Real World Clinicopathologic Observations of Patients with Metastatic Solid Tumors Receiving Immune Checkpoint Inhibitor Therapy: Analysis from Kentucky Cancer Registry

The state of Kentucky has the highest cancer incidence and mortality in the United States. High‐risk populations such as this are often underrepresented in clinical trials. The study aims to do a comprehensive analysis of molecular landscape of metastatic cancers among these patients with detailed evaluation of factors affecting response and outcomes to immune checkpoint inhibitor (ICI) therapy. We performed a retrospective analysis of metastatic solid tumor patients who received ICI and underwent molecular profiling at our institution. Sixty nine patients with metastatic solid tumors who received ICI were included in the study. Prevalence of smoking and secondhand tobacco exposure was 78.3% and 14.5%, respectively. TP53 (62.3%), CDKN1B/2A (40.5%), NOTCH and PIK3 (33.3%) were the most common alterations in tumors. 67.4% were PDL1 positive and 59.4% had intermediate‐high tumor mutational burden (TMB). Median TMB (12.6) was twofold to fourfold compared to clinical trials. The prevalence of mutations associated with smoking, homologous recombinant repair and PIK3/AKT/mTOR pathway mutations was higher compared to historic cohorts. PDL1 expression had no significant effect on radiologic response, but PFS improvement in patients with tumors expressing PDL1 trended toward statistical significance (median 18 vs. 40 weeks. HR = 1.43. 95%CI 0.93, 4.46). Median PFS was higher in the high‐TMB cohort compared to low‐intermediate TMB (median not reached vs. 26 weeks; HR = 0.37. 95%CI 0.13, 1.05). A statistically significant improvement in PFS was observed in the PIK3 mutated cohort (median 123 vs. 23 weeks. HR = 2.51. 95%CI 1.23, 5.14). This was independent of tumor mutational burden (TMB) status or PDL1 expression status. PIK3 mutants had a higher overall response rate than the wild type (69.6% vs. 43.5%, OR 0.34; p = 0.045). The results should prompt further evaluation of these potential biomarkers and more widespread real‐world data publications which might help determine biomarkers that could benefit specific populations

Web-Based Interactive Mapping from Data Dictionaries to Ontologies, with an Application to Cancer Registry

BACKGROUND: The Kentucky Cancer Registry (KCR) is a central cancer registry for the state of Kentucky that receives data about incident cancer cases from all healthcare facilities in the state within 6 months of diagnosis. Similar to all other U.S. and Canadian cancer registries, KCR uses a data dictionary provided by the North American Association of Central Cancer Registries (NAACCR) for standardized data entry. The NAACCR data dictionary is not an ontological system. Mapping between the NAACCR data dictionary and the National Cancer Institute (NCI) Thesaurus (NCIt) will facilitate the enrichment, dissemination and utilization of cancer registry data. We introduce a web-based system, called Interactive Mapping Interface (IMI), for creating mappings from data dictionaries to ontologies, in particular from NAACCR to NCIt. METHOD: IMI has been designed as a general approach with three components: (1) ontology library; (2) mapping interface; and (3) recommendation engine. The ontology library provides a list of ontologies as targets for building mappings. The mapping interface consists of six modules: project management, mapping dashboard, access control, logs and comments, hierarchical visualization, and result review and export. The built-in recommendation engine automatically identifies a list of candidate concepts to facilitate the mapping process. RESULTS: We report the architecture design and interface features of IMI. To validate our approach, we implemented an IMI prototype and pilot-tested features using the IMI interface to map a sample set of NAACCR data elements to NCIt concepts. 47 out of 301 NAACCR data elements have been mapped to NCIt concepts. Five branches of hierarchical tree have been identified from these mapped concepts for visual inspection. CONCLUSIONS: IMI provides an interactive, web-based interface for building mappings from data dictionaries to ontologies. Although our pilot-testing scope is limited, our results demonstrate feasibility using IMI for semantic enrichment of cancer registry data by mapping NAACCR data elements to NCIt concepts

