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Leon-Ferre RA, Whitaker KR, Suman VJ, Hoskin T, Giridhar KV, Moore RM, Al-Jarrad A, McLaughlin SA, Northfelt DW, Hunt KN, Conners AL, Moyer A, Carter JM, Kalari K, Weinshilboum R, Wang L, Ingle JN, Knutson KL, Ansell SM, Boughey JC, Goetz MP, Villasboas JC. Pre-treatment peripheral blood immunophenotyping and response to neoadjuvant chemotherapy in operable breast cancer. Breast Cancer Res 2024; 26:97. [PMID: 38858721 PMCID: PMC11165781 DOI: 10.1186/s13058-024-01848-z] [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: 02/22/2024] [Accepted: 05/22/2024] [Indexed: 06/12/2024] Open
Abstract
BACKGROUND Tumor immune infiltration and peripheral blood immune signatures have prognostic and predictive value in breast cancer. Whether distinct peripheral blood immune phenotypes are associated with response to neoadjuvant chemotherapy (NAC) remains understudied. METHODS Peripheral blood mononuclear cells from 126 breast cancer patients enrolled in a prospective clinical trial (NCT02022202) were analyzed using Cytometry by time-of-flight with a panel of 29 immune cell surface protein markers. Kruskal-Wallis tests or Wilcoxon rank-sum tests were used to evaluate differences in immune cell subpopulations according to breast cancer subtype and response to NAC. RESULTS There were 122 evaluable samples: 47 (38.5%) from patients with hormone receptor-positive, 39 (32%) triple-negative (TNBC), and 36 (29.5%) HER2-positive breast cancer. The relative abundances of pre-treatment peripheral blood T, B, myeloid, NK, and unclassified cells did not differ according to breast cancer subtype. In TNBC, higher pre-treatment myeloid cells were associated with lower pathologic complete response (pCR) rates. In hormone receptor-positive breast cancer, lower pre-treatment CD8 + naïve and CD4 + effector memory cells re-expressing CD45RA (TEMRA) T cells were associated with more extensive residual disease after NAC. In HER2 + breast cancer, the peripheral blood immune phenotype did not differ according to NAC response. CONCLUSIONS Pre-treatment peripheral blood immune cell populations (myeloid in TNBC; CD8 + naïve T cells and CD4 + TEMRA cells in luminal breast cancer) were associated with response to NAC in early-stage TNBC and hormone receptor-positive breast cancers, but not in HER2 + breast cancer. TRIAL REGISTRATION NCT02022202 . Registered 20 December 2013.
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Affiliation(s)
| | | | - Vera J Suman
- Department of Quantitative Health Sciences, Mayo Clinic, Rochester, MN, USA
| | - Tanya Hoskin
- Department of Quantitative Health Sciences, Mayo Clinic, Rochester, MN, USA
| | | | - Raymond M Moore
- Department of Quantitative Health Sciences, Mayo Clinic, Rochester, MN, USA
| | | | | | | | - Katie N Hunt
- Department of Radiology, Mayo Clinic, Rochester, MN, USA
| | | | - Ann Moyer
- Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, MN, USA
| | - Jodi M Carter
- Department of Laboratory Medicine and Pathology, University of Alberta, Edmonton, Alberta, Canada
| | - Krishna Kalari
- Department of Quantitative Health Sciences, Mayo Clinic, Rochester, MN, USA
| | | | - Liewei Wang
- Department of Quantitative Health Sciences, Mayo Clinic, Rochester, MN, USA
| | - James N Ingle
- Department of Oncology, Mayo Clinic, Rochester, MN, USA
| | - Keith L Knutson
- Department of Immunology, Mayo Clinic, Jacksonville, FL, USA
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Dieleman S, Kooreman LFS, van Kuijk SMJ, Zur Hausen A, Smidt ML, Grabsch HI. Germinal centres within tumour positive sentinel lymph nodes are positively associated with tumour infiltrating lymphocytes and tertiary lymphoid structures in breast cancer. Pathol Res Pract 2024; 254:155171. [PMID: 38306861 DOI: 10.1016/j.prp.2024.155171] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/01/2023] [Revised: 01/22/2024] [Accepted: 01/24/2024] [Indexed: 02/04/2024]
Abstract
BACKGROUND Stromal tumour infiltrating lymphocytes (sTILs) and presence of tertiary lymphoid structures have been proposed as indicators of tumour-related immune response in breast cancer. An increased number of germinal centres (GCs) in lymph nodes is considered a sign of humoral immune reactivity. AIMS It is unclear whether a relationship exists between number and size of GCs within tumour positive sentinel lymph nodes (SLNpos), sTILs and tertiary lymphoid structures within matched primary breast cancer and breast cancer subtype. METHODS Axillary SLNpos from 175 patients with breast cancer were manually contoured in digitized haematoxylin and eosin stained sections. Total SLN area, GC number and GC area were measured in SLNpos with the largest metastatic area. To correct for SLN size, GC number and GC area were divided by SLN area. sTILs and presence of tertiary lymphoid structures were assessed in the primary breast cancer. RESULTS A higher GC number and larger GC area were found in patients with high sTILs (≥2%) (both P < 0.001) and in patients with presence of tertiary lymphoid structures (PGC number = 0.034 and PGC area = 0.016). Triple negative and HER2-positive (N = 45) breast cancer subtypes had a higher GC number and higher sTILs compared to hormone receptor positive, HER2-negative breast cancer (N = 130) (PGC number < 0.001 and PsTILs= 0.001). CONCLUSION This study suggests GCs measured within SLNpos might be useful indicators of the humoral anti-tumour immune response in breast cancer. Future studies are needed investigating underlying biological mechanisms and prognostic value of GCs in SLNs.
