Evaluation Of Tumor-infiltrating Lymphocytes Research Articles

HISTOPATHOLOGICAL PARAMETERS AND INTRATUMORAL LYMPHOCYTES: CORRELATION WITH SURVIVAL OUTCOMES IN TRIPLE-NEGATIVE BREAST CANCER - A COMPREHENSIVE RETROSPECTIVE ANALYSIS

OBJECTIVE: Triple-negative breast cancer (TNBC) is a subtype characterized by aggressive tumor behavior and limited treatment options. This study aimed to investigate the relationship among age, pathological stage, proliferative index, presence of tumor infiltrating lymphocytes (TILs), and survival outcomes in TNBC. MATERIAL AND METHODS: Tumoral slides and blocks of 31 patients with triple negative breast cancer were retrieved from the pathology archive and retrospectively re-evaluated. The relationship among patient age, histopathological subtype of the tumor, tumor grade, lymph node grade, Ki-67 proliferation index and survival was evaluated. TILs were scored as mild, moderate and severe and the relationship with survival was evaluated. RESULTS: Regarding age and tumor stage, there was no significant correlation found (p=0,81 and p=0,89 respectively). However, when analyzing the N stage, a clear association was observed, with a higher proportion of patients aged 65 years or older displaying advanced N3 stage breast cancer (p=0.000013). A significant relationship was found between TILs and the Ki-67 proliferative index, with cases exhibiting high TILs also demonstrating a high proliferative index (p=0.003). Furthermore, increased TIL concentration was associated with a positive response to therapy and improved overall survival in TNBC patients (p=0.001). CONCLUSIONS: These findings emphasize the importance of considering age, pathological stage, proliferative index, and the presence of TILs in TNBC prognosis. Evaluation of TILs in routine histopathologic examination and inclusion in pathology reports, particularly in postmenopausal patients, could provide valuable information for future studies and guide treatment decisions. Additional research on immune-modulating therapies targeting TILs may hold promise for improving outcomes in TNBC patients.

An algorithm for standardization of tumor Infiltrating lymphocyte evaluation in head and neck cancers

PurposeThe prognostic and predictive significance of pathologist-read tumor infiltrating lymphocytes (TILs) in head and neck cancers have been demonstrated through multiple studies over the years. TILs have not been broadly adopted clinically, perhaps due to substantial inter-observer variability. In this study, we developed a machine-based algorithm for TIL evaluation in head and neck cancers and validated its prognostic value in independent cohorts. Experimental designA network classifier called NN3-17 was trained to identify and calculate tumor cells, lymphocytes, fibroblasts and “other” cells on hematoxylin-eosin stained sections using the QuPath software. These measurements were used to construct three predefined TIL variables. A retrospective collection of 154 head and neck squamous cell cancer cases was used as the discovery set to identify optimal association of TIL variables and survival. Two independent cohorts of 234 cases were used for validation. ResultsWe found that electronic TIL variables were associated with favorable prognosis in both the HPV-positive and -negative cases. After adjusting for clinicopathologic factors, Cox regression analysis demonstrated that electronic total TILs% (p = 0.025) in the HPV-positive and electronic stromal TILs% (p < 0.001) in the HPV-negative population were independent markers of disease specific outcomes (disease free survival). ConclusionsNeural network TIL variables demonstrated independent prognostic value in validation cohorts of HPV-positive and HPV-negative head and neck cancers. These objective variables can be calculated by an open-source software and could be considered for testing in a prospective setting to assess potential clinical implications.

Clinical and radiomics integrated nomogram for preoperative prediction of tumor-infiltrating lymphocytes in patients with triple-negative breast cancer.

