Tumor-Infiltrating Lymphocytes (TILs) in Early Breast Cancer Patients: High CD3+, CD8+, and Immunoscore Are Associated with a Pathological Complete Response

Predictive model based on multiple immunofluorescence quantitative analysis for pathological complete response to neoadjuvant immunochemotherapy in lung squamous cell carcinoma

Objective

This study aims to establish a prediction model for neoadjuvant immunochemotherapy (NICT) in lung squamous cell carcinoma to guide clinical treatment.

Methods

This retrospective study included 50 patients diagnosed with lung squamous cell carcinoma who received NICT. The patients were divided into the pathological complete response (PCR) group and the non-PCR group. HE staining and multiple immunofluorescence (mIF) techniques were utilized to analyze the differences in the immune microenvironment between these groups. LASSO regression and optimal subset regression were employed to identify the most significant variables and construct a prediction model.

Results

The PCR group showed higher densities of lymphocyte nuclei and karyorrhexis based on HE staining. Furthermore, based on mIF analysis, the PCR group showed higher cell densities of CD8+, PD-L1+, and CD8+PD-L1+ in the tumor region, while showing lower cell densities of CD3+Foxp3+, Foxp3+, and CD163+. Logistic univariate analysis revealed CD8+PD-L1+, PD-L1+, CD8+, CD4+LAG-3+, lymphocyte nuclei, and karyorrhexis as significant factors influencing PCR. By using diverse screening methods, the three most relevant variables (CD8+, PD-L1+, and CD8+PD-L1+ in the tumor region) were selected to establish the prediction model. The model exhibited excellent performance in both the training set (AUC=0.965) and the validation set (AUC=0.786). In the validation set, In comparison to the conventional TPS scoring criteria, the model attained superior accuracy (0.85), specificity(0.67), and sensitivity (0.92).

Conclusion

NICT treatment might induce anti-tumor effects by enriching immune cells and reactivating exhausted T cells. CD8+, PD-L1+, and CD8+PD-L1+ cell abundances within the tumor region have been closely associated with therapeutic efficacy. Incorporating these three variables into a predictive model allows accurate forecasting of treatment outcomes and provides a reliable basis for selecting NICT treatment strategies.

Plasma Gelsolin Inhibits Natural Killer Cell Function and Confers Chemoresistance in Epithelial Ovarian Cancer

Plasma gelsolin (pGSN) overexpression in ovarian cancer (OVCA) disarms immune function, contributing to chemoresistance. The aim of this study was to investigate the immunoregulatory effects of pGSN expression on natural killer (NK) cell function in OVCA. OVCA tissues from primary surgeries underwent immunofluorescent staining of pGSN and the activated NK cell marker natural cytotoxicity triggering receptor 1 to analyze the prognostic impact of pGSN expression and activated NK cell infiltration. The immunoregulatory effects of pGSN on NK cells were assessed using apoptosis assay, cytokine secretion, immune checkpoint-receptor expression, and phosphorylation of STAT3. In OVCA tissue analyses, activated NK cell infiltration provided survival advantages to patients. However, high pGSN expression attenuated the survival benefits of activated NK cell infiltration. In the in vitro experiment, pGSN in OVCA cells induced NK cell death through cell-to-cell contact. pGSN increased T-cell immunoglobulin and mucin-domain-containing-3 expression (TIM-3) on activated NK cells. Further, it decreased interferon-γ production in activated TIM-3+ NK cells, attenuating their anti-tumor effects. Thus, increased pGSN expression suppresses the anti-tumor functions of NK cells. The study provides insights into why immunotherapy is rarely effective in patients with OVCA and suggests novel treatment strategies.

Digital image analysis and machine learning-assisted prediction of neoadjuvant chemotherapy response in triple-negative breast cancer

BACKGROUND

Pathological complete response (pCR) is associated with favorable prognosis in patients with triple-negative breast cancer (TNBC). However, only 30-40% of TNBC patients treated with neoadjuvant chemotherapy (NAC) show pCR, while the remaining 60-70% show residual disease (RD). The role of the tumor microenvironment in NAC response in patients with TNBC remains unclear. In this study, we developed a machine learning-based two-step pipeline to distinguish between various histological components in hematoxylin and eosin (H&E)-stained whole slide images (WSIs) of TNBC tissue biopsies and to identify histological features that can predict NAC response.

