Tumor-infiltrating Lymphocytes In Breast Cancer Research Articles

Prediction value study of breast cancer tumor infiltrating lymphocyte levels based on ultrasound imaging radiomics.

Construct models based on grayscale ultrasound and radiomics and compare the efficacy of different models in preoperatively predicting the level of tumor-infiltrating lymphocytes in breast cancer. This study retrospectively collected clinical data and preoperative ultrasound images from 185 breast cancer patients confirmed by surgical pathology. Patients were randomly divided into a training set (n=111) and a testing set (n=74) using a 6:4 ratio. Based on a 10% threshold for tumor-infiltrating lymphocytes (TIL) levels, patients were classified into low-level and high-level groups. Radiomic features were extracted and selected using the training set. The evaluation included assessing the relationship between TIL levels and both radiomic features and grayscale ultrasound features. Subsequently, grayscale ultrasound models, radiomic models, and nomograms combining radiomics score (Rad-score) and grayscale ultrasound features were established. The predictive performance of different models was evaluated through receiver operating characteristic (ROC) analysis. Calibration curves assessed the fit of the nomograms, and decision curve analysis (DCA) evaluated the clinical effectiveness of the models. Univariate analyses and multivariate logistic regression analyses revealed that indistinct margin (P<0.001, Odds Ratio [OR]=0.214, 95% Confidence Interval [CI]: 0.103-1.026), posterior acoustic enhancement (P=0.027, OR=2.585, 95% CI: 1.116-5.987), and ipsilateral axillary lymph node enlargement (P=0.001, OR=4.214, 95% CI: 1.798-9.875) were independent predictive factors for high levels of TIL in breast cancer. In comparison to grayscale ultrasound model (Training set: Area under curve [AUC] 0.795; Testing set: AUC 0.720) and radiomics model (Training set: AUC 0.803; Testing set: AUC 0.759), the nomogram demonstrated superior discriminative ability on both the training (AUC 0.884) and testing (AUC 0.820) datasets. Calibration curves indicated high consistency between the nomogram model's predicted probability of breast cancer TIL levels and the actual occurrence probability. DCA revealed that the radiomics model and the nomogram model achieved higher clinical net benefits compared to the grayscale ultrasound model. The nomogram based on preoperative ultrasound radiomics features exhibits robust predictive capacity for the non-invasive evaluation of breast cancer TIL levels, potentially providing a significant basis for individualized treatment decisions in breast cancer.

Prognostic Significance of CD163+ and/or CD206+ Tumor-Associated Macrophages Is Linked to Their Spatial Distribution and Tumor-Infiltrating Lymphocytes in Breast Cancer.

Tumor-associated macrophages (TAMs) is a key element in the breast tumor microenvironment. CD163 and CD206 have been utilized for TAM identification, but the clinical implications of TAMs identified by these markers have not been thoroughly explored. This study conducted a comparative analysis of CD163 and CD206 TAMs using digital image analysis, focusing on their spatial distribution and prognostic significance in relation to tumor-infiltrating lymphocytes (TILs). Distinct clinico-pathological and prognostic characteristics were noted between the two types of TAMs. CD163 TAMs were linked to high-grade tumors (p = 0.006), whereas CD206 TAMs were associated with a higher incidence of nodal metastasis (p = 0.033). CD206 TAMs were predominantly found in the stroma, with more cases being stromal CD206-high (sCD206-high) than tumoral CD206-high (tCD206-high) (p = 0.024). Regarding prognostication, patients stratified according to stromal and tumoral densities of CD163 showed different disease-free survival (DFS) time. Specifically, those that were sCD163-low but tCD163-high exhibited the poorest DFS (chi-square = 10.853, p = 0.013). Furthermore, a high sCD163-to-stromal-TILs ratio was identified as an independent predictor of unfavorable survival outcomes (DFS: HR = 3.477, p = 0.018). The spatial distribution and interactions with TILs enhanced the prognostic value of CD163 TAMs, while CD206 TAMs appeared to have limited prognostic utility in breast cancer cases.

Emerging measurements for tumor-infiltrating lymphocytes in breast cancer.

