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Xiao M, Tu L, Zhou T, He Y, Li X, Zuo Q. Predictive model based on multiple immunofluorescence quantitative analysis for pathological complete response to neoadjuvant immunochemotherapy in lung squamous cell carcinoma. Front Oncol 2024; 14:1396439. [PMID: 38887237 PMCID: PMC11180808 DOI: 10.3389/fonc.2024.1396439] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/05/2024] [Accepted: 05/20/2024] [Indexed: 06/20/2024] Open
Abstract
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.
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Affiliation(s)
| | | | | | | | - Xiaohui Li
- The Geriatric Respiratory Department, Sichuan Provincial People’s Hospital, University of Electronic Science and Technology of China, Chengdu, China
| | - Qiunan Zuo
- The Geriatric Respiratory Department, Sichuan Provincial People’s Hospital, University of Electronic Science and Technology of China, Chengdu, China
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Onuma T, Asare-Werehene M, Fujita Y, Yoshida Y, Tsang BK. Plasma Gelsolin Inhibits Natural Killer Cell Function and Confers Chemoresistance in Epithelial Ovarian Cancer. Cells 2024; 13:905. [PMID: 38891037 PMCID: PMC11171658 DOI: 10.3390/cells13110905] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2024] [Revised: 05/18/2024] [Accepted: 05/22/2024] [Indexed: 06/20/2024] Open
Abstract
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.
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Affiliation(s)
- Toshimichi Onuma
- Department of Obstetrics and Gynecology, Faculty of Medicine, University of Ottawa, Ottawa, ON K1H 8L1, Canada; (T.O.); (M.A.-W.)
- Interdisciplinary School of Health Sciences, Faculty of Health Sciences, University of Ottawa, Ottawa, ON K1H 8L1, Canada
- Department of Cellular and Molecular Medicine & The Centre for Infection, Immunity and Inflammation (CI3), Faculty of Medicine, University of Ottawa, Ottawa, ON K1H 8M5, Canada
- Chronic Disease Program, Ottawa Hospital Research Institute, Ottawa, ON K1H 8L6, Canada
- Department of Obstetrics and Gynecology, University of Fukui, Fukui 910-1193, Japan;
| | - Meshach Asare-Werehene
- Department of Obstetrics and Gynecology, Faculty of Medicine, University of Ottawa, Ottawa, ON K1H 8L1, Canada; (T.O.); (M.A.-W.)
- Interdisciplinary School of Health Sciences, Faculty of Health Sciences, University of Ottawa, Ottawa, ON K1H 8L1, Canada
- Department of Cellular and Molecular Medicine & The Centre for Infection, Immunity and Inflammation (CI3), Faculty of Medicine, University of Ottawa, Ottawa, ON K1H 8M5, Canada
- Chronic Disease Program, Ottawa Hospital Research Institute, Ottawa, ON K1H 8L6, Canada
- Department of Laboratory Medicine and Pathobiology, University of Toronto, Toronto, ON M5S 1A1, Canada
| | - Yuko Fujita
- Department of Obstetrics and Gynecology, University of Fukui, Fukui 910-1193, Japan;
| | - Yoshio Yoshida
- Department of Obstetrics and Gynecology, University of Fukui, Fukui 910-1193, Japan;
| | - Benjamin K. Tsang
- Department of Obstetrics and Gynecology, Faculty of Medicine, University of Ottawa, Ottawa, ON K1H 8L1, Canada; (T.O.); (M.A.-W.)
- Interdisciplinary School of Health Sciences, Faculty of Health Sciences, University of Ottawa, Ottawa, ON K1H 8L1, Canada
- Department of Cellular and Molecular Medicine & The Centre for Infection, Immunity and Inflammation (CI3), Faculty of Medicine, University of Ottawa, Ottawa, ON K1H 8M5, Canada
- Chronic Disease Program, Ottawa Hospital Research Institute, Ottawa, ON K1H 8L6, Canada
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Rapoport BL, Anderson R. Strategies to optimize the promise of checkpoint-targeted anti-cancer therapy. Immunotherapy 2024; 16:565-568. [PMID: 38717385 PMCID: PMC11290365 DOI: 10.1080/1750743x.2024.2343271] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2024] [Accepted: 04/11/2024] [Indexed: 07/26/2024] Open
Affiliation(s)
- Bernardo L Rapoport
- Department of Immunology, Faculty of Health Sciences, University of Pretoria, Pretoria, 0001, South Africa
- Medical Oncology Centre of Rosebank, Johannesburg, 2196, South Africa
| | - Ronald Anderson
- Department of Immunology, Faculty of Health Sciences, University of Pretoria, Pretoria, 0001, South Africa
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Fisher TB, Saini G, Rekha TS, Krishnamurthy J, Bhattarai S, Callagy G, Webber M, Janssen EAM, Kong J, Aneja R. Digital image analysis and machine learning-assisted prediction of neoadjuvant chemotherapy response in triple-negative breast cancer. Breast Cancer Res 2024; 26:12. [PMID: 38238771 PMCID: PMC10797728 DOI: 10.1186/s13058-023-01752-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/07/2023] [Accepted: 12/11/2023] [Indexed: 01/22/2024] Open
Abstract
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.
