified four prognostic subgroups with distinct overall, disease specific and progression free survival (p ⁇ 0.001 ).
  • POLE EDM having the most favourable prognosis and p53 abn having the worst prognosis consistent with TCGA genomic subgroups and the initial discovery cohort with separation of the two middle survival curves (p53 wt and MMR-D) now observed.
  • p53 wt and MMR-D middle survival curves
  • a molecular classification method (interchangeably referred to as a "classifier” or a “classification system” or “classification scheme”, based on proactive molecular risk classification for EC that assigns EC patients to one of four risk groups based on a combination of mutation and protein expression analysis as shown in Figure 6 and described above.
  • This classification method was based on results from The Cancer Genome Atlas (TCGA) collaborative project in endometrial carcinomas that yielded four molecularly defined subgroups, but which utilized cost-prohibitive methods for group assignment in routine clinical practice, and was dependent on fresh frozen tumor samples, which requires special sample handling.
  • TCGA Cancer Genome Atlas
  • Clinical and pathology parameters collected included age, body mass index (kg/m 2 ), stage (updated according to FIGO 2009 classification), grade, histology, lymphovascular space invasion (LVSI, yes/no), myometrial invasion (none/ ⁇ 50%/>50%), positive nodes (not tested/tested positive/tested negative), and adjuvant treatment (any, including vaginal brachy therapy vs. none).
  • endpoints overall (OS), disease specific (DSS), progression-free survival (PFS)
  • JM and JK Two clinicians (referred to as: JM and JK), blinded to outcomes and molecular profiles, independently assigned ESMO risk group (low-, intermediate, or high-risk) to each case and the consensus was used for analysis
  • TMA Tissue Microarray
  • Tumours were considered to demonstrate aberrant protein expression (mismatch repair deficient or MMR-D) if tumour cells showed complete absence of nuclear staining, in conjunction with preserved expression in non-neoplastic internal control cells. Staining for individual MMR proteins was repeated on corresponding whole sections whenever there was aberrant, equivocal or uninterpretable staining (e.g., no tumour tissue) on the TMA. Results were ultimately dichotomized as MMR-D (one or more than one of two MMR proteins missing and confirmed on whole section), or intact (both proteins present).
  • DNA extraction Methods as described above were used, and were successful in both fresh frozen and FFPE samples. Briefly, DNA from frozen tumours and buffy coat were extracted using the QiagenTM Gentra PuregeneTM kit (Qiagen) as per manufacturer's protocols. FFPE tumour blocks were extracted using the Qiagen FFPE tissue kit, and all DNA was quantified using the QubitTM fluorometer kit (Life Technologies). To determine somatic status normal DNA was either extracted from available buffy coat or representative normal FFPE blocks.
  • QiagenTM Gentra PuregeneTM kit Qiagen
  • FFPE tumour blocks were extracted using the Qiagen FFPE tissue kit, and all DNA was quantified using the QubitTM fluorometer kit (Life Technologies). To determine somatic status normal DNA was either extracted from available buffy coat or representative normal FFPE blocks.
  • Targeted primers were designed to cover the POLE exonuclease domain exons 9-14.
  • PCR products 150-200bp
  • DNA barcodes (10bp) with lllumina cluster-generating adapters were added to the libraries, and 96 samples pooled.
  • the library pools were sequenced using the llluminaTM MiSeqTM for ultra-deep sequencing. All validated POLE mutations were bi-directionally sequenced twice at minimum, and once in the normal (to validate somatic or germline status) using either ultra- deep MiSeq sequencing or Sanger sequencing.
  • Figure 6 illustrates the steps in molecular classification with the described method, interchangeably referred to herein as the Proactive Molecular Risk Classifier for Endometrial Cancer.
  • the first assessment is immunohistochemistry for the presence of mismatch repair (MMR) deficiency proteins in order to identify women who may have Lynch syndrome to enable rapid referral to the hereditary cancer program and possibly direct surgical or therapeutic decisions.
  • Tumors are next assessed for POLE EDM mutations, and finally p53 IHC yielding four subgroups: MMR-D, POLE, p53 wt and p53 abn.
  • MMR-D mismatch repair
  • POLE p53 wt
  • p53 abn Statistical Analysis. Results of the interrogation of 16 molecular classification models, based on considerations to order of testing and methodologies applicable to clinical samples, i.e. requiring no special specimen handling and relatively low cost and yielded the described method of classification.
  • the initial discovery cohort and the subsequent larger confirmation cohort were compared using descriptive statistics on all parameters of interest.