Tumor-infiltrating lymphocytes are a general term for lymphocytes or immune cells infiltrating the tumor microenvironment. Numerous studies have demonstrated tumor-infiltrating lymphocytes to be robust prognostic and predictive biomarkers in breast cancer. Recently, immune checkpoint inhibitors, which directly target tumor-infiltrating lymphocytes, have become part of standard of care treatment for triple-negative breast cancer. Surprisingly, tumor-infiltrating lymphocytes quantified by conventional methods do not predict response to immune checkpoint inhibitors, which highlights the heterogeneity of tumor-infiltrating lymphocytes and the complexity of the immune network in the tumor microenvironment. Tumor-infiltrating lymphocytes are composed of diverse immune cell populations, including cytotoxic CD8-positive T lymphocytes, B cells and myeloid cells. Traditionally, tumor-infiltrating lymphocytes in tumor stroma have been evaluated by histology. However, the standardization of this approach is limited, necessitating the use of various novel technologies to elucidate the heterogeneity in the tumor microenvironment. This review outlines the evaluation methods for tumor-infiltrating lymphocytes from conventional pathological approaches that evaluate intratumoral and stromal tumor-infiltrating lymphocytes such as immunohistochemistry, to the more recent advancements in computer tissue imaging using artificial intelligence, flow cytometry sorting and multi-omics analyses using high-throughput assays to estimate tumor-infiltrating lymphocytes from bulk tumor using immune signatures or deconvolution tools. We also discuss higher resolution technologies that enable the analysis of tumor-infiltrating lymphocytes heterogeneity such as single-cell analysis and spatial transcriptomics. As we approach the era of personalized medicine, it is important for clinicians to understand these technologies.

Abstract 4149: Real-time intravital trafficking of tumor infiltrating lymphocytes in breast cancer models

Abstract Cancer immunotherapy utilizing the immunoreactive efficiency of immune cells via adoptive cell therapy (ACT) has been a novel method of cancer treatment. In tumor microenvironment, immune cell reactivity of tumor-infiltrating lymphocytes (TILs) can facilitate cancer cell attack, which finally derives efficient suppression of tumor growth. However, the conventional evaluation for cancer immunotherapy has been generally performed by ex vivo methods, providing restricted information on lymphocyte motility and immune cell dynamics within the tumor microenvironment. In vivo data with the dynamic behavior of TILs in breast cancer can provide new insight to investigate novel targets of immune checkpoint inhibitors. In this research, longitudinal intravital imaging of motility trafficking of TILs in breast cancer animal models has been performed by utilizing intravital microscopy to unveil the immunoreactive dynamics of T cell activity in early stage. In vivo analysis of TIL motility within different breast cell lines, including 4T1 (mouse breast cancer cell), MDA-MB-231 (human triple-negative breast cancer cell), and MCF7 (human breast cancer cell), respectively, has been investigated for immune responses to cancer. We’ve monitored spatiotemporal dynamics of TIL over 11 days in breast cancer xenograft mouse model generated by tumor implantation into the dorsal skinfold imaging chamber and adoptive T cell transfer by blood circulation. In the results, the adoptive immunoreactivity of TIL has been differentiated by the timepoint of adoptive T cell transfer, distance proximity of T cells in tumors, and breast cancer types. Furthermore, anti-cancer effect coordinated with dendritic cells behaviors showed induced immune responses with accumulated dendritic cells with increased TIL count over time in breast cancer model treated with various cancer therapy such as immunotherapy, radiation therapy and anti-cancer drug (anti-PD-L1), which implied a relationship between programmed death-ligand 1 expression levels and TIL/dendritic cell motility. The result demonstrates that intravital TIL motility trafficking analysis in cancer model can be an invaluable assessment method to explain the detailed cellular/molecular mechanism of immune response to cancer involved with T lymphocytes, dendritic cells, and immune checkpoint inhibitors to accelerate the efficiency of cancer immunotherapy. Citation Format: Soyeon Ahn, Kubra Akyildiz, Hyunseok Kim, Pilhan Kim. Real-time intravital trafficking of tumor infiltrating lymphocytes in breast cancer models [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 4149.

A simple and convenient model combining multiparametric MRI and clinical features to predict tumour-infiltrating lymphocytes in breast cancer

To develop a simple and convenient method based on multiparametric magnetic resonance imaging (MRI) and clinical features to non-invasively predict tumour-infiltrating lymphocytes (TILs) in breast cancer (BC) and to explore the relationship between TIL levels and disease-free survival (DFS). A total of 172 BC patients were enrolled between November 2017 and June 2021 in this retrospective study. The patients were divided into high (≥10%) and low (<10%) TIL groups. Clinicopathological data were collected. MRI features were reviewed by two radiologists. Predictors associated with TILs were determined by using multivariable logistic regression analyses. Kaplan-Meier survival curves based on TIL levels were used to estimate DFS. A total of 102 patients with low TILs and 70 patients with high TILs were included in the study. Tumour size (odds ratio [OR], 1.040; 95% confidence interval [CI]: 1.006, 1.075; p=0.020), apparent diffusion coefficient (ADC; OR, 1.003; 95% CI: 1.001, 1.005; p=0.015), clinical axillary lymph node status (CALNS; OR, 3.222; 95% CI: 1.372,7.568; p=0.007), and enhancement pattern (OR, 0.284; 95% CI: 0.143, 0.563; p<0.001) were independently associated with TIL levels. These features were used in the ALSE model (where A is ADC, L is CALNS, S is size, and E is enhancement pattern). High TILs were associated with better DFS (p=0.016). The ALSE model derived from multiparametric MRI and clinical features could non-invasively predict TIL levels in BC, and high TILs were associated with longer DFS, especially in human epidermal growth factor receptor 2 (HER2)-positive BC and triple-negative BC (TNBC).

