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

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.Gilead Breast Cancer Research Grant; Breast Cancer Research Foundation; Susan G Komen Leadership; Interne Fondsen KU Leuven/Internal Funds KU Leuven; Swedish Society for Medical Research; Swedish Breast Cancer Association; Cancer Research Program; US Department of Defense; Mayo Clinic Breast Cancer; Marie Sklodowska Curie; NHMRC; National Institutes of Health; Cancer Research UK; Japan Society for the Promotion of Science; Horizon 2020 European Union Research and Innovation Programme National Cancer Institute; National Heart, Lung and Blood Institute; National Institute of Biomedical Imaging and Bioengineering; VA Merit Review Award; US Department of Veterans Affairs Biomedical Laboratory Research Breast Cancer Research Program; Prostate Cancer Research Program; Lung Cancer Research Program; Kidney Precision Medicine Project (KPMP) Glue Grant; EPSRC; Melbourne Research Scholarship; Peter MacCallum Cancer Centre; KWF Kankerbestrijding; Dutch Ministry of Health, Welfare and Sport the Breast Cancer Research Foundation; Agence Nationale de la Recherche; Q-Life; National Breast Cancer Foundation of Australia; National Health and Medical Council of Australia; All-Island Cancer Research Institute; Irish Cancer Society; Science Foundation Ireland Investigator Programme; Science Foundation Ireland Strategic Partnership Programme. Open access funding provided by IReL.https://pathsocjournals.onlinelibrary.wiley.com/journal/10969896hj2024ImmunologySDG-03:Good heatlh and well-bein

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

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

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

Chitosan modulates Pochonia chlamydosporia gene expression during nematode egg parasitism

Climate change makes plant‐parasitic nematodes (PPN) an increasing threat to commercial crops. PPN can be managed sustainably by the biocontrol fungus Pochonia chlamydosporia (Pc). Chitosan generated from chitin deacetylation enhances PPN parasitism by Pc. In this work, we investigate the molecular mechanisms of Pc for chitosan resistance and root‐knot nematode (RKN) parasitism, using transcriptomics. Chitosan and RKN modify the expression of Pc genes, mainly those involved in oxidation–reduction processes. Both agents significantly modify the expression of genes associated to 113 GO terms and 180 Pc genes. Genes encoding putative glycoproteins (Pc adhesives) to nematode eggshell, as well as genes involved in redox, carbohydrate and lipid metabolism trigger the response to chitosan. We identify genes expressed in both the parasitic and endophytic phases of the Pc lifecycle; these include proteases, chitosanases and transcription factors. Using the Pathogen—Host Interaction database (PHI‐base), our previous RNA‐seq data and RT‐PCR of Pc colonizing banana we have investigated genes expressed both in the parasitic and endophytic phases of Pc lifecycle.This work was supported by H2020 MUSA 727624 European Project

Towards explainable motion prediction using heterogeneous graph representations

Motion prediction systems play a crucial role in enabling autonomous vehicles to navigate safely and efficiently in complex traffic scenarios. Graph Neural Network (GNN)-based approaches have emerged as a promising solution for capturing interactions among dynamic agents and static objects. However, they often lack transparency, interpretability and explainability - qualities that are essential for building trust in autonomous driving systems. In this work, we address this challenge by presenting a comprehensive approach to enhance the explainability of graph-based motion prediction systems. We introduce the Explainable Heterogeneous Graph based Policy (XHGP) model based on an heterogeneous graph representation of the traffic scene and lane-graph traversals. Distinct from other graph-based models, XHGP leverages object level and type-level attention mechanisms to learn interaction behaviors, providing information about the importance of agents and interactions in the scene. In addition, capitalizing on XHGPs architecture, we investigate the explanations provided by the GNNExplainer and apply counterfactual reasoning to analyze the sensitivity of the model to modifications of the input data. This includes masking scene elements, altering trajectories, and adding or removing dynamic agents. Our proposal advances towards achieving reliable and explainable motion prediction systems, addressing the concerns of users, developers and regulatory agencies alike. The insights gained from our explainability analysis contribute to a better understanding of the relationships between dynamic and static elements in traffic scenarios, facilitating the interpretation of the results, as well as the correction of possible errors in motion prediction models, and thus contributing to the development of trustworthy motion prediction systems.The code to reproduce this work is publicly available at https://github.com/sancarlim/ Explainable-MP/tree/v1.1.Funding Agencies|HUMAINT project at the Algorithmic Transparency Unit at the Directorate-General Joint Research Centre (JRC) of the European Commission; Spanish Ministry of Science and Innovation [PID2020-114924RB-I00, PDC2021-121324-I00]; AI Sweden</p

Chitosan induces differential transcript usage of chitosanase 3 encoding gene (csn3) in the biocontrol fungus Pochonia chlamydosporia 123

Background: Pochonia chlamydosporia is an endophytic fungus used for nematode biocontrol that employs its cellular and molecular machinery to degrade the nematode egg-shell. Chitosanases, among other enzymes, are involved in this process. In this study, we improve the genome sequence assembly of P. chlamydosporia 123, by utilizing long Pacific Biosciences (PacBio) sequence reads. Combining this improved genome assembly with previous RNA-seq data revealed alternative isoforms of a chitosanase in the presence of chitosan. This study could open new insights into understanding fungal resistance to chitosan and root-knot nematode (RKN) egg infection processes. Results: The P. chlamydosporia 123 genome sequence assembly has been updated using long-read PacBio sequencing and now includes 12,810 predicted protein-coding genes. Compared with the previous assembly based on short reads, there are 701 newly annotated genes, and 69 previous genes are now split. Eight of the new genes were differentially expressed in fungus interactions with Meloidogyne javanica eggs or chitosan. A survey of the RNA-seq data revealed alternative splicing in the csn3 gene that encodes a chitosanase, with four putative splicing variants: csn3_v1, csn3_v2, csn3_v3 and csn3_v4. When P. chlamydosporia is treated with 0.1 mg·mL− 1 chitosan for 4 days, csn3 is expressed 10-fold compared with untreated controls. Furthermore, the relative abundances of each of the four transcripts are different in chitosan treatment compared with controls. In controls, the abundances of each transcript are nil, 32, 55, and 12% for isoforms csn3_v1, csn3_v2, csn3_v3 and csn3_v4 respectively. Conversely, in chitosan-treated P. chlamydosporia, the abundances are respectively 80, 15%, 2—3%, 2—3%. Since isoform csn3_v1 is expressed with chitosan only, the putatively encoded enzyme is probably induced and likely important for chitosan degradation. Conclusions: Alternative splicing events have been discovered and described in the chitosanase 3 encoding gene from P. chlamydosporia 123. Gene csn3 takes part in RKN parasitism process and chitosan enhances its expression. The isoform csn3_v1 would be related to the degradation of this polymer in bulk form, while other isoforms may be related to the degradation of chitosan in the nematode egg-shell.This research was funded by European Project H2020 MUSA no. 727624