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Affiliation(s)
- Timothy B Fisher
- Department of Biology, Georgia State University, Atlanta, GA, 30302, USA
| | - Geetanjali Saini
- School of Health Professions, University of Alabama at Birmingham, Birmingham, AL, 35294, USA
| | - T S Rekha
- JSSAHER (JSS Academy of Higher Education and Research) Medical College, Mysuru, Karnataka, India
| | - Jayashree Krishnamurthy
- JSSAHER (JSS Academy of Higher Education and Research) Medical College, Mysuru, Karnataka, India
| | - Shristi Bhattarai
- School of Health Professions, University of Alabama at Birmingham, Birmingham, AL, 35294, USA
| | - Grace Callagy
- Discipline of Pathology, University of Galway, Galway, Ireland
| | - Mark Webber
- Discipline of Pathology, University of Galway, Galway, Ireland
| | - Emiel A M Janssen
- Department of Pathology, Stavanger University Hospital, Stavanger, Norway
- Department of Chemistry, Bioscience and Environmental Engineering, University of Stavanger, Stavanger, Norway
| | - Jun Kong
- Department of Mathematics and Statistics, Georgia State University, Atlanta, GA, 30303, USA.
| | - Ritu Aneja
- Department of Biology, Georgia State University, Atlanta, GA, 30302, USA.
- School of Health Professions, University of Alabama at Birmingham, Birmingham, AL, 35294, USA.
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Bhattarai S, Saini G, Li H, Seth G, Fisher TB, Janssen EAM, Kiraz U, Kong J, Aneja R. Predicting Neoadjuvant Treatment Response in Triple-Negative Breast Cancer Using Machine Learning. Diagnostics (Basel) 2023; 14:74. [PMID: 38201383 PMCID: PMC10871101 DOI: 10.3390/diagnostics14010074] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2023] [Revised: 12/19/2023] [Accepted: 12/21/2023] [Indexed: 01/12/2024] Open
Abstract
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.
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Affiliation(s)
- Shristi Bhattarai
- Department of Clinical and Diagnostic Sciences, School of Health Professions, University of Alabama at Birmingham, Birmingham, AL 35294, USA; (S.B.); (G.S.); (G.S.)
| | - Geetanjali Saini
- Department of Clinical and Diagnostic Sciences, School of Health Professions, University of Alabama at Birmingham, Birmingham, AL 35294, USA; (S.B.); (G.S.); (G.S.)
| | - Hongxiao Li
- Department of Mathematics and Statistics, Georgia State University, Atlanta, GA 30302, USA;
| | - Gaurav Seth
- Department of Clinical and Diagnostic Sciences, School of Health Professions, University of Alabama at Birmingham, Birmingham, AL 35294, USA; (S.B.); (G.S.); (G.S.)
| | - Timothy B. Fisher
- Department of Biology, Georgia State University, Atlanta, GA 30302, USA;
| | - Emiel A. M. Janssen
- Department of Pathology, Stavanger University Hospital, 4011 Stavanger, Norway; (E.A.M.J.); (U.K.)
- Department of Chemistry, Bioscience and Environmental Engineering, Stavanger University, 4021 Stavanger, Norway
| | - Umay Kiraz
- Department of Pathology, Stavanger University Hospital, 4011 Stavanger, Norway; (E.A.M.J.); (U.K.)
- Department of Chemistry, Bioscience and Environmental Engineering, Stavanger University, 4021 Stavanger, Norway
| | - Jun Kong
- Department of Mathematics and Statistics, Georgia State University, Atlanta, GA 30302, USA;
| | - Ritu Aneja
- Department of Clinical and Diagnostic Sciences, School of Health Professions, University of Alabama at Birmingham, Birmingham, AL 35294, USA; (S.B.); (G.S.); (G.S.)
