A novel approach to biomarker discovery in head and neck cancer using an autoantibody signature.

Despite the dismal prognosis for patients with squamous cell carcinoma of the head and neck (SCCHN), there have been no novel treatments in over 40 years. Identification of novel tumor antigens in SCCHN will facilitate the identification of potential novel treatment targets. Tumor antigens are proteins selectively expressed by tumor cells and recognized by the host immune system. Phage-displayed tumor antigens were enriched by biopanning with normal and then SCCHN-specific serum. Ninety-six phage clones were sequenced for identification, and 21 clones were validated using Luminex. One of these proteins, L23, a novel tumor antigen in SCCHN, was validated as an oncogene. L23 is upregulated in SCCHN compared with normal keratinocytes. Knockdown of L23 inhibited proliferation, invasion and cell survival. Overexpression of L23 had the reverse effect. Overexpression of L23 in non malignant cells led to transformation. Injection of SCCHN cells with knockdown of L23 in mice, induced tumors that were significantly smaller than control tumors. In conclusion, the immunomic screen yielded a panel of antigens specific to SCCHN; one of these proteins, L23, is a novel oncogene in SCCHN.

The tumor suppressor gene rap1GAP is silenced by miR-101-mediated EZH2 overexpression in invasive squamous cell carcinoma.

Rap1GAP is a critical tumor suppressor gene that is downregulated in multiple aggressive cancers, such as head and neck squamous cell carcinoma, melanoma and pancreatic cancer. However, the mechanistic basis of rap1GAP downregulation in cancers is poorly understood. By employing an integrative approach, we demonstrate polycomb-mediated repression of rap1GAP that involves Enhancer of Zeste Homolog 2 (EZH2), a histone methyltransferase in head and neck cancers. We further demonstrate that the loss of miR-101 expression correlates with EZH2 upregulation, and the concomitant downregulation of rap1GAP in head and neck cancers. EZH2 represses rap1GAP by facilitating the trimethylation of histone 3 at lysine 27, a mark of gene repression, and also hypermethylation of rap1GAP promoter. These results provide a conceptual framework involving a microRNA-oncogene-tumor suppressor axis to understand head and neck cancer progression.

Profile information matrix for nonlinear transformation models.

For semiparametric models, interval estimation and hypothesis testing based on the information matrix for the full model is a challenge because of potentially unlimited dimension. Use of the profile information matrix for a small set of parameters of interest is an appealing alternative. Existing approaches for the estimation of the profile information matrix are either subject to the curse of dimensionality, or are ad-hoc and approximate and can be unstable and numerically inefficient. We propose a numerically stable and efficient algorithm that delivers an exact observed profile information matrix for regression coefficients for the class of Nonlinear Transformation Models [A. Tsodikov (2003) J R Statist Soc Ser B 65:759-774]. The algorithm deals with the curse of dimensionality and requires neither large matrix inverses nor explicit expressions for the profile surface.

A population model of prostate cancer incidence.

Introduction of screening for prostate cancer using the prostate-specific antigen (PSA) marker of the disease led to remarkable dynamics of the incidence of the disease observed in the last two decades. A statistical model is used to provide a link between dissemination of PSA and the observed transient population responses. The model is used to estimate lead time, overdiagnosis and other relevant characteristics of prostate cancer screening.

Thermodynamic criteria for high hit rate antisense oligonucleotide design.

Antisense oligonucleotides are used for therapeutic applications and in functional genomic studies. In practice, however, many of the oligonucleotides complementary to an mRNA have little or no antisense activity. Theoretical strategies to improve the 'hit rate' in antisense screens will reduce the cost of discovery and may lead to identification of antisense oligonucleotides with increased potency. Statistical analysis performed on data collected from more than 1000 experiments with phosphorothioate-modified oligonucleotides revealed that the oligo-probes, which form stable duplexes with RNA (DeltaG(o)37 < or = -30 kcal/mol) and have small self-interaction potential, are more frequently efficient than molecules that form less stable oligonucleotide-RNA hybrids or more stable self-structures. To achieve optimal statistical preference, the values for self-interaction should be (DeltaG(o)37) > or = -8 kcal/mol for inter-oligonucleotide pairing and (DeltaG(o)37) > or = -1.1 kcal/mol for intra-molecular pairing. Selection of oligonucleotides with these thermodynamic values in the analyzed experiments would have increased the 'hit rate' by as much as 6-fold.

Thermodynamic calculations and statistical correlations for oligo-probes design.

