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      Classification of the tumor immune microenvironment and associations with outcomes in patients with metastatic melanoma treated with immunotherapies

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          Abstract

          Background

          Tumor microenvironment (TME) characteristics are potential biomarkers of response to immune checkpoint inhibitors in metastatic melanoma. This study developed a method to perform unsupervised classification of TME of metastatic melanoma.

          Methods

          We used multiplex immunohistochemical and quantitative pathology-derived assessment of immune cell compositions of intratumoral and peritumoral regions of metastatic melanoma baseline biopsies to classify TME in relation to response to anti-programmed cell death protein 1 (PD-1) monotherapy or in combination with anti-cytotoxic T-cell lymphocyte-4 (ipilimumab (IPI)+PD-1).

          Results

          Spatial profiling of CD8+T cells, macrophages, and melanoma cells, as well as phenotypic PD-1 receptor ligand (PD-L1) and CD16 proportions, were used to identify and classify patients into one of three mutually exclusive TME classes: immune-scarce, immune-intermediate, and immune-rich tumors. Patients with immune-rich tumors were characterized by a lower proportion of melanoma cells and higher proportions of immune cells, including higher PD-L1 expression. These patients had higher response rates and longer progression-free survival (PFS) than those with immune-intermediate and immune-scarce tumors. At a median follow-up of 18 months (95% CI: 6.7 to 49 months), the 1-year PFS was 76% (95% CI: 64% to 90%) for patients with an immune-rich tumor, 56% (95% CI: 44% to 72%) for those with an immune-intermediate tumor, and 33% (95% CI: 23% to 47%) for patients with an immune-scarce tumor. A higher response rate was observed in patients with an immune-scarce or immune-intermediate tumor when treated with IPI+PD-1 compared with those treated with PD-1 alone.

          Conclusions

          Our study provides an automatic TME classification method that may predict the clinical efficacy of immunotherapy for patients with metastatic melanoma.

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          Most cited references76

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          New response evaluation criteria in solid tumours: revised RECIST guideline (version 1.1).

          Assessment of the change in tumour burden is an important feature of the clinical evaluation of cancer therapeutics: both tumour shrinkage (objective response) and disease progression are useful endpoints in clinical trials. Since RECIST was published in 2000, many investigators, cooperative groups, industry and government authorities have adopted these criteria in the assessment of treatment outcomes. However, a number of questions and issues have arisen which have led to the development of a revised RECIST guideline (version 1.1). Evidence for changes, summarised in separate papers in this special issue, has come from assessment of a large data warehouse (>6500 patients), simulation studies and literature reviews. HIGHLIGHTS OF REVISED RECIST 1.1: Major changes include: Number of lesions to be assessed: based on evidence from numerous trial databases merged into a data warehouse for analysis purposes, the number of lesions required to assess tumour burden for response determination has been reduced from a maximum of 10 to a maximum of five total (and from five to two per organ, maximum). Assessment of pathological lymph nodes is now incorporated: nodes with a short axis of 15 mm are considered measurable and assessable as target lesions. The short axis measurement should be included in the sum of lesions in calculation of tumour response. Nodes that shrink to <10mm short axis are considered normal. Confirmation of response is required for trials with response primary endpoint but is no longer required in randomised studies since the control arm serves as appropriate means of interpretation of data. Disease progression is clarified in several aspects: in addition to the previous definition of progression in target disease of 20% increase in sum, a 5mm absolute increase is now required as well to guard against over calling PD when the total sum is very small. Furthermore, there is guidance offered on what constitutes 'unequivocal progression' of non-measurable/non-target disease, a source of confusion in the original RECIST guideline. Finally, a section on detection of new lesions, including the interpretation of FDG-PET scan assessment is included. Imaging guidance: the revised RECIST includes a new imaging appendix with updated recommendations on the optimal anatomical assessment of lesions. A key question considered by the RECIST Working Group in developing RECIST 1.1 was whether it was appropriate to move from anatomic unidimensional assessment of tumour burden to either volumetric anatomical assessment or to functional assessment with PET or MRI. It was concluded that, at present, there is not sufficient standardisation or evidence to abandon anatomical assessment of tumour burden. The only exception to this is in the use of FDG-PET imaging as an adjunct to determination of progression. As is detailed in the final paper in this special issue, the use of these promising newer approaches requires appropriate clinical validation studies.
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            Microenvironmental regulation of tumor progression and metastasis.