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.

Spatial Distribution and Densities of CD103+ and FoxP3+ Tumor Infiltrating Lymphocytes by Digital Analysis for Outcome Prediction in Breast Cancer.

The evaluation of tumor-infiltrating lymphocytes (TILs) for breast cancer prognosis is now established. However, the clinical value for their spatial distributions of specific immune subsets, namely CD103+ tissue-resident memory T cells FoxP3+ regulatory T ells, have not been thoroughly examined. Representative whole sections of breast cancers were subjected to CD103 and FoxP3 double staining. Their density, ratio, and spatial features were analyzed in tumor area and tumor-stromal interface. Their associations with clinicopathological parameters and patient's prognosis were analyzed. CD103 TILs were closer to tumor nests than FoxP3 TILs in the tumor-stromal interface. Their densities were associated with high-grade disease, TNBC, and stromal TILs. High stromal FoxP3 (sFoxP3) TILs and close proximity of sCD103 TILs to tumor were independently associated with better survival at multivariate analysis. Subgroup analysis showed the high FoxP3 TILs density associated better survival was seen in HER2-OE and TNBC subtypes while the proximity of CD103 TILs to tumor nests associated better survival was seen in luminal cancers. The prognostic impact of CD103 and FoxP3 TILs in breast cancer depends on their spatial localization. High sFoxP3 TIL density and the lower distance of CD103 TILs from the tumor nests had independent favorable prognostic values.

Abstract 5444: Integrated analysis of gene expression signatures and AI-based detection of tertiary lymphoid structures and stromal tumor-infiltrating lymphocytes in breast cancer H&amp;E samples

Abstract Background: Transcriptomic and morphological features of the tumor microenvironment (TME) are associated with favorable prognosis and can serve as surrogate markers for immune checkpoint inhibitor response in certain breast cancer subtypes. Tertiary lymphoid structures (TLS) and stromal tumor-infiltrating lymphocytes (sTILs) are integral components of the TME. New techniques for TLS characterization could improve reproducibility and automate the current workflow. Here, we correlated AI-based imaging platform predictions of TLS and sTILs on breast cancer H&amp;E whole slide images (WSIs) with expression data. Design: WSIs from 390 TCGA breast cancer samples were used to detect normalized tumoral TLS and tumor stroma infiltration areas by a convolutional neural network (CNN). High and low groups were defined by IQR1 and IQR3. Gene counts were compared using differential expression analysis followed by gene set enrichment analysis (GSEA) with hallmark gene sets. Kassandra's cell type percentage predictions, functional gene signature (Fges) single-sample GSEA, and PROGENy pathway activity median-scaled scores were compared with the Mann-Whitney U test. The Fges consisted of 4 TLS and 29 TME signatures and were clustered into 4 groups: fibrotic, myeloid, T cell, and TLS. Group medians were analyzed. Somatic deep amplifications, deletions, and missense mutations of cancer pathways were compared with Fisher’s exact test. The FDR method was used to adjust p-values. Results: FDCSP, CXCL13, CD79A, CCL19, and IGLL5 were upregulated in the TLS-high group (logFC &amp;gt; 1.5, p &amp;lt; 0.003), and CXCL13, CXCL9, CCL19, CD3E, and CD79A were upregulated in the sTIL-high group (logFC &amp;gt; 2, p &amp;lt; 4e-9). GSEA identified interferon γ and α response and allograft rejection signatures in TLS- and sTIL-high groups (p = 0.006, NES &amp;gt; |2.5|). sTIL-high samples expressed high levels of E2F targets, while the sTIL-low group was enriched with myogenesis and epithelial-mesenchymal transition gene sets. The TLS-high group was enriched in the TLS Fges cluster (p = 1e-5) and contained high B, CD4+ T, and CD8+ T cell content (p &amp;lt; 2e-4). sTIL samples expressed high levels of B, CD4+ T, CD8+ T, and myeloid cells (p &amp;lt; 0.001) and TLS and myeloid clusters (p &amp;lt; 1e-5). TGFβ and VEGF pathways were more active in sTIL-low samples (p &amp;lt; 0.001), while JAK-STAT was most active in the sTIL-high group (p = 0.0006). In the basal subtype (p &amp;lt; 0.001), the TLS-high group was associated with NOTCH2 amplification; the sTIL-low group had increased NRG1 deletions; and the sTIL-high group contained BRCA2 mutations. Conclusions: RNA-based methods used were concordant with AI-based methods of TLS and sTIL detection. Our CNN-based method of identifying immune structures in the TME with morphological features on H&amp;E WSIs may automate traditional pathology workflows with additional validation. Citation Format: Nadezhda Lukashevich, Vladimir Kushnarev, Daniil Dymov, Anastasia Zotova, Anna Belozerova, Ivan Valiev, Lev Popyvanov, Anna M. Love, Nathan Fowler, Alexander Bagaev, Ekaterina Postovalova. Integrated analysis of gene expression signatures and AI-based detection of tertiary lymphoid structures and stromal tumor-infiltrating lymphocytes in breast cancer H&amp;E samples. [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 5444.

