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PubMed1979

HLA system and testicular germinative tumours.

In 62 patients with testicular germinative tumours and 301 healthy unrelated subjects, 23 HLA antigens of A and B loci were tested. In the group of 40 seminomas the incidence of HLA-Bw35 antigen and in 22 patients with non-seminomas (embryonal carcinomas, teratocarcinoma and mixed forms) the frequency of HLA-A 10 antigen were significantly higher (27.50 vs. 14.28% in the controls, p greater than 0.025; 36.36 vs. 15.28%, p less than 0.025). After correction by multiplying p by the number of typed antigens there was no statistically significant result any more. The causes of dubious results of the studies about the association between HLA and malignancies are discussed.

Dysgerminoma↗
PubMed2005

An N-glycan structure correlates with pulmonary metastatic ability of cancer cells.

N-Glycan structures on the surface of cancer cells have diverse structures and play significant roles in metastatic process. However, little is known about their roles in organ-selective metastasis. Our study revealed that an alpha1,6-fucosylated biantennary N-glycan structure designated A2G2F is characteristic of lungs, with far more abundant expression in normal human and murine lungs than in other organs. In this study, we further examined the role of A2G2F in pulmonary metastasis. We stained metastatic cancers by alpha1,6-fucose-specific Lens culinaris agglutinin lectin and revealed that pulmonary metastatic nodules more abundantly expressed alpha1,6-fucosylated N-glycans than hepatic metastatic nodules from common primary cancers. The most specific alpha1,6-fucosylated N-glycan structure in pulmonary metastatic cancer was identified to be A2G2F. Using a B16 melanoma cell metastasis model, we showed that A2G2F-rich B16 cells formed more pulmonary metastatic nodules than A2G2F-poor cells. Our results suggest that A2G2F plays a critical role in pulmonary metastasis.

Animals↗
PubMed1999

A novel form of ascertainment bias in case-control studies of cancer screening.

In case-control studies of cancer screening, some have generally admonished investigators against case definitions based on diagnosis dates because of lead-time bias. However, perhaps partly due to vagueness, the admonitions have been frequently ignored. A recurrence-time model simulates case ascertainment when diagnosis must occur within a specific calendar period. The model depends on screening test sensitivity and rate, age-specific preclinical incidence rates, and preclinical duration time and survival time distributions. For one study of sigmoidoscopic screening for colorectal cancer, when the true odds ratio is 1, its estimate is 0.50 to 0.75 under plausible assumptions. This bias can affect any observational study wherein case definition depends on diagnosis times (e.g., health-plan enrollment data). To avoid bias in observational investigations of cancer screening wherein the case definition depends on the diagnosis date, one must ensure that both screening and preclinical incidence do not occur before the case definition period.

Adult↗
PubMed2006

A comprehensive study of chromosome 16q in invasive ductal and lobular breast carcinoma using array CGH.

We analysed chromosome 16q in 106 breast cancers using tiling-path array-comparative genomic hybridization (aCGH). About 80% of ductal cancers (IDCs) and all lobular cancers (ILCs) lost at least part of 16q. Grade I (GI) IDCs and ILCs often lost the whole chromosome arm. Grade II (GII) and grade III (GIII) IDCs showed less frequent whole-arm loss, but often had complex changes, typically small regions of gain together with larger regions of loss. The boundaries of gains/losses tended to cluster, common sites being 54.5-55.5 Mb and 57.4-58.8 Mb. Overall, the peak frequency of loss (83% cancers) occurred at 61.9-62.9 Mb. We also found several 'minimal' regions of loss/gain. However, no mutations in candidate genes (TRADD, CDH5, CDH8 and CDH11) were detected. Cluster analysis based on copy number changes identified a large group of cancers that had lost most of 16q, and two smaller groups (one with few changes, one with a tendency to show copy number gain). Although all morphological types occurred in each cluster group, IDCs (especially GII/GIII) were relatively overrepresented in the smaller groups. Cluster groups were not independently associated with survival. Use of tiling-path aCGH prompted re-evaluation of the hypothetical pathways of breast carcinogenesis. ILCs have the simplest changes on 16q and probably diverge from the IDC lineage close to the stage of 16q loss. Higher-grade IDCs probably develop from low-grade lesions in most cases, but there remains evidence that some GII/GIII IDCs arise without a GI precursor.