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Affiliation(s)
- Sabine Dieleman
- Department of Pathology, GROW School for Oncology and Reproduction, Maastricht University Medical Centre+, Maastricht, the Netherlands; Department of Surgery, GROW School for Oncology and Reproduction, Maastricht University Medical Centre+, Maastricht, the Netherlands
| | - Loes F S Kooreman
- Department of Pathology, GROW School for Oncology and Reproduction, Maastricht University Medical Centre+, Maastricht, the Netherlands
| | - Sander M J van Kuijk
- Department of Clinical Epidemiology and Medical Technology Assessment, Maastricht University Medical Centre+, Maastricht, Netherlands
| | - Axel Zur Hausen
- Department of Pathology, GROW School for Oncology and Reproduction, Maastricht University Medical Centre+, Maastricht, the Netherlands
| | - Marjolein L Smidt
- Department of Surgery, GROW School for Oncology and Reproduction, Maastricht University Medical Centre+, Maastricht, the Netherlands
| | - Heike I Grabsch
- Department of Pathology, GROW School for Oncology and Reproduction, Maastricht University Medical Centre+, Maastricht, the Netherlands; Pathology and Data Analytics, Leeds Institute of Medical Research at St. James's, University of Leeds, Leeds, UK.
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3
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Kooreman LFS, Dieleman S, van Kuijk SMJ, zur Hausen A, Smidt ML, Grabsch HI. The prognostic value of the histological shape of tumor negative sentinel nodes in breast cancer. Front Immunol 2023; 14:1258641. [PMID: 37965336 PMCID: PMC10642264 DOI: 10.3389/fimmu.2023.1258641] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2023] [Accepted: 10/11/2023] [Indexed: 11/16/2023] Open
Abstract
Introduction 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. Methods 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. Results 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. Conclusions 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.
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Affiliation(s)
- Loes F. S. Kooreman
- Department of Pathology, GROW School for Oncology and Reproduction, Maastricht University Medical Center+, Maastricht, Netherlands
- GROW School for Oncology and Reproduction, Maastricht University Medical Center+, Maastricht, Netherlands
| | - Sabine Dieleman
- Department of Pathology, GROW School for Oncology and Reproduction, Maastricht University Medical Center+, Maastricht, Netherlands
- GROW School for Oncology and Reproduction, Maastricht University Medical Center+, Maastricht, Netherlands
- Department of Surgery, Maastricht University Medical Center+, Maastricht, Netherlands
| | - Sander M. J. van Kuijk
- Department of Clinical Epidemiology and Medical Technology Assessment, Maastricht University Medical Center+, Maastricht, Netherlands
| | - Axel zur Hausen
- Department of Pathology, GROW School for Oncology and Reproduction, Maastricht University Medical Center+, Maastricht, Netherlands
- GROW School for Oncology and Reproduction, Maastricht University Medical Center+, Maastricht, Netherlands
| | - Marjolein L. Smidt
- GROW School for Oncology and Reproduction, Maastricht University Medical Center+, Maastricht, Netherlands
- Department of Surgery, Maastricht University Medical Center+, Maastricht, Netherlands
| | - Heike I. Grabsch
- Department of Pathology, GROW School for Oncology and Reproduction, Maastricht University Medical Center+, Maastricht, Netherlands
- GROW School for Oncology and Reproduction, Maastricht University Medical Center+, Maastricht, Netherlands
- Pathology and Data Analytics, Leeds Institute of Medical Research at St. James’s, University of Leeds, Leeds, United Kingdom
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Verghese G, Li M, Liu F, Lohan A, Kurian NC, Meena S, Gazinska P, Shah A, Oozeer A, Chan T, Opdam M, Linn S, Gillett C, Alberts E, Hardiman T, Jones S, Thavaraj S, Jones JL, Salgado R, Pinder SE, Rane S, Sethi A, Grigoriadis A. Multiscale deep learning framework captures systemic immune features in lymph nodes predictive of triple negative breast cancer outcome in large-scale studies. J Pathol 2023; 260:376-389. [PMID: 37230111 PMCID: PMC10720675 DOI: 10.1002/path.6088] [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: 09/09/2022] [Revised: 02/27/2023] [Accepted: 04/11/2023] [Indexed: 05/27/2023]