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Bagbudar S, Karanlık H, Cabioglu N, Bayram A, Tükenmez M, Aydıner A, Yavuz E, Onder S. Evaluation of immune density, PD-L1, and CXCR4 expressions in metaplastic breast carcinoma to predict potential immunotherapy benefit. Med Oncol 2023; 41:18. [PMID: 38102446 DOI: 10.1007/s12032-023-02243-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2023] [Accepted: 11/07/2023] [Indexed: 12/17/2023]
Abstract
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.
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Affiliation(s)
- Sidar Bagbudar
- Department of Pathology, Istanbul Faculty of Medicine, Istanbul University, Millet Caddesi, Çapa, Fatih, 34390, Istanbul, Turkey.
| | - Hasan Karanlık
- Department of Surgical Oncology Unit, Institute of Oncology, Istanbul University, Istanbul, Turkey
| | - Neslihan Cabioglu
- Department of General Surgery, Istanbul Faculty of Medicine, Istanbul University, Istanbul, Turkey
| | - Aysel Bayram
- Department of Pathology, Istanbul Faculty of Medicine, Istanbul University, Millet Caddesi, Çapa, Fatih, 34390, Istanbul, Turkey
| | - Mustafa Tükenmez
- Department of General Surgery, Istanbul Faculty of Medicine, Istanbul University, Istanbul, Turkey
| | - Adnan Aydıner
- Department of Medical Oncology, Institute of Oncology, Istanbul University, Istanbul, Turkey
| | - Ekrem Yavuz
- Department of Pathology, Istanbul Faculty of Medicine, Istanbul University, Millet Caddesi, Çapa, Fatih, 34390, Istanbul, Turkey
| | - Semen Onder
- Department of Pathology, Istanbul Faculty of Medicine, Istanbul University, Millet Caddesi, Çapa, Fatih, 34390, Istanbul, Turkey
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Eichhorn F, Weigert A, Nandigama R, Klotz LV, Wilhelm J, Kriegsmann M, Allgäuer M, Muley T, Christopoulos P, Savai R, Eichhorn ME, Winter H. Prognostic Impact of the Immune-Cell Infiltrate in N1-Positive Non-Small-Cell Lung Cancer. Clin Lung Cancer 2023; 24:706-716.e1. [PMID: 37460340 DOI: 10.1016/j.cllc.2023.06.013] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2023] [Revised: 05/29/2023] [Accepted: 06/24/2023] [Indexed: 11/24/2023]
Abstract
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.
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Affiliation(s)
- Florian Eichhorn
- Department of Thoracic Surgery, Thoraxklinik, Heidelberg University, Heidelberg, Germany; Translational Lung Research Center, German Center for Lung Research (DZL), Heidelberg, Germany.
| | - Andreas Weigert
- Institute of Biochemistry I, Faculty of Medicine, Goethe-University Frankfurt, Frankfurt, Germany; Frankfurt Cancer Institute (FCI), Goethe University, and German Cancer Consortium (DKTK), Partner Site Frankfurt, Frankfurt, Germany
| | - Rajender Nandigama
- Institute for Lung Health (ILH), Justus Liebig University, Giessen, Germany; Max Planck Institute for Heart and Lung Research, Member of the German Center for Lung Research (DZL), Member of the Cardio-Pulmonary Institute (CPI), Bad Nauheim, Germany
| | - Laura V Klotz
- Department of Thoracic Surgery, Thoraxklinik, Heidelberg University, Heidelberg, Germany; Translational Lung Research Center, German Center for Lung Research (DZL), Heidelberg, Germany
| | - Jochen Wilhelm
- Institute for Lung Health (ILH), Justus Liebig University, Giessen, Germany; Internal Medicine, University of Giessen and Marburg Lung Center, Member of the German Center for Lung Research, Giessen, Germany
| | - Mark Kriegsmann
- Translational Lung Research Center, German Center for Lung Research (DZL), Heidelberg, Germany; Institute of Pathology Wiesbaden, Wiesbaden, Germany
| | - Michael Allgäuer
- Institute of Pathology, Heidelberg University Hospital, Heidelberg, Germany
| | - Thomas Muley
- Translational Lung Research Center, German Center for Lung Research (DZL), Heidelberg, Germany; Section Translational Research (STF), Thoraxklinik, Heidelberg University, Heidelberg, Germany
| | - Petros Christopoulos
- Translational Lung Research Center, German Center for Lung Research (DZL), Heidelberg, Germany; Department of Thoracic Oncology, Thoraxklinik, Heidelberg University Hospital, Heidelberg, Germany
| | - Rajkumar Savai
- Frankfurt Cancer Institute (FCI), Goethe University, and German Cancer Consortium (DKTK), Partner Site Frankfurt, Frankfurt, Germany; Institute for Lung Health (ILH), Justus Liebig University, Giessen, Germany; Max Planck Institute for Heart and Lung Research, Member of the German Center for Lung Research (DZL), Member of the Cardio-Pulmonary Institute (CPI), Bad Nauheim, Germany
| | - Martin E Eichhorn
- Department of Thoracic Surgery, Thoraxklinik, Heidelberg University, Heidelberg, Germany; Translational Lung Research Center, German Center for Lung Research (DZL), Heidelberg, Germany
| | - Hauke Winter
- Department of Thoracic Surgery, Thoraxklinik, Heidelberg University, Heidelberg, Germany; Translational Lung Research Center, German Center for Lung Research (DZL), Heidelberg, Germany
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Xu JL, Yang MX, Lan HR, Jin KT. Could immunoscore improve the prognostic and therapeutic management in patients with solid tumors? Int Immunopharmacol 2023; 124:110981. [PMID: 37769534 DOI: 10.1016/j.intimp.2023.110981] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/12/2023] [Revised: 09/20/2023] [Accepted: 09/20/2023] [Indexed: 10/03/2023]
Abstract
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.