METHODS

H&E-stained WSIs of treatment-naïve biopsies from 85 patients (51 with pCR and 34 with RD) of the model development cohort and 79 patients (41 with pCR and 38 with RD) of the validation cohort were separated through a stratified eightfold cross-validation strategy for the first step and leave-one-out cross-validation strategy for the second step. A tile-level histology label prediction pipeline and four machine-learning classifiers were used to analyze 468,043 tiles of WSIs. The best-trained classifier used 55 texture features from each tile to produce a probability profile during testing. The predicted histology classes were used to generate a histology classification map of the spatial distributions of different tissue regions. A patient-level NAC response prediction pipeline was trained with features derived from paired histology classification maps. The top graph-based features capturing the relevant spatial information across the different histological classes were provided to the radial basis function kernel support vector machine (rbfSVM) classifier for NAC treatment response prediction.

RESULTS

The tile-level prediction pipeline achieved 86.72% accuracy for histology class classification, while the patient-level pipeline achieved 83.53% NAC response (pCR vs. RD) prediction accuracy of the model development cohort. The model was validated with an independent cohort with tile histology validation accuracy of 83.59% and NAC prediction accuracy of 81.01%. The histological class pairs with the strongest NAC response predictive ability were tumor and tumor tumor-infiltrating lymphocytes for pCR and microvessel density and polyploid giant cancer cells for RD.

CONCLUSION

Our machine learning pipeline can robustly identify clinically relevant histological classes that predict NAC response in TNBC patients and may help guide patient selection for NAC treatment.

Predicting Neoadjuvant Treatment Response in Triple-Negative Breast Cancer Using Machine Learning

BACKGROUND

Neoadjuvant chemotherapy (NAC) is the standard treatment for early-stage triple negative breast cancer (TNBC). The primary endpoint of NAC is a pathological complete response (pCR). NAC results in pCR in only 30-40% of TNBC patients. Tumor-infiltrating lymphocytes (TILs), Ki67 and phosphohistone H3 (pH3) are a few known biomarkers to predict NAC response. Currently, systematic evaluation of the combined value of these biomarkers in predicting NAC response is lacking. In this study, the predictive value of markers derived from H&E and IHC stained biopsy tissue was comprehensively evaluated using a supervised machine learning (ML)-based approach. Identifying predictive biomarkers could help guide therapeutic decisions by enabling precise stratification of TNBC patients into responders and partial or non-responders.

METHODS

Serial sections from core needle biopsies (n = 76) were stained with H&E and immunohistochemically for the Ki67 and pH3 markers, followed by whole-slide image (WSI) generation. The serial section stains in H&E stain, Ki67 and pH3 markers formed WSI triplets for each patient. The resulting WSI triplets were co-registered with H&E WSIs serving as the reference. Separate mask region-based CNN (MRCNN) models were trained with annotated H&E, Ki67 and pH3 images for detecting tumor cells, stromal and intratumoral TILs (sTILs and tTILs), Ki67+, and pH3+ cells. Top image patches with a high density of cells of interest were identified as hotspots. Best classifiers for NAC response prediction were identified by training multiple ML models and evaluating their performance by accuracy, area under curve, and confusion matrix analyses.

RESULTS

Highest prediction accuracy was achieved when hotspot regions were identified by tTIL counts and each hotspot was represented by measures of tTILs, sTILs, tumor cells, Ki67+, and pH3+ features. Regardless of the hotspot selection metric, a complementary use of multiple histological features (tTILs, sTILs) and molecular biomarkers (Ki67 and pH3) resulted in top ranked performance at the patient level.

CONCLUSIONS

Overall, our results emphasize that prediction models for NAC response should be based on biomarkers in combination rather than in isolation. Our study provides compelling evidence to support the use of ML-based models to predict NAC response in patients with TNBC.

