Associations between actigraphy-measured sleep duration, continuity, and timing with mortality in the UK Biobank.

STUDY OBJECTIVES: To examine the associations between sleep duration, continuity, timing, and mortality using actigraphy among adults. METHODS: Data were from a cohort of 88 282 adults (40-69 years) in UK Biobank that wore a wrist-worn triaxial accelerometer for 7 days. Actigraphy data were processed to generate estimates of sleep duration and other sleep characteristics including wake after sleep onset (WASO), number of 5-minute awakenings, and midpoint for sleep onset/wake-up and the least active 5 hours (L5). Data were linked to mortality outcomes with follow-up to October 31, 2021. We implemented Cox models (hazard ratio, confidence intervals [HR, 95% CI]) to quantify sleep associations with mortality. Models were adjusted for demographics, lifestyle factors, and medical conditions. RESULTS: Over an average of 6.8 years 2973 deaths occurred (1700 cancer, 586 CVD deaths). Overall sleep duration was significantly associated with risk for all-cause (p < 0.01), cancer (p < 0.01), and CVD (p = 0.03) mortality. For example, when compared to sleep durations of 7.0 hrs/d, durations of 5 hrs/d were associated with a 29% higher risk for all-cause mortality (HR: 1.29 [1.09, 1.52]). WASO and number of awakenings were not associated with mortality. Individuals with L5 early or late midpoints (<2:30 or ≥ 3:30) had a ~20% higher risk for all-cause mortality, compared to those with intermediate L5 midpoints (3:00-3:29; p ≤ 0.01; e.g. HR ≥ 3:30: 1.19 [1.07, 1.32]). CONCLUSIONS: Shorter sleep duration and both early and late sleep timing were associated with a higher mortality risk. These findings reinforce the importance of public health efforts to promote healthy sleep patterns in adults.

Actigraphy-derived measures of sleep and risk of prostate cancer in the UK Biobank.

BACKGROUND: Studies of sleep and prostate cancer are almost entirely based on self-report, with limited research using actigraphy. Our goal was to evaluate actigraphy-measured sleep and prostate cancer and to expand on findings from prior studies of self-reported sleep. METHODS: We prospectively examined 34 260 men without a history of prostate cancer in the UK Biobank. Sleep characteristics were measured over 7 days using actigraphy. We calculated sleep duration, onset, midpoint, wake-up time, social jetlag (difference in weekend-weekday sleep midpoints), sleep efficiency (percentage of time spent asleep between onset and wake-up time), and wakefulness after sleep onset. Cox proportional hazards models were used to estimate covariate-adjusted hazards ratios (HRs) and 95% confidence intervals (CIs). RESULTS: Over 7.6 years, 1152 men were diagnosed with prostate cancer. Sleep duration was not associated with prostate cancer risk. Sleep midpoint earlier than 4:00 am was not associated with prostate cancer risk, though sleep midpoint of 5:00 am or later was suggestively associated with lower prostate cancer risk but had limited precision (earlier than 4:00 am vs 4:00-4:59 am HR = 1.00, 95% CI = 0.87 to 1.16; 5:00 am or later vs 4:00-4:59 am HR = 0.79, 95% CI = 0.57 to 1.10). Social jetlag was not associated with greater prostate cancer risk (1 to <2 hours vs <1 hour HR = 1.06, 95% CI = 0.89 to 1.25; ≥2 hours vs <1 hour HR = 0.90, 95% CI = 0.65 to 1.26). Compared with men who averaged less than 30 minutes of wakefulness after sleep onset per day, men with 60 minutes or more had a higher risk of prostate cancer (HR = 1.20, 95% CI = 1.00 to 1.43). CONCLUSIONS: Of the sleep characteristics studied, higher wakefulness after sleep onset-a measure of poor sleep quality-was associated with greater prostate cancer risk. Replication of our findings between wakefulness after sleep onset and prostate cancer are warranted.

Sickle cell allele HBB-rs334(T) is associated with decreased risk of childhood Burkitt lymphoma in East Africa.