Abstract
The suggestion that the systemic immune response in lymph nodes (LNs) conveys prognostic value for triple-negative breast cancer (TNBC) patients has not previously been investigated in large cohorts. We used a deep learning (DL) framework to quantify morphological features in haematoxylin and eosin-stained LNs on digitised whole slide images. From 345 breast cancer patients, 5,228 axillary LNs, cancer-free and involved, were assessed. Generalisable multiscale DL frameworks were developed to capture and quantify germinal centres (GCs) and sinuses. Cox regression proportional hazard models tested the association between smuLymphNet-captured GC and sinus quantifications and distant metastasis-free survival (DMFS). smuLymphNet achieved a Dice coefficient of 0.86 and 0.74 for capturing GCs and sinuses, respectively, and was comparable to an interpathologist Dice coefficient of 0.66 (GC) and 0.60 (sinus). smuLymphNet-captured sinuses were increased in LNs harbouring GCs (p < 0.001). smuLymphNet-captured GCs retained clinical relevance in LN-positive TNBC patients whose cancer-free LNs had on average ≥2 GCs, had longer DMFS (hazard ratio [HR] = 0.28, p = 0.02) and extended GCs' prognostic value to LN-negative TNBC patients (HR = 0.14, p = 0.002). Enlarged smuLymphNet-captured sinuses in involved LNs were associated with superior DMFS in LN-positive TNBC patients in a cohort from Guy's Hospital (multivariate HR = 0.39, p = 0.039) and with distant recurrence-free survival in 95 LN-positive TNBC patients of the Dutch-N4plus trial (HR = 0.44, p = 0.024). Heuristic scoring of subcapsular sinuses in LNs of LN-positive Tianjin TNBC patients (n = 85) cross-validated the association of enlarged sinuses with shorter DMFS (involved LNs: HR = 0.33, p = 0.029 and cancer-free LNs: HR = 0.21 p = 0.01). Morphological LN features reflective of cancer-associated responses are robustly quantifiable by smuLymphNet. Our findings further strengthen the value of assessment of LN properties beyond the detection of metastatic deposits for prognostication of TNBC patients. © 2023 The Authors. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of The Pathological Society of Great Britain and Ireland.
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Affiliation(s)
- Gregory Verghese
- Cancer Bioinformatics, School of Cancer & Pharmaceutical Sciences, Faculty of Life Sciences and MedicineKing's College LondonLondonUK
- School of Cancer & Pharmaceutical Sciences, Faculty of Life Sciences and MedicineKing's College LondonLondonUK
- Breast Cancer Now Unit, School of Cancer and Pharmaceutical Sciences, Faculty of Life Sciences and MedicineKing's College LondonLondonUK
| | - Mengyuan Li
- Cancer Bioinformatics, School of Cancer & Pharmaceutical Sciences, Faculty of Life Sciences and MedicineKing's College LondonLondonUK
- School of Cancer & Pharmaceutical Sciences, Faculty of Life Sciences and MedicineKing's College LondonLondonUK
| | - Fangfang Liu
- Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Tianjin's Clinical Research Center for Cancer, Key Laboratory of Breast Cancer Prevention and Therapy, Tianjin Medical University, Ministry of EducationKey Laboratory of Cancer Prevention and TherapyTianjinPR China
| | - Amit Lohan
- Department of Electrical EngineeringIndian Institute of Technology BombayMumbaiIndia
| | - Nikhil Cherian Kurian
- Department of Electrical EngineeringIndian Institute of Technology BombayMumbaiIndia
| | - Swati Meena
- Department of Electrical EngineeringIndian Institute of Technology BombayMumbaiIndia
| | - Patrycja Gazinska
- Breast Cancer Now Unit, School of Cancer and Pharmaceutical Sciences, Faculty of Life Sciences and MedicineKing's College LondonLondonUK
- Biobank Research GroupLukasiewicz Research Network, PORT Polish Center for Technology DevelopmentWroclawPoland
| | - Aekta Shah
- Cancer Bioinformatics, School of Cancer & Pharmaceutical Sciences, Faculty of Life Sciences and MedicineKing's College LondonLondonUK