Incidence of CNS Tumors in Appalachian Children

Determine whether the risk of astrocytomas in Appalachian children is higher than the national average. We compared the incidence of pediatric brain tumors in Appalachia versus non-Appalachia regions, covering years 2000–2011. The North American Association of Central Cancer Registries (NAACCR) collects population-based data from 55 cancer registries throughout U.S. and Canada. All invasive primary (i.e. non-metastatic tumors), with age at diagnosis 0–19 years old, were included. Nearly 27,000 and 2200 central nervous system (CNS) tumors from non-Appalachia and Appalachia, respectively comprise the cohorts. Age-adjusted incidence rates of each main brain tumor subtype were compared. The incidence rate of pediatric CNS tumors was 8% higher in Appalachia, 3.31 [95% CI 3.17–3.45] versus non–Appalachia, 3.06, [95% CI 3.02–3.09] for the years 2001–2011, all rates are per 100,000 population. Astrocytomas accounted for the majority of this difference, with the rate being 16% higher in Appalachian children, 1.77, [95% CI 1.67–1.87] versus non-Appalachian children, 1.52, [95% CI 1.50–1.55]. Among astrocytomas, World Health Organization (WHO) grade I astrocytomas were 41% higher in Appalachia, 0.63 [95% CI 0.56–0.70] versus non-Appalachia 0.44 [95% CI 0.43–0.46] for the years 2004–2011. This is the first study to demonstrate that Appalachian children are at greater risk of CNS neoplasms, and that much of this difference is in WHO grade I astrocytomas, 41% more common. The cause of this increased incidence is unknown and we discuss the importance of this in relation to genetic and environmental findings in Appalachia

Global Burden of Neuroendocrine Tumors and Changing Incidence in Kentucky

Background: Neuroendocrine tumors (NETs) have a low incidence but relatively high prevalence. Over the last three decades, the incidence of NETs has risen 6-fold in the United States. We conducted an observational study to compare the incidence of NETs reported to the Kentucky Cancer Registry (KCR) versus that reported to Surveillance, Epidemiology, and End Results Program (SEER). We also provide a systematic review of the state of neuroendocrine tumors worldwide, and compare the available global and local published data. Methods: KCR and SEER databases were queried for NET cases between 1995 and 2015. A detailed literature review of epidemiological data for various nations worldwide summarize epidemiological data from various countries. Results: KCR recorded 6179 individuals with newly diagnosed NETs between 1995 and 2015. Between 1995-2012, the incidence of NETs in KCR increased from 3.1 to 7.1 per 100,000 cases, while it increased from 3.96 to 6.61 in the SEER database. The incidence rates in both KCR and SEER databases were linear. 90.57% were Caucasians with 54.74% females. 27.67% of the Kentucky population was from the Appalachian region. Patients aged 50-64 years had the highest prevalence (38%). Lung NET (30.60%) formed the bulk of cases, followed by small intestine (16.82%), rectum/anus (11.35%) and colon (9.71%). Conclusions: NETs incidence between 1995 and 2015 show a linear increase in both KCR and SEER databases. Because of this increased incidence it is imperative for community oncologists to familiarize themselves with this entity, which until recently was under-studied and with few viable treatment options

Using Case-Level Context to Classify Cancer Pathology Reports

Individual electronic health records (EHRs) and clinical reports are often part of a larger sequence-for example, a single patient may generate multiple reports over the trajectory of a disease. In applications such as cancer pathology reports, it is necessary not only to extract information from individual reports, but also to capture aggregate information regarding the entire cancer case based off case-level context from all reports in the sequence. In this paper, we introduce a simple modular add-on for capturing case-level context that is designed to be compatible with most existing deep learning architectures for text classification on individual reports. We test our approach on a corpus of 431,433 cancer pathology reports, and we show that incorporating case-level context significantly boosts classification accuracy across six classification tasks-site, subsite, laterality, histology, behavior, and grade. We expect that with minimal modifications, our add-on can be applied towards a wide range of other clinical text-based tasks