Burkitt lymphoma (BL) is an aggressive B-cell lymphoma that significantly contributes to childhood cancer burden in sub-Saharan Africa. Plasmodium falciparum, which causes malaria, is geographically associated with BL, but the evidence remains insufficient for causal inference. Inference could be strengthened by demonstrating that mendelian genes known to protect against malaria-such as the sickle cell trait variant, HBB-rs334(T)-also protect against BL. We investigated this hypothesis among 800 BL cases and 3845 controls in four East African countries using genome-scan data to detect polymorphisms in 22 genes known to affect malaria risk. We fit generalized linear mixed models to estimate odds ratios (OR) and 95% confidence intervals (95% CI), controlling for age, sex, country, and ancestry. The ORs of the loci with BL and P. falciparum infection among controls were correlated (Spearman's ρ = 0.37, p = .039). HBB-rs334(T) was associated with lower P. falciparum infection risk among controls (OR = 0.752, 95% CI 0.628-0.9; p = .00189) and BL risk (OR = 0.687, 95% CI 0.533-0.885; p = .0037). ABO-rs8176703(T) was associated with decreased risk of BL (OR = 0.591, 95% CI 0.379-0.992; p = .00271), but not of P. falciparum infection. Our results increase support for the etiological correlation between P. falciparum and BL risk.

Metabolomic Analysis of Vitamin E Supplement Use in the Prostate, Lung, Colorectal, and Ovarian Cancer Screening Trial.

The effects of vitamin E supplementation on cancer and other chronic diseases are not clear. We compared the serum metabolomic profile of differing vitamin E dosages in order to re-examine the previously observed changes in a novel C22 lactone sulfate compound, androgenic steroids, and other metabolites. A total of 3409 women and men previously selected for metabolomics studies in the PLCO Cancer Screening Trial were included in this investigation. Serum metabolites were profiled using ultrahigh-performance liquid and gas chromatography/tandem mass spectrometry. Seventy known metabolites including C22 lactone sulfate and androgens were significantly associated with vitamin E supplementation. In the sex-stratified analysis, 10 cofactors and vitamins (e.g., alpha-CEHC sulfate and alpha-CEHC glucuronide), two carbohydrates (glyceric and oxalic acids), and one lipid (glycocholenate sulfate) were significantly associated with vitamin E dose in both males and females (FDR-adjusted p-value < 0.01). However, the inverse association between C22 lactone sulfate and daily vitamin E supplementation was evident in females only, as were two androgenic steroids, 5-androstenediol and androsterone glucuronide. Our study provides evidence of distinct steroid hormone pathway responses based on vitamin E dosages. Further studies are needed to gain biological insights into vitamin E biochemical effects relevant to cancer and other chronic diseases.

Inference for High-Dimensional Censored Quantile Regression.

With the availability of high dimensional genetic biomarkers, it is of interest to identify heterogeneous effects of these predictors on patients' survival, along with proper statistical inference. Censored quantile regression has emerged as a powerful tool for detecting heterogeneous effects of covariates on survival outcomes. To our knowledge, there is little work available to draw inference on the effects of high dimensional predictors for censored quantile regression. This paper proposes a novel procedure to draw inference on all predictors within the framework of global censored quantile regression, which investigates covariate-response associations over an interval of quantile levels, instead of a few discrete values. The proposed estimator combines a sequence of low dimensional model estimates that are based on multi-sample splittings and variable selection. We show that, under some regularity conditions, the estimator is consistent and asymptotically follows a Gaussian process indexed by the quantile level. Simulation studies indicate that our procedure can properly quantify the uncertainty of the estimates in high dimensional settings. We apply our method to analyze the heterogeneous effects of SNPs residing in lung cancer pathways on patients' survival, using the Boston Lung Cancer Survivor Cohort, a cancer epidemiology study on the molecular mechanism of lung cancer.

Dietary Quality and Circulating Lipidomic Profiles in 2 Cohorts of Middle-Aged and Older Male Finnish Smokers and American Populations.

