Real time patient-reported outcome measures in patients with cancer: Early experience within an integrated health system

BACKGROUND: While patient-reported outcome measures (PROMs) have benefit in cancer clinical trials, real-world applications are lacking. This study describes the method of implementation of a cancer enterprise-wide PROMs platform. METHODS: After establishing a multispecialty stakeholder group within a large integrated health system, domain-specific instruments were selected from the National Institutes of Health\u27s Patient-Reported Outcomes Measurement Information System (PROMIS) instruments (pain interference, fatigue, physical function, and depression) and were administered at varying frequencies throughout each patient\u27s cancer journey. All cancer patients with an oncologic visit were eligible to complete the PROMs prior to the visit using a patient portal, or at the time of the visit using a tablet. PROMs were integrated into clinical workflow. Clinical partnerships were essential for successful implementation. Descriptive preliminary data were compared using multivariable logistic regression to determine the factors associated with method of PROMs completion. RESULTS: From September 16, 2020 to July 23, 2021, 23 of 38 clinical units (60.5%) implemented PROMs over 2392 encounters and 1666 patients. Approximately one third of patients (n = 629, 37.8%) used the patient portal. Black patients (aOR 0.70; 95% CI: 0.51-0.97) and patients residing in zip codes with higher percentage of unemployment (aOR: 0.07, 95% CI: 0.01-0.41) were among the least likely to complete PROMs using the patient portal. CONCLUSIONS: Successful system-wide implementation of PROMs among cancer patients requires engagement from multispecialty stakeholders and investment from clinical partners. Attention to the method of PROMs collection is required in order to reduce the potential for disparities, such as Black populations and those residing in areas with high levels of unemployment

Cancer and psychiatric diagnoses in the year preceding suicide

BACKGROUND: Patients with cancer are known to be at increased risk for suicide but little is known about the interaction between cancer and psychiatric diagnoses, another well-documented risk factor. METHODS: Electronic medical records from nine healthcare systems participating in the Mental Health Research Network were aggregated to form a retrospective case-control study, with ICD-9 codes used to identify diagnoses in the 1 year prior to death by suicide for cases (N = 3330) or matching index date for controls (N = 297,034). Conditional logistic regression was used to assess differences in cancer and psychiatric diagnoses between cases and controls, controlling for sex and age. RESULTS: Among patients without concurrent psychiatric diagnoses, cancer at disease sites with lower average 5-year survival rates were associated with significantly greater relative risk, while cancer disease sites with survival rates of \u3e70% conferred no increased risk. Patients with most psychiatric diagnoses were at higher risk, however, there was no additional risk conferred to these patients by a concurrent cancer diagnosis. CONCLUSION: We found no evidence of a synergistic effect between cancer and psychiatric diagnoses. However, cancer patients with a concurrent psychiatric illness remain at the highest relative risk for suicide, regardless of cancer disease site, due to strong independent associations between psychiatric diagnoses and suicide. For patients without a concurrent psychiatric illness, cancer disease sites associated with worse prognoses appeared to confer greater suicide risk

Examining provider perceptions and practices for comprehensive geriatric assessment among cancer survivors: a qualitative study with an implementation science focus

Introduction: Cancer rates increase with age, and older cancer survivors have unique medical care needs, making assessment of health status and identification of appropriate supportive resources key to delivery of optimal cancer care. Comprehensive geriatric assessments (CGAs) help determine an older person’s functional capabilities as cancer care providers plan treatment and follow-up care. Despite its proven utility, research on implementation of CGA is lacking.Methods: Guided by a qualitative description approach and through interviews with primary care providers and oncologists, our goal was to better understand barriers and facilitators of CGA use and identify training and support needs for implementation. Participants were identified through Cancer Prevention and Control Research Network partner listservs and a national cancer and aging organization. Potential interviewees, contacted via email, were provided with a description of the study purpose. Eight semi-structured interviews were conducted via Zoom, recorded, and transcribed verbatim by a professional transcription service. The interview guide explored providers’ knowledge and use of CGAs. For codebook development, three representative transcripts were independently reviewed and coded by four team members. The interpretive process involved reflecting, transcribing, coding, and searching for and identifying themes.Results: Providers shared that, while it would be ideal to administer CGAs with all new patients, they were not always able to do this. Instead, they used brief screening tools or portions of CGAs, or both. There was variability in how CGA domains were assessed; however, all considered CGAs useful and they communicated with patients about their benefits. Identified facilitators of implementation included having clinic champions, an interdisciplinary care team to assist with implementation and referrals for intervention, and institutional resources and buy-in. Barriers noted included limited staff capacity and competing demands on time, provider inexperience, and misaligned institutional priorities.Discussion: Findings can guide solutions for improving the broader and more systematic use of CGAs in the care of older cancer patients. Uptake of processes like CGA to better identify those at risk of poor outcomes and intervening early to modify treatments are critical to maximize the health of the growing population of older cancer survivors living through and beyond their disease

Pan-Cancer Analysis of lncRNA Regulation Supports Their Targeting of Cancer Genes in Each Tumor Context