Association of first primary cancer with risk of subsequent primary cancer among survivors of adult-onset cancers in Kentucky and Appalachian Kentucky

BackgroundAppalachia is a region with significant cancer disparities in incidence and mortality compared to Kentucky and the United States. However, the contribution of these cancer health disparities to subsequent primary cancers (SPCs) among survivors of adult-onset cancers is limited. This study aimed to quantify the overall and cancer type-specific risks of SPCs among adult-onset cancer survivors by first primary cancer (FPC) types, residence and sex.MethodsThis retrospective cohort study from the Kentucky Cancer Registry included 148,509 individuals aged 20-84 years diagnosed with FPCs from 2000-2014 (followed until December 31, 2019) and survived at least 5 years. Expected numbers of SPC were derived from incidence rates in the Kentucky population; standardized incidence ratio (SIR) compared with those expected in the general Kentucky population.ResultsAmong 148,509 survivors (50.2% women, 27.9% Appalachian), 17,970 SPC cases occurred during 829,530 person-years of follow-up (mean, 5.6 years). Among men, the overall risk of developing any SPCs was statistically significantly higher for 20 of the 30 FPC types, as compared with risks in the general population. Among women, the overall risk of developing any SPCs was statistically significantly higher for 20 of the 31 FPC types, as compared to the general population. The highest overall SIR were estimated among oral cancer survivors (SIR, 2.14 [95% CI, 1.97-2.33] among men, and among laryngeal cancer survivors (SIR, 3.62 [95% CI, 2.93-4.42], among women. Appalachian survivors had significantly increased risk of overall SPC and different site specific SPC when compared to non-Appalachian survivors. The highest overall SIR were estimated among laryngeal cancer survivors for both Appalachian and non-Appalachian residents (SIR, 2.50: 95%CI, 2.10-2.95; SIR, 2.02: 95% CI, 1.77-2.03, respectively).ConclusionAmong adult-onset cancer survivors in Kentucky, several FPC types were significantly associated with greater risk of developing an SPC, compared with the general population. Risk for Appalachian survivors was even higher when compared to non-Appalachian residents, but was not explained by higher risk of smoking related cancers. Cancers associated with smoking comprised substantial proportions of overall SPC incidence among all survivors and highlight the importance of ongoing surveillance and efforts to prevent new cancers among survivors

Automatic Extraction of ICD-O-3 Primary Sites from Cancer Pathology Reports

Abstract Although registry specific requirements exist, cancer registries primarily identify reportable cases using a combination of particular ICD-O-3 topography and morphology codes assigned to cancer case abstracts of which free text pathology reports form a main component. The codes are generally extracted from pathology reports by trained human coders, sometimes with the help of software programs. Here we present results that improve on the state-ofthe-art in automatic extraction of 57 generic sites from pathology reports using three representative machine learning algorithms in text classification. We use a dataset of 56,426 reports arising from 35 labs that report to the Kentucky Cancer Registry. Employing unigrams, bigrams, and named entities as features, our methods achieve a class-based micro F-score of 0.9 and macro F-score of 0.72. To our knowledge, this is the best result on extracting ICD-O-3 codes from pathology reports using a large number of possible codes. Given the large dataset we use (compared to other similar efforts) with reports from 35 different labs, we also expect our final models to generalize better when extracting primary sites from previously unseen reports

Elevated Integrin α6β4 Expression is Associated with Venous Invasion and Decreased Overall Survival in Non-Small Cell Lung Cancer

