Eukaryote initiation factor 6 modulates small-cell lung carcinoma plasticity via the integrin-FAK signaling axis

Domain: chemistry
Published: 2026-02-24
Editorial outcome: major_revision

Abstract
Small cell lung carcinoma (SCLC) is an aggressive neuroendocrine cancer that rapidly develops resistance to platinum-based chemotherapy. A key feature of SCLC is its ability to switch between neuroendocrine (NE) and non-neuroendocrine (non-NE) states, a process linked to therapeutic failure, yet the underlying mechanisms driving this plasticity remain in completely understood. Here, we show that the translation initiation factor eIF6 is a critical regulator of non-NE transdifferentiation in SCLC. eIF6 expression is consistently upregulated in non-NE states across cell lines, mouse models, and patient samples, accompanied by global remodelling of the translational landscape. Mechanistically, eIF6 dissociates from ribosomes and interacts with the CD104-FAK complex, leading to MAPK pathway activation. Intervening eIF6 suppresses non-NE transdifferentiation and enhances SCLC chemotherapy sensitivity in vitro and in vivo. These findings position the eIF6-CD104-FAK axis as a prognostic marker and therapeutic target, offering a potential strategy to mitigate SCLC resistance.

Introduction
Small cell lung carcinoma (SCLC), a high-grade neuroendocrine (NE) cancer that accounts for ~15% of all lung cancers1. SCLC is characterized by a high mutation burden, primarily driven by its strong association with tobacco exposure and frequent loss-of-function alterations in RB1 (~74%) and TP53 (~92%). Its recalcitrant nature is evident in the rapid disease progression and early metastasis, with over two-thirds of patients presenting with advanced-stage disease at the time of diagnosis2. For more than three decades, platinum-based combination chemotherapy has been the standard front-line treatment for SCLC. More recently, the addition of immune checkpoint blockade has extended the therapeutic options. Combining platinum-based chemotherapy with anti-PD-L1 immunotherapy, such as durvalumab or atezolizumab, has shown improved overall survival by ~3 months in patients with extensive-stage SCLC (ES-SCLC)3,4. Despite an initial treatment response rate of 60~80%, relapse (~90%) is common and rapid, with median survival for ES-SCLC patients remaining below 10 months, highlighting the urgent need for strategies to achieve durable clinical outcomes.

Tumour cell plasticity, an emerging hallmark of cancer, drives cell state diversity and contributes to therapy resistance5. Once viewed as a homogeneous disease, SCLC is now recognized as a highly heterogeneous and plastic tumour comprising distinct molecular subtypes: ASCL1+ neuroendocrine (SCLC-A), NEUROD1+ neuronal (SCLC-N), and POU2F3+ tuft-like (SCLC-P) cell states6. A fourth subset lacking ASCL1, NEUROD1, and POU2F3 expression, termed triple-negative, YAP1+, mesenchymal, or inflammatory (SCLC-Y/I), represents an inflammatory non-NE phenotype. High-NE subtypes such as SCLC-A (~70%) and SCLC-N (~15%) are generally sensitive to frontline chemotherapy, whereas low-NE states, including SCLC-P (7~15%) and SCLC-Y or -I (3~10%), exhibit greater chemoresistance but enhanced immune infiltration6. Human tumours exhibit dynamic NE to non-NE transitions during adaptive resistance and relapse2, a phenomenon mirrored in genetically engineered mouse models (GEMMs). However, genomic studies reveal minimal mutational differences between treatment-naïve and relapsed SCLC7–10, underscoring the role of non-genetic plasticity in drug resistance. Recent work has identified transcriptional and epigenetic mechanisms governing these transitions: c-MYC-driven Notch activation promotes sequential evolution from SCLC-A to SCLC-N and YAP1+ non-NE states11, while loss of Kdm1a, Kdm5a, or KDM6A facilitates NE-to-non-NE reprogramming through chromatin remodelling and ASCL1 suppression12–14. Beyond transcriptional and epigenetic regulation, post-transcriptional mechanisms are also increasingly recognized as key mediators of SCLC plasticity.

The mRNA-binding protein ZFP36L1 inhibits ASCL1 expression by binding to SOX2 and INSM1, driving a non-NE cell state reminiscent of SCLC-I tumours, highlighting the role of mRNA-level regulation in SCLC cell state transition15. While transcriptional regulation in SCLC has been extensively studied, mRNA translational regulation remains in its infancy. The initiation phase of mRNA translation, regulated by multiple initiation factors, is important for adjusting the proteome in cancer cell plasticity5. Among others, eIF4E, the cap-binding protein in eIF4F complex, and eIF4A, an RNA helicase, play critical roles in oncogenesis by modulating specific mRNA translation16. In addition, eIF6 governs 60S ribosomal subunit maturation and assembly into functional 80S ribosomes by acting as an anti-association factor, holding 60S subunits in a translationally inactive state until release factors Shwachman-Bodian-Diamond syndrome protein (SBDS) and elongation factor like GTPase 1 (EFL1) recycle them for translation17. Aberrant eIF6 expression is widely observed in cancers, including colorectal, head and neck, lung and ovarian cancer, with heterozygous loss of eIF6 shown to confer resistance to oncogene-induced transformation, emphasizing its key role in tumour growth and progression18. Furthermore, eIF6 phosphorylation by the RACK1-PKCβII complex downstream of RAS suggests its involvement in oncogenic signalling19. Despite these findings, the molecular mechanisms by which eIF6 contributes to cancer cell plasticity remain unclear.

Here, we identify a functional association between the translation initiation factor eIF6 and NE-to-non-NE transdifferentiation in SCLC. In non-NE cell states, increased expression of eIF6 partially dissociates from ribosomes and contributes to the assembly of integrin–FAK complexes and activation of the downstream MAPK pathway. Genetic perturbation of eIF6 limits non-NE transitions and increases SCLC sensitivity to frontline chemotherapy. Furthermore, in situ detection of eIF6 association with CD104/FAK complexes correlates with clinical outcomes in SCLC patients.

Results
SCLC neuroendocrine plasticity is associated with translational remodelling
To investigate additional mechanisms implicated in SCLC plasticity and drug resistance, we conducted a comparative analysis of gene expression profiles between relapsed SCLC patient samples post-chemotherapy (n=18)9 and treatment-naïve SCLC samples (n=81)20 using single-sample gene set enrichment analysis (ssGSEA)21 (Fig. 1a). We observed enrichment in pathways related to eukaryotic translation and 60S ribosome regulation in relapsed samples (Fig. 1b), hinting at translation regulation in SCLC’s chemotherapy tolerance. Intriguingly, pathways associated with NE cell states22 are diminished in relapsed compared to treatment-naïve SCLC samples, raising the possibility that global alterations in mRNA translation may represent a common feature of NE transdifferentiation (Fig. 1c). Similar enrichment patterns, including ribosome 60S large subunit biogenesis, cytoplasmic translation, and ribosome assembly, were observed in ASCL1low/NEUROD1low non-NE SCLC cells23 that are refractory to chemotherapy and anti-PD-L1 immunotherapy in the IMpower133 trial (Fig. 1d and Supplementary Fig 1a)3. Meanwhile, neuroendocrine-associated pathways, such as neurotransmitter secretion, synaptic vesicle exocytosis and axoneme assembly, were enriched in NE SCLC subtypes (Fig. 1d).

To validate these in silico findings, we assessed mRNA translational activity in distinct cell state transition models across multiple SCLC cell lines (Fig. 1e). These include the spontaneous transition model from MYCLhigh/ASCL1+ H209 cells to non-NE H209A cells, hepatocyte growth factor (HGF)-induced transition models24 involving MYCNhigh/ASCL1+ H69 cells (H69 vs YAP1+ H69M) or H209M cells (Fig. 1e), chemotherapy-resistant cell lines (NEUROD1+ H82 vs c-Mychigh H82R)25 (Fig. 1e). In these transdifferentiated or chemotherapy-refractory cell states, we observed variant non-NE phenotypes, ranging from smaller, less compact suspension cell clusters to fully attached, spread-out mesenchymal cell morphologies (Fig. 1e).

Consistent with transcriptomic analyses, non-NE cells exhibited a significant alteration in translation activity compared to their NE counterparts, as revealed by polysome profiling26. Ultracentrifugation of cell lysates on a sucrose gradient indicated a reduced content of polysome-bound mRNAs in variant non-NE cells compared to NE cells (Fig. 1f). This was evident by the decreased ratio of the area under curve (AUC) between polysome (ribosomes ≥ 2) and monosomes (40S-80S) (Supplementary Fig. 1b), as shown by multiple models (Fig. 1f, right), suggesting a global downregulation of translational activity in variant non-NE SCLC cells. Moreover, the reduced translation activity is not due to reduced cell proliferation as evidenced by CCK-8 cell number count during three days (Supplementary Fig. 1c). In line with these findings, variant non-NE cells across multiple models showed increased tolerance to carboplatin/etoposide combination treatment (Fig. 1g). We also observed downregulation of translation activity in two mesenchymal-like YAP1-high thoracic tumours, H196 (RB1/TP53 mutant) and DMS114 (SMARCA4 mutant) cells (Supplementary Fig. 1d, e). Transcriptomic pathway analysis further confirmed the transdifferentiated non-NE states, showing increased EMT pathway, inflammatory responses (TNFα signalling pathway and interferon-α response) across non-NE cell states from multiple models (Supplementary Fig. 2a). Alongside these polysome profile changes, we also noted an increase in the phosphorylation of RPS6 (p-RPS6) in non-NE SCLC cells compared to NE cells (Supplementary Fig. 2b). This elevated p-RPS6 consistently associated with poly-ribosomes in non-NE cell lysates (Supplementary Fig. 2c), whose dephosphorylation is recognized as necessary for actively translating ribosomes27. Given the central role of the mTOR pathway in regulating RPS6 phosphorylation and mRNA translation, we assessed its activation by monitoring phosphorylation of p70S6K1 (Thr389), 4EBP1 (Ser65), and AKT across SCLC transdifferentiation models. As shown in Supplementary Fig. 2b, phosphorylation of S6K1 and 4EBP1 remained largely unchanged, while p-AKT showed a modest increase only in the H69 model. These findings suggest that mTOR signalling is unlikely to account for the elevated p-RPS6 observed in non-NE cell states.

Translation landscape in NE versus non-NE SCLC
To characterize the translational landscape underlying NE-to-non-NE transition in SCLC, we performed genome-wide polysome profiling across HGF-induced (H69 vs. H69M), spontaneous (H209 vs. H209A), and chemotherapy-mediated (H82 vs. H82R) cell state models to identify mRNAs subject to altered translational regulation (Fig. 2a and Supplementary Fig. 3a and 3d). Polysome-associated RNA sequencing in H69M and H69 cells allowed us to categorize mRNAs into three distinct groups: Group 1 displayed at least a twofold change in cytoplasmic mRNA levels but less than a twofold change in polysomal mRNA levels (Fig. 2b, blue points); Group 2 exhibited at least a twofold change in polysomal mRNA levels but less than a twofold change in cytoplasmic mRNA levels (Fig. 2b, orange points); and Group 3 showed at least a twofold change, either an increase or decrease, in both cytoplasmic and polysomal mRNA levels (Fig. 2b, green points). This paired transcription-translation analysis revealed substantial alterations in translational regulation along the spectrum of SCLC transdifferentiation. Similar findings were replicated in H82 and H209 models (Supplementary Fig. 3b, 3e and Supplementary Data 1). Cumulative gene expression analysis of both ribosome-bound mRNAs and total RNAs showed stronger regulatory effects at the level of polysome-associated mRNAs (Fig. 2c). Notably, differentially transcribed (ΔmRNA, FDR < 1% and log2 |FC| > 1) and translated mRNAs (Δ translation efficiency [ΔTE], FDR < 10%) were largely independent, with minimal overlap between transcriptional and translational changes associated with the non-NE cell state (Fig. 2d and Supplementary Fig. 3b and 3e). Specifically, The SCLC neuroendocrine signature22 was downregulated at both transcriptional and translational levels in H69 cell model (Fig. 2e); Similar changes were also observed in the other two models (Supplementary Fig. 3c and 3f). We selected H69 as a representative model to further examine the 5′ untranslated regions (5′ UTRs) of differentially translated and transcribed mRNAs (Fig. 2f). Transcripts upregulated at the translational level in non-NE cell states exhibited lower GC content and increased 5’UTR length, whereas translationally downregulated transcripts had higher GC content and shorter 5’UTR length (Fig. 2f). Of note, reduced GC content in 5’UTR has been linked to enhanced translation under conditions of translational perturbation, though these features alone may not be sufficient to mediate translational control28.

The observed global downregulation of translation in the non-NE cell state may be linked to alterations in ribosome scanning or composition, potentially triggered by the cellular stress response29,30. To investigate these possibilities, we first measured ribosome transit rates using a harringtonine run-off assay (Supplementary Fig. 3g)31. Treatment with harringtonine in both NE and non-NE cells led to a reduction of polysomes and an accumulation of 80S ribosomes (Supplementary Fig. 3h), thereby ruling out ribosome stalling as the primary cause of the global translation remodelling. To further explore potential changes in ribosomal composition, we subsequently employed quantitative high-coverage tandem-mass-tag (TMT) mass spectrometry on H69 and H69M cells to evaluate the expression of all ribosome proteins (RPs) (Fig. 2g and Supplementary Fig. 4a)32. Cells were fractioned using sucrose gradient sedimentation, and monosomes (M, a single ribosome), light polysomes (LP, 2-4 ribosomes), and heavy polysomes (HP, ≥ 5 ribosomes) were collected from both H69 and H69M cells (Fig. 2g). On average, we detected peptides predominantly in the range of 8–20 amino acids, with a high protein coverage (Supplementary Fig. 4b–d and Supplementary Data 2). A diverse array of subcellular proteins potentially associated with polysomes was detected, with a substantial proportion belonging to nuclear proteins (37%) (Supplementary Fig. 4e). Gene ontology analysis showed an enrichment of translation-related components in our mass spectrometry analysis (Supplementary Fig. 4f). Differential proteomic analysis demonstrated distinct protein expression profiles associated with ribosomes in each sub-fraction (Supplementary Fig. 4g). Moreover, principal component analysis highlighted a clear segregation between NE and non-NE cell states, emphasizing translation remodelling during SCLC transdifferentiation (Fig. 2h). Intriguingly, despite the global downregulation of translation in the non-NE cell state compared to the NE cell state, the average composition of RPs within monosomes, light polysomes, and heavy polysomes remained largely invariant (Fig. 2i). These findings suggest that translation alterations associated with SCLC transdifferentiation are likely driven by regulatory mechanisms beyond ribosome stalling or modifications in ribosomal protein composition.

Translation initiation factor eIF6 is upregulated in the non-NE cell state
As neither mTOR pathway activity, ribosomal components, nor ribosome stalling showed substantial changes during SCLC transdifferentiation, we next examined whether translation initiation, the rate-limiting step of protein synthesis, contributes to the observed translational plasticity in SCLC. Intriguingly, we observed a discernible increase in the abundance of the 60S ribosomal subunit in the non-NE cell state compared to NE cell state (Fig. 1). eIF6 interacts with nascent 60S subunits, facilitating their assembly with 40S subunits to form active 80S ribosomes33. Notably, elevated eIF6 levels in c-Kit+ bone marrow cells reduce the 80S:60S ratio, leading to translational inactivation of 80S ribosomes17. This identifies eIF6 as a rate-limiting factor in tumorigenesis, as evidenced by studies in Eif6+/- mouse embryonic fibroblasts, which show reduced transformation capacity18. Among key translation initiation factors, we found that eIF6 was the only factor consistently upregulated at the protein (Fig. 3a and Supplementary Fig. 5a) and mRNA level (Fig. 3b and Supplementary Fig. 5b) during non-NE transition across multiple SCLC models.