Evaluation of immune density, PD-L1, and CXCR4 expressions in metaplastic breast carcinoma to predict potential immunotherapy benefit

Metaplastic breast carcinoma (MBC) -rare but fatal subtype of invasive breast carcinomas- provides limited benefit from conventional triple-negative breast carcinoma chemotherapy. We aimed to determine the immune density of this tumor and to evaluate of programmed death-ligand 1 (PD-L1) and chemokine receptor type 4 (CXCR4) expressions to determine whether it would benefit from immunotherapy. Clinicopathological characteristics of 85 patients diagnosed as MBC between 1997 and 2017 were retrospectively assessed. We evaluated the immunohistochemical expression of PD-L1 and CXCR4, and the extent of tumour infiltrating lymphocytes (TILs), with survival data. TILs groups were statistically significantly associated with lymph node status, histological subtype, squamous component, local recurrence and/or systemic metastasis, and disease-related deaths (p < 0.05). PD-L1 positivity in immune cells (ICs) has a statistically significant relationship with the presence of squamous component (p = 0.011) and HER2 positivity (p = 0.031). PD-L1 positivity in tumor cells (TCs) was found to be significantly more frequent in high-TILs density (p = 0.003). PD-L1 combined positive score was significantly associated with the tumors containing high-TILs density (p = 0.012) and squamous component (p = 0.035). Disease-free and disease-specific survival rates were found to be longer for the cases displaying PD-L1 positivity in ICs; and also PD-L1 positivity in ICs was found to be an independent prognostic factor. When the expression of CXCR4 was compared with clinicopathological and survival parameters, no statistically significant association was found (p > 0.05). Based on the results of this retrospective study, PD-L1 and TILs appear to be prognostic. This study provides rationale for further studies to determine whether a subset of patients with metaplastic breast cancer could derive a meaningful benefit from immune-targeting therapies.

Prognostic Impact of the Immune-Cell Infiltrate in N1-Positive Non-Small-Cell Lung Cancer

INTRODUCTION

The tumoral immune milieu plays a crucial role for the development of non-small-cell lung cancer (NSCLC) and may influence individual prognosis. We analyzed the predictive role of immune cell infiltrates after curative lung cancer surgery.

MATERIALS AND METHODS

The tumoral immune-cell infiltrate from 174 patients with pN1 NSCLC and adjuvant chemotherapy was characterized using immunofluorescence staining. The density and distribution of specific immune cells in tumor center (TU), invasive front (IF) and normal tissue (NORM) were correlated with clinical parameters and survival data.

RESULTS

Tumor specific survival (TSS) of all patients was 69.9% at 5 years. The density of tumor infiltrating lymphocytes (TIL) was higher in TU and IF than in NORM. High TIL density in TU (low vs. high: 62.0% vs. 86.7%; p = .011) and the presence of cytotoxic T-Lymphocytes (CTLs) in TU and IF were associated with improved TSS (positive vs. negative: 90.6% vs. 64.7% p = .024). High TIL-density correlated with programmed death-ligand 1 expression levels ≥50% (p < .001). Multivariate analysis identified accumulation of TIL (p = .016) and low Treg density (p = .003) in TU as negative prognostic predictors in squamous cell carcinoma (p = .025), whereas M1-like tumor- associated macrophages (p = .019) and high programmed death-ligand 1 status (p = .038) were associated with better survival in adenocarcinoma.

CONCLUSION

The assessment of specific intratumoral immune cells may serve as a prognostic predictor in pN1 NSCLC. However differences were observed related to adenocarcinoma or squamous cell carcinoma histology. Prospective assessment of the immune-cell infiltrate and further clarification of its prognostic relevance could assist patient selection for upcoming perioperative immunotherapies.

Could immunoscore improve the prognostic and therapeutic management in patients with solid tumors?

The Immunoscore (ISc) is an emerging immune-based scoring system that has shown potential in improving the prognostic and therapeutic management of patients with solid tumors. The ISc evaluates the immune infiltrate within the tumor microenvironment (TME) and has demonstrated superior predictive ability compared to traditional histopathological parameters. It has been particularly promising in colorectal, lung, breast, and melanoma cancers. This review summarizes the clinical evidence supporting the prognostic value of the ISc and explores its potential in guiding therapeutic decisions, such as the selection of adjuvant therapies and recognizing patients likely to profit from immune checkpoint inhibitors (ICIs). The challenges and future directions of ISc implementation are also discussed, including standardization and integration into routine clinical practice.

