Browser-based Data Annotation, Active Learning, and Real-Time Distribution of Artificial Intelligence Models: From Tumor Tissue Microarrays to COVID-19 Radiology.

BACKGROUND: Artificial intelligence (AI) is fast becoming the tool of choice for scalable and reliable analysis of medical images. However, constraints in sharing medical data outside the institutional or geographical space, as well as difficulties in getting AI models and modeling platforms to work across different environments, have led to a "reproducibility crisis" in digital medicine. METHODS: This study details the implementation of a web platform that can be used to mitigate these challenges by orchestrating a digital pathology AI pipeline, from raw data to model inference, entirely on the local machine. We discuss how this federated platform provides governed access to data by consuming the Application Program Interfaces exposed by cloud storage services, allows the addition of user-defined annotations, facilitates active learning for training models iteratively, and provides model inference computed directly in the web browser at practically zero cost. The latter is of particular relevance to clinical workflows because the code, including the AI model, travels to the user's data, which stays private to the governance domain where it was acquired. RESULTS: We demonstrate that the web browser can be a means of democratizing AI and advancing data socialization in medical imaging backed by consumer-facing cloud infrastructure such as Box.com. As a case study, we test the accompanying platform end-to-end on a large dataset of digital breast cancer tissue microarray core images. We also showcase how it can be applied in contexts separate from digital pathology by applying it to a radiology dataset containing COVID-19 computed tomography images. CONCLUSIONS: The platform described in this report resolves the challenges to the findable, accessible, interoperable, reusable stewardship of data and AI models by integrating with cloud storage to maintain user-centric governance over the data. It also enables distributed, federated computation for AI inference over those data and proves the viability of client-side AI in medical imaging. AVAILABILITY: The open-source application is publicly available at , with a short video demonstration at

A framework for transcriptome-wide association studies in breast cancer in diverse study populations

Background: The relationship between germline genetic variation and breast cancer survival is largely unknown, especially in understudied minority populations who often have poorer survival. Genome-wide association studies (GWAS) have interrogated breast cancer survival but often are underpowered due to subtype heterogeneity and clinical covariates and detect loci in non-coding regions that are difficult to interpret. Transcriptome-wide association studies (TWAS) show increased power in detecting functionally relevant loci by leveraging expression quantitative trait loci (eQTLs) from external reference panels in relevant tissues. However, ancestry- or race-specific reference panels may be needed to draw correct inference in ancestrally diverse cohorts. Such panels for breast cancer are lacking. Results: We provide a framework for TWAS for breast cancer in diverse populations, using data from the Carolina Breast Cancer Study (CBCS), a population-based cohort that oversampled black women. We perform eQTL analysis for 406 breast cancer-related genes to train race-stratified predictive models of tumor expression from germline genotypes. Using these models, we impute expression in independent data from CBCS and TCGA, accounting for sampling variability in assessing performance. These models are not applicable across race, and their predictive performance varies across tumor subtype. Within CBCS (N = 3,828), at a false discovery-adjusted significance of 0.10 and stratifying for race, we identify associations in black women near AURKA, CAPN13, PIK3CA, and SERPINB5 via TWAS that are underpowered in GWAS. Conclusions: We show that carefully implemented and thoroughly validated TWAS is an efficient approach for understanding the genetics underpinning breast cancer outcomes in diverse populations

