The dual role of the X-linked FoxP3 gene in human cancers

The FoxP3 (forkhead box P3) gene is an X-linked gene that is submitted to inactivation. It is an essential transcription factor in CD4(+)CD25(+)FoxP3 regulatory T cells, which are therapeutic targets in disseminated cutaneous melanoma. Moreover, FoxP3 is an important tumor suppressor gene in carcinomas and has putative cancer suppressor gene function in cutaneous melanoma as well. Therefore understanding the structure and function of the FoxP3 gene is crucial to gaining insight into the biology of melanoma to better develop immunotherapeutics and future therapeutic strategies

BORIS/CTCFL promotes a switch from a proliferative towards an invasive phenotype in melanoma cells

Melanoma is among the most aggressive cancers due to its tendency to metastasize early. Phenotype switching between a proliferative and an invasive state has been suggested as a critical process for metastasis, though the mechanisms that regulate state transitions are complex and remain poorly understood. Brother of Regulator of Imprinted Sites (BORIS), also known as CCCTC binding factor-Like (CTCFL), is a transcriptional modulator that becomes aberrantly expressed in melanoma. Yet, the role of BORIS in melanoma remains elusive. Here, we show that BORIS is involved in melanoma phenotype switching. Genetic modification of BORIS expression in melanoma cells combined with whole-transcriptome analysis indicated that BORIS expression contributes to an invasion-associated transcriptome. In line with these findings, inducible BORIS overexpression in melanoma cells reduced proliferation and increased migration and invasion, demonstrating that the transcriptional switch is accompanied by a phenotypic switch. Mechanistically, we reveal that BORIS binds near the promoter of transforming growth factor-beta 1 (TFGB1), a well-recognized factor involved in the transition towards an invasive state, which coincided with increased expression of TGFB1. Overall, our study indicates a pro-invasive role for BORIS in melanoma via transcriptional reprogramming

Integrating NGS-derived mutational profiling in the diagnosis of multiple lung adenocarcinomas

MICROABSTRACT: Integration of Next Generation Sequencing (NGS) information for use in distinguishing between Multiple Primary Lung Cancer and intrapulmonary metastasis was evaluated. We used a probabilistic model, comprehensive histologic assessment and NGS to classify patients. Integrating NGS data confirmed initial diagnosis (n = 41), revised the diagnosis (n = 12), while resulted in non-informative data (n = 8). Accuracy of diagnosis can be significantly improved with integration of NGS data. BACKGROUND: Distinguishing between multiple primary lung cancers (MPLC) and intrapulmonary metastases (IPM) is challenging. The goal of this study was to evaluate how Next Generation Sequencing (NGS) information may be integrated in the diagnostic strategy. PATIENTS AND METHODS: Patients with multiple lung adenocarcinomas were classified using both the comprehensive histologic assessment and NGS. We computed the joint probability of each pair having independent mutations by chance (thus being classified as MPLC). These probabilities were computed using the marginal mutation rates of each mutation, and the known negative dependencies between driver genes and different gene loci. With these NGS-driven data, cases were re-classified as MPLC or IPM. RESULTS: We analyzed 61 patients with a total of 131 tumors. The most frequent mutation was KRAS (57.3%) which occured at a rate higher than expected (p < 0.001) in lung cancer. No mutation was detected in 25/131 tumors (19.1%). Discordant molecular findings between tumor sites were found in 46 patients (75.4%); 11 patients (18.0%) had concordant molecular findings, and 4 patients (6.6%) had concordant molecular findings at 2 of the 3 sites. After integration of the NGS data, the initial diagnosis was confirmed for 41 patients (67.2%), the diagnosis was revised for 12 patients (19.7%) or was considered as non-informative for 8 patients (13.1%). CONCLUSION: Integrating the information of NGS data may significantly improve accuracy of diagnosis and staging

EORTC Melanoma Group achievements

AbstractSince its inception in 1969, the EORTC Melanoma Group has employed a multidisciplinary approach in the fight against melanoma and has registered significant achievements in many areas of melanoma treatment and research. The group showed that sentinel node (SN) tumor burden according to the Rotterdam Criteria and the microanatomic location were the most important prognostic factors for melanoma-specific survival and non-SN positivity in the completion lymph node dissection specimen. They demonstrated that extended schedule escalated dose temozolomide is feasible and has an acceptable safety profile. They also showed that the interferon-a targeted therapy should occur in a targeted patient population, and should probably not be offered to 70% of the patients that are currently being given this treatment. Through EORTC trial 18991, Sylatron™, pegylated interferon a-2b, for the treatment of melanoma patients with microscopic or gross nodal involvement within 84 days of definitive surgical resection including complete lymphadenectomy, was approved by the US FDA. The present article describes the achievements and future strategies of the Melanoma Group

