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Purpose of Review Elevated prevalence of autism characteristics is reported in genetic syndromes associated with intellectual disability. This review summarises recent evidence on the behavioural heterogeneity of autism in the following syndromes: Fragile X, Cornelia de Lange, Williams, Prader-Willi, Angelman, Down, Smith-Magenis, and tuberous sclerosis complex. Key considerations for assessment and support are discussed. Recent Findings The profile and developmental trajectory of autism-related behaviour in these syndromes indicate some degree of syndrome specificity which may interact with broader behavioural phenotypes (e.g. hypersociability), intellectual disability, and mental health (e.g. anxiety). Genetic subtype and co-occurring epilepsy within syndromes contribute to increased significance of autism characteristics. Autism-related strengths and challenges are likely to be overlooked or misunderstood using existing screening/diagnostic tools and criteria, which lack sensitivity and specificity within these populations. Summary Autism characteristics are highly heterogeneous across genetic syndromes and often distinguishable from non-syndromic autism. Autism diagnostic assessment practices in this population should be tailored to specific syndromes. Service provisions must begin to prioritise needs-led support.

Computerised surgical planning (CSP) and computer-aided design and manufacturing (CAD/CAM) have been demonstrated to increase surgical accuracy and reduce operative time in free flap mandibular reconstruction, but evidence is lacking as to their impact on patient-centred outcomes. Implant-supported dental prostheses, however, have been associated with improved quality of life outcomes following free flap mandibular reconstruction. We aim to review reported patient-centred outcomes in mandibular reconstruction with CSP and CAD/CAM and determine whether use of these technologies is associated with higher rates of dental implant placement following free flap mandibular reconstruction. On December 20, 2020, a systematic review and meta-analysis were conducted according to PRISMA guidelines for studies reporting quality of life, functional outcomes, and rates of dental implant placement in computer-aided free flap mandibular reconstruction. A random-effects meta-analysis was performed to compare dental implant placement rates between surgeries using CSP and those using conventional freehand techniques. A total of 767 articles were screened. Nine articles reporting patient-centred outcomes and 16 articles reporting dental implant outcomes were reviewed. Of those reporting dental implant outcomes, five articles, representing a total of 302 cases, were included in the meta-analysis. Use of CSP was associated with a significant increase in the likelihood of dental implant placement, with an odds ratio of 2.70 (95% CI 1.52 to 4.79, p = 0.0007). Standardised reporting methods and controlled studies are needed to further investigate the impact of CSP and CAD/CAM technologies on functional outcomes and patient-reported quality of life in free flap mandibular reconstruction. Use of CSP and CAD/CAM technologies is associated with higher rates of dental implant placement in patients undergoing free flap mandibular reconstruction when compared to conventional freehand techniques.

BACKGROUND AND PURPOSE: The study objective was to determine whether longitudinal changes in patient-reported outcomes (PROs) were associated with survival among early-stage, non-small cell lung cancer (NSCLC) patients undergoing stereotactic body radiation therapy (SBRT). MATERIALS AND METHODS: Data were obtained from January 2015 through March 2020. We ran a joint probability model to assess the relationship between time-to-death, and longitudinal PRO measurements. PROs were measured through the Edmonton Symptom Assessment Scale (ESAS). We controlled for other covariates likely to affect symptom burden and survival including stage, tumor diameter, comorbidities, gender, race/ethnicity, relationship status, age, and smoking status. RESULTS: The sample included 510 early-stage NSCLC patients undergoing SBRT. The median age was 73.8 (range: 46.3–94.6). The survival component of the joint model demonstrates that longitudinal changes in ESAS scores are significantly associated with worse survival (HR: 1.04; 95% CI: 1.02–1.05). This finding suggests a one-unit increase in ESAS score increased probability of death by 4%. Other factors significantly associated with worse survival included older age (HR: 1.04; 95% CI: 1.03–1.05), larger tumor diameter (HR: 1.21; 95% CI: 1.01–1.46), male gender (HR: 1.87; 95% CI: 1.36–2.57), and current smoking status (HR: 2.39; 95% CI: 1.25–4.56). CONCLUSION: PROs are increasingly being collected as a part of routine care delivery to improve symptom management. Healthcare systems can integrate these data with other real-world data to predict patient outcomes, such as survival. Capturing longitudinal PROs—in addition to PROs at diagnosis—may add prognostic value for estimating survival among early-stage NSCLC patients undergoing SBRT.

PURPOSE Electronic health record (EHR) data are widely used in precision medicine, quality improvement, disease surveillance, and population health management. However, a significant amount of EHR data are stored in unstructured formats including scanned documents external to the treatment facility presenting an informatics challenge for secondary use. Studies are needed to characterize the clinical information uniquely available in scanned outside documents (SODs) to understand to what extent the availability of such information affects the use of these real-world data for cancer research. MATERIALS AND METHODS Two independent EHR data abstractions capturing 30 variables commonly used in oncology research were conducted for 125 patients treated for advanced non–small-cell lung cancer at a comprehensive cancer center, with and without consideration of SODs. Completeness and concordance were compared between the two abstractions, overall, and by patient groups and variable types. RESULTS The overall completeness of the data with SODs was 77.6% as compared with 54.3% for the abstraction without SODs. The differences in completeness were driven by data related to biomarker tests, which were more likely to be uniquely available in SODs. Such data were prone to missingness among patients who were diagnosed externally. CONCLUSION There were no major differences in completeness between the two abstractions by demographics, diagnosis, disease progression, performance status, or oral therapy use. However, biomarker data were more likely to be uniquely contained in the SODs. Our findings may help cancer centers prioritize the types of SOD data being abstracted for research or other secondary purposes.

