BackgroundImmunotherapy, particularly cytokine-based therapy, has been gaining traction in the treatment of head and neck cancers. Cytokines are small proteins that play a crucial role in the immune ...response to cancer. However, not all patients respond to cytokine-based immunotherapies, and some may experience severe side effects. The ability to predict which patients are most likely to benefit from these therapies could greatly improve the efficacy and tolerability of treatment.1 In this context, the study of predictive biomarkers, such as the expression of immune checkpoint molecules like PD-L1 and TIGIT, and the profiling of cytokines within the tumor microenvironment, has become crucial. These biomarkers could provide valuable information about the patient‘s immune response to the tumor and their likelihood of responding to cytokine-based immunotherapies.2 3 MethodsA multimodal approach to stratify predictive biomarkers in head and neck cancers was used. The methodology was founded on combining metabolic, transcriptomic, and proteomic data to offer a comprehensive understanding of potential biomarkers. The data from different modalities were integrated using bioinformatics and machine learning algorithms. This comprehensive dataset was analyzed to identify multimodal biomarker signatures that could predict patient responses to cytokine-based therapies.ResultsPreliminary finding showed significant heterogeneity in the expression of PD-L1, TIGIT, and cytokines across the tumor microenvironment. This underscored the complexity of head and neck cancers. Certain multimodal biomarker signatures, which included specific patterns of immune checkpoint molecule expression, cytokine profiles, and immune cell infiltration, correlated robustly with patient responses to cytokine-based therapies.ConclusionsOur study demonstrates the considerable potential of a multimodal stratification approach in deciphering the complexity of head and neck cancers and predicting responses to cytokine-based immunotherapies. By integrating metabolomic, transcriptomic, and proteomic data, we identified unique biomarker signatures that strongly correlated with therapy responses. This work underscores the role of integrated predictive biomarker profiles in enhancing the precision of immunotherapy.ReferencesZemek RM, De Jong E, Chin WL, Schuster IS, Fear VS, Casey TH, Holt RA. Sensitization to immune checkpoint blockade through activation of a STAT1/NK axis in the tumor microenvironment. Science translational medicine. 2019;11(501):7816.Zhang Q, He Y, Luo N, Patel SJ, Han Y, Gao R, Hu Z. Landscape and Dynamics of Single Immune Cells in Hepatocellular Carcinoma. Cell. 2019;179(4):829–845.Azizi E, Carr AJ, Plitas G, Cornish AE, Konopacki C, Prabhakaran S. Single-Cell Map of Diverse Immune Phenotypes in the Breast Tumor Microenvironment. Cell. 2018;174(5):1293–1308.
Little is known about the molecular signatures associated with specific metastatic sites in breast cancer. Using comprehensive multi-omic molecular profiling, we assessed whether alterations or ...activation of the PI3K-AKT-mTOR pathway is associated with specific sites of breast cancer metastasis.
Next-generation sequencing-based whole-exome sequencing was coupled with reverse-phase protein microarray (RPPA) functional signaling network analysis to explore the PI3K-AKT-mTOR axis in 32 pretreated breast cancer metastases. RPPA-based signaling data were further validated in an independent cohort of 154 metastatic lesions from breast cancer and 101 unmatched primary breast tumors. The proportion of cases with PI3K-AKT-mTOR genomic alterations or signaling network activation were compared between hepatic and nonhepatic lesions.
mutation and activation of AKT (S473) and p70S6K (T389) were detected more frequently among liver metastases than nonhepatic lesions (
< 0.01,
= 0.056, and
= 0.053, respectively). However,
mutations alone were insufficient in predicting protein activation (
= 0.32 and
= 0.19 for activated AKT and p70S6K, respectively). RPPA analysis of an independent cohort of 154 tumors confirmed the relationship between pathway activation and hepatic metastasis AKT (S473), mTOR (S2448), and 4EBP1 (S65);
< 0.01,
= 0.02, and
= 0.01, respectively. Similar results were also seen between liver metastases and primary breast tumors AKT (S473)
< 0.01, mTOR (S2448)
< 0.01, 4EBP1 (S65)
= 0.01. This signature was lost when primary tumors were compared with all metastatic sites combined.
Breast cancer patients with liver metastasis may represent a molecularly homogenized cohort with increased incidence of
mutations and activation of the PI3K-AKT-mTOR signaling network.
.
