Abstract

   Background: Small cell lung cancer (SCLC) is an extremely aggressive
   cancer type with a patient median survival of 6-12 months. Epidermal
   growth factor (EGF) signaling plays an important role in triggering
   SCLC. In addition, growth factor-dependent signals and alpha-,
   beta-integrin (ITGA, ITGB) heterodimer receptors functionally cooperate
   and integrate their signaling pathways. However, the precise role of
   integrins in EGF receptor (EGFR) activation in SCLC remains elusive.

   Methods: We analyzed human precision-cut lung slices (hPCLS),
   retrospectively collected human lung tissue samples and cell lines by
   classical methods of molecular biology and biochemistry. In addition,
   we performed RNA-sequencing-based transcriptomic analysis in human lung
   cancer cells and human lung tissue samples, as well as high-resolution
   mass spectrometric analysis of the protein cargo from extracellular
   vesicles (EVs) that were isolated from human lung cancer cells.

   Results: Our results demonstrate that non-canonical ITGB2 signaling
   activates EGFR and RAS/MAPK/ERK signaling in SCLC. Further, we
   identified a novel SCLC gene expression signature consisting of 93
   transcripts that were induced by ITGB2, which may be used for
   stratification of SCLC patients and prognosis prediction of LC
   patients. We also found a cell-cell communication mechanism based on
   EVs containing ITGB2, which were secreted by SCLC cells and induced in
   control human lung tissue RAS/MAPK/ERK signaling and SCLC markers.

   Conclusions: We uncovered a mechanism of ITGB2-mediated EGFR activation
   in SCLC that explains EGFR-inhibitor resistance independently of EGFR
   mutations, suggesting the development of therapies targeting ITGB2 for
   patients with this extremely aggressive lung cancer type.

   Keywords: small cell lung cancer, integrin, EGFR, KRAS, extracellular
   vesicles

Introduction

   Lung cancer (LC) causes more deaths worldwide than the next three most
   prevalent cancers together (colorectal, breast and prostate) [63]^1.
   Based on histology, LC is classified into non-small cell (NSCLC) and
   small cell lung cancer (SCLC). NSCLC can be further classified into
   three subtypes: squamous cell carcinoma, adenocarcinoma, and large-cell
   lung cancer [64]^2. SCLC accounts for 15-20% of all LC cases and is
   strongly associated with cigarette smoking [65]^3. SCLC is a
   neuroendocrine type of lung cancer characterized by aggressive
   progression due to high cellular proliferation and early metastasis
   [66]^4. The first line of therapy includes a combination of
   platinum-based treatment (cisplatin or carboplatin) with etoposide or
   irinotecan. While SCLC is initially chemo- and radiosensitive, therapy
   resistance frequently arises. Consequently, patient prognosis is poor
   with a median survival of 6 to 12 months [67]^5.

   Extracellular vesicles (EVs) are nano-sized, phospholipid
   membrane-enclosed vesicles that are secreted by different cell types
   into the extracellular space and can be found in a wide spectrum of
   human body fluids including serum, plasma, saliva, breast milk,
   amniotic fluid, cerebrospinal fluid, urine, among others [68]^6^,
   [69]^7. EVs are classified based on their size, biogenesis and method
   of cellular release into three groups: exosomes, microvesicles and
   apoptotic bodies. Microvesicles and apoptotic bodies are formed by
   budding from the plasma membrane, and generally range in size from 0.1
   to 1 μm and 1 to 4 μm, respectively [70]^8^, [71]^9. In contrast,
   exosomes are smaller with a diameter ranging from 30 to 150 nm, and are
   formed by inward budding of the endosome lumen to form a multivesicular
   body, which fuses with the plasma membrane during secretion [72]^10.
   Due to an overlap in size (100-150 nm) and density (1.08-1.19 g/mL), as
   well as the presence of common markers, such as CD63 and CD81 [73]^11,
   it is difficult to differentiate exosomes and microvesicles. Thus,
   exosomes and microvesicles with a diameter below 150 nm are
   collectively referred to as small extracellular vesicles (small EVs)
   [74]^12. EV secretion is elevated in response to inflammation [75]^13
   and hypoxia [76]^14^, [77]^15 and is associated with human diseases. In
   cancer, for example, EV levels correlate with tumor invasiveness
   [78]^16^-[79]^18. Interestingly, EVs contain proteins and nucleic
   acids, and it has been reported that tumor cells can influence their
   microenvironment through EV-based cell-cell communication mechanisms
   [80]^19^-[81]^23.

   Epidermal growth factor (EGF) signaling plays an important role in LC
   development and metastasis. In particular, activation of EGF receptor
   (EGFR) tyrosine kinases (RTK) is crucial for triggering SCLC and NSCLC
   [82]^24. EGFR is composed of an extracellular ligand-binding domain, a
   transmembrane domain and an intracellular tyrosine kinase domain. The
   binding of a ligand to the extracellular domain of EGFR induces
   receptor dimerization, activation of the intracellular kinase domain
   and auto-phosphorylation of tyrosine residues within the cytoplasmic
   domain of the receptor [83]^25. The tyrosine-phosphorylated motifs of
   EGFR recruit adapters or signaling molecules that initiate various
   downstream signaling cascades including the RAS/MAPK/ERK, PIK3-Akt and
   STAT pathways [84]^26. These signaling cascades result in
   transcriptional activation of gene signatures that mediate specific
   cellular responses, such as cell proliferation, cell migration,
   epithelial-mesenchymal transition (EMT), among others.

   Integrins are heterodimeric transmembrane protein complexes resulting
   from noncovalent association of specific alpha (ITGA) and a beta (ITGB)
   subunits [85]^27. In general, each integrin subunit has a large
   extracellular domain, a single-pass transmembrane domain, and a short
   cytoplasmic tail [86]^27. Integrins mediate cell-cell and cell-ECM
   (extracellular matrix) interactions and transmit signals in both
   directions, outside-in and inside-out, across the cell membrane
   [87]^28. Recent studies have shown that growth factor- and
   integrin-dependent signals functionally cooperate to integrate their
   signaling pathways [88]^29^, [89]^30. The crosstalk between EGFR and
   integrins has been reported to play a key role in multiple biological
   processes in cancer [90]^31. Moreover, several integrin dimers,
   including ITGA5-ITGB1, ITGA6-ITGB4, ITGAv-ITGB3 and ITGAv-ITGB5, exert
   different effects on the regulation of EGF signaling [91]^32^-[92]^34.
   We have previously demonstrated that ITGA2, ITGB2 and ITGB6 are
   enriched in the membrane of alveolar type-II (ATII) cells, which are
   lung progenitor cells responsible for regeneration of the alveolar
   epithelium during homeostatic turnover and in response to injury
   [93]^35. In addition, ATII cells were also reported as cells of origin
   of lung adenocarcinoma [94]^36. These observations motivated us to
   investigate the function of integrin receptor subunits in LC.

Results

Non-canonical ITGB2 signaling activates EGFR in SCLC

   The previously reported enrichment of ITGA2, ITGB2 and ITGB6 in the
   membrane of lung progenitor cells [95]^35 suggests a potential
   interaction between these integrin receptor subunits. To test this
   hypothesis, we performed in silico analyses of proteomic data
   repositories derived from protein-protein interaction databases
   ([96]Figure S1A). Using the STRING database, we identified 50
   interaction partners of human ITGA2 with high confidence (combined
   score ≥ 0.9; 2 nodes, [97]Table S1), including ITGB2 (combined score =
   0.96) and ITGB6 (combined score = 0.97). To confirm the interaction of
   ITGA2 with ITGB2 and ITGB6, we performed co-immunoprecipitation (Co-IP)
   assays using total protein extracts from mouse lung epithelial cells
   (MLE-12) transiently transfected with HIS-ITGA2 and YFP-ITGB2 or
   GFP-ITGB6 (Figure [98]1A). ITGA2 precipitated both ITGB2 and ITGB6,
   thus validating our in silico analysis and demonstrating the
   interaction between these integrin receptor subunits. To assess whether
   ITGB2, ITGB6 and ITGA2 are expressed in SCLC, we performed
   qRT-PCR-based expression analysis using total RNA extracted from
   retrospectively collected formalin-fixed paraffin embedded (FFPE) human
   lung tissues from SCLC patients (n = 5) and control donors (Ctrl, n =
   4; [99]Table S2 and Figure [100]1B). We observed increased ITGB2 (P =
   0.02) and ITGA2 (P = 9 E-03) expression in SCLC as compared to Ctrl
   FFPE human lung tissue, whereas ITGB6 levels were reduced (P = 0.01).
   To perform an equivalent analysis in NSCLC, we retrieved RNA-sequencing
   (RNA-seq) data of lung adenocarcinoma patients (LUAD, n = 11) and
   control donors (Ctrl, n = 9) from The Cancer Genome Atlas (TCGA)
   ([101]Table S3). In contrast to SCLC, we observed decreased ITGB2 (P =
   0.05) in LUAD as compared to Ctrl human lung tissue (Figure [102]1C).
   Similarly to SCLC, ITGA2 expression also increased (P = 0.01) in LUAD
   compared to Ctrl. Consistent with these results, we found a positive
   correlation between the expression of ITGB2 and ITGA2 in SCLC (R^2 =
   0.84, P < 0.05, [103]Figure S1B, top) and a positive correlation
   between the expression of ITGB6 and ITGA2 in LUAD (R^2 = 0.94, P < 1
   E-04, [104]Figure S1B, bottom) by linear regression analysis. Our
   results demonstrated the complementary expression of ITGB2 and ITGB6 in
   SCLC and LUAD suggesting their use as markers for these cancer subtypes
   and supporting the formation of different integrin heterodimer
   receptors in SCLC and LUAD.

Figure 1.

   [105]Figure 1
   [106]Open in a new tab

   Mutual negative regulation of ITGB2 and ITGB6 levels in SCLC and NSCLC.
   (A) Protein extracts of MLE-12 cells co-transfected with ITGA2-HIS and
   ITGB2-YFP or ITGA2-HIS and ITGB6-GFP were immunoprecipitated (IP) using
   either immunoglobulin G (IgG, as control) or HIS-specific antibodies.
   Co-IP proteins were analyzed by WB using the indicated antibodies. In,
   input, 3% of material used for the IP. (B) Box plots of qRT-PCR-based
   expression analysis of indicated transcripts using RNA isolated from
   FFPE lung tissue sections from Ctrl (n = 4) and small cell-lung cancer
   (SCLC, n = 5) patients. Rel nor exp, relative normalized expression to
   GAPDH. (C) Box plots of RNA-seq-based expression analysis of indicated
   transcripts in matched control donors (Ctrl; n = 9) and matched lung
   adenocarcinoma (LUAD; n = 11) patients from the Cancer Genome Atlas
   (TCGA). Values were normalized using reads per kilobase per million
   (RPKM). (D) Box plots of RNA-seq-based expression analysis of indicated
   transcripts in non-small cell lung cancer (NSCLC; n = 34) and small
   cell lung cancer (SCLC; n = 17) cell lines. Values are represented as
   log2 RPKM. All box plots (B-D) indicate median (middle line), 25th,
   75th percentile (box) and 5th and 95th percentile (whiskers); P-values
   after two-tailed t-test. (E) qRT-PCR-based expression analysis of
   indicated mRNA in A549, NCI-H82 and NCI-H196 cell lines. (F)
   qRT-PCR-based expression analysis of indicated mRNA in A549 cells (left
   panel) transfected with ITGB2 or NCI-H196 cells (right panel)
   transfected with ITGB6. In the bar plots (E-F), data are shown as means
   ± SD (n = 3); asterisks, P-values after two-tailed t-test, *** P ˂
   0.001; ** P ˂ 0.01; * P ˂ 0.05. (G) Hematoxylin and eosin staining in
   human lung tissue from NSCLC (left) and SCLC (right) patients. Squares
   are respectively shown in E at higher magnification. (H) Fluorescence
   microscopy after immunostaining using ITGB6 or ITGB2-specific
   antibodies in NSCLC and SCLC FFPE lung tissues (in G). DAPI, nuclear
   staining. Scale bar, 500 µm. Source data for all plots are provided as
   Source Data S1. See also [107]Figure S1-S5 and Source Data S2.