Background: Racial/ethnic minority patients remain underrepresented in clinical trials potentially due to restrictive trial eligibility criteria that disproportionally affect minority cancer patients. Objective: To examine the prevalence of existing medical conditions and abnormal lab values that commonly serve as clinical trial eligibility criteria among cancer patients by race and ethnicity. Methods: A cross-sectional analysis was conducted among patients new to Moffitt Cancer Center in 2011-2021 with multiple myeloma (n=3,967), breast (n=14,348), lung (n=10,492), and prostate (n=7,823) cancers. Demographics, existing medical conditions, and lab values were obtained from the Electronic Health Record, whereas history of cancer and metastatic disease at diagnosis were obtained from the Cancer Registry. Prevalence of medical conditions and abnormal lab values were reported among all patients and compared by race groups (White, Black, Hispanic, and other races) using age-adjusted logistic regression. For factors with prevalence higher than 5%, stratified analysis was conducted with respect to cancer type and adjusted for multiple comparisons. Results: Compared to White patients, Black (B) and Hispanic (H) patients were found to have higher prevalence of diabetes (OR [odds ratio] = 2.26 [B]/1.40 [H]), organ transplantation (OR=1.58 [B]/1.77 [H]), hepatitis (OR=1.74 [B]/1.48 [H]), HIV (OR=4.25 [B]/1.92 [H]), and abnormal creatinine value (OR=1.77 [B]/1.23 [H]). In addition, Black patients were more likely to have hypertension (OR=1.41) while patients of other races were more likely to be diabetic (OR=1.36). Similar patterns were observed across cancer types. Conclusion: Restrictive cancer clinical trial eligibility criteria may post a structural barrier that disproportionately impact racial/ethnic minority patients. Investigators should consider leveraging real-world data to define and design appropriate trial eligibility criteria. Prevalence of factors commonly included as clinical trial eligibility by race/ethnicity. Factors All White Black Hispanic Other Prevalence (%) % % OR (95% CI) % OR (95% CI) % OR (95% CI) Diabetes 6.30 5.90 10.00 2.26 (1.97-2.58) 6.50 1.40 (1.20-1.63) 6.50 1.36 (1.07-1.70) Chronic obstructive pulmonary disease 10.30 11.40 6.20 0.61 (0.52-0.72) 6.10 0.61 (0.52-0.71) 5.90 0.57 (0.44-0.72) Hypertension 18.00 18.50 19.80 1.41 (1.27-1.55) 13.40 0.87 (0.78-0.97) 13.50 0.84 (0.71-1.00) Heart condition 3.00 3.10 2.60 1.10 (0.86-1.39) 2.00 0.80 (0.61-1.03) 1.80 0.70 (0.44-1.05) Organ transplant 0.90 0.90 1.40 1.58 (1.11-2.20) 1.50 1.77 (1.28-2.41) 0.40 0.46 (0.16-1.00) Autoimmune disease 2.50 2.60 2.10 0.84 (0.63-1.09) 1.90 0.78 (0.59-1.01) 2.20 0.90 (0.60-1.30) Hepatitis 0.70 0.70 1.20 1.74 (1.17-2.49) 1.00 1.48 (0.99-2.15) 0.40 0.60 (0.21-1.30) HIV infection 0.30 0.20 0.90 4.25 (2.62-6.74) 0.40 1.92 (1.01-3.40) 0.20 0.71 (0.12-2.30) History of any cancer 15.90 17.20 10.30 0.69 (0.60-0.78) 10.30 0.69 (0.61-0.77) 10.90 0.71 (0.59-0.85) Metastasis at diagnosis 21.20 21.40 22.60 1.13 (1.02-1.24) 19.80 0.96 (0.87-1.05) 18.60 0.88 (0.75-1.02) Abnormal neutrophil count 19.70 20.10 20.20 1.00 (0.86-1.16) 16.60 0.79 (0.67-0.93) 18.20 0.89 (0.69-1.13) Abnormal creatinine 14.00 13.50 19.30 1.77 (1.57-1.99) 14.10 1.23 (1.08-1.39) 13.40 1.13 (0.93-1.38) Abnormal glomerular filtration rate 14.00 13.50 22.40 2.32 (1.99-2.70) 11.20 1.07 (0.88-1.30) 10.10 0.91 (0.66-1.22) Abnormal bilirubin 2.40 2.50 2.40 1.02 (0.69-1.45) 2.40 1.03 (0.70-1.46) 2.40 0.99 (0.52-1.69) Abnormal aspartate aminotransferase 9.40 9.40 9.20 0.88 (0.72-1.07) 9.70 0.92 (0.76-1.12) 8.50 0.82 (0.59-1.12) Citation Format: Yayi Zhao, Rossybelle P. Amorrortu, Rachel Howard, Kedar S. Kirtane, Susan T. Vadaparampil, Dana E. Rollison. Prevalence of factors serving as common cancer clinical trial eligibility criteria by race and ethnicity. [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 5538.

The goal of the described work was to create a self-service tool for data exploration and cohort creation, facilitating feasibility assessments for research studies and providing insights into the primary clinical and demographic characteristics of the Moffitt patient population. The Moffitt Cancer Analytics Platform (MCAP) was designed to enable seamless end-to-end data lifecycle management, promote data democratization, and reduce data duplication by feeding diverse, typically siloed local data streams into a central data repository. Eight distinct sources including the Moffitt electronic medical record, cancer registry, billing systems and clinical trial management system feed into an Amazon S3-based data lake. Raw data are cleaned, standardized for research use, and stored in the enterprise data warehouse in Snowflake. To date, over 80 distinct tables and 2,200 data elements are captured, representing over 500,000 patients. To provide transparency into these data assets, Moffitt partnered with phData to create a custom tool for data self-service, MCAP Explore. Built on the Sigma cloud analytic platform, Explore is a highly customized interactive workbook that allows the user to filter the total patient population on a broad range of clinical and demographic characteristics to identify cohorts of interest, with applications ranging from research study feasibility assessments to operational oversight of the composition of the patient population and where disparities may exist in disease characteristics, treatment or outcomes. In addition, the user is able to view a broad range of visualizations summarizing the resulting cohort and deep-dive into the specific records available. The features that distinguish Explore from similar tools include: a) the broad range of data domains covered by the available filters (demographics, diagnoses, appointments, labs/vitals, treatment, study enrollment/consent, patient-reported, biospecimens, molecular); b) the ability to apply any number of filters in an arbitrary order, on data that is both one-to-one and many-to-one with the patient, while maintaining important linkages between record types; c) full customization of the product for our oncology-specific use cases, facilitating transparency into the underlying data model and flexibility for future expansion; d) no data being required to leave the Moffitt environment, and de-identification processes governed at an institutional level and applied within Snowflake being automatically inherited within Explore by connection to our institutional Active Directory accounts and Snowflake user roles. We will present detailed examples of tool functionality and describe the underlying data model and design decisions required to accommodate a broad range of complex oncology-specific use cases. In addition, we will present early usage metrics and next steps for expansion into new data domains including imaging. Citation Format: Rachel Howard, Phillip Reisman, Patricia Lewis, Rodrigo Carvajal, Chandan Challa, Mark Ruesink, Joe McFarren, Katrina Johnson, Mukund Sridhar, Kedar Kulkarni, Dana E. Rollison. MCAP Explore: A self-service data exploration and cohort building tool for oncologists [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 2068.