The present study aimed to develop a radiomics nomogram based on conventional ultrasound (CUS) to preoperatively distinguish high tumor-infiltrating lymphocytes (TILs) and low TILs in triple-negative breast cancer (TNBC) patients. In the present study, 145 TNBC patients were retrospectively included. Pathological evaluation of TILs in the hematoxylin and eosin sections was set as the gold standard. The patients were randomly allocated into training dataset and validation dataset with a ratio of 7:3. Clinical features (age and CUS features) and radiomics features were collected. Then, the Rad-score model was constructed after the radiomics feature selection. The clinical features model and clinical features plus Rad-score (Clin+RS) model were built using logistic regression analysis. Furthermore, the performance of the models was evaluated by analyzing the receiver operating characteristic (ROC) curve, calibration curve, and decision curve analysis (DCA). Univariate analysis and LASSO regression were employed to identify a subset of 25 radiomics features from a pool of 837 radiomics features, followed by the calculation of Rad-score. The Clin+RS integrated model, which combined posterior echo and Rad-score, demonstrated better predictive performance compared to both the Rad-score model and clinical model, achieving AUC values of 0.848 in the training dataset and 0.847 in the validation dataset. The Clin+RS integrated model, incorporating posterior echo and Rad-score, demonstrated an acceptable preoperative evaluation of the TIL level. The Clin+RS integrated nomogram holds tremendous potential for preoperative individualized prediction of the TIL level in TNBC.

A Deep Learning–Based Assessment Pipeline for Intraepithelial and Stromal Tumor-Infiltrating Lymphocytes in High-Grade Serous Ovarian Carcinoma

Tumor-infiltrating lymphocytes (TILs) are associated with improved survival in patients with epithelial ovarian cancer. However, the evaluation of TILs has not been applied to routine clinical practice due to reproducibility issues. We developed two convolutional neural network models to detect TILs and to determine their spatial location in whole-slide images, and established a spatial assessment pipeline to objectively quantify intraepithelial and stromal TILs in patients with high-grade serous ovarian carcinoma. The predictions of the established models showed a significant positive correlation with the number of CD8+ T cells and immune gene expressions. We demonstrated that patients with a higher density of intraepithelial TILs had a significantly prolonged overall survival (OS) and progression-free survival (PFS) in multiple cohorts. Based on the density of intraepithelial and stromal TILs, we classified patients into three immunophenotypes: immune-inflamed, excluded, and desert. The immune-desert subgroup showed the worst prognosis. Gene expression analysis showed that the immune-desert subgroup had lower immune cytolytic activity (CYT) and T-cell-inflamed gene-expression profile (GEP) scores, whereas immune-excluded subgroup had higher expression of interferon-γ and programmed death 1 receptor (PD-1) signaling pathway. Our established evaluation method provides detailed and comprehensive quantification of intraepithelial and stromal TILs throughout hematoxylin and eosin (H&E)-stained slides, and has potential for clinical application for personalized treatment of patients with ovarian cancer.

Genetically engineered mouse model of pleomorphic liposarcoma: Immunophenotyping and histologic characterization

IntroductionPleomorphic liposarcoma is a rare and aggressive subset of soft-tissue sarcomas with a high mortality burden. Local treatment largely consists of radiation therapy and wide surgical resection, but options for systemic therapy in the setting of metastatic disease are limited and largely ineffective, prompting exploration of novel therapeutic strategies and experimental models. As with other cancers, sarcoma cell lines and patient-derived xenograft models have been developed and used to characterize these tumors and identify therapeutic targets, but these models have inherent limitations. The establishment of genetically engineered mouse models represents a more realistic framework for reproducing clinically relevant conditions for studying pleomorphic liposarcoma. MethodsTrp53fl/fl/Rb1fl/fl/Ptenfl/fl (RPP) mice were used to reliably generate an immunocompetent model of mouse pleomorphic liposarcoma through Cre-mediated conditional silencing of the Trp53, Rb1, and Pten tumor suppressor genes. Evaluation of tumor-infiltrating lymphocytes was assessed with immunostaining for CD4, CD8, and PD-L1, and flow cytometry with analysis of CD45, CD3, CD4, CD8, CD19, F4/80, CD11b, and NKp46 sub-populations. ResultsMice reliably produced noticeable soft-tissue tumors in approximately 6 weeks with rapid tumor growth between 100 and 150 days of life, after which mice reached euthanasia criteria. Histologic features were consistent with pleomorphic liposarcoma, including widespread pleomorphic lipoblasts. Immunoprofiling and assessment of tumor-infiltrating lymphocytes was consistent with other soft-tissue sarcomas. ConclusionGenetically engineered RPP mice reliably produced soft-tissue tumors consistent with pleomorphic liposarcoma, which immunological findings similar to other soft-tissue sarcomas. This model may demonstrate utility in testing treatments for this rare disease, including immunomodulatory therapies.