BackgroundTertiary lymphoid structures (TLS) are ectopic lymphoid formations and sites of local immune response, where B-cells, among other immune cells, play critical roles.1 2 In the tumor ...microenvironment TLS play pivotal roles in the orchestration of immune responses in lung cancer. However, the spatial distribution of B-cell checkpoint inhibitors within these structures remains poorly understood.3 Understanding these interactions could lead to more effective use of checkpoint inhibitors and the development of novel therapeutic strategies for lung cancer, thereby improving clinical outcomes.4–6 This study aims to elucidate this complex spatial interface and its implications for therapy response.MethodsRetrospectively collected surgically resected NSCLC tumors treated with checkpoint inhibitors therapy were used. Using multiplexed immunohistochemistry and comprehensive spatial analytics, we analyzed the distribution, density, and interaction of B-cell checkpoint inhibitors, including PD-1 and CTLA-4, within the TLS. This technique allowed for an in-depth analysis of the protein expression patterns and spatial relationships at the single-cell level, providing an opportunity to explore correlations between spatial distribution of B-cell checkpoint inhibitors and therapy response.ResultsPreliminary findings showed a distinct heterogeneity in the spatial localization of these inhibitory markers, suggestive of a complex interplay between the B-cell subsets, checkpoint expression, and tumor characteristics. Additionally, we identified distinct spatial signatures correlating with patients‘ responses to checkpoint blockade therapies. Our results suggested that the spatial distribution of B-cell checkpoint inhibitors within TLS may serve as a novel biomarker for predicting immunotherapy responses in lung cancer patients.ConclusionsOur data enriches the understanding of the tumor-immune interface in lung cancer, and we propose novel strategies for tailoring immunotherapies based on the spatial distribution of B-cell checkpoint inhibitors within TLS. We anticipate this research to stimulate further discussion on optimizing lung cancer treatment strategies and identifying patients who are most likely to benefit from B-cell checkpoint blockade therapies.ReferencesFridman, W. H, Pagès, F, Sautès-Fridman, C, Galon, J. The immune contexture in human tumours: impact on clinical outcome. Nature Reviews Cancer. 2012. 12(4):298–306.Dieu-Nosjean, M. C., Antoine, M, Danel, C, Heudes, D, Wislez, M, Poulot, V, & Cadranel, J. Long-term survival for patients with non-small-cell lung cancer with intratumoral lymphoid structures. Journal of Clinical Oncology. 2008. 26(27):4410–4417.Germain, C, Gnjatic, S, & Dieu-Nosjean, M. C. Tertiary Lymphoid Structure-Associated B Cells are Key Players in Anti-Tumor Immunity. Frontiers in Immunology. 2019. 10:604.Ribas, A, & Wolchok, J. D. Cancer immunotherapy using checkpoint blockade. Science. 2018. 359(6382):1350–1355.Tsou, P, Katayama, H, Ostrin, E. J, Hanash, S. M. The Emerging Role of B Cells in Tumor Immunity. Cancer Research. 2016. 76(19):5597–5601.Petitprez, F, de Reyniès, A, Keung, E. Z, Chen, T. W, Sun, C. M, Calderaro, J,Fridman, W. H. B cells are associated with survival and immunotherapy response in sarcoma. Nature. 2020. 577(7791): 556–560.
Assembly of the nuclear pore, gateway to the genome, from its component subunits is a complex process. In higher eukaryotes, nuclear pore assembly begins with the binding of ELYS/MEL-28 to chromatin ...and recruitment of the large critical Nup107-160 pore subunit. The choreography of steps that follow is largely speculative. Here, we set out to molecularly define early steps in nuclear pore assembly, beginning with chromatin binding. Point mutation analysis indicates that pore assembly is exquisitely sensitive to the change of only two amino acids in the AT-hook motif of ELYS. The dependence on AT-rich chromatin for ELYS binding is borne out by the use of two DNA-binding antibiotics. AT-binding Distamycin A largely blocks nuclear pore assembly, whereas GC-binding Chromomycin A(3) does not. Next, we find that recruitment of vesicles containing the key integral membrane pore proteins POM121 and NDC1 to the forming nucleus is dependent on chromatin-bound ELYS/Nup107-160 complex, whereas recruitment of gp210 vesicles is not. Indeed, we reveal an interaction between the cytoplasmic domain of POM121 and the Nup107-160 complex. Our data thus suggest an order for nuclear pore assembly of 1) AT-rich chromatin sites, 2) ELYS, 3) the Nup107-160 complex, and 4) POM121- and NDC1-containing membrane vesicles and/or sheets, followed by (5) assembly of the bulk of the remaining soluble pore subunits.
iBiopsy® for Precision Medicine Brag, Johan; Auffret, Michaël; Ramos, Corinne ...