Combining the guidelines-method and multiphoton imaging method to improve the prognostic value of tumor-infiltrating lymphocytes in breast cancer.

Multiphoton microscopy (MPM) was introduced to label-freely obtain tumor-infiltrating lymphocytes (TILs) images from a total of 611 patients, and the prognostic value of TILs in breast cancer was assessed by the MPM method (TILs-MPM) and guidelines-method proposed by the International Immuno-Oncology Biomarker Working Group (TILs-WG), respectively. Moreover, the clinical (CLI) model, TILs-WG+TILs-MPM model, and full model (CLI+TILs-WG+TILs-MPM) were developed to investigate the prognostic value of TILs. The results show that TILs-WG performs better in ER-negative subgroup, and TILs-MPM is comparable with TILs-WG in the ER-negative subgroup, but has the best performance in the ER-positives subgroup. Furthermore, the TILs-WG+TILs-MPM model can significantly improve the prognostic power compared with the TILs-WG model, and the full model has excellent performance, with high AUC and HR in both ER-positive, ER-negative subgroups, and the complete cohort. Our results suggest that the combination of TILs-WG with TILs-MPM model can greatly improve the prognostic value of TILs.

A pathomic approach for tumor-infiltrating lymphocytes classification on breast cancer digital pathology images

Background and objectivesThe detection of tumor-infiltrating lymphocytes (TILs) could aid in the development of objective measures of the infiltration grade and can support decision-making in breast cancer (BC). However, manual quantification of TILs in BC histopathological whole slide images (WSI) is currently based on a visual assessment, thus resulting not standardized, not reproducible, and time-consuming for pathologists. In this work, a novel pathomic approach, aimed to apply high-throughput image feature extraction techniques to analyze the microscopic patterns in WSI, is proposed. In fact, pathomic features provide additional information concerning the underlying biological processes compared to the WSI visual interpretation, thus providing more easily interpretable and explainable results than the most frequently investigated Deep Learning based methods in the literature. MethodsA dataset containing 1037 regions of interest with tissue compartments and TILs annotated on 195 TNBC and HER2+ BC hematoxylin and eosin (H&E)-stained WSI was used. After segmenting nuclei within tumor-associated stroma using a watershed-based approach, 71 pathomic features were extracted from each nucleus and reduced using a Spearman's correlation filter followed by a nonparametric Wilcoxon rank-sum test and least absolute shrinkage and selection operator. The relevant features were used to classify each candidate nucleus as either TILs or non-TILs using 5 multivariable machine learning classification models trained using 5-fold cross-validation (1) without resampling, (2) with the synthetic minority over-sampling technique and (3) with downsampling. The prediction performance of the models was assessed using ROC curves. Results21 features were selected, with most of them related to the well-known TILs properties of having regular shape, clearer margins, high peak intensity, more homogeneous enhancement and different textural pattern than other cells. The best performance was obtained by Random-Forest with ROC AUC of 0.86, regardless of resampling technique. ConclusionsThe presented approach holds promise for the classification of TILs in BC H&E-stained WSI and could provide support to pathologists for a reliable, rapid and interpretable clinical assessment of TILs in BC.

