Platinum-based chemotherapy in combination with immune-checkpoint inhibitors is the current standard of care for patients with advanced lung adenocarcinoma (LUAD). However, tumor progression evolves in most cases. Therefore, predictive biomarkers are needed for better patient stratification and for the identification of new therapeutic strategies, including enhancing the efficacy of chemotoxic agents. Here, we hypothesized that discoidin domain receptor 1 (DDR1) may be both a predictive factor for chemoresistance in patients with LUAD and a potential target positively selected in resistant cells. By using biopsies from patients with LUAD, KRAS-mutant LUAD cell lines, and in vivo genetically engineered KRAS-driven mouse models, we evaluated the role of DDR1 in the context of chemotherapy treatment. We found that DDR1 is upregulated during chemotherapy both in vitro and in vivo. Moreover, analysis of a cohort of patients with LUAD suggested that high DDR1 levels in pretreatment biopsies correlated with poor response to chemotherapy. Additionally, we showed that combining DDR1 inhibition with chemotherapy prompted a synergistic therapeutic effect and enhanced cell death of KRAS-mutant tumors in vivo. Collectively, this study suggests a potential role for DDR1 as both a predictive and prognostic biomarker, potentially improving the chemotherapy response of patients with LUAD.

When triple-negative breast cancer (TNBC) patients have residual disease after neoadjuvant chemotherapy (NACT), they have a high risk of metastatic relapse. With immune infiltrate in TNBC being prognostic and predictive of response to treatment, our aim was to develop an immunologic transcriptomic signature using post-NACT samples to predict relapse. We identified 115 samples of residual tumors from post-NACT TNBC patients. We profiled the expression of 770 genes related to cancer microenvironment using the NanoString PanCancer IO360 panel to develop a prognostic transcriptomic signature, and we describe the immune microenvironments of the residual tumors. Thirty-eight (33%) patients experienced metastatic relapse. Hierarchical clustering separated patients into five clusters with distinct prognosis based on pathways linked to immune activation, epithelial-to-mesenchymal transition and cell cycle. The immune microenvironment of the residual disease was significantly different between patients who experienced relapse compared to those who did not, the latter having significantly more effector antitumoral immune cells, with significant differences in lymphoid subpopulations. We selected eight genes linked to immunity (BLK, GZMM, CXCR6, LILRA1, SPIB, CCL4, CXCR4, SLAMF7) to develop a transcriptomic signature which could predict relapse in our cohort. This signature was validated in two external cohorts (KMplot and METABRIC). Lack of immune activation after NACT is associated with a high risk of distant relapse. We propose a prognostic signature based on immune infiltrate that could lead to targeted therapeutic strategies to improve patient prognosis.

Whole genome and transcriptome sequencing of a cohort of 67 leiomyosarcomas has been revealed ATRX to be one of the most frequently mutated genes in leiomyosarcomas after TP53 and RB1. While its function is well described in the alternative lengthening of telomeres mechanism, we wondered whether its alteration could have complementary effects on sarcoma oncogenesis. ATRX alteration is associated with the down-expression of genes linked to differentiation in leiomyosarcomas, and to immunity in an additional cohort of 60 poorly differentiated pleomorphic sarcomas. In vitro and in vivo models showed that ATRX down-expression increases tumor growth rate and immune escape by decreasing the immunity load of active mast cells in sarcoma tumors. These data indicate that an alternative to unsuccessful targeting of the adaptive immune system in sarcoma could target the innate system. This might lead to a better outcome for sarcoma patients in terms of ATRX status.

Purpose: To investigate the immune biomarker in Leiomyosarcoma (LMS), which is rare and recognized as an immune cold cancer showing a poor response rate (<10%) to immune checkpoint inhibitors (ICIs). However, durable response and clinical benefit to ICIs has been observed in a few cases of LMS, including, but not only, LMS with tertiary lymphoid structure (TLS) structures. Patients and methods: We used comprehensive transcriptomic profiling and a deconvolution method extracted from RNA-sequencing gene expression data in two independent LMS cohorts, the International Cancer Genome Consortium (ICGC, N = 146) and The Cancer Genome Atlas (TCGA, N = 75), to explore tumor immune microenvironment (TIME) in LMS. Results: Unsupervised clustering analysis using the previously validated two methods, 90-gene signature and Cell-type Identification by Estimating Relative Subsets of RNA Transcripts (CIBERSORT), identified immune hot (I-H) and immune high (I-Hi) LMS, respectively, in the ICGC cohort. Similarly, immune active groups (T-H, T-Hi) were identified in the TCGA cohort using these two methods. These immune active (“hot”) clusters were significantly associated, but not completely overlapping, with several validated immune signatures such as sarcoma immune class (SIC) classification and TLS score, T cell inflamed signature (TIS) score, immune infiltration score (IIS), and macrophage score (M1/M2), with more patients identified by our clustering as potentially immune hot. Conclusions: Comprehensive immune profiling revealed a subset of LMS with a distinct active (“hot”) TIME, consistently associated with several validated immune signatures in other cancers. This suggests that the methodologies that we used in this study warrant further validation and development, which can potentially help refine our current immune biomarkers to select the right LMS patients for ICIs in clinical trials.

In leiomyosarcoma (LMS), a very aggressive disease, a relatively transcriptionally uniform subgroup of well-differentiated tumors has been described and is associated with poor survival. The question raised how differentiation and tumor progression, two apparently antagonist processes, coexist and allow tumor malignancy. We first identified the most transcriptionally homogeneous LMS subgroup in three independent cohorts, which we named ‘hLMS’. The integration of multi-omics data and functional analysis suggests that hLMS originate from vascular smooth muscle cells and show that hLMS transcriptional program reflects both modulations of smooth muscle contraction activity controlled by MYOCD/SRF regulatory network and activation of the cell cycle activity controlled by E2F/RB1 pathway. We propose that the phenotypic plasticity of vascular smooth muscle cells coupled with MYOCD/SRF pathway amplification, essential for hLMS survival, concomitant with PTEN absence and RB1 alteration, could explain how hLMS balance this uncommon interplay between differentiation and aggressiveness.