European Medical Journal (Chelmsford, England),
12/2018, Letnik:
3, Številka:
4
Journal Article
Recenzirano
Odprti dostop
A high-throughput artificial intelligence-powered image-based phenotyping platform, iBiopsy® (Median Technologies, Valbonne, France), which aims to improve precision medicine, is discussed in the ...presented review. The article introduces novel concepts, including high-throughput, fully automated imaging biomarker extraction; unsupervised predictive learning; large-scale content- based image-based similarity search; the use of large-scale clinical data registries; and cloud-based big data analytics to the problems of disease subtyping and treatment planning. Unlike electronic health record-based approaches, which lack the detailed radiological, pathological, genomic, and molecular data necessary for accurate prediction, iBiopsy generates unique signatures as fingerprints of disease and tumour subtypes from target images. These signatures are then merged with any additional omics data and matched against a large-scale reference registry of deeply phenotyped patients. Initial applications targeted include hepatocellular carcinoma and other chronic liver diseases, such as nonalcoholic steatohepatitis. This new disruptive technology is expected to lead to the identification of appropriate therapies targeting specific molecular pathways involved in the detected phenotypes to bring personalised treatment to patients, taking into account individual biological variability, which is the principal aim of precision medicine.
Post-mitotic reassembly of nuclear envelope (NE) and the endoplasmic reticulum (ER) has been reconstituted in a cell-free system based on interphase Xenopus egg extract. To evaluate the relative ...contributions of cytosolic and transmembrane proteins in NE and ER assembly, we replaced a part of native membrane vesicles with ones either functionally impaired by trypsin or N-ethylmaleimide treatments or with protein-free liposomes. Although neither impaired membrane vesicles nor liposomes formed ER and nuclear membrane, they both supported assembly reactions by fusing with native membrane vesicles. At membrane concentrations insufficient to generate full-sized functional nuclei, addition of liposomes and their fusion with membrane vesicles resulted in an extensive expansion of NE, further chromatin decondensation, restoration of the functionality, and spatial distribution of the nuclear pore complexes (NPCs), and, absent newly delivered transmembrane proteins, an increase in NPC numbers. This rescue of the nuclear assembly by liposomes was inhibited by wheat germ agglutinin and thus required active nuclear transport, similarly to the assembly of full-sized functional NE with membrane vesicles. Mechanism of fusion between liposomes and between liposomes and membrane vesicles was investigated using lipid mixing assay. This fusion required interphase cytosol and, like fusion between native membrane vesicles, was inhibited by guanosine 5′-3-O-(thio)triphosphate, soluble N-ethylmaleimide-sensitive factor attachment protein, and N-ethylmaleimide. Our findings suggest that interphase cytosol contains proteins that mediate the fusion stage of ER and NE reassembly, emphasize an unexpected tolerance of nucleus assembly to changes in concentrations of transmembrane proteins, and reveal the existence of a feedback mechanism that couples NE expansion with NPC assembly.
Background Non-small cell lung cancer (NSCLC) accounts for about 85% of all lung cancers. There is a strong rationale for incorporating immunotherapy into the treatment of early-stage NSCLC, given ...the breakthrough results with PD-1 checkpoint inhibitors in advanced-stage NSCLC. How immunotherapy should be implemented in patients who are operable is still unclear. Most of the efforts so far to identify clinically useful biomarkers do not preserve spatial information and leave us blind to the critical source of information revealed in the cell-to-cell biology of the tumor microenvironment (TME). In order to overcome these limitations, we used spatial biomarkers assays that preserve this critical information about which cells are influencing treatment response. Methods Frozen sections from retrospectively collected surgically resected NSCLC (adenocarcinoma and squamous cell carcinoma) tumors treated with adjuvant pembrolizumab therapy were used. Patients were classified in two groups according to their Objective Response Rate (ORR): Complete Response (CR) and Progression Disease (PD) for spatial transcriptomic and proteomics assays. The statistical analysis was performed through the GeoMx® DSP analysis suite. Cell deconvolution using the SpatialDecon® algorithm (Nanostring®) was then used to estimate the cell-type abundance in the spatially-resolved region of interest. Results were validated with single cell proteomic spatial analysis using proprietary workflow to identify which cells are influencing the treatment response and how they are spatially distributed relative to each other. Results A higher expression of the drug targets, PD1 (PDCD1) and PD-L1 (CD274), and genes related to T lymphocytes cytotoxicity (GZMB, CD8a) and activation (CD44, CD27, TNFRS9) were detected in the tumor microenvironment of the responder patient. The analysis of the non-responder patients highlighted the overexpression of inhibitory ligands CD86 and B7H3 (CD276). Interestingly, TIGIT, CTLA4 and TIM-3 were significantly overexpressed on the surface of the CD8a+ T cells. These results were validated by investigating the drug targets and immunosuppressive cells in the tumor microenvironment of patient samples that did not respond to immunotherapy. Conclusions These findings highlight the relevance of considering a set of spatial biomarkers involved in immune suppression pathways to obtain a comprehensive portrait of the tumor microenvironment for personalized therapy selection. Our results suggest that for patients who did not respond to monotherapy, it would have been preferable to resort to a combined immune checkpoint inhibitors treatment strategy, aimed at the complete inhibition of all the immune-suppressive pathways.
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