Abstract P1-05-17: Radiomic models based on dynamic contrast-enhanced magnetic resonance imaging predict the immunophenotype reflecting spatial distribution of CD8+ tumor-infiltrating lymphocytes in breast cancer

Abstract Purpose: This study aimed to develop noninvasive magnetic resonance imaging (MRI)-based radiomic models to predict immunophenotypes of breast cancer. Methods: A total of 182 breast cancer patients who underwent upfront surgery were analyzed, divided into training (n = 137) and validation (n = 45) cohorts. Dynamic contrast-enhanced (DCE)-MRI was acquired, and immunophenotype was determined on the surgical tumor sections: immune-inflamed (high degree of CD8+ T cells infiltrated), -excluded (CD8+ T cells accumulated at invasive margin but not efficiently infiltrated in tumor bed), and -desert (CD8+ T cells absent within tumor and at margins). Based on 833 radiomic features extracted after manual delineation, the least absolute shrinkage and selection operator method was used to build radiomic models. Results: Our radiomic models from the whole tumor sections showed moderate performance in predicting the immune-inflamed versus non-inflamed tumors, showing the highest AUC values, of 0.659 and 0.671 for training and validation cohorts, respectively. Combining the models, much improved predictability was observed, with AUC values of 0.973 and 0.985 for training and validation cohorts, respectively. Other radiomic features from tumor periphery data discriminated immune-excluded versus immune-desert status, with AUC values of 0.993 and 0.984 for training and validation cohorts, respectively. The combined models were also applicable to predicting immunophenotype for different molecular subtypes, with AUC values ≥ 0.867. Conclusions: By integrating the immunohistochemistry profiles, we established MRI-derived radiomic models to predict the detailed immunophenotype of breast cancer. This study suggests the feasibility of noninvasive assessment of tumor immune status in real-world clinics. Citation Format: Seung Hyuck Jeon, So-Woon Kim, Mirinae Seo, Yu Jin Lim. Radiomic models based on dynamic contrast-enhanced magnetic resonance imaging predict the immunophenotype reflecting spatial distribution of CD8+ tumor-infiltrating lymphocytes in breast cancer [abstract]. In: Proceedings of the 2022 San Antonio Breast Cancer Symposium; 2022 Dec 6-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2023;83(5 Suppl):Abstract nr P1-05-17.

Abstract P1-08-05: Educating pathologists in quantitating stromal tumor-infiltrating lymphocytes in breast cancer for artificial intelligence applications

Abstract Background Immune cells in the tumor microenvironment play an important role in cancer development [1]. In triple negative breast cancer (TNBC), stromal tumor-infiltrating lymphocytes (sTILs) have been identified as a biomarker with both predictive and prognostic clinical values [2]. The High Throughput Truthing project collected sTILs density estimates in hematoxylin and eosin stained invasive breast cancer biopsy specimens. The goal of the project is to produce a dataset to validate artificial intelligence and machine learning models [3]. After collecting annotations from pathologists for a pilot study, we observed a high level of interobserver variability in sTILs density estimates. To improve pathologist accuracy in breast cancer sTILs assessment, we created educational materials using an expert panel and pilot study data. Method The pilot study data consisted of 640 unique regions of interest (ROIs) derived from 64 digital whole slide images. We categorized ROIs based on their mean sTILs density as “10% or less”, “11% to 40%”, or “greater than 40%”, and selected 72 unique ROIs from those with the highest and lowest pathologist variability in each density bin. In a series of eight one-hour sessions, each ROI was reviewed in a group setting by at least three members of our expert panel, which consisted of one clinical scientist and seven board-certified pathologists trained in breast cancer sTILs assessment. Experts provided estimates of the percent of tumor-associated stroma and sTILs density, and commentary on features that confound sTILs assessment for each ROI. Results We created a set of educational materials to teach the sTILs assessment methodology in breast cancer. These materials include an introduction to the clinical relevance of tumor infiltrating lymphocytes in the breast cancer microenvironment, a tutorial for assessing sTILs according to published guidelines [4], and a discussion of specific pitfalls that may be encountered. Expert panel annotations, comments, and pitfalls were used to generate a reference document and interactive tests: one with expert feedback on each ROI and one to determine proficiency. Conclusions Educational materials designed by an expert panel will serve as reference materials for learning sTILs assessment in breast cancer. Our work provides valuable education for pathologists, and directly supports their ability to provide up-to-date diagnostic information used in caring for breast cancer patients.

Radiomic models based on magnetic resonance imaging predict the spatial distribution of CD8+ tumor-infiltrating lymphocytes in breast cancer.

