Digital Quantification of Tumor PD-L1 Predicts Outcome of PD-1-Based Immune Checkpoint Therapy in Metastatic Melanoma

Differential predictive value of tissue-specific PD-L1 expression scores in adjuvant immunotherapy of melanoma

BACKGROUND

Adjuvant treatment of stage II-IV melanoma with PD-1-based immune checkpoint inhibitors (ICI) has improved relapse-free survival (RFS) and has therefore become a standard-of-care treatment option. Approximately 25%-30% of patients still recur within 1 year. Predictive biomarkers reflecting real-world data are desired. The predictive relevance of tumour tissue PD-L1 expression in the adjuvant setting remains inconclusive.

OBJECTIVES

This retrospective, observational study was conducted to evaluate the value of PD-L1 expression scores in different tumour tissue locations in predicting response towards adjuvant immunotherapeutic treatment.

METHODS

Tumour tissue taken prior to anti-PD-1 adjuvant ICI in 243 stage II-IV melanoma patients was collected at University Skin Cancer Center Hamburg. PD-L1 expression was evaluated on immune cells (ICS), tumour cells (TPS) and combined (CPS). Scores were determined by independent pathological physician quantification and correlated with therapy outcome at different cut-off (CO) levels (relapse-free survival, RFS) for different tumour tissue locations (primary tumour, metastases).

RESULTS

A total of 104 patients were eligible for analysis. Positivity of ICS, TPS and CPS showed no predictive RFS outcome association at different CO levels when analysed irrespective of tissue origin. In primary tumours, ICS at CO 1% showed a significantly improved RFS upon positivity (HR 0.22). In contrast, positivity to TPS (CO 1%) correlated significantly and independently with improved RFS when evaluated in metastatic tumour tissue specimens (HR 0.37).

CONCLUSIONS

PD-L1 tumour tissue expression may serve as a predictive biomarker for adjuvant ICI treatment response stratification in melanoma, but caution should be spent on the origin of tumour tissue analysed. The cell-type relevant for the predictive value of PD-L1 expression is tissue-specific with immune cells being important in primary tumours while tumour cells are key in metastases. The present results should be validated in a multicentre cohort.

Immune features are associated with response to neoadjuvant chemo-immunotherapy for muscle-invasive bladder cancer

Neoadjuvant cisplatin-based chemotherapy is standard of care for muscle-invasive bladder cancer (MIBC). Immune checkpoint inhibition (ICI) alone, and ICI in combination with chemotherapy, have demonstrated promising pathologic response ( Collapse

Key Words

• predictive markers
• bladder cancer
• immunotherapy
• tumour immunology

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MESH Headings

• Humans
• Urinary Bladder Neoplasms/drug therapy
• Urinary Bladder Neoplasms/immunology
• Urinary Bladder Neoplasms/therapy
• Urinary Bladder Neoplasms/pathology
• Neoadjuvant Therapy/methods
• Deoxycytidine/analogs & derivatives
• Deoxycytidine/therapeutic use
• Deoxycytidine/administration & dosage
• Gemcitabine
• Immunotherapy/methods
• Male
• Cisplatin/therapeutic use
• Cisplatin/administration & dosage
• Female
• Antibodies, Monoclonal, Humanized/therapeutic use
• Antineoplastic Combined Chemotherapy Protocols/therapeutic use
• Immune Checkpoint Inhibitors/therapeutic use
• Aged
• Middle Aged
• Neoplasm Invasiveness
• Interleukin-9/metabolism
• B7-H1 Antigen/metabolism
• Biomarkers, Tumor/blood
• Treatment Outcome

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Grants

• F30 CA278317 NCI NIH HHS
• 1F30CA278317-01A1 U.S. Department of Health & Human Services | NIH | National Cancer Institute (NCI)
• Merck & Co., Inc. | Merck Sharp and Dohme (Merck Sharp & Dohme)
• U.S. Department of Health & Human Services | NIH | National Cancer Institute (NCI)

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Affiliation(s)