   To further investigate the differential expression of ITGB6 and ITGB2
   in lung cancer subtypes, we analyzed publicly available RNA-seq data of
   NSCLC and SCLC cell lines ([108]Figure S2) [109]^37. Consistent with
   our results in human lung tissue (Figure [110]1B-C), we detected high
   levels of ITGB2 in SCLC cell lines (Figure [111]1D, top), whereas ITGB6
   levels were high in NSCLC cell lines (Figure [112]1D, middle). In
   addition, linear regression analysis confirmed the positive correlation
   between ITGB2 and ITGA2 levels in SCLC cell lines (R^2 = 0.82; P = 4
   E-03; [113]Figure S1C, top) and the positive correlation between ITGB6
   and ITGA2 levels in NSCLC cell lines (R^2 = 0.72; P = 9 E-04;
   [114]Figure S1C, bottom). To further investigate these findings, we
   selected the human adenocarcinoma cell line A549 as a
   NSCLC-representative cell line, whereas the human cell lines NCI-H82
   and NCI-H196 were selected as representative cells for SCLC. The
   selected cell lines lack somatic mutations in the EGFR locus
   ([115]Figure S3) and are experimental systems commonly used in LC
   subtype-specific studies. Expression analysis by qRT-PCR (Figure
   [116]1E-F) confirmed the results obtained by RNA-seq (Figure [117]1D),
   high ITGB2 levels were detected in SCLC cell lines (P = 0.005 and P =
   0.001), whereas A549 cells showed high ITGB6 levels (P = 0.023 and P =
   0.019). Interestingly, transient transfection of ITGB2 in A549 cells
   significantly reduced ITGB6 expression (P = 0.0171; Figure [118]1F,
   left), whereas ITGB6 transfection in NCI-H82 and NCI-H196 cells
   significantly reduced ITGB2 expression (P = 0.0002 and P = 0.0098,
   respectively; Figure [119]1F, right). Moreover, our qRT-PCR-based
   expression analyses were also confirmed in FFPE of lung tissue obtained
   from NSCLC and SCLC patients stained with ITGB6- or ITGB2-specific
   antibodies (Figure [120]1G-H) and by Western Blot analysis (WB) of
   protein extracts from transfected A549, NCI-H82 and NCI-H196 cells
   (Figure [121]2A, top). Our results support a mutual negative regulation
   of ITGB6 and ITGB2 expression in NSCLC and SCLC.

Figure 2.

   [122]Figure 2
   [123]Open in a new tab

   Non-canonical ITGB2 signaling activates EGFR in SCLC. (A) Total protein
   extracts of A549, NCI-H82 and NCI-H196 cell lines transfected with
   ITGB2 or ITGB6 were analyzed by WB using the indicated antibodies. (B)
   Total protein extracts of A549 cells transfected with ITGB2,
   ligand-binding-deficient D134A ITGB2 mutant (mutITGB2) or
   Galectin-3-specific small interfering RNA (siGAL3) were analyzed by WB
   using the indicated antibodies. (C) Confocal microscopy after
   immunostaining with specific antibodies against EGFR, pEGFR, ITGB2 and
   ITGB6 in NCI-H196 and A549 cells. DAPI, nucleus. Scale bars, 10 μm. (D)
   Schematic representation of a EGFR-dimer highlighting specific key
   amino acid residues, including phosphorylation sites in two p38 target
   regions (R1 and R2; [124]^39) that were mutated in two expression
   constructs MutR1 and MutR2. (E) Total protein extracts of NCI-H196 cell
   lines were analyzed by WB using the indicated antibodies. NCI-H196
   cells were transiently transfected with control empty plasmid (Ctrl),
   or constructs for expression of MYC-tagged wild-type EGFR (EGFR-WT) or
   EGFR mutants, in which either S1015, T1017 and S1018 were mutated to
   alanine (MutR1); or S1046 and S1047 were mutated to alanine (MutR2)
   alone or in combination (MutR1R2). Exogenous EGFR (exoEGFR) was
   differentiated from endogenous EGFR (endoEGFR) using MYC-tag-specific
   antibodies. See also [125]Figure S1-S5 and Source Data S2.

   Additional analysis of the RNA-seq data from NCI-H82 and NCI-H196 cells
   showed enrichment of pathways related to EGFR signaling ([126]Figure
   S4A-B). We further observed increased levels of key downstream genes of
   EGFR signaling, such as VIM, NFKB2 and HIF1A, in RNA-seq data from SCLC
   cell lines as compared to NSCLC cell lines ([127]Figure S4C-D)
   [128]^37, which were confirmed by qRT-PCR-based expression analysis in
   FFPE human lung tissue ([129]Figure S4E). Our results indicate that
   EGFR signaling is active in SCLC and correlates with increased ITGB2
   expression. To further investigate this finding, we analyzed protein
   extracts from transiently transfected A549, NCI-H82 and NCI-H196 cells
   by WB (Figure [130]2A, middle). Overexpression of ITGB2 in A549 cells
   induced phosphorylation of EGFR (pEGFR) and the mitogen-activated
   protein kinase (pMAPK) as compared to Ctrl transfected cells. On the
   other hand, the levels of pEGFR and pMAPK in NCI-H82 and NCI-H196 cells
   were reduced after ITGB6 transfection. Moreover, the changes in pEGFR
   and pMAPK in all three cell lines did not affect total levels of EGFR
   and MAPK, thereby indicating that the observed effects were related to
   the post-translational phosphorylation of these proteins. To gain
   further insight into the mechanism of ITGB2-induced,
   phosphorylation-dependent activation of EGFR and MAPK, we investigated
   the involvement of non-canonical, ligand-independent integrin signaling
   [131]^33^, [132]^38. We generated a ITGB2 mutant (mutITGB2) that is
   ligand-binding-deficient, as aspartic acid 134 in the ligand-binding
   domain was substituted by alanine ([133]Figure S5A). Overexpression of
   mutITGB2 in A549 cells (Figure [134]2B and S5B) induced pEGFR and
   pMAPK, as well as increased the levels of VIM and ACTA2 in Galectin-3
   (GAL3) -dependent manner, demonstrating the involvement of
   non-canonical, ligand-independent integrin signaling during the
   phosphorylation-dependent activation of EGFR and MAPK. In addition, we
   observed co-localization of ITGB2 and pEGFR in NCI-H196 cells (Figure
   [135]2C, top), whereas ITGB6 and EGFR co-localized in A549 cells
   (Figure [136]2C, bottom). To further characterize the
   phosphorylation-dependent activation of EGFR during non-canonical,
   ligand-independent integrin signaling in SCLC, we analyzed protein
   extracts from transiently transfected NCI-H196 cells by WB (Figure
   [137]2D-E). Overexpression of an EGFR mutant (MutR1, in which serine
   1015, threonine 1017 and serine 1018 were substituted by alanine)
   [138]^39 acted as a dominant-negative EGFR abolishing pEGFR and pMAPK
   and reducing VIM and ITGB2 levels. Interestingly, overexpression of a
   second EGFR mutant (MutR2, in which serines 1046 and 1047 were mutated
   to alanine) also interfered with the phosphorylation-dependent
   activation of EGFR and MAPK and reduced VIM levels without affecting
   ITGB2 levels. In summary, our results demonstrate that non-canonical
   ITGB2 signaling activates EGFR in SCLC (Figure [139]2B). In addition,
   whereas both EGFR mutants (MutR1 and MutR2) acted as dominant-negative
   forms interfering with phosphorylation-dependent activation of EGFR and
   MAPK (Figure [140]2D-E), only MutR1 reduced ITGB2 levels suggesting
   specificity in the role of the phosphorylation of the mutated amino
   acids during non-canonical, ligand-independent integrin signaling in
   SCLC.

ITGB2 induces a novel SCLC gene expression signature

   We retrieved and analyzed RNA-seq data of a previously published cohort
   of SCLC patients from the European Genome-Phenome Archive ([141]Figure
   S6) [142]^40. Correlation analysis of these RNA-seq based
   transcriptomes allowed us to group the SCLC patients into two clusters
   (C1 and C2; Figure [143]3A). Over-representation analysis (ORA) based
   on the Reactome database [144]^41 for genes with increased expression
   in C2 (Figure [145]3B; 5,149 transcripts with fold change (FC) ≥ 3;
   Source Data S1) revealed a significant enrichment of genes related to
   the category “integrin cell surface interactions” (P = 9.1 E-03) as one
   of the top items of the ranked list. In addition, gene set enrichment
   analysis (GSEA) of the up-regulated transcripts in C2 (Figure [146]3C)
   showed a high enrichment score (ES) of 0.88 for the category “integrin
   cell surface interactions” (P = 9 E-03). Interestingly, lung tissue
   from SCLC patients in C2 showed significantly higher ITGB2 expression
   (M = 0.04; IQR = 0.03) than the lung tissue from SCLC patients in C1 (M
   = 0.01; IQR = 0.01; P = 2 E-03; Figure [147]3D; Source Data S1).
   Moreover, we identified 93 transcripts that were jointly upregulated in
   C2, in the SCLC cell line NCI-H196, as well as in A549 cells
   transiently transfected with ITGB2 or mutITGB2 (Figure [148]3E;
   [149]Table S4). We named the set of 93 genes coding for these common
   upregulated transcripts as SCLC-ITGB2 gene expression signature
   (SCLC-ITGB2-sig). As shown in the heatmap in Figure [150]3F, the
   expression levels of the 93 genes, constituting the SCLC-ITGB2-sig,
   differentiate the two clusters of SCLC patients. Comparing the
   enrichment of the genes grouped into the Kyoto Encyclopedia of Genes
   and Genomes (KEGG) category “SCLC” and the SCLC-ITGB2-sig ([151]Figure
   S7), we observed ES of 0.52 (P = 0.37) and 0.52 (P = 0.36) for the
   KEGG-SCLC set in SCLC patients of C2 and C1, respectively, whereas the
   ES of the SCLC-ITGB2-sig significantly improved to 0.97 (P = 8 E-03)
   and 0.98 (P = 8 E-03) in SCLC patients of C2 and C1, respectively.
   Moreover, an external validation (Figure [152]3G) using independent
   RNA-seq data from SCLC cell lines [153]^42 confirmed the improvement of
   the SCLC-ITGB2-sig (ES = 0.77; FDR = 0.50) as compared to the KEGG-SCLC
   category (ES = 0.42; FDR = 0.82). Thus, we propose the use of
   SCLC-ITGB2-sig for the identification of gene expression signatures
   related to SCLC.