Infiltration of CD8+ T cells and their spatial contexture, represented by immunophenotype, predict the prognosis and therapeutic response in breast cancer. However, a non-surgical method using radiomics to evaluate breast cancer immunophenotype has not been explored. Here, we assessed the CD8+ T cell-based immunophenotype in patients with breast cancer undergoing upfront surgery (n = 182). We extracted radiomic features from the four phases of dynamic contrast-enhanced magnetic resonance imaging, and randomly divided the patients into training (n = 137) and validation (n = 45) cohorts. For predicting the immunophenotypes, radiomic models (RMs) that combined the four phases demonstrated superior performance to those derived from a single phase. For discriminating the inflamed tumor from the non-inflamed tumor, the feature-based combination model from the whole tumor (RM-wholeFC) showed high performance in both training (area under the receiver operating characteristic curve [AUC] = 0.973) and validation cohorts (AUC = 0.985). Similarly, the feature-based combination model from the peripheral tumor (RM-periFC) discriminated between immune-desert and excluded tumors with high performance in both training (AUC = 0.993) and validation cohorts (AUC = 0.984). Both RM-wholeFC and RM-periFC demonstrated good to excellent performance for every molecular subtype. Furthermore, in patients who underwent neoadjuvant chemotherapy (n = 64), pre-treatment images showed that tumors exhibiting complete response to neoadjuvant chemotherapy had significantly higher scores from RM-wholeFC and lower scores from RM-periFC. Our RMs predicted the immunophenotype of breast cancer based on the spatial distribution of CD8+ T cells with high accuracy. This approach can be used to stratify patients non-invasively based on the status of the tumor-immune microenvironment.

Evaluation of tumor infiltrating lymphocytes in breast carcinomas

Background: Among the parameters that have a prognostic and/or predictive significance to therapeutic response, in the case of breast carcinoma, the study of tumor-infiltrating lymphocytes in breast carcinomas is emerging. The present study aimed to access the clinicopathological profiles of tumor-infiltrating lymphocytes in patients with primary breast carcinoma. Materials and Methods: Information was collected from the archive of the Department of Pathology, about invasive breast cancers diagnosed between April 2019 –March 2020. Hormone receptor status and Ki-67 index were assessed. For tumor-infiltrating lymphocytes, hematoxylin and eosin sections were evaluated following the guidelines of the “International Working Group for tumor-infiltrating lymphocytes in Breast Cancer—2014”. Results: High tumor-infiltrating lymphocytes were seen in 64.2% and 43.8% in ages &lt; 50 and &gt; 50 respectively. All cases of invasive lobular carcinoma had low tumor-infiltrating lymphocytes. 62.5% of T1 tumors were associated with low TILs whereas 80% of low tumor-infiltrating lymphocytes were T3. 71.4% of lymph node-positive tumors had low tumor-infiltrating lymphocytes. All breast cancers showed lymphocytic infiltrate. 40% of the tumors showed intermediate to high tumor-infiltrating lymphocytes. Lymphocyte-predominant breast cancers were 30%. Among the LPBC, 55.5% were triple-negative breast cancers, 33.3% were HR+, and 11.2% were HER2 positive. Conclusions: Low tumor-infiltrating lymphocytes presented at an advanced stage of the disease at the time of diagnosis. tumor-infiltrating lymphocytes in breasts decreased with increasing age. LPBCs were predominantly TNBCs. All breast cancers were immunogenic, stating the necessity to report tumor-infiltrating lymphocytes in all breast carcinomas.

Expression of immune checkpoints (PD-L1 and IDO) and tumour-infiltrating lymphocytes in breast cancer

BackgroundBreast cancer (BC) has become the most common cancer globally in 2020 as well as in the United Arab Emirates. The breast tumor microenvironment is composed of various immune cell types, including lymphocytes. Tumour-infiltrating lymphocytes (TILs) play a crucial role in tumor eradication and progression. Further, immune checkpoint markers such as programmed death receptor ligand 1 (PD-L1) and indoleamine-2,3-dioxygenase (IDO) have been associated with tumor evasion from the immune system. In this study, we aimed to explore the status of TILs, PD-L1 and IDO as well as to investigate their association with the clinicopathological parameters. Materials and methodsA total of 59 patients diagnosed with primary infiltrating BC were selected, after which tissue sections were stained to identify TILs along with immunohistochemical staining of PD-L1 and IDO. Moreover, in-silico tools were used to assess the expression of PD-L1, IDO and CD3ε in various molecular subtypes of BC. ResultsIt was found that the percentage of TILs correlated with estrogen receptor (ER) and progesterone receptor (PR) expression. This was supported by the finding that most of the triple-negative breast cancer (TNBC) cases belonged to the group with a high percentage of TILs (h-TILs). Similarly, the expression of PD-L1 and IDO was correlated with the ER and PR, whereas TNBC cases showed a high expression of PD-L1 and IDO. This goes in line with the in-silico findings where the TNBC group showed the highest expression of PD-L1 and IDO as well as the T cell marker CD3ε. ConclusionThis study highlighted a possible link between the immunosuppressive markers PD-L1 and IDO with TILs density in the BC microenvironment.

