Older Adults with Cancer: A Randomized Controlled Trial of Occupational and Physical Therapy

OBJECTIVES: The impact of occupational therapy (OT) and physical therapy (PT) on functional outcomes in older adults with cancer is unknown. DESIGN: Two-arm single-institution randomized controlled trial of outpatient OT/PT. SETTING: Comprehensive cancer center with two off-site OT/PT clinics. PARTICIPANTS: We recruited adults 65 years and older with a recent diagnosis or recurrence of cancer within 5 years, with at least one functional limitation as identified by a geriatric assessment. Participants were randomized to OT/PT or usual care. INTERVENTION: Rehabilitation consisted of individualized OT and PT that addressed functional activities and strength/endurance needs. MEASUREMENTS: Primary outcome was functional status as measured by the Nottingham Extended Activities of Daily Living scale. Secondary outcomes were Patient-Reported Outcomes Measurement Information System-Global Mental Health (GMH) and Global Physical Health (GPH), ability to participate in Social Roles (SR), physical function, and activity expectations and self-efficacy (Possibilities for Activity Scale [PActS]). RESULTS: Among those recruited (N = 63), only 45 patients (71%) were evaluable due to loss of follow-up and/or nonreceipt of intervention. The median age was 74 years; 53% were female, and 91% were white. Overall, 30% patients had hematologic malignancies, 30% breast cancer, and 16% colorectal cancers. A total of 65% were in active treatment; 49% had stage 3 or 4 disease. At follow-up, both OT/PT (P =.02) and usual care (P =.03) groups experienced a decline in functional status. PActS scores between groups (P =.04) was significantly improved in the intervention group. GMH and SR met criteria for minimally important clinical difference favoring the intervention, but not statistical significance. Several barriers were noted in the implementation of the intervention program: recruitment, concerns about cost, distance, scheduling, and limited treatment provided. CONCLUSION: OT/PT may positively influence activity expectations and self-efficacy. Future research needs to address significant barriers to implementation to increase use of OT/PT services and access to quality care

Large expert-curated database for benchmarking document similarity detection in biomedical literature search

Document recommendation systems for locating relevant literature have mostly relied on methods developed a decade ago. This is largely due to the lack of a large offline gold-standard benchmark of relevant documents that cover a variety of research fields such that newly developed literature search techniques can be compared, improved and translated into practice. To overcome this bottleneck, we have established the RElevant LIterature SearcH consortium consisting of more than 1500 scientists from 84 countries, who have collectively annotated the relevance of over 180 000 PubMed-listed articles with regard to their respective seed (input) article/s. The majority of annotations were contributed by highly experienced, original authors of the seed articles. The collected data cover 76% of all unique PubMed Medical Subject Headings descriptors. No systematic biases were observed across different experience levels, research fields or time spent on annotations. More importantly, annotations of the same document pairs contributed by different scientists were highly concordant. We further show that the three representative baseline methods used to generate recommended articles for evaluation (Okapi Best Matching 25, Term Frequency-Inverse Document Frequency and PubMed Related Articles) had similar overall performances. Additionally, we found that these methods each tend to produce distinct collections of recommended articles, suggesting that a hybrid method may be required to completely capture all relevant articles. The established database server located at https://relishdb.ict.griffith.edu.au is freely available for the downloading of annotation data and the blind testing of new methods. We expect that this benchmark will be useful for stimulating the development of new powerful techniques for title and title/abstract-based search engines for relevant articles in biomedical research.Peer reviewe

Scalable Open Science Approach for Mutation Calling of Tumor Exomes Using Multiple Genomic Pipelines

The Cancer Genome Atlas (TCGA) cancer genomics dataset includes over 10,000 tumor-normal exome pairs across 33 different cancer types, in total >400 TB of raw data files requiring analysis. Here we describe the Multi-Center Mutation Calling in Multiple Cancers project, our effort to generate a comprehensive encyclopedia of somatic mutation calls for the TCGA data to enable robust cross-tumor-type analyses. Our approach accounts for variance and batch effects introduced by the rapid advancement of DNA extraction, hybridization-capture, sequencing, and analysis methods over time. We present best practices for applying an ensemble of seven mutation-calling algorithms with scoring and artifact filtering. The dataset created by this analysis includes 3.5 million somatic variants and forms the basis for PanCan Atlas papers. The results have been made available to the research community along with the methods used to generate them. This project is the result of collaboration from a number of institutes and demonstrates how team science drives extremely large genomics projects

Spatial Organization and Molecular Correlation of Tumor-Infiltrating Lymphocytes Using Deep Learning on Pathology Images

Beyond sample curation and basic pathologic characterization, the digitized H&E-stained images of TCGA samples remain underutilized. To highlight this resource, we present mappings of tumor-infiltrating lymphocytes (TILs) based on H&E images from 13 TCGA tumor types. These TIL maps are derived through computational staining using a convolutional neural network trained to classify patches of images. Affinity propagation revealed local spatial structure in TIL patterns and correlation with overall survival. TIL map structural patterns were grouped using standard histopathological parameters. These patterns are enriched in particular T cell subpopulations derived from molecular measures. TIL densities and spatial structure were differentially enriched among tumor types, immune subtypes, and tumor molecular subtypes, implying that spatial infiltrate state could reflect particular tumor cell aberration states. Obtaining spatial lymphocytic patterns linked to the rich genomic characterization of TCGA samples demonstrates one use for the TCGA image archives with insights into the tumor-immune microenvironment. Tumor-infiltrating lymphocytes (TILs) were identified from standard pathology cancer images by a deep-learning-derived \u201ccomputational stain\u201d developed by Saltz et al. They processed 5,202 digital images from 13 cancer types. Resulting TIL maps were correlated with TCGA molecular data, relating TIL content to survival, tumor subtypes, and immune profiles