Search

Apoptosis is a form of regulated cell death (RCD) that involves proteases of the caspase family. Pharmacological and genetic strategies that experimentally inhibit or delay apoptosis in mammalian systems have elucidated the key contribution of this process not only to (post-)embryonic development and adult tissue homeostasis, but also to the etiology of multiple human disorders. Consistent with this notion, while defects in the molecular machinery for apoptotic cell death impair organismal development and promote oncogenesis, the unwarranted activation of apoptosis promotes cell loss and tissue damage in the context of various neurological, cardiovascular, renal, hepatic, infectious, neoplastic and inflammatory conditions. Here, the Nomenclature Committee on Cell Death (NCCD) gathered to critically summarize anabundant pre-clinical literature mechanistically linking the core apoptotic apparatus to organismal homeostasis in the context of disease.

Additional file 1: Appendix I.

Additional file 1:. Supplementary Methods.

Additional file 1: Table S1. Cancer cohort characteristics. Table S2. Peptides used as internal standards in this study. Table S3. E.Coli peptides used to evaluate instrumental variance in the LC-MRM validation assay. Table S4. Candidate peptide transitions employed for the LC-MRM validation assay. Table S5. Logistic regression analysis and validation of candidate peptide markers. Figure S1. Frequency distribution and histograms of molecular weight (A) and abundance (B) for unique plasma proteins identified across cases and controls. Enrichment of low abundance, low molecular weight proteins using nanoparticle capture is shown. Figure S2. Overlap of pathways identified by functional enrichment analysis across control and IDC cases (stages I-III). Enriched pathways were filtered for p 50% of samples. Figure S3. Correlation analysis of Filamin A peptides AUC in models 1 and 2. Pearson’s correlation coefficients are shown for each correlation pair. Analysis excludes outlier cases 22 and 44. Starred correlations: p

Additional file 11: Figure S5. ZEB2 expression in TNM stages, correlation with RFS and CMS in stage 2 CRC patients. ZEB2 transcript levels in the indicated number of CRC patients across all TNM stages. One-way ANOVA resulted in non-significant differences between stages. Outliers are depicted as crosses. n = 1079.

Additional file 8: Figure S2. Expression of pCRAF in vivo and in vitro and complementary RPPA data analysis. a Representative confocal microscopy images of PKH26-positive areas (yellow) in xenograft sections immunostained with anti-pCRAF S338 (green) and PROMININ1 (red). Scale bar, 80 μm. b Representative confocal microscopy images of SW480 cells treated for 48 h with 10 μM etoposide or 10 μM irinotecan and stained with anti-pCRAF S338 antibody. Scale bar, 20 μm. c Spatial representation of principal component (PC) analysis computed on a matrix having loading values of the two components, Factor 1 and Factor 2 that discriminates among PKH26+ and PKH26− samples. Results obtained on three PKH26+ versus PKH26− samples, n = 3 pools of 12 tumors each. d Spatial representation of scores of components representing relative RPPA antibodies values (Factor 1 and Factor 2). Results obtained on three PKH26+ versus PKH26− samples, n = 3 pools of 12 tumors each.

Additional file 1.

Additional file 10: Figure S4. In vivo effects of ZEB2 overexpression in CCSCs. a Volume of xenografts derived from CCSCs transduced with pLenti-GFP (Vector, black line/triangles) or with pLenti-GFP-ZEB2 (ZEB2, red line/squares). Graph shows the mean ± SEM, 6 tumors/group. **P

Additional file 2.

Additional file 9: Figure S3. Trends of ZEB2 overexpression in cultured cells. a Percentage of GFP positivity in CCSCs (diamonds) or SW480 (circles) cells transduced with empty pLenti-GFP (Vector) or with pLenti-GFP-ZEB2 (ZEB2) as assessed by flow cytometry for 5 weeks following lentiviral transduction and sorting (day 0). Graph shows the mean ± SD of three independent experiments. b Representative confocal image of CCSCs and SW480 cells transduced with GFP-ZEB2 and labeled with anti-Ki67 at day 28 after sorting. Circles indicate rare ZEB2+ cells, which are also Ki67−. Scale bar, 50 μm.