Given the observed alteration of eIF6 expression in multiple in vitro transdifferentiation models, we investigated whether this phenomenon occurs in vivo and in patient samples. We first analysed a public dataset of SCLC patient samples20, categorizing them into four groups based on the expression of classical NE-associated TFs, namely ASCL1high, NEUROD1high, dual-positive (ASCL1high NEUROD1high) and dual-negative (ASCL1low NEUROD1low). Notably, eIF6 expression was significantly elevated in dual-negative samples, suggesting an inverse relationship between eIF6 expression and the NE cell state (Supplementary Fig. 5c). Meanwhile, MYC, a known driver of SCLC transdifferentiation34, was also upregulated in non-NE tumours (Supplementary Fig. 5c). On the other hand, despite the observed inverse correlation between eIF6 and NE cell state, eIF6-associated proteins SBDS and EFL1 expression levels remained unchanged (Supplementary Fig. 5d). Further supporting these findings, we observed increased Eif6 and Myc expression, along with decreased Ascl1 expression, in murine RPM tumours compared to RPR2 (Rb1fl/fl/Trp53fl/fl/Rbl2fl/fl) tumours (Supplementary Fig 5e, f). This aligns with previous reports of reduced NE marker expression in invasive RPM tumours compared to RPR2 tumours11. To extend these observations, we performed immunohistochemistry of Eif6 and Ascl1 expression in the RPM mouse model, which exhibits NE-low histopathology34. Adenoviruses carrying Cre-driven by the neuroendocrine calcitonin gene-related peptide (Cgrp) promoter were nasally administered to mice (Fig. 3c), and micro-computed tomography revealed centrally located tumours with increased density at major bronchi between weeks 5 and 7 post-infection (Fig. 3c, top). Tumours were collected at various stages, including in situ carcinoma (402), locally advanced tumours (412), and locoregional metastatic tumours (404) (Fig. 3c). In in situ carcinoma, Ascl1 expression was high, while Eif6 was largely absent (Fig. 3d, 402). Similarly, in the locally advanced tumour, eIF6 expression remained low in regions with high Ascl1 expression (Fig. 3d, 412). However, in more advanced tumours, eIF6 staining was intensified in Ascl1-low regions, contrasting with lower eIF6 expression Ascl1-high regions (Fig. 3d, 404). To determine whether this inverse correlation between eIF6 and the NE cell state extends to human tumours, we analysed surgical specimens from limited-stage SCLC patients (n=38). Multiplex immunofluorescence (mIF) was performed on formalin-fixed, paraffin-embedded (FFPE) samples to simultaneously detect ASCL1, NEUROD1, and eIF6 (Fig. 3e). Single-cell protein quantification using QuPath software (Fig. 3e and Supplementary Fig. 6a)35 revealed significant intra-tumoral heterogeneity in ASCL1, NEUROD1, and eIF6 expression. Despite this variability, eIF6 and ASCL1 expression were inversely correlated (R2=−0.328, p-value=0.0285), consistent with our in vitro and murine findings (Fig. 3f and Supplementary Fig. 6b). However, we acknowledge that explicit cell-classification-based quantification of ASCL1 and eIF6 expression, aimed at assessing their co-expression, would be valuable for defining the relationship between these two proteins at the patient level. Definitely resolving their expression patterns in individual cells will require higher-resolution spatial single-cell transcriptomic and proteomic approaches in the future. Nevertheless, the anti-correlation between ASCL1 and eIF6 was further supported by RNA sequencing data from the IMpower133 cohort36 (Fig. 3g), and a similar inverse trend – although not reaching statistical significance – was also observed in treatment-naïve samples from the George et al. dataset20 (Supplementary Fig. 5g).

Frontline chemotherapy induces eIF6 expression
Reanalysis of the transcriptomic dataset shown in Fig. 1a revealed that an inverse correlation between eIF6 and ASCL1 (Supplementary Fig. 7a, b), while eIF6 expression showed a modest positive correlation with POU2F3 and MYC, but not with NEUROD1 (Supplementary Fig. 7b). Stratification of patient samples further demonstrated elevated eIF6 expression in ASCL1-low relapsed tumours compared with treatment-naïve counterparts (Supplementary Fig. 7c), other translation initiation factors, including EIF4E, EIF4G1 and EIF4A, were either downregulated or unchanged in relapsed samples, consistent with our in vitro findings across transdifferentiation models (Supplementary Fig. 7c). Carboplatin/etoposide treatment acutely increased eIF6 protein levels in human SCLC cell lines, as confirmed by western blot (Fig. 3h), and these levels remained elevated in time-course assays across human and RPM-derived mouse models34 (Supplementary Fig. 7d). To validate these observations in vivo, we examined EIF6 expression in a residual disease setting. Human H69 (Fig. 3i) and H82 (Supplementary Fig. 7f) tumours transplanted into Balb/cNj-Foxn1nu/Gpt mice were treated with carboplatin/etoposide for three cycles. Tumours initially regressed but relapsed around day 60 following a minimal residual disease phase (Fig. 3i and Supplementary Fig. 7f). Notably, eIF6 protein expression was selectively upregulated during the residual phase in both models (Fig. 3j and Supplementary Fig. 7e and 7g-h), mirroring our in vitro observations.

eIF6 regulates non-NE transdifferentiation across multiple models
To assess the functional role of eIF6 upregulation in SCLC neuroendocrine plasticity, we examined the effects of eIF6 knockdown in non-NE SCLC cells. We did not observe significant alterations of translation activity in H69 and H69M cells upon eIF6 knockdown (Supplementary Fig. 8a). RNA sequencing followed by GSEA revealed that eIF6 knockdown in H69M cells significantly downregulated epithelial-mesenchymal transition (EMT)-associated gene signatures (Fig. 4a, top), including NOTCH family proteins, integrins, and vimentin, all of which have been implicated in EMT-driven transdifferentiation in SCLC (Fig. 4b, left and Supplementary Data 3). Conversely, eIF6 knockdown led to a marked increase in the neuroendocrine gene signature15,37 (Fig. 4a, bottom and Supplementary Data 3), encompassing HES6, TOP2A, DLL3, NEUROD1, ASCL1, INSM1 and CHGA (Fig. 4b, right). To substantiate these findings, we performed transcriptomic analyses in the spontaneous transdifferentiation model H209A (Supplementary Fig 8b, c) and the chemotherapy-induced model H82R (Supplementary Fig 8d, e). Both models showed downregulation of EMT-associated signatures and upregulation of SCLC neuroendocrine programs. Consistent with these findings, western blot analysis across the three SCLC cell models showed a reduction in EMT-related transcription factors ZEB1/2 in non-NE cell state, although the effect was less pronounced in H82R cells. Partial restoration of ASCL1 expression was also observed in H209A and H82R cells, with a weaker effect in H69M cells (Fig. 4c).

Although eIF6 knockdown does not fully reverse non-NE transdifferentiation, these results suggest that its inhibition may restrain the transition toward a non-NE state across distinct transdifferentiation contexts. While global polysome profiles remained unchanged (Supplementary Fig. 8a), selective translational control might also underlie the observed phenotypic effects. We therefore performed ribosome profiling (Ribo-seq) to assess ribosome scanning and mRNA-specific translation upon eIF6 knockdown (Supplementary Data 4). Ribo-seq displayed canonical features, including 26–29 nt footprints, strong frame 0 enrichment, and clear three-nucleotide periodicity (Supplementary Fig. 8f-h). Notably, eIF6 depletion primarily induced ribosome stalling within the first 20-45 codons downstream of the start codon, consistent with its known anti-association activity (Supplementary Fig. 8i). Analysis of translation efficiency revealed that eIF6 knockdown predominantly affected mRNAs encoding RNA processing factors involved in splicing and ribosome biogenesis (Supplementary Fig. 8j), with only a limited set of selectively translated proteins, including WDR75, MARK3, MSN, PABPC1, and PPIG (Supplementary Data 4). No established regulators of SCLC non-NE transdifferentiation exhibited altered translation under these conditions.

We next performed a transdifferentiation assay using human H69 cell lines, which are known to transition into a non-NE cell state upon HGF treatment24. As shown in Fig. 4d, H69 cells expressing non-targeted control shRNA (sh_NTC) exhibited increased adhesion upon HGF treatment, as visualized by crystal violet staining. In contrast, eIF6 knockdown dramatically impeded this phenotypic change, maintaining cells in neuroendocrine spheroid aggregates in suspension and leading to a reduced crystal violet staining signal (Fig. 4d). A similar inhibitory effect of eIF6 knockdown was observed in EZH2 inhibitor-mediated SCLC transdifferentiation38 (Supplementary Fig. 9a). To further explore these findings, we performed a transdifferentiation assay14 using RPM-derived mouse SCLC cell lines. RPM-derived cells progressively and spontaneously shifted from neuroendocrine spheroid growth to a non-neuroendocrine adherent state34 (Supplementary Fig. 9b), accompanied by reduced EMT-associated factors and partial restoration of ASCL1 expression (Supplementary Fig. 9c). Re-expression of shRNA-resistant eIF6 restored HGF-induced transdifferentiation (Fig. 4e, f), confirming an on-target effect. Conversely, lentiviral eIF6 overexpression in NE-state cells (H69, H82, and H209) enhanced HGF-induced attachment (Supplementary Fig. 9d–f). Together, these data indicate that eIF6, while not a primary driver, contributes to the non-NE transition in SCLC.

Intervention of eIF6 sensitizes SCLC to chemotherapy
Given the heightened sensitivity of neuroendocrine-state SCLC to platinum-based chemotherapy36, we investigated whether eIF6 knockdown could enhance chemotherapy responsiveness across multiple models. First, we assessed short-term chemotherapy effects in vitro. H69M cells were treated with carboplatin/etoposide for 72 hours, followed by flow cytometry analysis (Supplementary Fig. 9g). eIF6 knockdown significantly reduced the percentage of viable cells compared to the non-targeted controls upon chemotherapy (Fig. 4g). This effect was consistently observed in RPM-derived murine SCLC cell line, where Eif6 knockdown led to increased cell death during short-term chemotherapy exposure (Supplementary Fig. 9h). Next, we examined long-term chemotherapy effects using a clonogenic assay26. RPM-derived mouse SCLC cells with Eif6 knockdown exhibited not only reduced proliferation but also a substantial loss of residual survival cells, as indicated by a significant reduction in colony formation (Supplementary Fig. 9i). The enhanced chemotherapy response was further validated in vivo. H69 cell xenografts in Balb/cNj-Foxn1nu/Gpt mice underwent three cycles of carboplatin/etoposide treatment. In the control group, 76.9% of mice (10/13) exhibited disease progression (PD) (Fig. 4h); In contrast, H69 xenografts with eIF6 knockdown demonstrated a marked response, with 61.5% (8/13) exhibiting disease regression, including two complete responses (CR) and six partial responses (PR) (Fig. 4h and Supplementary Fig. 9j). To explore the molecular underpinnings of this response, we collected FFPE tumour samples at the time of evaluation and performed immunohistochemistry of four key transcription factors associated with SCLC subtypes, including ASCL1, NEUROD1, POU2F3 and YAP1. Increased expression of POU2F3 (3/3) and, to a lesser extent, YAP1 (1/3) was observed in eIF6 control samples (sh_NTC), all of which showed PD at the time of evaluation (Figs. 4h, 4i, a–c). In contrast, tumours with eIF6 knockdown showed increased ASCL1 expression and low-to-absent levels of POU2F3 and YAP1 (Fig. 4i, d–f). Notably, one tumour (Fig. 4h, d) displayed lower ASCL1 expression, partial NEUROD1 expression, and low POU2F3 expression, correlating with relative chemotherapy resistance (Fig. 4i, d). Taken together, these findings suggest that targeting eIF6 suppresses non-NE transdifferentiation, thereby enhancing SCLC responsiveness to chemotherapy.

Reciprocal interplay between eIF6 and SCLC microenvironment
eIF6, a stress-responsive translation factor39, was induced in non-NE SCLC states and upon chemotherapy exposure (Fig. 3), but its relationship with the tumour microenvironment remained unclear. Given that non-NE SCLC subtypes (SCLC-I) exhibit inflammatory signatures and lymphocyte infiltration23, we examined whether eIF6 correlates with immune modulation. Cytokine profiling revealed increased secretion of GM-CSF, CXCL10 and CCL2 in non-NE H69M cells compared with H69 cells (Supplementary Fig. 10a), suggesting a pro-inflammatory secretome. In patient tumours, ASCL1-high regions displayed a “cold” phenotype with minimal lymphocytes and moderate CD68+ macrophages, whereas eIF6-high regions, exhibiting desmoplastic features40, were enriched for CD3+ lymphocytes and CD68+ (Supplementary Fig. 10b). This spatial pattern even persisted within the same tumour, where CD3+ T cells localized selectively to eIF6-high but not ASCL1-high areas (Supplementary Fig. 10b, PT892). Analysis of the George et al. cohort20 confirmed elevated IFNG and CD8A expression in eIF6-high tumours (Supplementary Fig. 10c), and CIBERSORTx deconvolution41 further showed that CD8+ T-cell fractions correlated with high eIF6 expression (Supplementary Fig. 10d), consistent with enhanced T-cell infiltration. To probe intercellular interactions, we re-analysed a published single-cell RNA-seq dataset using the original cell annotations42 (Supplementary Fig 10e). This reanalysis recapitulated an inverse relationship between eIF6 and ASCL1 at single-cell resolution, except in tumours lacking TP53 and/or RB1 mutations (Supplementary Fig. 10f). CellChat inference43 suggested that eIF6-high tumour cells interact with CD8+ T cells via multiple ligand–receptor pairs (e.g., NECTIN2, MIF, LCK, collagens), while T cells signal back through the IFNG–IFNGR1 axis, suggesting possible environmental feedback on eIF6 expressing cells (Supplementary Fig. 10g). Functionally, eIF6 knockdown reduced PD-L1 expression in human and RPM-derived mouse models (Supplementary Fig. 10h, i), implicating eIF6-mediated regulation in immune evasion. In accordance with cell-cell communication analysis in Supplementary Fig. 10g, IFNγ induced both eIF6 and PD-L1, whereas eIF6 silencing blunted IFNγ-mediated PD-L1 upregulation (Supplementary Fig. 10j). Although PD-L1 transcripts were not detected in our Ribo-seq dataset (Supplementary Fig. 8), likely reflecting limited sequencing depth, our polysome profiling (Supplementary Fig. 10k, l) showed reduced polysome association of PD-L1 mRNA and its upstream regulator STAT1 mRNA after eIF6 knockdown, indicating decreased translational efficiency (Supplementary Fig. 10m). CD47 was similarly translationally downregulated, while HLA-A translation increased, suggesting selective translational control of immune-evasion factors. Accordingly, eIF6 depletion in vivo delayed tumour progression only in immunocompetent, not nude, mice (Supplementary Fig. 10n), supporting a lymphocyte-associated immune-dependent role for eIF6 in SCLC. Together, these findings suggest a reciprocal interplay between eIF6-mediated translational control and immune-microenvironmental adaptation that may be involved in non-NE transdifferentiation and tumour immune evasion in SCLC, warranting further investigation in an immune-competent context.

eIF6 moonlighting integrin-FAK complex assembly in non-NE SCLC
Given that eIF6 knockdown does not directly affect the translation of known regulators of SCLC transdifferentiation, we next sought to understand how elevated eIF6 expression contributes to the non-NE transition in SCLC. To study this intrinsic role of eIF6 in SCLC neuroendocrine plasticity, we first established an in vitro eIF6-ribosome binding assay using sucrose gradient ultracentrifugation19. Cell lysates were subjected to sucrose gradient sedimentation, which allowed the separation of ribosome-associated and free eIF6. Specifically, 60S subunit-bound eIF6 pelleted in fraction F2 through a 30% sucrose cushion, whereas ribosome-free eIF6 remained in fraction F1 (Fig. 5a, top). Immunoblot analysis revealed a reduced abundance of eIF6 in the F2 fraction of non-NE H69M cells compared to their NE counterparts (H69) (Fig. 5a, bottom). Further analysis of eIF6 distribution within polysome profiles showed diminished eIF6 levels in monosome-associated fractions of non-NE cells (Supplementary Fig. 11a). These findings suggest that in the non-NE cell state, eIF6 undergoes dissociation from the ribosome pool, potentially altering its regulatory function in translation.

We next leveraged large-scale CRISPR screening data from the Cancer DepMap dataset to search for potential pathways that may be associated with eIF6 expression across ~1200 solid cancer cell lines44. We performed linear regression analyses to assess the correlation coefficient between eIF6 expression and gene dependency scores for each perturbed gene, identifying genes with the most negative correlation coefficients as those essential for the survival of eIF6-high tumour cells (Methods). Consistent with its role in ribosome biogenesis, eIF6-high tumour cells exhibited increased dependency on 60S ribosome assembly factors, including SBDS and EFL1, two main eIF6 interactors (Fig. 5b and Supplementary Data 5). Unexpectedly, components of two additional pathways – Hippo-YAP pathway and the integrin-FAK signal complex – emerged as essential for eIF6-high tumour cell survival (Fig. 5b and Supplementary Data 5). In contrast, the lysine demethylase EZH2, whose inhibition promotes non-NE transdifferentiation45, displayed a positive correlation with eIF6 expression, suggesting a potential opposing role in lineage plasticity.

We were particularly intrigued by the integrin-FAK pathway, given its established role in EMT across various contexts and the lack of prior studies linking eIF6 to its regulation in SCLC. To further explore this relationship, we analysed the eIF6 genome-wide interactome using the BioGrid database46, revealing an interaction between eIF6 and integrin β4 protein (ITGB4, also called CD104) (Fig. 5c). Notably, previous studies demonstrated eIF6 binding to CD104 in a yeast two-hybrid assay47, as well as its ability to localize to intermediate filaments and nuclear matrix48. Accordingly, CD104 expression was significantly upregulated in non-NE cell state (Fig. 5d). These lines of evidences suggest a non-classical role for eIF6, wherein it dissociates from ribosomes and associates with the CD104-FAK signalling complex in non-NE SCLC cells.

