Correction: Establishing standardized immune phenotyping of metastatic melanoma by digital pathology

BackgroundAnti-programmed cell death protein 1 (PD-1) antibodies are now routinely administered for metastatic melanoma and for increasing numbers of other cancers, but still only a fraction of patients respond. Better understanding of the modes of action and predictive biomarkers for clinical outcome is urgently required. Cancer rejection is mostly T cell-mediated. We previously showed that the presence of NY-ESO-1-reactive and/or Melan-A-reactive T cells in the blood correlated with prolonged overall survival (OS) of patients with melanoma with a heterogeneous treatment background. Here, we investigated whether such reactive T cells can also be informative for clinical outcomes in metastatic melanoma under PD-1 immune-checkpoint blockade (ICB).MethodsPeripheral blood T cell stimulation by NY-ESO-1 and Melan-A overlapping peptide libraries was assessed before and during ICB in two independent cohorts of a total of 111 patients with stage IV melanoma. In certain cases, tumor-infiltrating lymphocytes could also be assessed for such responses. These were characterized using intracellular cytokine staining for interferon gamma (IFN-γ), tumor negrosis factor (TNF) and CD107a. Digital pathology analysis was performed to quantify NY-ESO-1 and Melan-A expression by tumors. Endpoints were OS and progression-free survival (PFS).ResultsThe initial presence in the circulation of NY-ESO-1- or Melan-A-reactive T cells which became no longer detectable during ICB correlated with validated, prolonged PFS (HR:0.1; p>0.0001) and OS (HR:0.2; p=0.021). An evaluation of melanoma tissue from selected cases suggested a correlation between tumor-resident NY-ESO-1- and Melan-A-reactive T cells and disease control, supporting the notion of a therapy-associated sequestration of cells from the periphery to the tumor predominantly in those patients benefitting from ICB.ConclusionsOur findings suggest a PD-1 blockade-dependent infiltration of melanoma-reactive T cells from the periphery into the tumor and imply that this seminally contributes to effective treatment.

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821 Machine learning models can quantify CD8 positivity in lymphocytes in melanoma clinical trial samples

Journal for ImmunoTherapy of Cancer ◽

10.1136/jitc-2021-sitc2021.821 ◽

2021 ◽

Vol 9 (Suppl 3) ◽

pp. A859-A859

Author(s):

Benjamin Glass ◽

S Adam Stanford-Moore ◽

Diksha Meghwal ◽

Nishant Agrawal ◽

Mary Lin ◽

...

Keyword(s):

Machine Learning ◽

Clinical Trial ◽

Clinical Trials ◽

Tumor Microenvironment ◽

Cell Carcinoma ◽

Metastatic Melanoma ◽

Cell Types ◽

Cell Counts ◽

Immune Phenotyping ◽

Cd8 Cell

BackgroundAn accurate histological characterization of immune cells in the tumor microenvironment is essential for developing novel immune oncology targeted therapies and can assist in guiding patient treatment decisions. However, immune phenotyping is subject to challenges of manual scoring and inter-pathologist scoring variability. To support pathologist-scored immune phenotyping across tumor types, we are developing machine learning (ML)-based models that can identify and quantify CD8+ lymphocytes within the stromal and parenchyma regions of tumors from non-small cell lung cancer, renal cell carcinoma, breast cancer, gastric cancer, head and neck squamous cell carcinoma, urothelial carcinoma, and melanoma. Here, we focus on the ML model for melanoma showing recent results for ML-based identification and quantification of CD8+ lymphocytes and concordance with manual pathologic assessment in data derived from clinical trials.MethodsML algorithms were developed to quantify CD8+ lymphocytes in melanoma using 200 samples from a commercial dataset containing both primary and metastatic melanoma cases. Models were trained using the PathAI research platform on digitized whole slide images (WSI) stained for CD8 using clone C8/144b (Dako), and annotations were provided by the PathAI network of expert pathologists. Training included identification of slide artifacts, parenchyma, cancer stroma, and necrosis, as well as CD8+ lymphocytes and other CD8– cell types. Examples of melanin, such as pigmented macrophages, were added to non-CD8+ cell types. To evaluate the performance of the ML model, model-predicted CD8+ counts were compared to a consensus count from five independent pathologists for representative regions (“frames”) using the Pearson correlation. This was done in 112 held-out test frames from 90 WSI baseline samples from three clinical trials of immunotherapy treatment in individuals with metastatic melanoma. Inter-pathologist agreement among the five pathologists was also calculated.ResultsML-based quantitation of CD8 positivity in lymphocytes showed high concordance with manual pathologist consensus counts. In frames validation of CD8+ counts on the test set of WSI, there was high correlation between the ML model and pathologist consensus counts (r=0.92 [95% CI 0.88–0.94]). This correlation was comparable to the agreement among the five expert pathologists (r=0.88 [95% CI 0.85–0.91]).ConclusionsML model-predicted CD8+ cell counts are highly concordant with pathologist scores on WSI samples from melanoma-focused clinical trials. These data demonstrate the capability of AI-powered digital pathology for accurate and reproducible quantitation of CD8+ lymphocytes in clinical trial samples, contributing to improved evaluation of the tumor microenvironment and targeted development of therapeutics.