BACKGROUND: Higher dietary quality is associated with lower disease risks and has not been examined extensively with lipidomic profiles. OBJECTIVES: Our goal was to examine associations of the Healthy Eating Index (HEI)-2015, Alternate HEI-2010 (AHEI-2010), and alternate Mediterranean Diet Index (aMED) diet quality indices with serum lipidomic profiles. METHODS: We conducted a cross-sectional analysis of HEI-2015, AHEI-2010, and aMED with lipidomic profiles from 2 nested case-control studies within the Prostate, Lung, Colorectal and Ovarian Cancer Screening Trial (n = 627) and the Alpha-Tocopherol, Beta-Carotene Cancer Prevention Study (n = 711). We used multivariable linear regression to determine associations of the indices, derived from baseline food-frequency questionnaires (Prostate, Lung, Colorectal and Ovarian Cancer Screening Trial: 1993-2001, Alpha-Tocopherol, Beta-Carotene Cancer Prevention Study: 1985-1988) with serum concentrations of 904 lipid species and 252 fatty acids (FAs) across 15 lipid classes and 28 total FAs, within each cohort and meta-analyzed results using fixed-effect models for lipids significant at Bonferroni-corrected threshold in common in both cohorts. RESULTS: Adherence to HEI-2015, AHEI-2010, or aMED was associated positively with 31, 41, and 54 lipid species and 8, 6, and 10 class-specific FAs and inversely with 2, 8, and 34 lipid species and 1, 3, and 5 class-specific FAs, respectively. Twenty-five lipid species and 5 class-specific FAs were common to all indices, predominantly triacylglycerols, FA22:6 [docosahexaenoic acid (DHA)]-containing species, and DHA. All indices were positively associated with total FA22:6. AHEI-2010 and aMED were inversely associated with total FA18:1 (oleic acid) and total FA17:0 (margaric acid), respectively. The identified lipids were most associated with components of seafood and plant proteins and unsaturated:saturated fat ratio in HEI-2015; eicosapentaenoic acid plus DHA in AHEI-2010; and fish and monounsaturated:saturated fat ratio in aMED. CONCLUSIONS: Adherence to HEI-2015, AHEI-2010, and aMED is associated with serum lipidomic profiles, mostly triacylglycerols or FA22:6-containing species, which are related to seafood and plant proteins, eicosapentaenoic acid-DHA, fish, or fat ratio index components.

Association of Leisure Time Physical Activity Types and Risks of All-Cause, Cardiovascular, and Cancer Mortality Among Older Adults.

Importance: Higher amounts of physical activity are associated with increased longevity. However, whether different leisure time physical activity types are differentially associated with mortality risk is not established. Objectives: To examine whether participation in equivalent amounts of physical activity (7.5 to <15 metabolic equivalent of task [MET] hours per week) through different activity types is associated with mortality risk and to investigate the shape of the dose-response association. Design, Setting, and Participants: Participants in this cohort were respondents from the National Institutes of Health-AARP Diet and Health Study who completed the follow-up questionnaire between 2004 and 2005. This questionnaire collected data on weekly durations of different types of physical activities. Mortality was ascertained through December 31, 2019. Exposures: MET hours per week spent participating in the following activities: running, cycling, swimming, other aerobic exercise, racquet sports, golf, and walking for exercise. Main Outcomes and Measures: All-cause, cardiovascular, and cancer mortality. Separate multivariable-adjusted Cox proportional hazards regression models were fitted to estimate hazard ratios (HRs) and 95% CIs of mortality for each of the 7 types of leisure time physical activities, as well as the sum of these activities. Results: A total of 272 550 participants (157 415 men [58%]; mean [SD] age at baseline, 70.5 [5.4] years [range, 59-82 years]) provided information on types of leisure time activity, and 118 153 (43%) died during a mean (SD) follow-up of 12.4 (3.9) years. In comparison with those who did not participate in each activity, 7.5 to less than 15 MET hours per week of racquet sports (HR, 0.84; 95% CI, 0.75-0.93) and running (HR, 0.85; 95% CI, 0.78-0.92) were associated with the greatest relative risk reductions for all-cause mortality, followed by walking for exercise (HR, 0.91; 95% CI, 0.89-0.93), other aerobic activity (HR, 0.93; 95% CI, 0.90-0.95), golf (HR, 0.93; 95% CI, 0.90-0.97), swimming (HR, 0.95; 95% CI, 0.92-0.98), and cycling (HR, 0.97; 95% CI, 0.95-0.99). Each activity showed a curvilinear dose-response association with mortality risk; low MET hours per week of physical activity for any given activity type were associated with a large reduction in mortality risk, with diminishing returns for each increment in activity thereafter. Associations were similar for cardiovascular and cancer mortality. Conclusions and Relevance: This cohort study of older individuals found differences between different types of leisure time activities and mortality risk, but there were significant associations between participating in 7.5 to less than 15 MET hours per week of any activity and mortality risk.