To investigate whether eIF6 interacts with the CD104-FAK complex, we employed two complementary approaches, including immunoprecipitation and in situ proximity detection. Immunoprecipitation of endogenous FAK revealed an increased interaction between CD104 and FAK in ASCL1-negative non-NE H69M cells compared to ASCL1-positive NE H69 cells (Fig. 5e). Notably, ribosome-dissociated eIF6 also exhibited enhanced interaction with the CD104-FAK complex, coinciding with an increase in phosphorylated ERK1/2 within this complex. These findings suggest that integrin-FAK complex formation is elevated in the non-NE cell state and may activate downstream MAPK signalling (Fig. 5e). We also found increased total CD104 protein levels, aligning with its elevated mRNA expression detected by qPCR (Fig. 5d, 5e). To further validate these interactions in situ, we performed a proximity ligation assay (PLA) followed by our in-house PLA analysis pipeline49 (Fig. 5f). PLA consistently detected eIF6 in close proximity to FAK (Fig. 5g) and CD104 (Supplementary Fig. 11b), with a significant increase in FAK-eIF6 PLA complexes in H69M cells compared to H69 cells (Fig. 5h). Moreover, eIF6 knockdown substantially reduced the number of FAK-eIF6 PLA complexes (Fig. 5g, h) a trend similarly observed in CD104-eIF6 PLA complexes (Fig. 5i and Supplementary Fig 11b). This in situ observation was further corroborated by endogenous FAK immunoprecipitation, which showed that eIF6 knockdown in non-NE H69M cells led to a decrease in total CD104 levels, thereby reducing CD104-FAK complex formation and ERK1/2 association (Fig. 5j).

Previous studies reported that Ser235 phosphorylation by the RACK1–PKCβII complex promotes eIF6 release from the 60S ribosome in other systems33. We similarly observed increased Ser235 phosphorylation of eIF6 during non-NE transition across multiple models (Supplementary Fig. 11c). Structurally, eIF6 adopts a characteristic “pentein” fold composed of five α/β subdomains arranged around a pseudosymmetry axis50, with a C-terminal tail harbouring several phosphorylation sites, including Ser23551 (Supplementary Fig. 11d). Although we did not directly assess its function, this finding may provide a mechanistic clue for future investigation into whether Ser235 phosphorylation contributes to eIF6 dissociation during non-NE transdifferentiation. Given that such dissociation could expose eIF6 surfaces for alternative binding partners, we next used AlphaFold3 modelling52 to explore potential structural interfaces mediating its interaction with the CD104–FAK complex. The modelling suggested that the Calx-β domain of CD104, known to interact with the FAK linker region53 (Supplementary Fig. 11e), may engage the same surface of eIF6 that associates with the RPL23 (Supplementary Fig. 11f, g). Four residues, Arg57, Ser102, Ser150, and Asp209, were predicted to mediate this interaction, with Arg57 shared by both the RPL23 and CD104 interfaces. The other three residues lie in close proximity to ribosome-interacting residues (Asn106, Tyr151, Glu12, and Ser190)54 and to Arg61, a residue essential for maintaining eIF6 folding55 (Supplementary Fig. 11h). This spatial arrangement suggests potential competition between CD104 and RPL23 for eIF6 binding. We tried to generate alanine substitutions at these sites, but the mutations resulted in protein misfolding, likely because the mutations disrupted local interactions near Arg61 that are critical for eIF6 structural stability55, precluding further functional evaluation. Nevertheless, our in silico analyses and validation experiments suggest that eIF6 may exert a moonlighting function by dissociating from ribosomes and associating with the integrin-FAK complex, possibly modulating integrin-mediated downstream signalling in the non-NE cell state.

Targeting integrin-FAK signalling in chemoresistant non-NE SCLC
Following integrin-FAK complex formation, we observed increased focal adhesion kinase (FAK) phosphorylation across multiple SCLC transdifferentiation models (Fig. 6a), as well as upon carboplatin/etoposide treatment (Supplementary Fig. 12a). Transcriptomic analysis revealed a consistent reduction of the MAPK pathway gene signature upon eIF6 knockdown across multiple SCLC models (Fig. 6b, c and Supplementary Fig. 12b), including downregulation of the core MAPK-responsive genes such as DUSP3, DUSP5, DUSP6 and MAP2K1 (Fig. 6b). Concordantly, eIF6 knockdown selectively reduced ERK1/2 phosphorylation in the non-NE state, with a similar, albeit less pronounced, effect on FAK autophosphorylation in ASCL1+ H69 and ASCL1+ MYCL+ H209 cells (Fig. 6d). Interestingly, a recent study also demonstrated eIF6’s interaction with FAK for the local activation of ERK1/2 at focal adhesions in endothelial cells responding to mechanical forces56. By contrast, we did not observe measurable changes in AKT phosphorylation (Fig. 6d), in line with our earlier observation that the AKT pathway is not activated during non-NE transition (Supplementary Fig. 2b). To further elucidate the relevance of eIF6 to FAK-ERK