Additional file 4: Figure S1. Chemoresistance and marker expression of PKH26-positive cells. a Representative flow cytometry analysis of SW480 cells stained with PKH26 and treated with 2,5 μM oxaliplatin (OXA) starting from day 11, as described in Fig. 1a. Cell viability plots obtained with 7-AAD staining are shown below PKH26 plots, and the percentage of viable cells is indicated below each plot. b Representative flow cytometry analysis of EpCAM and PKH26 on xenograft-derived CCSCs 3 weeks upon subcutaneous injection of PKH26-stained cells in NSG mice. IgG, isotype control antibody. c Representative flow cytometry analysis of PKH26 and Ki67 in xenograft-derived EpCAM+ CCSCs at 3 weeks of tumor growth. d Representative flow cytometry analysis of PKH26 and PROMININ1 in xenograft-derived EpCAM+ CCSCs at 3 weeks of tumor growth.

Figure S1. Metabolites plasma concentrations (MPR and oxo-4-HPR) determined in mice after single oral treatment of 4 doses of Bio-nFeR: 10, 50, 100 and 200 mg/Kg. The analysis was performed after 0, 24, 48 h. Figure S2. Evaluation of the 50% inhibitory concentration (IC50) of BionFeR and NCI-FeR formulation was done on a representative lung cancer stem cell line (LCSC). The cells were exposed to the indicated drug doses (0–30 μM) and cell viability was evaluated by CellTiter-Glo after 72 h. Figure S3. A) Sphingomyelin-Ceramide (SM-CER) and Sphingomyelin- Dihydro-Ceramide (SM-DH-CER) content in lung (LC), melanoma (MEL) and colon (CRC) tumors treated with Bio-nFeR at single 100–150 mg/Kg administration were quantified through Ultra High Performance Liquid Chromatography (UHPLC), normalized to control and plotted as fold change. B) Quantification of Glucosyl-Ceramide (GLC-CER) and Glucosyl-Dihydro-Ceramide (GLC-DH-CER) in the same samples as in A, normalized to control and plotted as fold change. Figure S4. Cancer stem cell marker expression in CSC-culture conditions and in CSC-derived xenografts. A) Flow cytometry analysis of Aldefluor in (left panels) and CD44v6 (middle and right panels) in the indicated CSC and xenograft samples. B) Immunoblot analysis of CSC antigens c-Kit and SOX-2 in the indicated control (−) or BionFeR treated (+) melanoma xenografts. β-actin blot was used for equal loading control. Figure S5. Growth curves of lung CSC-derived xenografts in control mice or mice treated with Bio-nFeR or NCI-FeR at 100 mg/kg dose for the indicated times. Mean ± standard error is shown. *P

Over the past decade, the Nomenclature Committee on Cell Death (NCCD) has formulated guidelines for the definition and interpretation of cell death from morphological, biochemical, and functional perspectives. Since the field continues to expand and novel mechanisms that orchestrate multiple cell death pathways are unveiled, we propose an updated classification of cell death subroutines focusing on mechanistic and essential (as opposed to correlative and dispensable) aspects of the process. As we provide molecularly oriented definitions of terms including intrinsic apoptosis, extrinsic apoptosis, mitochondrial permeability transition (MPT)-driven necrosis, necroptosis, ferroptosis, pyroptosis, parthanatos, entotic cell death, NETotic cell death, lysosome-dependent cell death, autophagy-dependent cell death, immunogenic cell death, cellular senescence, and mitotic catastrophe, we discuss the utility of neologisms that refer to highly specialized instances of these processes. The mission of the NCCD is to provide a widely accepted nomenclature on cell death in support of the continued development of the field.

Additional file 2. Representative examples of immunohistochemical expression of TAZ and YAP in gastric cancer. Two cases are presented with combined nuclear expression of both TAZ and YAP (Aâ D).