In cancer patients, a high pre-treatment neutrophil-to-lymphocyte ratio (NLR) is associated with poorer survival outcomes. Significant variation in the magnitude of this association has been observed between studies, but sources of this variation are poorly understood. Here, we explore differences in the prognostic potential of NLR between patient subgroups stratified by demographic and clinical characteristics using a retrospective cohort of 5,363 patients treated at Moffitt Cancer Center (Tampa, FL). We identify patients for whom NLR has maximum prognostic potential via adjusted hazard ratios (HRs) calculated using multivariable Cox proportional hazards models and area under the curve analysis. NLR demonstrates stronger associations (HRs > 2) with survival among African-American patients, patients receiving radiation therapy, stage IV patients, and melanoma patients when compared with the overall study population (HR = 1.58). Sensitivity and specificity of NLR as a prognostic marker are also higher in these patient subgroups, and increase further with combinations of multiple “high-risk” demographic or clinical characteristics. In summary, NLR may have greater prognostic value in patients with certain demographic and clinical features. Future prospective studies could validate this hypothesis, after further characterization of populations in which NLR has maximum prognostic potential and the identification of meaningful thresholds for risk stratification.

Aim: This study assesses the technical performance and cost of a targeted next-generation sequencing (NGS) multigene pharmacogenetic (PGx) test. Materials & methods: A genetic test was developed for 21 PGx genes using molecular inversion probes to generate library fragments for NGS. Performance of this test was assessed using 53 unique reference control cell lines from the Genetic Testing Reference Materials Coordination Program (GeT-RM). Results: 93.7% of variants were successfully called and the repeatability rate was 99.9%. Reference calls were available for 78.4% of diplotype calls resulting from PGx testing, and concordance for the test was 85.7%. Cost per sample was $32–$56. Conclusion: A targeted NGS assay using molecular inversion probe technology is able to characterize the pharmacogenome efficiently.

BACKGROUND Information in pathology reports is critical for cancer care. Natural language processing (NLP) systems used to extract information from pathology reports are often narrow in scope or require extensive tuning. Consequently, there is growing interest in automated deep learning approaches. A powerful new NLP algorithm, bidirectional encoder representations from transformers (BERT), was published in late 2018. BERT set new performance standards on tasks as diverse as question answering, named entity recognition, speech recognition, and more. OBJECTIVE The aim of this study is to develop a BERT-based system to automatically extract detailed tumor site and histology information from free-text oncological pathology reports. METHODS We pursued three specific aims: extract accurate tumor site and histology descriptions from free-text pathology reports, accommodate the diverse terminology used to indicate the same pathology, and provide accurate standardized tumor site and histology codes for use by downstream applications. We first trained a base language model to comprehend the technical language in pathology reports. This involved unsupervised learning on a training corpus of 275,605 electronic pathology reports from 164,531 unique patients that included 121 million words. Next, we trained a question-and-answer (Q&A) model that connects a Q&A layer to the base pathology language model to answer pathology questions. Our Q&A system was designed to search for the answers to two predefined questions in each pathology report: What organ contains the tumor? and What is the kind of tumor or carcinoma? This involved supervised training on 8197 pathology reports, each with ground truth answers to these 2 questions determined by certified tumor registrars. The data set included 214 tumor sites and 193 histologies. The tumor site and histology phrases extracted by the Q&A model were used to predict International Classification of Diseases for Oncology, Third Edition (ICD-O-3), site and histology codes. This involved fine-tuning two additional BERT models: one to predict site codes and another to predict histology codes. Our final system includes a network of 3 BERT-based models. We call this CancerBERT network (caBERTnet). We evaluated caBERTnet using a sequestered test data set of 2050 pathology reports with ground truth answers determined by certified tumor registrars. RESULTS caBERTnet’s accuracies for predicting group-level site and histology codes were 93.53% (1895/2026) and 97.6% (1993/2042), respectively. The top 5 accuracies for predicting fine-grained ICD-O-3 site and histology codes with 5 or more samples each in the training data set were 92.95% (1794/1930) and 96.01% (1853/1930), respectively. CONCLUSIONS We have developed an NLP system that outperforms existing algorithms at predicting ICD-O-3 codes across an extensive range of tumor sites and histologies. Our new system could help reduce treatment delays, increase enrollment in clinical trials of new therapies, and improve patient outcomes.

Next-generation sequencing (NGS) is rapidly expanding into routine oncology practice. Genetic variations in both the cancer and inherited genomes are informative for hereditary cancer risk, prognosis, and treatment strategies. Herein, we focus on the clinical perspective of integrating NGS results into patient care to assist with therapeutic decision making. Five key considerations are addressed for operationalization of NGS testing and application of results to patient care as follows: (1) NGS test ordering and workflow design; (2) result reporting, curation, and storage; (3) clinical consultation services that provide test interpretations and identify opportunities for molecularly guided therapy; (4) presentation of genetic information within the electronic health record; and (5) education of providers and patients. Several of these key considerations center on informatics tools that support NGS test ordering and referencing back to the results for therapeutic purposes. Clinical decision support tools embedded within the electronic health record can assist with NGS test utilization and identifying opportunities for targeted therapy including clinical trial eligibility. Challenges for project and change management in operationalizing NGS-supported, evidence-based patient care in the context of current information technology systems with appropriate clinical data standards are discussed, and solutions for overcoming barriers are provided.