Methods

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "This paper investigates the molecular mechanisms underlying SCLC plasticity and resistance to chemotherapy. The authors\nuncover a non-canonical, “moonlighting\" role for eIF6 in regulating SCLC plasticity. They demonstrate that eIF6 dissociates\nfrom ribosomes in non-NE states, associates with the CD104/FAK complex, and activates MAPK signaling. Knockdown of\neIF6 inhibits non-NE transdifferentiation and enhances chemotherapy sensitivity in SCLC models. Importantly, they propose\nthat the eIF6-CD104-FAK axis serves as both a prognostic marker and a therapeutic target. Clinical data from limited-stage\nSCLC patients suggest that CD104-eIF6 proximity ligation assay (PLA) scores correlate strongly with patient outcomes,\nunderscoring the clinical relevance.\nThis is a very good and original manuscript that provides new mechanistic insights into small cell lung carcinoma (SCLC)\nplasticity. By identifying the translation initiation factor eIF6 as a key regulator of non-neuroendocrine (non-NE)\ntransdifferentiation and chemoresistance, the authors open new avenues for therapeutic targeting. The work is\ncomprehensive, combining in vitro, in vivo, and patient sample analyses, and addresses an urgent clinical problem in\nSCLC. While the manuscript is strong and well-constructed, certain aspects require\nclarification and additional validation to fully support the major claims.\nSpecific comments\n1. Mechanistic Depth and Post-Translational Regulation.\nWhile the discovery of eIF6's moonlighting role is novel, the mechanistic switch governing its ribosome-to-integrin transition\nremains speculative. Adding some kinetic aspects would help understand how this switch is triggered. Can the authors\nprovide evidence of post-translational modifications (PTMs), such as phosphorylation, driving this switch? Alternatively,\nauthors may include more discussion on this point, and discuss if they might be ways to prevent this relocation.\n2. In Vivo Validation of FAK Inhibitor Strategy\nThe combination of chemotherapy with FAK inhibitors is compelling but currently limited to in vitro data. In vivo xenograft or\nGEMM experiments combining FAK inhibitors with chemotherapy are essential to support the therapeutic claims and\nproposed clinical translation.\n3. Sample Size and Clinical Interpretation\nThe survival analyses based on CD104-eIF6 PLA scores are interesting, but the clinical cohort is relatively small (n=38).\nThe authors should add a note of caution about over-interpreting these findings and emphasize the need for validation in\nlarger, independent cohorts.\n4. Selective vs. Global Translation Effects\nThe study focuses on global translation suppression in non-NE states, but selective translation of specific mRNAs could\nalso drive phenotypic outcomes. Have the authors profiled or analyzed specific mRNA subsets whose translation is altered\nby eIF6? can they exclude this mechanism totally?\n5. Knockdown Specificity and Rescue Experiments\nSince the conclusions rely heavily on shRNA-mediated eIF6 knockdown, it would greatly strengthen the study to include\nrescue experiments using shRNA-resistant eIF6 constructs to rule out off-target effects. Having a mutant of eIF6 that would\nnot bind FAK would help understand the importance of this relocation in the phenotype (vs translation see point4).\nMinor comments:\n1. Fig.1b and 1c, what is the difference? What is the gene ratio item in 1b?\n2. Fig.3c, add the proteins detected in 3a. How do authors explain this rapid upregulation upon treatment with regards to the\npanel 3f (i.e. upregulation seen in the minimal phase = much more late). Show the blots in 3f. 3c & 3f: how specific is it to\neIF6?\nFig3d. Redundant with 3a? is HGF also used? do authors know if the upregulation is seen at RNA levels?\nSuppl Fig5a-b: legends are inverted.\n3. Fig4c: “partially restoring ASCL1 expression” I do not see this. This statement is not correct.\n4f: most of the effect is already seen in untreated conditions. Is not convincing that eIF6 would synergize with the\nchemotherapy.\n4g: a precise measurement of the tumor size should be provided to really appreciate the effect of eIF6 depletion. Data are\ntoo much normalized.\n4. Fig5b: “analysis of eIF6 distribution within polysome profiles showed diminished eIF6 levels in monosome-associated\nfractions of non-NE cells”. Is not clear to me how this conclusion is made – I do not see this data. The profiles are different\nbetween the conditions.\n5. The panels Fig5d and Suppl. Fig8 are difficult to read. Can you not simplify them highlighting the physical interactions\ndirectly?\n6. Provide additional quantitative data on MAPK/ERK pathway activation (e.g., densitometric analyses, kinase assays).\nGenerally speaking, one would like to have a clear idea of the size of the detected proteins…\n7. Clarify randomization and blinding procedures in the in vivo chemotherapy experiments in the Methods section.\n8. Can you discuss why direct ERK inhibition fails to mimic the chemosensitization effect seen with FAK inhibition?", "reviewer_2": "This manuscript provides novel insights into eIF6's role in SCLC transdifferentiation and plasticity using diverse models,\nincluding genetically engineered mice and limited-stage patient samples. The discovery of eIF6-CD104/FAK-MAPK axis-\ndriven translational rewiring and its prognostic relevance via PLA scoring is compelling. However, while the work\nrigorously examines cell-intrinsic mechanisms (e.g., ribosome dynamics, chemotherapy resistance), it overlooks the role of TME\nmodulating plasticity.\n- Chemotherapy sensitivity improvements upon eIF6 knockdown may involve immune-mediated mechanisms (e.g.,\nimmunogenic cell death, antigen presentation), which remain unexplored.\n- Immune compartment gap: transdifferentiation influence immune evasión?, checkpoint expression, or immune cell\ninfiltration (e.g., T cells, macrophages) in SCLC?\n- The GEMM and xenograft models lack analysis of immune-tumor crosstalk during plasticity transitions, despite known links\nbetween FAK/MAPK signaling.", "reviewer_3": "In this article, Peng, Wang M, Wang Z et al identify eIF6, a translation initiation factor, as a key regulator of non-NE\ntransdifferentiation in SCLC. eIF6 expression is upregulated in non-NE states across different models. It alters the\ntranslational landscape of SCLC by dissociating from ribosomes and interacting with the CD104/FAK complex. Inhibition of\neIF6 suppresses non-NE transdifferentiation and improves chemotherapy sensitivity in vitro and in vivo. These findings\nsuggest that the eIF6-CD104-FAK axis could serve as both a prognostic marker and a therapeutic in SCLC. However, there\nare numerous major technical and experimental design issues in this study (see below), which significantly affect the validity\nof the findings. The main concern is the limited number and inconsistent selection of models used. Each preclinical model\ndiffers significantly: one is derived from a chemoresistant model, another was generated by introducing HGF, and a third is\ndescribed as a spontaneous transition model. There is also a significant lack of consistency in model usage—while H69\n(arguably the most artificial model) is included in all experiments, most experiments have not been performed in H209 or\nH82 cells. I strongly recommend that all key findings be validated in at least three distinct cell lines. If the main claim of the\nstudy is that the low-NE state found in chemoresistant SCLC cells is driven by the upregulation of eIF6, the authors should\ngenerate chemoresistant models in vitro, isolate the non-NE cells from the resistant populations, and compare these to the\noriginal high-NE cells. This would represent a true transitional model from a high- to low-NE state driven by\nchemoresistance.\nMinor comments\n1. There is a typo in sentence 138: NERUOD1\n2. Fig. 1G lacks statistical analysis.\nMajor concerns\n1. Authors show in Fig. 1C the upregulation of ribosomal-related pathways in high-NE clinical samples by using data from\nIMpower 133. However, it is not shown whether the upregulation of ribosomal pathways in post-chemo versus naïve\nsamples (Fig. 1B) correlates with a reduced NE phenotype in their in-house sequencing database.\n2. In Fig. 1D, the H69 cell line is repeated in the western blot and H209M and H209A cell lines are missing. A recent report\nfrom the Sutherland lab (PMID: 38180245) reclassified H196 and DMS114 as SMARCA4 mutant poorly differentiated\nthoracic tumors, not SCLC cell lines. The authors should consider using mixed (floating and adherent) SCLC cell lines\nknown to be comprised by both high-NE (floating) and low-NE (adherent) cells and perform the same western blots in the\nlow and high-NE populations.\n3. Western blotting of RPS6 should be performed in all models used not only in selected cell lines. Why did the authors not\nanalyze the expression of p-RPS6/RPS6 in H82/H82R and H209/H209A/H209M cells? Authors should also analyze the\nlevels of this marker in low and high NE subpopulations as suggested in the previous point.\n4. Why did authors perform genome-wide polysome profiling only in H69 and H196 cells? Experiments should be done in all\nmodels used in Fig 1 and avoid using H196 and DMS114 as low NE cell lines. I also strongly recommend performing this\nexperiment in both low and high NE cells derived from the same cell line, as indicated above.\n5. In Fig. 2E, a better approach would be to use for example the NE and non-NE signatures from Zhang et al (PMID:\n29535911), to calculate the NE and non-NE scores rather than relying on pre-selected genes.\n6. The authors previously showed that translation-related pathways were among the top pathways upregulated in relapse\nversus treatment-naïve (TN) clinical samples (Fig. 1B), and they claimed that eIF6 is a key translation initiation factor\ninvolved in the non-NE transition. However, it is not shown whether eIF6 is upregulated in relapse versus naïve samples.\nAccording to Fig. 3B, it is not very evident that eIF6 is preferentially expressed in relapse cases. Can authors show the\nlevels of eIF6 is each of these groups?\n7. How do other genes involved in translation pathways look like in relapse versus naïve samples? The authors should\npresent the expression of key translation factors in both groups and justify why eIF6 was selected, as it is not clear how this\ntarget was identified.\n8. In order to confirm the inverse correlation between ASCL1 and eIF6 (Fig. 3B), the authors should perform a correlation\nanalysis. eIF6 also appears to correlate inversely with NEUROD1, while it correlates positively with MYC and POU2F3.\n9. Western blots of eIF6 expression after carboplatin/etoposide treatment (Fig. 3C) should be performed in a panel of SCLC\ncell lines, not only in one cell line, to validate the universality of this finding.\n10. The in vivo experiment shown in Fig. 3E should be performed in least one additional cell line–derived xenograft. The\nauthors use H69 as the primary model for most of their experiments, while other models are used selectively in only a few\nexperiments. This lack of consistency in the models used, along with the reliance on a single cell line in key experiments,\nraises concerns about the universality and robustness of the results.\n11. In Fig. 3H, did the authors quantify the H-score of both ASCL1 and eIF6 in tumors at different stages? Showing a single\nIHC image is insufficient to conclude that eIF6 is upregulated in advanced tumors lacking ASCL1. Furthermore, if the\nauthors wish to claim that high eIF6 expression is found in ASCL1-low regions (line 208), they should perform dual\nimmunofluorescence (IF) staining for ASCL1 and eIF6 and determine the co-localization of both markers. Also in Fig. 3H, the\nareas shown for ASCL1 and eIF6 IHC are different, so the authors cannot draw the aforementioned conclusion.\n12. I find quite surprising that the majority of clinical limited-stage specimens used in Fig. 3J lack ASCL1 expression, given\nthat up to 70% of all SCLC tumors exhibit high ASCL1 levels. Were these samples preselected based on subtype?\n13. In the IF experiment (Fig. 3J), the authors should quantify and present the percentage of cells that are ASCL1⁺/eIF6⁻,\nASCL1⁺/eIF6⁺, ASCL1-/eIF6⁺, and NEUROD1⁺/eIF6⁺, as the primary purpose of performing multiplex IF is to assess such\nco-expression patterns.\n14. The correlation between ASCL1 and eIF6 in the IMPOWER data appears to be weak, as at least half of the high ASCL1\ncases exhibit very high levels of eIF6. How does this correlation appear in other databases, such as George et al.?\n15. In general, I find the association between ASCL1 and eIF6 to be weak and hard to believe. Significant re-analysis and\nadditional experiments should be performed to validate this. Does ASCL1 genetic ablation lead to the upregulation of eIF6\nexpression?\n16. It cannot be concluded that eIF6 knockdown increases the expression of NE markers based on the RNA-seq performed\nin H69 cells. According to Fig. 4B, eIF6 KD with one guide upregulates ASCL1, INSM1, and NEUROD1, while the other\nguide inhibits them. Similarly, ASCL1 upregulation at protein level (Fig. 4C) is very mild and only detected in one of the two\nKD clones. In line with previous comments, why did the authors inhibit eIF6 only in H69 cells and not in other cell lines to\nvalidate this result?\n17. Why the levels of ASCL1 are not shown in the western blot in Fig. 4E?\n18. The tumor growth should be shown in Fig. 4G and untreated arms should have been included in the experiment to\ncompare effectively the effect of chemotherapy.\n19. I find interesting the interaction between eIF6 and FAK. Could this interaction be explored in the clinical specimens used\nin Fig. 3I to strengthen the universality of this finding?\n20. Experiments shown in Fig. 6C-G should be performed in additional cell lines."}, "author_response": "Point-by-point reply\nReviewer #1, Expertise: translation regulation, cancer resistance (Remarks to the\nAuthor):\nThis paper investigates the molecular mechanisms underlying SCLC plasticity and resistance\nto chemotherapy. The authors uncover a non-canonical, “moonlighting\" role for eIF6 in\nregulating SCLC plasticity. They demonstrate that eIF6 dissociates from ribosomes in non-\nNE states, associates with the CD104/FAK complex, and activates MAPK signaling.\nKnockdown of eIF6 inhibits non-NE transdifferentiation and enhances chemotherapy\nsensitivity in SCLC models. Importantly, they propose that the eIF6-CD104-FAK axis serves\nas both a prognostic marker and a therapeutic target. Clinical data from limited-stage SCLC\npatients suggest that CD104-eIF6 proximity ligation assay (PLA) scores correlate strongly\nwith patient outcomes, underscoring the clinical relevance.\nThis is a very good and original manuscript that provides new mechanistic insights into small\ncell lung carcinoma (SCLC) plasticity. By identifying the translation initiation factor eIF6 as a\nkey regulator of non-neuroendocrine (non-NE) transdifferentiation and chemoresistance, the\nauthors open new avenues for therapeutic targeting. The work is comprehensive, combining\nin vitro, in vivo, and patient sample analyses, and addresses an urgent clinical problem in\nSCLC. While the manuscript is strong and well-constructed, certain aspects require\nclarification and additional validation to fully support the major claims.\nResponse: We sincerely thank the reviewer for the very positive feedback and comprehensive\nsummary of our work, which greatly encourages us to further explore SCLC plasticity in future\nstudies. For clarity and validation, we have revised the manuscript as suggested by the\nreviewer as follows:\nSpecific comments\n1. Mechanistic Depth and Post-Translational Regulation.\nWhile the discovery of eIF6's moonlighting role is novel, the mechanistic switch governing its\nribosome-to-integrin transition remains speculative. Adding some kinetic aspects would help\nunderstand how this switch is triggered. Can the authors provide evidence of post-\ntranslational modifications (PTMs), such as phosphorylation, driving this switch?\nAlternatively, authors may include more discussion on this point, and discuss if they might be\nways to prevent this relocation.\nResponse: We thank the reviewer for this insightful suggestion. We fully agree that PTMs of\neIF6 may underlie its functional switch. eIF6 carries multiple phosphorylation sites regulating\nits localization and activity. Phosphorylation at Ser174 and Ser175 by casein kinase 1 (CK1)\ndrives its nucleo-cytoplasmic shuttling (PMID:21084295), while C-terminal sites (Thr231,\nSer235, Ser239, Ser243) are phosphorylated under stress via GSK3 (PMID:32703900).\nNotably, Ser235 phosphorylation by the RACK1–PKCβII complex promotes eIF6 release from\nthe 60S ribosome, sustaining minimal translation during starvation (PMID:14654845).\nConsistently, eIF6+/− fibroblasts fail to upregulate translation upon insulin or phorbol ester\nstimulation and accumulate inactive 80S ribosomes (PMID:26383020), suggesting that eIF6\ndissociation from ribosomes is a conserved stress response. Collectively, these studies\nindicate that C-terminal phosphorylation, particularly at Ser235, enables eIF6 release while\nmaintaining basal translation to support slow growth. Although not yet examined in SCLC, this\nmechanism parallels our finding that in non-NE SCLC cells, reduced translation coincides with\neIF6 ribosomal dissociation and integrin interaction during transdifferentiation.\nIn fact, this is an important and intriguing direction that we plan to pursue in future studies. We\nhave therefore included these data (Supplementary Fig. 11) showing increased\nphosphorylation of eIF6 at Ser235 in three independent SCLC cell models, while keeping the\nmain text focused on our core findings to maintain a coherent scope. Unfortunately, suitable\nantibodies for other phosphorylation sites are not available.\nIn line with the reviewer’s suggestion, we have expanded the discussion to integrate these new\ndata and relevant references, outlining the importance of PTMs in eIF6 functional switch and\nfuture research directions as follows:\n“…Like several ribosome-associated proteins that switch functions through post-\ntranslational modifications (PTMs)58,59, exemplified by phosphorylated RPS3 acquiring nuclear\nroles60,61, eIF6 also carries multiple phosphorylation sites including Ser174/17562 and Ser23563.\nAdditional phosphorylation sites, such as Tyr113, Ser239, Ser243, and Ser245, have been\nidentified, but their regulatory kinases and functional consequences remain unknown57. We\nshowed that phosphorylation of eIF6 at Ser235 is upregulated across multiple SCLC non-NE\ntransition models. Given that Ser235 phosphorylation promotes eIF6 release from 60S\nsubunits63, this modification may represent a regulatory axis enabling eIF6 to shift from\ntranslational control toward non-canonical interactions in the non-NE state. Using Alphafold-\nbased structural modeling, we explored the potential mechanism of interaction between eIF6\nand integrin complex. Notably, eIF6 residuals predicted to mediate CD104 binding (Ser102,\nSer150, Ser19) lie in close proximity to ribosome interacting residuals (Asn106, Tyr151, Glu12,\nSer190)54, and both interfaces appear to converge near Arg57. This spatial convergence raises\nthe possibility that phosphorylation-driven conformational changes may bias eIF6 toward one\ninteraction surface over another, thereby modulating its partitioning between ribosome-bound\nand integrin-associated states. Although our attempts to express mutant eIF6 containing all four\nkey residues were unsuccessful likely due to proper structural folding, the in silico observations\nhighlight a mechanistic framework worth testing. Future studies dissecting how individual\nresidual and the associated PTMs influence eIF6’s structural dynamics, ribosome affinity, and\nintegrin complex engagement will be essential for defining how eIF6 moonlighting behavior\ncontributes to SCLC plasticity.”\n2. In Vivo Validation of FAK Inhibitor Strategy\nThe combination of chemotherapy with FAK inhibitors is compelling but currently limited to in\nvitro data. In vivo xenograft or GEMM experiments combining FAK inhibitors with\nchemotherapy are essential to support the therapeutic claims and proposed clinical\ntranslation.\nResponse: We thank the reviewer for this valuable suggestion. We have now performed an\nin vivo xenograft study using the H69 SCLC model previously applied in our eIF6 knockdown\nexperiments. The combination of the FAK inhibitor GSK2256098 with etoposide and\ncarboplatin (EP) significantly delayed tumor growth compared with either treatment alone.\nNotably, tumors under FAK monotherapy resumed growth after 33 days, suggesting that\nFAK inhibition may preferentially suppress non-NE cells, allowing NE populations to expand.\nWhile our study focuses on the intrinsic mechanisms by which eIF6 regulates non-NE\ntransitions, we acknowledge that these in vivo assays were performed in immunodeficient\nmice. Future work in immune-competent models will be essential to assess the influence of\nthe tumor–immune microenvironment. These new data have been incorporated into the\nrevised manuscript (Figure 6g).