- Department of PathologyTata Memorial Centre, Tata Memorial Hospital, Homi Bhabha National InstituteMumbaiIndia
| | - Aasiyah Oozeer
- King's Health Partners Cancer Biobank, King's College LondonLondonUK
| | - Terry Chan
- Division of Molecular PathologyThe Netherlands Cancer InstituteAmsterdamThe Netherlands
| | - Mark Opdam
- Division of Molecular PathologyThe Netherlands Cancer InstituteAmsterdamThe Netherlands
| | - Sabine Linn
- Division of Molecular PathologyThe Netherlands Cancer InstituteAmsterdamThe Netherlands
- Department of Medical OncologyThe Netherlands Cancer Institute, Antoni van LeeuwenhoekAmsterdamThe Netherlands
- Department of PathologyUniversity Medical CentreUtrechtThe Netherlands
| | - Cheryl Gillett
- King's Health Partners Cancer Biobank, King's College LondonLondonUK
| | - Elena Alberts
- Cancer Bioinformatics, School of Cancer & Pharmaceutical Sciences, Faculty of Life Sciences and MedicineKing's College LondonLondonUK
- School of Cancer & Pharmaceutical Sciences, Faculty of Life Sciences and MedicineKing's College LondonLondonUK
| | - Thomas Hardiman
- Cancer Bioinformatics, School of Cancer & Pharmaceutical Sciences, Faculty of Life Sciences and MedicineKing's College LondonLondonUK
- School of Cancer & Pharmaceutical Sciences, Faculty of Life Sciences and MedicineKing's College LondonLondonUK
| | - Samantha Jones
- Centre for Tumour Biology, Barts Cancer Institute, Queen Mary University of LondonLondonUK
| | - Selvam Thavaraj
- Faculty of Dentistry, Oral & Craniofacial ScienceKing's College LondonLondonUK
- Head and Neck PathologyGuy's & St Thomas' NHS Foundation TrustLondonUK
| | - J Louise Jones
- Centre for Tumour Biology, Barts Cancer Institute, Queen Mary University of LondonLondonUK
| | - Roberto Salgado
- Department of PathologyGZA‐ZNA HospitalsAntwerpBelgium
- Division of ResearchPeter Mac Callum Cancer CentreMelbourneAustralia
| | - Sarah E Pinder
- School of Cancer & Pharmaceutical Sciences, Faculty of Life Sciences and MedicineKing's College LondonLondonUK
| | - Swapnil Rane
- Department of PathologyTata Memorial Centre‐ACTREC, HBNIMumbaiIndia
| | - Amit Sethi
- Department of Electrical EngineeringIndian Institute of Technology BombayMumbaiIndia
| | - Anita Grigoriadis
- Cancer Bioinformatics, School of Cancer & Pharmaceutical Sciences, Faculty of Life Sciences and MedicineKing's College LondonLondonUK
- School of Cancer & Pharmaceutical Sciences, Faculty of Life Sciences and MedicineKing's College LondonLondonUK
- Breast Cancer Now Unit, School of Cancer and Pharmaceutical Sciences, Faculty of Life Sciences and MedicineKing's College LondonLondonUK
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Verghese G, Lennerz JK, Ruta D, Ng W, Thavaraj S, Siziopikou KP, Naidoo T, Rane S, Salgado R, Pinder SE, Grigoriadis A. Computational pathology in cancer diagnosis, prognosis, and prediction - present day and prospects. J Pathol 2023; 260:551-563. [PMID: 37580849 PMCID: PMC10785705 DOI: 10.1002/path.6163] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2023] [Revised: 06/12/2023] [Accepted: 06/17/2023] [Indexed: 08/16/2023]
Abstract
Computational pathology refers to applying deep learning techniques and algorithms to analyse and interpret histopathology images. Advances in artificial intelligence (AI) have led to an explosion in innovation in computational pathology, ranging from the prospect of automation of routine diagnostic tasks to the discovery of new prognostic and predictive biomarkers from tissue morphology. Despite the promising potential of computational pathology, its integration in clinical settings has been limited by a range of obstacles including operational, technical, regulatory, ethical, financial, and cultural challenges. Here, we focus on the pathologists' perspective of computational pathology: we map its current translational research landscape, evaluate its clinical utility, and address the more common challenges slowing clinical adoption and implementation. We conclude by describing contemporary approaches to drive forward these techniques. © 2023 The Authors. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of The Pathological Society of Great Britain and Ireland.
