Image-based multiplex immune profiling of cancer tissues: translational implications. A report of the International Immuno-oncology Biomarker Working Group on Breast Cancer.

Recent advances in the field of immuno-oncology have brought transformative changes in the management of cancer patients. The immune profile of tumours has been found to have key value in predicting disease prognosis and treatment response in various cancers. Multiplex immunohistochemistry and immunofluorescence have emerged as potent tools for the simultaneous detection of multiple protein biomarkers in a single tissue section, thereby expanding opportunities for molecular and immune profiling while preserving tissue samples. By establishing the phenotype of individual tumour cells when distributed within a mixed cell population, the identification of clinically relevant biomarkers with high-throughput multiplex immunophenotyping of tumour samples has great potential to guide appropriate treatment choices. Moreover, the emergence of novel multi-marker imaging approaches can now provide unprecedented insights into the tumour microenvironment, including the potential interplay between various cell types. However, there are significant challenges to widespread integration of these technologies in daily research and clinical practice. This review addresses the challenges and potential solutions within a structured framework of action from a regulatory and clinical trial perspective. New developments within the field of immunophenotyping using multiplexed tissue imaging platforms and associated digital pathology are also described, with a specific focus on translational implications across different subtypes of cancer. © 2024 The Authors. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of The Pathological Society of Great Britain and Ireland.

Spatial analyses of immune cell infiltration in cancer: current methods and future directions: A report of the International Immuno-Oncology Biomarker Working Group on Breast Cancer.

Modern histologic imaging platforms coupled with machine learning methods have provided new opportunities to map the spatial distribution of immune cells in the tumor microenvironment. However, there exists no standardized method for describing or analyzing spatial immune cell data, and most reported spatial analyses are rudimentary. In this review, we provide an overview of two approaches for reporting and analyzing spatial data (raster versus vector-based). We then provide a compendium of spatial immune cell metrics that have been reported in the literature, summarizing prognostic associations in the context of a variety of cancers. We conclude by discussing two well-described clinical biomarkers, the breast cancer stromal tumor infiltrating lymphocytes score and the colon cancer Immunoscore, and describe investigative opportunities to improve clinical utility of these spatial biomarkers. © 2023 The Pathological Society of Great Britain and Ireland.

Pitfalls in machine learning-based assessment of tumor-infiltrating lymphocytes in breast cancer: A report of the International Immuno-Oncology Biomarker Working Group on Breast Cancer

The clinical significance of the tumor-immune interaction in breast cancer is now established, and tumor-infiltrating lymphocytes (TILs) have emerged as predictive and prognostic biomarkers for patients with triple-negative (estrogen receptor, progesterone receptor, and HER2-negative) breast cancer and HER2-positive breast cancer. How computational assessments of TILs might complement manual TIL assessment in trial and daily practices is currently debated. Recent efforts to use machine learning (ML) to automatically evaluate TILs have shown promising results. We review state-of-the-art approaches and identify pitfalls and challenges of automated TIL evaluation by studying the root cause of ML discordances in comparison to manual TIL quantification. We categorize our findings into four main topics: (1) technical slide issues, (2) ML and image analysis aspects, (3) data challenges, and (4) validation issues. The main reason for discordant assessments is the inclusion of false-positive areas or cells identified by performance on certain tissue patterns or design choices in the computational implementation. To aid the adoption of ML for TIL assessment, we provide an in-depth discussion of ML and image analysis, including validation issues that need to be considered before reliable computational reporting of TILs can be incorporated into the trial and routine clinical management of patients with triple-negative breast cancer. © 2023 The Authors. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of The Pathological Society of Great Britain and Ireland.

Moving Toward Findable, Accessible, Interoperable, Reusable Practices in Epidemiologic Research.

Data sharing is essential for reproducibility of epidemiologic research, replication of findings, pooled analyses in consortia efforts, and maximizing study value to address multiple research questions. However, barriers related to confidentiality, costs, and incentives often limit the extent and speed of data sharing. Epidemiological practices that follow Findable, Accessible, Interoperable, Reusable (FAIR) principles can address these barriers by making data resources findable with the necessary metadata, accessible to authorized users, and interoperable with other data, to optimize the reuse of resources with appropriate credit to its creators. We provide an overview of these principles and describe approaches for implementation in epidemiology. Increasing degrees of FAIRness can be achieved by moving data and code from on-site locations to remote, accessible ("Cloud") data servers, using machine-readable and nonproprietary files, and developing open-source code. Adoption of these practices will improve daily work and collaborative analyses and facilitate compliance with data sharing policies from funders and scientific journals. Achieving a high degree of FAIRness will require funding, training, organizational support, recognition, and incentives for sharing research resources, both data and code. However, these costs are outweighed by the benefits of making research more reproducible, impactful, and equitable by facilitating the reuse of precious research resources by the scientific community.

