BUSCADOR RECOLECTA

Supplementary Figure 1. Isolated EVs present typical size and topology. Supplementary Figure 2. Protein profiling from total cell lysates and their derived EVs from WT and KO-HDAC6 BMDCs. Supplementary Figure 3. Enrichment in acetylated and ubiquitinated DC proteins upon Lm infection. Supplementary Figure 4. Ubiquitination in K-48 and K-63 state in T lymphoblast total cell lysates and their derived EVs. Supplementary Figure 5. Pore filtration methods restrain Lm and do not induce strong antipathogenic responses. Supplementary Figure 6. IFN-β is detected following Lm infection. Table S1. List of antibodies used for Western-blot and Flow Cytometry and the used dilution. Table S2. List of primers, with their corresponding sequence, used for qPCR. Table S3: Protein quantification in total cell lysates Table S4: IPA analysis of total cell lysates: canonical pathways and diseases and functions category Table S5: Protein quantification in EVs Table S6: IPA analysis of EVs: diseases and functions category Table S7: Ubiquitinated and acetylated peptides in total cell lysates and EVs Table S8: Enrichment analysis of ubiquitinated and acetylated proteins, Peer reviewed

Supplementary Table 9. Gene ontology (GO) functional enrichment analysis of the differentially expressed genes (DEGs) for cellular component., Mouse models of cancer provide a powerful tool for investigating all aspects of cancer biology. In this study, we used our recently developed machine learning approach to identify the cellular morphometric biomarkers (CMB) from digital images of hematoxylin and eosin (H&E) micrographs of orthotopic Trp53-null mammary tumors (n = 154) and to discover the corresponding cellular morphometric subtypes (CMS). Of the two CMS identified, CMS-2 was significantly associated with shorter survival (p = 0.0084). We then evaluated the learned CMB and corresponding CMS model in MMTV-Erbb2 transgenic mouse mammary tumors (n = 53) in which CMS-2 was significantly correlated with the presence of metastasis (p = 0.004). We next evaluated the mouse CMB and CMS model on The Cancer Genome Atlas breast cancer (TCGA-BRCA) cohort (n = 1017). Kaplan–Meier analysis showed significantly shorter overall survival (OS) of CMS-2 patients compared to CMS-1 patients (p = 0.024) and added significant prognostic value in multi-variable analysis of clinical and molecular factors, namely, age, pathological stage, and PAM50 molecular subtype. Thus, application of CMS to digital images of routine workflow H&E preparations can provide unbiased biological stratification to inform patient care., Peer reviewed

Identification of genomic alterations that influence the immune response within the tumor microenvironment is mandatory in order to identify druggable vulnerabilities. In this article, by interrogating public genomic datasets we describe copy number variations (CNV) present in breast cancer (BC) tumors and corresponding subtypes, associated with different immune populations. We identified regulatory T-cells associated with the Basal-like subtype, and type 2 T-helper cells with HER2 positive and the luminal subtype. Using gene set enrichment analysis (GSEA) for the Type 2 T-helper cells, the most relevant processes included the ERBB2 signaling pathway and the Fibroblast Growth Factor Receptor (FGFR) signaling pathway, and for CD8+ T-cells, cellular response to growth hormone stimulus or the JAK-STAT signaling pathway. Amplification of ERBB2, GRB2, GRB7, and FGF receptor genes strongly correlated with the presence of type 2 T helper cells. Finally, only 8 genes were highly upregulated and present in the cellular membrane: MILR1, ACE, DCSTAMP, SLAMF8, CD160, IL2RA, ICAM2, and SLAMF6. In summary, we described immune populations associated with genomic alterations with different BC subtypes. We observed a clear presence of inhibitory cells, like Tregs or Th2 when specific chromosomic regions were amplified in basal-like or HER2 and luminal groups. Our data support further evaluation of specific therapeutic strategies in specific BC subtypes, like those targeting Tregs in the basal-like subtype., Peer reviewed