Locoregional recurrences of breast cancer after reconstructive plastic and organ-preserving surgery

Objective. To analyze locoregional relapses (LRR) after reconstructive plastic and organ-preserving operations in breast cancer (BC)Materials and methods. A retrospective analysis of the results of the treatment of 303 patients with BC who underwent reconstructive plastic and organ-preserving operations in the period from 2014 to 2019 in the Oncological Department of General Oncology and Rehabilitation of Gomel Regional Clinical Oncological Dispensary was carried out. The median age was 44 years (25-70 years). Statistical data processing was carried out using the application software package “Statistica”, 10.0. To characterize the surveyed groups, a standard methodological approach based on the calculation of descriptive statistics data was used. In order to statistically assess the relationship between the frequency of relapses depending on the stage and the molecular biological type of neoplasm, the Spearman correlation coeffi cient was used. The accepted level of statistical signifi cance (p) was 0.05.Results. Locoregional relapses were recorded in 11 (3.6%) patients, of which local — in 8 (2.6%), regional — in 3 (1.0%). The minimum period of development of LRR was 15 months, the maximum was 74 months; the median period of occurrence of LRR was 47 months. Depending on the stage of breast cancer, relapses developed: at stage I — in 4 (3.7%) cases, II — 5 (3.5%), III — 2 (4.2%). Depending on the molecular biological subtype of the neoplasm, LRR were distributed as follows: with luminal A — 1 (1.3%) case, luminal In HER2-negative — 1 (0.9%), luminal In HER2-positive — 6 (9.7%), non-luminal HER2-positive 1 (6.3%), three times negative — 2 (5.6%). Discordance of the receptor status was detected in 8 (72.7%) patients with recurrent tumor, most often due to the loss or decrease in the number of progesterone receptors (PR). The level of tumor-infi ltrating lymphocytes (TILs) in primary neoplasm ranged from 4 to 12%, in recurrent tumors it remained low: 5-10%.Conclusion. Given the heterogeneity of BC, the risk of developing LRR depends on many factors. LRR developed in luminal HER2-positive cancer in 9.7% of cases (p 0.05), stage III breast cancer — 4.2% (p 0.05). One of the important predictive factors is the evaluation of TILs. We noted a low level of TILs in both primary and recurrent tumors. There is a high discordance in the receptor status — 72.7%, which is important to take into account when prescribing systemic therapy.

The Value of Tumor Infiltrating Lymphocytes (TIL) for Predicting the Response to Neoadjuvant Chemotherapy (NAC) in Breast Cancer according to the Molecular Subtypes.

The antitumor host immune response is an important factor in breast cancer, but its role is not fully established. The role of tumor infiltrating lymphocytes (TIL) as an immunological biomarker in breast cancer has been significantly explored in recent years. The number of patients treated with neoadjuvant chemotherapy (NAC) has increased and the identification of a biomarker to predict the probability of pCR (pathological complete response) is a high priority. We evaluated 334 cases of BC treated with NAC followed by surgical resection from 2020-2022 at the Ist Clinic of Oncological Surgery, Oncological Institute "Prof Dr I Chiricuta" Cluj Napoca. Of the above, 122 cases were available for histological evaluation both in pre-NAC biopsy and post-NAC resection tissue. Evaluation of biopsy fragments and resection parts were performed using hematoxylin eosin (H&E). The TIL evaluation took place according to the recommendations of the International TIL Working Group (ITILWG). There was a strong association between elevated levels of pre-NAC TIL. At the same time, there is a statistically significant correlation between stromal TIL and tumor grade, the number of lymph node metastases, the molecular subtype and the number of mitoses (p < 0.005). Intratumoral TIL showed a significant correlation with tumor size, distant metastasis, molecular subtype, number of mitosis, stage and lymph node metastasis (p < 0.005). We also demonstrated that high pre-NAC STIL represents a strong predictive marker for pCR. This study reveals the role of TIL as a predictive biomarker in breast cancer not only for the well-established TNBC (triple negative breast cancer) and HER2+ (Her2 overexpressed) subtypes but also in Luminal A and B molecular subtypes. In this scenario, the evaluation of sTIL as a novel predictive and therapy-predicting factor should become a routinely performed analysis that could guide clinicians when choosing the most appropriate therapy.

