Method to measure myeloid suppressor cells for diagnosis and prognosis of cancer

A method of using an array to determine proportions in a biological sample of a subject of leukocyte types to prognose and/or diagnose a disease state in the subject, the method comprising:

• analyzing extent of hybridization of patient sample DNA to each of a plurality of oligonucleotide probes, the probes being affixed to at least two surfaces for each of methylated and unmethylated CpG sequences and otherwise identical in nucleotide sequence, the plurality of the nucleotide sequences selected from at least one of the group of SEQ ID NO:
• 1-100, for determining methylation status of at least one CpG dinucleotide in the DNA of the sample;
• comparing methylation status of the plurality of CpG dinucleotides analyzed in the patient sample to a DNA methylation reference library, to determine proportion of each leukocyte type in the sample;
• displaying the methylation status of the plurality of hybridized genes in the sample in a graphical representation, thereby generating an image of the methylation profile (methylome) of the leukocyte types in the patient sample;
• and,
• prognosing and/or diagnosing the disease state in the patient associated with the methylation status of CpG sites in leukocyte types, the disease state selected from a cancer, a cardiac condition, inflammation, an autoimmune disease, and infection/sepsis.

In a method of predicting a methylation class membership of leukocytes in a bodily fluid sample of a patient, the methylation class membership corresponding to an epigenetic signature of a plurality of leukocyte types, in which the method includes steps of measuring amounts of DNA methylation in each of a plurality of leukocyte type populations to determine differentially methylated regions (DMRs), ranking leukocyte DMRs for each leukocyte type according to statistical strength of association of each of at least one DMR with each leukocyte type, clustering samples in a training set using a defined number of highest ranked leukocyte DMRs to determine clustering solutions, a clustering solution corresponding to the methylation class membership, and predicting the methylation class membership for the leukocyte types within a testing set by applying the clustering solutions obtained from the training set to highest ranked leukocyte DMRs in the testing set, the predicted methylation class membership being determined by testing association of the predicted methylation class membership with the statistical discriminatory strength of the at least one DMR among the leukocyte types, the improvement comprising:

• obtaining leukocyte methylation data of the sample using an array containing a plurality of nucleotide sequences each having a CpG site affixed to the array;
• identifying statistically predictive subset DNA methylation libraries by scanning candidate sets of putative leukocyte-specific methylation markers to find sets of CpG sites that characterize each of the respective leukocyte types in the sample estimated by a cell mixture deconvolution;
• constructing and evolving subset libraries of DMRs consisting of CpG sites differentially methylated among leukocyte types, by iteratively selecting subsets of DMRs at each iteration based on the statistical contribution of each DMR to methylation class membership prediction accuracy;
• modifying a probability of selection of the DMRs at each iteration, the probability of selection of a CpG being modified proportional to contribution of the at least one DMR to methylation class membership prediction accuracy;
• and,
• comparing the subset library of the patient DMRs sample to DMRs of a reference-based library of a plurality of control samples from a plurality of normal patients, to obtain a prognosis and/or a diagnosis of a cancer of the patient.

A method of obtaining selection probabilities of leukocyte differentially methylated regions (DMRs) for inclusion in a statistically predictive subset library of DMRs for predicting leukocyte type methylation class membership of leukocytes in a blood sample from a subject for prognosis and/or diagnosis of cancer in the subject, the method comprising:

• constructing a candidate DMR search space to compare mean methylation values among leukocyte types by identifying CpGs that uniquely characterize each leukocyte cell type, and randomly assembling subset DMR libraries with CpGs that uniquely characterize the leukocyte cell types through multiple iterations;
• estimating leukocyte cell compositions in the sample using the assembled subset DMR libraries and cell mixture deconvolution, and computing leukocyte ratios from the estimated leukocyte compositions of the sample;
• assessing the accuracy of leukocyte cell composition estimates by comparing statistical differences among observed cell compositions obtained by at least one method selected from the group of:
• fluorescence-activated cell sorting (FACS) and complete blood cell counts (CBC), to predicted cell compositions obtained from cell mixture deconvolution of normal control samples, and implementing an iterative leave-one out procedure to assess individual contributions of each CpG to statistical prediction performance of the methylation class membership of the leukocytes, and further computing a dispersion separability criterion (DSC) score to assess a DMR subset power for discriminating among leukocyte types, to select CpGs, and updating subset DMR library selection probabilities by modifying the CpGs selected using the statistical prediction performance of a relative and of an absolute prediction accuracy of each CpG compared to remaining CpGs in the library, and using the updated probabilities in successive iterations to obtain updated probabilities, resulting statistically predictive subset DNA methylation libraries containing CpGs with the largest selection probabilities for improved accuracy of predicting leukocyte type methylation class membership;
• and,
• fitting the multivariate proportional hazards ratio calculated from the sample to the updated subset DMR libraries thereby prognosing and/or diagnosing cancer in the blood sample from the subject.