Figure S6. (A) Freshly dissociated tissues maintained for one week in serum-free stem cell-isolating medium supplemented with Epidermal Growth Factor (EGF), basic Fibroblast Growth Factor (b-FGF), DMEM (Dulbecco Modified Eagle Medium), or Glutamine and FBS (Fetal Bovine Serum) supplemented medium, and analyzed by cytofluorimetric analysis. CD45 (PE-Cy7), CD146 (PE), CD44 (H450-Pacific Blue) and EpCAM (FITC) antigens were analyzed. TOPRO3 was used for gating vital cells. (B) The histograms report growth rate fold change of cells described in A, 4 and 10 days after sorting. Control represents (red dashed line) value = 1 i.e. reference relative count at sorting and plating day. Mean of three independent experiments is reported. Values are mean ± s.d (C) Colony forming assay of EpCAM+/CD146+/CD44+ and triple negative sorted cells and non-sorted population maintained in culture one week in stem serum free medium (D) Mean colony size of EpCAM+/CD146+/CD44+ and triple negative sorted cells and non-sorted population maintained in culture one week in stem serum free medium. Mean of three independent experiments is reported. Values are mean ± s.d. (PDF 231 kb)

Figure S8. (A) Representative images of Western blot analysis of pAKT S473 and pERK T202/Y204 proteins in clear renal cancer isolated cells (G3 and G4) and commercial lines [primary tumor 786–0 (786) and metastatic Caki-1 (CK1)]. GAPDH expression was used as internal control (B) Anti-mTOR and VEGFR2 protein staining in two representative samples classified by (ISUP) grading as G3 and G4 cases by immunohistochemistry assay. Microscope used Nikon Eclipse 55i, magnification 20X. (C) Representative images of Western blot analysis of pAKT S473 and pERK T202/Y204 proteins in non-sorted stem serum free clear renal cancer enriched cells (Stem Tot.), EpCAM+/CD146+/CD44+ (Stem+) and triple negative (Stem-) sorted cells vs non-sorted clear renal cancer cells maintained in DMEM-FBS condition (DMEM Tot) and evaluated one week after culture. (D) Two-way unsupervised hierarchical clustering of 18 ccRCC samples for the expression of proteins belonging to the angiogenesis pathway. Highlighted in the yellow box are overexpressed protein commonly shared in samples of patients that underwent progression (red arrows). N1 and M1 samples were excluded from the analysis (E) Receiver operating characteristic (ROC) curve showing sensitivity and specificity of HIF-1 alpha and phospho-mTOR (S2448) protein RPPA expressions in predicting progression. The true positive rate (sensitivity) is plotted in function of the false positive rate (100-specificity). The area under the ROC curve (AUC) represents a measure of how well the HIF-1 alpha and phospho-mTOR (S2448) protein RPPA expressions distinguishes progression group from no progression [0.96 (p

Figure S5. Freshly dissociated tissues were maintained three days in serum-free stem cell-isolating medium supplemented with Epidermal Growth Factor (EGF) and basic Fibroblast Growth Factor (b-FGF). On the left a representative image of the sorting of EpCAM+/CD146+/CD44+ populations (EpCAM+/CD146+/CD44+) and triple negative (EpCAM-/CD146-/CD44-) by FACS ARIA cytometer was reported. Images of colonies of both sorted sub-populations were reported on the right. Yellow and pink boxes mirror cytometer density plot. Pink dashed line represents matrigel front of cell invasion. (PDF 179 kb)

Additional file 1. Baseline characteristics of gastric cancer (GC) patients included in this study (N = 83).

Additional file 1. First-line chemotherapy regimens and schedules (NÂ =Â 86).

Figure S7. (A) Two-way unsupervised hierarchical clustering of RPPA data of 2 G1, 7 G2, 8 G3, and 3 G4 ccRCC populations for the expression of stem cell markers reported as heatmap. (B) mRNA expression of SOX2 gene in G2, G3, and G4 ccRCC samples as assessed by RT-qPCR. Mean of three independent experiments is reported. RRN18S was used as endogenous control. *p