PLCOjs, a FAIR GWAS web SDK for the NCI Prostate, Lung, Colorectal and Ovarian Cancer Genetic Atlas project.

MOTIVATION: The Division of Cancer Epidemiology and Genetics (DCEG) and the Division of Cancer Prevention (DCP) at the National Cancer Institute (NCI) have recently generated genome-wide association study (GWAS) data for multiple traits in the Prostate, Lung, Colorectal, and Ovarian (PLCO) Genomic Atlas project. The GWAS included 110 000 participants. The dissemination of the genetic association data through a data portal called GWAS Explorer, in a manner that addresses the modern expectations of FAIR reusability by data scientists and engineers, is the main motivation for the development of the open-source JavaScript software development kit (SDK) reported here. RESULTS: The PLCO GWAS Explorer resource relies on a public stateless HTTP application programming interface (API) deployed as the sole backend service for both the landing page's web application and third-party analytical workflows. The core PLCOjs SDK is mapped to each of the API methods, and also to each of the reference graphic visualizations in the GWAS Explorer. A few additional visualization methods extend it. As is the norm with web SDKs, no download or installation is needed and modularization supports targeted code injection for web applications, reactive notebooks (Observable) and node-based web services. AVAILABILITY AND IMPLEMENTATION: code at https://github.com/episphere/plco; project page at https://episphere.github.io/plco.

Quest markup for developing FAIR questionnaire modules for epidemiologic studies.

BACKGROUND: Online questionnaires are commonly used to collect information from participants in epidemiological studies. This requires building questionnaires using machine-readable formats that can be delivered to study participants using web-based technologies such as progressive web applications. However, the paucity of open-source markup standards with support for complex logic make collaborative development of web-based questionnaire modules difficult. This often prevents interoperability and reusability of questionnaire modules across epidemiological studies. RESULTS: We developed an open-source markup language for presentation of questionnaire content and logic, Quest, within a real-time renderer that enables the user to test logic (e.g., skip patterns) and view the structure of data collection. We provide the Quest markup language, an in-browser markup rendering tool, questionnaire development tool and an example web application that embeds the renderer, developed for The Connect for Cancer Prevention Study. CONCLUSION: A markup language can specify both the content and logic of a questionnaire as plain text. Questionnaire markup, such as Quest, can become a standard format for storing questionnaires or sharing questionnaires across the web. Quest is a step towards generation of FAIR data in epidemiological studies by facilitating reusability of questionnaires and data interoperability using open-source tools.

Mortality Tracker: the COVID-19 case for real time web APIs as epidemiology commons.

MOTIVATION: Mortality Tracker is an in-browser application for data wrangling, analysis, dissemination and visualization of public time series of mortality in the United States. It was developed in response to requests by epidemiologists for portable real time assessment of the effect of COVID-19 on other causes of death and all-cause mortality. This is performed by comparing 2020 real time values with observations from the same week in the previous 5 years, and by enabling the extraction of temporal snapshots of mortality series that facilitate modeling the interdependence between its causes. RESULTS: Our solution employs a scalable 'Data Commons at Web Scale' approach that abstracts all stages of the data cycle as in-browser components. Specifically, the data wrangling computation, not just the orchestration of data retrieval, takes place in the browser, without any requirement to download or install software. This approach, where operations that would normally be computed server-side are mapped to in-browser SDKs, is sometimes loosely described as Web APIs, a designation adopted here. AVAILABILITYAND IMPLEMENTATION: https://episphere.github.io/mortalitytracker; webcast demo: youtu.be/ZsvCe7cZzLo. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Impact of Population Growth and Aging on Estimates of Excess U.S. Deaths During the COVID-19 Pandemic, March to August 2020.