Diagnostics and Therapeutics in Early Stage Breast Cancer Receiving Neoadjuvant Systemic Therapy

Breast cancer (BC) remains a major challenge for oncology today, impacting the lives of countless individuals worldwide [...].

Digital image analysis and machine learning-assisted prediction of neoadjuvant chemotherapy response in triple-negative breast cancer

Background

Pathological complete response (pCR) is associated with favorable prognosis in patients with triple-negative breast cancer (TNBC). However, only 30-40% of TNBC patients treated with neoadjuvant chemotherapy (NAC) show pCR, while the remaining 60-70% show residual disease (RD). The role of the tumor microenvironment (TME) in NAC response in patients with TNBC remains unclear. In this study, we developed a machine learning-based two-step pipeline to distinguish between various histological components in hematoxylin and eosin (H&E)-stained whole slide images (WSIs) of TNBC tissue biopsies and to identify histological features that can predict NAC response.

Methods

H&E-stained WSIs of treatment-naïve biopsies from 85 patients (51 with pCR and 34 with RD) were separated through a stratified 8-fold cross validation strategy for the first step and leave one out cross validation strategy for the second step. A tile-level histology label prediction pipeline and four machine learning classifiers were used to analyze 468,043 tiles of WSIs. The best-trained classifier used 55 texture features from each tile to produce a probability profile during testing. The predicted histology classes were used to generate a histology classification map of the spatial distributions of different tissue regions. A patient-level NAC response prediction pipeline was trained with features derived from paired histology classification maps. The top graph-based features capturing the relevant spatial information across the different histological classes were provided to the radial basis function kernel support vector machine (rbfSVM) classifier for NAC treatment response prediction.

Results

The tile-level prediction pipeline achieved 86.72% accuracy for histology class classification, while the patient-level pipeline achieved 83.53% NAC response (pCR vs. RD) prediction accuracy. The histological class pairs with the strongest NAC response predictive ability were tumor and tumor tumor-infiltrating lymphocytes for pCR and microvessel density and polyploid giant cancer cells for RD.

Conclusion

Our machine learning pipeline can robustly identify clinically relevant histological classes that predict NAC response in TNBC patients and may help guide patient selection for NAC treatment.

Immune Biomarkers in Triple-Negative Breast Cancer: Improving the Predictivity of Current Testing Methods

Triple-negative breast cancer (TNBC) poses a significant challenge in terms of prognosis and disease recurrence. The limited treatment options and the development of resistance to chemotherapy make it particularly difficult to manage these patients. However, recent research has been shifting its focus towards biomarker-based approaches for TNBC, with a particular emphasis on the tumor immune landscape. Immune biomarkers in TNBC are now a subject of great interest due to the presence of tumor-infiltrating lymphocytes (TILs) in these tumors. This characteristic often coincides with the presence of PD-L1 expression on both neoplastic cells and immune cells within the tumor microenvironment. Furthermore, a subset of TNBC harbor mismatch repair deficient (dMMR) TNBC, which is frequently accompanied by microsatellite instability (MSI). All of these immune biomarkers hold actionable potential for guiding patient selection in immunotherapy. To fully capitalize on these opportunities, the identification of additional or complementary biomarkers and the implementation of highly customized testing strategies are of paramount importance in TNBC. In this regard, this article aims to provide an overview of the current state of the art in immune-related biomarkers for TNBC. Specifically, it focuses on the various testing methodologies available and sheds light on the immediate future perspectives for patient selection. By delving into the advancements made in understanding the immune landscape of TNBC, this study aims to contribute to the growing body of knowledge in the field. The ultimate goal is to pave the way for the development of more personalized testing strategies, ultimately improving outcomes for TNBC patients.

