Targeting NHEJ activates STING signaling through MYC degradation to boost antitumor immunity in SCLC

Domain: neuroscience
Published: 2026-02-10
Editorial outcome: minor_revision

Abstract
Small-cell lung cancer (SCLC) is the most lethal type of lung cancer. Paradoxically, this tumor displays a high mutation burden; however, a modest response to immunotherapy. Improving Immunotherapy response in SCLC patients remains an unmet need. Here, we report that across 24 tumor types, including over 179,000 real-world patient tumors, SCLC has the highest expression of nonhomologous end joining (NHEJ) DNA repair regulator PRKDC (DNAPKcs). High PRKDC expression predicts poor response to immunotherapy in SCLC. DNAPKcs depletion causes activation of cGAS/STING pathway due to cytoplasmic accumulation of double-stranded DNA, inducing immunogenicity and enhancing sensitivity of SCLC models to immunotherapy. Analyses in SCLC cell lines and mouse models show that depletion of DNAPKcs leads to proteasomal degradation of MYC via GSK3β pathway. We show that DNAPKcs upregulation contributes to immunotherapy resistance and DNAPKcs inhibition represents a promising therapeutic strategy to induce antitumor immunity and potentiate immunotherapy efficacy in immunologically suppressed SCLC.

Introduction
Small cell lung cancers (SCLCs) are highly aggressive neuroendocrine cancers that account for 15% of all lung cancer cases, with a median overall survival of only 9–11 months1,2. SCLC accounts for 30,000 new cases diagnosed annually in the United States and has an overall 5-year survival rate of about 7%3. While treatment options have recently broadened, however, overall outcomes only modestly improved, indicating a pressing need for novel therapies. The recent addition of surface-targeted drugs shows promise but is also accompanied by severe toxicities4. Efforts to expand the therapeutic arsenal toward SCLC have focused on immune checkpoint blockade (ICB) using agents targeting programmed cell death protein 1 (PD-1) and its ligand (programmed death ligand 1 [PD-L1]). One particularly understudied but common feature of SCLC is an immunosuppressed phenotype characterized by relatively low levels of infiltrating T-cells, suppression of class I major histocompatibility complex (MHC-I), or reduced antigen presentation5. Consistent with a compromised tumor microenvironment immune milieu, only a few patients with SCLC have a deep and durable response to ICB. The approved first-line treatment of platinum doublet and PD-L1 blockade leads to a modest increase in overall survival by approximately 2 months2,6–8. The mechanisms preventing ICB from reactivating antitumor immunity in SCLC are not fully understood.

Identifying therapeutic targets in SCLC has been challenging, partly because primary alterations are loss-of-function mutations in tumor suppressor genes and patients with SCLC exhibit heterogeneous transcriptomic and proteomic profiles9. SCLC is characterized by the ubiquitous loss of TP53 and RB1 and amplification of MYC family genes in a subset of SCLC2,9. Observed in approximately 20% of patients with SCLC, amplification of the MYC family genes (MYCL, MYCN, and MYC) has been reported and associated with worse prognosis10. MYC is often termed “undruggable” due to its critical role and complex protein structure11. Owing to the alterations in the genes involved in replication stress, we have previously established that targeting the replication stress response pathways is an effective therapeutic strategy in SCLC2,12–16.

Nonhomologous DNA end joining (NHEJ) is the primary pathway for repair of double-strand DNA breaks in human cells, and disruption of this pathway has been implicated in tumorigenesis and immune evasion. NHEJ confers resistance to chemotherapy in multiple cancers17. KU and DNA-PKcs are overexpressed in multiple cancers and repair DNA double-strand breaks (DSBs)18. DNA-dependent protein kinase catalytic subunit (DNAPKcs, gene name: PRKDC) is a multifunctional serine–threonine protein kinase that orchestrates the NHEJ-mediated repair upon detection of double-strand break in DNA19. DNAPKcs overexpression is often associated with a poor prognosis in patients with non-SCLC (NSCLC) and other solid tumors20,21. DNAPKcs can also drive antiviral responses via a STING-independent pathway21,22. These reports suggest that DNAPKcs is a potential regulator of the innate immune response in cancer cells. Multiple DNAPKcs inhibitors are undergoing clinical trials as monotherapies or in combination with other therapies23–25 for cancer. However, the biological significance of NHEJ signaling and the effects of targeting NHEJ signaling via DNAPKcs inhibition has not been previously explored in SCLC.