BACKGROUND: Excess death estimates quantify the full impact of the coronavirus disease 2019 (COVID-19) pandemic. Widely reported U.S. excess death estimates have not accounted for recent population changes, especially increases in the population older than 65 years. OBJECTIVE: To estimate excess deaths in the United States in 2020, after accounting for population changes. DESIGN: Surveillance study. SETTING: United States, March to August 2020. PARTICIPANTS: All decedents. MEASUREMENTS: Age-specific excess deaths in the United States from 1 March to 31 August 2020 compared with 2015 to 2019 were estimated, after changes in population size and age were taken into account, by using Centers for Disease Control and Prevention provisional death data and U.S. Census Bureau population estimates. Cause-specific excess deaths were estimated by month and age. RESULTS: From March through August 2020, 1 671 400 deaths were registered in the United States, including 173 300 COVID-19 deaths. An average of 1 370 000 deaths were reported over the same months during 2015 to 2019, for a crude excess of 301 400 deaths (128 100 non-COVID-19 deaths). However, the 2020 U.S. population includes 5.04 million more persons aged 65 years and older than the average population in 2015 to 2019 (a 10% increase). After population changes were taken into account, an estimated 217 900 excess deaths occurred from March through August 2020 (173 300 COVID-19 and 44 600 non-COVID-19 deaths). Most excess non-COVID-19 deaths occurred in April, July, and August, and 34 900 (78%) were in persons aged 25 to 64 years. Diabetes, Alzheimer disease, and heart disease caused the most non-COVID-19 excess deaths. LIMITATION: Provisional death data are underestimated because of reporting delays. CONCLUSION: The COVID-19 pandemic resulted in an estimated 218 000 excess deaths in the United States between March and August 2020, and 80% of those deaths had COVID-19 as the underlying cause. Accounting for population changes substantially reduced the excess non-COVID-19 death estimates, providing important information for guiding future clinical and public health interventions. PRIMARY FUNDING SOURCE: National Cancer Institute.

Racial and Ethnic Disparities in Excess Deaths During the COVID-19 Pandemic, March to December 2020.

BACKGROUND: Although racial/ethnic disparities in U.S. COVID-19 death rates are striking, focusing on COVID-19 deaths alone may underestimate the true effect of the pandemic on disparities. Excess death estimates capture deaths both directly and indirectly caused by COVID-19. OBJECTIVE: To estimate U.S. excess deaths by racial/ethnic group. DESIGN: Surveillance study. SETTING: United States. PARTICIPANTS: All decedents. MEASUREMENTS: Excess deaths and excess deaths per 100 000 persons from March to December 2020 were estimated by race/ethnicity, sex, age group, and cause of death, using provisional death certificate data from the Centers for Disease Control and Prevention (CDC) and U.S. Census Bureau population estimates. RESULTS: An estimated 2.88 million deaths occurred between March and December 2020. Compared with the number of expected deaths based on 2019 data, 477 200 excess deaths occurred during this period, with 74% attributed to COVID-19. Age-standardized excess deaths per 100 000 persons among Black, American Indian/Alaska Native (AI/AN), and Latino males and females were more than double those in White and Asian males and females. Non-COVID-19 excess deaths also disproportionately affected Black, AI/AN, and Latino persons. Compared with White males and females, non-COVID-19 excess deaths per 100 000 persons were 2 to 4 times higher in Black, AI/AN, and Latino males and females, including deaths due to diabetes, heart disease, cerebrovascular disease, and Alzheimer disease. Excess deaths in 2020 resulted in substantial widening of racial/ethnic disparities in all-cause mortality from 2019 to 2020. LIMITATIONS: Completeness and availability of provisional CDC data; no estimates of precision around results. CONCLUSION: There were profound racial/ethnic disparities in excess deaths in the United States in 2020 during the COVID-19 pandemic, resulting in rapid increases in racial/ethnic disparities in all-cause mortality between 2019 and 2020. PRIMARY FUNDING SOURCE: National Institutes of Health Intramural Research Program.

Utilizing Automated Breast Cancer Detection to Identify Spatial Distributions of Tumor-Infiltrating Lymphocytes in Invasive Breast Cancer.

Quantitative assessment of spatial relations between tumor and tumor-infiltrating lymphocytes (TIL) is increasingly important in both basic science and clinical aspects of breast cancer research. We have developed and evaluated convolutional neural network analysis pipelines to generate combined maps of cancer regions and TILs in routine diagnostic breast cancer whole slide tissue images. The combined maps provide insight about the structural patterns and spatial distribution of lymphocytic infiltrates and facilitate improved quantification of TILs. Both tumor and TIL analyses were evaluated by using three convolutional neural network networks (34-layer ResNet, 16-layer VGG, and Inception v4); the results compared favorably with those obtained by using the best published methods. We have produced open-source tools and a public data set consisting of tumor/TIL maps for 1090 invasive breast cancer images from The Cancer Genome Atlas. The maps can be downloaded for further downstream analyses.