Breast Neoplasms↗
PubMed1999

Validation studies for models projecting the risk of invasive and total breast cancer incidence.

BACKGROUND: In 1989, Gail and colleagues developed a model for estimating the risk of breast cancer in women participating in a program of annual mammographic screening (designated herein as model 1). A modification of this model to project the absolute risk of developing only invasive breast cancer is referred to herein as model 2. We assessed the validity of both models by employing data from women enrolled in the Breast Cancer Prevention Trial. METHODS: We used data from 5969 white women who were at least 35 years of age and without a history of breast cancer. These women were in the placebo arm of the trial and were screened annually. The average follow-up period was 48.4 months. We compared the observed number of breast cancers with the predicted numbers from the models. RESULTS: In terms of absolute risk, the ratios of total expected to observed numbers of cancers (95% confidence intervals [CIs]) were 0.84 (0. 73-0.97) for model 1 and 1.03 (0.88-1.21) for model 2, respectively. Within the age groups of 49 years or less, 50-59 years, and 60 years or more, the ratios of expected to observed numbers of breast cancers (95% CIs) for model 1 were 0.91 (0.73-1.14), 0.96 (0.73-1. 28), and 0.66 (0.52-0.86), respectively. Thus, model 1 underestimated breast cancer risk in women more than 59 years of age. For model 2, the risk ratios (95% CIs) were 0.93 (0.72-1.22), 1.13 (0.83-1.55), and 1.05 (0.80-1.41), respectively. Both models exhibited a tendency to overestimate risk for women classified in the higher quintiles of predicted 5-year risk and to underestimate risk for those in the lower quintiles of the same. CONCLUSION: Despite some limitations, these methods provide useful information on breast cancer risk for women who plan to participate in an annual mammographic screening program.

Age Factors↗
PubMed1993

Estrogen receptor status and dietary intakes in breast cancer patients.

We used data from a study of racial differences in cancer patient survival to examine the association between estrogen receptor status and the intake of nutrients and food groups among 689 black and white women, ages 20-79, with breast cancer newly diagnosed in 1985 and 1986. We reviewed medical records and collected interview data, including a 34-item food frequency questionnaire. Consistent with published reports, we found positive estrogen receptor status to be positively associated with age and inversely associated with parity and oral contraceptive use. Whites were more likely than blacks to have estrogen receptor-positive tumors. We examined eight nutrients and six food groups in multivariate analyses for association with estrogen receptor status. After adjusting for age, race, usual body mass index, and parity, a high percentage of calories from fat was associated with estrogen receptor-positive cancer, and a high percentage of calories from carbohydrates was associated with estrogen receptor-negative breast cancer. These findings indicate that women with breast cancer who are on diets with a high percentage of calories from fat may be more likely to develop estrogen receptor-positive cancers.

Adult↗
PubMed2006

Tissue self-affinity and polarized light scattering in the born approximation: a new model for precancer detection.

Light scattered from biological tissues can exhibit an inverse power law spectral component. We develop a model based on the Born approximation and von Karman (self-affine) spatial correlation of submicron tissue refractive index to account for this. The model is applied to light scattering spectra obtained from excised esophagi of normal and carcinogen-treated rats. Power law exponents used to fit dysplastic tissue site spectra are significantly smaller than those from normal sites, indicating that changes in tissue self-affinity can serve as a potential biomarker for precancer.

Algorithms↗
PubMed1999

Using a mixed effects model to estimate geographic variation in cancer rates.

Commonly used methods for depicting geographic variation in cancer rates are based on rankings. They identify where the rates are high and low but do not indicate the magnitude of the rates nor their variability. Yet such measures of variability may be useful in suggesting which types of cancer warrant further analytic studies of localized risk factors. We consider a mixed effects model in which the logarithm of the mean Poisson rate is additive in fixed stratum effects (e.g., age effects) and in logarithms of random relative risk effects associated with geographic areas. These random effects are assumed to follow a gamma distribution with unit mean and variance 1/alpha, similar to Clayton and Kaldor (1987, Biometrics 43, 671-681). We present maximum likelihood and method-of-moments estimates with standard errors for inference on alpha -1/2, the relative risk standard deviation (RRSD). The moment estimates rely on only the first two moments of the Poisson and gamma distributions but have larger standard errors than the maximum likelihood estimates. We compare these estimates with other measures of variability. Several examples suggest that the RRSD estimates have advantages compared to other measures of variability.

