Feminist identification, actions, beliefs and commitments at CSB/SJU

Does self-identification as a feminist, non-feminist, or non-labeler, affect actions, beliefs, and commitments regarding feminist ideals? Is there any dissonance between identifying with feminist beliefs and identifying with the feminist label? Will our data give us insight into education about feminist ideas at CSB/SJU (College of Saint Benedict and Saint John’s University)? Is the “feminist” label stigmatized on our campuses

Pitfalls in machine learning‐based assessment of tumor‐infiltrating lymphocytes in breast cancer: a report of the international immuno‐oncology biomarker working group

The clinical significance of the tumor-immune interaction in breast cancer (BC) has been well established, and tumor-infiltrating lymphocytes (TILs) have emerged as a predictive and prognostic biomarker for patients with triple-negative (estrogen receptor, progesterone receptor, and HER2 negative) breast cancer (TNBC) and HER2-positive breast cancer. How computational assessment of TILs can complement manual TIL-assessment in trial- and daily practices is currently debated and still unclear. Recent efforts to use machine learning (ML) for the automated evaluation of TILs show promising results. We review state-of-the-art approaches and identify pitfalls and challenges by studying the root cause of ML discordances in comparison to manual TILs quantification. We categorize our findings into four main topics; (i) technical slide issues, (ii) ML and image analysis aspects, (iii) data challenges, and (iv) 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 in TILs 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 TNBC

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.http://www.thejournalofpathology.com/hj2024ImmunologySDG-03:Good heatlh and well-bein

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

First-Generation Bennies and Johnnies: Who they are, what they need, how CSB/SJU is supporting them, and what you can do in your position

In the fall of 2016, CSB hired Hannah Salto (December ’16 Grad) as the College Navigator to begin work in the spring semester of 2017. The College Navigator position focuses on providing support for first-generation students, locating and connecting already existing support on campus for these students, and helping the incoming class of first-generation Bennies and Johnnies successfully transition into the college environment and culture. This presentation will cover statistical information and data about first-generation students at CSB/SJU gathered from the registrars office, surveys, and focus groups. It will also cover the work of the College Navigator up until this point and tips on how you can support these students in the position you hold in our community. Come learn who CSB and SJU\u27s first-generation students are, what they need, the work of the College Navigator on campus, and what you can do in your position to help support these students

Swarm learning for decentralized artificial intelligence in cancer histopathology

Artificial intelligence (AI) can predict the presence of molecular alterations directly from routine histopathology slides. However, training robust AI systems requires large datasets for which data collection faces practical, ethical and legal obstacles. These obstacles could be overcome with swarm learning (SL), in which partners jointly train AI models while avoiding data transfer and monopolistic data governance. Here, we demonstrate the successful use of SL in large, multicentric datasets of gigapixel histopathology images from over 5,000 patients. We show that AI models trained using SL can predict BRAF mutational status and microsatellite instability directly from hematoxylin and eosin (H&E)-stained pathology slides of colorectal cancer. We trained AI models on three patient cohorts from Northern Ireland, Germany and the United States, and validated the prediction performance in two independent datasets from the United Kingdom. Our data show that SL-trained AI models outperform most locally trained models, and perform on par with models that are trained on the merged datasets. In addition, we show that SL-based AI models are data efficient. In the future, SL can be used to train distributed AI models for any histopathology image analysis task, eliminating the need for data transfer

Metastasis and immune evasion from extracellular cGAMP hydrolysis

Cytosolic DNA is characteristic of chromosomally unstable metastatic cancer cells, resulting in constitutive activation of the cGAS-STING innate immune pathway. How tumors co-opt inflammatory signaling while evading immune surveillance remains unknown. Here we show that the ectonucleotidase ENPP1 promotes metastasis by selectively degrading extracellular cGAMP, an immune stimulatory metabolite whose breakdown products include the immune suppressor, adenosine. ENPP1 loss suppresses metastasis, restores tumor immune infiltration, and potentiates response to immune checkpoint blockade in a manner dependent on tumor cGAS and host STING. Conversely, overexpression of wildtype ENPP1, but not an enzymatically weakened mutant, promotes migration and metastasis, in part, through the generation of extracellular adenosine, and renders otherwise sensitive tumors completely resistant to immunotherapy. In human cancers, ENPP1 expression correlates with reduced immune cell infiltration, increased metastasis, and resistance to anti-PD1/PD-L1 treatment. Thus, cGAMP hydrolysis by ENPP1 enables chromosomally unstable tumors to transmute cGAS activation into an immune suppressive pathway

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

Abstract 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