Evaluation of tumor-infiltrating lymphocytes, PD-L1, and PIK3CA mutations and association with prognosis in HER2-positive early stage breast cancer

Background Tumor-infiltrating lymphocytes (TILs) have predictive and prognostic potential in HER2-positive breast cancer (HER2+ BC). Programmed death-ligand 1 (PD-L1) is an immune checkpoint protein, with important roles in the tumor microenvironment, possibly in both tumor and immune cells (ICs), providing rationale for targeting with immune-checkpoint therapy. PIK3CA mutations are oncogenic, activating mutations, which are also of relevance in breast cancer. Herein, we investigate the frequency of TILs, PD-L1 and PIK3CA mutations, and whether these factors influence outcome, in early HER2+ BC. Materials and methods Stromal TILs (sTILs) and PD-L1 expressions were assessed using full tumor-sections and TMA, respectively, from 236 patients with HER2+ BC. TILs were assessed, according to a standardized method, as continuous measurement and according to three predefined categories: low (0–10%), intermediate (11–59%), and high (60–100%). PD-L1 immunohistochemistry (Ventana SP263) was evaluated and positivity defined as ≥1% expression in tumor and ICs. PIK3CA mutations (exons 9 and 20) were determined by pyrosequencing. Results Fourteen percent of patients had high sTILs and 25% had a PIK3CA mutation. PD-L1 expression was more frequent in ICs (68%) than tumor cells (24%). Patients with low sTILs had a significantly worse overall survival (multivariate: HR 2.80; 95% CI 1.36–5.78; p = .02). Discussion Patients with low sTILs had a significantly poorer survival, despite adequate treatment with adjuvant therapy.

Evaluation of tumor-infiltrating lymphocytes and mammographic density as predictors of response to neoadjuvant systemic therapy in breast cancer

Background Response rates vary among breast cancer patients treated with neoadjuvant systemic therapy (NAST). Thus, there is a need for reliable treatment predictors. Evidence suggests tumor-infiltrating lymphocytes (TILs) predict NAST response. Still, TILs are seldom used clinically as a treatment determinant. Mammographic density (MD) is another potential marker for NAST benefit and its relationship with TILs is unknown. Our aims were to investigate TILs and MD as predictors of NAST response and to study the unexplored relationship between TILs and MD. Material and methods We studied 315 invasive breast carcinomas treated with NAST between 2013 and 2020. Clinicopathological data were retrieved from medical records. The endpoint was defined as pathological complete response (pCR) in the breast. TILs were evaluated in pre-treatment core biopsies and categorized as high (≥10%) or low (<10%). MD was scored (a–d) according to the breast imaging reporting and data system (BI-RADS) fifth edition. Binary logistic regression and Spearman’s test of correlation were performed using SPSS. Results Out of 315 carcinomas, 136 achieved pCR. 94 carcinomas had high TILs and 215 had low TILs. Six carcinomas had no available TIL data. The number of carcinomas in each BI-RADS category were 37, 122, 112, and 44 for a, b, c, and d, respectively. High TILs were independently associated with pCR (OR: 2.95; 95% CI: 1.59–5.46) compared to low TILs. In the univariable analysis, MD (BI-RADS d vs. a) showed a tendency of higher likelihood for pCR (OR: 2.43; 95% CI: 0.99–5.98). However, the association was non-significant, which is consistent with the result of the multivariable analysis (OR: 2.51; 95% CI: 0.78–8.04). We found no correlation between TILs and MD (0.02; p = .80). Conclusion TILs significantly predicted NAST response. We could not define MD as a significant predictor of NAST response. These findings should be further replicated.