  • the prognostic signature of the described classification method in the confirmation cohort was compared to the one obtained in the previous Examples as well as in the TCGA study, by considering the survival pattern of the molecular subgroups, obtained from Kaplan Meier survival plots. Harell's C-index was used to evaluate the classification method's ability to discriminate between good and bad outcomes in new patients from the confirmation cohort, using the previously developed model parameters.
  • Estimated hazard ratios associated with the described molecular subgroups, obtained using cox proportional hazard model adjusted for treatment, were compared across cohorts. Cases from the discovery cohort were combined with the new cases from the confirmation cohort to form the full cohort, which was used to establish the described classification method as an independent prognostic marker in EC.
  • clinicopathological parameters with the described subgroups were compared using one-way analysis of variance for continuous data (age at surgery, BMI) and chi-squared test for categorical data (stage, grade, histological subtype at diagnosis, LVSI, myometrial invasion, and nodal status).
  • Multivariable associations were modeled using a multinomial logistic regression, with subgroups as outcome and all clinicopathological parameters as additive covariates.
  • grade was recoded as low grade (grades 1 and 2) versus high grade, similarly stage was recoded as advanced stage (disease spread beyond the uterus encompassing stages ll-IV) versus low stage, and histological subtype was recoded as endometrioid subtype versus non-endometrioid.
  • Adjuvant treatment was additionally dichotomized into any treatment (chemotherapy, external beam
  • a cox proportional hazard model was considered with classification subgroups and prognostic factors available from time of diagnosis as it is desirable to ultimately apply this classification method to specimens PRIOR to surgical staging.
  • groups were corrected for age, BMI, grade and histotype in addition to molecular subgroup as assigned by the classification method.
  • Multivariable analysis was also performed using additional parameters available from post-surgical staging as several of these are known to be important prognostic factors (e.g., stage, nodal status, myometrial invasion and presence of LVSI).
  • Adjuvant treatment status was included as a covariate in all multivariable models to account for the possible confounding effect of treatment, since all cases were not treated.
  • the 'discovery cohort' is as described in Example 1 . Together, the 'confirmation' and 'discovery cohorts' encompass 460 cases and are termed the 'full cohort'.
  • Figure 8A shows a breakdown of cases evaluated in both the discovery and confirmation cohorts. Cases were excluded for reasons listed, ultimately yielding 460 fully evaluable EC's (full cohort).
  • the confirmation cohort included cases from a longer time period (1983-2013) than the original discovery series (2002-2009). Overall follow up was 5.2 years (reverse Kaplan-Meier), and this was consistent in both cohorts. Full details on the cohorts including comparison of age, BMI, stage histotype, presence of LVSI, depth of myometrial invasion, nodal status, adjuvant treatment administered, ESMO risk group, and the molecular subgroup across the discovery, confirmation and full cohorts (discovery + confirmation) were assessed and revealed a slight enrichment for grade 3 and serous cases in the confirmation cohort. The described molecular subgroup distribution of the classification method is consistent with this observation with more p53 abn cases (27%) in the confirmation cohort than in the discovery set ( ⁇ 18%).
  • the confirmation cohort had slightly less tumors displaying MMR-deficiency (MMR-D) (20% compared with 29% in the discovery cohort). Both cohorts are selected, in that they are drawn from a tertiary referral center (majority of low grade endometrioid ECs are managed in the community) and do not represent EC incidence/case distribution in the general population.
  • MMR-D MMR-deficiency
  • the discovery cohort consisted of mostly fresh frozen samples (87.9%) and the confirmation cohort mostly (85.3%) FFPE samples yielding a total of 289 (62.8%) FFPE and 171 (37.2%) frozen samples in the full cohort.
  • the described molecular classification method can discriminate outcomes (OS, DSS,
  • FIG. 8B outlines the breakdown of cases in the full cohort with statistically significant univariable associations seen between molecular subgroups for all clinical and pathological parameters measured shown in Table 9A and Table 9B. Details were obtained on follow up times and number of events by subtypes, and further details on the clinicopathological parameters and molecular features were evaluated, including tissue type examined and a breakdown of IHC scores for the full cohort. Missing data analysis revealed no associations with clinicopathologic parameters or outcomes, with grade and histotype available in all cases and ⁇ 10 cases with missing values for age, stage, myometrial invasion, nodal status, treatment and ESMO risk group.
  • p53 abn tumors had aggressive pathologic features (92% grade 3, 76% non-endometrioid histology, 61 % LVSI and highest proportion of lymph node positivity (17%), advanced stage), but aggressive pathologic features were also noted (66% grade 3, 21 % non- endometrioid histotype, 52.5% with LVSI, 32% advanced stage II-IV) associated with MMR-D ECs (Table 8A and 8B) as compared to p53 wild type subgroup.