Lung cancer carries a poor prognosis and is the most common cause of cancer-related death worldwide. The integrin α6β4, a laminin receptor, promotes carcinoma progression in part by cooperating with various growth factor receptors to facilitate invasion and metastasis. In carcinoma cells with mutant TP53, the integrin α6β4 promotes cell survival. TP53 mutations and integrin α6β4 overexpression co-occur in many aggressive malignancies. Because of the high frequency of TP53 mutations in lung squamous cell carcinoma (SCC), we sought to investigate the association of integrin β4 expression with clinicopathologic features and survival in non–small cell lung cancer (NSCLC). We constructed a lung cancer tissue microarray and stained sections for integrin β4 subunit expression using immunohistochemistry. We found that integrin β4 expression is elevated in SCC compared with adenocarcinoma (P \u3c .0001), which was confirmed in external gene expression data sets (P \u3c .0001). We also determined that integrin β4 overexpression associates with the presence of venous invasion (P = .0048) and with reduced overall patient survival (hazard ratio, 1.46; 95% confidence interval, 1.01-2.09; P = .0422). Elevated integrin β4 expression was also shown to associate with reduced overall survival in lung cancer gene expression data sets (hazard ratio, 1.49; 95% confidence interval, 1.31-1.69; P \u3c .0001). Using cBioPortal, we generated a network map demonstrating the 50 most highly altered genes neighboring ITGB4 in SCC, which included laminins, collagens, CD151, genes in the EGFR and PI3K pathways, and other known signaling partners. In conclusion, we demonstrate that integrin β4 is overexpressed in NSCLC where it is an adverse prognostic marker

Deep Active Learning for Classifying Cancer Pathology Reports

Background: Automated text classification has many important applications in the clinical setting; however, obtaining labelled data for training machine learning and deep learning models is often difficult and expensive. Active learning techniques may mitigate this challenge by reducing the amount of labelled data required to effectively train a model. In this study, we analyze the effectiveness of 11 active learning algorithms on classifying subsite and histology from cancer pathology reports using a Convolutional Neural Network as the text classification model. Results: We compare the performance of each active learning strategy using two differently sized datasets and two different classification tasks. Our results show that on all tasks and dataset sizes, all active learning strategies except diversity-sampling strategies outperformed random sampling, i.e., no active learning. On our large dataset (15K initial labelled samples, adding 15K additional labelled samples each iteration of active learning), there was no clear winner between the different active learning strategies. On our small dataset (1K initial labelled samples, adding 1K additional labelled samples each iteration of active learning), marginal and ratio uncertainty sampling performed better than all other active learning techniques. We found that compared to random sampling, active learning strongly helps performance on rare classes by focusing on underrepresented classes. Conclusions: Active learning can save annotation cost by helping human annotators efficiently and intelligently select which samples to label. Our results show that a dataset constructed using effective active learning techniques requires less than half the amount of labelled data to achieve the same performance as a dataset constructed using random sampling

Preliminary Research on a COVID-19 Test Strategy to Guide Quarantine Interval in University Students

Following COVID-19 exposure, the Centers for Disease Control (CDC) recommends a 10–14-day quarantine for asymptomatic individuals and more recently a 7-day quarantine with a negative PCR test. A university-based prospective cohort study to determine if early polymerase chain reaction (PCR) negativity predicts day 14 negativity was performed. A total of 741 asymptomatic students in quarantine was screened and 101 enrolled. Nasopharyngeal swabs were tested on days 3 or 4, 5, 7, 10, and 14, and the proportion of concordant negative results for each day versus day 14 with a two-sided 95% exact binomial confidence interval was determined. Rates of concordant negative test results were as follows: day 5 vs. day 14 = 45/50 (90%, 95% CI: 78–97%); day 7 vs. day 14 = 47/52 (90%, 95% CI: 79–97%); day 10 vs. day 14 = 48/53 (91%, 95% CI:79–97%), with no evidence of different negative rates between earlier days and day 14 by McNemar’s test, p \u3e 0.05. Overall, 14 of 90 (16%, 95% CI: 9–25%) tested positive while in quarantine, with seven initial positive tests on day 3 or 4, 5 on day 5, 2 on day 7, and none on day 10 or 14. Based on concordance rates between day 7 and 14, we anticipate that 90% (range: 79–97%) of individuals who are negative on day 7 will remain negative on day 14, providing the first direct evidence that exposed asymptomatic students ages 18–44 years in a university setting are at low risk if released from quarantine at 7 days if they have a negative PCR test prior to release. In addition, the 16% positive rate supports the ongoing need to quarantine close contacts of COVID-19 cases