In this work, we demonstrate that PRKDC is significantly overexpressed in SCLC and predicts poor prognosis. DNAPKcs inhibition in vivo leads to increased immunogenicity and augmentes the response to chemotherapy and PD-L1 blockade. Therefore, we establish the biological effects of targeting NHEJ in SCLC and highlight the role DNAPKcs inhibition as a promising therapeutic strategy to augment the efficacy of chemotherapy and immunotherapy in SCLC.

Results
SCLC tumors have a significantly high expression of PRKDC compared to other cancers and PRKDC expression predicts worse prognosis in patients with SCLC

NHEJ is a major DNA double-strand break repair pathway whose effect has been previously unexplored in SCLC. Therefore, we investigated the expression of PRKDC (key regulator of the NHEJ pathway) in 179,508 real-world tumor samples across 24 tumor types, including SCLC. Encouragingly, SCLC had the highest expression of PRKDC among the 24 types of cancer (Fig. 1A and Supplementary Table S1). This is a significant finding as this reports of the significantly high expression of the NHEJ gene, PRKDC in SCLC.

Since there is a substantial biological and therapeutic heterogeneity within lung cancer subtypes, we next examined PRKDC expression in clinical samples (n = 36,896) from multiple lung cancer subtypes, including lung carcinoid (n = 177), NSCLC (n = 35,324), atypical lung carcinoid (n = 19), large cell lung carcinoma (n = 432), and SCLC (n = 944). As expected, SCLC clinical samples had significantly higher expression of PRKDC compared with other lung cancer subtypes (Fig. 1B and Supplementary Table S2). To confirm the finding in cell lines, we next assessed PRKDC expression in NSCLC and SCLC cell lines using the Cancer Cell Line Encyclopedia database. Consistent with our observation in clinical samples, PRKDC expression was significantly higher (p = 0.00026) in SCLC compared with that in other lung cancer cell lines (Supplementary Fig. S1A).

SCLC has been subdivided into four major subtypes based on relative expression of transcription factors like ASCL1 (SCLC-A), NEUROD1 (SCLC-N) and POU2F3 (SCLC-P)2,26–28. A fourth subtype defined by relatively higher expression of immune-related genes (inflamed) and YAP1 (SCLC-Y). Hence, we next investigated whether there are any subtype-specific differences in the expression of PRKDC in over 900 SCLC patient tumors. In addition to the four well-known subtypes (ASCL1, NEUROD1, POU2F3, and YAP1) our cohort also included cases with no known lineage determining transcription factors (TFs; TF negative) and tumors with more than one TFs (mixed tumors). Interestingly, TF-negative tumors had the lowest expression, and mixed TF tumors had the highest expression of PRKDC (statistical significance detailed in figure legends) (Fig. 1C and Supplementary Table S3). Among the well-known subtypes, the ASCL1-driven subtype had the highest expression of PRKDC (TF-neg/ASCL1, p = 2.9475e-16). This is the largest SCLC dataset that has reported the transcriptomic expression of PRKDC in SCLC.

Since the role of the NHEJ pathway in the prognosis of patients with SCLC is obscure, we next investigated whether higher PRKDC expression affects the overall survival (OS) of patients with SCLC. Higher PRKDC expression predicted significantly shorter OS in 21 patients with SCLC29 (Fig. 1D). To further investigate, we leveraged the IMpower133 Phase 3 clinical trial dataset (NCT02763579)7, which to our knowledge, is the largest available clinical cohort with transcriptomic and survival data for SCLC. We stratified patients into quartiles based on PRKDC expression and then compared survival between the highest and lowest quartiles (n = 68 patients per group). Patients in the lowest PRKDC expression quartile had significantly better overall survival (OS) (Supplementary Fig. S1B) as well as progression-free survival (PFS) (Supplementary Fig. S1C) compared to those in the highest quartile.

