Melissa L. Bondy

Environmental risk factors for primary malignant brain tumors: A review

ConclusionsThis review suggests that existing theories of carcinogenesis provide much of the necessary framework for understanding the etiologies of malignant gliomas. Most of the environmental risk factors associated with brain tumors involve exposures to infectious, chemical, or physical agents capable of inflicting genetic damage. It is equally important to recognize that some potential risk factors for brain tumors, if their association proves causal, suggest as yet unknown or undefined mechanisms of carcinogenesis. For example, the mechanisms through which extremely-low-frequency electromagnetic fields might induce cancer are unknown, but interest in this area is intensifying [165].Large population based studies currently being conducted throughout the world will help to estimate the relative importance of some suspected factors. Several of the most serious limitations of previous studies can be overcome by careful study design including sample size planning to ensure adequate power for important hypotheses and subgroup analyses, systematic pathology review to enable analyses by histologie type, collecting sufficiently detailed exposure data to minimize recall problems and permit flexibility in classifications, and using rapid case ascertainment systems to minimize the need for interviews with proxies and concomitant reporting bias. Furthermore, as important genes for brain tumors are identified, they can be used to further refine subtype classifications and to improve sensitivity in the search for environmental culprits.

Selective Genomic Copy Number Imbalances and Probability of Recurrence in Early-Stage Breast Cancer

A number of studies of copy number imbalances (CNIs) in breast tumors support associations between individual CNIs and patient outcomes. However, no pattern or signature of CNIs has emerged for clinical use. We determined copy number (CN) gains and losses using high-density molecular inversion probe (MIP) arrays for 971 stage I/II breast tumors and applied a boosting strategy to fit hazards models for CN and recurrence, treating chromosomal segments in a dose-specific fashion (-1 [loss], 0 [no change] and +1 [gain]). The concordance index (C-Index) was used to compare prognostic accuracy between a training (nu200a=u200a728) and test (nu200a=u200a243) set and across models. Twelve novel prognostic CNIs were identified: losses at 1p12, 12q13.13, 13q12.3, 22q11, and Xp21, and gains at 2p11.1, 3q13.12, 10p11.21, 10q23.1, 11p15, 14q13.2-q13.3, and 17q21.33. In addition, seven CNIs previously implicated as prognostic markers were selected: losses at 8p22 and 16p11.2 and gains at 10p13, 11q13.5, 12p13, 20q13, and Xq28. For all breast cancers combined, the final full model including 19 CNIs, clinical covariates, and tumor marker-approximated subtypes (estrogen receptor [ER], progesterone receptor, ERBB2 amplification, and Ki67) significantly outperformed a model containing only clinical covariates and tumor subtypes (C-Index full model, train[test] u200a=u200a 0.72[0.71] ± 0.02 vs. C-Index clinical + subtype model, train[test] u200a=u200a 0.62[0.62] ± 0.02; p<10−6). In addition, the full model containing 19 CNIs significantly improved prognostication separately for ER–, HER2+, luminal B, and triple negative tumors over clinical variables alone. In summary, we show that a set of 19 CNIs discriminates risk of recurrence among early-stage breast tumors, independent of ER status. Further, our data suggest the presence of specific CNIs that promote and, in some cases, limit tumor spread.