Evaluating Cognitive Impairment in a Large Health Care System: The Cognition in Primary Care Program.

Background: The prevalence of Alzheimer's disease and related disorders (ADRD) is rising. Primary care providers (PCPs) will increasingly be required to play a role in its detection but lack the training to do so. Objective: To develop a model for cognitive evaluation which is feasible in primary care and evaluate its implementation in a large health system. Methods: The Cognition in Primary Care Program consists of web-based training together with integrated tools built into the electronic record. We implemented the program among PCPs at 14 clinics in a large health system. We (1) surveyed PCPs to assess the impact of training on their confidence to evaluate cognition, (2) measured the number of cognitive assessments they performed, and (3) tracked the number of patients diagnosed with mild cognitive impairment (MCI). Results: Thirty-nine PCPs completed the training which covered how to evaluate cognition. Survey response rate from those PCPs was 74%. Six months after the end of the training, they reported confidence in assessing cognition (mean 4.6 on 5-point scale). Cognitive assessments documented in the health record increased from 0.8 per month before the training to 2.5 in the six months after the training. Patients who were newly diagnosed with MCI increased from 4.2 per month before the training to 6.0 per month in the six months after the training. Conclusions: This model for cognitive evaluation in a large health system was shown to increase cognitive testing and increase diagnoses of MCI. Such improvements are essential for the timely detection of ADRD.

Integrating patient voices into the extraction of social determinants of health from clinical notes: ethical considerations and recommendations.

Identifying patients' social needs is a first critical step to address social determinants of health (SDoH)-the conditions in which people live, learn, work, and play that affect health. Addressing SDoH can improve health outcomes, population health, and health equity. Emerging SDoH reporting requirements call for health systems to implement efficient ways to identify and act on patients' social needs. Automatic extraction of SDoH from clinical notes within the electronic health record through natural language processing offers a promising approach. However, such automated SDoH systems could have unintended consequences for patients, related to stigma, privacy, confidentiality, and mistrust. Using Floridi et al's "AI4People" framework, we describe ethical considerations for system design and implementation that call attention to patient autonomy, beneficence, nonmaleficence, justice, and explicability. Based on our engagement of clinical and community champions in health equity work at University of Washington Medicine, we offer recommendations for integrating patient voices and needs into automated SDoH systems.

Responding to COVID-19: The UW Medicine Information Technology Services Experience.

BACKGROUND: UW Medicine was one of the first health systems to encounter and treat COVID-19 patients in the United States, starting in late February 2020. OBJECTIVE: Here we describe the rapid rollout of capabilities by UW Medicine Information Technology Services (ITS) to support our clinical response to the COVID-19 pandemic and provide recommendations for health systems to urgently consider, as they plan their own response to this and potentially other future pandemics. METHODS: Our recommendations include establishing a hospital incident command structure that includes tight integration with IT, creating automated dashboards for incident command, optimizing emergency communication to staff and patients, and preparing human resources, security, other policies, and equipment to support the transition of all nonessential staff to telework.We describe how UW Medicine quickly expanded telemedicine capabilities to include most primary care providers and increasing numbers of specialty providers. We look at how we managed expedited change control processes to quickly update electronic health records (EHR) with new COVID-19 laboratory and clinical workflows. We also examine the integration of new technology such as tele-intensive care (ICU) equipment and improved integration with teleconferencing software into our EHR. To support the rapid preparation for COVID-19 at other health systems, we include samples of the UW Medicine's COVID-19 order set, COVID-19 documentation template, dashboard metric categories, and a list of the top 10 things your health care IT organization can do now to prepare. CONCLUSION: The COVID-19 response requires new and expedited ways of approaching ITS support to clinical needs. UW Medicine ITS leadership hope that by quickly sharing our nimble response to clinical and operational requests, we can help other systems prepare to respond to this public health emergency.

A community-based lung cancer rapid tissue donation protocol provides high-quality drug-resistant specimens for proteogenomic analyses.

BACKGROUND: For the advancement of cancer research, the collection of tissue specimens from drug-resistant tumors after targeted therapy is crucial. Although patients with lung cancer are often provided targeted therapy, post-therapy specimens are not routinely collected due to the risks of collection, limiting the study of targeted therapy resistance mechanisms. Posthumous rapid tissue donation (RTD) is an expedient collection process that provides an opportunity to understand treatment-resistant lung cancers. METHODS: Consent to participate in the thoracic RTD protocol was obtained during patient care. When death occurred, tumor and paired non-tumor, cytology, and blood specimens were collected within 48 hours and preserved as formalin-fixed and frozen specimens. Tissue sections were evaluated with hematoxylin and eosin staining and immunohistochemistry (IHC) against multiple biomarkers, including various programmed death ligand 1 (PD-L1) clones. Next-generation sequencing was performed on 13 specimens from 5 patients. RESULTS: Postmortem specimens (N = 180) were well preserved from 9 patients with lung cancer. PD-L1 IHC revealed heterogeneity within and between tumors. An AGK-BRAF fusion was newly identified in tumor from a donor with a known echinoderm microtubule-associated protein-like 4 to anaplastic lymphoma kinase (EML4-ALK) fusion and history of anaplastic lymphoma kinase (ALK) inhibitor therapy. RNA expression analysis revealed a clonal genetic origin of metastatic cancer cells. CONCLUSIONS: Post-therapy specimens demonstrated PD-L1 heterogeneity and an acyl glycerol kinase to B-rapidly accelerated fibrosarcoma (AGK-BRAF) fusion in a patient with an EML4-ALK-positive lung adenocarcinoma as a potential resistance mechanism to ALK inhibitor therapy. Rapid tissue donation collection of postmortem tissue from lung cancer patients is a novel approach to cancer research that enables studies of molecular evolution and drug resistance.

