Future Directions for Postdoctoral Training in Cancer Prevention: Insights from a Panel of Experts

Cancer prevention postdoctoral fellowships have existed since the 1970s. The National Cancer Institute facilitated a meeting by a panel of experts in April 2013 to consider four important topics for future directions for cancer prevention postdoctoral training programs: 1) future research needs; 2) underrepresented disciplines; 3) curriculum; and 4) career preparation. Panelists proffered several areas needing more research or emphasis, ranging from computational science to culture. Health care providers, along with persons from non-traditional disciplines such as engineers and lawyers, were among disciplines recognized as being underrepresented in training programs. Curriculum suggestions were that fellows receive training in topics such as leadership and human relations, in addition to learning the principles of epidemiology, cancer biological mechanisms, and behavioral science. For career preparation, there was a clear recognition of the diversity of employment options available besides academic positions, and that program leaders should do more to help fellows identify and prepare for different career paths. The major topics and strategies covered at this meeting can help form the basis for cancer prevention training program leaders to consider modifications or new directions, and keep them current with the changing scientific and employment climate for doctoral degree recipients and postdoctoral fellows

Federated learning enables big data for rare cancer boundary detection.

Although machine learning (ML) has shown promise across disciplines, out-of-sample generalizability is concerning. This is currently addressed by sharing multi-site data, but such centralization is challenging/infeasible to scale due to various limitations. Federated ML (FL) provides an alternative paradigm for accurate and generalizable ML, by only sharing numerical model updates. Here we present the largest FL study to-date, involving data from 71 sites across 6 continents, to generate an automatic tumor boundary detector for the rare disease of glioblastoma, reporting the largest such dataset in the literature (n = 6, 314). We demonstrate a 33% delineation improvement for the surgically targetable tumor, and 23% for the complete tumor extent, over a publicly trained model. We anticipate our study to: 1) enable more healthcare studies informed by large diverse data, ensuring meaningful results for rare diseases and underrepresented populations, 2) facilitate further analyses for glioblastoma by releasing our consensus model, and 3) demonstrate the FL effectiveness at such scale and task-complexity as a paradigm shift for multi-site collaborations, alleviating the need for data-sharing

Author Correction: Federated learning enables big data for rare cancer boundary detection.

10.1038/s41467-023-36188-7NATURE COMMUNICATIONS14

Cerebellar Volume and Disease Staging in Parkinson's Disease: An ENIGMA-PD Study.

peer reviewed[en] BACKGROUND: Increasing evidence points to a pathophysiological role for the cerebellum in Parkinson's disease (PD). However, regional cerebellar changes associated with motor and non-motor functioning remain to be elucidated. OBJECTIVE: To quantify cross-sectional regional cerebellar lobule volumes using three dimensional T1-weighted anatomical brain magnetic resonance imaging from the global ENIGMA-PD working group. METHODS: Cerebellar parcellation was performed using a deep learning-based approach from 2487 people with PD and 1212 age and sex-matched controls across 22 sites. Linear mixed effects models compared total and regional cerebellar volume in people with PD at each Hoehn and Yahr (HY) disease stage, to an age- and sex- matched control group. Associations with motor symptom severity and Montreal Cognitive Assessment scores were investigated. RESULTS: Overall, people with PD had a regionally smaller posterior lobe (dmax  = -0.15). HY stage-specific analyses revealed a larger anterior lobule V bilaterally (dmax  = 0.28) in people with PD in HY stage 1 compared to controls. In contrast, smaller bilateral lobule VII volume in the posterior lobe was observed in HY stages 3, 4, and 5 (dmax  = -0.76), which was incrementally lower with higher disease stage. Within PD, cognitively impaired individuals had lower total cerebellar volume compared to cognitively normal individuals (d = -0.17). CONCLUSIONS: We provide evidence of a dissociation between anterior "motor" lobe and posterior "non-motor" lobe cerebellar regions in PD. Whereas less severe stages of the disease are associated with larger motor lobe regions, more severe stages of the disease are marked by smaller non-motor regions