Cancer incidence in British vegetarians

Background: Few prospective studies have examined cancer incidence among vegetarians. Methods: We studied 61 566 British men and women, comprising 32 403 meat eaters, 8562 non-meat eaters who did eat fish ('fish eaters') and 20 601 vegetarians. After an average follow-up of 12.2 years, there were 3350 incident cancers of which 2204 were among meat eaters, 317 among fish eaters and 829 among vegetarians. Relative risks (RRs) were estimated by Cox regression, stratified by sex and recruitment protocol and adjusted for age, smoking, alcohol, body mass index, physical activity level and, for women only, parity and oral contraceptive use. Results: There was significant heterogeneity in cancer risk between groups for the following four cancer sites: stomach cancer, RRs (compared with meat eaters) of 0.29 (95% CI: 0.07–1.20) in fish eaters and 0.36 (0.16–0.78) in vegetarians, P for heterogeneity=0.007; ovarian cancer, RRs of 0.37 (0.18–0.77) in fish eaters and 0.69 (0.45–1.07) in vegetarians, P for heterogeneity=0.007; bladder cancer, RRs of 0.81 (0.36–1.81) in fish eaters and 0.47 (0.25–0.89) in vegetarians, P for heterogeneity=0.05; and cancers of the lymphatic and haematopoietic tissues, RRs of 0.85 (0.56–1.29) in fish eaters and 0.55 (0.39–0.78) in vegetarians, P for heterogeneity=0.002. The RRs for all malignant neoplasms were 0.82 (0.73–0.93) in fish eaters and 0.88 (0.81–0.96) in vegetarians (P for heterogeneity=0.001). Conclusion: The incidence of some cancers may be lower in fish eaters and vegetarians than in meat eaters

Gene-Level Germline Contributions to Clinical Risk of Recurrence Scores in Black and White Patients with Breast Cancer

Continuous risk of recurrence scores (CRS) based on tumor gene expression are vital prognostic tools for breast cancer. Studies have shown that Black women (BW) have higher CRS than White women (WW). Although systemic injustices contribute substantially to breast cancer disparities, evidence of biological and germline contributions is emerging. In this study, we investigated germline genetic associations with CRS and CRS disparity using approaches modeled after transcriptome-wide association studies (TWAS). In the Carolina Breast Cancer Study, using race-specific predictive models of tumor expression from germline genetics, we performed race-stratified (N = 1,043 WW, 1,083 BW) linear regressions of three CRS (ROR-S: PAM50 subtype score; proliferation score; ROR-P: ROR-S plus proliferation score) on imputed tumor genetically regulated tumor expression (GReX). Bayesian multivariate regression and adaptive shrinkage tested GReXprioritized genes for associations with tumor PAM50 expression and subtype to elucidate patterns of germline regulation underlying GReX-CRS associations. At FDR-adjusted P < 0.10, 7 and 1 GReX prioritized genes among WW and BW, respectively. Among WW, CRS were positively associated with MCM10, FAM64A, CCNB2, and MMP1 GReX and negatively associated with VAV3, PCSK6, and GNG11 GReX. Among BW, higher MMP1 GReX predicted lower proliferation score and ROR-P. GReX-prioritized gene and PAM50 tumor expression associations highlighted potential mechanisms for GReX-prioritized gene to CRS associations. Among patients with breast cancer, differential germline associations with CRS were found by race, underscoring the need for larger, diverse datasets in molecular studies of breast cancer. These findings also suggest possible germline trans-regulation of PAM50 tumor expression, with potential implications for CRS interpretation in clinical settings

Genetic and Non-genetic Predictors of LINE-1 Methylation in Leukocyte DNA.