\n3. Sample Size and Clinical Interpretation\nThe survival analyses based on CD104-eIF6 PLA scores are interesting, but the clinical\ncohort is relatively small (n=38). The authors should add a note of caution about over-\ninterpreting these findings and emphasize the need for validation in larger, independent\ncohorts.\nResponse: We thank the reviewer for this valuable suggestion. We acknowledge that our\nclinical cohort includes a limited number of SCLC samples, which reflects a major challenge\nin translational SCLC research. Unlike NSCLC, surgical resection is rarely performed for\nSCLC, and available biopsies are often small, necrotic, or damaged, making high-quality\ntissue acquisition extremely difficult. Despite these challenges, we made substantial efforts to\ncollect surgical samples with patient consent, many of which were initially identified by\nimaging and later confirmed as SCLC upon histopathological analysis. Consequently,\nalthough a larger number of samples were collected overall, only a few were confirmed as\nSCLC and suitable for analysis. We deeply appreciate the patients’ contribution, which has\nbeen instrumental in linking our mechanistic findings to the clinical setting. Following his/her\nsuggestions, we have now emphasized this limitation and the need for larger validation\ncohorts in the revised manuscript :\n“…Our study has several limitations that warrant future investigations. First, the clinical\nanalyses, including eIF6 expression and PLA-based biomarkers, were performed in a relatively\nsmall patient cohort (n = 38) restricted to limited-stage SCLC (LS-SCLC), underscoring the\nneed for validation in larger, independent, prospective collected cohorts. Second, the\nmechanisms driving eIF6 upregulation in the non-NE state remain unresolved. Non-NE cells\nexhibit low ASCL1 expression, which can activate Wnt signaling in high-grade neuroendocrine\nlung cancer76 while suppress a SOX9 associated nonendodermal stem-like fate in SCLC77.\nASCL1 strongly binds a canonical E-box motif 5’-CAGCTG-3’ that we did not observe in eIF6-\npromoter regions78. Moreover, chemotherapy rapidly increases and maintains eIF6 expression\nwithout reducing ASCL1, suggesting that eIF6 is unlikely to be a direct ASCL1 suppressive\ntarget. Instead, its inducibility by starvation and IFNg points toward regulation by extrinsic cues\nor the integrated stress response (ISR). Whether ISR factors such as ATF4 contribute to eIF6\nupregulation remains an important question. A further limitation is that our study largely\nfocuses on cell-intrinsic mechanisms. Because inflammation and the non-NE state are closely\nlinked, the observed enrichment of CD8+ T cells in eIF6-high tumour regions raises the\npossibility of IFNG-IFNR1 signaling shaping eIF6 expression in vivo. Knockdown of eIF6\nreduces PD-L1 and CD47 expression in non-NE cells, primarily through translational\nmechanisms, suggesting that eIF6 may modulate immune evasion inflammatory SCLC context.\nThese observations highlight the need for future work in immunocompetent models to dissect\nthe reciprocal interactions between tumour-expressed eIF6 and the microenvironment during\nSCLC plasticity.”\n4. Selective vs. Global Translation Effects\nThe study focuses on global translation suppression in non-NE states, but selective\ntranslation of specific mRNAs could also drive phenotypic outcomes. Have the authors\nprofiled or analyzed specific mRNA subsets whose translation is altered by eIF6? can they\nexclude this mechanism totally?\nResponse: This is an insightful suggestion. We therefore performed additional Ribo-seq\nfollowing eIF6 knockdown to assess its impact on ribosome scanning and eIF6-dependent\ntranslation. Although global polysome profiles remained unchanged, Ribo-seq revealed\nprominent ribosome stalling within the first 0–50 nt downstream of the start codon, consistent\nwith the known anti-association role of eIF6. This was accompanied by reduced translation of\nmRNAs encoding RNA processing factors involved in splicing and ribosome biogenesis, with\nonly a limited set of selectively translated proteins, including WDR75, MARK3, MSN,\nPABPC1, and PPIG. Importantly, no established regulators of SCLC non-NE\ntransdifferentiation showed altered translation under these conditions. We therefore focused\nsubsequent mechanistic studies on the moonlighting functions of eIF6. These data are\npresented in Supplementary Fig. 8.\n5. Knockdown Specificity and Rescue Experiments\nSince the conclusions rely heavily on shRNA-mediated eIF6 knockdown, it would greatly\nstrengthen the study to include rescue experiments using shRNA-resistant eIF6 constructs to\nrule out off-target effects. Having a mutant of eIF6 that would not bind FAK would help\nunderstand the importance of this relocation in the phenotype (vs translation see point4).\nResponse: We thank the reviewer for this important and constructive suggestion. We have\nnow performed rescue experiments by expressing shRNA-resistant eIF6 in eIF6-knockdown\ncells and show that re-expression of eIF6 restores HGF-induced non-NE transdifferentiation\n(Fig. 4e-f).\nIn addition, baseline overexpression of eIF6 in NE-state cells produced the opposite effect of\neIF6 knockdown and significantly enhanced non-NE transdifferentiation across three different\nSCLC models, as shown in Supplementary Fig. 9d-f.\nH69 H82 H209\nWe fully agree that generating an eIF6 mutant unable to bind the CD104–FAK complex\nwould be critical for mechanistically validating this interaction. Since the residues mediating\nthis binding are currently unknown, we applied AlphaFold-based structural modeling to\npredict the interaction interface. Our analysis suggests that the Calx-β domain of CD104,\nwhich interacts with the FAK linker region (Supplementary Fig. 11e), may engage the same\nsurface of eIF6 that binds RPL23 (Supplementary Fig. 11f–g). Four residues—Arg57,\nSer102, Ser150, and Asp209—were predicted to contribute to this interface, with Arg57\nshared by both RPL23- and CD104-binding surfaces. The remaining residues lie in close\nproximity to known ribosome-interacting residues (Asn106, Tyr151, Glu12, and Ser190) and\nto Arg61, which is essential for maintaining eIF6 structural integrity (Supplementary Fig.\n11h). This spatial organization suggests potential competitive binding between CD104 and\nRPL23 for eIF6.\nWe attempted to generate alanine substitutions at these predicted sites; however, these\nmutations caused protein misfolding, likely due to disruption of local interactions near Arg61\nthat are essential for eIF6 stability, thereby precluding further functional characterization. As\nshown below, the eIF6-4mut construct failed to produce detectable eIF6 protein, despite\nsuccessful expression of the GFP reporter encoded by the same plasmid. Moreover,\ntreatment with proteasome and lysosome inhibitors did not restore eIF6-4mut protein levels,\nindicating that the mutant is not degraded but is instead likely unable to be properly\nexpressed or folded.\nWe therefore chose not to include these mutation results in the revised manuscript, and\ninstead addressed this point as a future research direction in the Discussion, as follows:\n“…We showed that phosphorylation of eIF6 at Ser235 is upregulated across multiple\nSCLC non-NE transition models. Given that Ser235 phosphorylation promotes eIF6 release\nfrom 60S subunits63, this modification may represent a regulatory axis enabling eIF6 to shift\nfrom translational control toward non-canonical interactions in the non-NE state. Using\nAlphafold-based structural modeling, we explored the potential mechanism of interaction\nbetween eIF6 and integrin complex. Notably, eIF6 residuals predicted to mediate CD104\nbinding (Ser102, Ser150, Ser19) lie in close proximity to ribosome interacting residuals\n(Asn106, Tyr151, Glu12, Ser190)54, and both interfaces appear to converge near Arg57. This\nspatial convergence raises the possibility that phosphorylation-driven conformational changes\nmay bias eIF6 toward one interaction surface over another, thereby modulating its partitioning\nbetween ribosome-bound and integrin-associated states. Although our attempts to express\nmutant eIF6 containing all four key residues were unsuccessful likely due to proper structural\nfolding, the in silico observations highlight a mechanistic framework worth testing. Future\nstudies dissecting how individual residual and the associated PTMs influence eIF6’s structural\ndynamics, ribosome affinity, and integrin complex engagement will be essential for defining\nhow eIF6 moonlighting behavior contributes to SCLC plasticity.”\nMinor comments:\n1. Fig.1b and 1c, what is the difference? What is the gene ratio item in 1b?\nResponse: We apologize for the confusion. In Fig. 1b, we show pathway enrichment\nanalysis of the transcriptomic dataset in Fig. 1a, comparing treatment-naïve and relapsed\ntumors. The GeneRatio on the x-axis indicates the proportion of differentially expressed\ngenes within each pathway. Fig. 1c presents a similar analysis using single-sample GSEA\n(ssGSEA) on an independent cohort (IMpower133). We have revised the figure legend for\nclarity and expanded the methodological description accordingly. We thank the reviewer for\nthis helpful suggestion.\n2. Fig.3c, add the proteins detected in 3a. How do authors explain this rapid upregulation\nupon treatment with regards to the panel 3f (i.e. upregulation seen in the minimal phase =\nmuch more late). Show the blots in 3f. 3c & 3f: how specific is it to eIF6?\nResponse: We thank the reviewer for this helpful suggestion. We have added the\ncorresponding protein data from EP-treated experiments using three independent SCLC cell\nline pairs to ensure consistency across panels. The figure layout has been reorganized\naccordingly, and the original Fig. 3c is now presented as Fig. 3h in the revised version:\nMeanwhile, the western blots from the original Fig 3f (H69 model) are now shown in\nSupplemental Fig 7e, along with additional in vivo data generated from an independent H82\ncell model (Supplemental Fig 7f-h), as shown below for your convenience:\nWe agree with the reviewer’s observation. As shown now in Supplemental Fig. 7d, eIF6 is\nrapidly induced within 24 h of EP-based chemotherapy across four SCLC models, reflecting\nan immediate cellular stress response that persists beyond 24 h. These in vitro data indicate\nthat eIF6 upregulation alone is not sufficient to determine cell fate, as cells are not yet\nundergoing death at this early timescale. In vivo, the timing of tumor collection during\nregression is less precise, and bulk measurements capture a heterogeneous mix of cells:\nsensitive cells die, leaving surviving populations in which eIF6 upregulation becomes\ndetectable at the residual tumor stage. We have now discussed this temporal discrepancy\nand moderated our statement in the revised manuscript.\nFig3d. Redundant with 3a? is HGF also used? do authors know if the upregulation is seen at\nRNA levels?\nResponse: Fig. 3d shows flow cytometry analysis of eIF6 protein levels in established non-\nNE (H69M) and NE (H69) cells, without HGF treatment. While related to Fig. 3a, it offers an\northogonal single-cell perspective on eIF6 heterogeneity. Following the reviewer’s\nsuggestion, we have moved Fig. 3d to the Supplementary Figure 5a to improve the main\nfigure flow. We have also assessed eIF6 RNA levels across models and integrated these\ndata into Fig. 3b. Additionally, qPCR analysis of other translation factors examined by\nwestern blot (Supplementary Fig. 5) revealed that eIF4A1, eIF4E, and eIF4G exhibit variable\nexpression across models and are not consistently upregulated in non-NE cells.\nSuppl Fig5a-b: legends are inverted.\nResponse: We apologize for this mistake. In the revised manuscript, we have corrected the\ninverted legend. We also thank the reviewer for his/her careful evaluation of the manuscript.\nFig4c: “partially restoring ASCL1 expression” I do not see this. This statement is not correct.\nResponse: We agree with the reviewer that eIF6 knockdown does not fully revert non-NE\ncells to a typical neuroendocrine phenotype. Rather, its inhibition mitigates the transition\ntoward the non-NE state (Fig. 4d–e and Supplementary Fig. 9a–b). In line with this\nsuggestion, we extended our analyses to additional SCLC models using RNA-seq and\nimmunoblotting (Fig. 4c and Supplementary Fig. 8b–e). Across all three models, eIF6\nknockdown consistently downregulated EMT-associated gene signatures while upregulating\nneuroendocrine-associated programs. At the protein level, we similarly observed reduced\nEMT marker expression and a partial but reproducible increase in ASCL1, particularly in the\nH209 and H82 models (Fig. 4c). Based on these results, we have moderated our statements\nin the revised manuscript as follows:\n“…Partial restoration of ASCL1 expression was also observed in H209A and H82R cells, with\na weaker effect in H69M cells (Fig 4c). Although eIF6 knockdown does not fully reverse\nnon-NE transdifferentiation, these results suggest that its inhibition may restrain the transition\ntoward a non-NE state across distinct transdifferentiation contexts.”\n4f: most of the effect is already seen in untreated conditions. Is not convincing that eIF6\nwould synergize with the chemotherapy.\nResponse: We thank the reviewer for this comment. RPM-derived mouse SCLC cells are\nknown to exhibit phenotypic instability (PMID: 32473656), spontaneously transitioning from\nASCL1-positive, NE-high aggregates to amorphous, chain-like clusters characteristic of non-\nNE cells within 7–14 days in culture. Consistent with this property, this Myc-high line provides\na robust model to examine eIF6 function during non-NE transition in the absence of external\nstimuli. As shown in the original Fig. 4f (now relocated to Supplementary Fig. 9), crystal violet\nstaining of attached cells, representing the non-NE phenotype, demonstrates that eIF6\nknockdown significantly reduces colony formation, consistent with its ability to mitigate non-\nNE transition (Fig. 4d–e and Supplementary Fig. 9). Notably, combining eIF6 knockdown\nwith first-line chemotherapy further decreased the number of attached colonies compared\nwith eIF6 knockdown alone, indicating that eIF6 inhibition also enhances the\nchemosensitivity of non-NE cells. We have revised the manuscript to clarify that these results\nreflect the combined effects of altered phenotypic transition and increased chemotherapy\nsensitivity.\n4g: a precise measurement of the tumor size should be provided to really appreciate the\neffect of eIF6 depletion. Data are too much normalized.\nResponse: We have added tumor size measurements in Supplementary Fig. 9, while\nretaining the waterfall plot in the main figure to highlight combination effects, as individual\nresponses showed substantial inter-mouse heterogeneity, mirroring patient treatment\noutcomes.\n4. Fig5b: “analysis of eIF6 distribution within polysome profiles showed diminished eIF6\nlevels in monosome-associated fractions of non-NE cells”. Is not clear to me how this\nconclusion is made – I do not see this data. The profiles are different between the conditions.\nResponse: We thank the reviewer for this insightful suggestion. As correctly noted, the\npolysome profiles differ between conditions, showing a global downregulation of translation\nin non-NE compared with NE cells. Consistently, the overall binding of eIF6 in polysome\nfractions is reduced in non-NE cells, in line with the results shown in Fig. 5a. While this\nfraction-by-fraction analysis provides higher resolution, it is somewhat redundant with Fig.\n5a. We have therefore moved this figure to Supplemental Fig. 11a to streamline the main\ndata flow and have moderated the corresponding statements in the revised manuscript.\n5. The panels Fig5d and Suppl. Fig8 are difficult to read. Can you not simplify them\nhighlighting the physical interactions directly?\nResponse: We apologize for any confusion caused by the complexity of this figure. The eIF6\ninteractome was primarily derived from the BioGRID database, which integrates yeast two-\nhybrid data and bioinformatic predictions, and therefore represents a broad overview of\npotential eIF6-interacting proteins. Following the reviewer’s suggestion, we now specifically\nhighlight the physical interaction between eIF6 and ITGB4 in the revised Fig. 5c. We have\nalso removed the original Supplementary Fig. 8, as its excessive size and low readability did\nnot provide additional relevant information.\n6. Provide additional quantitative data on MAPK/ERK pathway activation (e.g., densitometric\nanalyses, kinase assays). Generally speaking, one would like to have a clear idea of the size\nof the detected proteins…\nResponse: We apologize for this oversight. Protein levels have now been quantified by\ndensitometric analysis and normalized to the corresponding internal controls. In accordance\nwith the reviewer’s suggestion, the molecular weights of the detected proteins have also\nbeen indicated throughout the manuscript.\n7. Clarify randomization and blinding procedures in the in vivo chemotherapy experiments in\nthe Methods section.\nResponse: We thank the reviewer for this valuable suggestion. We have now revised the\ncorresponding Methods section to clearly describe the randomization and blinding\nprocedures used in all in vivo chemotherapy experiments.\n8. Can you discuss why direct ERK inhibition fails to mimic the chemosensitization effect\nseen with FAK inhibition?\nResponse: We thank the reviewer for the thoughtful evaluation of the downstream\nmechanisms. Indeed, our data show that the MEK inhibitor cobimetinib does not fully\nsuppress ERK phosphorylation in non-NE SCLC cells, nor does ERK inhibition phenocopy\nthe effects of FAK inhibition. These findings suggest that additional factors may contribute to\nERK activation independently of the FAK pathway. For instance, a recent high-throughput\nscreening study reported that PKC, which phosphorylates eIF6 at Ser235, can also promote\nERK activation (PMID: 37858338). In parallel, FAK-mediated signaling may depend on\nPKN2, a candidate identified in our DepMap analysis as specifically associated with eIF6-\nhigh tumors (originally Fig. 5c). Notably, Darovasertib, an FDA-approved drug for uveal\nmelanoma, targets both the FAK–PKN2 axis and PKC activation, and could therefore\nrepresent a promising therapeutic candidate to test in our context. We have now expanded\nand refined the discussion of this point in the revised manuscript while maintaining our\noriginal rationale.\n“…Downstream of integrin-FAK complex, MAPK signaling is a central determinant of\nproliferation and differentiation71, and combined FAK-MEK inhibition is already being tested\nclinically (NCT02428270). Although our findings suggest an eIF6-CD104/FAK axis\nregulating MAPK activation in non-NE SCLC, yet direct inhibition of downstream MEK or\nERK kinase cannot completely reverse the non-NE transition or sensitize SCLC to\nchemotherapy, thus other contributors likely intersect. For instance, KRasV12 overexpression\ncan drive NE-to-non-NE transition72, and hyperactive RAS-MEK-ERK signaling suppresses\nNE identity and NE-SCLC growth73. Given that RAS can either activate or suppress integrin\nsignaling in a context-dependent manner74, dissecting how eIF6 interfaces with FAK and RAS\ncircuitry will be important to understanding SCLC plasticity. Additional pathways may also\nexplain why direct ERK inhibition does not phenocopy FAK blockade. A recent high-\nthroughput screen in uveal melanoma showed that PKC, which phosphorylates eIF6 at\nSer235, can drive ERK activation independently of FAK and RAS75. Moreover, FAK\nsignaling in eIF6-high tumours may rely on PKN2, a dependency highlighted in our DepMap\nanalysis. Intriguingly, Darovasertib, an FDA-approved therapy for uveal melanoma, can\ntarget both FAK-PKN2 and PKC, making it a potential candidate for evaluation in the SCLC\nsetting.”\nReviewer #2, Expertise: drug resistance (Remarks to the Author):\nThis manuscript provides novel insights into eIF6's role in SCLC transdifferentiation and\nplasticity using diverse models, including genetically engineered mice and limited-stage\npatient samples. The discovery of eIF6-CD104/FAK-MAPK axis-driven translational rewiring\nand its prognostic relevance via PLA scoring is compelling. However, while the work\nrigorously examines cell-intrinsic mechanisms (e.g., ribosome dynamics, chemotherapy\nresistance), it overlooks the role of TME modulating plasticity.\nResponse: We sincerely thank the reviewer for acknowledging the importance of our work and\nfor the comprehensive summary of our study. As correctly noted, this work focuses on the\nintrinsic mechanisms governing SCLC transdifferentiation and plasticity, through which we\nreveal a previously unrecognized function of the translation initiation factor eIF6.\nWe fully agree that the tumor microenvironment (TME) is an important and fascinating\nregulator of plasticity that may interact with intrinsic factors such as eIF6. However, we\nrespectfully contend that dissecting the TME-associated mechanisms lies beyond the scope\nof the current study, which was designed to delineate the intrinsic regulatory role of eIF6.