Long noncoding RNAs (lncRNAs) are commonly dys-regulated in tumors, but only a handful are known toplay pathophysiological roles in cancer. We inferredlncRNAs that dysregulate cancer pathways, onco-genes, and tumor suppressors (cancer genes) bymodeling their effects on the activity of transcriptionfactors, RNA-binding proteins, and microRNAs in5,185 TCGA tumors and 1,019 ENCODE assays.Our predictions included hundreds of candidateonco- and tumor-suppressor lncRNAs (cancerlncRNAs) whose somatic alterations account for thedysregulation of dozens of cancer genes and path-ways in each of 14 tumor contexts. To demonstrateproof of concept, we showed that perturbations tar-geting OIP5-AS1 (an inferred tumor suppressor) andTUG1 and WT1-AS (inferred onco-lncRNAs) dysre-gulated cancer genes and altered proliferation ofbreast and gynecologic cancer cells. Our analysis in-dicates that, although most lncRNAs are dysregu-lated in a tumor-specific manner, some, includingOIP5-AS1, TUG1, NEAT1, MEG3, and TSIX, synergis-tically dysregulate cancer pathways in multiple tumorcontexts

Genomic, Pathway Network, and Immunologic Features Distinguishing Squamous Carcinomas

This integrated, multiplatform PanCancer Atlas study co-mapped and identified distinguishing molecular features of squamous cell carcinomas (SCCs) from five sites associated with smokin

Pan-cancer Alterations of the MYC Oncogene and Its Proximal Network across the Cancer Genome Atlas

Although theMYConcogene has been implicated incancer, a systematic assessment of alterations ofMYC, related transcription factors, and co-regulatoryproteins, forming the proximal MYC network (PMN),across human cancers is lacking. Using computa-tional approaches, we define genomic and proteo-mic features associated with MYC and the PMNacross the 33 cancers of The Cancer Genome Atlas.Pan-cancer, 28% of all samples had at least one ofthe MYC paralogs amplified. In contrast, the MYCantagonists MGA and MNT were the most frequentlymutated or deleted members, proposing a roleas tumor suppressors.MYCalterations were mutu-ally exclusive withPIK3CA,PTEN,APC,orBRAFalterations, suggesting that MYC is a distinct onco-genic driver. Expression analysis revealed MYC-associated pathways in tumor subtypes, such asimmune response and growth factor signaling; chro-matin, translation, and DNA replication/repair wereconserved pan-cancer. This analysis reveals insightsinto MYC biology and is a reference for biomarkersand therapeutics for cancers with alterations ofMYC or the PMN

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

Beyond sample curation and basic pathologic characterization, the digitized H&E-stained images of TCGA samples remain underutilized. To highlight this resource, we present mappings of tumorinfiltrating lymphocytes (TILs) based on H&E images from 13 TCGA tumor types. These TIL maps are derived through computational staining using a convolutional neural network trained to classify patches of images. Affinity propagation revealed local spatial structure in TIL patterns and correlation with overall survival. TIL map structural patterns were grouped using standard histopathological parameters. These patterns are enriched in particular T cell subpopulations derived from molecular measures. TIL densities and spatial structure were differentially enriched among tumor types, immune subtypes, and tumor molecular subtypes, implying that spatial infiltrate state could reflect particular tumor cell aberration states. Obtaining spatial lymphocytic patterns linked to the rich genomic characterization of TCGA samples demonstrates one use for the TCGA image archives with insights into the tumor-immune microenvironment

Determinants of penetrance and variable expressivity in monogenic metabolic conditions across 77,184 exomes

Penetrance of variants in monogenic disease and clinical utility of common polygenic variation has not been well explored on a large-scale. Here, the authors use exome sequencing data from 77,184 individuals to generate penetrance estimates and assess the utility of polygenic variation in risk prediction of monogenic variants

Addressing climate change with behavioral science: a global intervention tournament in 63 countries

Effectively reducing climate change requires marked, global behavior change. However, it is unclear which strategies are most likely to motivate people to change their climate beliefs and behaviors. Here, we tested 11 expert-crowdsourced interventions on four climate mitigation outcomes: beliefs, policy support, information sharing intention, and an effortful tree-planting behavioral task. Across 59,440 participants from 63 countries, the interventions’ effectiveness was small, largely limited to nonclimate skeptics, and differed across outcomes: Beliefs were strengthened mostly by decreasing psychological distance (by 2.3%), policy support by writing a letter to a future-generation member (2.6%), information sharing by negative emotion induction (12.1%), and no intervention increased the more effortful behavior—several interventions even reduced tree planting. Last, the effects of each intervention differed depending on people’s initial climate beliefs. These findings suggest that the impact of behavioral climate interventions varies across audiences and target behaviors

AI is a viable alternative to high throughput screening: a 318-target study

: High throughput screening (HTS) is routinely used to identify bioactive small molecules. This requires physical compounds, which limits coverage of accessible chemical space. Computational approaches combined with vast on-demand chemical libraries can access far greater chemical space, provided that the predictive accuracy is sufficient to identify useful molecules. Through the largest and most diverse virtual HTS campaign reported to date, comprising 318 individual projects, we demonstrate that our AtomNet® convolutional neural network successfully finds novel hits across every major therapeutic area and protein class. We address historical limitations of computational screening by demonstrating success for target proteins without known binders, high-quality X-ray crystal structures, or manual cherry-picking of compounds. We show that the molecules selected by the AtomNet® model are novel drug-like scaffolds rather than minor modifications to known bioactive compounds. Our empirical results suggest that computational methods can substantially replace HTS as the first step of small-molecule drug discovery