The FFA, an American agricultural educational organization, was studied to determine its influence on the meat paradox, an ambivalent response towards eating meat while being troubled about its moral consequences. The study was completed using survey research, which involved asking FFA and non-FFA members about their feelings towards eating different types of meat, such as cows and rabbits. Participants were asked whether they would eat a particular animal and then their reasoning when choosing not to. The results were collected and compared between the FFA and non-FFA members’ responses. Overall, FFA members were more likely to eat abnormal animals. However, whenever participants decided that they were unwilling to eat an animal, FFA members reasoned that they wouldn’t like its taste while non-FFA members chose that it was against their morals. This may reveal a lower moral connection to animals when FFA members, or other individuals who work with animals regularly, spend more time with specific species. This study has implications in the field of psychology, but more specifically paradox, cognitive dissonance, and dehumanization studies. These results suggest for further research into the influence of other variables on ambivalence as well as additional maintenance strategies involved in paradoxes.

Background/aim Gastric adenocarcinoma is the fourth most common cancer worldwide. While gastric cancer prevalence varies globally and incidence rates are decreasing in the West, many cases continue to be diagnosed at an advanced stage and the 5-year survival rate still falls below 30%. Early treatment of gastric cancer by endoscopic and/or surgical therapy may decrease mortality; yet reliable, universally applicable biomarkers for early detection of gastric cancer have still not been established. Materials and methods The present work compares the expression of CD133 (prominin-1), a potential biomarker of disease progression in gastric cancer, between independent cohorts of H. pylori (+) and H. pylori (-) patients at each respective stage of carcinogenesis. H. pylori (-) patients (N=45) who underwent gastric biopsy at the Moffitt Cancer Center (MCC) in Tampa, Florida, and H. pylori (+) patients (N=59) who underwent gastric biopsy at the Instituto de Patologia Mejia Jimenez (IPMJ) in Cali, Colombia were evaluated and immunostained for CD133. Results A statistically significant increase in CD133 expression (in terms of the Allred score) was observed between all stages of progression (normal mucosa, inflammation/metaplasia, low-grade dysplasia and gastric adenocarcinoma) for each respective patient cohort. No statistically significant difference in CD133 expression at each respective stage of disease was observed between the H. pylori-positive and negative-cohorts. Conclusion The observation of distinct stepwise increases in CD133 expression in both patient cohorts, and the lack of any significant difference between groups, suggests that CD133 expression may serve as a biomarker for early detection of gastric cancer independent of bacterial status and strain, and corresponding differences in disease histomorphology and classification. This warrants further validation on larger independent cohorts across multiple geographic regions and incorporating multiple bacterial strain types.

Female breast cancer (BC) became the most commonly diagnosed cancer globally in 2020. One of the hallmarks of BC is both intratumor and intertumor heterogeneity. Efforts have been made to categorize this heterogeneity based on the presence or absence of estrogen receptor (ER), progesterone receptor (PR) and human epidermal growth factor receptor 2 (HER2/ERBB2). Receptor expression patterns are used to stratify BC and the subsequent treatments. Much of the current knowledge of breast cancer comes from in vitro and in vivo studies using well established cell lines, some of which have been cultured since the 1950s. Given that serial passaging over many years has been reported to alter the mutational profile of cell lines and therefore their relevance to clinical decisions on the primary tumour, new near-tumor cell models need to be generated. In this study, tumour biopsies or ascitic fluids were obtained from a local cancer research biobank, as well as directly from metastatic BC patients across the globe. Using mechanical dissociation and cell separation techniques we isolated tumor cells and grew them in proprietary media. The resultant models were subjected to whole exome and RNA sequencing. In addition, the models were tested in a 3-day cell death assay against a library of more than 50 FDA-approved drugs. The models and four BC cell lines (HCC1937, ZR-75-1, MDA-MB-436 and HCC1428) were also tested in a longer term 7-day assay against a panel of four poly(ADP-ribose) polymerase (PARP) inhibitors. A total of 10 patient-derived cell (PDC) models were successfully established from biopsies or ascitic fluids taken from patients with primary (1/10) or metastatic (9/10) BC. Models were established from patients with ER+ BC, HER2+ BC and triple negative BC (TNBC) within an average of 23 days from sample arrival. All models displayed a greater sensitivity to the non-targeted drugs in the library compared with the targeted drugs. Mutations in breast cancer associated 1 (BRCA1) and BRCA2 genes that were not present in BC cell lines were identified in a subset of the PDCs. All models showed some sensitivity to PARP inhibitors, and most were exquisitely sensitive to talazoparib with IC50s of less than 100 nM. Determining cell death responses in near-tumour PDCs should thus be adopted to enable more informed clinical decisions for treatment pathways and in parallel will be promising tools for oncology drug discovery. Citation Format: Immaculate Nalubowa, Subir Singh, Yuen Ngan Fan, Rachel Howard-Jones, Albert Bezman, Dominic I. James, Geoff Muckle, Gareth J. Griffiths. Differential sensitivity to poly(ADP-ribose) polymerase inhibitors in patient-derived cell models of breast cancer [abstract]. In: Proceedings of the AACR-NCI-EORTC Virtual International Conference on Molecular Targets and Cancer Therapeutics; 2021 Oct 7-10. Philadelphia (PA): AACR; Mol Cancer Ther 2021;20(12 Suppl):Abstract nr P119.

“Deprescribing – is less more?” was the theme of this year's annual scientific meeting organised by the Prescribing and Research in Medicines Management (PRIMM) group, held in London in January. The meeting covered a range of topics relating to deprescribing, including a seven-step deprescribing model, identifying patients with polypharmacy problems, and providing individualised prescribing.

High pre-treatment values of the neutrophil-to-lymphocyte ratio (NLR) are strongly associated with poorer survival outcomes in cancer patients. Here, we assess heterogeneity in the magnitude of this association and the prognostic potential of the NLR between patient subgroups. We conducted a random effects meta-analysis of 228 published studies (N=75,555 patients) relating NLR with overall survival across 18 cancer types. Cochran’s Q test and Higgins I2 statistic were used to assess study heterogeneity. Pooled hazard ratios were compared between groups of studies classified by cancer type, geographic region, therapy type, and cut-off for high NLR to identify study-level characteristics associated with increased prognostic potential of the NLR. Pooled hazard ratios are highest in studies of melanoma and breast cancer and lowest in studies of brain cancer and lung cancer. Radiation as primary treatment also demonstrates a large pooled effect size as compared to other therapies. The NLR has greater prognostic value in certain cancer types and therapeutic regimens. Efforts are needed to comprehensively examine populations in which NLR has maximum prognostic power. Clinically meaningful thresholds for risk stratification should be identified within these patient subgroups to permit prospective validation of the prognostic potential of the NLR.