\nIndeed, we are currently pursuing a dedicated project aimed at elucidating how TME\ncomponents influence SCLC plasticity and interact with eIF6. Including these data here, in our\nview, would risk diluting the central mechanistic focus and overcomplicating the narrative of\nthis work.\nIn full consideration of the reviewer’s suggestion, we have nonetheless incorporated\ndata indicating that tumour microenvironment (TME)–derived factors may modulate eIF6\nactivity, now presented in Supplementary Fig. 10, and have added a dedicated, independent\nsection “Reciprocal interplay between eIF6 and SCLC microenvironment” to the Results.\nThese additions provide a more balanced perspective while preserving the conceptual clarity\nof the manuscript and highlighting directions for future investigation. We have also explicitly\ndiscussed the limitations of the current study in the context of the tumour microenvironment as\nfollows:\n“.. A further limitation is that our study largely focuses on cell-intrinsic mechanisms.\nBecause inflammation and the non-NE state are closely linked, the observed enrichment of\nCD8+ T cells in eIF6-high tumour regions raises the possibility of IFNG-IFNR1 signaling\nshaping eIF6 expression in vivo. Knockdown of eIF6 reduces PD-L1 and CD47 expression in\nnon-NE cells, primarily through translational mechanisms, suggesting that eIF6 may modulate\nimmune evasion inflammatory SCLC context. These observations highlight the need for future\nwork in immunocompetent models to dissect the reciprocal interactions between tumour-\nexpressed eIF6 and the microenvironment during SCLC plasticity.”\n- Chemotherapy sensitivity improvements upon eIF6 knockdown may involve immune-\nmediated mechanisms (e.g., immunogenic cell death, antigen presentation), which remain\nunexplored.\nResponse: We fully agree with the reviewer’s insightful comment. However, the\nimmunogenic cell death–related data are part of another study from our group currently\nunder revision elsewhere. To provide a balanced perspective, we have included results\nshowing the effects of IFNγ and eIF6 knockdown on PD-L1 expression in the revised\nmanuscript, while keeping the intrinsic mechanism of eIF6 as the main focus.\n- Immune compartment gap: transdifferentiation influence immune evasión?, checkpoint\nexpression, or immune cell infiltration (e.g., T cells, macrophages) in SCLC?\n- The GEMM and xenograft models lack analysis of immune-tumor crosstalk during plasticity\ntransitions, despite known links between FAK/MAPK signaling.\nResponse: As noted, we fully agree with the reviewer that these aspects are highly important\nfor a comprehensive understanding of plasticity, immune evasion, and cell–cell interactions\nin SCLC. However, we respectfully feel that a detailed investigation of these mechanisms\nrepresents an independent study and including all such data here would risk overwhelming\nthe current work.\nTo address the reviewer’s suggestion while maintaining focus, we have now added a\ndedicated, independent section ‘Reciprocal interplay between eIF6 and SCLC\nmicroenvironment’ in the revised manuscript. This section demonstrates that eIF6 is closely\nlinked to immune modulation within the SCLC tumour microenvironment. eIF6 expression is\nelevated in non-neuroendocrine (non-NE) states and correlates with a pro-inflammatory\nsecretome, including increased GM-CSF, CXCL10 and CCL2. Spatial analyses of patient\ntumours revealed that eIF6-high regions exhibit desmoplastic features and are enriched with\nCD3⁺ lymphocytes and CD68⁺ macrophages, contrasting with the immune-cold phenotype of\nASCL1-high areas. Transcriptomic and deconvolution analyses further confirmed higher\nCD8⁺ T-cell infiltration and IFNG signalling in eIF6-high tumours. Single-cell reanalysis and\nCellChat inference suggested bidirectional communication between eIF6-high tumour cells\nand CD8⁺ T cells, including IFNγ-mediated feedback. Functionally, eIF6 may potentially\nenhance immune evasion by promoting translational efficiency of PD-L1, STAT1 and CD47,\nwhile its depletion impaired tumour growth only in immunocompetent mice. Collectively,\nthese findings highlight a reciprocal crosstalk between eIF6-driven translational control and\nimmune adaptation in SCLC. The related data were added to Supplemental Fig 10 as\nfollows:\nMain comments\n(Line 234). Fig 1a revealed that EIF6 expression was inversely correlated with the\nneuroendocrine transcription factor ASCL1\nIt is not clear why this inverse correlation occurs. Fig 1A only shows a schematic of the\ntranscriptomic analysis. It would be helpful to show the analysis that justifies the selection of\nEIF6 as a factor to study. Some patients with relapse show upregulation of both EIF6 and\nASCL1, indicating a possible positive correlation of gene expression; you could further\ndiscuss this.\nResponse: We thank the reviewer for the insightful suggestion. We understand the comment\nrefers to Fig. 3b (Line 234). As noted, Fig. 1a–c shows enrichment analyses of translational\nalterations in relapsed samples and non-NE cell states. In original Fig. 3b, which we have\nnow moved to Supplemental Fig. 7a to keep the main figures focused, we show that eIF6\nexpression is inversely correlated with ASCL1. We apologize for the initial insufficient\nanalysis and have now added additional analysis to strengthen this point. As the reviewer\nmay appreciate, we now provide multiple lines of evidence supporting the relevance of eIF6\nin non-NE transition:\n(1) eIF6 is upregulated, whereas ASCL1 is downregulated, in relapsed compared with\ntreatment-naïve tumors (Supplementary Fig. 7c).\nSupplementaryFig7\nSupplementaryFig7\n(2) eIF6 and ASCL1 expression display an inverse correlation across patient samples\n(Supplementary Fig. 7b).\n(3) eIF6 is consistently elevated in the non-NE state at both mRNA and protein levels across\nthree independent SCLC models (Fig. 3a).\nFig3\nFiSgu3pplementaryFig7\n(4) Chemotherapy specifically induces eIF6 expression (Fig. 3h and Supplementary Fig. 7d),\nwhereas other translation initiation factors, including eIF4E, eIF4G1 and eIF4A, are either\ndownregulated or unchanged. Finally, we clarify that eIF6 was prioritized for mechanistic\ninvestigation because polysome profiling revealed a relative enrichment of 60S subunits in\nnon-NE states.\nSupplementaryFig7\nIn the RPM mouse model, it would be beneficial to further study tissue obtained at different\nstages—carcinoma, locoregional, and advanced carcinoma—to check additional markers\nrelated to the aggressiveness of those tumors by immunohistochemistry. It would also be\nuseful to observe the crosstalk with the immune system to determine if there is heterogeneity\namong the different stages.\nResponse: We thank the reviewer for this thoughtful suggestion. We agree that a\ncomprehensive assessment of tumor aggressiveness markers and immune crosstalk across\ncarcinoma, locoregional, and advanced stages in the RPM model would be valuable for\nfuture work. However, the primary focus of the present study is distinct: our goal is to define\nthe intrinsic role of eIF6 in SCLC neuroendocrine–non-NE transdifferentiation across\ncomplementary systems, including in vitro models, the RPM mouse model, and human\nsamples. In the RPM model, our analyses were designed specifically to evaluate eIF6\nexpression dynamics and their relationship to NE/non-NE states, rather than to characterize\ntumor aggressiveness. As shown in the manuscript, H&E staining already documents the\nexpected spectrum of in situ, locoregional, and metastatic lesions in the lungs. Importantly,\nIHC on serial sections demonstrates an inverse relationship between ASCL1 and eIF6\nexpression in these lesions, consistent with our in vitro and patient-sample observations.\nRegarding immune interactions, we fully agree that this is an important aspect of SCLC\nbiology. However, immune regulation and tumor–immune crosstalk fall outside the central\nmechanistic question addressed here, which focuses on the intrinsic function of eIF6 in\nlineage plasticity. To acknowledge this point, we have added a dedicated section section\n‘Reciprocal interplay between eIF6 and SCLC microenvironment’ in the revised\nmanuscript as summarized in the previous point, and addressed the limitations of the current\nwork in the Discussion.\nWe hope the reviewer agrees that these additions appropriately address the conceptual\naspects of the comment while maintaining a focused and coherent narrative aligned with the\nstudy’s main objective.\nThe conclusion of Figure 5 is overstated: eIF6, which dissociates from ribosomes and\nfacilitates integrin-FAK complex formation, leads to global translational downregulation and\npotentially modulates integrin-mediated downstream signaling in the non-NE cell state. You\nneed to give additional data or tune down the message.\nResponse: We thank the reviewer for this comment and agree that our conclusions should\nremain tightly aligned with the experimental evidence. We have therefore carefully evaluated\nthe strength of our data and have tuned the wording in the revised manuscript accordingly.\nImportantly, our conclusions in the revised Fig. 5 and Supplementary Fig 11 are supported\nby multiple, independent lines of evidence:\n1) Ribosome dissociation of eIF6 in non-NE cells: Sucrose-gradient assays and polysome\nprofiling (Fig. 5a and Supplementary Fig. 11a) demonstrate a clear reduction in 60S-bound\nand monosome-associated eIF6 in non-NE cells, indicating dissociation of eIF6 from\nribosomes during transdifferentiation.\n2) Genetic dependency analyses identifying integrin–FAK signaling: Large-scale CRISPR-\ndependency correlations across ~1200 cell lines show that eIF6-high tumors are highly\ndependent on components of the integrin–FAK complex, alongside known 60S-assembly\npartners (Fig. 5b). This unbiased analysis independently points to integrin–FAK signaling as\nlinked to elevated eIF6.\n3) Existence of a physical eIF6–integrin axis: BioGrid interactome data and prior yeast two-\nhybrid results identify a reported interaction between eIF6 and integrin β4 (CD104) (Fig. 5c),\nwhich we confirm is upregulated in non-NE states (Fig. 5d).\n4) Biochemical and in situ validation of eIF6 association with CD104–FAK complex:Co-IP\nexperiments show increased interaction among CD104, FAK, and ribosome-free eIF6 in non-\nNE cells, accompanied by increased ERK1/2 association (Fig. 5e). PLA analysis further\nvalidates enhanced eIF6–FAK and eIF6–CD104 proximity in non-NE cells and shows that\nthese interactions decrease upon eIF6 knockdown (Fig. 5f–i; Supplementary Fig. 11b).\nEndogenous FAK IP also demonstrates that eIF6 depletion reduces CD104–FAK complex\nabundance and downstream ERK1/2 recruitment (Fig. 5j).\n5) Supporting mechanistic clues: Increased Ser235 phosphorylation of eIF6 in the non-NE\nstate and AlphaFold-based modeling suggest a structural basis for ribosome dissociation and\na potential competition between RPL23 and CD104 for eIF6 binding (Supplementary Fig.\n11c–h). Although we did not functionally evaluate mutations at predicted residues due to\nmisfolding, these analyses provide mechanistic coherence.\nTogether, these observations support a model in which ribosome-dissociated eIF6 gains\naccess to integrin–FAK complexes in the non-NE state, with functional consequences for\ndownstream signaling. Nonetheless, we agree that our original wording may have implied a\nbroader or more causal role than directly demonstrated. In the revised manuscript, we have\ntherefore softened the language as follows:\n“..our in silico analyses and validation experiments suggest that eIF6 may exert a\nmoonlighting function by dissociating from ribosomes and associating with integrin-FAK\ncomplex, possibly modulating integrin-mediated downstream signaling in the non-NE cell\nstate.”\nThe question HOW eIF6 is connected to intergrin-FAK is not very clear. The term\n\"facilitates\" suggests a direct mechanism, but this is not supported by the data presented here.\nResponse: We thank the reviewer for this insightful comment. We agree that defining the\nprecise mechanism linking eIF6 to the CD104–FAK complex is an important direction for\nfuture work. In the revision, we have expanded the Discussion and added in silico structural\nanalyses using AlphaFold to outline a plausible interaction model. Our analyses highlight\nknown structural features of eIF6 and its phosphorylation-dependent dissociation from the\n60S subunit, and we observe increased Ser235 phosphorylation in non-NE states across\nthree SCLC models, consistent with this mechanism. AlphaFold did not predict a direct eIF6–\nFAK interaction, but suggested that CD104 may serve as a structural bridge, with a predicted\ninterface on eIF6 that overlaps partially with the RPL23-contacting surface. We attempted to\ngenerate alanine mutants at these residues, but the mutants were possibly miss folded and\ncould not be reliably expressed; therefore, these preliminary data were not included.\nGiven these limitations, we have moderated our wording to avoid overinterpreting causality\nand now present the eIF6–CD104–FAK link as a testable hypothesis supported by\nbiochemical data and structural plausibility. We hope the expanded discussion clarifies our\ncautious interpretation and outlines a clear path for future mechanistic work. Therefore, we\ndiscussed the limitation of the molecular mechanistic study on the interactions between eIF6\nand CD104-FAK complex as follows:\n“..We showed that phosphorylation of eIF6 at Ser235 is upregulated across multiple\nSCLC non-NE transition models. Given that Ser235 phosphorylation promotes eIF6 release\nfrom 60S subunits63, this modification may represent a regulatory axis enabling eIF6 to shift\nfrom translational control toward non-canonical interactions in the non-NE state. Using\nAlphafold-based structural modeling, we explored the potential mechanism of interaction\nbetween eIF6 and integrin complex. Notably, eIF6 residuals predicted to mediate CD104\nbinding (Ser102, Ser150, Ser19) lie in close proximity to ribosome interacting residuals\n(Asn106, Tyr151, Glu12, Ser190)54, and both interfaces appear to converge near Arg57. This\nspatial convergence raises the possibility that phosphorylation-driven conformational changes\nmay bias eIF6 toward one interaction surface over another, thereby modulating its partitioning\nbetween ribosome-bound and integrin-associated states. Although our attempts to express\nmutant eIF6 containing all four key residues were unsuccessful likely due to proper structural\nfolding, the in silico observations highlight a mechanistic framework worth testing. Future\nstudies dissecting how individual residual and the associated PTMs influence eIF6’s structural\ndynamics, ribosome affinity, and integrin complex engagement will be essential for defining\nhow eIF6 moonlighting behavior contributes to SCLC plasticity.”\nMinor Comments\nFigure 1.\nThe manuscript should clarify whether the compared cell lines (H209 vs. H209A, H69 vs.\nH69M, and HB2 vs. HB2R) display similar proliferation rates under the experimental\nconditions used for polysome profiling. Differences in proliferation rates can significantly\nimpact global translation and ribosome loading, potentially confounding the interpretation of\npolysome profiles. Please provide quantitative proliferation data (e.g., cell counts, doubling\ntimes, or growth curves) for each cell line during the profiling experiments.\nResponse: We thank the reviewer for this helpful suggestion. We have now evaluated cell\ngrowth in all three pairs of cell models used in this study, and the corresponding data have\nbeen incorporated into the revised Supplemental Fig. 1.\nFigure2.\nThe manuscript would be strengthened by including an immunoblot analysis for TORC1.\nSince TORC1 regulates Rps6 phosphorylation on Ser-233 via Ypk3, it would be valuable to\nshow how TORC1 activity and Rps6 phosphorylation are modulated under your experimental\nconditions.\nResponse: We thank the reviewer for this insightful suggestion. Indeed, Ypk3, an AGC kinase\nin Saccharomyces cerevisiae, shares strong homology with human S6 kinase (S6K) and can\nphosphorylate Rps6 downstream of TORC1 signaling in yeast. Following the reviewer’s advice,\nwe have revised Supplemental Fig. 2b to include western blots of phospho-S6K1, phospho-\nRPS6, and phospho-4EBP1, three well-established downstream readouts of mTORC1 activity,\nacross all three independent pairs of SCLC models comparing NE and non-NE cell states.\nWe did not observe significant changes in phospho-S6K1 or phospho-4EBP1 levels in\nany of the models. Consistently, AKT phosphorylation also remained largely unaltered. These\nfindings suggest that the constitutive phosphorylation of RPS6 may be mediated by kinases\nother than those in the canonical mTORC1 pathway. Notably, RSK has been reported to\nphosphorylate RPS6 independently of mTORC1 (PMID: 17360704; PMID: 34871442), and\nintriguingly, RSK can also phosphorylate CD104 at several sites, including Ser1364 in the\nconnecting segment (PMID: 20870721) and Thr1736 in the C-terminal tail (PMID: 26580203).\nWhether RSK contributes to alternative signaling in non-NE cells that integrates integrin–FAK\nsignaling with RPS6 phosphorylation to coordinate translation and intracellular signaling—and\nwhether this occurs independently of eIF6 modulation—remains an open question. We have\nexpanded the Discussion to include these perspectives.\nFigure 2E.\nThe transcripts associated with NE are translationally downregulated; however, the polyA-\nRNA is going in the opposite direction in ADSC1, TTF3, and SYP genes. Why do you have\nthis discrepancy?\nResponse: We thank the reviewer for this critical insight. Indeed, NE and non-NE–associated\ntranscripts are regulated at both the transcriptional and translational levels. In our original\nFig. 2e, ASCL1 was modestly downregulated at the transcriptional level and showed a slight,\nnon-significant increase at the translational level, while TTF3 and SYP were not significantly\naltered at either level.\nFollowing Reviewer 3’s suggestion to use the established NE and non-NE signatures from\nZhang et al. (PMID: 29535911) instead of selected individual genes, we revised this analysis\nin the new Fig. 2e and performed additional polysome RNA-seq in two more SCLC models\n(H82 and H209; Supplemental Fig. 3). Using these curated signatures, we now show\nconsistent enrichment patterns at both the total RNA-seq and polysome RNA-seq levels.\nFigure 3A.\nIt would be better to include a quantification of the Western blot, because only showing the\nimmunoblot is not very clear regarding the upregulation of some of the proteins; only the\nincrease in Vimentin is clear.\nResponse: We thank the reviewer for this insightful suggestion. In response, we have\nperformed densitometric quantification of the western blot signals to more clearly illustrate\nthe changes in protein levels observed in this study. Following the reviewer 1’s suggestion,\nwe have now also included the qPCR quantification of the corresponding translation initiation\nfactor genes.\nFigure 4h.\nIt would be helpful to check protein downregulation in situ in the tissue. EZH2 is positively\ncorrelated with eIF6. Is this also observed with other components of PRC2?\nResponse: We thank the reviewer for highlighting this important control and apologize for\nthe earlier omission. The eIF6 IHC experiment had been performed, and we have now\nincorporated the corresponding in situ expression data in the revised Fig. 4i.\nWe thank the reviewer for this comment and apologize for the earlier misunderstanding. Our\nin silico analysis using the DepMap database was based on the dependency scores\n(Chronos scores from genome-wide CRISPR-Cas9 knockout screens). In the DepMap\ndataset, a more negative Chronos score indicates stronger dependency (i.e., greater growth\ninhibition upon gene knockout), whereas a positive score suggests that knockout of the gene\nhas little or even a slightly growth-promoting effect. Therefore, our analysis does not imply\nthat eIF6 is positively correlated with EZH2 expression; rather, it indicates that cell lines with\nhigh eIF6 expression display positive EZH2 dependency scores, meaning that EZH2 loss\ndoes not impair—and may even promote—the growth of eIF6-high cells. We have clarified\nthis concept in the revised manuscript as ‘‘We next leveraged large-scale CRISPR screening\ndata from the Cancer DepMap dataset to search potential pathways that may be associated\nwith eIF6 expression across ~1200 solid cancer cell lines. We performed linear regression\nanalyses to assess the correlation coefficient between eIF6 expression and gene dependency\nscores for each perturbed gene, identifying genes with the most negative correlation\ncoefficients as those essential for the survival of eIF6-high tumour cells”, and explicitly\nexplained it in the Methods section.\nFor the reviewer’s reference, we further analyzed RNA-seq data from the George et al.