The prognostic value of the histological shape of tumor negative sentinel nodes in breast cancer.

Sentinel lymph node (SLN) metastasis is an important predictor of prognosis in breast cancer (BC) patients, guiding treatment decisions. However, patients with the same BC subtype and tumor negative SLN (SLNneg) can have different survival outcomes. We hypothesized that the host anti-tumor immune reaction in SLNneg is important and results in morphometrically measurable changes in SLN size or shape which are related to patient prognosis. Surface area, circumference, long axis and short axis were histologically measured in 694 SLNneg from 356 cases of invasive BC and 67 ductal carcinoma in situ cases. The area occupied by fat was categorized as less or more than 50%. The long to short axis (L/S) ratio was calculated. The relationship between SLNneg morphometries and clinicopathological variables like tumor-infiltrating lymphocytes (TILs) within the primary tumor, as well as prognosis at 10 years follow up were analyzed. The mean SLNneg surface area was 78.7mm2, circumference 40.3mm, long axis 13.1mm, short axis 8.2mm and L/S ratio 1.7. Larger surface area, long axis and short axis, including age >55 years were associated with higher body mass index (BMI) and SLN fat over 50% (p<0.003). In invasive BC, a high SLNneg L/S ratio (≥1.9) was related to poorer disease-free (HR=1.805, 95%CI 1.182-2.755, p=0.006) and overall (HR=2.389, 95%CI 1.481-3.851, p<0.001) survival. A low SLNneg L/S ratio (<1.9) was associated with high TILs in the primary BC (≥10%) (p=0.005). However a high TIL count was not of prognostic relevance. This is the first study to suggest that morphometric characteristics of axillary SLNneg, like L/S ratio, could be used to predict prognosis in patients with SLNneg invasive BC of all subtypes. The association between low L/S ratio and high TILs suggest that SLN shape is related to immunological functioning of the SLN and could be used in addition to TIL evaluation. Regarding the dubious role of TILs in hormone receptor positive breast cancer, SLNneg morphometry to gain information about host immune status could especially be of benefit in this subtype. Further studies are warranted to better understand the underlying biological mechanisms.

Tumor-infiltrating lymphocytes in oral cavity squamous cell carcinoma and its association with clinicopathological parameters

BackgroundOral squamous cell carcinoma is one of the most common cancers of the head and neck region. Tumor-infiltrating lymphocytes are naturally occurring mononuclear cells that infiltrate the tumor microenvironment. The aim of the present study is the evaluation of stromal tumor-infiltrating lymphocytes (TILs) in oral squamous cell carcinoma and its association with clinicopathological parameters like age, gender, site, tumor stage, nodal status, grade, lymphovascular and perineural invasion. MaterialsA total of 51 patients of oral cavity squamous cell carcinomas who underwent resection were included in the study. Assessment of the stromal TILs was done on sections stained with Hematoxylin and eosin using the scoring criteria proposed by the International Immuno-Oncology Biomarker Working Group. TILs were assessed in percentages as continuous scores and subsequently categorized into low TILs (<20%) and high TILs (≥20%). The association between TILs and clinicopathological parameters was evaluated using the Fischer exact/Chi-square test. ResultsAmong the 51 cases evaluated in the present study, low and high TILs were found in 25.5% and 74.5% of cases respectively. TILs were found to show a significant association with tumor size and lymphovascular invasion. However, no significant association was found with age, gender, nodal status, grade, perineural invasion, and site in the oral cavity. ConclusionThe evaluation of TILs as proposed by the International Immuno-Oncology Biomarker Working Group is a simple, inexpensive test. Morphological evaluation of TIL can provide valuable prognostic information in oral cavity squamous cell carcinoma cases and can be incorporated as a part of routine histopathological reporting.