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Affiliation(s)
- Gregory Verghese
- School of Cancer & Pharmaceutical Sciences, Faculty of Life Sciences and MedicineKing's College LondonLondonUK
- The Breast Cancer Now Research Unit, School of Cancer and Pharmaceutical Sciences, Faculty of Life Sciences and MedicineKing's College LondonLondonUK
| | - Jochen K Lennerz
- Center for Integrated Diagnostics, Department of PathologyMassachusetts General Hospital/Harvard Medical SchoolBostonMAUSA
| | - Danny Ruta
- Guy's CancerGuy's and St Thomas’ NHS Foundation TrustLondonUK
| | - Wen Ng
- Department of Cellular PathologyGuy's and St Thomas NHS Foundation TrustLondonUK
| | - Selvam Thavaraj
- Head & Neck PathologyGuy's and St Thomas NHS Foundation TrustLondonUK
- Centre for Clinical, Oral & Translational Science, Faculty of Dentistry, Oral & Craniofacial SciencesKing's College LondonLondonUK
| | - Kalliopi P Siziopikou
- Department of Pathology, Section of Breast PathologyNorthwestern University Feinberg School of MedicineChicagoILUSA
| | - Threnesan Naidoo
- Department of Laboratory Medicine and Pathology, Walter Sisulu University, Mthatha, Eastern CapeSouth Africa and Africa Health Research InstituteDurbanSouth Africa
| | - Swapnil Rane
- Department of PathologyTata Memorial Centre – ACTRECHBNINavi MumbaiIndia
- Computational Pathology, AI & Imaging LaboratoryTata Memorial Centre – ACTREC, HBNINavi MumbaiIndia
| | - Roberto Salgado
- Department of PathologyGZA–ZNA ZiekenhuizenAntwerpBelgium
- Division of ResearchPeter MacCallum Cancer CentreMelbourneVictoriaAustralia
| | - Sarah E Pinder
- School of Cancer & Pharmaceutical Sciences, Faculty of Life Sciences and MedicineKing's College LondonLondonUK
- Department of Cellular PathologyGuy's and St Thomas NHS Foundation TrustLondonUK
| | - Anita Grigoriadis
- School of Cancer & Pharmaceutical Sciences, Faculty of Life Sciences and MedicineKing's College LondonLondonUK
- The Breast Cancer Now Research Unit, School of Cancer and Pharmaceutical Sciences, Faculty of Life Sciences and MedicineKing's College LondonLondonUK
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Li M, Quintana A, Alberts E, Hung MS, Boulat V, Ripoll MM, Grigoriadis A. B Cells in Breast Cancer Pathology. Cancers (Basel) 2023; 15:1517. [PMID: 36900307 PMCID: PMC10000926 DOI: 10.3390/cancers15051517] [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: 12/22/2022] [Revised: 02/13/2023] [Accepted: 02/20/2023] [Indexed: 03/06/2023] Open
Abstract
B cells have recently become a focus in breast cancer pathology due to their influence on tumour regression, prognosis, and response to treatment, besides their contribution to antigen presentation, immunoglobulin production, and regulation of adaptive responses. As our understanding of diverse B cell subsets in eliciting both pro- and anti-inflammatory responses in breast cancer patients increases, it has become pertinent to address the molecular and clinical relevance of these immune cell populations within the tumour microenvironment (TME). At the primary tumour site, B cells are either found spatially dispersed or aggregated in so-called tertiary lymphoid structures (TLS). In axillary lymph nodes (LNs), B cell populations, amongst a plethora of activities, undergo germinal centre reactions to ensure humoral immunity. With the recent approval for the addition of immunotherapeutic drugs as a treatment option in the early and metastatic settings for triple-negative breast cancer (TNBC) patients, B cell populations or TLS may resemble valuable biomarkers for immunotherapy responses in certain breast cancer subgroups. New technologies such as spatially defined sequencing techniques, multiplex imaging, and digital technologies have further deciphered the diversity of B cells and the morphological structures in which they appear in the tumour and LNs. Thus, in this review, we comprehensively summarise the current knowledge of B cells in breast cancer. In addition, we provide a user-friendly single-cell RNA-sequencing platform, called "B singLe cEll rna-Seq browSer" (BLESS) platform, with a focus on the B cells in breast cancer patients to interrogate the latest publicly available single-cell RNA-sequencing data collected from diverse breast cancer studies. Finally, we explore their clinical relevance as biomarkers or molecular targets for future interventions.