\ndataset to examine correlations between eIF6 and PRC2 component expression. Consistent\nwith the DepMap results, EZH2 expression is anti-correlated with eIF6 expression. Similarly,\neIF6 expression is negatively correlated with SUZ12, a core structural component required for\nEZH2 catalytic function. By contrast, no significant correlations were observed between eIF6\nand other PRC2 components such as EED, RBBP4, RBBP7, or EZHIP. Together, these\nfindings suggest that EZH2 activity may be less essential—or even counter-selected—in eIF6-\nhigh contexts, consistent with a potential shift in epigenetic regulation accompanying non-NE\ntransition.\nFigure 6C .\nIf the mechanism is correct and there is an association between eIF6 and FAK axis, in which\nyou do not see a change downstream of AKT, it would be important to show AKT\nphosphorylation. The immunoblot is not enough to sustain the conclusion about AKT or the\nFAK pathway, as FAK phosphorylation is not easy to assess, but the downstream effect at\nAKT level should be quantifiable.\nResponse: We thank the reviewer for this helpful suggestion. We re-examined phospho-AKT\nlevels across all three independent SCLC model pairs and found no significant differences\nbetween NE and non-NE cell states. To further address the reviewer’s concern, we also\ntested potential PTMs of eIF6 and found increased Ser235 phosphorylation in non-NE cells,\ndespite unchanged phospho-AKT levels. Moreover, knockdown of eIF6 does not impact on\nAKT phosphorylation as shown now in Fig 6d. Together, these results suggest that the AKT\npathway, along with the mTORC1-dependent signaling cascade, is likely not a major\ncontributor in our experimental context.\nReviewer #3, Expertise: SCLC biology, mouse models(Remarks to the Author):\nIn this article, Peng, Wang M, Wang Z et al identify eIF6, a translation initiation factor, as a\nkey regulator of non-NE transdifferentiation in SCLC. eIF6 expression is upregulated in non-\nNE states across different models. It alters the translational landscape of SCLC by\ndissociating from ribosomes and interacting with the CD104/FAK complex. Inhibition of eIF6\nsuppresses non-NE transdifferentiation and improves chemotherapy sensitivity in vitro and in\nvivo. These findings suggest that the eIF6-CD104-FAK axis could serve as both a prognostic\nmarker and a therapeutic in SCLC. However, there are numerous major technical and\nexperimental design issues in this study (see below), which significantly affect the validity of\nthe findings. The main concern is the limited number and inconsistent selection of models\nused. Each preclinical model differs significantly: one is derived from a chemoresistant\nmodel, another was generated by introducing HGF, and a third is described as a\nspontaneous transition model. There is also a significant lack of consistency in model\nusage—while H69 (arguably the most artificial model) is included in all experiments, most\nexperiments have not been performed in H209 or H82 cells. I strongly recommend that all\nkey findings be validated in at least three distinct cell lines. If the main claim of the study is\nthat the low-NE state found in chemoresistant SCLC cells is driven by the upregulation of\neIF6, the authors should generate chemoresistant models in vitro, isolate the non-NE cells\nfrom the resistant populations, and compare these to the original high-NE cells. This would\nrepresent a true transitional model from a high- to low-NE state driven by chemoresistance.\nResponse: We thank the reviewer for the thoughtful summary and constructive comments. In\nresponse, we have now validated all key findings in two additional and biologically distinct\nSCLC models, H209 (MYCL⁺/ASCL1⁺) and H82 (NEUROD1-high), which consistently\nreproduced the effects of eIF6 modulation. H69 remains our primary mechanistic and in vivo\nmodel because it is one of the most widely used classical SCLC lines and robustly\nrecapitulates intrinsic plasticity among SCLC-A, -N and -P states. Our H69 xenografts\ncontain ASCL1⁺, NEUROD1⁺ and POU2F3⁺ subpopulations in vivo, underscoring its\nsuitability for studying lineage transitions.\nRegarding the reviewer’s concern about model heterogeneity, we respectfully view\nthe use of spontaneous, HGF-induced and chemotherapy-resistant systems as a strength.\nThis diversity enabled us to test whether eIF6 upregulation is a shared feature across distinct\npaths to the non-NE state. Remarkably, all models converged on a common phenotype—\nglobal translational downregulation accompanied by increased eIF6 expression—supporting\nthe robustness and generalizability of this mechanism. Moreover, consistent with Reviewer\n2’s suggestion, we show that environmental signals such as IFNγ, which reduce translational\nactivity, also induce eIF6, further highlighting its broad regulatory relevance. Moreover, we\nperformed an shRNA-resistant eIF6 rescue experiment, demonstrating that re-expression of\neIF6 restores the induction of a non-NE transition in the H69 model (Fig 4e-f). In addition,\nforced overexpression of eIF6 in NE cell states, including H69, H209, and H82, enhanced\nnon-NE–like properties such as increased adherent growth and led to elevated\nphosphorylation of FAK and ERK across all models (Supplemental Fig 9d-f).\nWe have revised the figures and expanded the Discussion to clarify the rationale for\nmodel selection and to emphasize that convergence across diverse systems strengthens the\ncentral conclusions of our study.\nMinor comments\n1. There is a typo in sentence 138: NERUOD1\nResponse: We thank the reviewer for this helpful comment. We have carefully rechecked\nthe entire manuscript and corrected any typos or formatting errors in the revised version.\n2. Fig. 1G lacks statistical analysis.\nResponse: We have now performed an extra sum-of-squares F-test to statistically compare\nthe dose-response curves between groups.\nMajor concerns\n1. Authors show in Fig. 1C the upregulation of ribosomal-related pathways in high-NE clinical\nsamples by using data from IMpower 133. However, it is not shown whether the upregulation\nof ribosomal pathways in post-chemo versus naïve samples (Fig. 1B) correlates with a\nreduced NE phenotype in their in-house sequencing database.\nResponse: We thank the reviewer for this helpful suggestion. We have now incorporated the\nNE and non-NE signature analysis derived from Zhang et al. (PMID: 29535911) and applied\nit to stratified relapsed versus treatment-naïve samples, as presented in Fig. 1c.\n2. In Fig. 1D, the H69 cell line is repeated in the western blot and H209M and H209A cell\nlines are missing. A recent report from the Sutherland lab (PMID: 38180245) reclassified\nH196 and DMS114 as SMARCA4 mutant poorly differentiated thoracic tumors, not SCLC cell\nlines. The authors should consider using mixed (floating and adherent) SCLC cell lines\nknown to be comprised by both high-NE (floating) and low-NE (adherent) cells and perform\nthe same western blots in the low and high-NE populations.\nResponse: We thank the reviewer for this valuable suggestion and acknowledge our earlier\noversight in reference verification. As rigorously established by the Sutherland laboratory,\nDMS114 has been reclassified as a SMARCA4-deficient undifferentiated thoracic tumour\nwith high YAP1 expression. This line carries a truncating TP53 mutation, retains wild-type\nRB1, and transcriptionally clusters with SMARCA4-mutant SCCOHT models. Conversely,\nH196 exhibits thoracic sarcomatoid carcinoma histology but maintains canonical SCLC\nmutations in TP53 and RB1 while expressing wild-type SMARCA4 (PMID: 38180245). In line\nwith these clarifications, we now present only the translational downregulation observed in\nthese two YAP1-high thoracic carcinoma models and have removed them from further\nmechanistic analyses.\nAs the reviewer notes, both H69 and H209 exhibit mixed floating and adherent\ngrowth. Given the limited availability of non-NE transdifferentiation models, we used the\nH209A sublines to represent adherent, low-NE derivatives that arise sequentially from the\nfloating, high-NE parental population and were further enriched for analysis. Importantly,\ngrowth morphology alone does not define the low-NE state; contemporary classification\nrelies on transcription factor signatures, now broadly adopted in the SCLC field. We have\nthoroughly revised Fig. 1e–g to more clearly delineate translational alterations in each paired\nmodel, as well as transcriptomic analysis in all models in the revised Supplemental Fig 2a.\n3. Western blotting of RPS6 should be performed in all models used not only in selected cell\nlines. Why did the authors not analyze the expression of p-RPS6/RPS6 in H82/H82R and\nH209/H209A/H209M cells? Authors should also analyze the levels of this marker in low and\nhigh NE subpopulations as suggested in the previous point.\nResponse: We thank the reviewer for this valuable suggestion. As the reviewer may appreciate,\nwe have incorporated p-RPS6 analyses across these additional models. Notably, while the\nmTORC1 pathway—including p-S6K and p-4EBP1—was not significantly altered between\nhigh- and low-NE states, and AKT phosphorylation also remained largely unchanged, we\nconsistently observed elevated p-RPS6 in high-NE cells. These findings imply that constitutive\nRPS6 phosphorylation may be driven by kinases outside the canonical mTORC1 axis. RSK,\nfor example, can phosphorylate RPS6 independently of mTORC1 (PMID: 17360704; PMID:\n34871442) and is also known to phosphorylate CD104 at several sites, including Ser1364 in\nthe connecting segment (PMID: 20870721) and Thr1736 in the C-terminal tail (PMID:\n26580203).\nWhether RSK contributes to alternative signaling in non-NE cells—potentially linking integrin–\nFAK signaling with RPS6 phosphorylation to coordinate translation and intracellular\ncommunication, and whether this occurs independently of eIF6 regulation—remains an\nintriguing open question. We have incorporated these results into Supplemental Fig. 2b and\nexpanded the Discussion accordingly.\n4. Why did authors perform genome-wide polysome profiling only in H69 and H196 cells?\nExperiments should be done in all models used in Fig 1 and avoid using H196 and DMS114\nas low NE cell lines. I also strongly recommend performing this experiment in both low and\nhigh NE cells derived from the same cell line, as indicated above.\nResponse: We thank the reviewer for this important suggestion and agree that validation\nacross diverse SCLC models is essential. In response, we performed additional polysome\nprofiling RNA-seq analyses in three independent model systems: H82/H82R (a chemotherapy-\nresistant pair), H209/H209A (spontaneous low- and high-NE subpopulations from the same\nparental line), and H69/H69M (a growth factor–induced transition model). We also excluded\nH196 cells from the revised analysis. As recommended, we applied an established NE-\nassociated gene signature, rather than relying on a limited set of pre-selected markers, to\nassess subtype-related programs at both the transcriptional and translational levels. These\nnew data are now incorporated into Fig. 2 and Supplementary Fig. 3 and further support the\ngenerality and robustness of our conclusions.\n5. In Fig. 2E, a better approach would be to use for example the NE and non-NE signatures\nfrom Zhang et al. (PMID: 29535911), to calculate the NE and non-NE scores rather than\nrelying on pre-selected genes.\nResponse: As the reviewer may appreciate, we are a young group venturing into the territory\nof non-genetic adaptation and plasticity in SCLC, where we hope our efforts will illuminate the\nrole of translation initiation factors in shaping SCLC biology. We sincerely apologize for the\noversight in reference checking. In response to the reviewer’s suggestion, we have revised Fig.\n2e and the corresponding Supplemental Figure 3 to calculate NE and non-NE scores directly\nfrom the transcriptome and translatome, providing an unbiased and comprehensive view of\ncell states without relying on pre-selected genes as shown in the previous point.\n6. The authors previously showed that translation-related pathways were among the top\npathways upregulated in relapse versus treatment-naïve (TN) clinical samples (Fig. 1B), and\nthey claimed that eIF6 is a key translation initiation factor involved in the non-NE transition.\nHowever, it is not shown whether eIF6 is upregulated in relapse versus naïve samples.\nAccording to Fig. 3B, it is not very evident that eIF6 is preferentially expressed in relapse\ncases. Can authors show the levels of eIF6 is each of these groups?\nResponse: We thank the reviewer for this valuable suggestion and apologize for the\ninsufficient analysis in the initial submission. In the revised manuscript, we have added a\nviolin plot showing eIF6 expression in relapsed versus treatment-naïve samples and\nextended our analysis to other related translation initiation factors (revised Supplementary\nFig. 7a–c). As suggested, we now discuss that a subset of samples co-expresses ASCL1\nand eIF6, which likely reflects an early adaptive state in which NE cells begin to upregulate\neIF6 before transitioning to non-NE phenotypes. This early increase in eIF6 is further\nsupported by data from early chemotherapy time points (Fig. 3h and Supplementary Fig. 7b).\nNotably, while eIF6 and ASCL1 are inversely correlated, they are not strictly mutually\nexclusive. Finally, we clarify that eIF6 was selected for further study based on polysome\nprofiling evidence showing a relative enrichment of 60S subunits in non-NE cell states.\nSupplementaryFig7\n7. How do other genes involved in translation pathways look like in relapse versus naïve\nsamples? The authors should present the expression of key translation factors in both\ngroups and justify why eIF6 was selected, as it is not clear how this target was identified.\nResponse: We thank the reviewer for this suggestion and we have added the other\ntranslation key factors in both groups. Meanwhile, as this reviewer may appreciate, we have\nalso expanded our western blot and qPCR analysis across three different models to show\nthat other key translation factors, such as eIF4E, eIF4G, eIF4A1, was not significantly or\nconsistently altered in low- and high-NE cell states, nor up chemotherapy treatment.\na. Relapsed vs Naïve samples for other translation factors (Supplemental Fig 7c):\nSupplementaryFig7\nb. Western blot analysis (Fig 3a):\nc. qPCR analysis (Fig 3b and Supplemental Fig 5b):\n8. In order to confirm the inverse correlation between ASCL1 and eIF6 (Fig. 3B), the authors\nshould perform a correlation analysis. eIF6 also appears to correlate inversely with\nNEUROD1, while it correlates positively with MYC and POU2F3.\nResponse: We thank the reviewer for this insightful suggestion. In response, we performed a\nPearson correlation analysis of SCLC-associated transcription factors in relation to eIF6\nexpression levels. These results have now been incorporated into the revised Supplementary\nFig. 7b.\nSupplementaryFig7\n9. Western blots of eIF6 expression after carboplatin/etoposide treatment (Fig. 3C) should be\nperformed in a panel of SCLC cell lines, not only in one cell line, to validate the universality of\nthis finding.\nResponse: In line with the reviewer’s suggestions in this and the preceding points, we\nperformed additional analyses of eIF6 across all three models. These results are now\nintegrated into the revised Fig. 3h.\n10. The in vivo experiment shown in Fig. 3E should be performed in least one additional cell\nline–derived xenograft. The authors use H69 as the primary model for most of their\nexperiments, while other models are used selectively in only a few experiments. This lack of\nconsistency in the models used, along with the reliance on a single cell line in key experiments,\nraises concerns about the universality and robustness of the results.\nResponse: We thank the reviewer for this constructive suggestion. In addition to the widely\nused H69/H69M neuroendocrine transdifferentiation model, we performed an H82-derived\nxenograft experiment to further evaluate eIF6 dynamics following EP-based chemotherapy in\nvivo, representing a distinct NEUROD1⁺ context, integrated in the revised Supplemental Fig\n7f-h.. As shown in Supplementary Fig. 7b, eIF6 is rapidly induced within 24 h of EP treatment\nacross four SCLC models, indicating an immediate and sustained stress response. These\nearly in vitro changes suggest that eIF6 upregulation alone is insufficient to determine cell\nfate. In vivo, sample timing is less precise, and bulk analyses reflect heterogeneous\npopulations, with eIF6 induction observed in residual, surviving tumor cells. We now discuss\nthese temporal dynamics and have accordingly moderated our statement in the revised\nmanuscript.\n11. In Fig. 3H, did the authors quantify the H-score of both ASCL1 and eIF6 in tumors at\ndifferent stages? Showing a single IHC image is insufficient to conclude that eIF6 is\nupregulated in advanced tumors lacking ASCL1. Furthermore, if the authors wish to claim\nthat high eIF6 expression is found in ASCL1-low regions (line 208), they should perform dual\nimmunofluorescence (IF) staining for ASCL1 and eIF6 and determine the co-localization of\nboth markers. Also in Fig. 3H, the areas shown for ASCL1 and eIF6 IHC are different, so the\nauthors cannot draw the aforementioned conclusion.\nResponse: We thank the reviewer for this careful and helpful comment. We agree that a\nsingle IHC image is insufficient to support quantitative conclusions regarding eIF6\nupregulation in ASCL1-low tumors, and we acknowledge that the limited number of samples\nobtained from the RPM mouse model precluded reliable H-score quantification at the\nindividual tumor level. Accordingly, we have revised the manuscript to avoid any over-\ninterpretation of these data.\nThe primary purpose of the RPM model in Fig. 3h was therefore not to provide a\nstatistical assessment of eIF6 and ASCL1 levels, but rather to qualitatively illustrate that,\nacross different disease stages, the pattern of eIF6 expression appears inversely associated\nwith ASCL1. We have clarified in the revised text and figure legend that the regions of\ninterest shown for ASCL1 and eIF6 were derived from adjacent consecutive sections, rather\nthan from the same slide, and therefore should be interpreted as indicative rather than\ndefinitive. Taken together, the new data from in vitro models, RNA-seq analyses, human\ntissue co-staining, and the illustrative RPM model provide a more coherent and convergent\nbody of evidence. We hope the reviewer will find that these results now more robustly\nsupport our conclusions.\n12. I find quite surprising that the majority of clinical limited-stage specimens used in Fig. 3J\nlack ASCL1 expression, given that up to 70% of all SCLC tumors exhibit high ASCL1 levels.\nWere these samples preselected based on subtype?\nResponse: We thank the reviewer for this insightful comment. As the reviewer correctly\nnoted, several studies have reported that approximately 70% of SCLC tumors exhibit high\nASCL1 expression, often even in advanced stages such as IIIA/B (PMID: 26168399;\n28089889; 28534531). Most of these cohorts, however, comprise predominantly Caucasian\npatients. In our study, samples were not preselected by subtype but were collected routinely\nfrom our Thoracic Department. Our cohort includes 14 stage IIIA/B SCLC cases (as detailed\nin Supplementary Table 5), representing tumors with local advancement and increased\nmolecular heterogeneity. Importantly, our cohort consists entirely of East-Asian patients, in\nwhom the proportion of ASCL1-high tumors was 44.7%, consistent with the 42.5% reported\nin the SCLC cohort from the National Cancer Center of China (PMID: 34931456; 33191657).\nAlthough we cannot at this stage conclude that the lower frequency of ASCL1-high tumors\nreflects ethnic differences, we have moderated our statement and discussed this limitation in\nthe revised manuscript as follows:\n“..Our study has several limitations that warrant future investigations. First, the clinical\nanalyses, including eIF6 expression and PLA-based biomarkers, were performed in a\nrelatively small patient cohort (n = 38) restricted to limited-stage SCLC (LS-SCLC),\nunderscoring the need for validation in larger, independent, prospective collected cohorts. In\naddition, our cohort consisted exclusively of East Asian patients, among whom 44.7% of\ntumors were classified as ASCL1-high, considerably lower than the ~70% ASCL1-positive\nfrequency reported in predominantly Caucasian cohorts20,76. Interestingly, a recent study from\nthe National Cancer Center of China reported a similar ASCL1 positivity of 42.5% in Chinese\nSCLC patients77,78, consistent with our observations. These findings raise the possibility that\nSCLC subtype distribution may vary across ethnic populations, highlighting the need for\ndirect, comparative studies to address potential population-specific differences.”\n13. In the IF experiment (Fig. 3J), the authors should quantify and present the percentage of\ncells that are ASCL1⁺/eIF6⁻, ASCL1⁺/eIF6⁺, ASCL1-/eIF6⁺, and NEUROD1⁺/eIF6⁺, as the\nprimary purpose of performing multiplex IF is to assess such co-expression patterns.