Tumor-Infiltrating Lymphocyte Recognition in Primary Melanoma by Deep Learning Convolutional Neural Network

The presence of tumor-infiltrating lymphocytes (TILs) is associated with a favorable prognosis of primary melanoma (PM). Recently, artificial intelligence (AI)-based approach in digital pathology was proposed for the standardized assessment of TILs on hematoxylin and eosin-stained whole slide images (WSIs). Herein, the study applied a new convolution neural network (CNN) analysis of PM WSIs to automatically assess the infiltration of TILs and extract a TIL score. A CNN was trained and validated in a retrospective cohort of 307 PMs including a training set (237 WSIs, 57,758 patches) and an independent testing set (70 WSIs, 29,533 patches). An AI-based TIL density index (AI-TIL) was identified after the classification of tumor patches by the presence or absence of TILs. The proposed CNN showed high performance in recognizing TILs in PM WSIs, showing 100% specificity and sensitivity on the testing set. The AI-based TIL index correlated with conventional TIL evaluation and clinical outcome. The AI-TIL index was an independent prognostic marker associated directly with a favorable prognosis. A fully automated and standardized AI-TIL appeared to be superior to conventional methods at differentiating the PM clinical outcome. Further studies are required to develop an easy-to-use tool to assist pathologists to assess TILs in the clinical evaluation of solid tumors.

The germline HLA-A02B62 supertype is associated with a PD-L1-positive tumour immune microenvironment and poor prognosis in stage I lung cancer

BackgroundGermline HLA class I molecule supertypes are shown to correlate with response to anti-PD-1 therapy. Here, we investigate the significance of germline HLA-A and HLA-B supertypes in tumour microenvironment of non-small-cell lung cancer. MethodsTotally 278 NSCLC patients were collected retrospectively. HLA genotyping was conducted using next-generation sequencing. The evaluation of tumour-infiltrating lymphocytes was performed by multiplex immunohistochemistry assay. Correlations among HLA supertypes, tumour infiltrating lymphocytes, and clinicopathological characteristics were assessed. ResultsHLA-A03 and HLA-B62 were the supertypes with the highest proportions, at 69.1% and 52.2%, respectively. HLA-A02 or HLA-B62, but not HLA-A03, associated with higher PD-L1+ tumour and stromal cells levels, CD68+ cells, and CD68+PD-L1+ cells. Patients with both HLA-A02 and HLA-B62 supertypes displayed significantly higher PD-L1+ cells, CD68+ cells, and CD8+ cells levels than patients with other supertypes (P = 0.0301, P = 0.0479, P = 0.0192). These cells collectively constitute a hot but immunosuppressive tumour microenvironment. Accordingly, patients with both HLA-A02 and HLA-B62 supertypes had short progression-free survival after surgery (HR = 2.27, P = 0.0373). ConclusionsThe HLA-A02B62 supertype could serve as a possible indicator of poor prognosis in early-stage lung cancer. However, it may also act as a favorable prognostic factor for immunotherapy, given its association with a PD-L1-positive tumour microenvironment.

Deep learning based quantitative models to distinguish tumoral and stromal tumor-infiltrating lymphocytes and the prognosis patients with high-grade serous ovarian cancer.