Figure S3 (A) RPPA-TCGA elaboration of E-Cadherin and Fibronectin expressions. Data were obtained from macrodissected clear cell renal cancer tissues (GDC-database- https://tcga-data.nci.nih.gov/docs/publications/kirc_2013/ ) and reported for grading, stage and for progression rate by RPPA. (B) mRNA level elaboration of EpCAM, CD146(MCAM) and CD44 antigens. Data were obtained from GSE48550 microarray and were analyzed on different kinds of renal stem cells. (C) TOPRO3 staining for cell viability evaluation of populations maintained for three days (upper panels) and one week (Lower panels) in serum-free stem cell-isolating medium supplemented with Epidermal Growth Factor (EGF), basic Fibroblast Growth Factor (b-FGF), DMEM (Dulbecco Modified Eagle Medium), Glutamine and FBS (Fetal Bovine Serum) supplemented medium and evaluated by cytofluorimetric analysis. Blue and Black areas represent vital and dead cells respectively. (PDF 389 kb)

Table S2. Clinical features of 20 collected ccRCC patients including: 2 G1; 7 G2; 8 G3 and 3 G4 processed by RPPA. (PDF 488 kb)

Figure S10. (A) Kaplan-Meier curve for the Disease Free Survival (DFS) in the engrafted (Engraf) and not engrafted (No Engraf) groups. (B) Histogram showing FACS analysis results of a PDX derived from a G4 metastatic patient with a sarcomatoid phenotype at esordium for the expression of selected markers. (C) Luciferase analysis representative image of G4 ccRCC injected mice (seven mice/group) and after 21Â days of treatment with Sunitinib by IVIS imaging. (D) Histogram showing luciferase photon emission of the vehicle and Sunitinib treated mice at days 0 and 21 of treatment and evaluated by IVIS imaging. (E) Representative Haematoxylin and Eosin staining of tumor sections from treated mice. Microscope used Nikon Eclipse 55i, magnification 10X (upper panel) and 20X (lower panel) (F) Two-way unsupervised hierarchical clustering of 20 samples for the expression of endpoints representing the most common drug targets. Green arrows represent recurrent patients, while the black arrow represents a metastatic (M1) sample at esordium. (PDF 542 kb)

Additional file 3. Multivariate Cox regression model for progression-free survival (PFS) evaluating three different molecular signatures (N = 83).

Figure S4. (A) Fresh dissociated tissues maintained for three days in serum-free stem cell-isolating medium supplemented with Epidermal Growth Factor (EGF), basic Fibroblast Growth Factor (b-FGF), DMEM (Dulbecco Modified Eagle Medium), or in Glutamine and FBS (Fetal Bovine Serum) supplemented medium, and analyzed by cytofluorimetric analysis. CD45 (PE-Cy7), CD146(PE), CD44 (H450-Pacific Blue) and EpCAM(FITC) antigens were analyzed. TOPRO3 was used for gating vital cells. (B-C) Images and clonogenic population percentage of cells maintained in both conditions after three days of culture by Colony forming assay. Colonies distinguished on the basis of their shape in the two conditions: spheroidal (blue box) and bidimensional (red box). (D) Percentage of colonies distinguished on the basis of their shape in the two conditions was reported: spheroidal (blue box) and bidimentional (red box) such as in B. (PDF 229 kb)

Table S3. List of total and phosphorylated proteins analyzed by RPPA. In blue, positively expressed proteins. (PDF 87 kb)

Figure S9. Hematoxylin and Eosin staining of PDXs versus parental primary tumor. G4 tumors often retain both epithelial and sarcomatoid phenotypes. Xenografts are frequently representative of most aggressive parental part. Representative images report epithelial and sarcomatoid phenotype belonging to the same patient. Xenograft image mirrors parental tumor aggressive phenotype. (PDF 114 kb)

Figure S1. (A) (PANEL1) Table of 1286 ccRCC patient distribution: 1013 tumor free patients at 36 months from surgery, 130 metastatic (M1) patients at diagnosis time and 143 recurrent patients at 36 months after surgery were reported. (PANEL2) Table of 57 ccRCC cancer patient distribution: 37 tumor free patients at 36 months from surgery, 6 metastatic (M1) patients at diagnosis time and 14 recurrent patients at 36 months from surgery were reported. (B) Representative immunofluorescence of DAPI-stained tumor derived spheroids. (C) Representative image of 7- aminoactinomycin D staining (7AAD) of in vitro isolated populations by flow cytometry. (D) Table reporting distribution of specific antigen expression percentages (%) in all studied ccRCC populations. (E) Representative images of flow cytometry analysis showing the expression of the epithelial and undifferentiated cell markers EpCAM, CD24, CD10, CD90, CD44 and CD146 mesenchymal stem cell markers in ccRCC isolated populations. Background staining was calculated by using appropriate isotype controls. (F) Flow cytometry analysis of cell lines 786–0 and Caki-1 representative of primary and metastatic tumor, respectively. One representative staining of three independent experiments is shown. (PDF 243 kb)