Background: Altered DNA methylation has been associated with various diseases. Objective: We evaluated the association between levels of methylation in leukocyte DNA at long interspersed nuclear element 1 (LINE-1) and genetic and non-genetic characteristics of 892 control participants from the Spanish Bladder Cancer/EPICURO study. Methods: We determined LINE-1 methylation levels by pyrosequencing. Individual data included demographics, smoking status, nutrient intake, toenail concentrations of 12 trace elements, xenobiotic metabolism gene variants, and 515 polymorphisms among 24 genes in the one-carbon metabolism pathway. To assess the association between LINE-1 methylation levels (percentage of methylated cytosines) and potential determinants, we estimated beta coefficients (βs) by robust linear regression. Results: Women had lower levels of LINE-1 methylation than men (β = –0.7, p = 0.02). Persons who smoked blond tobacco showed lower methylation than nonsmokers (β = –0.7, p = 0.03). Arsenic toenail concentration was inversely associated with LINE-1 methylation (β = –3.6, p = 0.003). By contrast, iron (β = 0.002, p = 0.009) and nickel (β = 0.02, p = 0.004) were positively associated with LINE-1 methylation. Single nucleotide polymorphisms (SNPs) in DNMT3A (rs7581217-per allele, β = 0.3, p = 0.002), TCN2 (rs9606756-GG, β = 1.9, p = 0.008; rs4820887-AA, β = 4.0, p = 4.8 × 10–7; rs9621049-TT, β = 4.2, p = 4.7 × 10–9), AS3MT (rs7085104-GG, β = 0.7, p = 0.001), SLC19A1 (rs914238, TC vs. TT: β = 0.5 and CC vs. TT: β = –0.3, global p = 0.0007) and MTHFS (rs1380642, CT vs. CC: β = 0.3 and TT vs. CC; β = –0.8, global p = 0.05) were associated with LINE-1 methylation. Conclusions: We identified several characteristics, environmental factors, and common genetic variants that predicted DNA methylation among study participants.This work was partially supported by the Association for International Cancer Research (AICR; grant 09-0780, and a doctoral scholarship awarded to S.M.T.); Fondo de Investigaciones Sanitarias, Instituto de Salud Carlos III, MINECO, Spain (grants 00/0745, PI051436, PI061614, PI09-02102, and G03/174); Red Temática de Investigación Cooperativa en Cáncer (grant RD06/0020-RTICC); the U.S. National Institutes of Health (grant RO1-CA089715); a postdoctoral fellowship awarded to A.F.S.A. from the Fundación Científica de la AECC; Fundació Marató TV3; and The Johns Hopkins University Vredenburg Scholarship awarded to A.L.C

Improved detection and management of advanced HIV disease through a community adult TB‐contact tracing intervention with same‐day provision of the WHO‐recommended package of care including ART initiation in a rural district of Mozambique

Introduction: AIDS-mortality remains unacceptably high in sub-Saharan Africa, largely driven by advanced HIV disease (AHD). We nested a study in an existing tuberculosis (TB) contact-tracing intervention (Xpatial-TB). The aim was to assess the burden of AHD among high-risk people living with HIV (PLHIV) identified and to evaluate the provision of the WHO-recommended package of care to this population. Methods: All PLHIV ≥14 years old identified between June and December 2018 in Manhiça District by Xpatial-TB were offered to participate in the study if ART naïve or had suboptimal ART adherence. Consenting individuals were screened for AHD. Patients with AHD (CD4 < 200 cells/μL or WHO stage 3 or 4) were offered a package of interventions in a single visit, including testing for cryptococcal antigen (CrAg) and TB-lipoarabinomannan (TB-LAM), prophylaxis and treatment for opportunistic infections, adherence support or accelerated ART initiation. We collected information on follow-up visits carried out under routine programmatic conditions for six months. Results: A total of 2881 adults were identified in the Xpatial TB-contact intervention. Overall, 23% (673/2881) were HIV positive, including 351 TB index (64.2%) and 322 TB contacts (13.8%). Overall, 159/673 PLHIV (24%) were ART naïve or had suboptimal ART adherence, of whom 155 (97%, 124 TB index and 31 TB-contacts) consented to the study and were screened for AHD. Seventy percent of TB index-patients (87/124) and 16% of TB contacts (5/31) had CD4 < 200 cells/µL. Four (13%) of the TB contacts had TB, giving an overall AHD prevalence among TB contacts of 29% (9/31). Serum-CrAg was positive in 4.6% (4/87) of TB-index patients and in zero TB contacts. All ART naïve TB contacts without TB initiated ART within 48 hours of HIV diagnosis. Among TB cases, ART timing was tailored to the presence of TB and cryptococcosis. Six-month mortality was 21% among TB-index cases and zero in TB contacts. Conclusions: A TB contact-tracing outreach intervention identified undiagnosed HIV and AHD in TB patients and their contacts, undiagnosed cryptococcosis among TB patients, and resulted in an adequate provision of the WHO-recommended package of care in this rural Mozambican population. Same-day and accelerated ART initiation was feasible and safe in this population including among those with AHD

A multi-stage genome-wide association study of bladder cancer identifies multiple susceptibility loci.