Purpose The neutrophil-to-lymphocyte ratio (NLR) is a marker of systemic inflammation with established prognostic value in patients with cancer. Although high NLR is associated with poorer clinical outcomes, factors that influence the magnitude of NLR independently of disease are poorly understood. Methods We identified 48,023 adults who participated in the National Health and Nutrition Examination Survey (1999-2016). Demographic, socioeconomic, and lifestyle factors associated with the magnitude of NLR after adjusting for comorbidities including heart disease, cancer, diabetes, and hypertension, and medications including aspirin, were identified. Effect modification by comorbidity status and demographics was explored. Results Female gender, age less than 60 years, and non-Hispanic black race/ethnicity were associated with lower NLR. Marital statuses of widowed, separated, or never married demonstrated increased NLR as compared with those who were currently married. Never-smoking and moderate alcohol consumption were associated with lower NLR. Participation in physical activity was associated with decreased NLR after adjustment for potential confounders, primarily among non-Hispanic whites. Conclusions Multiple demographic and lifestyle factors are independently associated with NLR. Sex, age, race, marital status, body mass index, physical activity, smoking history, and alcohol consumption should all be routinely collected and adjusted for to improve the accuracy of assessment of the prognostic power of NLR.

Condensins are highly conserved proteins that are important for chromosome maintenance in nearly all forms of life. Although many organisms employ two forms of the condensin complex, the condensin genes in Tetrahymena have expanded even further. Here we report a form of condensin that is specifically active during sexual reproduction. This complex, condensin D, is composed of the core condensin proteins, Smc2 and Smc4, and two unique subunits, the kleisin Cph5 and Cpd2. Cpd2 is also found in somatic nuclei in vegetative cells, but is dispensable for growth and nuclear division. Immunoprecipitation experiments show that condensin D interacts with a putative member of a chromatin-remodeling complex during development. Condensin D is required for sexual reproduction and for endoreplication and genome reduction of the progeny’s somatic nuclei. Altogether, Tetrahymena possesses at least four forms of condensin to fulfill the needs of maintaining chromosomes in two different nuclei containing the somatic and germline genomes.

Intracellular thermometry has recently demonstrated temperatures in the nucleus, mitochondria, and centrosome to be significantly higher than those of the cytoplasm and cell membrane. This local thermogenesis and the resulting temperature gradient could facilitate the development of persistent, self-organizing convection currents in the cytoplasm of large eukaryotes. Using 3-dimensional computational simulations of intracellular fluid motion, we quantify the convective velocities that could result from the temperature differences observed experimentally. Based on these velocities, we identify the conditions necessary for this temperature-driven bulk flow to dominate over random thermal diffusive motion at the scale of a single eukaryotic cell. With temperature gradients of the order 1°C and diffusion coefficients comparable to those described in the literature, Péclet numbers ≥ 1 are feasible and permit comparable or greater effects of convection than diffusion in determining intracellular mass flux. In addition to the temperature gradient, the resulting flow patterns would also depend on the spatial localization of the heat source, the shape of the cell membrane, and the complex intracellular structure including the cytoskeleton. While this intracellular convection would be highly context-dependent, in certain settings, convective motion could provide a previously unrecognized mechanism for directed, bulk transport within eukaryotic cells., Author summary Individual cells are the building blocks of all living things, and a myriad of processes occur within their walls. To facilitate the generation of energy and manufacturing of proteins and other molecules critical for cell survival, work and growth, intracellular material often must be shuttled to different locations or compartments within the cell. It is vital that we understand this intracellular transport if we hope to fully understand cell functioning. Some mechanisms of intracellular transport are well established, including random diffusive motion and active transport by molecular motor proteins. Recently, experimental evidence has suggested there are areas of substantial local heating within the cell, introducing the possibility of temperature gradient-driven convective circulation. Here, we explore the theoretical possibility of such mechanisms competing with diffusion to drive intracellular flows, and identify the conditions necessary for this to occur. For intracellular material with low diffusivity and in the presence of temperature gradients described in the recent literature, simulations suggest this mode of intracellular transport may indeed feasible and should be further validated experimentally.

Immune checkpoint inhibitors (ICIs) have improved overall survival for cancer patients, however, optimal duration of ICI therapy has yet to be defined. Given ICIs were first used to treat patients with metastatic melanoma, a condition that at the time was incurable, little attention was initially paid to how much therapy would be needed for a durable response. As the early immunotherapy trials have matured past 10 years, a significant per cent of patients have demonstrated durable responses; it is now time to determine whether patients have been overtreated, and if durable remissions can still be achieved with less therapy, limiting the physical and financial toxicity associated with years of treatment. Well-designed trials are needed to identify optimal duration of therapy, and to define biomarkers to predict who would benefit from shorter courses of immunotherapy. Here, we outline key questions related to health, financial and societal toxicities of over treating with ICI and present four unique clinical trials aimed at exposing criteria for early cessation of ICI. Taken together, there is a serious liability to overtreating patients with ICI and future work is warranted to determine when it is safe to stop ICI.

Introduction Cancer patients often choose complementary and alternative medicine (CAM) in palliative care, often in addition to conventional treatment and without medical advice or approval. Herbal medicines (HM) are the most commonly used type of CAM, but rarely available on an in-patient basis for palliative care. The motivations which lead very ill patients to travel far to receive such therapies are not clear. A qualitative study was therefore carried out to investigate influences on choosing to attend a CAM herbal hospice, to identify cancer patients’ main concerns about end-of-life care. Methods Semi-structured interviews with 32 patients were conducted and analysed using thematic analysis. Patients were recruited from Arokhayasala, a Buddhist cancer hospice in Thailand which provides CAM, in the form of HM, a restricted diet, Thai yoga, deep-breathing exercises, meditation, chanting, Dhamma, laughter and music therapy, free-of-charge. Results The main factors influencing decision-making were a positive attitude towards HMs and previous use of them, dissatisfaction with conventional treatment, the home environment and their relationships with hospital doctors. Conclusions Patients’ own perceptions and experiences were more important in making the decision to use CAM, and especially HM, in palliative cancer care than referral by healthcare professionals or scientific evidence of efficacy. Patients were prepared to travel far and live away from home to receive such care, especially as it was cost-free. In view of patients’ previously stated satisfaction with the regime at the Arokhayasala, these findings may be relevant to the provision of in-patient cancer palliative care to other patients.