Federated Learning Enables Big Data for Rare Cancer Boundary Detection

Although machine learning (ML) has shown promise across disciplines, out-of-sample generalizability is concerning. This is currently addressed by sharing multi-site data, but such centralization is challenging/infeasible to scale due to various limitations. Federated ML (FL) provides an alternative paradigm for accurate and generalizable ML, by only sharing numerical model updates. Here we present the largest FL study to-date, involving data from 71 sites across 6 continents, to generate an automatic tumor boundary detector for the rare disease of glioblastoma, reporting the largest such dataset in the literature (n = 6, 314). We demonstrate a 33% delineation improvement for the surgically targetable tumor, and 23% for the complete tumor extent, over a publicly trained model. We anticipate our study to: 1) enable more healthcare studies informed by large diverse data, ensuring meaningful results for rare diseases and underrepresented populations, 2) facilitate further analyses for glioblastoma by releasing our consensus model, and 3) demonstrate the FL effectiveness at such scale and task-complexity as a paradigm shift for multi-site collaborations, alleviating the need for data-sharing

2012 ACCF/AHA/ACP/AATS/PCNA/SCAI/STS guideline for the diagnosis and management of patients with stable ischemic heart disease

The recommendations listed in this document are, whenever possible, evidence based. An extensive evidence review was conducted as the document was compiled through December 2008. Repeated literature searches were performed by the guideline development staff and writing committee members as new issues were considered. New clinical trials published in peer-reviewed journals and articles through December 2011 were also reviewed and incorporated when relevant. Furthermore, because of the extended development time period for this guideline, peer review comments indicated that the sections focused on imaging technologies required additional updating, which occurred during 2011. Therefore, the evidence review for the imaging sections includes published literature through December 2011

TUSC1, a putative tumor suppressor gene, reduces tumor cell growth in vitro and tumor growth in vivo.

We previously reported the identification of TUSC1 (Tumor Suppressor Candidate 1), as a novel intronless gene isolated from a region of homozygous deletion at D9S126 on chromosome 9p in human lung cancer. In this study, we examine the differential expression of TUSC1 in human lung cancer cell lines by western blot and in a primary human lung cancer tissue microarray by immunohistochemical analysis. We also tested the functional activities and mechanisms of TUSC1 as a tumor suppressor gene through growth suppression in vitro and in vivo. The results showed no expression of TUSC1 in TUSC1 homozygously deleted cells and diminished expression in some tumor cell lines without TUSC1 deletion. Interestingly, the results from a primary human lung cancer tissue microarray suggested that higher expression of TUSC1 was correlated with increased survival times for lung cancer patients. Our data demonstrated that growth curves of tumor cell lines transfected with TUSC1 grew slower in vitro than those transfected with the empty vector. More importantly, xenograph tumors in nude mice grew significantly slower in vivo in cells stably transfected with TUSC1 than those transfected with empty vector. In addition, results from confocal microscopy and immunohistochemical analyses show distribution of TUSC1 in the cytoplasm and nucleus in tumor cell lines and in normal and tumor cells in the lung cancer tissue microarray. Taken together, our results support TUSC1 has tumor suppressor activity as a candidate tumor suppressor gene located on chromosome 9p

Altered prostanoid signaling contributes to increased skin tumorigenesis in Tpl2 knockout mice.

Squamous cell carcinoma is the second most common form of skin cancer with the incidence expected to double over the next 20 years. Inflammation is believed to be a critical component in skin cancer progression. Therefore, understanding genes involved in the regulation of inflammatory pathways is vital to the design of targeted therapies. Numerous studies show cyclooxygenases (COXs) play an essential role in inflammation-associated cancers. Tpl2 (MAP3K8) is a protein kinase in the MAP Kinase signal transduction cascade. Previous research using a two-stage skin carcinogenesis model revealed that Tpl2(-/-) mice have significantly higher tumor incidence and inflammatory response than wild-type (WT) controls. The current study investigates whether cyclooxygenase-2 (COX-2) and COX-2- regulated prostaglandins and prostaglandin receptors drive the highly tumorigenic state of Tpl2(-/-) mice by investigating the relationship between Tpl2 and COX-2. Keratinocytes from newborn WT or Tpl2(-/-) mice were treated with 12-O-tetradecanoylphorbol-13-acetate (TPA) for various times over 24 hours. Western analysis revealed significant differences in COX-2 and COX-2 dependent prostanoids and prostanoid receptors. Additionally, in vivo experiments confirmed that COX-2 and COX-2 downstream factors were elevated in TPA-treated Tpl2(-/-) skin, as well as in papillomas from Tpl2(-/-) mice. Use of the selective COX-2 inhibitor Celecoxib showed the increased tumorigenesis in the Tpl2(-/-) mice to primarily be mediated through COX-2. These experiments illustrate COX-2 induction in the absence of Tpl2 may be responsible for the increased tumorigenesis found in Tpl2(-/-) mice. Defining the relationship between Tpl2 and COX-2 may lead to new ways to downregulate COX-2 through the modulation of Tpl2