Wolfgang Beckabir Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA Department of Microbiology and Immunology, UNC School of Medicine, Chapel Hill, NC, USA
Mi Zhou Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
Jin Seok Lee Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA Curriculum in Bioinformatics and Computational Biology, UNC School of Medicine, Chapel Hill, NC, USA
Steven P Vensko Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
Mark G Woodcock Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA Division of Oncology, Department of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
Hsing-Hui Wang Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA Department of Microbiology and Immunology, UNC School of Medicine, Chapel Hill, NC, USA
Sara E Wobker Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA Department of Pathology and Laboratory Medicine, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
Gatphan Atassi Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
Alec D Wilkinson Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
Kenneth Fowler Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
Leah M Flick Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
Jeffrey S Damrauer Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA Division of Oncology, Department of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
Michael R Harrison Division of Medical Oncology, Department of Medicine, Duke Cancer Institute, Duke University, Durham, NC, USA
Karen P McKinnon Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA Department of Microbiology and Immunology, UNC School of Medicine, Chapel Hill, NC, USA
Tracy L Rose Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA Division of Oncology, Department of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
Matthew I Milowsky Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA Division of Oncology, Department of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
Jonathan S Serody Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA. Department of Microbiology and Immunology, UNC School of Medicine, Chapel Hill, NC, USA. Division of Oncology, Department of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA. Department of Pharmacology, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
William Y Kim Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA. Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA. Division of Oncology, Department of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA. Division of Hematology, Department of Medicine, UNC School of Medicine, Chapel Hill, NC, USA.
Benjamin G Vincent Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA. Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA. Curriculum in Bioinformatics and Computational Biology, UNC School of Medicine, Chapel Hill, NC, USA. Division of Oncology, Department of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA. Department of Pharmacology, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA. Computational Medicine Program, UNC School of Medicine, Chapel Hill, NC, USA.

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Programmed Cell Death Ligand 1 Immunohistochemical Expression and Cutaneous Melanoma: A Controversial Relationship

Cutaneous melanoma (CM) is traditionally considered one of the most "immunogenic" tumors, eliciting a high immune response. However, despite the presence of tumor-infiltrating lymphocytes (TILs), melanoma cells use strategies to suppress antitumor immunity and avoid being eliminated by immune surveillance. The PD-1 (programmed death-1)/PD-L1 (programmed death-ligand 1) axis is a well-known immune escape system adopted by neoplastic cells. Therefore, immunotherapy with PD-1 and PD-L1 inhibitors is quickly becoming the main treatment approach for metastatic melanoma patients. However, the clinical utility of PD-L1 expression assessment in CM is controversial, and the interpretation of PD-L1 scores in clinical practice is still a matter of debate. Nonetheless, the recent literature data show that by adopting specific PD-L1 assessment methods in melanoma samples, a correlation between the expression of such a biomarker and a positive response to PD-1-based immunotherapy can be seen. Our review aims to describe the state-of-the-art knowledge regarding the prognostic and predictive role of PD-L1 expression in CM while also referring to possible biological explanations for the variability in its expressions and related treatment responses.

Correlation Between ImageJ and Conventional Manual Scoring Methods for Programmed Death-Ligand 1 Immuno-Histochemically Stained Sections

Background: One of the most frequently used methods for quantifying PD-L1 (programmed cell death-ligand 1) expression in tumor tissue is IHC (immunohistochemistry). This may predict the patient's response to anti-PD1/PD-L1 therapy in cancer. Methods: ImageJ software was used to score IHC-stained sections for PD-L1 and compare the results with the conventional manual method. Results: In diffuse large B cell lymphoma, no significant difference between the scores obtained by the conventional method and ImageJ scores obtained using the option "RGB" or "Brightness/Contrast." On the other hand, a significant difference was found between the conventional and HSB scoring methods. ImageJ faced some challenges in analyzing head and neck squamous cell carcinoma tissues because of tissue heterogenicity. A significant difference was found between the conventional and ImageJ scores using HSB or RGB but not with the "Brightness/Contrast" option. Scores obtained by ImageJ analysis after taking images using 20 × objective lens gave significantly higher readings compared to 40 × magnification. A significant difference between camera-captured images' scores and scanner whole slide images' scores was observed. Conclusion: ImageJ can be used to score homogeneous tissues. In the case of highly heterogeneous tissues, it is advised to use the conventional method rather than ImageJ scoring.