\nResponse: We thank the reviewer for raising this important point. We agree that an explicit\nquantification of the different cell populations expressing ASCL1, eIF6, and NEUROD1 could,\nin principle, provide additional insight. However, QuPath-based single-cell segmentation can\nbe ambiguous in densely packed regions, where clustered cells may partially overlap and\nfluorescence or chromogenic signals from adjacent cells cannot be reliably separated. In this\ncontext, quantifying discrete cellular classes could introduce substantial error and may not\nfaithfully reflect true single-cell expression patterns.\nFor this reason, we instead calculated the fraction of ASCL1+ and eIF6+ cells based\non single-marker positivity and used these values to perform Pearson’s correlation analysis\n(Fig. 3f), which provides an approximate yet robust representation of the relationship\nbetween the two proteins within each patient sample. Importantly, we do not claim that\nASCL1 and eIF6 expression is mutually exclusive, a point that is now further supported by\nour analysis of an independent single-cell RNA-seq dataset (Supplementary Fig. 10).\nAccordingly, we chose not to perform a strict classification-based quantification as\nsuggested. This limitation is now explicitly discussed in the revised manuscript, and we\nhighlight that higher-resolution spatial single-cell transcriptomic and proteomic approaches\nwill be required in the future to definitively resolve this question as follows:\n“..However, we acknowledge that explicit cell-classification-based quantification of ASCL1\nand eIF6 expression, aimed at assessing their co-expression, would be valuable for defining\nthe relationship between these two proteins at the patient level. Definitively resolving their\nexpression patterns in individual cells will require higher-resolution spatial single cell\ntranscriptomic and proteomic approaches in the future.”\n14. The correlation between ASCL1 and eIF6 in the IMPOWER data appears to be weak, as\nat least half of the high ASCL1 cases exhibit very high levels of eIF6. How does this\ncorrelation appear in other databases, such as George et al.?\nResponse: We apologize for the incorrect subtype annotation in the previous version. In the\nrevised manuscript, we have adopted the SCLC subtype classification defined by Gay et al.\n(PMID: 33482121). While eIF6 expression is not strictly mutually exclusive with ASCL1, the\noverall pattern demonstrates a clear inverse correlation, now presented in revised Fig. 3g. As\nthe reviewer is well aware, cell state transitions—particularly EMT-like processes—are\ninherently continuous rather than binary, reflecting the dynamic nature of regulatory\nprograms (PMID: 38987605). Pearson correlation analysis in the George et al. dataset\nshows a trend toward an inverse relationship between ASCL1 and eIF6 (p = 0.1,\nSupplementary Fig. 5g). Although this does not reach conventional statistical significance, it\naligns with the inverse pattern observed in our in vitro models, human tissue co-staining,\nand the RPM model. Taken together, these complementary datasets provide coherent and\nconvergent evidence supporting a general inverse association between eIF6 and ASCL1 in\nSCLC.\n15. In general, I find the association between ASCL1 and eIF6 to be weak and hard to\nbelieve. Significant re-analysis and additional experiments should be performed to validate\nthis. Does ASCL1 genetic ablation lead to the upregulation of eIF6 expression?\nResponse: We thank the reviewer for these insightful comments (points 11–15). We agree that\nASCL1 and eIF6 expression are not mutually exclusive, and we did not intend to imply a strict\nanti-correlation. Rather, our data indicate that eIF6 exhibits stress-responsive dynamics during\nthe continuum of the NE-to-non-NE transition. Consistent with the reviewer’s observation,\ndatasets including the relapsed cohort, IMPower133, and George et al. reveal an overall trend\nof inverse correlation, although some samples co-express both eIF6 and ASCL1.\nIn the revised Supplemental Fig. 10, we further analyzed a single-cell RNA-seq dataset\n(PMID: 34653364), showing that eIF6-high cells are generally ASCL1-low, while a small subset\nof ASCL1+ cells also express eIF6, likely representing transitional states. These findings\nemphasize the dynamic nature of eIF6 function, which shifts from a ribosome anti-association\nfactor to engaging integrin complexes, contributing to non-NE transdifferentiation. We hope\nthis perspective helps highlight the continuum of NE-to-non-NE transition rather than focusing\nsolely on absolute expression patterns. Our study focuses on how eIF6 modulates translation\nactivity and alternative signaling during this transition. While ASCL1 remains a canonical NE\nmarker, our bulk and single-cell data together indicate a general inverse trend rather than a\nstrict binary relationship. In response to the reviewer’s suggestion, we have moderated the\ncorresponding statements in the revised manuscript to avoid overinterpretation and better\nreflect the dynamic remodeling during cell-state transitions.\nWe appreciate the reviewer’s suggestion regarding ASCL1 genetic ablation to assess\npotential direct regulation of eIF6. At the outset of our study, we examined the promoter\nregulatory region of eIF6 but did not identify any canonical ASCL1-binding motifs, which led us\nnot to pursue this axis further. We consider that eIF6 induction is more likely driven by stress-\nresponsive pathways, such as the integrated stress response (ISR), and by external cues\nincluding IFNG (Supplemental Fig. 10), which could be a very intriguing direction of research\nin the future. In light of the reviewer’s comment, we have now explicitly noted this as a limitation\nin the Discussion as follows:\n“..Second, the mechanisms driving eIF6 upregulation in the non-NE state remain\nunresolved. Non-NE cells exhibit low ASCL1 expression, which can activate Wnt signaling in\nhigh-grade neuroendocrine lung cancer79 while suppress a SOX9 associated nonendodermal\nstem-like fate in SCLC80. ASCL1 strongly binds a canonical E-box motif 5’-CAGCTG-3’ that\nwe did not observe in eIF6 promoter regions81. Moreover, chemotherapy rapidly increases and\nmaintains eIF6 expression without reducing ASCL1, suggesting that eIF6 is unlikely to be a\ndirect ASCL1 suppressive target. Instead, its inducibility by starvation and IFNg points toward\nregulation by extrinsic cues or the integrated stress response (ISR). Whether ISR factors such\nas ATF4 contribute to eIF6 upregulation remains an important question.”\n16. It cannot be concluded that eIF6 knockdown increases the expression of NE markers\nbased on the RNA-seq performed in H69 cells. According to Fig. 4B, eIF6 KD with one guide\nupregulates ASCL1, INSM1, and NEUROD1, while the other guide inhibits them. Similarly,\nASCL1 upregulation at protein level (Fig. 4C) is very mild and only detected in one of the two\nKD clones. In line with previous comments, why did the authors inhibit eIF6 only in H69 cells\nand not in other cell lines to validate this result?\nResponse: We sincerely thank the reviewer for emphasizing validation across multiple\nmodels, which has strengthened our study. We have now performed eIF6 knockdown in all\nthree models (revised Fig. 4 and Supplementary Fig. 8) and observed consistent modulation\nof EMT-like and neuroendocrine (NE) signatures.\nFig4. SupplementaryFig8.\nAt the protein level, ASCL1 increased in all three models, with a mild effect in H69M and\nmore pronounced upregulation in H82R and H209A.\nFig4.\nThese data suggest that eIF6, as a stress-responsive factor, may contribute to the adaptive\nnon-NE transition, and that its suppression can partially attenuate this process but is not\nsufficient to fully restore a stable NE phenotype. Accordingly, we have moderated the\nrelevant statements in the revised manuscript to avoid overinterpretation:\n“.. Although eIF6 knockdown does not fully reverse non-NE transdifferentiation, these results\nsuggest that its inhibition may restrain the transition toward a non-NE state across distinct\ntransdifferentiation contexts.”\n17. Why the levels of ASCL1 are not shown in the western blot in Fig. 4E?\nResponse: We apologize for the oversight and have added the ASCL1 western blot in this\nfigure which is now relocated in the Supplemental Fig 9b-c.\n18. The tumor growth should be shown in Fig. 4G and untreated arms should have been\nincluded in the experiment to compare effectively the effect of chemotherapy.\nResponse: We thank the reviewer for the helpful suggestion and have now added the growth\ncurve back into Supplemental Fig. 9.\n19. I find interesting the interaction between eIF6 and FAK. Could this interaction be explored\nin the clinical specimens used in Fig. 3I to strengthen the universality of this finding?\nResponse: As the reviewer will appreciate, the acquisition of high-quality clinical material for\nSCLC is inherently challenging, as surgical resections are rare and most available biopsies\nare limited in size or affected by necrosis. Despite these constraints, we obtained patient-\nconsented surgical specimens, of which only a small number were confirmed as SCLC and\nsuitable for downstream analyses. As a consequence, conventional biochemical approaches\nsuch as immunoprecipitation were not feasible using the available FFPE samples.\nTo address this limitation, we instead employed in situ proximity ligation assays to\nassess the spatial proximity of eIF6 with CD104 and FAK. While this approach does not\ndemonstrate direct physical binding, it provides supportive evidence for a potential eIF6–\nCD104–FAK association in patient samples, which is correlated with clinical outcome (Fig. 7).\nFollowing Reviewer 1’s suggestion, we further modeled these interactions using AlphaFold.\nAlthough no direct eIF6–FAK interaction was predicted, the Calx-β domain of CD104 was\nidentified as a potential bridging interface involving several key eIF6 residues (Arg57,\nSer102, Ser150, Asp209).\nWe subsequently attempted to generate alanine substitutions at these predicted sites.\nHowever, these mutations resulted in apparent protein misfolding, likely due to disruption of\nlocal structural interactions, particularly near Arg61, which is critical for eIF6 stability.\nAccordingly, the eIF6-4mut construct failed to produce detectable eIF6 protein, despite robust\nexpression of the co-encoded GFP reporter. Moreover, treatment with proteasome or\nlysosome inhibitors did not restore eIF6-4mut protein levels, suggesting that the mutant protein\nnot being actively degraded, but rather is possibly unable to be properly expressed and/or\nfolded, thereby precluding further functional characterization.\nWe therefore chose not to include these mutation results in the revised manuscript, and\ninstead addressed this point as a future research direction in the Discussion, as follows:\n“…We showed that phosphorylation of eIF6 at Ser235 is upregulated across multiple\nSCLC non-NE transition models. Given that Ser235 phosphorylation promotes eIF6 release\nfrom 60S subunits63, this modification may represent a regulatory axis enabling eIF6 to shift\nfrom translational control toward non-canonical interactions in the non-NE state. Using\nAlphafold-based structural modeling, we explored the potential mechanism of interaction\nbetween eIF6 and integrin complex. Notably, eIF6 residuals predicted to mediate CD104\nbinding (Ser102, Ser150, Ser19) lie in close proximity to ribosome interacting residuals\n(Asn106, Tyr151, Glu12, Ser190)54, and both interfaces appear to converge near Arg57. This\nspatial convergence raises the possibility that phosphorylation-driven conformational changes\nmay bias eIF6 toward one interaction surface over another, thereby modulating its partitioning\nbetween ribosome-bound and integrin-associated states. Although our attempts to express\nmutant eIF6 containing all four key residues were unsuccessful likely due to proper structural\nfolding, the in silico observations highlight a mechanistic framework worth testing. Future\nstudies dissecting how individual residual and the associated PTMs influence eIF6’s structural\ndynamics, ribosome affinity, and integrin complex engagement will be essential for defining\nhow eIF6 moonlighting behavior contributes to SCLC plasticity.”\n20. Experiments shown in Fig. 6C-G should be performed in additional cell lines.\nResponse: We have now expanded our analyses to additional cell lines and comprehensively\nrevised Fig. 6 and the corresponding Supplementary Fig. 12 as follows:\n(1) MAPK pathway signature enrichment was analyzed in three independent models by RNA-\nseq (Fig. 6b–c and Supplementary Fig. 12b);\n(2) MAPK signaling was further validated at the protein level by western blot analysis in both\nH69 and H209 models (Fig. 6d);\n(3) eIF6 overexpression was performed in all three models, demonstrating its consistent impact\non the upregulation of p-ERK and p-FAK (Supplementary Fig. 12c);\n(4) in vitro combination treatments using a FAK inhibitor and chemotherapy were carried out\nacross all three models (Fig. 6e–f and Supplementary Fig. 12e);\n(5) following Reviewer 1’s suggestion, we conducted an in vivo H69 xenograft experiment,\nwhich indicates that the combination of FAK inhibition with frontline chemotherapy may\nenhance tumour response (Fig. 6g).\nPoint-by-point Reply\nReviewer #1 (Remarks to the Author):\nI wish to thank the authors for their extensive work in revising the manuscript.\nBest regards\nResponse: We thank the reviewer’s positive feedback.\nReviewer #2 (Remarks to the Author):\nThank you for your thoughtful and detailed response to our comments. We\nappreciate the effort and time invested in this revision and in addressing the\nreviewer's concerns.\nResponse: We thank the reviewer’s suggestions.\nRegarding the point on the molecular mechanism of eIF6, we acknowledge your\nclarification that a detailed mechanistic elucidation falls beyond the scope of the\npresent manuscript and represents a valid direction for future work. We agree that\nthe polysome profiling data, which showed a relative enrichment of 60S subunits,\nprovides a solid rationale for prioritizing eIF6 for further study, as stated in the\nmanuscript.\nResponse: We thank the reviewer for this positive feedback, which has helped\nimprove the clarity and flow of the manuscript.\nIn the RPM mouse model, it would be beneficial to further study tissue obtained at\ndifferent stages—carcinoma, locoregional, and advanced carcinoma—to check\nadditional markers related to the aggressiveness...\nIt is commendable that the authors have directly acknowledged these limitations in\nthe revised discussion section.\nResponse: We thank the reviewer for acknowledging the improvements in the\nDiscussion section.\nThe conclusion of Figure 5 is overstated: eIF6, which dissociates from ribosomes and\nfacilitates integrin-FAK complex formation, leads to global translational\ndownregulation and potentially modulates integrin-mediated downstream signaling in\nthe non-NE cell state...\nThe question HOW eIF6 is connected to intergrin-FAK is not very clear. The term\n\"facilitates\" suggests a direct mechanism, but this is not supported by the data\npresented here\nThe authors have carefully addressed the major concern regarding the integrin-FAK\npathway association in Figure 5. Toning down the associated message was an\nappropriate and crucial revision, as avoiding overstatements is essential for honest\nscientific work. My concerns on this point are now satisfied.\nResponse : We thank the reviewer for acknowledging our revisions and agree that\ntoning down the conclusions regarding the integrin-FAK pathway was important for\naccurately reflecting the data.\nAll minor comments have been satisfactorily addressed. Thank you for the careful\nattention to these details.\nResponse: We thank the reviewer for their constructive suggestions, which have\nhelped improve the quality of this work.\nReviewer #3 (Remarks to the Author):\nThe authors have addressed my concerns, and the manuscript has been significantly\nstrengthened.\nResponse : We thank the reviewer’s positive feedback.\nI just have two minor comments:\n1. Add the NES value and p-value to the newly added GSEA plots.\nResponse: As suggested by the reviewer, we have now added the NES scores and\np-values to all GSEA plots. In addition, the corresponding full table data are provided\nin the Source Data file, with clear indication of the associated figure panels.\n2. Quantify the OD of the western blots in Fig. 4C to confirm ASCL1 upregulation.\nResponse: Following the reviewer’s suggestion, densitometric quantification was\nperformed on the blots in Fig. 4c, with ASCL1 expression normalized to the β-tubulin\nloading control for each condition. Although a minor upregulation was observed, we\nhave clarified in the main text that eIF6 intervention does not fully restore the\nneuroendocrine state, but rather plays a role in mitigating the transition to the non-\nneuroendocrine state, which has been recognized by the reviewer.\nSummary\nThis manuscript provides novel insights into eIF6's role in SCLC\ntransdifferentiation and plasticity using diverse models, including genetically\nengineered mice and limited-stage patient samples. The discovery of eIF6-\nCD104/FAK-MAPK axis-driven translational rewiring and its prognostic relevance\nvia PLA scoring is compelling. However, while the work rigorously examines cell-\nintrinsic mechanisms (e.g., ribosome dynamics, chemotherapy resistance), it\noverlooks the role of TME modulating plasticity.\n- Chemotherapy sensitivity improvements upon eIF6 knockdown may\ninvolve immune-mediated mechanisms (e.g., immunogenic cell death,\nantigen presentation), which remain unexplored.\n- Immune compartment gap: transdifferentiation influence immune\nevasión?, checkpoint expression, or immune cell infiltration (e.g., T cells,\nmacrophages) in SCLC?\n- The GEMM and xenograft models lack analysis of immune-tumor crosstalk\nduring plasticity transitions, despite known links between FAK/MAPK\nsignaling.\nMain comments\n(Line 234). Fig 1a revealed that EIF6 234 expression was inversely correlated\nwith the neuroendocrine transcription factor ASCL1\nIt is not clear why this inverse correlation occurs. Fig 1A only shows a schematic\nof the transcriptomic analysis. It would be helpful to show the analysis that\njustifies the selection of EIF6 as a factor to study. Some patients with relapse\nshow upregulation of both EIF6 and ASCL1, indicating a possible positive\ncorrelation of gene expression; you could further discuss this\nIn the RPM mouse model, it would be beneficial to further study tissue obtained\nat different stages—carcinoma, locoregional, and advanced carcinoma—to\ncheck additional markers related to the aggressiveness of those tumors by\nimmunohistochemistry. It would also be useful to observe the crosstalk with the\nimmune system to determine if there is heterogeneity among the different stages.\nThe conclusion of Figure 5 is overstated: eIF6, which dissociates from ribosomes\nand facilitates integrin-FAK complex formation, leads to global translational\ndownregulation and potentially modulates integrin-mediated downstream\nsignaling in the non-NE cell state. You need to give additional data or tune down\nthe message.\nThe question HOW eIF6 is connected to intergrin-FAK is not very clear. The term\n\"facilitates\" suggests a direct mechanism, but this is not supported by the data\npresented here\nMinor Comments\nFigure 1.\nThe manuscript should clarify whether the compared cell lines (H209 vs. H209A,\nH69 vs. H69M, and HB2 vs. HB2R) display similar proliferation rates under the\nexperimental conditions used for polysome profiling. Differences in proliferation\nrates can significantly impact global translation and ribosome loading, potentially\nconfounding the interpretation of polysome profiles. Please provide quantitative\nproliferation data (e.g., cell counts, doubling times, or growth curves) for each cell\nline during the profiling experiments.\nFigure2.\nThe manuscript would be strengthened by including an immunoblot analysis for\nTORC1. Since TORC1 regulates Rps6 phosphorylation on Ser-233 via Ypk3, it\nwould be valuable to show how TORC1 activity and Rps6 phosphorylation are\nmodulated under your experimental conditions\nFigure 2E.\nThe transcripts associated with NE are translationally downregulated; however,\nthe polyA-RNA is going in the opposite direction in ADSC1, TTF3, and SYP\ngenes. Why do you have this discrepancy?\nFigure 3A.\nIt would be better to include a quantification of the Western blot, because only\nshowing the immunoblot is not very clear regarding the upregulation of some of\nthe proteins; only the increase in Vimentin is clear\nFigure 4h.\nIt would be helpful to check protein downregulation in situ in the tissue. EZH2 is\npositively correlated with eIF6. Is this also observed with other components of\nPRC2?\nFigure 6C.\nIf the mechanism is correct and there is an association between eIF6 and FAK\naxis, in which you do not see a change downstream of AKT, it would be important\nto show AKT phosphorylation. The immunoblot is not enough to sustain the\nconclusion about AKT or the FAK pathway, as FAK phosphorylation is not easy\nto assess, but the downstream effect at AKT level should be quantifiable"}]

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