Blood Cell Count

Table 2 provides an overview of our measures of epigenetic age acceleration. The universal measure of age acceleration (AgeAccel), which is valid for a wide range of tissue types, is defined as the residual resulting from a linear regression model that regresses the Horvath estimate of epigenetic age on chronological age. Thus, a positive value for AgeAccel indicates that the observed epigenetic age is higher than that predicted, based on chronological age. AgeAccel has a relatively weak correlation with blood cell counts [25 (link)], but it still relates to estimated blood cell counts, as seen in Supplementary Table 4.
To estimate “pure” epigenetic aging effects that are not influenced by differences in blood cell counts (“intrinsic” epigenetic age acceleration, IEAA), we obtained the residual resulting from a multivariate regression model of epigenetic age on chronological age and various blood immune cell counts (naive CD8+ T cells, exhausted CD8+ T cells, plasmablasts, CD4+ T cells, natural killer cells, monocytes, and granulocytes) imputed from methylation data.
Extrinsic epigenetic age acceleration measures capture both cell intrinsic methylation changes and extracellular changes in blood cell composition. Our measure of EEAA is defined using the following three steps. First, we calculated the epigenetic age measure from Hannum et al [2 (link)], which already correlated with certain blood cell types [5 (link)]. Second, we increased the contribution of immune blood cell types to the age estimate by forming a weighted average of Hannum's estimate with 3 cell types that are known to change with age: naïve (CD45RA+CCR7+) cytotoxic T cells, exhausted (CD28-CD45RA-) cytotoxic T cells, and plasmablasts using the Klemera-Doubal approach [32 (link)]. The weights used in the weighted average are determined by the correlation between the respective variable and chronological age [32 (link)]. The weights were chosen on the basis of the WHI data. Thus, the same (static) weights were used for all data sets. EEAA was defined as the residual variation resulting from a univariate model regressing the resulting age estimate on chronological age. By construction, EEAA is positively correlated with the estimated abundance of exhausted CD8+ T cells, plasmablast cells, and a negative correlated with naive CD8+ T cells. Blood cell counts were estimated based on DNA methylation data as described in the next section. By construction, the measures of EEAA track both age related changes in blood cell composition and intrinsic epigenetic changes. None of our four measures of epigenetic age acceleration are correlated with chronological age.

To evaluate the performance of the proposed method, we used three real data sets from the study of the methylation change associated with gastric cancer [24 (link)] (GSE30601, n = 297), serum IgE concentration [22 (link)] (n = 357) and human aging [23 (link)] (GSE40279, n = 656). The first two data sets were generated using Illumina HumanMethylation27 BeadChip and the third data set was generated using Illumina HumanMethylation450 BeadChip. Gastric tissue was used to profile methylation for the first study, and peripheral whole blood was used for the last two studies. Probes with detection P values >0.01 in more than 5% of the samples, probes with single-nucleotide polymorphisms (MAF > 0.05, European population, 1000 Genomes Project), and probes on the sex chromosomes were excluded from analysis. We performed the methylation association tests on the raw data since a previous study found that the raw data were already highly reproducible and some normalization approaches might introduce more variability into the data [29 (link)]. We did not remove consistently methylated and unmethylated probes since there was no substantial evidence to justify that. For the IgE data set, whole blood cell counts were available for neutrophils, lymphocytes, monocytes, eosinophils and basophils.

This protocol was approved by the Mayo Clinic institutional review board and each site institutional review board within the participating Translational Breast Cancer Research Consortium (TBCRC) institutions (Supplement 1). Participants provided written informed consent. Postmenopausal women with ER⁺/ERBB2⁻ MBC or ER⁻/ERBB2⁻ MBC with a history of primary ER⁺/ERBB2⁻ disease were preregistered. The study defined ER⁺ disease (by clinical assay) as 10% or more cells positive to limit inclusion of basal-like ER⁻/ERBB2⁻ MBC that did not acquire ET resistance due to ERα downregulation. Additional key preregistration criteria included 2 or fewer prior MBC chemotherapy regimens, prior treatment with fulvestrant for ER⁺ MBC, measurable disease by Response Evaluation Criteria in Solid Tumors (RECIST) criteria, willingness to limit daily alcohol intake, and no visceral crisis. Unlimited prior treatment with ET was allowed.
During the preregistration period, a biopsy specimen of a metastatic site was obtained for central ERα testing (for stratification). Key registration criteria included adequate blood cell counts and chemistry results, an Eastern Cooperative Oncology Group (ECOG) performance score of 0 to 1, no systemic therapy 21 days or fewer before registration, no need for treatment with a chronic proton pump inhibitor or H2 antagonist, and no visceral crisis.

Alisertib was administered as 50 mg, oral, twice daily on days 1 to 3, 8 to 10, and 15 to 17 of a 28-day cycle. Fulvestrant was administered as 500 mg, intramuscularly on days 1 and 15 of a 28-day cycle (cycle 1) and then day 1 of all subsequent cycles.
Arm 1 patients experiencing progression of disease (PD) could crossover to arm 2 if (1) ER was 10%or greater positive in preregistration or PD tumor tissue; (2) recovery from toxic effects to grade 0 to 1; (3) adequate blood cell counts and chemistry results; and (4) treatment initiated within 35 days of PD.

The data collected in this retrospective study were obtained through the hospital's electronic medical record system and included baseline information (sex, age, and past medical history), clinic symptoms (main onset phenotype and state of consciousness), laboratory examinations (blood cell count, total protein, Alb, globulin, coagulation function index, CRP, and FARP), electroencephalogram (EEG), magnetic resonance imaging (MRI), and treatment options. Fasting venous blood was collected from the patients early in the morning after admission. All blood samples acquired for routine blood tests and biomarker identification were analyzed in the biochemistry laboratory of the First Affiliated Hospital of Zhengzhou University. All tests were performed according to the manufacturer's instructions and relevant guidelines, and the inspectors were blinded to all clinical information.