Figure 3.

   [154]Figure 3
   [155]Open in a new tab

   ITGB2 induces a novel SCLC gene expression signature. (A) Correlogram
   showing the correlation score matrix (RPKM values, Spearman correlation
   coefficient) across RNA-seq data of lung tissue of SCLC patients from
   the European Genome Archive (EGAS00001000299). SCLC patients were
   grouped in cluster 1 (C1) and cluster 2 (C2). (B) Bubble plot of top
   six enrichment of Reactome pathways in C2 by Overrepresentation
   Analysis (ORA). P-values after two-tailed t-test are shown by different
   color, the size of bubble indicate the gene count of each pathway.
   Sig., signaling; int., interactions; chemok., chemokine; rec.,
   receptor. (C) Gene Set Enrichment Analysis (GSEA) using the fold change
   of genes inside the integrin pathway in B. ES, enrichment score;
   P-value after two-tailed t-test. (D) Box plots of RNA-seq-based
   expression analysis of indicated transcripts in SCLC patients C1 (n =
   7) and C2 (n = 7). Values are represented as log2 RPKM. Nor exp,
   normalized expression to GAPDH. Box plots indicate median (middle
   line), 25th, 75th percentile (box) and 5th and 95th percentile
   (whiskers); P-values after two-tailed t-test. Source data are provided
   as Source Data S1. (E) Venn diagram comparing transcripts that were
   significantly increased in SCLC patients C2 compared to C1 (FC ≥ 3; P ≤
   0.05 after two-tailed t-test), NCI-H196 cells, A549 cells transfected
   either with ITGB2 or mutITGB2 (for all 3 cell lines, coding
   transcripts; FC ≥ 1.15; P ≤ 0.05 after two-tailed t-test) highlights a
   group of 93 transcripts that are common in all four groups, the
   SCLC-ITGB2 gene expression signature (SCLC-ITGB2-sig). See also
   [156]Table S4. (F) Hierarchical heatmap using RPKM of all 93 IDs of the
   SCLC-ITGB2-sig comparing SCLC patients in C1 to C2. Hierarchical
   clustering was performed using Person's correlation based distance and
   average linkage. (G) External validation of the SCLC-ITGB2-sig. GSEA
   using independent RNA-seq data from SCLC cell lines [157]^42 comparing
   the conventional SCLC signature in KEGG (left) versus the
   SCLC-ITGB2-sig (right) identified in E. ES, enrichment score; FDR,
   false discovery rate. See also [158]Figure S6 and S7.

   Recent reports suggest a SCLC classification in subtypes based on the
   expression of ASCL1, NEUROD1, POU2F3 and YAP1 [159]^43^, [160]^44.
   Interestingly, we did not detect significant differences in the
   expression levels of these markers between the clusters C1 and C2 of
   SCLC patients (Figure [161]4A-B), nor a significant linear correlation
   between the expression of ITGB2 and ASCL1 (R^2 = 0.43), NEUROD1 (R^2 =
   0.11), POU2F3 (R^2 = 0.13) and YAP1 (R^2 = 0.00) by linear regression
   analysis (Figure [162]4C). These results suggest that the
   SCLC-ITGB2-sig arises in all four SCLC subtypes. Further, a survival
   analysis of 673 LC patients from Kaplan-Meier plotter [163]^45 (Figure
   [164]4D) showed a significantly shorter survival of patients with
   increased levels of these 93 transcripts (n = 372) compared to patients
   with low expression levels (n = 300). The median survival in the two
   groups were 88.7 and 127 months, respectively (hazard ratio = 1.38, P =
   0.01, Cox regression). These findings support the clinical relevance of
   the SCLC-ITGB2-sig not only for stratification of SCLC patients, which
   might help to develop patient-tailored therapies, but also for
   prognosis prediction of LC patients.

Figure 4.

   [165]Figure 4
   [166]Open in a new tab

   SCLC-ITGB2 gene expression signature occurs in all SCLC subtypes. (A)
   Box plots of RNA-seq-based expression analysis of the transcripts used
   for SCLC subtype classification (ASCL1, NEUROD1, POU2F3 and YAP1)
   [167]^43, [168]^44 in SCLC patients C1 (n = 7) and C2 (n = 7). Values
   are represented as log2 RPKM. Nor exp, normalized expression to GAPDH.
   Box plots indicate median (middle line), 25th, 75th percentile (box)
   and 5th and 95th percentile (whiskers); P-values after two-tailed
   t-test. Source data are provided as Source Data S1. (B) Heat map
   representing the RNA-seq-based expression levels of the indicated
   transcripts in SCLC patients C1 (n = 7) and C2 (n = 7). Blue, low
   expression; red, high expression. (C) Correlation analysis between
   ITGB2 and ASCL1, NEUROD1, POU2F3 and YAP1 by linear regression of
   relative normalized expression from RNA-seq-based expression analysis
   of indicated transcripts in SCLC patients C1 (n = 7) and C2 (n = 7).
   (D) Overall survival rates by Kaplan-Meier plotter of LUAD patients
   expressing low (n = 300) or high (n = 372) SCLC-ITGB2-sig (127 vs 88.7
   months, respectively, P = 0.011 after after Log Rank test). HR, hazard
   ratio. See also [169]Figure S6 and S7.

Non-canonical ITGB2 signaling activates RAS/MAPK/ERK signaling

   To further characterize the SCLC-ITGB2-sig, we performed a gene
   ontology (GO) enrichment analysis based on Biological Processes and
   found significant enrichment of genes related to GO terms involved in
   extracellular signal-regulated kinase 1/2 (ERK1/2) signaling pathway
   (Figure [170]5A). In addition, we also detected significant enrichment
   of genes related to RAS pathway (P = 2.2 E-02; FDR = 0.26) and MAPK
   pathway (P = 2.9 E-03; FDR = 0.1) (Figure [171]5B). These findings
   corroborated our previous results (Figure [172]2A-C and S4-S5), as Ras
   family proteins are known to be activated by signaling through EGFR
   [173]^46.

Figure 5.

   [174]Figure 5
   [175]Open in a new tab

   Non-canonical ITGB2 signaling activates RAS/MAPK/ERK signaling. (A)
   Gene Ontology (GO)-based enrichment analysis of biological pathways in
   the 93 IDs of the SCLC-ITGB2 gene expression signature (SCLC-ITGB2-sig)
   from Figure [176]3E using Webgestalt bioinformatics tool and plotted by
   highest enrichment ratio. Reg., regulation. (B) GSEA line profiles of
   SCLC-ITGB2-sig in RAS (Panther) and MAPK signaling pathways (KEGG).
   P-values after two-tailed t-test. (C) RAS activation assay. Protein
   extracts of A549, NCI-H196 and NCI-H82 were immunoprecipitated (IP)
   using a KRAS-specific antibody (KRAS) or RAF-RBD (RBD, active KRAS)
   coated beads. Co-IP proteins were analyzed by WB using the indicated
   antibodies. In, input, 5% of material used for the IP. (D) Total
   protein extracts of A549 cells transfected with ITGB2 or
   ligand-binding-deficient D134A ITGB2 mutant (mutITGB2) were analyzed by
   WB using the indicated antibodies. pEGFR, active phosphorylated
   epidermal growth factor receptor; pMAPK, phosphorylated
   mitogen-activated protein kinase; pRAF1, phosphorylated proto-oncogene
   serine/threonine-protein kinase; pERK, phosphorylated extracellular
   signal-regulated kinase. (E) Total protein extracts of NCI-H82 and
   NCI-H196 cells transfected with small interfering RNA specific for
   ITGB2 (siITGB2) or KRAS (siKRAS) were analyzed by WB using the
   indicated antibodies. Vimentin (VIM) as product of a downstream gene
   target of EGF signaling is highlighted in green. See also Source Data
   S2.

   To further investigate the involvement of active RAS/MAPK/ERK signaling
   in SCLC, we performed RAS activation assay using whole cell protein
   extracts from A549, NCI-H196 and NCI-H82 cells (Figure [177]5C). This
   assay is based on the principle that the RAS binding domain (RBD) of
   the RAF kinase, one of the downstream RAS effector proteins, binds
   specifically to the GTP-bound form of RAS [178]^47. Using RAF-RBD
   coated beads, we pulled down active, GTP-bound KRAS from protein
   extracts of NCI-H196 and NCI-H82 cells, supporting that RAS/MAPK/ERK
   signaling is active in SCLC. Interestingly, transfection of ITGB2 or
   mutITGB2 into A549 cells (Figure [179]5D) induced phosphorylation of
   EGFR, MAPK, RAF1 and ERK without significantly affecting the total
   levels of these proteins, thereby showing that non-canonical ITGB2
   activates the EGFR and the RAS/MAPK/ERK signaling pathway. Further, we
   determined whether ITGB2 and KRAS are required for the intrinsic levels
   of pEGFR observed in both SCLC cell lines NCI-H82 and NCI-H196 (Figure
   [180]2A). We analyzed by WB protein extracts from NCI-H82 and NCI-H196
   cells that were transfected with Ctrl, ITGB2- or KRAS-specific small
   interfering RNAs (siRNA, siCtrl, siITGB2 or siKRAS; Figure [181]5E).
   Specific and efficient siRNA-mediated ITGB2 or KRAS loss-of-function
   (LOF) reduced the levels of pEGFR and the downstream target of EGFR
   signaling VIM, demonstrating the requirement of ITGB2 and KRAS for the
   activation of EGFR and the RAS/MAPK/ERK signaling in SCLC.
   Interestingly, siITGB2 or siKRAS transfection increased ITGB6 levels in
   both SCLC cell lines, supporting the mutual exclusive function of ITGB2
   and ITGB6 shown in Figure [182]1F, 2A and S5B-C.