Nanobody-mediated SPECT/CT imaging reveals the spatiotemporal expression of programmed death-ligand 1 in response to a CD8+ T cell and iNKT cell activating mRNA vaccine

Rationale: Although promising responses are obtained in patients treated with immune checkpoint inhibitors targeting programmed death ligand 1 (PD-L1) and its receptor programmed death-1 (PD-1), only a fraction of patients benefits from this immunotherapy. Cancer vaccination may be an effective approach to improve the response to immune checkpoint inhibitors anti-PD-L1/PD-1 therapy. However, there is a lack of research on the dynamics of PD-L1 expression in response to cancer vaccination. Methods: We performed non-invasive whole-body imaging to visualize PD-L1 expression at different timepoints after vaccination of melanoma-bearing mice. Mice bearing ovalbumin (OVA) expressing B16 tumors were i.v. injected with the Galsome mRNA vaccine: OVA encoding mRNA lipoplexes co-encapsulating a low or a high dose of the atypical adjuvant α-galactosylceramide (αGC) to activate invariant natural killer T (iNKT) cells. Serial non-invasive whole-body immune imaging was performed using a technetium-99m (99mTc)-labeled anti-PD-L1 nanobody, single-photon emission computerized tomography (SPECT) and X-ray computed tomography (CT) images were quantified. Additionally, cellular expression of PD-L1 was evaluated with flow cytometry. Results: SPECT/CT-imaging showed a rapid and systemic upregulation of PD-L1 after vaccination. PD-L1 expression could not be correlated to the αGC-dose, although we observed a dose-dependent iNKT cell activation. Dynamics of PD-L1 expression were organ-dependent and most pronounced in lungs and liver, organs to which the vaccine was distributed. PD-L1 expression in lungs increased immediately after vaccination and gradually decreased over time, whereas in liver, vaccination-induced PD-L1 upregulation was short-lived. Flow cytometric analysis of these organs further showed myeloid cells as well as non-immune cells with elevated PD-L1 expression in response to vaccination. SPECT/CT imaging of the tumor demonstrated that the expression of PD-L1 remained stable over time and was overall not affected by vaccination although flow cytometric analysis at the cellular level demonstrated changes in PD-L1 expression in various immune cell populations following vaccination. Conclusion: Repeated non-invasive whole-body imaging using 99mTc-labeled anti-PD-L1 nanobodies allows to document the dynamic nature of PD-L1 expression upon vaccination. Galsome vaccination rapidly induced systemic upregulation of PD-L1 expression with the most pronounced upregulation in lungs and liver while flow cytometry analysis showed upregulation of PD-L1 in the tumor microenvironment. This study shows that imaging using nanobodies may be useful for monitoring vaccine-mediated PD-L1 modulation in patients and could provide a rationale for combination therapy. To the best of our knowledge, this is the first report that visualizes PD-L1 expression upon cancer vaccination.

Lag-3 expression and clinical outcomes in metastatic melanoma patients treated with combination anti-lag-3 + anti-PD-1-based immunotherapies

Lymphocyte-activation gene-3 (LAG-3), an immune checkpoint receptor, negatively regulates T-cell function and facilitates immune escape of tumors. Dual inhibition of LAG-3 and programmed cell death receptor-1 (PD-1) significantly improved progression-free survival (PFS) in metastatic melanoma patients compared to anti-PD-1 therapy alone. Investigating the utility of LAG-3 expression as a biomarker of response to anti-LAG-3 + anti-PD-1 immunotherapy is of great clinical relevance. This study sought to evaluate the association between baseline LAG-3 expression and clinical outcomes following anti-LAG-3 and anti-PD-1-based immunotherapy in metastatic melanoma. LAG-3 immunohistochemistry (clone D2G4O) was performed on pre-treatment formalin-fixed, paraffin-embedded metastatic melanoma specimens from 53 patients treated with combination anti-LAG-3 + anti-PD-1-based therapies. Eleven patients had received prior anti-PD-1-based treatment. Patients were categorized as responders (complete/partial response; n = 36) or non-responders (stable/progressive disease; n = 17) based on the Response Evaluation Criteria in Solid Tumours (RECIST). Tumor-infiltrating lymphocytes (TILs) were scored on hematoxylin and eosin-stained sections. LAG-3 expression was observed in 81% of patients, with staining in TILs and dendritic cells. Responders displayed significantly higher proportions of LAG-3+ cells compared to non-responders (P = .0210). LAG-3 expression positively correlated with TIL score (P < .01). There were no significant differences in LAG-3 expression between different sites of metastases (P > .05). Patients with ≥ 1% LAG-3+ cells in their tumors had significantly longer PFS compared to patients with < 1% LAG-3 expression (P = .0037). No significant difference was observed in overall survival between the two groups (P = .1417). Therefore, the assessment of LAG-3 expression via IHC warrants further evaluation to determine its role as a predictive marker of response and survival in metastatic melanoma.

