There is an exponentially growing interest in targeting immune checkpoint molecules in breast cancer (BC), particularly in the triple-negative subtype where unmet treatment needs remain. This study was designed to analyze the expression, localization, and prognostic role of PD-1, PD-L1, PD-L2, CTLA-4, LAG3, and TIM3 in primary BC. Gene expression analysis using the METABRIC microarray dataset found that all six immune checkpoint molecules are highly expressed in basal-like and HER2-enriched compared to the other BC molecular subtypes. Flow cytometric analysis of fresh tissue homogenates from untreated primary tumors show that PD-1 is principally expressed on CD4⁺ or CD8⁺ T cells and CTLA-4 is expressed on CD4⁺ T cells. The global proportion of PD-L1⁺, PD-L2⁺, LAG3⁺, and TIM3⁺ tumor-infiltrating lymphocytes (TIL) was low and detectable in only a small number of tumors. Immunohistochemically staining fixed tissues from the same tumors was employed to score TIL and tertiary lymphoid structures (TLS). PD-L1⁺, PD-L2⁺, LAG3⁺, and TIM3⁺ cells were detected in some TLS in a pattern that resembles secondary lymphoid organs. This observation suggests that TLS are important sites of immune activation and regulation, particularly in tumors with extensive baseline immune infiltration. Significantly improved overall survival was correlated with PD-1 expression in the HER2-enriched and PD-L1 or CTLA-4 expression in basal-like BC. PD-1 and CTLA-4 proteins were most frequently detected on TIL, which supports the correlations observed between their gene expression and improved long-term outcome in basal-like and HER2-enriched BC. PD-L1 expression by tumor or immune cells is uncommon in BC. Overall, the data presented here distinguish PD-1 as a marker of T cell activity in both the T and B cell areas of BC associated TLS. We found that immune checkpoint molecule expression parallels the extent of TIL and TLS, although there is a noteworthy amount of heterogeneity between tumors even within the same molecular subtype. These data indicate that assessing the levels of immune checkpoint molecule expression in an individual patient has important implications for the success of therapeutically targeting them in BC.

Sixty percent of papillary thyroid cancers (PTCs) are driven by BRAF ^V600E , a mutation associated with high inter- and intra-tumoral heterogeneity. PTCs may become highly aggressive anaplastic thyroid cancers (ATC). While single cell transcriptomics may resolve this heterogeneity, it is potentially confounded by technical effects whose correction may dampen inter-tumor variations. Here we profiled ATCs and BRAF ^V600E PTCs with single nuclei RNA-seq and spatial transcriptomics, and an experimental design disentangling biological and technical variations. It reveals that much transcriptional variation in cancer cells and several immune cell types is idiosyncratic, i.e. tumor-specific, a phenomenon obscured by batch integration in a number of single cell studies. Idiosyncrasies are associated in some cases with genomic aberrations and global tissue states like hypoxia. Beyond idiosyncrasies, differentiation markers SLC5A5 ( NIS ), TPO , TG and TSHR are lost in a sequence mirrored by their gain during human thyroid organoids maturation, suggesting a new classification of cancer cell states. PTC cells retain TSHR expression and show features of partial EMT with a massive expression of FN1 , which promotes proliferation via an autocrine loop. In contrast, ATCs undergo full blown EMT, with expression of mesenchymal extracellular components and loss of TSHR . Finally, we show that the microenvironment of cancer cells is driven by inflammation. These findings may help future stratifications of BRAF ^V600E PTCs.

Background: The diagnosis of malignant thyroid nodules is mainly based on the fine-needle aspiration biopsy (FNAB). To improve the detection of malignant nodules, different molecular tests have been developed. We present a new molecular signature based on altered miRNA expressions and specific mutations. Methods: This is a prospective non-interventional study, including all Bethesda categories, carried out on an FNAB sampled in suspicious nodule(s) during thyroidectomy. miRNA quantification and mutations detection were performed. The reference diagnosis was the pathological assessment of the surgical specimen. Different classification algorithms were trained with molecular data to correctly classify the samples. Results: A total of 294 samples were recorded and randomly divided in two equal groups. The random forest algorithm showed the highest accuracy and used mostly miRNAs to classify the nodules. The sensitivity and the specificity of our signature were, respectively, 76% and 96%, and the positive and negative predictive values were both 90% (disease prevalence of 30%). Conclusions: We have identified a molecular classifier that combines miRNA expressions with mutations detection. This signature could potentially help clinicians, as complementary to the Bethesda classification, to discriminate indeterminate FNABs.

Purpose Pancreatic ductal adenocarcinoma (PDAC) remains one of the most aggressive malignancies, largely due to chemoresistance arising from prominent cellular heterogeneity and a complex tumor microenvironment. Given this challenge, understanding the mechanisms of driving chemoresistance is essential for guiding our chemotherapeutic choice. Emerging evidence suggests that methylglyoxal (MG), a reactive glycolytic byproduct, may play a role in modulating tumor behavior and therapy response, offering a novel angle for patient stratification. Our recent study indicates that the accumulation of MG in PDAC cells leads to MG-induced cellular stress associated with gemcitabine (GEM) resistance. The primary objective of this study was to identify a predictive transcriptomic signature associated with MG stress in PDAC and compare it to the recently reported GemPred signature. Patients and methods MG stress classification was applied to the Puleo et al. cohort (n=309), enabling differential gene expression analysis and the development of a prognostic 16-gene MG-GEM signature through LASSO Cox regression. The signature was validated for survival prediction and diagnostic performance using cross-validation and ROC analysis. Results The prognostic significance and predictive performance of the signature were validated in both internal and external cohorts, including this from the phase III PRODIGE-24/CCTG PA6 trial, composed of 167 GEM and 183 FOLFIRINOX (mFFX) patients. Importantly, the MG-GEM signature demonstrated independent and complementary predictive value relative to GemPred. Conclusion We present a novel clinically actionable 16-gene transcriptomic signature reflecting MG-induced stress that enables robust stratification of PDAC patients likely to benefit from adjuvant GEM therapy. This model supports the development of personalized treatment strategies in pancreatic cancer. Key Objective Can metabolic gemcitabine (GEM) resistant inducer methylglyoxal stress be used to predict resistance to adjuvant GEM through a transcriptomic signature? Knowledge Generated The transcriptomic MG-GEM signature demonstrated the ability to distinguish between GEM sensitive and GEM resistant patients in the internal cohorts from Erasme University Hospital. MG-GEMs predictive capacity was further validated in the independent PRODIGE-24/CCTG PA6 clinical trial data. When compared to the established GemPred transcriptomic signature, MG-GEM exhibited an independent predictive behaviour. Moreover, combining both independent signatures enhanced the identification of GEM sensitive and GEM resistant patient subgroups. This combined approach may be particularly valuable in identifying patient subsets with treatment- specific responses.