            Cancers develop in complex tissue environments, which they depend on for sustained growth, invasion and metastasis. Unlike tumor cells, stromal cell types within the tumor microenvironment (TME) are genetically stable and thus represent an attractive therapeutic target with reduced risk of resistance and tumor recurrence. However, specifically disrupting the pro-tumorigenic TME is a challenging undertaking, as the TME has diverse capacities to induce both beneficial and adverse consequences for tumorigenesis. Furthermore, many studies have shown that the microenvironment is capable of normalizing tumor cells, suggesting that re-education of stromal cells, rather than targeted ablation per se, may be an effective strategy for treating cancer. Here we discuss the paradoxical roles of the TME during specific stages of cancer progression and metastasis, as well as recent therapeutic attempts to re-educate stromal cells within the TME to have anti-tumorigenic effects.
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              Understanding the tumor immune microenvironment (TIME) for effective therapy

              The clinical successes in immunotherapy have been both astounding and at the same time unsatisfactory. Countless patients with varied tumor types have seen pronounced clinical response with immunotherapeutic intervention; however, many more patients have experienced minimal or no clinical benefit when provided the same treatment. As technology has advanced, so has the understanding of the complexity and diversity of the immune context of the tumor microenvironment and its influence on response to therapy. It has been possible to identify different subclasses of immune environment that have an influence on tumor initiation and response and therapy; by parsing the unique classes and subclasses of tumor immune microenvironment (TIME) that exist within a patient’s tumor, the ability to predict and guide immunotherapeutic responsiveness will improve, and new therapeutic targets will be revealed.
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                Author and article information

                Journal
                J Immunother Cancer
                J Immunother Cancer
                jitc
                jitc
                Journal for Immunotherapy of Cancer
                BMJ Publishing Group (BMA House, Tavistock Square, London, WC1H 9JR )
                2051-1426
                2023
                20 October 2023
                : 11
                : 10
                : e007144
                Affiliations
                [1 ] departmentMelanoma Institute Australia , Ringgold_213551The University of Sydney , Sydney, New South Wales, Australia
                [2 ] departmentFaculty of Medicine and Health , Ringgold_522555The University of Sydney , Sydney, New South Wales, Australia
                [3 ] departmentCentre for Cancer Research , Ringgold_107640Westmead Institute for Medical Research , Westmead, New South Wales, Australia
                [4 ] departmentSchool of Mathematics and Statistics , Ringgold_4334The University of Sydney , Sydney, New South Wales, Australia
                [5 ] Ringgold_213551Westmead and Blacktown Hospitals , Sydney, New South Wales, Australia
                [6 ] Ringgold_560845Royal North Shore and Mater Hospitals , Sydney, New South Wales, Australia
                [7 ] departmentDepartment of Tissue Oncology and Diagnostic Pathology , Ringgold_4334Royal Prince Alfred Hospital and NSW Health Pathology , Sydney, New South Wales, Australia
                Author notes
                [Correspondence to ] Dr James S Wilmott; james.wilmott@ 123456sydney.edu.au
                Author information
                http://orcid.org/0000-0001-7592-5460
                http://orcid.org/0000-0002-5253-4747
                http://orcid.org/0000-0001-5092-5544
                http://orcid.org/0000-0003-3540-8906
                http://orcid.org/0000-0001-8894-3545
                http://orcid.org/0000-0002-8991-0013
                http://orcid.org/0000-0002-6750-5244
                Article
                jitc-2023-007144
                10.1136/jitc-2023-007144
                10603328
                37865395
                598e52dd-cfe5-4db3-b9c5-6f51e4c51665
                © Author(s) (or their employer(s)) 2023. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.

                This is an open access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited, appropriate credit is given, any changes made indicated, and the use is non-commercial. See http://creativecommons.org/licenses/by-nc/4.0/.

                History
                : 10 August 2023
                Funding
                Funded by: Melanoma Research Alliance young investigator fellowship;
                Award ID: 700455
                Funded by: National Health and Medical Research Council of Australia (NHMRC);
                Award ID: APP2006415 and APP2018514
                Funded by: University of Sydney Medical Foundation;
                Award ID: N/A
                Funded by: NHMRC Fellowship;
                Award ID: APP1174325
                Funded by: FundRef http://dx.doi.org/10.13039/501100001102, Cancer Council NSW;
                Award ID: RG19-15
                Funded by: CINSW Early Career Fellowship;
                Award ID: 2020/ECF1153, 2020/ECF1244, and 2021/ECF1376
                Funded by: CINSW Translational Program Grant;
                Award ID: TPG 2021/TPG2114
                Categories
                Clinical/Translational Cancer Immunotherapy
                1506
                2435
                Original research
                Custom metadata
                unlocked

                pathology,immunotherapy,tumor microenvironment,melanoma
                pathology, immunotherapy, tumor microenvironment, melanoma

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