Figure S2. (A) Hematoxylin and Eosin (H&E) and CD31 staining of formalin-fixed and paraffin- embedded (FPPE) of primary tumors. Three patients for each grading were analyzed. A representative image for samples is reported. (B) RPPA-TCGA elaboration of CD31 expression. Data were obtained from macrodissected clear cell renal cancer tissues (GDC-database- https://tcga-data.nci.nih.gov/docs/publications/kirc_2013/ ) and reported for grading, stage and for progression rate by RPPA. (C) Representative images of flow cytometry analysis showing the expression of the endothelial CD31, VE-Cadherin (VE-Cadh) and putative stem cell markers (CD133, CD105) in ccRCC isolated populations. The analysis was combined with CD44 expression. Background staining was calculated by using appropriate isotype controls. (PDF 402 kb)

Pattern of recurrence in the 19 TNBC patients. (DOCX 12 kb)

Expression of TAZ and YAP in cancer cells, non-lymphocytic stromal cells, endothelial cells, and tumor-infiltrating lymphocytes. Subcellular localization of TAZ/YAP in tumors is also reported (Nâ =â 61). (DOCX 13 kb)

associations between TAZ/YAP, assessed in the tumor and in the microenvironment, and clinical-molecular features and pCR (Nâ =â 61). (DOCX 12 kb)