Mineralisation of collagen rich soft tissues and osteocyte lacunae in Enpp1(-/-) mice

Ecto-nucleotide pyrophosphatase/phosphodiesterases (NPPs) hydrolyse nucleotide triphosphates to the corresponding nucleotide monophosphates and the mineralisation inhibitor, pyrophosphate (PPi). This study examined the role of NPP1 in osteocytes, osteoclasts and cortical bone, using a mouse model lacking NPP1 (Enpp1−/−). We used microcomputed tomography (μCT) to investigate how NPP1 deletion affects cortical bone structure; excised humerus bones from 8, 15 and 22-week old mice were scanned at 0.9 μm. Although no changes were evident in the cortical bone of 8-week old Enpp1−/− mice, significant differences were observed in older animals. Cortical bone volume was decreased 28% in 22-week Enpp1−/− mice, whilst cortical porosity was reduced 30% and 60% at 15 and 22-weeks, respectively. This was accompanied by up to a 15% decrease in closed pore diameter and a 55% reduction in the number of pores. Cortical thickness was reduced up to 35% in 15 and 22-week Enpp1−/− animals and the endosteal diameter was increased up to 23%. Thus, the cortical bone from Enpp1−/− mice was thinner and less porous, with a larger marrow space. Scanning electron microscopy (SEM) revealed a decrease in the size and number of blood vessel channels in the cortical bone as well as a 40% reduction in the mean plan area of osteocyte lacunae. We noted that the number of viable osteocytes isolated from the long bones of Enpp1−/− mice was decreased ≤ 50%. In contrast, osteoclast formation and resorptive activity were unaffected by NPP1 deletion. μCT and histological analysis of Enpp1−/− mice also revealed calcification of the joints and vertebrae as well as soft tissues including the whisker follicles, ear pinna and trachea. This calcification worsened as the animals aged. Together, these data highlight the key role of NPP1 in regulating calcification of both soft and skeletal tissues

Mitotic Rate and Younger Age Are Predictors of Sentinel Lymph Node Positivity: Lessons Learned From the Generation of a Probabilistic Model

Background: Sentinel lymph node (SLN) biopsy allows surgeons to identify patients with subclinical nodal involvement who may benefit from lymphadenectomy and, possibly, adjuvant therapy. Several factors have been variably, and sometimes discordantly, reported to have predictive value for SLN metastasis to best select which patients require SLN biopsy.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/41402/1/10434_2004_Article_247.pd

The biology of melanoma prognostic factors

Cutaneous melanoma still represents a paradox among all solid tumors. It is the cancer for which the best prognostic markers ever identified in solid tumors are available, yet there is very little understanding of their biological significance. This review focuses on recent biological data that shed light on the clinical-biological correlations underlining the 2010 American Joint Committee on Cancer (AJCC) melanoma staging system. A major challenge is to replace outcome clustering based on artificial biomarker breakpoints by a continuous multidimensional prognostic model. Major improvement will come from shared computerized tools that allow the generation of continuous likelihood scores for diagnosis, prognosis, and response prediction. This will lead to the development of platforms which can be used by scientists from different fields to integrate and share high-quality data in the pre-competitive setting and generate new probabilistic causal models.</p

Generalizability of Machine Learning Models: Quantitative Evaluation of Three Methodological Pitfalls

Despite the great potential of machine learning, the lack of generalizability has hindered the widespread adoption of these technologies in routine clinical practice. We investigate three methodological pitfalls: (1) violation of independence assumption, (2) model evaluation with an inappropriate performance indicator, and (3) batch effect and how these pitfalls could affect the generalizability of machine learning models. We implement random forest and deep convolutional neural network models using several medical imaging datasets, including head and neck CT, lung CT, chest X-Ray, and histopathological images, to quantify and illustrate the effect of these pitfalls. We develop these models with and without the pitfall and compare the performance of the resulting models in terms of accuracy, precision, recall, and F1 score. Our results showed that violation of the independence assumption could substantially affect model generalizability. More specifically, (I) applying oversampling before splitting data into train, validation and test sets; (II) performing data augmentation before splitting data; (III) distributing data points for a subject across training, validation, and test sets; and (IV) applying feature selection before splitting data led to superficial boosts in model performance. We also observed that inappropriate performance indicators could lead to erroneous conclusions. Also, batch effect could lead to developing models that lack generalizability. The aforementioned methodological pitfalls lead to machine learning models with over-optimistic performance. These errors, if made, cannot be captured using internal model evaluation, and the inaccurate predictions made by the model may lead to wrong conclusions and interpretations. Therefore, avoiding these pitfalls is a necessary condition for developing generalizable models.Comment: 13 pages, 7 Figure