Ethnopharmacological relevance Cancer patients in all cultures are high consumers of herbal medicines (HMs) usually as part of a regime consisting of several complementary and alternative medicine (CAM) modalities, but the type of patient, the reasons for choosing such HM-CAM regimes, and the benefits they perceive from taking them are poorly understood. There are also concerns that local information may be ignored due to language issues. This study investigates aspects of HM-CAM use in cancer patients using two different abstracting sources: Medline, which contains only peer-reviewed studies from SCI journals, and in order to explore whether further data may be available regionally, the Thai national databases of HM and CAM were searched as an example. Materials and methods The international and Thai language databases were searched separately to identify relevant studies, using key words chosen to include HM use in all traditions. Analysis of these was undertaken to identify socio-demographic and clinical factors, as well as sources of information, which may inform the decision to use HMs. Results Medline yielded 5638 records, with 49 papers fitting the criteria for review. The Thai databases yielded 155, with none relevant for review. Factors associated with HM-CAM usage were: a younger age, higher education or economic status, multiple chemotherapy treatment, late stage of disease. The most common purposes for using HM-CAM cited by patients were to improve physical symptoms, support emotional health, stimulate the immune system, improve quality of life, and relieve side-effects of conventional treatment. Conclusions Several indicators were identified for cancer patients who are most likely to take HM-CAM. However, interpreting the clinical reasons why patients decide to use HM-CAM is hampered by a lack of standard terminology and thematic coding, because patients' own descriptions are too variable and overlapping for meaningful comparison. Nevertheless, fears that the results of local studies published regionally are being missed, at least in the case of Thailand, appeared to be unfounded.

Introduction: Much of the information in electronic medical records (EMRs) required for the practice of clinical oncology is contained in unstructured text. While natural language processing (NLP) has been used to extract information from EMR text, accuracy is suboptimal. In late 2018 a powerful new deep-learning NLP algorithm was published: Bidirectional Encoder Representations from Transformers (BERT). BERT set new accuracy records and for the first time achieved human-level performance on several NLP benchmarks. Our goal was to train BERT to extract clinically relevant data from pathology reports with high accuracy. Procedures: Like many cancer centers nationwide, Moffitt Cancer Center employs Certified Tumor Registrars (CTRs) to collect and report data about cancer patients to state and federal agencies. The CTR extracted data are labels that identify, with high accuracy, important information in each pathology report. Consequently, we used this data to tune BERT to perform a question-and-answering (Q&A) task. Our system sought the answers to 2 predetermined questions in each pathology report: “What organ contains the tumor?”, and “What is the kind of tumor or carcinoma?” To achieve this, we matched surgical pathology reports created at Moffitt from January 1, 2007 onwards with structured data extracted by CTRs. The resulting dataset was randomly divided into training (80%) and testing (20%) subsets. After Q&A training, model performance was assessed using the test dataset. Two metrics were calculated for each question: a true-or-false indication of a perfect word-for-word match between the BERT-extracted data and CTR-extracted data; and, the F1 statistic. The latter produces a value between 0% and 100% indicating the degree of overlap between words in the BERT-extracted data and words in the CTR-extracted data. Results: The final dataset contained 14,143 pathology reports (11,520 for training, 2,623 for testing). This dataset included tumors from 228 organ sites involving 232 histological classifications. The three most common organ sites / histological classifications were: Prostate Gland / Adenocarcinoma (6.7%); Breast / Invasive Carcinoma (6.1%); and, Breast Overlapping Lesion / Invasive Carcinoma (5.9%). Our BERT-based Q&A system searched for answers to both questions in each test report. Thus, a total of 5,246 answers were generated. Of these, 4,667 (89%) were a perfect word-for-word match with the corresponding CTR extracted phrases. The mean F1 statistic between the BERT answers and the CTR extracted phrases was 92%. Conclusions: Future efforts will focus on improving performance via unsupervised training of the BERT language model using 484,000 Moffitt pathology reports. We will also extract additional data fields with CTR-matched ground truth labels. Ultimately new NLP transformer models could aid extraction of information from pathology reports and other EMR documents. This, in turn, could greatly facilitate personalized medicine. Citation Format: Ross Mitchell, Rachel Howard, Patricia Lewis, Katie Fellows, Jennie Jones, Phillip Reisman, Brooke Fridley, Dana Rollison. Deep learning for automatic extraction of tumor site and histology from unstructured pathology reports [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 2101.

The vast wealth of medical data collected over the last decade holds great promise for accelerating novel research, discovery, and clinical translation. Specifically, the rapid expansion of genomic testing provides new opportunities for the clinical management of cancer patients, influencing diagnosis, risk stratification, and treatment planning. Moffitt Cancer Center's Personalized Medicine Clinical Service integrates next-generation sequencing test results into patient care, using the data to guide individualized treatment plans. To maximize the efficiency and efficacy of this service, creative solutions for data harmonization, storage, and management are required. We implemented a commercial molecular data warehouse (MDW), directly linked to our existing clinical data warehouse, to store and manage molecular data ranging from genotypic alterations to annotations from public resources (HUGO, COSMIC, Ensembl) and clinically actionable targets (4,256 records currently loaded). A centralized, cloud-based data and analytics platform is also being implemented at Moffitt that will integrate a broad range of multi-modal data. In the cloud environment, the data from the MDW will be linked to typically siloed data streams from the electronic health record, cancer registry management system, biospecimen management system, billing and scheduling systems, patient-reported information and outcomes, and patient-generated health data, creating a unique and customized Personalized Medicine Curated Data Mart (CDM). In addition to describing the features of the MDW and the challenges faced during its implementation, we will provide an overview of the extensive data cleaning and curation required to facilitate such a CDM. This includes the extraction of disease characteristics from unstructured clinical text via natural language processing, creation of new derived data fields, approaches to extracting and managing complex treatment data, and the inclusion of detailed, manually-abstracted recurrence and outcomes data for historical patients from existing institutional datasets such as the Clinical Genomics Action Committee (CGAC) database. Finally, we will present prototypes of analytics dashboards that will interface directly with our CDM, facilitating intuitive data exploration for all members of our personalized medicine teams. Citation Format: Rachel Howard, Kevin Hicks, Jamie Teer, Phillip Reisman, Mandy O'Leary, Steven Eschrich, Ross Mitchell, Howard McLeod, Dana Rollison. Facilitating personalized medicine with cloud-based storage and analytics [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 3226.