Our results indicate that SCLC tumors and cell lines have significantly high expression of NHEJ master regulator, PRKDC, and that PRKDC expression predicts a worse prognosis in SCLC.

PRKDC expression is correlated with lower expression of immune-related genes and predicts resistance to several drugs targeting the replication stress proteins

To understand the biological implications of high baseline expression of PRKDC in SCLC, we investigated the genes significantly correlated to PRKDC expression in SCLC cell lines and patient tumors. Multiple immunogenic pathways like hallmark inflammatory response, IFNγ response, IFNα response, were found to be enriched in SCLC clinical samples with low baseline expression of PRKDC (Fig. 1E). Whereas E2F target genes, G2M checkpoint and MYC target genes were observed to be enriched in SCLC patient samples with high expression of PRKDC (Fig. 1F). Furthermore, high PRKDC expression was positively correlated with DNA damage repair genes and MYC expression (Supplementary Fig. S1D).

To confirm our findings in preclinical models, we investigated the top genes that were positively or negatively correlated with PRKDC in SCLC cell lines. We demonstrated that replication stress genes, such as CHEK1, CHEK2, and PARP1 were positively correlated with PRKDC expression (Supplementary Fig. S1E). We also observed that the inhibitory NOTCH ligand, DLL3, which is an important therapeutic target in SCLC along with mTOR were positively correlated with PRKDC expression (Supplementary Fig. S1E). Interestingly, the chemokine C-X-C motif chemokine ligand 5 (CXCL5), TMEM173 (STING), and CD274 (PD-L1) were among the top negatively correlated genes with PRKDC expression (Supplementary Fig. S1F). Other immune-related genes, TGF-beta-I, and TNFRSF1A were also significantly negatively correlated with PRKDC expression (Supplementary Fig. S1F) in SCLC. These results indicate that PRKDC is positively correlated with replication stress genes and other SCLC-related oncogenes and is negatively correlated with immune-related genes in SCLC clinical samples and preclinical models.

Primary and acquired resistance to drugs targeting the DNA damage response pathways has been one of the major hurdles in the clinical translation of this class of drugs in SCLC. Therefore, to investigate whether PRKDC expression has any implications in drug resistance in SCLC, we compared the baseline expression of PRKDC in 63 SCLC cell lines that were treated with 103 US FDA-approved drugs and 423 investigational agents30. Interestingly, higher PRKDC expression was correlated with significantly higher IC50 of drugs targeting HSP90 (A1A7-AAG, A1A7-DMAG), PARP (BMN-673), IMPDH (AVN944), and topoisomerase (irinotecan, teniposide) (Supplementary Fig. S1G). Interestingly, all of these drugs target proteins linked to DNA repair and replication stress, which are key vulnerabilities in SCLC.

Therefore, we demonstrate that higher PRKDC expression is correlated with lower expression of key immune-related genes and is a potential biomarker of resistance to drugs targeting the DNA damage response pathways.

SCLC tumors with low PRKDC expression have better prognosis and derive greater benefit from chemotherapy and immunotherapy

Next, we investigated the role of NHEJ regulator, DNAPKcs, in the prognosis of patients treated with cisplatin in SCLC patients. High PRKDC expression predicted significantly poorer OS in a total of 423 patients treated with cisplatin (HR=1.529 (95% CI: 1.032 - 2.265, p=0.033,) (Fig. 1G). We further extended our analysis on OS and PFS (Progression free survival) using the IMPower133 dataset, which was the first randomized trial to demonstrate PFS or OS improvements with ICB in SCLC. We used the controlled access IMPower133 dataset (Phase 3 clinical trial dataset evaluating efficacy of atezolizumab in combination with chemotherapy), which includes 271 patients of various ages, genders, treated with either atezolizumab or placebo. First, we set out to test for the association of PRKDC expression with progression-free survival (OS). We used a cox regression model with censored time-to-disease progression (in months) as the dependent variable, and PRKDC as the independent variable (continuous, tpm), exploring possible covariates such as age, sex, and treatment, as well as interaction terms. This analysis revealed a significant association between OS and PRKDC (p-value=0.0193), after adjusting for treatment as a covariate. Indeed, treatment (Atezo or Placebo) was also significantly associated with OS (p=0.0193) (Fig. 1H). Next, we utilized the same statistical approach to test for the association of PRKDC expression with progression free survival (PFS) of SCLC patients. As expected, similar to OS, we demonstarte a significant association between PFS and PRKDC (p-value=0.000302) (Fig. 1I). We demonstrate that high baseline PRKDC expression may be predictive of ICB benefit in SCLC.