Biometry↗
PubMed2005

Tumour matrilysin expression predicts metastatic potential of stage I (pT1) colon and rectal cancers.

BACKGROUND AND AIMS: Nodal metastases are indisputable determinants of prognosis for colon and rectal cancer. Using classical histological criteria, many attempts to predict nodal metastasis have failed, preventing the adequate management of stage I (pT1) cancer. We investigated the role of tumour matrilysin in predicting metastatic potential, and discuss its potential use in individualising treatment of pT1 colon and rectal cancer. METHODS: The gene signature associated with nodal metastasis was investigated by cDNA array in 24 colon and rectal cancers. We studied 494 colon and rectal cancer patients to identify risk factors for nodal metastasis and evaluated the potential to predict nodal metastasis by either the logistic regression model or the Bayesian neural network model with built-in matrilysin. We then inferred possible causality of nodal metastasis from structural equation modelling. RESULTS: cDNA array revealed that matrilysin was maximally upregulated in the metastasis signature identified. Tumour matrilysin expression emerged as a stage independent risk factor for nodal metastasis, resulting in a similar predictive performance in receiver operating characteristic curve analysis in the two models. A Bayesian approach called automatic relevance determination identified matrilysin as one of the most relevant predictors examined. Structural equation modelling suggested possible direct causality between matrilysin and nodal metastasis. CONCLUSIONS: We have provided evidence that tumour matrilysin expression is a promising biomarker predicting nodal metastasis of colon and rectal cancer. Analysis of tumour matrilysin expression would help clinicians achieve the goal of individualised cancer treatment based on the metastatic potential of pT1 colon and rectal cancer.

Adenocarcinoma↗
PubMed2004

Two-stage normalization using background intensities in cDNA microarray data.

BACKGROUND: In the microarray experiment, many undesirable systematic variations are commonly observed. Normalization is the process of removing such variation that affects the measured gene expression levels. Normalization plays an important role in the earlier stage of microarray data analysis. The subsequent analysis results are highly dependent on normalization. One major source of variation is the background intensities. Recently, some methods have been employed for correcting the background intensities. However, all these methods focus on defining signal intensities appropriately from foreground and background intensities in the image analysis. Although a number of normalization methods have been proposed, no systematic methods have been proposed using the background intensities in the normalization process. RESULTS: In this paper, we propose a two-stage method adjusting for the effect of background intensities in the normalization process. The first stage fits a regression model to adjust for the effect of background intensities and the second stage applies the usual normalization method such as a nonlinear LOWESS method to the background-adjusted intensities. In order to carry out the two-stage normalization method, we consider nine different background measures and investigate their performances in normalization. The performance of two-stage normalization is compared to those of global median normalization as well as intensity dependent nonlinear LOWESS normalization. We use the variability among the replicated slides to compare performance of normalization methods. CONCLUSIONS: For the selected background measures, the proposed two-stage normalization method performs better than global or intensity dependent nonlinear LOWESS normalization method. Especially, when there is a strong relationship between the background intensity and the signal intensity, the proposed method performs much better. Regardless of background correction methods used in the image analysis, the proposed two-stage normalization method can be applicable as long as both signal intensity and background intensity are available.

Cell Line, Tumor↗
PubMed2002

Multiple comparisons model-based clustering and ternary pattern tree numerical display of gene response to treatment: procedure and application to the preclinical evaluation of chemopreventive agents.

Microarray technology has greatly aided the identification of genes that are expressed differentially. Statistical analysis of such data by multiple comparisons procedures has been slow to develop, in part, because methods to cluster the results of such comparisons in biologically meaningful ways have not been available. We isolated and analyzed, by Northern blot and GeneChip, replicate liver RNA samples (n = 4/group) from rats fed with control diet or diet containing one of three chemopreventive compounds, selected because their pharmacological activities, including RNA expression response, are relatively well understood. We report on a classification tree, based on the results of nonparametric multiple comparisons, which results in the bipolar hierarchical clustering of genes in relation to their response to treatment. In addition to identifying treatment-responsive genes, application of this procedure to our test study identified the known pharmacological relationships among the treatment groups without supervision. Also, small treatment-specific subsets of genes were identified that may be indicative of additional pharmacophores present in the test compounds.

Animals↗