bims-tumhet 2024-11-10 papers

Issue of 2024–11–10
three papers selected by
Sergio Marchini, Humanitas Research

Cancer Cell. 2024 Oct 17. pii: S1535-6108(24)00367-2. [Epub ahead of print]

Finding the right friends: Stromal composition influences TLS formation in ovarian cancer.

In this issue of Cancer Cell, MacFawn et al. reveal that tertiary lymphoid structures (TLSs) in high-grade serous ovarian cancer (HGSOC) vary significantly by anatomical site. They highlight a distinct stromal composition associated with TLS formation, with cancer-educated mesenchymal stem cells (CA-MSCs) inversely linked to TLS activity and patient prognosis.

DOI: https://doi.org/10.1016/j.ccell.2024.10.004

J Surg Oncol. 2024 Nov 05.

Molecular Classification of Endometrial Cancers Using an Integrative DNA Sequencing Panel.

Soyoun R Kim, Leslie Oldfield, Raymond H Kim, Osvaldo Espin-Garcia, Kathy Han, Danielle Vicus, Lua Eiriksson, Alicia Tone, Aaron Pollett, Matthew Cesari, Blaise Clarke, Marcus Q Bernardini, Trevor J Pugh, Sarah E Ferguson.

BACKGROUND AND OBJECTIVES: Adoption of molecular classification in endometrial cancer (EC) into clinical practice remains challenging due to complexity in coordination of multiple assays. We aimed to develop a simple molecular technique to classify ECs into four subgroups using our custom-designed targeted sequencing panel.
METHODS: Patients with newly diagnosed ECs were prospectively recruited from three cancer centres in Ontario, Canada. Using our panel, 181 ECs were sequenced. Variants were analysed for pathogenicity and clinicopathologic information was collected through medical records retrospectively.
RESULTS: Of 181, 86 (48%) were mismatch repair deficient (MMRd), of which 62 (72%) harboured MLH1 promoter methylation and 24 (28%) had pathogenic variants in MMR genes. Of single classifiers, three (1.8%) had pathogenic POLE (POLEmut), 15 (9%) had TP53 mutations (p53abn) and 61 (37%) had no specific molecular profile subtype (NSMP). Sixteen (9%) had more than one molecular classifying feature, with eight (4%) MMRd-p53abn, six (3%) POLEmut-MMRd, one (0.5%) POLEmut-MMRd-p53abn and one (0.5%) POLEmut-p53abn. When MMRd group was further subclassified according to mechanism of MMR loss, MLH1 promoter methylated group had worse outcomes than those with somatic MMR pathogenic variants.
CONCLUSIONS: Our panel can classify ECs into four subgroups through a simplified process and can be implemented reflexively in clinical practice.

Keywords: endometrial cancer; molecular classification; molecular subtype; next‐generation sequencing; panel

DOI: https://doi.org/10.1002/jso.27973

Front Immunol. 2024 ;15 1483497

Tertiary lymphoid structure-related score as a predictor for survival prognosis and immunotherapy response in head and neck squamous cell carcinoma.

Fan Wu, Haotian Cao, Siqi Ren, Jiaying Wu, Xin Liu, Qunxing Li, Qiuping Xu, Jiali Chen, Ruixin Wang, Suling Chen, Shijia Kuang, Binbin Xia, Yanyan Li, Liansheng Wang, Jintao Li, Bowen Li, Jinsong Li, Tianjun Lan.

Background: Substantial studies reveal that tertiary lymphoid structure (TLS) correlate with prognosis and immunotherapy response in various types of cancers. However, the predictive value of TLS, the specific immune cell subtype within TLS and their anti-tumor mechanisms remain unclear.
Methods: Based on 23 TLS-related genes (TLSRGs), we utilized bioinformatics methods to construct a scoring system, named TLSscore. By integrating RNA and single-cell sequencing data, we assessed the utility of TLSscore in head and neck squamous cell carcinoma (HNSCC). Flow cytometric sorting was used to isolate specific T cells subtypes, in vivo and in vitro experiments were conducted to demonstrate its anti-tumor effects.
Results: The TLSscore model was constructed and specific TLSscore-genes were found to consistently align with the spatial location of TLS. TLSscore has proven to be a robust predictive model for predicting survival prognosis, immune cell infiltration, somatic mutation and immunotherapy response. Notably, a specific PD1+CXCL13+CD8+T cell subtype was identified within TLS. Both in vivo and in vitro experiments demonstrated that PD1+CXCL13+CD8+T cell might represent a functional cell subtype exerting anti-tumor effects during the process of immune surveillance.
Conclusions: Our study presents a predictive model for TLS, which can evaluate its presence and predicts survival prognosis and immunotherapy response in HNSSC patients. Additionally, we identify a specific subtype of T cells that might elucidate the mechanism of TLS function in anti-tumor activities. This T cell subtype holds the potential to be a prognostic marker and a target for adoptive cell therapy (ACT) in the future.

Keywords: TLSRGs; TLSscore; head and neck squamous cell carcinoma; immunotherapy; tertiary lymphoid structure

DOI: https://doi.org/10.3389/fimmu.2024.1483497