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Affiliation(s)
- Mengyuan Li
- Cancer Bioinformatics, School of Cancer & Pharmaceutical Sciences, Faculty of Life Sciences and Medicine, King’s College London, London SE1 9RT, UK
- School of Cancer & Pharmaceutical Sciences, Faculty of Life Sciences and Medicine, King’s College London, London SE1 9RT, UK
| | | | - Elena Alberts
- Cancer Bioinformatics, School of Cancer & Pharmaceutical Sciences, Faculty of Life Sciences and Medicine, King’s College London, London SE1 9RT, UK
- School of Cancer & Pharmaceutical Sciences, Faculty of Life Sciences and Medicine, King’s College London, London SE1 9RT, UK
- Immunity and Cancer Laboratory, The Francis Crick Institute, London NW1 1AT, UK
| | - Miu Shing Hung
- Cancer Bioinformatics, School of Cancer & Pharmaceutical Sciences, Faculty of Life Sciences and Medicine, King’s College London, London SE1 9RT, UK
- School of Cancer & Pharmaceutical Sciences, Faculty of Life Sciences and Medicine, King’s College London, London SE1 9RT, UK
| | - Victoire Boulat
- Cancer Bioinformatics, School of Cancer & Pharmaceutical Sciences, Faculty of Life Sciences and Medicine, King’s College London, London SE1 9RT, UK
- School of Cancer & Pharmaceutical Sciences, Faculty of Life Sciences and Medicine, King’s College London, London SE1 9RT, UK
- Immunity and Cancer Laboratory, The Francis Crick Institute, London NW1 1AT, UK
| | - Mercè Martí Ripoll
- Immunology Unit, Department of Cell Biology, Physiology and Immunology, Universitat Autònoma de Barcelona, 08193 Barcelona, Spain
- Biosensing and Bioanalysis Group, Institute of Biotechnology and Biomedicine, Universitat Autònoma de Barcelona, 08193 Barcelona, Spain
| | - Anita Grigoriadis
- Cancer Bioinformatics, School of Cancer & Pharmaceutical Sciences, Faculty of Life Sciences and Medicine, King’s College London, London SE1 9RT, UK
- School of Cancer & Pharmaceutical Sciences, Faculty of Life Sciences and Medicine, King’s College London, London SE1 9RT, UK
- Breast Cancer Now Unit, School of Cancer & Pharmaceutical Sciences, Faculty of Life Sciences and Medicine, King’s College London, London SE1 9RT, UK
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Egger EK, Buchen MA, Recker F, Stope MB, Strunk H, Mustea A, Marinova M. Predicting incomplete cytoreduction in patients with advanced ovarian cancer. Front Oncol 2022; 12:1060006. [PMID: 36591482 PMCID: PMC9798233 DOI: 10.3389/fonc.2022.1060006] [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: 10/02/2022] [Accepted: 11/21/2022] [Indexed: 12/23/2022] Open
Abstract
Purpose The most important prognostic factor for survival in ovarian cancer patients is complete cytoreduction. The preoperative prediction of suboptimal cytoreduction, considered as any residual disease at the end of surgery, could prevent futile surgery and morbidity. Here, we aimed to identify markers in the preoperative abdominal CT scans of an unselected cohort of patients with ovarian cancer that are predictive of incomplete cytoreduction. Methods This is a single-institution retrospective analysis of 105 epithelial ovarian cancer (EOC) patients treated with surgical cytoreduction between 2010 and 2020. Twenty-two variables on preoperative abdominal CT scans were compared to the intraoperative macroscopic findings by Fisher's exact test. Parameters with a significant correlation between intraoperative findings and imaging were analyzed by multivariate binary logistic regression analysis regarding the surgical outcome of complete versus incomplete cytoreduction. Results Complete cytoreduction (CC), indicated by the absence of macroscopic residual disease, was achieved in 79 (75.2%) of 105 patients and 46 (63.9%) of 72 International Federation of Gynecology and Obstetrics (FIGO) stage III and IV patients. Twenty patients (19%) were incompletely cytoreduced due to miliary carcinomatosis of the small bowel, and six patients (5.7%) had various locations of residual disease. Thirteen variables showed a significant correlation between imaging and surgical findings. Large-volume ascites, absence of numerically increased small lymph nodes at the mesenteric root, and carcinomatosis of the transverse colon in FIGO stage III and IV patients decreased the rate of CC to 26.7% in the multivariate analysis. Conclusion Large-volume ascites, the absence of numerically increased small lymph nodes at the mesenteric root, and carcinomatosis of the transverse colon are markers in preoperative CT scans predicting a low chance for complete cytoreduction in unselected ovarian cancer patients in a real-world setting.
