Biology subjects

de Miranda, N. F. C. C.

Publications and source records attributed to de Miranda, N. F. C. C..

4 recordsLinked to original sources

bioRxiv2023

Multimodal profiling of chordoma immunity reveals distinct immune contextures

Chordomas are cancers from the axial skeleton presenting immunological hallmarks of unknown significance. In recent years, some clinical trials demonstrated that chordomas can respond to immunotherapy. We present a comprehensive characterisation of immunological features of 76 chordomas through application of a multimodal approach comprising transcriptional profiling, multidimensional immunophenotyping and TCR profiling. Chordomas generally presented an immune "hot" microenvironment in comparison to other sarcomas, as indicated by the immunologic constant of rejection transcriptional signature. We identified two distinct groups of chordomas based on T cell infiltration. The highly infiltrated group was further characterised by high dendritic cell infiltration and the presence of multicellular immune aggregates in tumours, whereas low T cell infiltration was associated with lower overall cell densities of immune and stromal cells. Interestingly, patients with higher T cell infiltration displayed a more pronounced clonal enrichment of the T cell receptor repertoire compared to those with low T cell counts. Furthermore, we observed that the majority of chordomas maintained HLA class I expression. Our findings shed light on the natural immunity against chordomas. Understanding their immune landscape could guide the development and application of immunotherapies in a tailored manner, ultimately leading to an improved clinical outcome for chordoma patients.

cancer biology↗

bioRxiv2021

OX40 agonism enhances efficacy of PD-L1 checkpoint blockade by shifting the cytotoxic T cell differentiation spectrum

Immune checkpoint therapy (ICT) has the potency to eradicate cancer but the mechanisms that determine effective versus non-effective therapy-induced immune responses are not fully understood. Here, using high-dimensional single-cell profiling we examined whether T cell states in the blood circulation could predict responsiveness to a combined ICT, sequentially targeting OX40 costimulatory and PD-1 inhibitory pathways, which effectively eradicated syngeneic mouse tumors. Unbiased assessment of transcriptomic alterations by single-cell RNA sequencing and profiling of cell-surface protein expression by mass cytometry revealed unique activation states for therapy-responsive CD4+ and CD8+ T cells. Effective ICT elicited T cells with dynamic expression of distinct NK cell and chemokine receptors, and these cells were systemically present in lymphoid tissues and in the tumor. Moreover, NK cell receptor-expressing CD8+ T cells were also present in the peripheral blood of immunotherapy-responsive cancer patients. Targeting of the NK cell and chemokine receptors in tumor-bearing mice showed their functional importance for therapy-induced anti-tumor immunity. These findings provide a better understanding of ICT and highlight the use of dynamic biomarkers on effector CD4+ and CD8+ T cells to improve cancer immunotherapy.

immunology↗

bioRxiv2021

γδ T cells are effectors of immune checkpoint blockade in mismatch repair-deficient colon cancers with antigen presentation defects

DNA mismatch repair deficient (MMR-d) cancers present an abundance of neoantigens that likely underlies their exceptional responsiveness to immune checkpoint blockade (ICB)1,2. However, MMR-d colon cancers that evade CD8+ T cells through loss of Human Leukocyte Antigen (HLA) class I-mediated antigen presentation3-6, frequently remain responsive to ICB7 suggesting the involvement of other immune effector cells. Here, we demonstrate that HLA class I-negative MMR-d cancers are highly infiltrated by {gamma}{delta} T cells. These {gamma}{delta} T cells are mainly composed of V{delta}1 and V{delta}3 subsets, and express high levels of PD-1, activation markers including cytotoxic molecules, and a broad repertoire of killer-cell immunoglobulin-like receptors (KIRs). In vitro, PD-1+ {gamma}{delta} T cells, isolated from MMR-d colon cancers, exhibited a cytolytic response towards HLA class I-negative MMR-d colon cancer cell lines and {beta}2-microglobulin (B2M)-knockout patient-derived tumor organoids (PDTOs), which was enhanced as compared to antigen presentation-proficient cells. This response was diminished after blocking the interaction between NKG2D and its ligands. By comparing paired tumor samples of MMR-d colorectal cancer patients obtained before and after dual PD-1 and CTLA-4 blockade, we found that ICB profoundly increased the intratumoral frequency of {gamma}{delta} T cells in HLA class I-negative cancers. Taken together, these data indicate that {gamma}{delta} T cells contribute to the response to ICB therapy in patients with HLA class I-negative, MMR-d colon cancers, and illustrate the potential of {gamma}{delta} T cells in cancer immunotherapy.

cancer biology↗

bioRxiv2020

Semi-automated background removal limits loss of data and normalises the images for downstream analysis of imaging mass cytometry data

Imaging mass cytometry (IMC) allows the detection of multiple antigens (approximately 40 markers) combined with spatial information, making it a unique tool for the evaluation of complex biological systems. Due to its widespread availability and retained tissue morphology, formalin-fixed, paraffin-embedded (FFPE) tissues are often a material of choice for IMC studies. However, antibody performance and signal-to-noise ratio can differ considerably between FFPE tissues as a consequence of variations in tissue processing, including fixation. We investigated the effect of immunodetection-related signal intensity fluctuations on IMC analysis and phenotype identification in a cohort of twelve colorectal cancer tissues. Furthermore, we explored different normalisation strategies and propose a workflow to normalise IMC data by semi-automated background removal, using publicly available tools. This workflow can be directly applied to previously obtained datasets and considerably improves the quality of IMC data, thereby supporting the analysis and comparison of multiple samples.

immunology↗