Therneau, Terry M.

Abstract We found no significant excess of fractures among Rochester, MN, residents with diabetes mellitus initially recognized in 1950–1969, but more recent studies elsewhere have documented an apparent increase in hip fracture risk. To explore potential explanations for any increase in fractures, we performed an historical cohort study among 1964 Rochester residents who first met glycemic criteria for diabetes in 1970–1994 (mean age, 61.7 ± 14.0 yr; 51% men). Fracture risk was estimated by standardized incidence ratios (SIRs), and risk factors were evaluated in Andersen-Gill time-to-fracture regression models. In 23,236 person-years of follow-up, 700 diabetic residents experienced 1369 fractures documented by medical record review. Overall fracture risk was elevated (SIR, 1.3; 95% CI, 1.2–1.4), but hip fractures were increased only in follow-up beyond 10 yr (SIR, 1.5; 95% CI, 1.1–1.9). As expected, fracture risk factors included age, prior fracture, secondary osteoporosis, and corticosteroid use, whereas higher physical activity and body mass index were protective. Additionally, fractures were increased among patients with neuropathy (hazard ratio [HR], 1.3; 95% CI, 1.1–1.6) and those on insulin (HR, 1.3; 95% CI, 1.1–1.5); risk was reduced among users of biquanides (HR, 0.7; 95% CI, 0.6–0.96), and no significant influence on fracture risk was seen with sulfonylurea or thiazolidinedione use. Thus, contrary to our earlier study, the risk of fractures overall (and hip fractures specifically) was increased among Rochester residents with diabetes, but there was no evidence that the rise was caused by greater levels of obesity or newer treatments for diabetes.

We hypothesized that increased monoclonal free kappa or lambda immunoglobulin light chains in smoldering multiple myeloma (SMM), as detected by the serum free light chain (FLC) assay, indicates an increased risk of progression to active myeloma. Baseline serum samples obtained within 30 days of diagnosis were available in 273 patients with SMM seen from 1970 to 1995. At a median follow-up of surviving patients of 12.4 years, transformation to active disease has occurred in 59%. The best breakpoint for predicting risk of progression was an FLC ratio of 0.125 or less, or 8 or more (hazard ratio, 2.3; 95% CI, 1.6-3.2). The extent of abnormality of FLC ratio was independent of SMM risk categories defined by number of bone marrow plasma cells (BMPCs) and size of serum M proteins (BMPC ≥ 10% and serum M protein ≥ 3 g/dL; BMPC ≥ 10% but serum M protein < 3 g/dL; and serum M protein≥ 3 g/dL but BMPC < 10%). Incorporating the FLC ratio into the risk model, the 5-year progression rates in high-, intermediate-, and low-risk groups were 76%, 51%, and 25%, respectively. The serum immunoglobulin FLC ratio is an important additional determinant of clinical outcome in patients with SMM.

AbstractWe describe a method to identify candidate cancer biomarkers by analyzing numeric approximations of tissue specificity of human genes. These approximations were calculated by analyzing predicted tissue expression distributions of genes derived from mapping expressed sequence tags (ESTs) to the human genome sequence using a binary indexing algorithm. Tissue-specificity values facilitated high-throughput analysis of the human genes and enabled the identification of genes highly specific to different tissues. Tissue expression distributions for several genes were compared to estimates obtained from other public gene expression datasets and experimentally validated using quantitative RT-PCR on RNA isolated from several human tissues. Our results demonstrate that most human genes (∼98%) are expressed in many tissues (low specificity), and only a small number of genes possess very specific tissue expression profiles. These genes comprise a rich dataset from which novel therapeutic targets and novel diagnostic serum biomarkers may be selected.Contact: vasm@mayo.eduSupplementary information: Supplementary data are available at Bioinformatics online.

Abstract Motivation: Using stable isotopes in global proteome scans, labeled molecules from one sample are pooled with unlabeled molecules from another sample and subsequently subjected to mass-spectral analysis. Stable-isotope methodologies make use of the fact that identical molecules of different stable-isotope compositions are differentiated in a mass spectrometer and are represented in a mass spectrum as distinct isotopic clusters with a known mass shift. We describe two multivariable linear regression models for 16O/18O stable-isotope labeled data that jointly model pairs of resolved isotopic clusters from the same peptide and quantify the abundance present in each of the two biological samples while concurrently accounting for peptide-specific incorporation rates of the heavy isotope. The abundance measure for each peptide from the two biological samples is then used in down-stream statistical analyses, e.g. differential expression analysis. Because the multivariable regression models are able to correct for the abundance of the labeled peptide that appear as an unlabeled peptide due to the inability to exchange the natural C-terminal oxygen for the heavy isotope, they are particularly advantageous for a two-step digestion/labeling procedure. We discuss how estimates from the regression model are used to quantify the variability of the estimated abundance measures for the paired samples. Although discussed in the context of 16O/18O stable-isotope labeled data, the multivariable regression models are generalizable to other stable-isotope labeled technologies. Contact: eckel@mayo.edu

Abstract Motivation: The need for normalization in microarray experiments has been well documented in the literature. Currently, many analysis methods treat normalization and analysis as a series of steps, with summarized data carried forward to the next step. Results: We present a unified algorithm which incorporates normalization and class comparison in one analysis using probe level perfect match and mismatch data. The algorithm is based on calibration models common to most biological assays, and the resulting chip-specific parameters have a natural interpretation. We show that the algorithm fits into the statistical generalized linear models framework, describe a practical fitting strategy and present results of the algorithm applied to an example dataset as well as based on metrics used in affycomp. The algorithm ranks amongst the top third of the affycomp competitors, performing best in measures of bias. Availability: R functions are available on request from the authors. Contact: oberg.ann@mayo.edu

