Identification and validation of gene expression models that predict clinical outcome in patients with early-stage laryngeal cancer.

BACKGROUND: Despite improvement in therapeutic techniques, patients with early-stage laryngeal cancer still recur after treatment. Gene expression prognostic models could suggest which of these patients would be more appropriate for testing adjuvant strategies. MATERIALS AND METHODS: Expression profiling using whole-genome DASL arrays was carried out on 56 formalin-fixed paraffin-embedded tumor samples of patients with early-stage laryngeal cancer. We split the samples into a training and a validation set. Using the supervised principal components survival analysis in the first cohort, we identified gene expression profiles that predict the risk of recurrence. These profiles were then validated in an independent cohort. RESULTS: Gene models comprising different number of genes identified a subgroup of patients who were at high risk of recurrence. Of these, the best prognostic model distinguished between a high- and a low-risk group (log-rank P<0.005). The prognostic value of this model was reproduced in the validation cohort (median disease-free survival: 38 versus 161 months, log-rank P=0.018), hazard ratio=5.19 (95% confidence interval 1.14-23.57, P<0.05). CONCLUSIONS: We have identified gene expression prognostic models that can refine the estimation of a patient's risk of recurrence. These findings, if further validated, should aid in patient stratification for testing adjuvant treatment strategies.

Formative evaluation of a prototype system for automated analysis of mass spectrometry data.

Mass Spectrometry (MS) is emerging as a breakthrough mass-throughput technology capable of producing powerful clinical diagnostic and prognostic models and of identifying important disease biomarkers. Few individuals possess the necessary skills to carry out MS analyses competently, and access to such individuals is limited in most settings, hindering progress in this field. We seek to ease this burden by creating a fully automated system (FAST-AIMS) capable of analyzing mass spectra to produce high-quality diagnostic and outcome prediction models and identify related biomarkers. In the present report we introduce the system and conduct a formative evaluation in which 6 users apply it to a challenging dataset. FAST-AIMS' performance is compared to that of an expert statistician as well as to a previously published analysis by an independent group. In our experiments FAST-AIMS when used by both MS-sophisticated users (n=4) and naïve users (n=2) achieves performance (a) comparable to our human expert, and (b) superior to the previously published manual analysis; in addition (c) the system's estimates future performance accurately, thus avoiding overfitting.

Adult height and menarcheal age of young women in Greece.

Several factors contribute to the attainment of adult height, including genetic and environmental variables. To assess the relationship between menarcheal age and adult height, measured height was regressed on recalled menarcheal age in 286 young women, 18-24 years old, candidates for recruitment in the Greek army. Height was significantly associated with menarcheal age (b = 0.52, 95% CI = 0.04-1.00, p = 0.03). Joint evaluation of age, body mass index (BMI) and menarcheal age as predictors of final height revealed that only age at menarche represents an independent predictor of final height. Finally, education in completed years of schooling and place of birth or residence did not influence adult height, and no interaction between age at menarche and these factors was observed in the present study. These data suggest that adult height of Greek women is independently associated with menarcheal age, whereas BMI, place of birth or residence and educational level do not seem to play a role of comparable significance.