Predicting neoadjuvant treatment response in triple-negative breast cancer using machine learning

Background

Neoadjuvant chemotherapy (NAC) is the standard treatment for early-stage triple negative breast cancer (TNBC). The primary endpoint of NAC is a pathological complete response (pCR). NAC results in pCR in only 30%â€"40% of TNBC patients. Tumor-infiltrating lymphocytes (TILs), Ki67 and phosphohistone H3 (pH3) are a few known biomarkers to predict NAC response. Currently, systematic evaluation of the combined value of these biomarkers in predicting NAC response is lacking. In this study, the predictive value of markers derived from H&E and IHC stained biopsy tissue was comprehensively evaluated using a supervised machine learning (ML)-based approach. Identifying predictive biomarkers could help guide therapeutic decisions by enabling precise stratification of TNBC patients into responders and partial or non-responders.

Methods

Serial sections from core needle biopsies (n=76) were stained with H&E, and immunohistochemically for the Ki67 and pH3 markers, followed by whole slide image (WSI) generation. The resulting WSI triplets were co-registered with H&E WSIs serving as the reference. Separate mask region-based CNN (MRCNN) models were trained with annotated H&E, Ki67 and pH3 images for detecting tumor cells, stromal and intratumoral TILs (sTILs and tTILs), Ki67 + , and pH3 + cells. Top image patches with a high density of cells of interest were identified as hotspots. Best classifiers for NAC response prediction were identified by training multiple ML models, and evaluating their performance by accuracy, area under curve, and confusion matrix analyses.

Results

Highest prediction accuracy was achieved when hotspot regions were identified by tTIL counts and each hotspot was represented by measures of tTILs, sTILs, tumor cells, Ki67 + , and pH3 + features. Regardless of the hotspot selection metric, a complementary use of multiple histological features (tTILs, sTILs) and molecular biomarkers (Ki67 and pH3) resulted in top ranked performance at the patient level.

Conclusions

Overall, our results emphasize that prediction models for NAC response should be based on biomarkers in combination rather than in isolation. Our study provides compelling evidence to support the use of ML-based models to predict NAC response in patients with TNBC.

Opportunities and challenges to engineer 3D models of tumor-adaptive immune interactions

Augmenting adaptive immunity is a critical goal for developing next-generation cancer therapies. T and B cells infiltrating the tumor dramatically influence cancer progression through complex interactions with the local microenvironment. Cancer cells evade and limit these immune responses by hijacking normal immunologic pathways. Current experimental models using conventional primary cells, cell lines, or animals have limitations for studying cancer-immune interactions directly relevant to human biology and clinical translation. Therefore, engineering methods to emulate such interplay at local and systemic levels are crucial to expedite the development of better therapies and diagnostic tools. In this review, we discuss the challenges, recent advances, and future directions toward engineering the tumor-immune microenvironment (TME), including key elements of adaptive immunity. We first offer an overview of the recent research that has advanced our understanding of the role of the adaptive immune system in the tumor microenvironment. Next, we discuss recent developments in 3D in-vitro models and engineering approaches that have been used to study the interaction of cancer and stromal cells with B and T lymphocytes. We summarize recent advancement in 3D bioengineering and discuss the need for 3D tumor models that better incorporate elements of the complex interplay of adaptive immunity and the tumor microenvironment. Finally, we provide a perspective on current challenges and future directions for modeling cancer-immune interactions aimed at identifying new biological targets for diagnostics and therapeutics.

Exosomal Plasma Gelsolin Is an Immunosuppressive Mediator in the Ovarian Tumor Microenvironment and a Determinant of Chemoresistance

Ovarian Cancer (OVCA) is the most fatal gynecologic cancer and has a 5-year survival rate less than 45%. This is mainly due to late diagnosis and drug resistance. Overexpression of plasma gelsolin (pGSN) is key contributing factor to OVCA chemoresistance and immunosuppression. Gelsolin (GSN) is a multifunctional protein that regulates the activity of actin filaments by cleavage, capping, and nucleation. Generally, it plays an important role in cytoskeletal remodeling. GSN has three isoforms: cytosolic GSN, plasma GSN (pGSN), and gelsolin-3. Exosomes containing pGSN are released and contribute to the progression of OVCA. This review describes how pGSN overexpression inhibits chemotherapy-induced apoptosis and triggers positive feedback loops of pGSN expression. It also describes the mechanisms by which exosomal pGSN promotes apoptosis and dysfunction in tumor-killing immune cells. A discussion on the potential of pGSN as a prognostic, diagnostic, and therapeutic marker is also presented herein.