Prognostic Significance of Fibrotic Focus and Tumor Infiltrating Lymphocytes in Breast Cancer According to Molecular Subtypes

Purpose: This study aimed to analyze the association between fibrotic focus (FF) and tumor-infiltrating lymphocytes (TILs) and to determine the prognostic significance of FF and TILs in the breast according to its molecular subtypes.Methods: The study included patients who underwent surgical treatment for breast cancer, for whom tissue samples were available. FF within the tumor and TILs in breast cancer tissues were evaluated. Clinicopathological characteristics were reviewed from medical records.Results: FF and TILs were present in 31.3% and 81.7% of the patients, respectively. FF and TILs showed a positive correlation. FF were significantly associated with tumor size, lymphovascular invasion, regional lymph node metastasis, and tumor stage. TILs were significantly associated with menopausal status, histologic grade, tubule formation, nuclear grade, mitosis, human epidermal growth factor receptor 2 (HER2) overexpression, molecular subtype of breast cancer, and the number of cluster of differentiation 8+ T cells. In TIL-positive cases, FF were significantly associated with tumor size, regional lymph node metastasis, extranodal extension, lymphovascular invasion, tumor stage, recurrence-free survival, and overall survival (OS). Based on HER2 overexpression status, TILs were significantly associated with tumor size, tumor necrosis, histologic grade, estrogen receptor status, and epidermal growth factor receptor expression in HER2-negative breast cancer. Further, in HER2-negative breast cancer, OS and recurrence-free survival were significantly associated with FF. The OS of FF-positive patients was significantly shorter than that of FF-negative patients.Conclusion: Our study showed an association between FF and TIL levels in breast cancer, indicating that FF are associated with poor prognostic factors for breast cancer and poor OS, and that TILs are associated with HER2 overexpression. However, further studies are needed to elucidate the interactions between FF and TILs in breast cancer.

CD45-positive tumor infiltrating lymphocytes in early-stage hormone-positive, HER2-negative (ER+/HER2-) breast cancer: Correlation with proliferation and prognostic signature scores.

e12555 Background: Tumour infiltrating lymphocytes (TILs), have been associated in higher responses to treatment and are thought to be a prognostic marker in a number of cancer types. The role of TILs in breast cancer varies depending on the molecular subtype of the disease. In hormone positive, HER2-negative breast cancer, the significance of TILs is less clear, partly due to the paucity of data in this area. We aim to assess overall TILs in early-stage, hormone-positive, HER2-negative (ER+/HER2-) breast cancer and its relationship with proliferation index, Ki67, and the 21-gene recurrence score. Methods: Archival patient tissue samples of early-stage, ER+/HER2- breast cancer from the Irish arm of the TAILORx clinical trial were used. Full-face sections of these tumours were stained with anti-CD45 antibody (DAKA clone M0701) via the automated Leica Bond III system. A watershed cell-detection algorithm from the open-source digital pathology software, QuPath, was fine-tuned to identify true cell objects and positive immune markers. Optimised detection parameters were used to calculate the percentage of overall CD45+ cells (CD45%) within the tumour area. Patient TIL categories were dichotomised into High v Low CD45%, with 10% chosen as the threshold. CD45% was further correlated with proliferation index, clinical data, and 21-gene recurrence score. Results: 409 samples were included in the analysis. The mean CD45% was 5.96, with samples ranging from 0.13 to 78.86. The majority of samples were in the range of 0-10% of CD45% positivity with 89% (n = 367) patients in this category. Stratification of the cohort by 21-gene-recurrence score (RS) into Low, Intermediate and High, showed a statistically significantly high CD45% in patients with a High RS compared to Low RS (p = 0.0015). When divided by recurrence score group, high CD45% trended toward a lower disease free survival (DFS) when compared to low CD45%, with statistical significance found in those in the Intermediate RS chemotherapy treatment arm (p = 0.009). Conclusions: The role of TILs in ER+/HER2- breast cancer is still undefined. While a High RS was associated with a higher percentage of CD45-positive lymphocytes, the impact on recurrence and response to treatment remains unclear. Further studies to assess the role of different tumour infiltrating lymphocyte phenotypes, and the spatial distribution of assessed markers, are needed.

Development of Training Materials for Pathologists to Provide Machine Learning Validation Data of Tumor-Infiltrating Lymphocytes in Breast Cancer.