Although histopathological diagnosis is essential in decision of therapeutic strategy for gliomas, sometimes the tumors diagnosed in one histological entity show thoroughly different clinical courses. This phenomenon is believed to be due primarily to the presence of the genetic subgroup. In fact, relationship between treatment response and certain genetic characteristics is indicated (e.g. better chemosensitivity in glioma with losses of 1p/19q (−1p/19q)). It is highly likely that genetic classification of glioma is useful to select the adjuvant treatment. Additionally, gain of 7q (+7q) and −1p/19q are early events in 2 distinct tumor lineages, astrocytic tumors and oligodendroglial tumors, respectively, and these tumors obtain additional genetic aberration (−9p, 10q) with tumor progression. On the other hand, concerning the tumors without +7q or −1p/19q, little is known about clinically important genetic aberration. Therefore the study on such tumors could provide useful information for the prognosis prediction and the determination of treatment strategy. METHODS: We selected 39 cases of gliomas without +7q or −1p/19q from 200 adult supratentorial glioma cases surgically treated and analyzed chromosomal DNA copy number aberrations (CNAs) by comparative genomic hybridization (CGH) from 2005 to 2012. We correlated clinical features of these tumors with histological characteristics, CNAs and IDH1 status. RESULTS: The clinical course of gliomas without +7q or −1p/19q was not correlated with additional genetic aberration of -9p or 10q, which have been known as genetic markers for poor prognosis, and absence of +7q or −1p/19q was maintained at the time of recurrence. The tumors without +7q or −1p/19q showed relatively favorable prognosis although mutation of IDH1 was infrequent in these tumors (35.8 %). CONCLUSION: The gliomas without +7q or −1p/19q have clinical features distinct from the +7q and −1p/19q gliomas. Prognostic markers for each subgroups could help establish therapeutic strategy against the tumor. DNA methylation is a mechanism altering the normal state of cells implicated in many cancers. Currently the methylation status of MGMT is one of the most widely utilized clinical genetic tests performed on glioblastoma multiforme (GBM). While several global gene expression signatures have been developed, it is unclear if genome-wide DNA methylation signatures can predict prognosis in cancer. We used a computational algorithm (MethylMix) to analyze genome-wide DNA methylation in GBM data obtained from The Cancer Genome Atlas (TCGA). MethylMix identified a set of driver genes that met criteria for being both differential and functional. Differential refers to a difference in cancer methylation compared to normal tissue; functional refers to having a significant correlation with matched gene expression changes. We then used these MethylMix driver genes to build multivariate models of overall survival using linear regression and validated these models in independent data sets. Applying MethylMix and linear regression we identified a novel methylation signature predictive of overall survival, which we here define as the GLIOMETH signature. Interestingly, GLIOMETH did not include MGMT, suggesting that MGMT methylation is not essential to predict prognosis in GBM. GLIOMETH was prognostically significant even in a multivariate analysis with known prognostic covariates, including MGMT methylation. We validated GLIOMETH in two external DNA methylation data sets and two gene expression data sets, using a leveraging technique predicting methylation in terms of gene expression, showing also a significant survival correlation. Differential and functional DNA methylation is predictive of overall survival in GBM independent of known prognostic factors. We identified GLIOMETH as a DNA methylation signature that is predictive of overall survival in GBM, outperforming MGMT methylation. The GLIOMETH model validated across multiple independent DNA methylation and gene expression validation data sets demonstrating its robustness as an independent predictor of prognosis in GBM. Recent next-generation genomic studies of medulloblastoma have revealed an unexpected and overwhelming convergence of somatic alterations affecting chromatin-modifying genes. Estimates informed by next-generation sequencing implicate that at least one third of all medulloblastomas have somatic mutations in a chromatin modifier, including those targeting histone methyltransfereses, histone demethylases, and related chromatin modulators that collectively function to influence chromatin conformation associated gene expression states. These mutations occur across all four medulloblastoma subgroups although different sets of genes appear to be selectively altered in a subgroup-specific manner. Despite the abundance of evidence implicating deregulation of chromatin modifiers as a key event in medulloblastoma pathogenesis, the medulloblastoma epigenome remains largely unexplored, and studies cataloguing histone modification states on a genome-wide scale have yet to be reported. To comprehensively investigate the histone code in medulloblastoma and the consequences associated with mutations affecting histone-modifying genes, we have performed ChIP-sequencing on a set of well-characterized primary medulloblastoma specimens. Histone marks examined in this study include the six modifications mandated by the International Human Epigenome Consortium (IHEC), including H3K4me3, H3K9me3, H3K27me3, H3K27ac, H3K4me1 and H3K36me3. Chromatin isolates from primary fresh-frozen tissues representative of each medulloblastoma subgroup were immunoprecipitated with the indicated antibodies and sequenced with a HiSeq Illumina sequencer to obtain at least 10 million unique reads per ChIP experiment. Peak calling was performed using multiple publicly available tools and data integrated with existing ENCODE data for the same histone marks. Inter-subgroup comparisons of histone modification states revealed a wealth of distinguishing genomic regions that were highly correlated with alternative patterns of gene expression existing between the subgroups. Moreover, integration with existing mutational profiles demonstrated aberrant chromatin states that could be linked to underlying mutations in select chromatin modifiers. Ongoing work will focus on expanding the cohort and integration with all levels of ‘omic data.

Supplementary Methods. Figure S1. RAF inhibition induces BRAF/CRAF heterodimerization in KRAS-mut contexts. Figure S2. Effects of trametinib, dabrafenib and their combination on cell growth and cell cycle distribution in A549 and MiaPaCa2 cells. Figure S3. Effects of trametinib and dabrafenib combination in lung cancer cells. Figure S4. Effects of trametinib and dabrafenib combination in pancreatic cancer cells. Figure S5. Effects of trametinib, dabrafenib and their combination in LCSC. Figure S6. Statistical correlation between KRAS status and pharmacological interactions. Figure S7. Effects of lapatinib in EGFR/HER2 amplified lung cancer cell lines. Table S1. Summary of the genetic status of the cell lines analyzed and response to treatments. (PDF 2519 kb)

Over the past decade, the Nomenclature Committee on Cell Death (NCCD) has formulated guidelines for the definition and interpretation of cell death from morphological, biochemical, and functional perspectives. Since the field continues to expand and novel mechanisms that orchestrate multiple cell death pathways are unveiled, we propose an updated classification of cell death subroutines focusing on mechanistic and essential (as opposed to correlative and dispensable) aspects of the process. As we provide molecularly oriented definitions of terms including intrinsic apoptosis, extrinsic apoptosis, mitochondrial permeability transition (MPT)-driven necrosis, necroptosis, ferroptosis, pyroptosis, parthanatos, entotic cell death, NETotic cell death, lysosome-dependent cell death, autophagy-dependent cell death, immunogenic cell death, cellular senescence, and mitotic catastrophe, we discuss the utility of neologisms that refer to highly specialized instances of these processes. The mission of the NCCD is to provide a widely accepted nomenclature on cell death in support of the continued development of the field.