  • a multinomial logistic regression model was used to assess the association between clinicopathological parameters that were most significantly associated with the described molecular subgroups, accounting for all other parameters: age, BMI, stage, grade, histotype, LVSI, myometrial invasion, nodal status and any adjuvant therapy. Older age at surgery was significantly associated with increased odds of having a p53 abn tumor. Increased BMI was associated with decreased odds of having a tumor with a POLE EDM. Women with grade 3 (vs. grade 1 or 2) tumours were more likely to have p53 abn or MMR-D tumors.
  • Non-endometrioid histotype and > 50% myometrial invasion were associated with p53 abn subgroup whereas tumors that exhibited 0-50% myometrial invasion were more likely to be MMR-D. Patients with LVSI had higher odds of having tumors that were p53 abn or MMR-D.
  • Table 9 notes: Age at diagnosis, recurrence free survival (RFS) and overall survival
  • Treatment refers to primary adjuvant treatment and does not include treatment that may be given at recurrence.
  • C carboplatin
  • P paclitaxel
  • EBRT external beam radiotherapy to pelvis
  • VF3 vaginal brachytherapy
  • PA para-aortic boost. Mutation frequencies are given in % where known. All mutations shown have been validated. MMR IHC results for two panel testing (MSH6 and PMS2) given. * p53 staining mixed on older studies (1 + and 2+).
  • High grade grade 3 vs. grade 1/2
  • Figures 10A, 10B, and 10C show Kaplan-Meier survival analyses for the full cohort
  • ESMO risk group assignment (low-intermediate-high-risk) between the two clinicians revealed agreement in all but four cases of the total 460 cases in the full cohort included in analysis. These four cases were re-reviewed and a final risk group assigned with agreement of both parties.
  • MMR-D tumors mostly fell in the high-risk group and most p53 wt tumors were low risk, but again, they did range across ESMO subgroups.
  • the molecular classification method described herein can be stated as a decision tree of binary parameters, with the result being determined by the presence or absence of a protein or of a mutation. Consistent subgroup assignment should therefore be achievable with only limitations related to sample quality e.g. low cellularity of samples or low quality of DNA extracted from FFPE. A very small (3.4%) subset of patients was observed with more than one discerning molecular parameter identified. It will be important to know how to classify such uncommon cases in practice, but a multicenter study will likely be needed to gather enough cases to fully understand their natural history. Some comments are possible, however, based on this case series.
  • the herein described molecular classification method may be shown to be more accurate than traditional risk-group classification with regard to determining differences in clinical outcomes, patient reported outcomes/quality of life, and health economic impact.
  • the classification method described herein offers benefits that can be immediately realized.
  • Implementation of MMR IHC for all cases will help identify women who may have Lynch Syndrome, who should be referred for counseling and testing to distinguish somatic events from germline mutations. Identifying women with Lynch syndrome will direct screening for other Lynch-associated cancers, initiate referrals for family members to be tested, and allow subsequent risk reducing interventions to be undertaken, saving lives.
  • MMR-D including ECs
  • clinicians may consider directing therapy based on molecular subgroup with high neoantigen load and associated immune infiltrates (MMR-D or even POLE EDM in the rare cases of recurrence).
  • the molecular classification method described herein addresses the greatest obstacles currently faced by clinicians and patients in management of EC; namely the inability to consistently classify EC tumors, and deficient risk stratification systems to direct care.
  • the described method provides biologically relevant information to patients and physicians; using molecular data to group EC patients based on risk of recurrence and death.
  • the method described herein is simple enough to be performed in any cancer center, on formalin-fixed paraffin embedded material, and at relatively low cost enabling easy translation into the clinic.
  • the information provided by the classification method is particularly relevant for young women struggling with difficult decisions about their reproductive health.
  • Molecular classification using the method described may advance both clinical management and research for endometrial cancers.
  • Embodiments of the disclosure can be represented as a computer program product stored in a machine-readable medium (also referred to as a computer-readable medium, a processor- readable medium, or a computer usable medium having a computer-readable program code embodied therein).
  • the machine-readable medium can be any suitable tangible, non-transitory medium, including magnetic, optical, or electrical storage medium including a diskette, compact disk read only memory (CD-ROM), memory device (volatile or non-volatile), or similar storage mechanism.
  • the machine-readable medium can contain various sets of instructions, code sequences, configuration information, or other data, which, when executed, cause a processor to perform steps in a method according to an embodiment of the disclosure. Other instructions and operations necessary to implement the described implementations can also be stored on the machine-readable medium.
  • the instructions stored on the machine-readable medium can be executed by a processor or other suitable processing device, and can interface with circuitry to perform the described tasks.