Abstract An abnormal serum immunoglobulin free light chain (FLC) ratio at diagnosis may identify risk of progression to myeloma in patients with solitary bone plasmacytoma (SBP). In the cohort of 116 patients, 43 have progressed to myeloma, with a median time to progression of 1.8 years. The FLC ratio was determined in all 116 patients on serum collected at time of diagnosis and was abnormal in 54 patients (47%). An abnormal FLC ratio was associated with a higher risk of progression to myeloma (P = .039). The risk of progression at 5 years was 44% in patients with an abnormal serum FLC ratio at diagnosis compared with 26% in those with a normal FLC ratio. One to 2 years following diagnosis, a persistent serum M protein level of 5 g/L (0.5 g/dL) or higher was an additional risk factor for progression. A risk stratification model was constructed using the 2 variables of FLC ratio and M protein level: patients with a normal FLC ratio at baseline and M protein level less than 5 g/L (0.5 g/dL) at 1 to 2 years following diagnosis (low risk, n = 31); with either risk factor abnormal (intermediate risk, n = 26); and with both an abnormal FLC ratio and M protein level of 5 g/L (0.5 g/dL) or higher (high risk, n = 18). The corresponding progression rates at 5 years were significantly different in the low, intermediate, and high groups: 13%, 26%, and 62%, respectively (P < .001).

AbstractThe clinical efficacy of evaluating genetic anomalies in metaphase cells versus interphase nuclei for multiple myeloma (MM) is poorly understood. Therefore, survival for 154 patients with newly diagnosed untreated MM was compared with results from analysis of metaphase and interphase cells. Metaphases were studied by conventional cytogenetics and fluorescent-labeled DNA probes (fluorescence in situ hybridization [FISH]), whereas inter-phase nuclei were evaluated only by FISH. All FISH studies were done using DNA probes to detect t(4;14)(p16;q32), t(11;14)(q13;q32), t(14;16)(q32;q23), del(17) (p13.1), and chromosome 13 anomalies. Metaphases were abnormal by cytogenetics and/or metaphase FISH in 61 (40%) patients. Abnormal interphase nuclei were observed in 133 (86%) patients, including each patient with abnormal metaphases. FISH was a necessary adjunct to cytogenetics to detect t(4;14) and t(14;16) in metaphase cells. Patient survival was especially poor for patients with greater than 50% abnormal interphase nuclei, although this result was more likely due to level of plasma cells than specific chromosome anomalies. For metaphase data, patients with t(4;14), t(14;16), del(17) (p13.1), and/or chromosome 13 anomalies (primarily monosomy 13) had poor survival. A different outcome was observed for interphase data as patients with t(4;14) or t(14;16) had poor survival, whereas patients with chromosome 13 anomalies had intermediate survival: interphase FISH did not substitute for metaphase analysis.

Abstract We hypothesized that the presence of monoclonal free kappa or lambda immunoglobulin light chains in monoclonal gammopathy of undetermined significance (MGUS), as detected by the serum free light chain (FLC) assay increases the risk of progression to malignancy. Of 1384 patients with MGUS from Southeastern Minnesota seen at the Mayo Clinic from 1960 to 1994, baseline serum samples obtained within 30 days of diagnosis were available in 1148. At a median follow-up of 15 years, malignant progression had occurred in 87 (7.6%) patients. An abnormal FLC ratio (kappa-lambda ratio &lt; 0.26 or &gt; 1.65) was detected in 379 (33%) patients. The risk of progression in patients with an abnormal FLC ratio was significantly higher compared with patients with a normal ratio (hazard ratio, 3.5; 95% confidence interval [CI], 2.3-5.5; P &lt; .001) and was independent of the size and type of the serum monoclonal (M) protein. Patients with an abnormal serum FLC ratio, non–immunoglobulin G (non-IgG) MGUS, and a high serum M protein level (≥ 15 g/L) had a risk of progression at 20 years of 58% (high-risk MGUS) versus 37% with any 2 of these risk factors (high-intermediate risk), 21% with one risk factor (low-intermediate risk), and 5% when none of the risk factors were present (low risk).

Purpose Primary systemic amyloidosis (AL) is a multisystemic disorder resulting from an underlying plasma cell dyscrasia. There is no formal staging system for AL, making comparisons between studies and treatment centers difficult. Our group previously identified elevated serum cardiac troponin T (cTnT) as the most powerful predictor of overall survival. Others have reported that N-terminal pro-brain natriuretic peptide (NT-proBNP) is a valuable prognostic marker. We sought to develop a staging system for patients with AL. Patients and Methods Two hundred forty-two patients with newly diagnosed AL who were seen at the Mayo Clinic between April 1979 and November 2000, and who had echocardiograms and stored serum samples at presentation were eligible for this retrospective review. NT-proBNP measurements were performed on 242 patients in whom cTnT and cardiac troponin I (cTnI) had been previously run. Two prognostic models were designed using threshold values of NT-proBNP and either cTnT or cTnI (NT-proBNP < 332 ng/L, cTnT < 0.035 μg/L, and cTnI < 0.1 μg/L). Depending on whether NT-proBNP and troponin levels were both low, were high for only one level, or were both high, patients were classified as stage I, II, or III, respectively. Results Using the cTnT+NT-proBNP model 33%, 30%, and 37% of patients were stages I, II, and III, respectively, with median survivals of 26.4, 10.5, and 3.5 months, respectively. The alternate cTnI+NT-proBNP model predicted median survivals of 27.2, 11.1, and 4.1 months, respectively. Conclusion Stratification of AL patients into three stages is possible with two readily available and reproducible tests setting the stage for more consistent and reliable cross comparisons of therapeutic outcomes.