Correlation of tumor PD-L1 expression in different tissue types and outcome of PD-1-based immunotherapy in metastatic melanoma - analysis of the DeCOG prospective multicenter cohort study ADOREG/TRIM

BACKGROUND

PD-1-based immune checkpoint inhibition (ICI) is the major backbone of current melanoma therapy. Tumor PD-L1 expression represents one of few biomarkers predicting ICI therapy outcome. The objective of the present study was to systematically investigate whether the type of tumor tissue examined for PD-L1 expression has an impact on the correlation with ICI therapy outcome.

METHODS

Pre-treatment tumor tissue was collected within the prospective DeCOG cohort study ADOREG/TRIM (CA209-578; NCT05750511) between February 2014 and May 2020 from 448 consecutive patients who received PD-1-based ICI for non-resectable metastatic melanoma. The primary study endpoint was best overall response (BOR), secondary endpoints were progression-free (PFS) and overall survival (OS). All endpoints were correlated with tumor PD-L1 expression (quantified with clone 28-8; cutoff ≥5%) and stratified by tissue type.

FINDINGS

Tumor PD-L1 was determined in 95 primary tumors (PT; 36.8% positivity), 153 skin/subcutaneous (34.0% positivity), 115 lymph node (LN; 50.4% positivity), and 85 organ (40.8% positivity) metastases. Tumor PD-L1 correlated with BOR if determined in LN (OR = 0.319; 95% CI = 0.138-0.762; P = 0.010), but not in skin/subcutaneous metastases (OR = 0.656; 95% CI = 0.311-1.341; P = 0.26). PD-L1 positivity determined on LN metastases was associated with favorable survival (PFS, HR = 0.490; 95% CI = 0.310-0.775; P = 0.002; OS, HR = 0.519; 95% CI = 0.307-0.880; P = 0.014). PD-L1 positivity determined in PT (PFS, HR = 0.757; 95% CI = 0.467-1.226; P = 0.27; OS; HR = 0.528; 95% CI = 0.305-0.913; P = 0.032) was correlated with survival to a lesser extent. No relevant survival differences were detected by PD-L1 determined in skin/subcutaneous metastases (PFS, HR = 0.825; 95% CI = 0.555-1.226; P = 0.35; OS, HR = 1.083; 95% CI = 0.698-1.681; P = 0.72).

INTERPRETATION

For PD-1-based immunotherapy in melanoma, tumor PD-L1 determined in LN metastases was stronger correlated with therapy outcome than that assessed in PT or organ metastases. PD-L1 determined in skin/subcutaneous metastases showed no outcome correlation and therefore should be used with caution for clinical decision making.

FUNDING

Bristol-Myers Squibb (ADOREG/TRIM, NCT05750511); German Research Foundation (DFG; Clinician Scientist Program UMEA); Else Kröner-Fresenius-Stiftung (EKFS; Medical Scientist Academy UMESciA).

Characterisation and outcome of RAC1 mutated melanoma

BACKGROUND

Activating hot spot R29S mutations in RAC1, a small GTPase influencing several cellular processes including cell proliferation and cytoskeleton rearrangement, have been reported in up to 9% of sun-exposed melanomas. Clinical characteristics and treatment implications of RAC1 mutations in melanoma remain unclear.

METHODS

We investigated the largest set (n = 64) of RAC1 mutated melanoma patients reported to date, including a retrospective single institution cohort (n = 34) from the University Hospital Essen and a prospective multicentre cohort (n = 30) from the translational study Tissue Registry in Melanoma (TRIM; CA209-578), for patient and tumour characteristics as well as therapy outcomes.