   To gain further insight into the mechanism of ITGB2-induced activation
   of EGFR, we performed Co-IP using ITGB6- or ITGB2-specific antibodies
   in protein extracts from A549 cells transiently transfected with Ctrl
   (empty vector), ITGB6 or ITGB2 (Figure [183]6A). We found that
   endogenous ITGB6 interacts with non-phosphorylated EGFR and ITGB2
   overexpression abolished this interaction. Further, ITGB2 transfection
   induced pEGFR and reduced ITGB6 levels, confirming our previous results
   (Figure [184]1F, 2A and S5B-C). Moreover, over-expressed ITGB2
   co-precipitated pEGFR demonstrating the interaction of ITGB2 with the
   active form of this receptor. These results were complemented by Co-IP
   experiments using protein extracts from NCI-H196 cells transiently
   transfected with Ctrl or ITGB6 (Figure [185]6B). We detected
   interaction of endogenous ITGB2 with pEGFR, confirming the results in
   A549 cells after overexpression of ITGB2. Further, ITGB6 overexpression
   abolished the ITGB2-pEGFR-interaction. Our results demonstrate the
   interaction between endogenous ITGB6 and EGFR in the NSCLC cell line
   A549, whereas endogenous ITGB2 interacts with pEGFR in the SCLC cell
   line NCI-H196. Moreover, these interactions appear mutually exclusive,
   since overexpression of ITGB2 or ITGB6 abolished the interaction of the
   complementary integrin receptor beta subunit with EGFR. Remarkably,
   precipitation of endogenous EGFR in NCI-H196 cells confirmed the
   pEGFR-ITGB2 interaction (Figure [186]6C). Moreover, siRNA-mediated LOF
   of ITGB2, GAL3 or KRAS abolished the pEGFR-ITGB2 interaction, thereby
   demonstrating the specificity of the pEGFR-ITGB2 interaction and the
   requirement of GAL3 and KRAS for this interaction. Further Co-IP
   experiments in A549 cells (Figure [187]6D) showed that endogenous KRAS
   interacted with overexpressed ITGB2, mutITGB2, endogenous EGFR, pEGFR
   and GAL3 in a GAL3-dependent manner (lanes 5 to 8). Interestingly, pull
   down of active, GTP-bound KRAS using RAF-RBD coated beads (lanes 9 to
   12) co-precipitated overexpressed ITGB2, mutITGB2, GAL3 and endogenous
   EGFR, but not pEGFR, in a GAL3-dependent manner. Our results support
   that the KRAS fraction interacting with pEGFR is inactive (compare
   lanes 5 to 8 with 9 to 12). The model in Figure [188]6E summarizes our
   results. Endogenous ITGB6 interacts with EGFR in the NSCLC cell line
   A549 (Figure [189]6E, left). The ITGB6-EGFR-interaction is abolished
   upon ITGB2 or mutITGB2 transfection, suggesting a functional switching
   of a complex containing ITGB6-EGFR in NSCLC to a complex containing
   ITGB2-EGFR in SCLC. Supporting this line of ideas, over-expressed ITGB2
   or mutITGB2 in A549 cells, or endogenous ITGB2 in the SCLC cell line
   NCI-H196 interact with endogenous pEGFR (Figure [190]6E, middle).
   However, our results indicate the formation of a multimeric protein
   complex in at least two different forms. One form contains ITGB2,
   pEGFR, GAL3 and inactive, GDP-bound KRAS (Figure [191]6E, middle). The
   other form contains ITGB2, EGFR, GAL3 and active, GTP-bound KRAS
   (Figure [192]6E, right). Considering the results presented in Figure
   [193]1, 2 and 5, we propose that these forms of the multimeric protein
   complex occur in sequential order during non-canonical ITGB2-mediated
   activation of KRAS/MAPK/ERK signaling in SCLC.

Figure 6.

   [194]Figure 6
   [195]Open in a new tab

   Different multimeric protein complexes sequentially occur during
   non-canonical ITGB2-mediated activation of KRAS/MAPK/ERK signaling in
   SCLC. (A) Total protein extracts of A549 cells transfected with empty
   vector (Ctrl) or ITGB2 were immunoprecipitated (IP) using either
   immunoglobulin G (IgG, as control) or ITGB6 and ITGB2-specific
   antibodies. Co-IP proteins were analyzed by WB using the indicated
   antibodies. Input, 5% of material used for the IP. Squares indicate
   conditions in which endogenous ITGB6 interacts with inactive EGFR
   (green) and overexpressed ITGB2 interacts with active pEGFR (gold). (B)
   Total protein extracts of NCI-H196 cells transfected with empty vector
   (Ctrl) or ITGB6 were immunoprecipitated (IP) using either
   immunoglobulin G (IgG, as control) or ITGB2-specific antibodies. Co-IP
   proteins were analyzed by WB using the indicated antibodies. Input, 5%
   of material used for the IP. Gold square indicates conditions in which
   endogenous ITGB2 interacts with endogenous, active pEGFR. (C) Protein
   extracts of NCI-H196 cells transfected with small interfering RNA
   specific for ITGB2 (siITGB2), GAL3 (siGAL3) or KRAS (siKRAS) were
   immunoprecipitated (IP) using either immunoglobulin G (IgG, as control)
   or EGFR-specific antibodies. Co-IP proteins were analyzed by WB using
   the indicated antibodies. In, input, 5% of material used for the IP.
   Gold square indicates conditions showing the ITGB2-pEGFR-interaction is
   specific and GAL3- and KRAS-dependent. (D) RAS activation assay.
   Protein extracts of A549 cells transfected with ITGB2 or
   ligand-binding-deficient D134A ITGB2 mutant (mutITGB2) and siGAL3 were
   immunoprecipitated (IP) using KRAS-specific antibody (KRAS) or RAF-RBD
   (RBD, active KRAS) coated beads. Co-IP proteins were analyzed by WB
   using the indicated antibodies. In, input, 5% of material used for the
   IP. Gold square highlights conditions in which KRAS interacts with
   ITGB2, mutITGB2, GAL3, EGFR and pEGFR in GAL3-dependent manner. Magenta
   square highlights conditions in which active, GTP-bound KRAS interacts
   with ITGB2, mutITGB2, GAL3 and EGFR, but not with pEGFR. (E) Model.
   Left, endogenous ITGB6 interacts with EGFR in the NSCLC cell line A549.
   Middle, endogenous ITGB2 interacts with endogenous pEGFR in the SCLC
   cell line NCI-H196, or in A549 cells after ITGB2 or mutITGB2
   transfection. Further, results from RAS activation assays indicate the
   formation of a multimeric protein complex in two different forms, one
   form containing ITGB2, pEGFR, GAL3 and inactive, GDP-bound KRAS
   (middle) and the other form containing ITGB2, EGFR, GAL3 and active,
   GTP-bound KRAS (right), both forms occurring in sequential order during
   non-canonical ITGB2-mediated activation of KRAS/MAPK/ERK signaling in
   SCLC. See also Source Data S2.

Extracellular vesicles containing ITGB2 activate RAS/MAPK/ERK signaling and
induce SCLC proteins

   ORA of the SCLC-ITGB2-sig based on the Reactome database revealed
   significant enrichment of genes related to glycosphingolipid metabolism
   (Figure [196]7A). In addition, we found elevated expression of the
   proto-oncogenes MYCN and MYCL in SCLC cell lines (Figure [197]7B).
   Furthermore, GO enrichment analysis based on Biological Processes of
   transcripts with high levels in SCLC cell lines revealed significant
   enrichment of genes related to the GO term “Vesicle transport”
   ([198]Figure S8A-B). Since all these events are related to the
   secretion of EVs [199]^48, we isolated EVs from the cell culture
   medium, in which A549, NCI-H82 and NCI-H196 cells were grown, and
   characterized them by various downstream analyses (Figure [200]7C and
   S8C). EVs produced by these three cell lines showed similar size with a
   radius of approximately 45 nm ([201]Figure S8C). Further,
   high-resolution mass spectrometry analysis of the protein cargo of the
   isolated EVs revealed 189 proteins that were common in EVs from
   NCI-H196 cells transfected with control plasmid (Ctrl) and from A549
   cells transfected either with ITGB2 or mutITGB2 (Figure [202]7D;
   [203]Table S5 and [204]Figure S8D). Panther-based ORA of these common
   189 proteins (Figure [205]7E) showed significant enrichment of proteins
   involved in “Ras pathway” and “Integrins signaling pathway”,
   correlating with our previous results (Figure [206]1-[207]6). In
   addition, ORA using the KEGG database for pathways enriched in the
   common 189 proteins ([208]Figure S8E) revealed significant enrichment
   of the oxidative phosphorylation pathway correlating with a previously
   reported metabolic switch in cancer associated fibroblasts mediated by
   ITGB2 [209]^49. Further, WB of the protein cargo of EVs produced by
   A549 cells that were transiently transfected with Ctrl, ITGB2 or
   mutITGB2 (Figure [210]7F) showed similar levels of ITGA2, as well as of
   the EVs markers TSG101 (tumor susceptibility gene 101 protein) and the
   CD63 antigen [211]^50^, [212]^51, whereas ITGB2 and MYCN were
   specifically detected after ITGB2 or mutITGB2 transfection, confirming
   the mass spectrometry results ([213]Table S5) and suggesting that
   ITGA2-ITGB2 heterodimers and MYCN are transferred from the EVs
   producing cells to hPCLS. Interestingly, the mass spectrometry results
   showed not significant differences in the levels of cell adhesion
   proteins in the EVs produced by A549 and NCI-H196 cells transfected as
   described above ([214]Figure S8F).

Figure 7.

   [215]Figure 7
   [216]Open in a new tab

   Extracellular vesicles containing ITGB2 activate RAS/MAPK/ERK signaling
   and induce SCLC proteins. (A) Reactome-based enrichment analysis of
   significant pathways in the 93 IDs of the SCLC-ITGB2 gene expression
   signature (SCLC-ITGB2-sig) from Figure [217]3E using Webgestalt
   bioinformatics tool and plotted by highest enrichment ratio. Int.,
   interactions; metab., metabolism. (B) RNA-seq-based expression analysis
   of indicated transcripts in non-small cell lung cancer (NSCLC; n = 33)
   and small cell lung cancer (SCLC; n = 17) cell lines. Values were
   normalized to GAPDH and represented as log2 of reads per kilobase per
   million (RPKM). Bar plots show data as means; error bars, SD. (C)
   Scheme of experiments with EVs isolated from the cell culture medium of
   NSCLC and SCLC cell lines. Characterization of the protein cargo of the
   isolated EVs by high-resolution mass spectrometry (HRMS) analysis.
   Characterization of human precision-cut lung slices (hPCLS) that were
   treated with isolated EVs. (D) Venn diagram comparing proteins that
   were detected by HRMS in EVs from control transfected A549 cells,
   NCI-H196 cells, as well as from A549 cells transfected either with
   ITGB2 or mutITGB2 highlights a group of 189 proteins that are common
   for the last 3 conditions. See also [218]Table S5. (E) Panther-based
   enrichment analysis of significant pathways in the 189 proteins
   highlighted in D, using Webgestalt bioinformatics tool and plotted by
   highest significance enrichment ratio. (F) Total protein extracts of
   EVs from A549 cells transfected with ITGB2 or mutITGB2 were analyzed by
   WB using the indicated antibodies. (G) Confocal microscopy after
   immunostaining with specific antibodies against ITGB2 in hPCLS
   incubated with EVs from A549 cells previously transfected with Ctrl or
   ITGB2. DAPI, nucleus. Scale bars, 500 μm. (H) Total protein extracts of
   hPCLS incubated with EVs from A549 cells previously transfected with
   ITGB2 or mutITGB2 alone or in combination with binase were analyzed by
   WB using the indicated antibodies. Products of downstream gene targets
   of EGF signaling (green) and SCLC proteins (red) are highlighted. See
   also [219]Figure S8, S9 and Data Source S2.

   In addition, confocal microscopy in sections of hPCLS that were
   incubated with EVs produced by A549 cells revealed ITGB2 staining when
   the A549 cells were transfected with ITGB2 prior EVs isolation (Figure
   [220]7G). These results together support that EVs can influence the
   gene expression signature of the treated hPCLS and argue against an
   increased uptake of EVs by hPCLS under the conditions analyzed here.
   This interpretation was confirmed by WB of protein extracts from hPCLS
   incubated with A549 EVs (Figure [221]7H). Protein extracts of hPCLS
   showed increased ITGB2 levels when they were incubated with EVs
   produced by A549 cells that were transfected with ITGB2 or mutITGB2.
   Interestingly, EVs produced by A549 cells transfected with ITGB2 or
   mutITGB2 induced phosphorylation-dependent activation of EGFR and MAPK,
   as well as increased levels of the downstream targets of EGFR signaling
   VIM and FN1, demonstrating activation of the RAS/MAPK/ERK signaling
   pathway in the treated hPCLS. Moreover, EVs isolated from ITGB2- or
   mutITGB2-transfected A549 cells induced NKX2-1, EZH2, ASH-1 and MYCN in
   treated hPCLS, whereas TP53 levels were reduced, thereby mimicking gene
   expression patterns that are characteristic of SCLC [222]^52^,
   [223]^53. As we have previously shown that the ribonuclease (RNase)
   binase inhibits oncogenic KRAS [224]^47, we included binase treatment
   in our experimental setting. Remarkably, binase treatment counteracted
   the effects caused by EVs isolated from ITGB2- or mutITGB2-transfected
   A549 cells, thereby demonstrating the causal involvement of KRAS
   activation.