As precision medicine research and its clinical applications continue to advance, it is critical for pharmacists to be involved in these developments to deliver optimal, tailored drug therapies for patients. To ensure pharmacists remain leaders in the field, the annual Pharmaceutical Sciences Conference convened by the University of North Carolina at Chapel Hill Eshelman School of Pharmacy focused on the role of pharmacy within precision medicine. This is a summary of the conference, highlighting the major challenges and solutions that will help advance individualized pharmacological methods within practice and research.

Pharmacogenomics provides a personalized approach to pharmacotherapy by using genetic information to guide drug dosing and selection. However, partly due to lack of education, pharmacogenomic testing has not been fully implemented in clinical practice. With pharmacotherapy training and patient accessibility, pharmacists are ideally suited to apply pharmacogenomics to patient care. Student pharmacists (n = 222) participated in an educational intervention that included voluntary personal genotyping using 23andMe. Of these, 31% of students completed both pre- and post-educational interventions to evaluate their attitudes and confidence towards the use of pharmacogenomics data in clinical decision making, and 55% of this paired subset obtained personal genotyping. McNemar&rsquo, s test and the Wilcoxon signed-rank test were used to analyze responses. Following the educational intervention, students regardless of genotyping were more likely to recommend personal genotyping (36% post-educational intervention versus 19% pre-educational intervention, p = 0.0032), more confident in using pharmacogenomics in the management of drug therapy (51% post-educational intervention versus 29% pre-educational intervention, p = 0.0045), and more likely to believe that personalized genomics would have an important role in their future pharmacy career (90% post-educational intervention versus 51% pre-educational intervention, p = 0.0072) compared to before receiving the educational intervention. This educational intervention positively influenced students&rsquo, attitudes and confidence regarding pharmacogenomics in the clinical setting. Future studies will examine the use of next-generation sequencing assays that selectively examine pharmacogenes in the education of student pharmacists.

PURPOSE/OBJECTIVES We set out to predict response and progression risk in recurrent high-grade glioma patients treated with hypofractionated stereotactic radiation plus pembrolizumab and bevacizumab (NCT02313272). At present RANO criteria define progression as 25% increase in sum of the products of perpendicular diameters of enhancing lesions compared with the smallest tumor measurement (either at baseline or best response) or significant increase in T2/FLAIR non-enhancing lesion on stable or increasing doses of corticosteroids. To this extent, a predictive model is needed to adaptively learn and forecast individual response to therapy. We evaluate if early tumor volume evolution can train a mathematical model to predict subsequent response to therapy. MATERIALS/METHODS We develop a mathematical model that describes patient-uniform tumor growth rate and initial pembrolizumab and bevacizumab responses, and patient-specific treatment response dynamics. A total of 154 radiology scans were delineated to derive longitudinal tumor volumes of 26 patients. In a leave-one-out study, patient-uniform model parameters are derived and then applied to the left-out patient to adaptively learn treatment response dynamics to forecast tumor volume evolution and progression risk. Model prediction performance is evaluated based on classification accuracy, including sensitivity and specificity. RESULTS Patient-uniform glioma growth rate and initial treatment response rates are estimated to achieve acceptable fits to the longitudinal data of all untrained patients with R2 = 0.81 [0.75, 0.89]. Response dynamics are predicted with high accuracy (0.78 [0.76, 0.79]), with positive and negative predictive values of 0.90 [0.86, 0.92] and 0.81 [0.78, 0.86], and sensitivity and specificity of 0.71 [0.69, 0.73] and 0.88 [0.83, 0.91] respectively. CONCLUSIONS Two patient-specific parameters in a mathematical model can be adaptively learned from early tumor volume evolution to predict subsequent response to therapy and progression risk for individual patients. Future validation is required in an independent dataset and prospective evaluation in another clinical trial.

Objectives To explore factors affecting communication between Foundation Year (FY) 1 doctors and hospital pharmacists about prescribing from the junior doctors’ perspective.Methods Trained interviewers (n=4) conducted semistructured interviews with FY1 doctors who were purposively sampled from three hospitals in England. FY1 doctors were asked about their experiences of communication with hospital pharmacists about their prescribing; instances where they disagreed with or did not implement a hospital pharmacist’s recommendation; and their preferences for communicating with hospital pharmacists about prescribing. Interviews were audiorecorded, transcribed verbatim and analysed thematically.Results A total of 27 FY1 doctors were interviewed. Findings were categorised into four main themes: (1) nature and context of communication; (2) FY1 doctors’ perceptions of communication with hospital pharmacists; (3) factors influencing FY1 doctors’ decision whether to act on pharmacists’ prescribing recommendations; and (4) suggestions to improve communication with pharmacists. FY1 doctors and hospital pharmacists generally communicated well. FY1 doctors appreciated and frequently acted on pharmacists’ advice yet there was deference to senior medical staff when advice differed. Joint ward rounds, pharmacist-led teaching sessions and a standardised approach to communication were all suggested as ways to improve communication and may increase the likelihood of pharmacists’ recommendations being acted on.Conclusions FY1 doctors and hospital pharmacists communicated frequently about medication prescribing. Issues occurred when there were differences in professional judgement between senior medical staff and pharmacists but these were usually resolved satisfactorily for the FY1 doctor. Further interventions to improve communication and safe prescribing could involve a multidisciplinary and systems approach.