Optimization of probe design for array-based experiments requires improved predictability of oligonucleotide hybridization behavior. Currently, designing oligonucleotides capable of interacting efficiently and specifically with the relevant target is not a routine procedure. Multiple examples demonstrate that oligonucleotides targeting different regions of the same RNA differ in their hybridization ability. The present work shows how thermodynamic evaluations of oligo-target duplex or oligo self-structure stabilities can facilitate probe design. Statistical analysis of large sets of hybridization data reveals that thermodynamic evaluation of oligonucleotide properties can be used to avoid poor RNA binders. Thermodynamic criteria for the selection of 20 and 21mers, which, with high probability, interact efficiently and specifically with their targets, are suggested. The design of longer oligonucleotides can also be facilitated by the same calculations of DeltaG(o) (T) values for oligo-target duplex or oligo self-structure stabilities and similar selection schemes.

Semiparametric models: a generalized self-consistency approach.

In semiparametric models, the dimension d of the maximum likelihood problem is potentially unlimited. Conventional estimation methods generally behave like O(d(3)). A new O(d) estimation procedure is proposed for a large class of semiparametric models. Potentially unlimited dimension is handled in a numerically efficient way through a Nelson-Aalen-like estimator. Discussion of the new method is put in the context of recently developed minorization-maximization algorithms based on surrogate objective functions. The procedure for semiparametric models is used to demonstrate three methods to construct a surrogate objective function: using the difference of two concave functions, the EM way and the new quasi-EM (QEM) approach. The QEM approach is based on a generalization of the EM-like construction of the surrogate objective function so it does not depend on the missing data representation of the model. Like the EM algorithm, the QEM method has a dual interpretation, a result of merging the idea of surrogate maximization with the idea of imputation and self-consistency. The new approach is compared with other possible approaches by using simulations and analysis of real data. The proportional odds model is used as an example throughout the paper.

Estimating Cure Rates From Survival Data: An Alternative to Two-Component Mixture Models.

This article considers the utility of the bounded cumulative hazard model in cure rate estimation, which is an appealing alternative to the widely used two-component mixture model. This approach has the following distinct advantages: (1) It allows for a natural way to extend the proportional hazards regression model, leading to a wide class of extended hazard regression models. (2) In some settings the model can be interpreted in terms of biologically meaningful parameters. (3) The model structure is particularly suitable for semiparametric and Bayesian methods of statistical inference. Notwithstanding the fact that the model has been around for less than a decade, a large body of theoretical results and applications has been reported to date. This review article is intended to give a big picture of these modeling techniques and associated statistical problems. These issues are discussed in the context of survival data in cancer.

Semi-parametric models of long- and short-term survival: an application to the analysis of breast cancer survival in Utah by age and stage.

A flexible class of semi-parametric survival models is proposed that takes account of long- and short-term covariate effects in cancer survival. The diversity of responses described by the models include non-proportional and crossing survival curves as well as a fraction of long-term survivors. Restricted non-parametric maximum likelihood estimation procedures (RNPMLE) are developed to provide point estimates, confidence intervals and tests for the models. Numerical algorithms to fit semi-parametric survival models are emphasized. The methods are applied to analyse post-treatment survival of breast cancer patients diagnosed in Utah by age and stage.

Adjustments and measures of differential expression for microarray data.

MOTIVATION: Existing analyses of microarray data often incorporate an obscure data normalization procedure applied prior to data analysis. For example, ratios of microarray channels intensities are normalized to have common mean over the set of genes. We made an attempt to understand the meaning of such procedures from the modeling point of view, and to formulate the model assumptions that underlie them. Given a considerable diversity of data adjustment procedures, the question of their performance, comparison and ranking for various microarray experiments was of interest. RESULTS: A two-step statistical procedure is proposed: data transformation (adjustment for slide-specific effect) followed by a statistical test applied to transformed data. Various methods of analysis for differential expression are compared using simulations and real data on colon cancer cell lines. We found that robust categorical adjustments outperform the ones based on a precisely defined stochastic model, including some commonly used procedures.

Variable selection and pattern recognition with gene expression data generated by the microarray technology.

Lack of adequate statistical methods for the analysis of microarray data remains the most critical deterrent to uncovering the true potential of these promising techniques in basic and translational biological studies. The popular practice of drawing important biological conclusions from just one replicate (slide) should be discouraged. In this paper, we discuss some modern trends in statistical analysis of microarray data with a special focus on statistical classification (pattern recognition) and variable selection. In addressing these issues we consider the utility of some distances between random vectors and their nonparametric estimates obtained from gene expression data. Performance of the proposed distances is tested by computer simulations and analysis of gene expression data on two different types of human leukemia. In experimental settings, the error rate is estimated by cross-validation, while a control sample is generated in computer simulation experiments aimed at testing the proposed gene selection procedures and associated classification rules.

Electron arc irradiation of the postmastectomy chest wall with CT treatment planning: 20-year experience.