   Since EVs produced by ITGB2- or mutITGB2-transfected A549 cells
   contained the transcription factor MYCN (Figure [225]7F and [226]Table
   S5) and EVs seem to influence gene expression of hPCLS (Figure
   [227]7G-H), we tested whether the ITGB2 gene is a direct target of
   MYCN. Published data of next generation sequencing (NGS) after
   chromatin immunoprecipitation (ChIP-seq) using MYC-specific antibodies
   [228]^54 and assay for transposase-accessible chromatin with sequencing
   (ATAC-Seq) [229]^55 revealed enrichment of MYC at the ITGB2 promoter in
   an area of the genome with accessible chromatin and with predicted
   binding elements for MYC ([230]Figure S9A). Since MYC proteins dimerize
   with MAX to regulate transcription of a broad number of genes
   [231]^56^, [232]^57, we also retrieved published CUT&RUN sequencing
   data, in which MAX-specific antibodies and controlled cleavage by
   micrococcal nuclease were combined [233]^58 and found also MAX
   enrichment in the same area of the at the ITGB2 promoter as MYC
   ([234]Figure S9A). These results suggest that the ITGB2 gene is
   directly regulated by MYC-MAX heterodimers. Supporting this
   interpretation, lung tissue from SCLC patients in C2 showed
   significantly higher MYC expression (median = 1.82; IQR = 1.10; n = 6)
   than the lung tissue from SCLC patients in C1 (median = 0.78; IQR =
   0.84; n = 6, P = 0.006; [235]Figure S9B left; Source Data S1). Similar
   results were observed for the expression of MAX and VIM ([236]Figure
   S9B middle and right; Source Data S1). Moreover, qRT-PCR-based
   expression analysis using total RNA extracted from A549 cells
   ([237]Figure S9C; Source Data S1) showed significantly increased ITGB2
   levels after treatment with EVs isolated from NCI-H196 cells (mean =
   1.38; SD = 0.09; n = 3) as compared to non-treated cells (mean = 0.75,
   SD = 0.25; n = 3, P = 0.01). We also observed increased expression of
   the of the downstream targets of EGFR signaling VIM, HIF1A and FN1 in
   A549 cells treated with EVs isolated from NCI-H196 cells as compared to
   non-treated cells ([238]Figure S9C; Source Data S1). These results
   confirmed that EVs from NCI-H196 cells activated the RAS/MAPK/ERK
   signaling pathway and influenced gene expression in the treated A549
   cells.

ITGB2 loss-of-function and binase inhibit SCLC-associated proteins

   Since EVs produced by ITGB2- or mutITGB2-transfected A549 cells were
   able to induce RAS/MAPK/ERK signaling and SLCL markers in hPCLS (Figure
   [239]7H), we decided to investigate the EVs produced by the SCLC cell
   line NCI-H196 either non-transfected or transfected with siCtrl or
   siITGB2 or treated with binase (Figure [240]8). WB of the protein cargo
   of EVs produced by non-transfected or siCtrl-transfected NCI-H196 cells
   showed similar levels of ITGB2, MYCN, TSG101 and CD63 (Figure [241]8A),
   whereas siITGB2 transfection or binase treatment of the NCI-H196 cells
   prior EVs isolation reduced the ITGB2 and MYCN levels in the isolated
   EVs. Further, we analyzed the effects caused on hPCLS by EVs produced
   by NCI-H196 cells under the four conditions specified above (Figure
   [242]8B-D). EVs produced by siCtrl-transfected NCI-H196 cells increased
   cell proliferation and cell number of hPCLS after 96h treatment (Figure
   [243]8B).

Figure 8.

   [244]Figure 8
   [245]Open in a new tab

   ITGB2 loss-of-function and binase inhibit SCLC. (A) Total protein
   extracts of EVs from NCI-H196 cells transfected with control or
   ITGB2-specific small interfering RNA (siCtrl or siITGB2), or treated
   with binase, were analyzed by WB using the indicated antibodies. (B)
   Left, cell proliferation of hPCLS co-cultured without or with NCI-H196
   cells previously transfected with siITGB2 or treated with binase was
   measured by the colorimetric method using BrdU incorporation (top) cell
   number quantification (bottom). Data are shown as means ± SD (n = 3
   independent experiments); asterisks, P-values after two tailed t-test,
   *** P ˂ 0.001; ** P ˂ 0.01; * P ˂ 0.05. Right, representative live
   microscopy images to the bar plots. Quadrants used for quantification
   are indicated. Scale bars, 500 μm. (C) Confocal microscopy after
   immunostaining with specific antibodies against ITGB2 and VIM in hPCLS
   incubated with EVs from A549 cells previously transfected with ITGB2
   and non-treated or treated with binase. DAPI, nucleus. Scale bars, 500
   μm. Squares are respectively shown below at higher magnification. (D)
   Total protein extracts of hPCLS incubated with EVs from NCI-H196 cells
   previously transfected with siCtrl or siITGB2 alone or in combination
   with binase were analyzed by WB using the indicated antibodies.
   Products of downstream gene targets of EGF signaling (green) and SCLC
   proteins (red) are highlighted. (E) Model. In SCLC, high ITGB2 induces
   a KRAS-driven secretory phenotype of ITGB2/ITGA2 loaded EVs, which
   total protein cargo induces a SCLC-like phenotypic transformation in
   normal cells. See also [246]Figure S10, S11 and Source Data S1 and S2.

   We also detected ITGB2 and VIM by confocal microscopy in sections of
   hPCLS that were incubated with EVs produced by A549 cells transfected
   wit ITGB2 (Figure [247]8C), supporting that such EVs induced in the
   treated hPCLS gene expression patterns that are similar to SCLC. These
   results were complemented by WB of protein extracts from hPCLS
   incubated with NCI-H196 EVs (Figure [248]8D). EVs produced by NCI-H196
   cells induced in hPCLS ITGB2, phosphorylation-dependent activation of
   EGFR and MAPK, as well as increased levels of the downstream targets of
   EGFR signaling VIM and FN1, demonstrating activation of the
   RAS/MAPK/ERK signaling pathway in the treated hPCLS. Moreover, EVs
   produced by NCI-H196 cells induced EZH2, H3K27me3 (Histone 3
   tri-methylated at lysine 27) and NKX2-1 in treated hPCLS, thereby
   mimicking gene expression patterns that are characteristic of SCLC
   [249]^52^, [250]^53. Remarkably, the effects induced by NCI-H196 EVs on
   hPCLS were counteracted by siITGB2 transfection or binase treatment of
   NCI-H196 cells prior EVs isolation (Figure [251]8B-D), suggesting both,
   ITGB2-LOF as well as binase treatment, for the development of therapies
   against SCLC. Supporting this interpretation, [252]Table S6 summarizes
   pre-clinical and clinical studies, in which binase and other RNases
   have been tested in the context of different cancer types.

   We decided to determine the effects of binase treatment and ITGB2-LOF
   on cancer hallmarks in SCLC cells ([253]Figure S10-S11). Binase
   treatment of NCI-H82 and NCI-H196 cells reduced the levels of KRAS,
   pEGFR and VIM ([254]Figure S10A), as well as reduced cell viability and
   colony formation ([255]Figure S10B-C). On the other hand, ITGB2
   knockout using CRISPR-Cas9 gene-editing technology [256]^59 in NCI-H196
   cells was lethal with all five guide RNAs used, supporting the
   requirement of the ITGB2 gene for this SCLC cell line. Further, we
   targeted the ITGB2 gene using CRISPR-Cas9 technology in A549 cells and
   in the human hepatocellular carcinoma cells HepG2, a liver cell line
   exhibiting epithelial-like morphology, being the liver an organ that
   arises from the embryonic endoderm as the lung. CRISPR-Cas9-mediated
   ITGB2 knockout in A549 and HepG2 cells ([257]Figure S10D) reduced
   colony formation and cell viability in both cell lines as compared to
   the control cells ([258]Figure S10E-G). A literature search summarized
   in [259]Table S7 shows that resistance to EGFR-Tyrosine
   kinase-inhibitors (EGFR-TKI) in SCLC occurs without any of the known
   somatic mutations in EGFR responsible for acquired EGFR-TKI resistance
   observed in NSCLC. On the other hand, our results showed that ITGB2 is
   sufficient and required for EGFR activation in SCLC (Figures [260]2, 5,
   6, 7 and 8), suggesting that targeting ITGB2 may sensitize SCLC cells
   to EGFR-TKI treatment. To test this hypothesis, we analyzed by WB
   protein extracts from both SCLC cell lines, NCI-H82 and NCI-H196, that
   were treated with the EGFR-TKI Erlotinib [261]^60 after siCtrl or
   siITGB2 transfection ([262]Figure S11A). Erlotinib treatment after
   siCtrl did not reduce the levels of the EGF signaling marker VIM in
   either SCLC cell line, showing the resistance of these cells to the
   EGFR-TKI. Interestingly, in NCI-H196 cells siITGB2 was sufficient to
   reduce VIM levels, which were not further reduced after Erlotinib
   treatment. However, in NCI-H82 cells siITGB2 alone was not sufficient
   to reduce VIM levels, but combining siITGB2 and Erlotinib did. Thus, we
   decided to determine the effect of siITGB2 on cell proliferation, one
   of the cancer hallmarks, by BrdU (5-Bromo-2´-Deoxyuridine)
   incorporation assay ([263]Figure S11B). Erlotinib treatment did not
   affect cell proliferation in either SCLC cell line. However, Erlotinib
   in combination with siITGB2 reduced cell proliferation. Our results
   indicate that ITGB2-LOF may be beneficial against SCLC, since it
   sensitizes SCLC cells to EGFR-TKI treatment.

