BUSCADOR RECOLECTA

Additional file 3: Figure S3. a, Representative two-color contour plots of B cell populations in a peritoneal wash and the spleen of 12 wk-old mice according to the expression of the CD11b and CD5 markers in the CD19+ population. The blue square indicates CD11b + CD5- B1b cells in the peritoneum. Red square, the presence of CD11b + CD5+ B1a cells in control mice and leukemic cells. Quantification of CD11b + CD5+ cells is shown to the right in box and whiskers plots showing all points and median value. **p < 0.01; *** p < 0.001, two-tailed unpaired t-test with Welch’s correction. b, Representative two-color contour plots of IgM and GFP expression within the CD11b + CD5+ populations shown in a. Quantification of IgMbright cells within the CD11b + CD5+ B cell population is shown to the right in box and whiskers plots showing all points and median value. **** p < 0.0001, two-tailed unpaired t-test with Welch’s correction., Fundación Científica Asociación Española Contra el Cáncer Ministerio de Ciencia, Innovación y Universidades H2020 European Research Council Instituto de Salud Carlos III Consejería de Educación, Junta de Castilla y León, Peer reviewed

Additional file 1: Supplementary Figure 1. Schematic flow chart representation of the genomic and proteomic approaches used to identify cell surface proteins in TNBC. Supplementary Figure 2. A) The table shows the data generated form the microarray analyses to identify cell surface proteins upregulated in TNBC. B) Venn diagram showing the number of genes specifically identified in each array and those that are common among them. Supplementary Figure 3. A) Procedure used to obtain enriched plasma membrane microsomal fraction, used to identify plasma membrane proteins in TNBC cell lines. B) The table shows the total number of proteins identified, as well as those that correspond to plasma membrane proteins. C) Venn diagram showing the number of proteins specifically identified in each cell line and those that are common among them. Supplementary Figure 4. A) Schematic representation of the protocol used in cell surface biotinylation experiments. B) The table shows the proteins identified and those that correspond to plasma membrane proteins. C) Venn diagram showing the number of proteins identified in each cell line and those that are common among them. Supplementary Figure 5. BT549 (A and B) and MDA-MB231 (C and D) cells were infected with lentivirus containing the shRNA control (sh-Control) or the shRNA sequences targeting GLUT1 or LAT1. Knockdown efficiency was verified by western (A and C), and the effect of the knockdowns on cell proliferation was analyzed by MTT metabolization (B and D). GAPDH was used as a loading control. Supplementary Figure 6. BT549 and HCC3153 cells were seeded on coverslips and treated with 10 nM of anti-CD98hc for the indicated times. Cells were fixed and stained for CD98hc (red), LAMP1 (green) and DNA (blue). Scale bar = 25 μm. Magnification of one cell at 24 hours of treatment is shown. Scale bar = 10 and 7.5 μm. Supplementary Figure 7. A) Dose-response analyses of the anti-proliferative effect of anti-CD98hc-DM1 in MDA-MB231 CD98hc CRISPR #B3, #G3 and parental MDA-MB231 cells. Cells were treated with anti-CD98hc-DM1 for four days at the indicated doses. Results are shown as the mean ± SD of quadruplicates of an experiment repeated three times. B and D) BT549 (B) and MDA-MB231 (D) cells were infected with lentivirus containing the shRNA control (sh-Control) or two shRNA sequences targeting CD98hc (sh-CD98hc #3 and #7). To verify the knockdown efficiency, levels of CD98hc were analyzed by Western blot. Calnexin was used as a loading control. C and E) BT549 (C) and MDA-MB231 (E) cells infected with lentivirus containing the shRNA control (sh-Control) or two shRNA sequences targeting CD98hc were plated and the MTT metabolization was measured at the times indicated. Supplementary Figure 8. Cell cycle profiles of TNBC cells treated with CD98hc-DM1. Cells were treated for one day with CD98hc-DM1 (10 nM), and then harvested and stained with propidium iodide for cell cycle analysis, following the procedure described in the materials and methods section. Supplementary Figure 9. Graphical representation of the surfaceome and the strategy to develop ADCs against differentially expressed proteins. Genomic as well as proteomic strategies allow the identification of proteins overexpressed or newly expressed by tumors with respect to normal tissue. That information may be used to develop an antibody that targets the differentially expressed protein, and that may be used as a backbone for the preparation of an ADC. Once prepared, in vitro and in vivo models can be used to define the antitumoral activity of the ADC as well as its mechanism of action., Instituto de Salud Carlos III Consejo Superior de Investigaciones Científicas Consejería de Educación, Junta de Castilla y León CRIS Cancer Foundation ACMUMA UCCTA ALMOM, Peer reviewed