Simple SummaryThe High Throughput Truthing project aims to develop a dataset of stromal tumor-infiltrating lymphocytes (sTILs) density evaluations in hematoxylin and eosin-stained invasive breast cancer specimens fit for a regulatory purpose. After completion of the pilot study, the analysis demonstrated inconsistencies and gaps in the provided training to pathologists. Select regions of interest (ROIs) were reviewed by an expert panel, who provided annotations and commentary on the challenges of the sTILs assessment. We used these annotations to develop a training document and reference standard for new training materials. These materials will train crowd-sourced pathologists to help create an algorithm validation dataset and contribute to sTILs evaluations in clinical practice.The High Throughput Truthing project aims to develop a dataset for validating artificial intelligence and machine learning models (AI/ML) fit for regulatory purposes. The context of this AI/ML validation dataset is the reporting of stromal tumor-infiltrating lymphocytes (sTILs) density evaluations in hematoxylin and eosin-stained invasive breast cancer biopsy specimens. After completing the pilot study, we found notable variability in the sTILs estimates as well as inconsistencies and gaps in the provided training to pathologists. Using the pilot study data and an expert panel, we created custom training materials to improve pathologist annotation quality for the pivotal study. We categorized regions of interest (ROIs) based on their mean sTILs density and selected ROIs with the highest and lowest sTILs variability. In a series of eight one-hour sessions, the expert panel reviewed each ROI and provided verbal density estimates and comments on features that confounded the sTILs evaluation. We aggregated and shaped the comments to identify pitfalls and instructions to improve our training materials. From these selected ROIs, we created a training set and proficiency test set to improve pathologist training with the goal to improve data collection for the pivotal study. We are not exploring AI/ML performance in this paper. Instead, we are creating materials that will train crowd-sourced pathologists to be the reference standard in a pivotal study to create an AI/ML model validation dataset. The issues discussed here are also important for clinicians to understand about the evaluation of sTILs in clinical practice and can provide insight to developers of AI/ML models.

Necroptosis-Associated lncRNA Prognostic Model and Clustering Analysis: Prognosis Prediction and Tumor-Infiltrating Lymphocytes in Breast Cancer.

Necroptosis plays an important role in tumor genesis and progression. This study aims to identify necroptosis-related lncRNAs (NR-lncRNAs) in breast cancer (BC), and their prognostic value and relationship with the tumor immune environment (TIE) through bioinformatics. Methods. A total of 67 necroptosis-related genes (NRGs) are retrieved, and 13 prognostically relevant NR-lncRNAs are identified by co-expression and Univariate Cox regression analyses. After unsupervised clustering analysis, the patients are classified into three clusters, and their survival and immune infiltration are compared. Lasso regression analysis is conducted to construct a prognostic model using eight lncRNAs (USP30-AS1, AC097662.1, AC007686.3, AL133467.1, AP006284.1, NDUFA6-DT, LINC01871, AL135818.1). The model is validated by Kaplan-Meier survival analysis, Multivariate Cox regression analysis, and receiver-operating characteristic (ROC) curves. Correlation analysis is useful to identify associations between risk scores and clinicopathological features. GSEA, drug prediction, and immune checkpoints analysis are further used to differentiate between the risk groups. Results. The C3 cluster has longer overall survival (OS) and the highest immune score, indicative of an immunologically hot tumor that may be sensitive to immunotherapy. Furthermore, the OS is significantly higher in the low-risk group, even after dividing the patients into subgroups with different clinical characteristics. The area under the ROC curve (AUC) for 1-, 3-, and 5-year survival in the training set are 0.761, 0.734, and 0.664, respectively, which indicate the moderate predictive performance of the model. Conclusion. NR-lncRNAs can predict the prognosis of BC, distinguish between hot and cold tumors, and are potential predictive markers of the immunotherapy response.

THE RELEVANCE AND IMPORTANCE OF TUMOR INFILTRATING LYMPHOCYTES (TILS) IN BREAST CANCER

Tumor infiltrating lymphocytes (TILs) were evaluated in many cancers like ovarian tumors, endometrial tumors, colon cancer and melanoma. Now there is increasing trend in evaluating TILs in breast cancer. Tumor infiltrating lymphocytes gives the patients immune response to the tumor. 100 cases of Modified radical mastectomy specimen were chosen from surgical pathology records. The representative formalin fixed paraffin embedded tissue samples were collected. Detailed history regarding Patients age, sex, side of the breast involved , Grade and neoadjuvant chemotherapy were assessed for 100 cases. Tumor Infiltrating Lymphocytes are evaluated with Immuno HistoChemistry CD45 and CD3 for 100 cases , in which 93% cases were Infiltrating Ductal carcinoma-NOS cases and 07 % cases are of special types like Mucinous, Metaplastic, Apocrine, and Medullary carcinoma. Results: In our study, Highest incidence of breast carcinoma occur in the age group of 41-50 years. Infiltrating Ductal Carcinoma NOS is the most common primary malignant neoplasm of breast constituting 93% of cases. Most of the malignant tumors are left sided. Forty seven percent of cancers are presented in the size range of 2-5cms. Majority of the tumors are of Grade II accounting for 63.5% of all cases. TILs were evaluated with three variables 0- 10 % 20- 40 % and 50- 90 %.Younger the age, more the TILs. Neo adjuvant chemotherapy had significant correlation with TILs (P= 0.05).