We conducted a multi-stage, genome-wide association study of bladder cancer with a primary scan of 591,637 SNPs in 3,532 affected individuals (cases) and 5,120 controls of European descent from five studies followed by a replication strategy, which included 8,382 cases and 48,275 controls from 16 studies. In a combined analysis, we identified three new regions associated with bladder cancer on chromosomes 22q13.1, 19q12 and 2q37.1: rs1014971, (P = 8 × 10⁻¹²) maps to a non-genic region of chromosome 22q13.1, rs8102137 (P = 2 × 10⁻¹¹) on 19q12 maps to CCNE1 and rs11892031 (P = 1 × 10⁻⁷) maps to the UGT1A cluster on 2q37.1. We confirmed four previously identified genome-wide associations on chromosomes 3q28, 4p16.3, 8q24.21 and 8q24.3, validated previous candidate associations for the GSTM1 deletion (P = 4 × 10⁻¹¹) and a tag SNP for NAT2 acetylation status (P = 4 × 10⁻¹¹), and found interactions with smoking in both regions. Our findings on common variants associated with bladder cancer risk should provide new insights into the mechanisms of carcinogenesis

Application of Multi-SNP Approaches Bayesian LASSO and AUC-RF to Detect Main Effects of Inflammatory-Gene Variants Associated with Bladder Cancer Risk

The relationship between inflammation and cancer is well established in several tumor types, including bladder cancer. We performed an association study between 886 inflammatory-gene variants and bladder cancer risk in 1,047 cases and 988 controls from the Spanish Bladder Cancer (SBC)/EPICURO Study. A preliminary exploration with the widely used univariate logistic regression approach did not identify any significant SNP after correcting for multiple testing. We further applied two more comprehensive methods to capture the complexity of bladder cancer genetic susceptibility: Bayesian Threshold LASSO (BTL), a regularized regression method, and AUC-Random Forest, a machine-learning algorithm. Both approaches explore the joint effect of markers. BTL analysis identified a signature of 37 SNPs in 34 genes showing an association with bladder cancer. AUC-RF detected an optimal predictive subset of 56 SNPs. 13 SNPs were identified by both methods in the total population. Using resources from the Texas Bladder Cancer study we were able to replicate 30% of the SNPs assessed. The associations between inflammatory SNPs and bladder cancer were reexamined among non-smokers to eliminate the effect of tobacco, one of the strongest and most prevalent environmental risk factor for this tumor. A 9 SNP-signature was detected by BTL. Here we report, for the first time, a set of SNP in inflammatory genes jointly associated with bladder cancer risk. These results highlight the importance of the complex structure of genetic susceptibility associated with cancer risk.The work was partially supported by the Fondo de Investigacion Sanitaria, Instituto de Salud Carlos III (G03/174, 00/0745, PI051436, PI061614, PI09-02102, G03/174 and Sara Borrell fellowship to ELM) and Ministry of Science and Innovation (MTM2008-06747-C02-02 and FPU fellowship award to VU), Spain; AGAUR-Generalitat de Catalunya (Grant 2009SGR-581); Fundaciola Maratode TV3; Red Tematica de Investigacion Cooperativa en Cancer (RTICC); Asociacion Espanola Contra el Cancer (AECC); EU-FP7-201663; and RO1-CA089715 and CA34627; the Spanish National Institute for Bioinformatics (www.inab.org); and by the Intramural Research Program of the Division of Cancer Epidemiology and Genetics, National Cancer Institute, USA. MD Anderson support for this project included U01 CA 127615 (XW); R01 CA 74880 (XW); P50 CA 91846 (XW, CPD); Betty B. Marcus Chair fund in Cancer Prevention (XW); UT Research Trust fund (XW) and R01 CA 131335 (JG)