Condensin is a protein complex with diverse functions in chromatin packaging and chromosome condensation and segregation. We studied condensin in the evolutionarily distant protist model

Objectives Using strong structuration theory, we aimed to understand the adoption and implementation of an electronic clinical audit and feedback tool to support medicine optimisation for patients in primary care.Design This is a qualitative study informed by strong structuration theory. The analysis was thematic, using a template approach. An a priori set of thematic codes, based on strong structuration theory, was developed from the literature and applied to the transcripts. The coding template was then modified through successive readings of the data.Setting Clinical commissioning group in the south of England.Participants Four focus groups and five semi-structured interviews were conducted with 18 participants purposively sampled from a range of stakeholder groups (general practitioners, pharmacists, patients and commissioners).Results Using the system could lead to improved medication safety, but use was determined by broad institutional contexts; by the perceptions, dispositions and skills of users; and by the structures embedded within the technology. These included perceptions of the system as new and requiring technical competence and skill; the adoption of the system for information gathering; and interactions and relationships that involved individual, shared or collective use. The dynamics between these external, internal and technological structures affected the adoption and implementation of the system.Conclusions Successful implementation of information technology interventions for medicine optimisation will depend on a combination of the infrastructure within primary care, social structures embedded in the technology and the conventions, norms and dispositions of those utilising it. Future interventions, using electronic audit and feedback tools to improve medication safety, should consider the complexity of the social and organisational contexts and how internal and external structures can affect the use of the technology in order to support effective implementation.

BACKGROUND: Monitoring for potentially hazardous prescribing is increasingly important to improve medication safety. Healthcare information technology can be used to achieve this aim, for example by providing access to prescribing data through surveillance of patients' electronic health records. The aim of our study was to examine the implementation and adoption of an electronic medicines optimisation system that was intended to facilitate clinical audit in primary care by identifying patients at risk of an adverse drug event. We adopted a sociotechnical approach that focuses on how complex social, organisational and institutional factors may impact upon the use of technology within work settings.METHODS: We undertook a qualitative realist evaluation of the use of an electronic medicines optimisation system in one Clinical Commissioning Group in England. Five semi-structured interviews, four focus groups and one observation were conducted with a range of stakeholders. Consistent with a realist evaluation methodology, the analysis focused on exploring the links between context, mechanism and outcome to explain the ways the intervention might work, for whom and in what circumstances.RESULTS: Using the electronic medicines optimisation system could lead to a number of improved patient safety outcomes including pre-emptively reviewing patients at risk of adverse drug events. The effective use of the system depended upon engagement with the system, the flow of information between different health professionals centrally placed at the Clinical Commissioning Group and those locally placed at individual general practices, and upon variably adapting work practices to facilitate the use of the system. The use of the system was undermined by perceptions of ownership, lack of access, and lack of knowledge and awareness.CONCLUSIONS: The use of an electronic medicines optimisation system may improve medication safety in primary care settings by identifying those patients at risk of an adverse drug event. To fully realise the potential benefits for medication safety there needs to be better utilisation across primary care and with a wider range of stakeholders. Engaging with all potential stakeholders and users prior to implementation of such systems might allay perceptions that the system is owned centrally and increase knowledge of the potential benefits.

Herbal medicines and dietary supplements are commonly taken by patients with cancer, leading to concern over interactions with conventional medicines. A literature search was carried out to identify published studies exploring supplement use by patients with a cancer diagnosis. A total of 818 articles were retrieved using the key words, but only 41 are judged to be relevant based on title. Following the review of the abstracts, ten papers were considered to be potentially relevant, but of these, only two met the selection criteria, and three additional papers were identified from published reviews. Of 806 patients surveyed, 433 (53.7%) were reported to be taking combinations of supplements and drugs, and 167 incidents of risk were identified, affecting 60 patients (13.9%). The interactions identified were mainly theoretical and not supported by clinical data. No studies reported any adverse events associated with these combinations; most did not record the actual drug combinations taken, and the risk potential of some supplements appears to have been over-estimated. More effort should be made to investigate supplement use in this vulnerable patient group, based on sound evidence of plausible interaction, not only to avoid harm but also to provide reassurance where appropriate if the patient wishes to take a particular supplement. Copyright © 2014 John Wiley & Sons, Ltd.

Background: The neutrophil-to-lymphocyte ratio (NLR) is an established marker of subclinical inflammation, and a high NLR is associated with poorer clinical outcomes in cancer patients. Despite this, factors that influence the magnitude of NLR independently of disease are poorly understood. Here, we systematically assess the influence of socio-demographic and lifestyle factors on NLR to identify a) potential confounders and effect modifiers in the relationship between NLR and cancer outcomes, and b) possible targets for intervention in patients with high NLR at diagnosis. Methods: Based on a total participant cohort of 48,023 adults with available NLR data, we identified 20 factors of interest relating to patient demographics, socioeconomic status and lifestyle that were included in the National Health and Nutrition Examination Survey (NHANES) between 1999 and 2016. Multivariable regression analysis identified factors significantly associated with the magnitude of NLR after adjusting for comorbidities and medications. Interactions between factors of interest were evaluated, and effect modification by demographic characteristics including age, sex and race was quantified. Results: Seven demographic and lifestyle factors were found to be significantly and independently associated with NLR: age, sex, race, marital status, physical activity, smoking history and alcohol use. Females, under 60s and non-Hispanic blacks demonstrated significantly lower NLR than males, over 60s and non-Hispanic whites. Participation in vigorous activity for at least ten minutes at a time was associated with decreased NLR, as was never-smoker status. Moderate drinking was also associated with a decreased NLR as compared to complete abstinence from alcohol. Conclusions: Multiple demographic and lifestyle factors are independently associated with elevated NLR. For an accurate assessment of the prognostic power of the NLR in the clinical setting, these factors should be routinely adjusted for in studies of the association between NLR and cancer outcomes. The present study reiterates that physical activity may have the potential to improve clinical outcomes by way of reducing systemic inflammation. Citation Format: Rachel Howard, Aaron Scheiner, Kathleen M. Egan. Socio-demographic and lifestyle factors associated with the neutrophil-to-lymphocyte ratio: A systematic evaluation of the United States National Health and Nutrition Examination Survey [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 3299.