Supplementary Figure 1 | Identification of K-RAS most common genomic alterations in patients with NSCLC by molecular subtypes. (i) Percentage of amplifications, mutations and deletions of K-RAS gene in patients with Squamous Cell Carcinoma or Adenocarcinoma. (ii) Graphical representation of the percentage of K-RAS genomic alterations according to TCGA, Firehose Legacy and MSKCC, 2020 databases in (A) or to TCGA, PanCancer Atlas and TCGA, Firehose Legacy databases in (B). Supplementary Figure 2 | Negative correlation between the expression of upregulated genes (CLRF1, HOPX, IRS2, KIT, PDE4D, and SMOC1) and most of immune infiltrates (CD8+ T cells, Neutrophils, Macrophages and dendritic cells). A green square encircles the dots with higher expression and little infiltration level. Supplementary Figure 3 | Positive correlation between the expression of downregulated genes (CD24, CDK6, HDAC9, TIAM1, TRFC, VTRC1, and VAV3) and most of immune infiltrates (CD8+ T cells, Neutrophils, Macrophages and dendritic cells). A yellow square encircles the dots with less expression and more infiltration level. Supplementary Figure 4 | Expression of CLDN10 and TMPRSS6 in different human cancer types. Bar graph showing the expression of individual CLDN10 (in (A), TMPRSS6 in (B) or both genes combined (C) in those cancer types where expression is significantly higher in tumor samples than in normal tissue (D). No correlation between CLDN10 and TMPRSS6 gene expression with most of immune infiltrates. Supplementary Figure 5 | Higher expression of CLDN10 and TMPRSS6 in K-RAS p.G12C. (A) Table presenting 15 NSLC cell lines that present the G12C variant in K-RAS gene. Bar graph showing the expression of CLDN10 in (B), and TMPRSS6 in (C) in 92 NSLC cell lines compared to those 15 selected in A. Expression of CLDN10 in (D), and TMPRSS6 in (E) in LUAD tumor samples comparing those that harbor or not the G12C mutation. Supplementary Table 1 | Investigational and approved drugs against K-RAS identified mutations. Specific K-RAS mutation, name of the drug, status (approved or investigational), identification code (NCT) and clinical studies with links and phases are included. Intervention is included if the drug is given in combination with others. Supplementary Table 2 | Gene functions of thirteen selected deregulated genes. Supplementary Table 3 | Upregulation details of cell surface-related genes analyzed. Name of the gene, mean of expression in mutant and wildtype K-RAS tumors, fold change (FC), direction and p-value are included. Supplementary Table 4 | Kaplan-Meier survival values of cell surface-related genes. Table includes the name of the gene, the hazard ratio (HR) (in blue, significant good prognosis, and in red, bad one), p-value and fold discovery rate (FDR) for FP and in LUAD patients., Targeting K-RAS-mutant non-small cell lung cancer (NSCLC) with novel inhibitors has shown promising results with the recent approval of sotorasib in this indication. However, progression to this agent is expected, as it has previously been observed with other inhibitors. Recently, new immune therapeutics, including vectorized compounds with antibodies or modulators of the host immune response, have demonstrated clinical activity. By interrogating massive datasets, including TCGA, we identified genes that code for surface membrane proteins that are selectively expressed in K-RAS mutated NSCLC and that could be used to vectorize novel therapies. Two genes, CLDN10 and TMPRSS6, were selected for their clear differentiation. In addition, we discovered immunologic correlates of outcome that were clearly de-regulated in this particular tumor type and we matched them with immune cell populations. In conclusion, our article describes membrane proteins and immunologic correlates that could be used to better select and optimize current therapies., Peer reviewed