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Affiliation(s)
- Eva K. Egger
- Department of Gynecology and Gynecological Oncology, University Hospital Bonn, Bonn, Germany
| | - Marie Antonia Buchen
- Department of Gynecology and Gynecological Oncology, University Hospital Bonn, Bonn, Germany
| | - Florian Recker
- Department of Gynecology and Gynecological Oncology, University Hospital Bonn, Bonn, Germany
| | - Matthias B. Stope
- Department of Gynecology and Gynecological Oncology, University Hospital Bonn, Bonn, Germany
| | - Holger Strunk
- Medicine Center Bonn, Medical Care Center, Bonn, Germany
| | - Alexander Mustea
- Department of Gynecology and Gynecological Oncology, University Hospital Bonn, Bonn, Germany
| | - Milka Marinova
- Department of Nuclear Medicine, University Hospital Bonn, Bonn, Germany
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8
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Li Z, Shi Y, Wu L, Zhang H, Xue J, Li W, Wang X, Zhang L, Wang Q, Duo L, Wang M, Wang G. Establishment and verification of a nomogram to predict tumor-specific mortality risk in triple-negative breast cancer: a competing risk model based on the SEER cohort study. Gland Surg 2022; 11:1961-1975. [PMID: 36654948 PMCID: PMC9840986 DOI: 10.21037/gs-22-650] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2022] [Accepted: 12/12/2022] [Indexed: 12/24/2022]
Abstract
Background Triple-negative breast cancer (TNBC) is the subtype of breast cancer with the worst prognosis, and traditional survival analysis methods are biased when predicting mortality. To predict the risk of death in patients with triple-negative breast cancer more precisely, a competing risk model was developed. Methods The clinicopathological data of the TNBC patients from 2010 to 2015 were collected from the Surveillance, Epidemiology, and End Results (SEER) database. The data were assigned into a training set and testing set at a ratio of 7:3 in a randomized pattern. Univariate and multivariate competing risk models were applied to find the independent prognostic factors. A prediction nomogram for cancer-specific mortality (CSM) risk was constructed. The accuracy and discrimination of the nomogram were assessed using receiver operating characteristic (ROC) area under the curve (AUC), concordance index (C-index), and a calibration curve using the training and testing sets, respectively. Results A total of 28,430 TNBC patients were randomly grouped into the training set (n=19,900) and the testing set (n=8,530). The median time for follow-up was 59 [1-107] months. A total of 7,014 (24.67%) patients died, among whom 4,801 (68.45%) died from breast cancer and 2,213 (31.55%) due to non-breast cancer events. The independent risk factors were primary site of tumor, grade, tumor-node-metastasis (TNM) stage, T stage, approach of surgery, chemotherapy, axillary lymph node metastases, brain metastases, and liver metastases. The prediction nomogram was constructed by using the aforementioned variables. The 1-, 3-, and 5-year AUC of CSM were predicted to be 0.856, 0.81, and 0.782, respectively, in the training set, and 0.856, 0.81, and 0.782 in the testing set, respectively. The C-index of the nomogram was 0.801 and 0.799 in the training and testing sets, respectively. As confirmed by the validation and training calibration curves, the nomogram was consistent with the results. Conclusions We used competing risk models to identify risk factors for CSM and constructed a CSM risk prediction nomogram for TNBC patients, that may be utilized to predict CSM risk in TNBC patients clinically and assist in the creation of individualised clinical treatment options.