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Establishing standardized immune phenotyping of metastatic melanoma by digital pathology

Laboratory Investigation ◽

10.1038/s41374-021-00653-y ◽

2021 ◽

Author(s):

Bettina Sobottka ◽

Marta Nowak ◽

Anja Laura Frei ◽

Martina Haberecker ◽

Samuel Merki ◽

...

Keyword(s):

T Cells ◽

Cd8 T Cells ◽

Validation Cohort ◽

Checkpoint Inhibitors ◽

Digital Pathology ◽

Diagnostic Category ◽

Predictive Biomarkers ◽

Diagnostic Algorithm ◽

Tumor Center ◽

Immune Phenotyping

AbstractCD8+ tumor-infiltrating T cells can be regarded as one of the most relevant predictive biomarkers in immune-oncology. Highly infiltrated tumors, referred to as inflamed (clinically “hot”), show the most favorable response to immune checkpoint inhibitors in contrast to tumors with a scarce immune infiltrate called immune desert or excluded (clinically “cold”). Nevertheless, quantitative and reproducible methods examining their prevalence within tumors are lacking. We therefore established a computational diagnostic algorithm to quantitatively measure spatial densities of tumor-infiltrating CD8+ T cells by digital pathology within the three known tumor compartments as recommended by the International Immuno-Oncology Biomarker Working Group in 116 prospective metastatic melanomas of the Swiss Tumor Profiler cohort. Workflow robustness was confirmed in 33 samples of an independent retrospective validation cohort. The introduction of the intratumoral tumor center compartment proved to be most relevant for establishing an immune diagnosis in metastatic disease, independent of metastatic site. Cut-off values for reproducible classification were defined and successfully assigned densities into the respective immune diagnostic category in the validation cohort with high sensitivity, specificity, and precision. We provide a robust diagnostic algorithm based on intratumoral and stromal CD8+ T-cell densities in the tumor center compartment that translates spatial densities of tumor-infiltrating CD8+ T cells into the clinically relevant immune diagnostic categories “inflamed”, “excluded”, and “desert”. The consideration of the intratumoral tumor center compartment allows immune phenotyping in the clinically highly relevant setting of metastatic lesions, even if the invasive margin compartment is not captured in biopsy material.

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Abstract #917: Ipilimumab Induced Hypophysitis in a Patient with Metastatic Melanoma

Endocrine Practice ◽

10.1016/s1530-891x(20)45207-3 ◽

2016 ◽

Vol 22 ◽

pp. 202

Author(s):

Samaneh Dowlatshahi ◽

Ronald A. Codario

Keyword(s):

Metastatic Melanoma

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Systemic therapy of metastatic melanoma

Therapeutische Umschau ◽

10.1024/0040-5930.56.6.330 ◽

1999 ◽

Vol 56 (6) ◽

pp. 330-333

Author(s):

Dummer ◽

Nestle ◽

Hofbauer ◽

Burg

Keyword(s):

Metastatic Melanoma ◽

Systemic Therapy ◽

Interleukin 2 ◽

Kurative Therapie

Das metastasierende Melanom (MM) gehört zu den schwierig behandelbaren Malignomen, wobei Allgemeinzustand und Motivation des Patienten neben Zahl und Lokalisation der Metastasen das therapeutische Vorgehen bestimmen. Solitäre Metastasen in Lunge, ZNS, Weichteilen und Lymphknoten sollten primär chirurgisch entfernt werden. Multiple Metastasen, insbesondere abdominal, werden nur in Ausnahmefällen chirurgisch angegangen. Hier ist vielmehr ein systemische Chemoimmuntherapie angebracht. Aussichtsreiche Behandlungskonzepte beinhalten Interleukin-2, Interferon, und verschiedenen Zytostatika wie DTIC, Temozolamid, Vindesine oder Cisplatin. Bei ZNS- und Skelettfiliae ist die Radiotherapie einzusetzen. Durch diese Chemoimmuntherapien hat sich die Prognose des metastasierenden Melanoms bezüglich des Überlebens verbessert. Langfristig wird aber nur eine Kombination von zeitraubenden Multicenterstudien und experimentellen Ansätzen in der Lage sein, uns langsam an eine kurative Therapie heranzuführen.

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