The Efficacy of Neoadjuvant Versus Adjuvant Therapy for Resectable Esophageal Cancer Patients: A Systematic Review and Meta-Analysis.

OBJECTIVE: Inconclusive results are available as to whether chemo/radiotherapy should be administered to resectable esophageal cancer patients before surgery (neoadjuvant therapy) or after surgery (adjuvant therapy). The paper, via a meta-analysis of effects of treatment modalities when administering chemo/radiotherapy, aims to systematically evaluate the effect of timing of chemo/radiotherapy and surgery. METHODS: We performed a systematic literature search for clinical trials of neoadjuvant and adjuvant therapy for patients with esophageal cancer. Using meta-analysis, we conducted direct and adjusted indirect comparisons of overall survival, complete resection rate (R0 resection), perioperative mortality, leakage rate and local recurrence in patients with resectable esophageal cancer. RESULTS: A total of 32 studies involving 7985 patients with esophageal cancer were included in the meta-analysis. Twenty-five randomized controlled studies indirectly compared neoadjuvant/adjuvant therapy with surgery alone, while five non-randomized controlled studies and two randomized controlled studies directly compared neoadjuvant with adjuvant therapy. Neoadjuvant therapy followed by surgery, compared with surgery along with adjuvant therapy, showed a significant overall survival advantage in our pooled analysis (HR 0.88; 95% CI 0.79-0.98). Directly compared with adjuvant therapy, neoadjuvant therapy demonstrated a lower local recurrence rate (OR 0.56; 95% CI 0.43-0.74) with low heterogeneity (I2 = 1%). Neoadjuvant therapy, comparing to surgery with or without adjuvant therapy, showed a significantly higher R0 resection rate (OR 2.86; 95% CI 2.02-4.04) with moderate heterogeneity (I2 = 38%) and no significant differences in postoperative anastomotic leakage (P = 0.50). However, neoadjuvant therapy, compared with surgery adjuvant therapy, significantly increased perioperative mortality in both direct and indirect comparisons (P < 0.01). CONCLUSIONS: We found that neoadjuvant therapy was associated with higher overall survival and R0 resection rate without increasing postoperative anastomotic leakage for patients with resectable esophageal cancer, whereas neoadjuvant therapy was associated with higher perioperative mortality after esophagectomy.

A network-based variable selection approach for identification of modules and biomarker genes associated with end-stage kidney disease.

AIMS: Intervention for end-stage kidney disease (ESKD), which is associated with adverse prognoses and major economic burdens, is challenging due to its complex pathogenesis. The study was performed to identify biomarker genes and molecular mechanisms for ESKD by bioinformatics approach. METHODS: Using the Gene Expression Omnibus dataset GSE37171, this study identified pathways and genomic biomarkers associated with ESKD via a multi-stage knowledge discovery process, including identification of modules of genes by weighted gene co-expression network analysis, discovery of important involved pathways by Gene Ontology and Kyoto Encyclopedia of Genes and Genomes enrichment analyses, selection of differentially expressed genes by the empirical Bayes method, and screening biomarker genes by the least absolute shrinkage and selection operator (Lasso) logistic regression. The results were validated using GSE70528, an independent testing dataset. RESULTS: Three clinically important gene modules associated with ESKD, were identified by weighted gene co-expression network analysis. Within these modules, Gene Ontology and Kyoto Encyclopedia of Genes and Genomes enrichment analyses revealed important biological pathways involved in ESKD, including transforming growth factor-ß and Wnt signalling, RNA-splicing, autophagy and chromatin and histone modification. Furthermore, Lasso logistic regression was conducted to identify five final genes, namely, CNOT8, MST4, PPP2CB, PCSK7 and RBBP4 that are differentially expressed and associated with ESKD. The accuracy of the final model in distinguishing the ESKD cases and controls was 96.8% and 91.7% in the training and validation datasets, respectively. CONCLUSION: Network-based variable selection approaches can identify biological pathways and biomarker genes associated with ESKD. The findings may inform more in-depth follow-up research and effective therapy.

Building generalized linear models with ultrahigh dimensional features: A sequentially conditional approach.