Chronic lymphocytic leukemia (CLL) is a lymphoid neoplasm characterized by the accumulation of mature B cells. The diagnosis is established by the detection of monoclonal B lymphocytes in peripheral blood, even in early stages [monoclonal B-cell lymphocytosis (MBLhi)], and its clinical course is highly heterogeneous. In fact, there are well-characterized multiple prognostic factors that are also related to the observed genetic heterogenicity, such as immunoglobulin heavy chain variable region (IGHV) mutational status, del17p, and TP53 mutations, among others. Moreover, a dysregulation of the immune system (innate and adaptive immunity) has been observed in CLL patients, with strong impact on immune surveillance and consequently on the onset, evolution, and therapy response. In addition, the tumor microenvironment is highly complex and heterogeneous (i.e., matrix, fibroblast, endothelial cells, and immune cells), playing a critical role in the evolution of CLL. In this study, a quantitative profile of 103 proteins (cytokines, chemokines, growth/regulatory factors, immune checkpoints, and soluble receptors) in 67 serum samples (57 CLL and 10 MBLhi) has been systematically evaluated. Also, differential profiles of soluble immune factors that discriminate between MBLhi and CLL (sCD47, sCD27, sTIMD-4, sIL-2R, and sULBP-1), disease progression (sCD48, sCD27, sArginase-1, sLAG-3, IL-4, and sIL-2R), or among profiles correlated with other prognostic factors, such as IGHV mutational status (CXCL11/I-TAC, CXCL10/IP-10, sHEVM, and sLAG-3), were deciphered. These results pave the way to explore the role of soluble immune checkpoints as a promising source of biomarkers in CLL, to provide novel insights into the immune suppression process and/or dysfunction, mostly on T cells, in combination with cellular balance disruption and microenvironment polarization leading to tumor escape., Peer reviewed

Chronic lymphocytic leukemia (CLL) is a lymphoid neoplasm characterized by the accumulation of mature B cells. The diagnosis is established by the detection of monoclonal B lymphocytes in peripheral blood, even in early stages [monoclonal B-cell lymphocytosis (MBLhi)], and its clinical course is highly heterogeneous. In fact, there are well-characterized multiple prognostic factors that are also related to the observed genetic heterogenicity, such as immunoglobulin heavy chain variable region (IGHV) mutational status, del17p, and TP53 mutations, among others. Moreover, a dysregulation of the immune system (innate and adaptive immunity) has been observed in CLL patients, with strong impact on immune surveillance and consequently on the onset, evolution, and therapy response. In addition, the tumor microenvironment is highly complex and heterogeneous (i.e., matrix, fibroblast, endothelial cells, and immune cells), playing a critical role in the evolution of CLL. In this study, a quantitative profile of 103 proteins (cytokines, chemokines, growth/regulatory factors, immune checkpoints, and soluble receptors) in 67 serum samples (57 CLL and 10 MBLhi) has been systematically evaluated. Also, differential profiles of soluble immune factors that discriminate between MBLhi and CLL (sCD47, sCD27, sTIMD-4, sIL-2R, and sULBP-1), disease progression (sCD48, sCD27, sArginase-1, sLAG-3, IL-4, and sIL-2R), or among profiles correlated with other prognostic factors, such as IGHV mutational status (CXCL11/I-TAC, CXCL10/IP-10, sHEVM, and sLAG-3), were deciphered. These results pave the way to explore the role of soluble immune checkpoints as a promising source of biomarkers in CLL, to provide novel insights into the immune suppression process and/or dysfunction, mostly on T cells, in combination with cellular balance disruption and microenvironment polarization leading to tumor escape., Peer reviewed