DNAPKcs inhibition decreases the viability of SCLC cells and causes tumor regression in SCLC PDX models

Next, we investigated the effect of DNAPKcs inhibition on SCLC cells. We used a panel of 16 SCLC cell lines (including 14 human-derived and 2 mouse-derived models), representing all four subtypes of SCLC. We treated the cells with two different DNAPKcs inhibitors (NU7441 and nedisertib) that are currently in clinical trials for other cancers31. Cells were treated with either NU7441 or nedisertib for 5 days with multiple doses and the resulting change in cell viability was assessed by cell titer. SCLC cells showed a range of sensitivity to NU7441 and nedisertib (Fig. 2A, B), with IC50s ranging starting at 40 nM to 10 µM for NU7441 and 68 nM to 10 µM for nedisertib. The IC50 for nedisertib for several cell lines was as low as 68 nM. To further extend the scope of our study, we treated four thoracic neuroendocrine cell lines with NU7441. Our data indicated that the thoracic neuroendocrine lines were also sensitive to NU7441 treatment, with IC50 concentration starting at 2.947 µM (Fig. 2A, B). This provides compelling evidence for the sensitivity of SCLC and other thoracic neuroendocrine cells to DNAPKcs inhibition.

We next investigated the anti-tumor effect of NU7441 in SCLC PDX models representing the three major subtypes. We included three PDX models previously characterized using genomic, transcriptomic, and proteomic assays with correlative clinical data32. The three PDX models had high expression of ASCL1 (LX110), NEUROD1 (LX33), and POU2F3 (LX1322). Tumor bearing mice from each model were treated with either vehicle, or single agent NU7441 (10 mg/kg, PO, 5 out of 7, QD). We measured the rate of tumor reduction when the vehicle treated group reached maximum tumor volume.

We observed the modest anti-tumor efficacy in the ASCL1 high PDX model (LX110) with 28% reduction in tumor growth upon NU7441 treatment relative to control at day 50 (Fig. 2C). In NEUROD1 high (LX33) PDX model, we observed a higher degree of tumor growth inhibition with a 58% reduction in tumor volume post-NU7441 treatment relative to control at day 50 (Fig. 2D). Encouragingly, in the POU2F3 high (LX1322) we observed a remarkable ~76% reduction of tumor growth post-NU7441 treatment relative to control by day 29 (Fig. 2E). Together these results demonstrate that a subgroup of SCLC preclinical models is sensitive to DNAPKcs targeting.

To optimize patient selection for clinical trials, we next investigated the biomarkers that predict the sensitivity of SCLC cell lines to DNAPKcs inhibition. We compare the cell line IC50 to the baseline gene expression of the cell lines. Pathway analysis with GSEA demonstrated MYC targets, G2M checkpoint pathway, and E2F pathway to be the top pathways correlated with sensitivity to both NU7441 and nedisertib in SCLC cell lines (Supplementary Fig. S2A, S2B). Next, we investigated the probable correlation between any of the top three lineage-defining transcription factors (LDTFs) (ASCL1, NEUROD1 and POU2F3) with PRKDC expression. Our results did not show a strong correlation of PRKDC expression with basal expression of the three LDTFs (Supplementary Fig. S2C).