84 Background: The better prognostic value of tumor-infiltrating lymphocytes (TILs) has been reported in high-grade serous ovarian cancer (HGSOC). However, the TILs evaluation of previous reports largely depends on manual quantification based on a visual assessment within limited section of pathological slides or tissue microarrays, which is subject to inter-observer variability. Since tumor immune microenvironment (TIME) of HGSOC is highly heterogeneous, there is a need to quantify TILs in large areas of pathological slides in an objective and time-saving manner. Here we present an AI-powered quantification method to distinguish tumoral TILs and stromal TILs using hematoxylin and eosin (H&amp;E)-stained whole-slide images (WSIs) of HGSOC. Methods: 161 H&amp;E stained WSIs of HGSOC patients treated at our hospital between 1998 and 2021 were prepared, and by using these WSIs we generated two AI-powered models. Initially, a TILs detection model (TIL-model) was created using a publicly available dataset of 86104 pan-cancer TILs images of 100 × 100 pixels at magnification of x200 (Saltz et al., 2018, Cell Reports). Secondary, a tumor and stroma segmentation model (T/S seg-model) was developed using 136962 patches of 224 × 224 pixels at magnification of x50 which were manually annotated with tumoral regions and stromal regions in 102 H&amp;E stained WSIs of HGSOC from The Cancer Genome Atlas (TCGA). Deep convolutional neural networks of NASNet-Large for the TIL-model and TransUNet for the T/S seg-model were used. TILs density score (number of TILs positive tiles per tumor area) of each sample was calculated, and association between TILs density score and the prognosis was investigated. Results: The established models achieved high accuracy: the TIL-model showed an area under the curve (AUC) value of 0.896, and the T/S seg-model showed an intersection over union value of 0.967. By combining the two models, we successfully distinguished tumoral TILs and stromal TILs. Using our dataset of 161 WSIs, we split the patients by TILs density score at top quartile into high tumoral TILs subgroup (n=40) and low tumoral TILs subgroup (n=121). High tumoral TILs subgroup showed significantly longer overall survival (OS) and progression free survival (PFS) (OS: median 70 months vs not reached, p &lt; 0.005. PFS: median 34 months vs 21 months, p = 0.02). Conclusions: We developed deep learning based pathological AI models to detect TILs and tumors using H&amp;E stained WSIs, which achieved to quantify tumoral and stromal TILs. The objective assessment of TILs using these models will lead to the development of clinically applicable tools for the evaluation of the immune status in HGSOC.

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.

Assessment of the Tumor-Stroma Ratio and Tumor-Infiltrating Lymphocytes in Colorectal Cancer: Inter-Observer Agreement Evaluation.

To implement the new marker in clinical practice, reliability assessment, validation, and standardization of utilization must be applied. This study evaluated the reliability of tumor-infiltrating lymphocytes (TILs) and tumor-stroma ratio (TSR) assessment through conventional microscopy by comparing observers' estimations. Intratumoral and tumor-front stromal TILs, and TSR, were assessed by three pathologists using 86 CRC HE slides. TSR and TILs were categorized using one and four different proposed cutoff systems, respectively, and agreement was assessed using the intraclass coefficient (ICC) and Cohen's kappa statistics. Pairwise evaluation of agreement was performed using the Fleiss kappa statistic and the concordance rate and it was visualized by Bland-Altman plots. To investigate the association between biomarkers and patient data, Pearson's correlation analysis was applied. For the evaluation of intratumoral stromal TILs, ICC of 0.505 (95% CI: 0.35-0.64) was obtained, kappa values were in the range of 0.21 to 0.38, and concordance rates in the range of 0.61 to 0.72. For the evaluation of tumor-front TILs, ICC was 0.52 (95% CI: 0.32-0.67), the overall kappa value ranged from 0.24 to 0.30, and the concordance rate ranged from 0.66 to 0.72. For estimating the TSR, the ICC was 0.48 (95% CI: 0.35-0.60), the kappa value was 0.49 and the concordance rate was 0.76. We observed a significant correlation between tumor grade and the median of TSR (0.29 (95% CI: 0.032-0.51), p-value = 0.03). The agreement between pathologists in estimating these markers corresponds to poor-to-moderate agreement; implementing immune scores in daily practice requires more concentration in inter-observer agreements.

PD-L1 and TILs expression and their association with clinicopathological characteristics in Vietnamese women with primary invasive breast cancer.