RESULTS

From 3037 sequenced melanoma samples screened RAC1 mutations occurred in ^∼2% of samples (64/3037). The most common RAC1 mutation was P29S (95%, 61/64). The majority of tumours had co-occuring MAP kinase mutations (88%, 56/64); mostly activating NRAS (47%, 30/64) mutations, followed by activating BRAF (28%, 18/64) and NF1 (25%, 16/64) mutations. RAC1 mutated melanomas were almost exclusively of cutaneous origin (84%, 54/64) or of unknown primary (MUP, 14%, 9/64). C > T alterations were the most frequent mutation type identified demonstrating a UV-signature for RAC1 mutated melanoma. Most patients with unresectable disease (39) received immune checkpoint inhibitors (ICI) (77%, 30/39). Objective response rate of first-line treatment in patients with stage III/IV disease was 21%; median overall survival was 47.8 months.

CONCLUSIONS

RAC1 mutated melanomas are rare, mostly of cutaneous origin and frequently harbour concomitant MAP kinase mutations, particularly in NRAS. Patients with advanced disease benefit from systemic treatment with ICI.

The Spatial Landscape of Progression and Immunoediting in Primary Melanoma at Single-Cell Resolution

Cutaneous melanoma is a highly immunogenic malignancy that is surgically curable at early stages but life-threatening when metastatic. Here we integrate high-plex imaging, 3D high-resolution microscopy, and spatially resolved microregion transcriptomics to study immune evasion and immunoediting in primary melanoma. We find that recurrent cellular neighborhoods involving tumor, immune, and stromal cells change significantly along a progression axis involving precursor states, melanoma in situ, and invasive tumor. Hallmarks of immunosuppression are already detectable in precursor regions. When tumors become locally invasive, a consolidated and spatially restricted suppressive environment forms along the tumor-stromal boundary. This environment is established by cytokine gradients that promote expression of MHC-II and IDO1, and by PD1-PDL1-mediated cell contacts involving macrophages, dendritic cells, and T cells. A few millimeters away, cytotoxic T cells synapse with melanoma cells in fields of tumor regression. Thus, invasion and immunoediting can coexist within a few millimeters of each other in a single specimen.

SIGNIFICANCE

The reorganization of the tumor ecosystem in primary melanoma is an excellent setting in which to study immunoediting and immune evasion. Guided by classic histopathology, spatial profiling of proteins and mRNA reveals recurrent morphologic and molecular features of tumor evolution that involve localized paracrine cytokine signaling and direct cell-cell contact. This article is highlighted in the In This Issue feature, p. 1397.

Genetic and Clinical Characteristics of ARID1A Mutated Melanoma Reveal High Tumor Mutational Load without Implications on Patient Survival

Simple Summary

Melanoma is a highly malignant skin cancer with the highest mortality of all cutaneous tumors. Relevant genetic events have been identified, which shape the tumor and also the response to treatment. Recurrent ARID1A mutations have been identified, which are associated with improved outcomes to immune checkpoint inhibition in various tumors. Not much was known about the role of ARID1A mutations in melanoma to date. We investigated the largest cohort of ARID1A mutated melanoma to date and were able to show that despite a high mutational load the described beneficial treatment response is not apparent in melanoma.

Abstract

(1) Background: Melanoma has the highest mortality of all cutaneous tumors, despite recent treatment advances. Many relevant genetic events have been identified in the last decade, including recurrent ARID1A mutations, which in various tumors have been associated with improved outcomes to immunotherapy. (2) Methods: Retrospective analysis of 116 melanoma samples harboring ARID1A mutations. Assessment of clinical and genetic characteristics was performed as well as correlations with treatment outcome applying Kaplan–Meier (log-rank test), Fisher’s exact and Chi-squared tests. (3) Results: The majority of ARID1A mutations were in cutaneous and occult melanoma. ARID1A mutated samples had a higher number of mutations than ARID1A wild-type samples and harbored UV-mutations. A male predominance was observed. Many samples also harbored NF1 mutations. No apparent differences were noted between samples harboring genetically inactivating (frame-shift or nonsense) mutations and samples with other mutations. No differences in survival or response to immunotherapy of patients with ARID1A mutant melanoma were observed. (4) Conclusions: ARID1A mutations primarily occur in cutaneous melanomas with a higher mutation burden. In contrast to findings in other tumors, our data does not support ARID1A mutations being a biomarker of favorable response to immunotherapies in melanoma. Larger prospective studies would still be warranted.