Next, we looked at individual genes that may predict sensitivity to DNAPKcs inhibition. Interestingly, high baseline expression of SLFN11 predicted lower IC50 of nedisertib (p=0.036, Supplementary Fig. S2D). We and others have demonstrated the role of SLFN11 as a predictive biomarker to chemotherapy and PARP inhibition in SCLC33. This is translationally very relevant as SLFN11 can be detected by IHC, and a recent clinical trial (SWOG S1929) has demonstrated the feasibility of including SLFN11 expression as a predictive biomarker in selecting ES-SCLC patients for treatment with PARP inhibitors and immunotherapy in the maintenance setting. Therefore, if validated in clinical samples, SLFN11 can be a predictive biomarker for DNAPKcs inhibition in SCLC clinical trials.

DNAPKcs inhibition induced DNA damage, apoptosis and micronuclei formation in SCLC models

To establish the downstream effect of DNAPKcs inhibition in SCLC, we treated DMS114, H446, and H69 cells with the concentration of NU7441, where we observed appreciable target inhibition (4µM NU7441 for 4 days). Western blotting analysis showed an increase in apoptosis markers, cleaved PARP, and cleaved caspase-3, in post-NU7441 treatment compared to control in all three cell lines (Fig. 2F), indicating apoptotic induction. We performed annexin V-PI based flow cytometry to further validate apoptosis induction post-NU7441 treatment (4µM NU7441 treatment for 4 days). We included five SCLC models that showed differential IC50 to NU7441 treatment (Fig. 2G and Supplementary Fig. S2E). Interestingly, DMS114 representing the SCLC-Y subtype showed highest induction of apoptosis (~24%) post-NU7441 treatment followed by H1341 (~20% increase) (Fig. 2G and Supplementary Fig. S2E). SCLC-N subtype, H82 cells showed increase in apoptosis, from 1.5% to ~12% (Fig. 2G and Supplementary Fig. S2E). However, H841 (SCLC-Y) and H526 (SCLC-P) cell lines showed modest yet statistically significant increase in apoptotic population (~6–8%) post-NU7441 treatment (Fig. 2G and Supplementary Fig. S2E). These findings establish that DNAPKcs inhibition causes appreciable apoptosis in SCLC models.

Next, we determined whether this apoptotic induction is a drug specific event or due to the inhibition of the NHEJ regulator, DNAPKcs. Therefore, we performed siRNA-mediated knockdown of PRKDC in SCLC cell lines using 2 different siRNA sequences. PRKDC knockdown also resulted in an appreciable increase in cleaved PARP (Supplementary Fig. S2F). Moreover, both pharmacological inhibition (with NU7441) of DNAPKcs, or genetic knockdown of PRKDC led to a notable increase in phospho-H2AX protein level indicating increased double-stranded DNA damage in SCLC models (Fig. 2F and Supplementary Fig. S2F). To further validate double stranded DNA damage, we performed confocal microscopy in multiple SCLC models pre- and post-NU7441 treatment. We observed significant increase of γH2AX puncta formation inside the nucleus in both DMS114 and H446 cell lines post 4 days of NU7441 treatment (Fig. 2H, I and Supplementary Fig. S2G).

Next, we investigated the effect of DNAPKcs targeting cell cycle progression in SCLC cell lines. Four SCLC cell lines with a range of sensitivity to NU7441 were treated with 4µM NU7441 for 4 days. Flow cytometry-based cell cycle analysis showed an appreciable G2/M arrest in H526, and DMS114 cell lines (more than 1.5-fold change, p<0.001) (Fig. 2J) and only a modest increase in H841 and H446 cells.

The induction of DNA damage is associated with aberrant breaks in DNA and the presence of dsDNA in the cytoplasm. Hence, we investigated the presence of DNA in the cytoplasm. Western blotting analysis showed increased levels of cytosolic histone H3 in DMS114, H446, and H69 cells, most prominently at day 4 after NU7441 treatment, indicating the presence of cytosolic DNA (Supplementary Fig. S3A). Unresolved DNA damage leads to nuclear envelope rupture, exposing double-stranded DNA to the cytosol. The presence of this dsDNA in cytoplasm can invoke inflammatory signaling in the cell34. To confirm micronuclei formation, we performed confocal microscopy in multiple SCLC models pre- and post-NU7441 treatment. We observed the presence of micronuclei in both DMS114 and H446 cell lines after 1 and 2 days of NU7441 treatment (Fig. 2K, L and Supplementary Fig. S3B).