Tumor-infiltrating lymphocytes (TILs) and programmed death ligand 1 (PD-L1) are promising new factors in the prognosis and prediction of breast cancer patients. Our study evaluated the prevalence of expression of TILs on hematoxylin and eosin (H&E) slides, PD-L1 expression on immunohistochemistry, and their association with clinicopathological characteristics in Vietnamese women with invasive breast cancer. This study was conducted on 216 women with primary invasive breast cancer. The evaluation of TILs on the HE slides was based on the International TILs Working Group 2014 recommendation. PD-L1 protein expression was determined using the Combined Positive Score, the number of tumor cells, lymphocytes, and macrophages stained by PD-L1 divided by the total viable tumor cells multiplied by 100. Based on the cutoff of 11%, the prevalence of TILs expression was 35.6%, of which highly expressed TILs (≥50%) accounted for 15.3%. Postmenopausal women and those who had a body mass index of 25 kg/m2 or greater had a higher odds of having TILs expression. However, patients who had the expression of Ki-67, HER-positive molecular subtype, and triple-negative subtype were more likely to have TILs expression. The prevalence of PD-L1 expression was 30.1%. A significantly higher odds of having PD-L1 was found in patients who had a history of benign breast disease, self-detected tumor and had TILs expression. The expression of TILs and PD-L1 is common in Vietnamese women with invasive breast cancer. Because of the importance of these expressions, routine evaluation to find women who had TILs and PD-L1 is needed so that treatment and prognosis can be optimized. Such routine evaluation can be targeted to those who had a high-risk profile found in this study.

Evaluation of tumor-infiltrating lymphocytes (TILs) in molecular subtypes of an Indian cohort of breast cancer patients

ObjectivesEvaluation of tumor-infiltrating lymphocytes (TILs) distribution in an Indian cohort of breast cancer patients for its prognostic significance.MethodsA retrospective cohort of breast cancer patients from a single onco-surgeon’s breast cancer clinic with a uniform treatment strategy was evaluated for TILs. Tumor sections were H&E stained and scored for the spatial distribution and percent stromal TILs infiltration by a certified pathologist. The scores were analysed for association with treatment response and survival outcomes across molecular subtypes.ResultsTotal 229 breast cancer tumors were evaluated. Within spatial distribution categories, intra-tumoral TILs were observed to be associated with complete pathological response and lower recurrence frequency for the entire cohort. Subtype-wise analysis of stromal TILs (sTILs) re-enforced significantly higher infiltration in TNBC compared to HER2-positive and ER-positive tumors. A favourable association of higher stromal infiltration was observed with treatment response and disease outcomes, specifically in TNBC.ConclusionIntra-tumoral TILs showed a higher proportion with favourable association with better patient outcomes in an Indian cohort, unlike western cohorts where both stromal and intra-tumoral TILs show similar association with prognosis. With further validation, TILs can be developed as a cost-effective surrogate marker for treatment response, especially in a low-resource setting such as India.

Multiplex immunohistochemistry and high-throughput image analysis for evaluation of spatial tumor immune cell markers in human breast cancer.

The clinicopathological significance of spatial tumor-infiltrating lymphocytes (TILs) subpopulations is not well studied due to lack of high-throughput scalable methodology for studies with large human sample sizes. Establishing a cyclic fluorescent multiplex immunohistochemistry (mIHC/IF) method coupled with computer-assisted high-throughput quantitative analysis to evaluate associations of six TIL markers (CD3, CD8, CD20, CD56, FOXP3, and PD-L1) with clinicopathological factors of breast cancer. Our 5-plex mIHC/IF staining was shown to be reliable and highly sensitive for labeling three biomarkers per tissue section. Through repetitive cycles of 5-plex mIHC/IF staining, more than 12 biomarkers could be detected per single tissue section. Using open-source software CellProfiler, the measurement pipelines were successfully developed for high-throughput multiplex evaluation of intratumoral and stromal TILs. In analyses of 188 breast cancer samples from the Nashville Breast Health Study, high-grade tumors showed significantly increased intratumoral CD3+CD8+ cytotoxic T lymphocyte density (P= 0.0008, false discovery rate (FDR) adjusted P= 0.0168) and intratumoral PD-L1 expression (P= 0.0061, FDR adjusted P= 0.0602) compared with low-grade tumors. The high- and low-grade breast cancers exhibit differential immune responses which may have clinical significance. The multiplexed imaging quantification strategies established in this study are reliable, cost-efficient and applicable in regular laboratory settings for high-throughput tissue biomarker studies, especially retrospective and population-based studies using archived paraffin tissues.