Discussion

   In the present study, we showed that ITGB6 interacts with the inactive
   EGFR in NSCLC, whereas in SCLC, ITGB2 reduces the levels of ITGB6, and
   interacts with and activates EGFR. In addition, we demonstrated that
   the EVs produced by a SCLC cell line induced in hPCLS both,
   non-canonical ITGB2-mediated activation of KRAS/MAPK/ERK signaling and
   SCLC proteins, supporting the hypothesis that the cargo of EVs may
   influence the gene expression signature of hPCLS (Figure [264]8D).
   Following this line of ideas, siRNA-mediated ITGB2-LOF or binase
   treatment of the SCLC cell line led to the production of EVs that lost
   the capacity to induce KRAS/MAPK/ERK signaling and SCLC proteins in
   hPCLS. On the one hand, our results provide a model (Figure [265]8E) to
   study the effects of EVs on specific tissues by targeted cargo
   modification through manipulation of the gene expression in
   EV-producing cells. On the other hand, they also provide a plausible
   explanation for the resistance of SCLC to tyrosine EGFR-TKIs (including
   Erlotinib, Gefitinib and Afatinib, [266]Table S7), and suggest the
   development of novel therapeutic strategies for SCLC combining
   EGFR-TKIs and ITGB2-LOF. Our results showing that ITGB2-LOF sensitized
   SCLC cell lines to Erlotinib ([267]Figure S11A-S11B) support this line
   of ideas. Alternatively, treating the lung of SCLC patients with EVs
   produced by NSCLC cell lines to achieve lung tissue sensitivity to
   EGFR-TKIs may be one treatment strategy worthy of exploration.
   Furthermore, determining high levels of ITGB2 in cases of NSCLC showing
   enhanced EGFR signaling in the absence of somatic mutations may
   validate ITGB2 as a promising therapeutic target in NSCLC. This
   reasoning is supported by the current use of EGFR-TKIs in lung
   adenocarcinoma patients with EGFR mutations [268]^61^, [269]^62. The
   majority of these hyperactive EGFR mutants harbor either a point
   mutation, in which leucine 858 is substituted by arginine (L858R), or a
   deletion involving 5 codons coding for the amino acids at the positions
   746-750 (ΔEx19) [270]^63. However, a secondary point mutation in the
   EGFR kinase domain that substitutes threonine 790 by methionine (T790M)
   produces drug-resistant EGFR variants, which are present in 50% of
   patients that developed resistance to EGFR-TKIs treatment [271]^64.
   Surprisingly, studies of biopsies have shown that 5-15% NSCLC patients
   undergo histological transformation to SCLC upon acquisition of therapy
   resistance [272]^65. It will be extremely interesting to determine
   whether this histological transformation from NSCLC to SCLC upon
   acquired EGFR-inhibitor resistance is the result of a functional switch
   from ITGA2-ITGB6 to ITGA2-ITGB2 during EGFR complex formation.

   Various aspects of the signaling model proposed here may be applicable
   in a broader context. Specific integrin receptor subunits have been
   identified as biological markers and potential therapy targets to tumor
   progression and metastasis in a wide range of cancers including
   glioblastoma, pancreatic carcinomas, breast cancer, and leukemia
   [273]^33^, [274]^66. In addition, recent reports demonstrated
   functional correlation between the switch of specific integrin subunits
   and an aggressive phenotype of cancer cells. For instance, ITGA2-ITGB1
   promotes chemotherapy resistance of T-cell acute lymphoblastic leukemia
   [275]^67. Further, ITGB1 has been reported to trigger EGFR
   ligand-independent proliferation signaling in pancreatic ductal
   adenocarcinoma, bypassing the EGFR-blocking effect of the anti-EGFR
   monoclonal antibody Cetuximab [276]^68. These reports suggest that
   targeting specific integrin subunits may be beneficial against a wider
   spectrum of cancer types. Supporting this hypothesis, depletion of
   ITGB3, ITGB4 and ITGB5 reduced angiogenesis and tumor growth in breast
   cancer [277]^31.

   Another interesting aspect of our model is the functional competition
   between ITGA2-ITGB2 in SCLC and ITGA2-ITGB6 in NSCLC. Similar
   competitions have been observed between ITGAM-ITGB2 and ITGA5-ITGB1 or
   ITGAv-ITGB3 and ITGA5-ITGB1 heterodimers regulating migration or
   trafficking of leukocytes [278]^69^, [279]^70. Recently, a mutual
   competition between ITGA5-ITGB1 blocking EGF signaling and ITGA6-ITGB4
   enhancing EGF signaling has been reported [280]^71, highlighting the
   specific interaction between integrin subunits mediating different
   functions in EGF signaling. Interestingly, EGFR interacts in the cell
   membrane with glycosylated regulatory partners including proteoglycans
   like syndecans [281]^72^, [282]^73. Remarkably, N-glycosylation of
   specific domains in the ITGA5 subunit appears critical to different
   processes mediating its biological function, such as ITGA5-ITGB1
   heterodimer formation, its expression on the cell surface, ligand
   binding, EGFR-ITGA5-ITGB1 complex formation and its inhibitory effect
   on EGFR [283]^71^, [284]^74^, [285]^75. Similar to ITGA5, it has been
   also reported that N-glycosylation of ITGB4 is essential for
   EGFR-ITGB4-ITGA6 complex formation on the cell surface [286]^76. Future
   work will determine whether a similar mechanism of N-glycosylation
   participates in switching the EGFR complex formation from ITGA2-ITGB6
   to ITGA2-ITGB2 in the course of the histological transformation from
   NSCLC to SCLC observed upon acquired EGFR-TKIs resistance [287]^65.

   We uncovered a mechanism of non-canonical ITGB2-mediated EGFR
   activation that explains EGFR-TKIs resistance in SCLC cases lacking
   EGFR mutations. Our results were obtained implementing in vitro and ex
   vivo experimental systems including human cells and tissue, and they
   may be confirmed and further investigated using in vivo animal models
   in future projects. Nevertheless, our results not only support the use
   of ITGB2 and the newly identified SCLC-ITGB2-sig as diagnostic markers
   for SCLC, but also as targets to develop therapeutic strategies against
   this extremely aggressive type of LC.

Methods

Study population

   The study was performed according to the principles set out in the WMA
   Declaration of Helsinki and the protocols approved by the institutional
   review board and ethical committee of Regional Hospital of High
   Specialties of Oaxaca (HRAEO), which belongs to the Ministry of Health
   in Mexico (HRAEO-CIC-CEI 006/13), the Medicine Faculty of the Justus
   Liebig University in Giessen, Germany (Ethical Votum 68/13) and the
   Hannover Medical School (no. 2701-2015). In this line, all patient and
   control materials were obtained through the HRAEO in Mexico, the
   Institute for Pathology of Justus Liebig University in Giessen and the
   Biobank from the Institute for Pathology of the Hannover Medical School
   as part of the BREATH Research Network. We used anonymized patient
   material.

   Formalin-fixed paraffin-embedded (FFPE) human lung tissue samples of
   either diagnostic transbronchial or bronchial biopsies or oncologic
   resections were retrospectively collected. All cases were reviewed and
   staged by an expert panel of pulmonologist and oncologist. FFPE tissue
   samples of LC patients comprised approximately 80% tumor cells. The
   control population for the analysis included lung tissue that was taken
   from macroscopically healthy adjacent regions of the lung of LC
   patients. Corresponding clinical data for matched patients with LUAD (n
   = 11) were obtained from The Genome Cancer Atlas (TCGA,
   tcga‑data.nci.nih.gov/doc/publications/tcga/). Data are publicly
   available and open‑access. Clinical characteristics of LUAD patients
   are presented in [288]Table S3.

Cell culture, transfection, treatment and siRNA-mediated knockdown

   Mouse lung epithelial cells MLE-12 (ATCC CRL-2110) were cultured in
   complete DMEM/F12 (5% FCS, 1% Penn-strep) at 37 ^°C in 5% CO[2]. Human
   SCLC cells NCI-H82 (ATCC HTB-175) and NCI-H196 (ATCC CRL-5823), and
   NSCLC cells A549 (ATCC CCL-185) were cultured in complete RPMI (10%
   FCS, 1% Penn-strep) at 37 °C in 5% CO[2]. During subculturing, cells
   were 1x PBS washed, trypsinized with 0.25% (w/v) Trypsin and
   subcultivated at the ratio of 1:5 to 1:10. The cell lines used in this
   paper were mycoplasma free. They were regularly tested for mycoplasma
   contamination. In addition, they are not listed in the database of
   commonly misidentified cell lines maintained by ICLAC. Cells were
   transfected with plasmid DNA or siRNA using Lipofectamine 2000
   (Invitrogen) following the manufacturer's instructions, and harvested
   48 h later for further analysis. ITGA2-HIS (Addgene, #51910), ITGB2-YFP
   (Addgene, #8638) and ITGB6-GFP (Addgene, #13293) mammalian expression
   constructs were used for respective gene overexpression in cell lines.
   siITGB2 (EHU133911) was purchased from Sigma. Empty vectors and siCtrl
   were used as a negative control.

Bacterial culture and cloning

   For cloning experiments, chemically competent E. coli TOP10
   (ThermoFisher Scientific) were used for plasmid transformation. TOP10
   strains were grown in Luria broth (LB) at 37 °C with shaking at 180 rpm
   for 16 h or on LB agar at 37 °C overnight.

RNA isolation, reverse transcription, quantitative PCR and TaqMan assay

   Expression analysis by qRT-PCR were performed as previously described
   [289]^77. Briefly, total RNA from cell lines was isolated using the
   RNeasy Mini kit (Qiagen) and quantified using a Nanodrop
   Spectrophotometer (ThermoFisher Scientific, Germany). Human lung tissue
   samples were obtained as FFPE tissues, and eight sections of 10 μm
   thickness were used for total RNA isolation using the RecoverAll Total
   Nucleic Acid Isolation Kit for FFPE (Ambion). Clinical characteristics
   of SCLC patients are presented in [290]Table S2. Synthesis of cDNA was
   performed using 0.2-1 µg total RNA and the High-Capacity cDNA Reverse
   Transcription kit (Applied Biosystems). Quantitative real-time PCR
   reactions were performed using SYBR^® Green on the Step One plus
   Real-time PCR system (Applied Biosystems). Housekeeping genes HPRT and
   GAPDH were used to normalize gene expression. Primer pairs used for
   gene expression analysis are described in Table [291]1.

Table 1.

   Sequence of primers used for qRT-PCR expression analysis.
      Gene           Primer sequence (5'→3')
   hHPRT     Forward TTTGCTTTCCTTGGTCAGGCAGT
             Reverse CGTGGGGTCCTTTTCACCAGCA
   hGAPDH    Forward GGCCCGATTTCTCCTCCGGGT
             Reverse GGTGACCAGGCGCCCAATACG
   hITGB2    Forward TGCGTCCTCTCTCAGGAGTG
             Reverse GGTCCATGATGTCGTCAGCC
   hmutITGB2 Forward CCTGTACTATCTGATGGCCTCTCCTACTCCATG
             Reverse CATGGAGTAGGAGAGGCCATCAGATAGTACAGG
   hITGB6    Forward CCACATGGGGCCTCGCTGTG
             Reverse CAGTCCAGCCGCTCCTGCAC
   hITGA2    Forward TTGGAACGGGACTTTCGCAT
             Reverse GGTACTTCGGCTTTCTCATCA
   hVIM      Forward GGAAATGGCTCGTCACCTTCGT
             Reverse GCAGAGAAATCCTGCTCTCCTCG
   hCDH1     Forward CCCACCACGTACAAGGGTC
             Reverse ATGCCATCGTTGTTCACTGGA
   [292]Open in a new tab

Immunofluorescence and confocal microscopy

   Immunostaining was performed as previously described [293]^78. Briefly,
   cells were grown on coverslips, fixed with 4% PFA for 10min at RT and
   permeabilized with 0.4% Triton-X100/1xPBS for 10 min at RT. For
   non-adherent cells, slides were previously coated with poly-L-lysine.
   During immunostaining procedure, all incubations and washes were
   performed with histobuffer containing 3% bovine serum albumin and 0.2%
   Triton X-100 in 1xPBS, pH 7.4. Non-specific binding was blocked by
   incubating with donkey serum and histobuffer (1:1 (v/v) ratio) for 1 h.
   Cells were then incubated with primary and then secondary antibodies
   for 1 h followed by nuclear staining. Immunostaining of cells were
   examined with a confocal microscope (Zeiss). Antibodies used were
   specific anti-ITGB2 (R&D Systems), anti-ITGB6 (R&D Systems), anti-GFP
   (Santa Cruz), anti-pEGFR (Cell Signaling, Antibody #2237, for phospho
   Tyr1045), and anti-EGFR (Cell Signaling, Antibody #2232), were used.
   Alexa 488, Alexa 555 or Alexa 594 tagged secondary antibodies
   (Invitrogen, Germany, dilution 1:2000) were used. DAPI (Sigma, Germany)
   used as nuclear dye.