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Affiliation(s)
- Jing-Lun Xu
- Department of Dermatology, Jinhua Fifth Hospital, Jinhua, Zhejiang 321000, China
| | - Meng-Xiang Yang
- Department of Colorectal Surgery, Affiliated Jinhua Hospital, Zhejiang University School of Medicine, Jinhua, Zhejiang 321000, China
| | - Huan-Rong Lan
- Department of Surgical Oncology, Hangzhou Cancer Hospital, Hangzhou, Zhejiang 310002, China.
| | - Ke-Tao Jin
- Department of Colorectal Surgery, Affiliated Jinhua Hospital, Zhejiang University School of Medicine, Jinhua, Zhejiang 321000, China.
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Belli P, Palma S, Costantini M. Diagnostics and Therapeutics in Early Stage Breast Cancer Receiving Neoadjuvant Systemic Therapy. Cancers (Basel) 2023; 15:4874. [PMID: 37835568 PMCID: PMC10571562 DOI: 10.3390/cancers15194874] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2023] [Accepted: 09/26/2023] [Indexed: 10/15/2023] Open
Abstract
Breast cancer (BC) remains a major challenge for oncology today, impacting the lives of countless individuals worldwide [...].
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Affiliation(s)
- Paolo Belli
- Department of Bioimaging, Radiation Oncology and Hematology, UOC of Radiologia, Fondazione Policlinico Universitario A. Gemelli IRCSS, Largo A. Gemelli 8, 00168 Rome, Italy; (S.P.); (M.C.)
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10
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Fisher TB, Saini G, Ts R, Krishnamurthy J, Bhattarai S, Callagy G, Webber M, Janssen EAM, Kong J, Aneja R. Digital image analysis and machine learning-assisted prediction of neoadjuvant chemotherapy response in triple-negative breast cancer. RESEARCH SQUARE 2023:rs.3.rs-3243195. [PMID: 37645881 PMCID: PMC10462230 DOI: 10.21203/rs.3.rs-3243195/v1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/31/2023]
Abstract
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.
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Affiliation(s)
| | | | - Rekha Ts
- JSSAHER (JSS Academy of Higher Education and Research) Medical College
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Porta FM, Sajjadi E, Venetis K, Frascarelli C, Cursano G, Guerini-Rocco E, Fusco N, Ivanova M. Immune Biomarkers in Triple-Negative Breast Cancer: Improving the Predictivity of Current Testing Methods. J Pers Med 2023; 13:1176. [PMID: 37511789 PMCID: PMC10381494 DOI: 10.3390/jpm13071176] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2023] [Revised: 07/17/2023] [Accepted: 07/20/2023] [Indexed: 07/30/2023] Open
Abstract
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.