Conditional screening approaches have emerged as a powerful alternative to the commonly used marginal screening, as they can identify marginally weak but conditionally important variables. However, most existing conditional screening methods need to fix the initial conditioning set, which may determine the ultimately selected variables. If the conditioning set is not properly chosen, the methods may produce false negatives and positives. Moreover, screening approaches typically need to involve tuning parameters and extra modeling steps in order to reach a final model. We propose a sequential conditioning approach by dynamically updating the conditioning set with an iterative selection process. We provide its theoretical properties under the framework of generalized linear models. Powered by an extended Bayesian information criterion as the stopping rule, the method will lead to a final model without the need to choose tuning parameters or threshold parameters. The practical utility of the proposed method is examined via extensive simulations and analysis of a real clinical study on predicting multiple myeloma patients' response to treatment based on their genomic profiles.

Lymph Node Station-Based Nodal Staging System for Esophageal Squamous Cell Carcinoma: A Large-Scale Multicenter Study.

BACKGROUND: The American Joint Committee on Cancer (AJCC) nodal staging for esophageal squamous cell carcinoma (ESCC) has been defined by the number of metastatic lymph nodes (N system). However, the precise counting of individual positive lymph nodes is difficult and unreliable in some clinical settings, which calls for a more available and reliable system. This study examined the performance of a newly proposed nodal staging category, termed the S system, based on the number of metastatic lymph node stations. METHODS: Using the Kaplan-Meier method and Cox-regression analysis, this study retrospectively analyzed the overall survival (OS) of 2285 ESCC patients who underwent esophagectomy in three major China hospitals. Predictive models were constructed, and C-indices were computed to evaluate the discriminatory power of the S system, and to compare it with the N system. RESULTS: The categories defined by the S system were more homogeneous in terms of OS than those defined by the N system. Overall, the S system had a slightly better C-index (p = 0.659) than the N system ((p = 0.658). Subgroup analyses also showed that the C-index of the S system was slightly better than that of the N system for each subgroup of sex and age, but the two were comparable for each subgroup defined by the tumor location. CONCLUSION: The S system demonstrated a competing prognostic performance compared with the current AJCC N system. Due to the relatively easy accessibility of the number of metastatic lymph node stations, the S system may offer an easier option for cancer staging without a loss of discriminative power.

Quantile regression for survival data in modern cancer research: expanding statistical tools for precision medicine.

Quantile regression links the whole distribution of an outcome to the covariates of interest and has become an important alternative to commonly used regression models. However, the presence of censored data such as survival time, often the main endpoint in cancer studies, has hampered the use of quantile regression techniques because of the incompleteness of data. With the advent of the precision medicine era and availability of high throughput data, quantile regression with high-dimensional predictors has attracted much attention and provided added insight compared to traditional regression approaches. This paper provides a practical guide for using quantile regression for right censored outcome data with covariates of low- or high-dimensionality. We frame our discussion using a dataset from the Boston Lung Cancer Survivor Cohort, a hospital-based prospective cohort study, with the goals of broadening the scope of cancer research, maximizing the utility of collected data, and offering useful statistical alternatives. We use quantile regression to identify clinical and molecular predictors, for example CpG methylation sites, associated with high-risk lung cancer patients, for example those with short survival.

Forward regression for Cox models with high-dimensional covariates.

Forward regression, a classical variable screening method, has been widely used for model building when the number of covariates is relatively low. However, forward regression is seldom used in high-dimensional settings because of the cumbersome computation and unknown theoretical properties. Some recent works have shown that forward regression, coupled with an extended Bayesian information criterion (EBIC)-based stopping rule, can consistently identify all relevant predictors in high-dimensional linear regression settings. However, the results are based on the sum of residual squares from linear models and it is unclear whether forward regression can be applied to more general regression settings, such as Cox proportional hazards models. We introduce a forward variable selection procedure for Cox models. It selects important variables sequentially according to the increment of partial likelihood, with an EBIC stopping rule. To our knowledge, this is the first study that investigates the partial likelihood-based forward regression in high-dimensional survival settings and establishes selection consistency results. We show that, if the dimension of the true model is finite, forward regression can discover all relevant predictors within a finite number of steps and their order of entry is determined by the size of the increment in partial likelihood. As partial likelihood is not a regular density-based likelihood, we develop some new theoretical results on partial likelihood and use these results to establish the desired sure screening properties. The practical utility of the proposed method is examined via extensive simulations and analysis of a subset of the Boston Lung Cancer Survival Cohort study, a hospital-based study for identifying biomarkers related to lung cancer patients' survival.