PURPOSE: Since 1980, electron arc irradiation of the postmastectomy chest wall has been the preferred radiotherapy technique at the University of Utah for patients with advanced breast cancer. We report the results of this technique in 156 consecutive Stage IIA-IIIB patients treated from 1980 to 1998. METHODS: CT treatment planning was used in all patients to identify chest wall thickness and internal mammary lymph node depth. Computerized dosimetry was used to deliver total doses of 50 Gy in 5-1/2 weeks to the chest wall and the internal mammary lymph nodes with electron arc therapy. Patients were assessed for local, regional, and distant control of disease and for survival. Univariate and multivariate proportional hazards were modeled using a hierarchical nonproportional semiparametric model testing the following prognostic factors: age, stage, tumor size, number of positive lymph nodes, estrogen receptor status, and dose. End points evaluated included disease-free survival, cause-specific survival, and overall survival. RESULTS: Eighty-one percent of patients were at high risk for local-regional failure because of > T2 primary tumor or > 3 positive axillary lymph nodes. The median number of positive lymph nodes was 5, and the median tumor size was 3.5 cm. Actuarial 10-year local-regional control and overall survival were 95% and 52%, respectively. In multivariate analysis, the only factor prognostic for disease-free survival, cause-specific survival, and overall survival was the number of positive lymph nodes (p < 0.001). The 10-year rates of local-regional control for patients with 0, 1-3, 4-9, and > or = 10 involved lymph nodes were 100%, 98%, 93%, and 89%, respectively. The only rates of acute and chronic radiotherapy toxicity > or = 2 by RTOG/EORTC criteria were skin related and observed in 44% and 10% for acute and late reactions, respectively. CONCLUSION: These data demonstrate excellent local-regional control rates with electron arc therapy of the postmastectomy chest wall in patients with advanced breast cancer. Our 20-year experience with electron arc radiotherapy has demonstrated the safety and efficacy of this technique. The advantage of this technique is that the internal mammary lymph node chain can be easily encompassed while the dose to heart and lung is minimized; it also obviates match lines in areas of high risk.

A survival model for fractionated radiotherapy with an application to prostate cancer.

This paper explores the applicability of a mechanistic survival model, based on the distribution of clonogens surviving a course of fractionated radiation therapy, to clinical data on patients with prostate cancer. The study was carried out using data on 1,100 patients with clinically localized prostate cancer who were treated with three-dimensional conformal radiation therapy. The patients were stratified by radiation dose (group 1: <67.5 Gy; group 2: 67.5-72.5 Gy; group 3: 72.5-77.5 Gy; group 4: 77.5-87.5 Gy) and prognosis category (favourable, intermediate and unfavourable as defined by pre-treatment PSA and Gleason score). A relapse was recorded when tumour recurrence was diagnosed or when three successive prostate specific antigen (PSA) elevations were observed from a post-treatment nadir PSA level. PSA relapse-free survival was used as the primary end point. The model, which is based on an iterated Yule process, is specified in terms of three parameters: the mean number of tumour clonogens that survive the treatment, the mean of the progression time of post-treatment tumour development and its standard deviation. The model parameters were estimated by the maximum likelihood method. The fact that the proposed model provides an excellent description both of the survivor function and of the hazard rate is prima facie evidence of the validity of the model because closeness of the two survivor functions (empirical and model-based) does not generally imply closeness of the corresponding hazard rates. The estimated cure probabilities for the favourable group are 0.80, 0.74 and 0.87 (for dose groups 1-3, respectively); for the intermediate group: 0.25, 0.51, 0.58 and 0.78 (for dose groups 1-4, respectively) and for the unfavourable group: 0.0, 0.27, 0.33 and 0.64 (for dose groups 1-4, respectively). The distribution of progression time to tumour relapse was found to be independent of prognosis group but dependent on dose. As the dose increases the mean progression time decreases (41, 28.5, 26.2 and 14.7 months for dose groups 1-4, respectively). This analysis confirms that, in terms of cure rate, dose escalation has a significant positive effect only in the intermediate and unfavourable groups. It was found that progression time is inversely proportional to dose, which means that patients recurring in higher dose groups have shorter recurrence times, yet these groups have better survival, particularly long-term. The explanation for this seemingly illogical observation lies in the fact that less aggressive tumours, potentially recurring after a long period of time, are cured by higher doses and do not contribute to the recurrence pattern. As a result, patients in higher dose groups are less likely to recur; however, if they do, they tend to recur earlier. The estimated hazard rates for prostate cancer pass through a clear-cut maximum, thus revealing a time period with especially high values of instantaneous cancer-specific risk; the estimates appear to be nonproportional across dose strata.