   Paraffin-embedded lung tissue sections (3-μm thick) were deparaffinized
   in xylene and rehydrated in a graded ethanol series to PBS (pH 7.2).
   Antigen retrieval was performed by pressure cooking in citrate buffer
   (pH 6.0) for 15 min. Double immunofluorescence staining was performed
   with primary antibodies anti-ITGB2 (R&D Systems), anti-ITGB6 (R&D
   Systems), anti-GFP (Santa Cruz), anti-VIM (Cell Signaling) and
   anti-ASH1 (Chemicon) were used. After overnight incubation with
   specific primary antibodies, slides were washed and incubated with the
   respective secondary antibodies, Alexa 488-, Alexa 555- and Alexa
   594-conjugated goat anti-rabbit IgG (dilution 1:1000, Molecular Probes,
   Eugene, OR) for 1 h. All sections were counterstained with nuclear DAPI
   (1:1000) and mounted with fluorescent mounting medium (Dako).

Co-immunoprecipitation (Co-IP) and Western blot

   Total protein extracts from different cell lines were prepared in 1 mL
   ice cold RIPA buffer [(50 mM (pH7.5) Tris-HCl, 150 mM NaCl, 1% Triton
   X-100 (Sigma), 0.5% Na-deoxycholate (Sigma), 0.1% SDS, 0.2 M imidazole
   (Sigma), 10 mM NaF (Sigma), 2 mM Na3VO4 (Sigma), 1 mM
   phenylmethylsulfonyl fluoride (PMSF) and protease inhibitor cocktail
   (Calbiochem)]. Detergent-insoluble material was precipitated by
   centrifugation at 14,000 rpm for 30 min at 4 °C. The supernatant was
   transferred to a fresh tube and stored at -20 °C. Protein concentration
   was estimated using Bradford assay, using serum albumin as standard. 5
   μL of serial dilutions of standard protein and samples were mixed with
   250 μL of Bradford reagent (500-0205, BIO-RAD Quick Start™). Samples
   were incubated 10 min at room temperature and measured at OD595 using
   an ELISA plate reader (TECAN Infinite M200 Pro). Co-IP was performed as
   described [294]^79 with minor adaptations. For immunoprecipitation of
   membrane proteins, a total of 5 x 10^7 cells were collected and washed
   three times in cold PBS, spun down at 270g for 10 min at 4 °C. Protein
   extracts were obtained as described above using cell
   immunoprecipitation (IP) buffer [(50 mM, pH 7.5) Tris-HCl, 1 mM MgCl2,
   1 mM CaCl2, 150 mM NaCl, 1% NP40, 1 mM phenylmethylsulfonyl fluoride
   (PMSF) and protease inhibitor (Calbiochem)] Protein concentrations were
   determined as described above. Precleared protein lysates (500 μg per
   immunoprecipitation) were resuspended in 500 μL IP buffer and incubated
   with the appropriate antibodies on ice for 2 h and then 30 μL
   protein-G-sepharose beads (GE Healthcare; equilibrated once in 10 mL
   water and three times in washing buffer) were added and incubated
   overnight at 4 °C. Beads were collected and washed 5 times with 500 μL
   ice-cold washing buffer. 30 μL 2x SDS sample loading buffer was added
   to beads, boiled at 95 °C for 5 min, spun down and loaded on SDS-PAGE
   for western blot analysis. Western blotting was performed using
   standard methods and antibodies specific for 6x-HIS-Tag (Abcam), GFP
   (Santa Cruz), ITGB2 (R&D Systems), ITGB6 (R&D Systems), ITGA2 (R&D
   Systems), pMAPK (Cell Signaling), MAPK (Cell Signaling), pEGFR (Cell
   Signaling), EGFR (Cell Signaling), GAL3 (Santa Cruz), KRAS (Santa
   Cruz), pRAF1 (Cell signaling), RAF1 (Cell signaling), pERK (Cell
   signaling), ERK (Cell signaling), MYCN (Santa Cruz), TSG101 (Santa
   Cruz), CD63 (Santa Cruz), FN1 (Millipore), ACTA2 (Sigma), VIM (Cell
   Signaling), NKX2-1 (Santa Cruz), EZH2 (Abcam), H3K27me3 (Millipore),
   ASH-1 (Chemicon), TP53 (Cell signaling) and GAPDH (Sigma) were used.
   Immunoreactive proteins were visualized with the corresponding
   HRP-conjugated secondary antibodies (Santa Cruz) using the Super Signal
   West Femto detection solutions (ThermoFisher Scientific). Signals were
   detected and analyzed with Luminescent Image Analyzer (Las 4000,
   Fujifilm).

KRAS activation assay

   RAS Activation Assay Biochem Kit™ (BK008; Cytoskeleton, Inc) was used
   to assess KRAS activity following manufacturer's instructions. Briefly,
   A549, NCI-H196 and NCI-H82 cell protein lysates were produced in cell
   lysis buffer containing 50 mM Tris-HCl pH 7.5, 10 mM MgCl2, 500 mM
   NaCl, 2% Igepal, 0-5% BSA, 20 mM Imidazole, 20 mM NaF, 0.5 mM Na3VO4,
   40 μg/mL PMSF and protease inhibitor) for 10 min at 37 °C on rotator
   with 200 rpm. RAF-RBD beads (100 μg) were added to the reactions and
   incubated at 4 °C on a rotator for 1 h. The beads were washed once with
   500 μL wash buffer (25 mM Tris pH 7.5, 30 mM MgCl2, 40 mM NaCl, 20 mM
   Imidazole, 20 mM NaF, 0.5 mM Na3VO4, 40 μg/mL PMSF and protease
   inhibitor). Precipitated proteins were analyzed by Western Blotting as
   described above.

Proliferation Assay

   NCI-H196 cells were treated with Placebo, with binase or transfected
   with siITGB2 and grown until 90-95% confluence in a 6-well plate. After
   24 h or 96 h, cells were re-plated in 96-well plate in a density of
   10^4/well in a final volume of 100 μL and cultured in a humidified
   atmosphere at 37 °C. 10 μM BrdU (Cell proliferation, colorimetric kit,
   Sigma #11647229001) were added and the cells were further incubated for
   additional 6 h, fixed and washed. Subsequently the immunoassay was done
   by measuring the absorbance of the samples in an ELISA reader at 370 nm
   (reference wavelength: 492 nm).

Generation of ITGB2 knockout cell lines

   Guide RNAs against human ITGB2 were designed using CRISPOR tool
   [295]^80, and cloned in BbsI (New England BioLabs, Cat# R3539S)
   digested pX459 V2 (Addgene Cat# 62988) vector. NCI-H196, A549 and HepG2
   cells were transfected with control pX459 V2 vector and vector
   containing ITGB2 guide RNAs. Cells were selected using 2.5 µg/mL
   puromycin for 48 h and ITGB2 protein expression level was checked by
   WB. Table [296]2 shows the sequences of the ITGB2 guide RNAs used.

Table 2.

   Sequence of guide RNAs used for generating ITGB2 -/- cells.
         Name            Sequence (5' → 3')
   c794 sg1_ITGB2_FP CACCGCATCCCCCGGGCCACGGGATT
   c795 sg1_ITGB2_RP AAACAATCCGTGGCCCGGGGGATGC
   c796 sg2_ITGB2_FP CACCGTCTCCCAATCCGTGGCCCG
   c797 sg2_ITGB2_RP AAACCGGGCCACGGATTGGGAGAC
   c798 sg3_ITGB2_FP CACCGTGGAGCCCGCTGCGCGCAG
   c799 sg3_ITGB2_RP AAACCTGCGCGCAGCGGGCTCCATC
   [297]Open in a new tab

Cell proliferation assay using CCK-8

   1000 cells of each A549 and HepG2 were seeded in 96 well plate and
   incubated in 100 µL corresponding growth medium. At time points 24 h,
   48 h, 72 h and 96 h, 10 µL CCK-8 reagent (Sigma-Aldrich, Cat#96992) was
   added and incubated for 2 h. Absorbance was measured at 450 nm using
   microplate reader.

Colony Formation Assay

   NCI-H196 cells transfected with siCtrl or siITGB2 alone or in
   combination with binase treatment were plated in a 6-well culture plate
   at a density of 1000 or 5000 cells/well. The plate was swirled to
   ensure an even distribution of cells. The cells were grown in a 37 °C
   incubator with 5% CO2 for 3 to 5 d with media replacement every 2 d. At
   10 d, the media was removed and cells were washed twice with PBS. The
   colonies were analyzed using ImageJ software
   ([298]https://imagej.nih.gov/ij/). A549 (1500 cells/well) and HepG2
   (1000 cells/well) cells were seeded in 6 well and incubated in
   corresponding growth medium for 1 week. Cells were washed with PBS,
   fixed using methanol and stained with 0.1% crystal violet solution in
   PBS. Number of colonies were counted using ImageJ.

Protein-interaction prediction

   Prediction of protein-protein interaction was observed using the STRING
   online database [299]^81 (Retrieval of Interacting
   Genes-Proteins-[300]http://string.embl.de/) with a cut-off criterion of
   a combined score 0.9 (highest confidence) and including a maximum of 50
   interactors on the 1^st shell and 20 interactors on the 2^nd shell.
   Network nodes represent proteins, while edges are the protein‑protein
   associations. Small nodes represent protein of unknown 3D structure and
   large nodes proteins with some known or predicted 3D structures.
   Colored nodes represent query proteins and first shell of interactors
   and white nodes second shell of interactors. Interactions are depicted
   by color as follows: known interactions were obtained from curated
   databases (turquoise), or experimentally determined (purple); predicted
   interactions were defined by neighborhood (green), gene fusions (red)
   and gene co-occurrence (blue), textmining (light green), co-expression
   (black) and protein homology (violet). Top ITGA2 interactors were
   processed using the functional enrichment analyses Kyoto Encyclopedia
   of Genes and Genomes (KEGG). KEGG is the major public
   pathway‑associated database, which identifies significantly enriched
   metabolic pathways or signal transduction pathways in target genes
   compared with the whole genome background.