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Affiliation(s)
- Zhi Li
- Department of General Surgery, Taihe Hospital, Hubei University of Medicine, Shiyan, China;,Hubei Clinical Research Center for Precise Diagnosis and Treatment of Liver Cancer, Taihe Hospital, Hubei University of Medicine, Shiyan, China;,Hubei Key Laboratory of Embryonic Stem Cell Research, Taihe Hospital, Hubei University of Medicine, Shiyan, China
| | - Yun Shi
- Department of General Surgery, Taihe Hospital, Hubei University of Medicine, Shiyan, China
| | - Lihua Wu
- Department of General Surgery, Taihe Hospital, Hubei University of Medicine, Shiyan, China
| | - Hua Zhang
- Department of General Surgery, Taihe Hospital, Hubei University of Medicine, Shiyan, China
| | - Jiapeng Xue
- Department of General Surgery, Taihe Hospital, Hubei University of Medicine, Shiyan, China
| | - Wenfang Li
- Department of General Surgery, Taihe Hospital, Hubei University of Medicine, Shiyan, China
| | - Xixi Wang
- Department of General Surgery, Taihe Hospital, Hubei University of Medicine, Shiyan, China
| | - Ligen Zhang
- Department of General Surgery, Taihe Hospital, Hubei University of Medicine, Shiyan, China
| | - Qun Wang
- Department of General Surgery, Taihe Hospital, Hubei University of Medicine, Shiyan, China
| | - Long Duo
- Department of Oncology, Renmin Hospital, Hubei University of Medicine, Shiyan, China
| | - Minghua Wang
- Department of General Surgery, Taihe Hospital, Hubei University of Medicine, Shiyan, China
| | - Geng Wang
- Department of General Surgery, Taihe Hospital, Hubei University of Medicine, Shiyan, China
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Wall I, Boulat V, Shah A, Blenman KRM, Wu Y, Alberts E, Calado DP, Salgado R, Grigoriadis A. Leveraging the Dynamic Immune Environment Triad in Patients with Breast Cancer: Tumour, Lymph Node, and Peripheral Blood. Cancers (Basel) 2022; 14:4505. [PMID: 36139665 PMCID: PMC9496983 DOI: 10.3390/cancers14184505] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2022] [Revised: 09/09/2022] [Accepted: 09/12/2022] [Indexed: 11/29/2022] Open
Abstract
During the anti-tumour response to breast cancer, the primary tumour, the peripheral blood, and the lymph nodes each play unique roles. Immunological features at each site reveal evidence of continuous immune cross-talk between them before, during and after treatment. As such, immune responses to breast cancer are found to be highly dynamic and truly systemic, integrating three distinct immune sites, complex cell-migration highways, as well as the temporal dimension of disease progression and treatment. In this review, we provide a connective summary of the dynamic immune environment triad of breast cancer. It is critical that future studies seek to establish dynamic immune profiles, constituting multiple sites, that capture the systemic immune response to breast cancer and define patient-selection parameters resulting in more significant overall responses and survival rates for breast cancer patients.
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Affiliation(s)
- Isobelle Wall
- Cancer Bioinformatics, School of Cancer & Pharmaceutical Sciences, King’s College London, Guy’s Hospital, London SE1 9RT, UK
| | - Victoire Boulat
- Cancer Bioinformatics, School of Cancer & Pharmaceutical Sciences, King’s College London, Guy’s Hospital, London SE1 9RT, UK
- Immunity and Cancer Laboratory, The Francis Crick Institute, London NW1 1AT, UK
| | - Aekta Shah
- Cancer Bioinformatics, School of Cancer & Pharmaceutical Sciences, King’s College London, Guy’s Hospital, London SE1 9RT, UK
- Department of Pathology, Tata Memorial Centre, Homi Bhabha National Institute, Mumbai 400012, India
| | - Kim R. M. Blenman
- Department of Internal Medicine, Section of Medical Oncology, Yale School of Medicine, Yale University, New Haven, CT 06510, USA
- Department of Computer Science, School of Engineering and Applied Science, Yale University, New Haven, CT 06511, USA
| | - Yin Wu
- Breast Cancer Now Research Unit, School of Cancer & Pharmaceutical Sciences, King’s College London, Guy’s Hospital, London SE1 9RT, UK
- Peter Gorer Department of Immunobiology, School of Immunology & Microbial Sciences, King’s College London, London SE1 9RT, UK
- Centre for Inflammation Biology and Cancer Immunology, School of Immunology & Microbial Sciences, King’s College London, London SE1 9RT, UK
| | - Elena Alberts
- Cancer Bioinformatics, School of Cancer & Pharmaceutical Sciences, King’s College London, Guy’s Hospital, London SE1 9RT, UK
- Immunity and Cancer Laboratory, The Francis Crick Institute, London NW1 1AT, UK
| | - Dinis Pedro Calado
- Immunity and Cancer Laboratory, The Francis Crick Institute, London NW1 1AT, UK
| | - Roberto Salgado
- Department of Pathology, GZA-ZNA Hospitals, 2610 Antwerp, Belgium
- Division of Research, Peter MacCallum Cancer Centre, Melbourne, VIC 3000, Australia
| | - Anita Grigoriadis
- Cancer Bioinformatics, School of Cancer & Pharmaceutical Sciences, King’s College London, Guy’s Hospital, London SE1 9RT, UK
- Breast Cancer Now Research Unit, School of Cancer & Pharmaceutical Sciences, King’s College London, Guy’s Hospital, London SE1 9RT, UK
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