Supplementary Figure S5: Immune signatures and IMMETCOLS in 75 samples of 75 mCRC. A) heatmap of transcriptomics immune signatures in 75 mCRC stratified according to IMMETCOLS. GEP is the average expression of the genes of the GEP signature. Immunophenoscore is the aggregate of the MHC (Antigen Processing), EC (Effector cells), CP (Checkpoints and Immunomodulators) and SC (Suppressor cells) scores. B) Average immunophenogram in each IMMETCOLS cluster in the 75 mCRC. Inner circle plots each of the four Immunophenoscore components with higher values representing a more immunogenic phenotype. The outer cycle plots the expression of markers used to compute each of the immunophenoscore components., Existing immune signatures and tumor mutational burden have only modest predictive capacity for the efficacy of immune check point inhibitors. In this study, we developed an immune-metabolic signature suitable for personalized ICI therapies. A classifier using an immune-metabolic signature (IMMETCOLS) was developed on a training set of 77 metastatic colorectal cancer (mCRC) samples and validated on 4,200 tumors from the TCGA database belonging to 11 types. Here, we reveal that the IMMETCOLS signature classifies tumors into three distinct immune-metabolic clusters. Cluster 1 displays markers of enhanced glycolisis, hexosamine byosinthesis and epithelial-to-mesenchymal transition. On multivariate analysis, cluster 1 tumors were enriched in pro-immune signature but not in immunophenoscore and were associated with the poorest median survival. Its predicted tumor metabolic features suggest an acidic-lactate-rich tumor microenvironment (TME) geared to an immunosuppressive setting, enriched in fibroblasts. Cluster 2 displays features of gluconeogenesis ability, which is needed for glucose-independent survival and preferential use of alternative carbon sources, including glutamine and lipid uptake/β-oxidation. Its metabolic features suggest a hypoxic and hypoglycemic TME, associated with poor tumor-associated antigen presentation. Finally, cluster 3 is highly glycolytic but also has a solid mitochondrial function, with concomitant upregulation of glutamine and essential amino acid transporters and the pentose phosphate pathway leading to glucose exhaustion in the TME and immunosuppression. Together, these findings suggest that the IMMETCOLS signature provides a classifier of tumors from diverse origins, yielding three clusters with distinct immune-metabolic profiles, representing a new predictive tool for patient selection for specific immune-metabolic therapeutic approaches., Peer reviewed

Raw microscopy data for Fig2A and G in .lif format in a single zip file., Resources available on the publisher's site: https://doi.org/10.25452/figshare.plus.19064324.v1, Marie Skłodowska-Curie grant agreement No. 754510 Ministry of Science, Innovation and Universities PGC2018-095616-B-I00 Ministry of Science, Innovation and Universities PGC2018-099562-B-I00 NIH Intramural Funding, National Cancer Institute, Peer reviewed

Raw microscopy data for Fig6A, F, G, S1 and S2 (.lif or .jpg format) in a single zip file., Resources available on the publisher's site: https://doi.org/10.25452/figshare.plus.18310361.v1, Deutsche Forschungsgemeinschaft DFG PH144/4-1 and PH144/6-1, Peer reviewed

Source microscopy data for Fig8A, B, C, D and S1 (.lif and .jpg file) in a single zip file., Resources available on the publisher's site: https://doi.org/10.25452/figshare.plus.18312410.v1, Deutsche Forschungsgemeinschaft DFG PH144/4-1 and PH144/6-1, Peer reviewed

Additional file 12 Figure S7. Scheme of the effect of Myosin over F-actin. (1), Myosin movement in parallel f-Actin. (2) F-actin sliding. (3) Tension building in the filaments. (4) Release from cortex after threshold tension is reached. After release, tension (illustrated in red) is redistributed to other F-actin in the network., Ministerio de Ciencia, Innovación y Universidades, Peer reviewed