Identification of genomic alterations that influence the immune response within the tumor microenvironment is mandatory in order to identify druggable vulnerabilities. In this article, by interrogating public genomic datasets we describe copy number variations (CNV) present in breast cancer (BC) tumors and corresponding subtypes, associated with different immune populations. We identified regulatory T-cells associated with the Basal-like subtype, and type 2 T-helper cells with HER2 positive and the luminal subtype. Using gene set enrichment analysis (GSEA) for the Type 2 T-helper cells, the most relevant processes included the ERBB2 signaling pathway and the Fibroblast Growth Factor Receptor (FGFR) signaling pathway, and for CD8+ T-cells, cellular response to growth hormone stimulus or the JAK-STAT signaling pathway. Amplification of ERBB2, GRB2, GRB7, and FGF receptor genes strongly correlated with the presence of type 2 T helper cells. Finally, only 8 genes were highly upregulated and present in the cellular membrane: MILR1, ACE, DCSTAMP, SLAMF8, CD160, IL2RA, ICAM2, and SLAMF6. In summary, we described immune populations associated with genomic alterations with different BC subtypes. We observed a clear presence of inhibitory cells, like Tregs or Th2 when specific chromosomic regions were amplified in basal-like or HER2 and luminal groups. Our data support further evaluation of specific therapeutic strategies in specific BC subtypes, like those targeting Tregs in the basal-like subtype., Peer reviewed

S1. Sample preparation protocol for evaluation of OneFlow kits (BD). Figure S1. Impact of different BSA concentrations and different pH of the washing buffer on the relative percentage distribution of different cell populations identifiable in normal PB samples with LST. Figure S2. Impact of different BSA concentrations and different pH of the washing buffer on the expression levels of individual LST markers as assessed on different cell populations in normal PB samples. Table S1. Detailed information on the antibody reagents used in the current study, including the antibody titer used per test. Table S2. Absolute and relative percentage changes of particular cell populations, debris and doublets detectable in BM samples stained with ALOT tube at day 0 and after 24-h storage. Table S3. Ratios of percentage of debris, doublets, and pathological/clonal B-cells in particular BM and PB samples stained with LST tube at different time points. Table S4. Ratios of percentage of debris, doublets, and pathological/clonal B-cells in particular BM and PB samples stained with the first tube of B-CLPD panel at different time points. Table S5. Ratios of percentage of debris, doublets, and pathological/clonal plasma cells in particular BM and PB samples stained with PCD panel at different time points. Table S6. MFI values of LST markers evaluated on relevant cell populations obtained at different pH and BSA concentrations. Table S7. Expression of markers on relevant PB cell populations stained with LST tube at different pH on averaging the results for different BSA concentrations. Table S8. Selected additional markers that were not directly studied in the current study that may be influenced by the preparation-associated variables., Peer reviewed

Additional file 1: Figure S1. a, Relative mRNA expression of RRAS2 in different types of leukemia. Data comes from (Haferlach et al., 2010) and has been retrieved from www.oncomine.org . b, Schematic representation of the overexpression cassette inserted into the Rosa26 locus. c, Relative expression of RRAS2 measured by RT-qPCR in different organs of Rosa26-RRAS2fl/flxSox2-Cre (Sox2-Cre+) mice compared to that of WT C57BL/6 J Control mice using 18S as the reference gene. All expression numbers were normalized to those of liver from WT Control mice (mean = 1). Data show relative expression of RRAS2 in the indicated organs in n = 3–4 8 month-old independent mice. d, Quantification of spleen weight from control and 6 month-old Sox2-Cre + mice. Data shown correspond to four control mice and eleven Sox2-Cre mice. Two-tailed unpaired t-test with Welch’s correction. e, Two-parameter flow cytometry of the expression of CD5 and IgM in B cells in the spleen of 6 month-old control and Sox2-Cre + mice. f, Quantification of the number of CD5 + IgM+ B cells in the spleens and bone marrow of 6 month-old control and Sox2-Cre + mice. Data correspond to triplicate measurements of one control and three Sox2-Cre mice. Unpaired t-test with Welch’s correction. g, Quantification of the serum IgM concentration in the blood of 35–40 wk-old control (n = 3) and mb1-Cre (n = 8) mice by ELISA. Unpaired t-test with Welch’s correction. h, Representative images from Giemsa stainings of blood smears of 36 wk-old control and mb1-Cre mice. i, Two-parameter flow cytometry of the forward scatter and CD5 expression in CD19+ cells in the blood of 16 wk-old mb1-Cre mice. The gated population represents large cells. j, Two-parameter flow cytometry of CD5 expression and BrdU incorporation in CD19+ cells in the blood of 16 wk-old mb1-Cre mice. k, Quantification of the percentage of CD19+ cells that are CD5+ blasts and of the CD19+ CD5+ cells that have incorporated BrdU., 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 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