Covariance-Insured Screening.

Modern bio-technologies have produced a vast amount of high-throughput data with the number of predictors far greater than the sample size. In order to identify more novel biomarkers and understand biological mechanisms, it is vital to detect signals weakly associated with outcomes among ultrahigh-dimensional predictors. However, existing screening methods, which typically ignore correlation information, are likely to miss weak signals. By incorporating the inter-feature dependence, a covariance-insured screening approach is proposed to identify predictors that are jointly informative but marginally weakly associated with outcomes. The validity of the method is examined via extensive simulations and a real data study for selecting potential genetic factors related to the onset of multiple myeloma.

Management of tunneled-cuffed catheter-related right atrial thrombosis in hemodialysis patients.

OBJECTIVES: Catheter-related right atrial thrombosis (CRAT) is an underreported but potentially life-threatening complication associated with the use of tunneled-cuffed catheters among hemodialysis (HD) patients. Because little is known about the evidence-based guidelines for the optimal management of CRAT among HD patients, this article reports findings based on 20 patients diagnosed with CRAT after catheter replacement and anticoagulation treatment. METHODS: The article retrospectively reviews the hospital records of 20 HD patients treated in the West China Hospital with diagnosis of CRAT from March 2013 to May 2016. Once CRAT was diagnosed, tunneled-cuffed catheters were exchanged over a guidewire in situ and the locations of the new catheter tips were adjusted to be away from the original sites. Immediately after the insertion of a new tunneled-cuffed catheter and at the end of each HD session, both ports of the catheters were locked with unfractionated heparin solution. Patients younger than 70 years of age were treated with warfarin at a target International Normalized Ratio of 1.5 to 1.9, whereas those older than 70 years were treated with dual antiplatelet therapy. All patients were on regular dialysis without thrombolysis or thrombectomy. RESULTS: During the follow-up, two patients died of gastrointestinal massive hemorrhage and one died of acute myocardial infarction. No fatal pulmonary embolism or other CRAT complication-related deaths were observed. A total of eight patients had complete dissolution of CRAT, and 12 patients had reduction in thrombi size. CONCLUSIONS: Maintenance of HD by replacing catheters and providing oral anticoagulation/antiplatelet therapies may be an effective strategy for treating HD patients with CRAT.

Conditional screening for ultra-high dimensional covariates with survival outcomes.

Identifying important biomarkers that are predictive for cancer patients' prognosis is key in gaining better insights into the biological influences on the disease and has become a critical component of precision medicine. The emergence of large-scale biomedical survival studies, which typically involve excessive number of biomarkers, has brought high demand in designing efficient screening tools for selecting predictive biomarkers. The vast amount of biomarkers defies any existing variable selection methods via regularization. The recently developed variable screening methods, though powerful in many practical setting, fail to incorporate prior information on the importance of each biomarker and are less powerful in detecting marginally weak while jointly important signals. We propose a new conditional screening method for survival outcome data by computing the marginal contribution of each biomarker given priorily known biological information. This is based on the premise that some biomarkers are known to be associated with disease outcomes a priori. Our method possesses sure screening properties and a vanishing false selection rate. The utility of the proposal is further confirmed with extensive simulation studies and analysis of a diffuse large B-cell lymphoma dataset. We are pleased to dedicate this work to Jack Kalbfleisch, who has made instrumental contributions to the development of modern methods of analyzing survival data.

Integrated powered density: Screening ultrahigh dimensional covariates with survival outcomes.

Modern biomedical studies have yielded abundant survival data with high-throughput predictors. Variable screening is a crucial first step in analyzing such data, for the purpose of identifying predictive biomarkers, understanding biological mechanisms, and making accurate predictions. To nonparametrically quantify the relevance of each candidate variable to the survival outcome, we propose integrated powered density (IPOD), which compares the differences in the covariate-stratified distribution functions. The proposed new class of statistics, with a flexible weighting scheme, is general and includes the Kolmogorov statistic as a special case. Moreover, the method does not rely on rigid regression model assumptions and can be easily implemented. We show that our method possesses sure screening properties, and confirm the utility of the proposal with extensive simulation studies. We apply the method to analyze a multiple myeloma study on detecting gene signatures for cancer patients' survival.