Supplementary Methods. Figure S1. RAF inhibition induces BRAF/CRAF heterodimerization in KRAS-mut contexts. Figure S2. Effects of trametinib, dabrafenib and their combination on cell growth and cell cycle distribution in A549 and MiaPaCa2 cells. Figure S3. Effects of trametinib and dabrafenib combination in lung cancer cells. Figure S4. Effects of trametinib and dabrafenib combination in pancreatic cancer cells. Figure S5. Effects of trametinib, dabrafenib and their combination in LCSC. Figure S6. Statistical correlation between KRAS status and pharmacological interactions. Figure S7. Effects of lapatinib in EGFR/HER2 amplified lung cancer cell lines. Table S1. Summary of the genetic status of the cell lines analyzed and response to treatments. (PDF 2519 kb)

Obesity is a strong risk factor for cancer progression, posing obesity-related cancer as one of the leading causes of death. Nevertheless, the molecular mechanisms that endow cancer cells with metastatic properties in patients affected by obesity remain unexplored. Here, we show that IL-6 and HGF, secreted by tumor neighboring visceral adipose stromal cells (V-ASCs), expand the metastatic colorectal (CR) cancer cell compartment (CD44v6 + ), which in turn secretes neurotrophins such as NGF and NT-3, and recruits adipose stem cells within tumor mass. Visceral adipose-derived factors promote vasculogenesis and the onset of metastatic dissemination by activation of STAT3, which inhibits miR-200a and enhances ZEB2 expression, effectively reprogramming CRC cells into a highly metastatic phenotype. Notably, obesity-associated tumor microenvironment provokes a transition in the transcriptomic expression profile of cells derived from the epithelial consensus molecular subtype (CMS2) CRC patients towards a mesenchymal subtype (CMS4). STAT3 pathway inhibition reduces ZEB2 expression and abrogates the metastatic growth sustained by adipose-released proteins. Together, our data suggest that targeting adipose factors in colorectal cancer patients with obesity may represent a therapeutic strategy for preventing metastatic disease., Obesity is a major risk factor for cancer related death. Here, the authors show that visceral adipose-derived factors promote vasculogenesis and metastatic dissemination by activation of STAT3, which inhibits miR-200a and enhances ZEB2 expression, effectively reprogramming colorectal cancer cells into a highly metastatic phenotype.

Summary Limited therapeutic options are available for advanced colorectal cancer (CRC). Herein, we report that exposure to a neo-synthetic bis(indolyl)thiazole alkaloid analog, nortopsentin 234 (NORA234), leads to an initial reduction of proliferative and clonogenic potential of CRC sphere cells (CR-CSphCs), followed by an adaptive response selecting the CR-CSphC-resistant compartment. Cells spared by the treatment with NORA234 express high levels of CD44v6, associated with a constitutive activation of Wnt pathway. In CR-CSphC-based organoids, NORA234 causes a genotoxic stress paralleled by G2-M cell cycle arrest and activation of CHK1, driving the DNA damage repair of CR-CSphCs, regardless of the mutational background, microsatellite stability, and consensus molecular subtype. Synergistic combination of NORA234 and CHK1 (rabusertib) targeting is synthetic lethal inducing death of both CD44v6-negative and CD44v6-positive CRC stem cell fractions, aside from Wnt pathway activity. These data could provide a rational basis to develop an effective strategy for the treatment of patients with CRC., Graphical abstract, Highlights • CR-CSCs acquire a long-term resistance to the NORA234 treatment • Replicative and genotoxic stress induces the activation of CHK1 • Adaptive response to NORA234 is associated with high expression levels of CHK1 • NORA234 together with targeting of CHK1 leads to depletion of CR-CSC compartment, Cancer; Cell biology; Drugs; Molecular physiology