RNA sequencing and data analysis

   RNA sequencing generated for this paper was sequenced as previously
   described [301]^78^, [302]^82. Briefly, total RNA from A549 (Ctrl,
   ITGB2 or mutITGB2) and NCI-H196 cells was isolated using the Trizol
   method. RNA was treated with DNase (DNase-Free DNase Set, Qiagen) and
   repurified using the RNeasy micro plus Kit (Qiagen). Total RNA and
   library integrity were verified on LabChip Gx Touch 24 (Perkin Elmer).
   Sequencing was performed on the NextSeq500 instrument (Illumina) using
   v2 chemistry with 1 x 75 bp single end setup. Raw reads were visualized
   by FastQC to determine the quality of the sequencing. Trimming was
   performed using trimmomatic with the following parameters LEADING:3
   TRAILING:3 SLIDINGWINDOW:4:15 HEADCROP:4, MINLEN:4. High quality reads
   were mapped using with STAR with reads corresponding to the transcript
   with default parameters. RNA-seq reads were mapped to human genome
   hg19. After mapping, Tag directories were obtained with
   MakeTagDirectory from HOMER (default setting). Samples were quantified
   by using analyzeRepeats.pl with the parameters (hg19 -count genes
   -rpkm; reads per kilobase per millions mapped). Gene expression was
   quantified in reads per kilo base million (RPKM). Expression values of
   zero were set to the overall minimum value and all data were log2
   transformed. Genes expressed (log2 transformed expression > 0.2) were
   included in the analysis. The correlations of genes were measured using
   Pearson's correlation. Overlapping genes in the cell lines RNAseq
   dataset were processed using the functional enrichment analyses KEGG,
   Gene Ontology and Reactome.

RNA sequencing meta-analysis

   RNAseq data of human lung adenocarcinoma (LUAD) (n = 16) were
   downloaded from the TCGA data portal. The RNAseq data of SCLC and NSCLC
   cell lines was obtained from GEO under the accession number
   [303]GSE30611 [304]^42. 34 out of 675 samples were selected as NSCLC
   and 17 as SCLC for analysis based on the following sample annotations:
   “Organism part” is lung and “Diseases” is lung carcinoma, lung
   adenocarcinoma, lung anaplastic carcinoma, non-small cell lung
   carcinoma, squamous cell lung carcinoma, large cell lung carcinoma,
   lung mucoepidermoid carcinoma, lung papillary adenocarcinoma, lung
   adenosquamous carcinoma, bronchioloalveolar adenocarcinoma, or squamous
   cell carcinoma. For details on the original processing of the data,
   refer to the original paper [305]^37. The transcriptome profile in
   NCI-H82 and NCI-H196 was measured by the mean normalized expression of
   the genes in the A549 cell line.

   For analysis of RNA-seq data retrieved from the European Genome Archive
   [306]^83, RNAseq was performed on cDNA libraries prepared from PolyA+
   RNA extracted from SCLC tissues and cells. A library with an insert
   size of 250 bp allowed to sequence 95 bp paired-end reads without
   overlap. Raw reads were visualized by FastQC to determine the quality
   of the sequencing. Trimming was performed using trimmomatic with the
   following parameters LEADING:3 TRAILING:3 SLIDINGWINDOW:4:15
   HEADCROP:4, MINLEN:4. High quality reads were mapped using with STAR
   with reads corresponding to the transcript with default parameters.
   RNA-seq reads were mapped to human genome hg19. After mapping, Tag
   directories were obtained with MakeTagDirectory from HOMER (default
   setting). Samples were quantified by using analyzeRepeats.pl with the
   parameters (hg19 -count genes -rpkm; reads per kilobase per millions
   mapped). Gene expression was quantified in reads per kilo base million
   (RPKM). RPKM values were used to compare gene expression between
   clusters and calculate linear regression of expression.

MYC-MAX binding site analysis

   MYC ChIPseq bigwig files ([307]GSM3073949) [308]^54, MAX CUT&RUN bigwig
   files ([309]GSM6222857) [310]^58 and ATACseq bed files
   ([311]GSM4729164) [312]^55 were downloaded from the GEO repository.
   Integrative Genomics Viewer was used for data visualization [313]^84
   and ConTra v3 was used to calculate the predicted MYC-MAX binging sites
   [314]^85.

EVs purification, characterization and co-culture assays

   For the collection of EVs, cells were cultured in media supplemented
   with 10% exosome-depleted FBS, in which EVs were depleted by overnight
   centrifugation at 100,000 g. Supernatants were then collected 72 h
   later for EV purification. Cell culture supernatants were centrifuged
   at 500 g for 5 min to pellet and discard cells, followed by 2,000 g for
   30 min to remove cell debris and apoptotic bodies. A 1:1 volume of 2X
   PEG solution (16% w/v, polyethylene glycol, 1 M NaCl) was added.
   Samples were inverted to mix, then incubated overnight. Next day,
   medium/PEG mixture was centrifuged at 3,300 g for 1 h. Crude vesicle
   pellets were resuspended in 1 mL of exosome-depleted 1X PBS and
   re-pelleted by centrifugation at 100,000 g for 70 min at 4 °C (Beckman
   45 Ti). Pellets at the bottom of the centrifugation tubes were
   resuspended in approximately 50 µL of 1X PBS. Differential Light
   Scattering (DLS) was used to validate the EVs size range. WB was
   performed as described above to characterize the protein cargo of EVs
   using specific antibodies to detect EVs constitutive markers. For
   co-culture assays, A549 cells cultured in exosome-free media were
   treated with the EVs isolated from NCI-H196 cells or NCI-H196 cells
   cultured in exosome-free media were treated with the EVs isolated from
   A549 cells using ExoQuick-TC Cat# EXOTC10A-1). Expression analysis by
   qRT-PCR was performed as previously described [315]^77.

Mass spectrometry: sample preparation, methods and data analysis

   Extracellular vesicle samples were subjected to in gel digest as
   described [316]^86. The resulting peptides were analyzed by liquid
   chromatography/tandem mass spectrometry (LC-MS2) utilizing in-house
   packed reverse phase column emitters (70 μm ID, 15 cm; ReproSil-Pur 120
   C18-AQ, 1.9 μm, Dr. Maisch GmbH) and a buffer system comprising solvent
   A (5% acetonitrile, 0.1% formic acid) and solvent B (80% acetonitrile,
   0.1% formic acid). The MaxQuant suite of algorithms (v.1.6.1.43)
   [317]^87^-[318]^89 was used for peptide/spectrum matching, protein
   group assembly as well as label free quantitation in the context of
   human Uniprot database (canonical and isoforms; downloaded on
   2020/02/05; 210,349 entries). Relevant instrumentation parameters were
   extracted using MARMoSET [319]^90 and are included in the
   [320]supplementary material together with MaxQuant parametrization.
   Common proteins detected by LC-MS2 in the EVs were subjected to an
   estimated overall survival curve analysis for LUAD using KM plotter
   [321]^45. KEGG pathway enrichment analysis was performed using the
   WebGestalt toolkit [322]^91. Cell adhesion molecules (CAMs) retrieved
   from the KEGG database [323]^92. The log-transformed label-free
   quantification (LFQ) intensities of the CAMs identified in the EVs were
   plotted.

Experiments with human PCLS

   PCLS were prepared from tumor free lung explants from patients who
   underwent lung resection for cancer at KRH Hospital
   Siloah-Oststadt-Heidehaus or the Hanover Medical School (both Hanover,
   Germany). Tissue was processed immediately within one day of resection
   as described before [324]^93. Briefly, human lung lobes were cannulated
   with a flexible catheter and the selected lung segments were inflated
   with warm (37 °C) low melting agarose (1.5%) dissolved in Dulbecco's
   Modified Eagle's Medium Nutrient Mixture F-12 Ham (DMEM) supplemented
   with L-glutamine, 15 mM HEPES without phenol red, pH 7.2-7.4
   (Sigma-Aldrich, Hamburg, Germany), 100 U/mL penicillin, and 100 µg/mL
   streptomycin (both from Biochrom, Berlin, Germany). After
   polymerization of the agarose solution on ice, tissue cores of a
   diameter of 8 mm were prepared using a sharp rotating metal tube.
   Subsequently, the cores were sliced into 300-350 µm thin slices in DMEM
   using a Krumdieck tissue slicer (Alabama Research and Development,
   Munford, AL). PCLS were washed 3× for 30 min in DMEM and used for
   experiments. Viability of the tissue was assessed by a LDH Cytotoxicity
   Detection Kit (Roche, Mannheim, Germany) according to manufacturer's
   instruction.

   For immunofluorescence staining in human PCLS, PCLS from Ctrl patients
   were fixed with acetone/methanol (Roth) 50:50 by volume for 20 min,
   blocked for 1 h with 5% bovine serum albumin (w/v, Sigma) in 1x PBS, pH
   7.4. Cells were then incubated with primary antibody overnight at 4 °C.
   After incubation with a secondary antibody for 1 h, nuclei were DAPI
   stained and PCLS were examined with a confocal microscope (Zeiss).
   Antibodies used were specific for ITGB2 (1:500 dilution, R&D Systems),
   and VIM (1:500 dilution, Cell Signaling). Alexa 488, Alexa555 or Alexa
   594-tagged secondary antibodies (Invitrogen) were used. DAPI (Sigma
   Aldrich) used as nuclear dye.

Statistical Analysis

   The source data for all the plots presented in the article, including
   the values for statistical significance and the implemented statistical
   tests, are provided in Source Data S1. Further details of statistical
   analysis in different experiments are included in the Figures and
   Figure legends. Briefly, expression analysis of samples was analyzed by
   next generation sequencing in duplicates of one experiment. Three
   independent experiments of the mass spectrometry-based proteomic
   approach were performed. For the rest of the experiments presented
   here, samples were analyzed at least in triplicates and experiments
   were performed three times. Statistical analysis was performed using
   Excel Solver and Prism9. Data in bar plots are represented as mean ±
   standard deviation (mean ± SD). Two-tailed t-tests were used to
   determine the levels of difference between the groups and P-values for
   significance. P-values after two-tailed t-test, * P ≤ 0.05; ** P <
   0.01, and *** P < 0.001.

Data availability

   The data that support this study are available from the corresponding
   author upon reasonable request. In addition, sequencing data of RNA
   have been deposited in NCBI's Gene Expression Omnibus [325]^94 and is
   accessible through SRA Sequence Read Archives NCBI with accession
   number PRJNA835424. The mass spectrometry-based interactome data have
   been deposited into the PRIDE archive and assigned to the project
   accession px-submission #576520.

   In addition, we retrieved and used a number of publicly available
   datasets to aid analysis of our data:

   Total RNA-seq in NSCLC and SCLC cell lines: European Genome Archive:
   EGAS00001000610

   Total RNA-seq in SCLC patients and cell lines: European Genome Archive:
   EGAS00001002115, EGAS00001000299

   The source data are provided with this paper in the file with the name
   Source Data S1, Source Data S2 and in the [326]Tables S1 to S7.

Supplementary Material

   Supplementary figures and tables 6-7, legends and explanations for data
   1-2, tables 1-5.
   [327]Click here for additional data file.^ (2.6MB, pdf)

   Supplementary source data 1.
   [328]Click here for additional data file.^ (53.6KB, xlsx)

   Supplementary source data 2.
   [329]Click here for additional data file.^ (1.2MB, pdf)

   Supplementary table 1.
   [330]Click here for additional data file.^ (47.5KB, xlsx)

   Supplementary table 2.
   [331]Click here for additional data file.^ (9.9KB, xlsx)

   Supplementary table 3.
   [332]Click here for additional data file.^ (9.4KB, xlsx)

   Supplementary table 4.
   [333]Click here for additional data file.^ (11.6KB, xlsx)

   Supplementary table 5.
   [334]Click here for additional data file.^ (13.8KB, xlsx)

Acknowledgments