Elucidating therapeutic molecular targets in premenopausal Asian women with recurrent breast cancers.

Breast cancer is an increasing problem in Asia, with a higher proportion of premenopausal patients who are at higher risk of recurrence. Targeted sequencing was performed on DNA extracted from primary tumor specimens of 63 premenopausal Asian patients who relapsed after initial diagnosis of non-metastatic breast cancer. The most prevalent alterations included: TP53 (65%); PIK3CA (32%); GATA3 (29%); ERBB2 (27%); MYC (25%); KMT2C (21%); MCL1 (17%); PRKDC, TPR, BRIP1 (14%); MDM4, PCDH15, PRKAR1A, CDKN1B (13%); CCND1, KMT2D, STK11, and MLH1 (11%). Sixty of the 63 patients (95%) had at least one genetic alteration in a signaling pathway related to cell cycle or p53 signaling. The presence of MCL1 amplification, HIF-1-alpha transcription factor network pathway alterations, and direct p53 effectors pathway alterations were independent predictors of inferior overall survival from initial diagnosis. Comparison with non-Asian premenopausal tumors in The Cancer Genome Atlas (TCGA) revealed a higher prevalence of TP53 mutations among HER2-positive cancers, and more frequent TP53, TET2, and CDK12 mutations among hormone receptor-positive HER2-negative cancers in our cohort. Given the limited number of non-Asian premenopausal breast cancers that had relapsed in TCGA, we compared the frequency of mutations in our cohort with 43 premenopausal specimens from both TCGA and International Cancer Genome Consortium that had relapsed. There was a trend toward higher prevalence of TP53 mutations in our cohort. Certain genomic aberrations may be enriched in tumors of poor-prognosis premenopausal Asian breast cancers. The development of novel therapies targeting these aberrations merit further research.

PureCN: copy number calling and SNV classification using targeted short read sequencing.

BACKGROUND: Matched sequencing of both tumor and normal tissue is routinely used to classify variants of uncertain significance (VUS) into somatic vs. germline. However, assays used in molecular diagnostics focus on known somatic alterations in cancer genes and often only sequence tumors. Therefore, an algorithm that reliably classifies variants would be helpful for retrospective exploratory analyses. Contamination of tumor samples with normal cells results in differences in expected allelic fractions of germline and somatic variants, which can be exploited to accurately infer genotypes after adjusting for local copy number. However, existing algorithms for determining tumor purity, ploidy and copy number are not designed for unmatched short read sequencing data. RESULTS: We describe a methodology and corresponding open source software for estimating tumor purity, copy number, loss of heterozygosity (LOH), and contamination, and for classification of single nucleotide variants (SNVs) by somatic status and clonality. This R package, PureCN, is optimized for targeted short read sequencing data, integrates well with standard somatic variant detection pipelines, and has support for matched and unmatched tumor samples. Accuracy is demonstrated on simulated data and on real whole exome sequencing data. CONCLUSIONS: Our algorithm provides accurate estimates of tumor purity and ploidy, even if matched normal samples are not available. This in turn allows accurate classification of SNVs. The software is provided as open source (Artistic License 2.0) R/Bioconductor package PureCN (http://bioconductor.org/packages/PureCN/).

High-throughput screening using patient-derived tumor xenografts to predict clinical trial drug response.

Profiling candidate therapeutics with limited cancer models during preclinical development hinders predictions of clinical efficacy and identifying factors that underlie heterogeneous patient responses for patient-selection strategies. We established ∼1,000 patient-derived tumor xenograft models (PDXs) with a diverse set of driver mutations. With these PDXs, we performed in vivo compound screens using a 1 × 1 × 1 experimental design (PDX clinical trial or PCT) to assess the population responses to 62 treatments across six indications. We demonstrate both the reproducibility and the clinical translatability of this approach by identifying associations between a genotype and drug response, and established mechanisms of resistance. In addition, our results suggest that PCTs may represent a more accurate approach than cell line models for assessing the clinical potential of some therapeutic modalities. We therefore propose that this experimental paradigm could potentially improve preclinical evaluation of treatment modalities and enhance our ability to predict clinical trial responses.

Studying clonal dynamics in response to cancer therapy using high-complexity barcoding.

Resistance to cancer therapies presents a significant clinical challenge. Recent studies have revealed intratumoral heterogeneity as a source of therapeutic resistance. However, it is unclear whether resistance is driven predominantly by pre-existing or de novo alterations, in part because of the resolution limits of next-generation sequencing. To address this, we developed a high-complexity barcode library, ClonTracer, which enables the high-resolution tracking of more than 1 million cancer cells under drug treatment. In two clinically relevant models, ClonTracer studies showed that the majority of resistant clones were part of small, pre-existing subpopulations that selectively escaped under therapeutic challenge. Moreover, the ClonTracer approach enabled quantitative assessment of the ability of combination treatments to suppress resistant clones. These findings suggest that resistant clones are present before treatment, which would make up-front therapeutic combinations that target non-overlapping resistance a preferred approach. Thus, ClonTracer barcoding may be a valuable tool for optimizing therapeutic regimens with the goal of curative combination therapies for cancer.