[Parametric survival analysis in patients with cervical cancer following fractionated radiotherapy: a new procedures and results]. / Parametricheskii analiz vyzhivaemosti bol'nykh rakom sheiki matki posle fraktsionirovannoi radioterapii. Novyi metod i rezul'taty ego primeneniia.

A recent theoretical result of Kendal (1998) enabled us to develop a survival model which allows for proliferation of tumor clonogenic cells in the course of fractionated radiotherapy. We explored this model during an analysis of clinical data on survival of 982 patients with cancer of the cervix uteri. The model provided a good description of survival patterns in different groups of patients. The estimated cure probability did not correlate with the rates of cell proliferation between exposures to radiation. Also, our results showed that this parameter cannot be estimated from survival data. Some light has been thrown on the relationship of cell proliferations taking place between exposures, on the one hand, and end-results of treatment for cancer of the cervix uteri, on the other.

Modeling cancer detection: tumor size as a source of information on unobservable stages of carcinogenesis.

This paper is concerned with modern approaches to mechanistic modeling of the process of cancer detection. Measurements of tumor size at diagnosis represent a valuable source of information to enrich statistical inference on the processes underlying tumor latency. One possible way of utilizing this information is to model cancer detection as a quantal response variable. In doing so, one relates the chance of detecting a tumor to its current size. We present various theoretical results emerging from this approach and illustrate their usefulness with numerical examples and analyses of epidemiological data. An alternative approach based on a threshold type mechanism of tumor detection is briefly described.

Identification of sequence motifs in oligonucleotides whose presence is correlated with antisense activity.

Design of antisense oligonucleotides targeting any mRNA can be much more efficient when several activity-enhancing motifs are included and activity-decreasing motifs are avoided. This conclusion was made after statistical analysis of data collected from >1000 experiments with phosphorothioate-modified oligonucleotides. Highly significant positive correlation between the presence of motifs CCAC, TCCC, ACTC, GCCA and CTCT in the oligonucleotide and its antisense efficiency was demonstrated. In addition, negative correlation was revealed for the motifs GGGG, ACTG, AAA and TAA. It was found that the likelihood of activity of an oligonucleotide against a desired mRNA target is sequence motif content dependent.

A random walk model of oligodendrocyte generation in vitro and associated estimation problems.

A branching stochastic process proposed earlier to model oligodendrocyte generation by O-2A progenitor cells under in vitro conditions does not allow invoking the maximum likelihood techniques for estimation purposes. To overcome this difficulty, we propose a partial likelihood function based on an embedded random walk model of clonal growth and differentiation of O-2A progenitor cells. Under certain conditions, the partial likelihood function yields consistent estimates of model parameters. The usefulness of this approach is illustrated with computer simulations and data analyses.

The shape of the hazard function in breast carcinoma: curability of the disease revisited.

BACKGROUND: The question of curability of breast carcinoma remains controversial. Because the probability of cure essentially is an asymptotic notion, the corresponding estimation problems call for special statistical methods. Such methods should account for an intimate connection between the probability of cure and the shape of the hazard function. METHODS: The study was performed on survival data for 13,166 women with breast carcinoma identified through the Utah Cancer Registry and stratified by clinical stage and age at diagnosis. For these patients, the follow-up period was 30 years. Three estimation procedures were used for estimating the hazard function from the data: the life table estimator, a kernel counterpart of the Nelson-Aalen estimator, and a parametric estimator specifically designed for two-component hazards. The parametric estimate of the hazard function was used to provide estimates of cure rates for each category of patients. RESULTS: For all categories of patients under study, the estimated hazard functions passed through a clear-cut maximum, showing a tendency to decrease as time approached the end of a follow-up period. The hazards appeared to be nonproportional across the strata. The estimated values of the cure rate and the corresponding confidence intervals were determined for each stratum of patients with breast carcinoma. CONCLUSIONS: The results of the current study strongly suggest that cure is a possible outcome of breast carcinoma treatment. The condition of proportionality of risks is not met in breast carcinoma survival data.

Deficiency of platelet-activating factor acetylhydrolase is a severity factor for asthma.

Asthma, a family of airway disorders characterized by airway inflammation, has an increasing incidence worldwide. Platelet-activating factor (PAF) may play a role in the pathophysiology of asthma. Its proinflammatory actions are antagonized by PAF acetylhydrolase. A missense mutation (V279F) in the PAF acetylhydrolase gene results in the complete loss of activity, which occurs in 4% of the Japanese population. We asked if PAF acetylhydrolase deficiency correlates with the incidence and severity of asthma in Japan. We found that the prevalence of PAF acetylhydrolase deficiency is higher in Japanese asthmatics than healthy subjects and that the severity of this syndrome is highest in homozygous-deficient subjects. We conclude that the PAF acetylhydrolase gene is a modulating locus for the severity of asthma.