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Affiliation(s)
- Francesca Maria Porta
- Division of Pathology, IEO, European Institute of Oncology IRCCS, University of Milan, 20122 Milan, Italy
| | - Elham Sajjadi
- Division of Pathology, IEO, European Institute of Oncology IRCCS, University of Milan, 20122 Milan, Italy
- Department of Oncology and Hemato-Oncology, University of Milan, 20122 Milan, Italy
| | - Konstantinos Venetis
- Division of Pathology, IEO, European Institute of Oncology IRCCS, University of Milan, 20122 Milan, Italy
| | - Chiara Frascarelli
- Division of Pathology, IEO, European Institute of Oncology IRCCS, University of Milan, 20122 Milan, Italy
- Department of Oncology and Hemato-Oncology, University of Milan, 20122 Milan, Italy
| | - Giulia Cursano
- Division of Pathology, IEO, European Institute of Oncology IRCCS, University of Milan, 20122 Milan, Italy
| | - Elena Guerini-Rocco
- Division of Pathology, IEO, European Institute of Oncology IRCCS, University of Milan, 20122 Milan, Italy
- Department of Oncology and Hemato-Oncology, University of Milan, 20122 Milan, Italy
| | - Nicola Fusco
- Division of Pathology, IEO, European Institute of Oncology IRCCS, University of Milan, 20122 Milan, Italy
- Department of Oncology and Hemato-Oncology, University of Milan, 20122 Milan, Italy
| | - Mariia Ivanova
- Division of Pathology, IEO, European Institute of Oncology IRCCS, University of Milan, 20122 Milan, Italy
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Bhattarai S, Saini G, Li H, Duanmu H, Seth G, Fisher TB, Janssen EAM, Kiraz U, Kong J, Aneja R. Predicting neoadjuvant treatment response in triple-negative breast cancer using machine learning. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.04.17.536459. [PMID: 37131688 PMCID: PMC10153161 DOI: 10.1101/2023.04.17.536459] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/04/2023]
Abstract
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.
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Visalakshan RM, Lowrey MK, Sousa MGC, Helms HR, Samiea A, Schutt CE, Moreau JM, Bertassoni LE. Opportunities and challenges to engineer 3D models of tumor-adaptive immune interactions. Front Immunol 2023; 14:1162905. [PMID: 37081897 PMCID: PMC10110941 DOI: 10.3389/fimmu.2023.1162905] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2023] [Accepted: 03/14/2023] [Indexed: 04/09/2023] Open
Abstract
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.
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Onuma T, Asare-Werehene M, Yoshida Y, Tsang BK. Exosomal Plasma Gelsolin Is an Immunosuppressive Mediator in the Ovarian Tumor Microenvironment and a Determinant of Chemoresistance. Cells 2022; 11:cells11203305. [PMID: 36291171 PMCID: PMC9600545 DOI: 10.3390/cells11203305] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/26/2022] [Revised: 10/15/2022] [Accepted: 10/18/2022] [Indexed: 12/03/2022] Open
Abstract
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.
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Affiliation(s)
- Toshimichi Onuma
- Department of Obstetrics & Gynecology, Faculty of Medicine & Interdisciplinary School of Health Sciences, Faculty of Health Sciences, University of Ottawa, Ottawa, ON K1H 8L1, Canada
- Department of Cellular and Molecular Medicine & the Centre for Infection, Immunity and Inflammation (CI3), Faculty of Medicine, University of Ottawa, Ottawa, ON K1H 8M5, Canada
- Chronic Disease Program, Ottawa Hospital Research Institute, Ottawa, ON K1H 8L6, Canada
- Department of Obstetrics and Gynecology, University of Fukui, Fukui 910-8507, Japan
| | - Meshach Asare-Werehene
- Department of Obstetrics & Gynecology, Faculty of Medicine & Interdisciplinary School of Health Sciences, Faculty of Health Sciences, University of Ottawa, Ottawa, ON K1H 8L1, Canada
- Department of Cellular and Molecular Medicine & the Centre for Infection, Immunity and Inflammation (CI3), Faculty of Medicine, University of Ottawa, Ottawa, ON K1H 8M5, Canada
- Chronic Disease Program, Ottawa Hospital Research Institute, Ottawa, ON K1H 8L6, Canada
| | - Yoshio Yoshida
- Department of Obstetrics and Gynecology, University of Fukui, Fukui 910-8507, Japan
- Correspondence: (Y.Y.); (B.K.T.)
| | - Benjamin K. Tsang
- Department of Obstetrics & Gynecology, Faculty of Medicine & Interdisciplinary School of Health Sciences, Faculty of Health Sciences, University of Ottawa, Ottawa, ON K1H 8L1, Canada
- Department of Cellular and Molecular Medicine & the Centre for Infection, Immunity and Inflammation (CI3), Faculty of Medicine, University of Ottawa, Ottawa, ON K1H 8M5, Canada
- Chronic Disease Program, Ottawa Hospital Research Institute, Ottawa, ON K1H 8L6, Canada
- Correspondence: (Y.Y.); (B.K.T.)
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