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

Trends in food insecurity rates at an academic primary care clinic: a retrospective cohort study

BACKGROUND: Healthcare organizations are increasingly screening and addressing food insecurity (FI); yet, limited data exists from clinic-based settings on how FI rates change over time. The objective of this study was to evaluate household FI trends over a two-year period at a clinic that implemented a FI screening and referral program. METHODS: In this retrospective cohort study, data were extracted for all visits at one academic primary care clinic for all children aged 0-18 years whose parents/guardians had been screened for FI at least once between February 1, 2018 to February 28, 2019 (Year 1) and screened at least once between March 1, 2019 to February 28, 2020 (Year 2). Bivariate analyses tested for differences in FI and demographics using chi-square tests. Mixed effects logistic regression was used to assess change in FI between Years 1 and 2 with random intercept for participants controlling for covariates. The interaction between year and all covariates was evaluated to determine differences in FI change by demographics. RESULTS: Of 6182 patients seen in Year 1, 3691 (59.7%) were seen at least once in Year 2 and included in this study. In Year 1, 19.6% of participants reported household FI, compared to 14.1% in Year 2. Of those with FI in Year 1, 40% had FI in Year 2. Of those with food security in Year 1, 92.3% continued with food security in Year 2. Compared to Hispanic/Latinx participants, African American/Black (OR: 3.53, 95% CI: 2.33, 5.34; p \u3c 0.001) and White (OR: 1.88, 95% CI: 1.06, 3.36; p = 0.03) participants had higher odds of reporting FI. African American/Black participants had the largest decrease in FI between Years 1 and 2 (- 7.9, 95% CI: - 11.7, - 4.1%; p \u3c 0.0001). CONCLUSIONS: Because FI is transitional, particularly for racial/ethnic minorities, screening repeatedly can identify families situationally experiencing FI

High metabolomic microdiversity within co-occurring isolates of the extremely halophilic bacterium Salinibacter ruber

Salinibacter ruber is an extremely halophilic member of the Bacteroidetes that thrives in crystallizer ponds worldwide. Here, we have analyzed two sets of 22 and 35 co-occurring S. ruber strains, newly isolated respectively, from 100 microliters water samples from crystalizer ponds in Santa Pola and Mallorca, located in coastal and inland Mediterranean Spain and 350 km apart from each other. A set of old strains isolated from the same setting were included in the analysis. Genomic and taxonomy relatedness of the strains were analyzed by means of PFGE and MALDI-TOF, respectively, while their metabolomic potential was explored with high resolution ion cyclotron resonance Fourier transform mass spectrometry (ICR-FT/MS). Overall our results show a phylogenetically very homogeneous species expressing a very diverse metabolomic pool. The combination of MALDI-TOF and PFGE provides, for the newly isolated strains, the same scenario presented by the previous studies of intra-specific diversity of S. ruber using a more restricted number of strains: the species seems to be very homogeneous at the ribosomal level while the genomic diversity encountered was rather high since no identical genome patterns could be retrieved from each of the samples. The high analytical mass resolution of ICR-FT/MS enabled the description of thousands of putative metabolites from which to date only few can be annotated in databases. Some metabolomic differences, mainly related to lipid metabolism and antibiotic-related compounds, provided enough specificity to delineate different clusters within the co-occurring strains. In addition, metabolomic differences were found between old and new strains isolated from the same ponds that could be related to extended exposure to laboratory conditions.This work was supported by the projects CLG2009-12651-C02-01 and 02; and CE-CSD2007-0005 of the Spanish Ministry of Science and Innovation, and all three projects were also co-financed with FEDER support from the European Union. JBE was financed by the Government of the Balearic Islands, Ministry of Economy and Finances

Translating research into practice-implementation recommendations for pediatric rheumatology; Proceedings of the childhood arthritis and rheumatology research alliance 2020 implementation science retreat

The translation of research findings into clinical practice is challenging, especially fields like in pediatric rheumatology, where the evidence base is limited, there are few clinical trials, and the conditions are rare and heterogeneous. Implementation science methodologies have been shown to reduce the research- to- practice gap in other clinical settings may have similar utility in pediatric rheumatology. This paper describes the key discussion points from the inaugural Childhood Arthritis and Rheumatology Research Alliance Implementation Science retreat held in February 2020. The aim of this report is to synthesize those findings into an Implementation Science Roadmap for pediatric rheumatology research. This roadmap is based on three foundational principles: fostering curiosity and ensuring discovery, integration of research and quality improvement, and patient-centeredness. We include six key steps anchored in the principles of implementation science. Applying this roadmap will enable researchers to evaluate the full range of research activities, from the initial clinical design and evidence acquisition to the application of those findings in pediatric rheumatology clinics and direct patient care

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