  • POLE Polymerase varepsilon
  • Kalloger SE Kobel M (2013) Reproducibility of histological cell type in high-grade endometrial carcinoma. Modern pathology : an official journal of the United States and Canadian Academy of Pathology, Inc 26(12): 1594-604.
  • Gynecologic cancer as a "sentinel cancer" for women with hereditary nonpolyposis colorectal cancer syndrome. Obstetrics and gynecology 105(3): 569-74.

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Abstract

La classification de cancers de l'endomètre par des caractéristiques morphologiques est incohérente, et ne permet d'obtenir que des informations limitées de pronostic et de prédiction. Une méthode de classification de cancers de l'endomètre et de traitement sur la base d'une classification consiste à obtenir un échantillon d'un sujet atteint d'un cancer de l'endomètre ; à évaluer l'échantillon à la recherche : (a) d'une protéine de réparation des mésappariements hypermutés comprenant un phénotype d'instabilité élevée des microsatellites pour classifier le cancer comme : (i) anormal pour la réparation des mésappariements si celle-ci est présente ou présentant une stabilité des microsatellites pour la réparation des mésappariements si celle-ci est absente ; (b) à évaluer ensuite l'échantillon présentant une stabilité des microsatellites pour la réparation des mésappariements à partir (a) d'une mutation d'un domaine d'exonucléase POLE pour classifier le cancer comme (ii) ayant subi une mutation de POLE si celle-ci est présente ou POLE de type sauvage si celle-ci est absente ; et (c) à évaluer ensuite l'échantillon de POLE de type sauvage de (b) à la recherche d'une anomalie du nombre de copies de p53 comprenant un nombre élevé de copies ou un faible nombre de copies pour classifier le cancer comme (iii) p53 de type sauvage, si l'anomalie est absente, et comme (iv) p53 anormal si l'anomalie est présente.
PCT/CA2016/050829 2015-07-14 2016-07-13 Méthode de classification et traitement de cancers de l'endomètre Ceased WO2017008165A1 (fr)

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CN117385036A (zh) * 2023-10-12 2024-01-12 华中科技大学同济医学院附属同济医院 SIGLEC10基因的p.Q144K突变作为子宫内膜癌保育治疗耐药标志物的应用
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CN112955570A (zh) * 2018-09-14 2021-06-11 莱森特生物公司 评估微卫星不稳定性的方法和系统
WO2020056347A1 (fr) * 2018-09-14 2020-03-19 Lexent Bio, Inc. Procédés et systèmes pour évaluer l'instabilité de microsatellites
CN112877438A (zh) * 2021-03-26 2021-06-01 中国医学科学院北京协和医院 纳入分子分型和pdl1检测的高危子宫内膜癌预后评价系统
CN112961921A (zh) * 2021-03-26 2021-06-15 中国医学科学院北京协和医院 一种用于判断早期子宫内膜癌预后的制剂及复发风险模型
CN113046438A (zh) * 2021-03-26 2021-06-29 中国医学科学院北京协和医院 一种纳入分子分型和免疫评分的子宫内膜癌预后评价系统
CN113156120A (zh) * 2021-03-26 2021-07-23 中国医学科学院北京协和医院 B7h4在制备子宫内膜癌分子分型试剂及系统中的应用
CN113604571A (zh) * 2021-09-02 2021-11-05 北京大学第一医院 一种用于人肿瘤分级的基因组合及其用途
CN113759132B (zh) * 2021-10-08 2022-06-03 首都医科大学附属北京妇产医院 用于预测子宫内膜癌预后的模型、产品和方法
CN113759132A (zh) * 2021-10-08 2021-12-07 首都医科大学附属北京妇产医院 用于预测子宫内膜癌预后的模型、产品和方法
CN114438210A (zh) * 2022-03-29 2022-05-06 复旦大学附属妇产科医院 一种基于高通量测序子宫内膜癌分子分型的文库构建方法
KR102612020B1 (ko) * 2022-10-24 2023-12-07 연세대학교 산학협력단 자궁내막암의 예후 예측 방법
WO2024090863A1 (fr) * 2022-10-24 2024-05-02 연세대학교 산학협력단 Procédé pour prédire le pronostic du cancer de l'endomètre
CN117385036A (zh) * 2023-10-12 2024-01-12 华中科技大学同济医学院附属同济医院 SIGLEC10基因的p.Q144K突变作为子宫内膜癌保育治疗耐药标志物的应用
CN120852412A (zh) * 2025-09-22 2025-10-28 同济大学 一种基于数字病理图像的子宫内膜癌分子分型与预后预测系统及方法

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