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      A Phase II Open-Label Randomized Clinical Trial of Preoperative Durvalumab or Durvalumab plus Tremelimumab in Resectable Head and Neck Squamous Cell Carcinoma

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          Abstract

          Purpose:

          Clinical implications of neoadjuvant immunotherapy in patients with locally advanced but resectable head and neck squamous cell carcinoma (HNSCC) remain largely unexplored.

          Patients and Methods:

          Patients with resectable HNSCC were randomized to receive a single dose of preoperative durvalumab (D) with or without tremelimumab (T) before resection, followed by postoperative (chemo)radiotherapy based on multidisciplinary discretion and 1-year D treatment. Artificial intelligence (AI)–powered spatial distribution analysis of tumor-infiltrating lymphocytes and high-dimensional profiling of circulating immune cells tracked dynamic intratumoral and systemic immune responses.

          Results:

          Of the 48 patients enrolled (D, 24 patients; D+T, 24 patients), 45 underwent surgical resection per protocol (D, 21 patients; D+T, 24 patients). D±T had a favorable safety profile and did not delay surgery. Distant recurrence-free survival (DRFS) was significantly better in patients treated with D+T than in those treated with D monotherapy. AI-powered whole-slide image analysis demonstrated that D+T significantly reshaped the tumor microenvironment toward immune-inflamed phenotypes, in contrast with the D monotherapy or cytotoxic chemotherapy. High-dimensional profiling of circulating immune cells revealed a significant expansion of T-cell subsets characterized by proliferation and activation in response to D+T therapy, which was rare following D monotherapy. Importantly, expansion of specific clusters in CD8+ T cells and non-regulatory CD4+ T cells with activation and exhaustion programs was associated with prolonged DRFS in patients treated with D+T.

          Conclusions:

          Preoperative D±T is feasible and may benefit patients with resectable HNSCC. Distinct changes in the tumor microenvironment and circulating immune cells were induced by each treatment regimen, warranting further investigation.

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          Most cited references60

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          Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries

          This article provides an update on the global cancer burden using the GLOBOCAN 2020 estimates of cancer incidence and mortality produced by the International Agency for Research on Cancer. Worldwide, an estimated 19.3 million new cancer cases (18.1 million excluding nonmelanoma skin cancer) and almost 10.0 million cancer deaths (9.9 million excluding nonmelanoma skin cancer) occurred in 2020. Female breast cancer has surpassed lung cancer as the most commonly diagnosed cancer, with an estimated 2.3 million new cases (11.7%), followed by lung (11.4%), colorectal (10.0 %), prostate (7.3%), and stomach (5.6%) cancers. Lung cancer remained the leading cause of cancer death, with an estimated 1.8 million deaths (18%), followed by colorectal (9.4%), liver (8.3%), stomach (7.7%), and female breast (6.9%) cancers. Overall incidence was from 2-fold to 3-fold higher in transitioned versus transitioning countries for both sexes, whereas mortality varied <2-fold for men and little for women. Death rates for female breast and cervical cancers, however, were considerably higher in transitioning versus transitioned countries (15.0 vs 12.8 per 100,000 and 12.4 vs 5.2 per 100,000, respectively). The global cancer burden is expected to be 28.4 million cases in 2040, a 47% rise from 2020, with a larger increase in transitioning (64% to 95%) versus transitioned (32% to 56%) countries due to demographic changes, although this may be further exacerbated by increasing risk factors associated with globalization and a growing economy. Efforts to build a sustainable infrastructure for the dissemination of cancer prevention measures and provision of cancer care in transitioning countries is critical for global cancer control.
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            Is Open Access

            SCANPY : large-scale single-cell gene expression data analysis

            Scanpy is a scalable toolkit for analyzing single-cell gene expression data. It includes methods for preprocessing, visualization, clustering, pseudotime and trajectory inference, differential expression testing, and simulation of gene regulatory networks. Its Python-based implementation efficiently deals with data sets of more than one million cells (https://github.com/theislab/Scanpy). Along with Scanpy, we present AnnData, a generic class for handling annotated data matrices (https://github.com/theislab/anndata).
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              Cancer immunotherapy using checkpoint blockade

              The release of negative regulators of immune activation (immune checkpoints) that limit antitumor responses has resulted in unprecedented rates of long-lasting tumor responses in patients with a variety of cancers. This can be achieved by antibodies blocking the cytotoxic T lymphocyte antigen-4 (CTLA-4) or the programmed death-1 (PD-1) pathway, either alone or in combination. The main premise for inducing an immune response is the pre-existence of antitumor T cells that were limited by specific immune checkpoints. Most patients who have tumor responses maintain long lasting disease control, yet one third of patients relapse. Mechanisms of acquired resistance are currently poorly understood, but evidence points to alterations that converge on the antigen presentation and interferon gamma signaling pathways. New generation combinatorial therapies may overcome resistance mechanisms to immune checkpoint therapy.
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                Author and article information

                Contributors
                Journal
                Clinical Cancer Research
                American Association for Cancer Research (AACR)
                1078-0432
                1557-3265
                May 15 2024
                March 08 2024
                May 15 2024
                March 08 2024
                : 30
                : 10
                : 2097-2110
                Article
                10.1158/1078-0432.CCR-23-3249
                38457288
                d05948ac-936d-4a92-bfdc-cd2d21fd1405
                © 2024
                History

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