Taken together, we demonstrate that pharmacologic and genetic targeting of DNAPKcs leads to appreciable DNA damage, apoptosis induction and formation of micronuclei in SCLC models.

DNAPKcs inhibition activates the cGAS-STING pathway and increases IFN signaling in SCLC

Cancers often evade immune detection by silencing the Stimulator of Interferon Genes (STING)-Interferon (IFN) signaling. We have previously shown that therapeutic reactivation of STING by epigenetic therapies and DNA damage response inhibitors can restore antitumor immunity in multiple preclinical models. Moreover, we and others have shown that DNA damage itself leads to the formation of micronuclei and cytosolic GMP-AMP synthase (cGAS) in the cytoplasm can colocalize with and recognize broken DNA in micronuclei12,35.

Hence, to further investigate the effect of DNAPKcs targeting on cGAS/STING pathway we performed confocal microscopy in H446 and H841 with or with treatment without NU7441 treatment (4µM, 24h treatment). We stained the cells with DAPI for micronuclei, cGAS, and lamin B1 to stain the nuclear envelope. We observed appreciable micronuclei formation in the treated group, and colocalization of cGAS to the micronuclei in both models after NU7441 treatment. Therefore, we conclude that DNAPKcs targeting leads to colocalization of the cytoplasmic cGAS with the DNA in micronuclei (Fig. 3A, B and Supplementary Fig. S3C).

Colocalization of cytoplasmic cGAS with DNA in micronuclei can lead to the activation of the cGAS-STING–dependent immune response12,35. Next, we investigated whether DNAPKcs inhibition leads to the activation of the STING pathway. Pharmacological inhibition of DNAPKcs (by NU7441 treatment) increased protein expressions of cGAS, and other key components of the STING pathway like phosphorylated (p-) STING, p-TBK1, and phospho-IRF3, in three SCLC cell lines, DMS114 (SCLC-Y), H446 (SCLC-N), and H82 (SCLC-N) (Fig. 3C), and the neuro-endocrine tumor cells (NETs) cell line H720 (Supplementary Fig. S4A). This demonstrates the activation of the cGAS–STING pathway upon DNAPKcs inhibition. Next, to exclude the possibility of off-target drug effects, we performed siRNA-mediated PRKDC knockdown in DMS114 and H446 cells and assessed the activation of the cGAS–STING pathway by western blot (Fig. 3D). As with pharmacological inhibition, genetic knockdown of PRKDC by 2 independent siRNA sequences also increased expression of p-STING, p-TBK1, and p-IRF3 (Fig. 3D), confirming STING pathway activation.

Next, to confirm the importance of STING activation on the NU7441-mediated downstream effects, we treated DMS114 and H446 cells with NU7441 in the presence or absence of the STING inhibitor H151 (Fig. 3E). As before, NU7441 treatment led to an increase in p-STING, p-TBK1, and p-IRF3 protein expressions, which was successfully reversed in the presence of STING inhibition in either the DMS114 or H446 cell line. This confirms STING-mediated activation of the TBK1/IRF3 signaling after NU7441 treatment (Fig. 3E). Thus, our data indicate the activation of the cGAS–STING pathway in response to DNAPKcs inhibition in SCLC and other neuroendocrine tumor cell lines, extending the scope and utility of DNAPKcs inhibition.

We and others have shown that activation of IRF3 leads to the transcriptional upregulation of the interferon genes. Therefore, we next investigated the expression of the expression of interferons (IFNs; type I/II IFNs) and pro-inflammatory chemokines following NU7441 treatment. NU7441 treatment led to a significant time-dependent increase in the mRNA expression of IFNA, IFNB1, IFNG, and CCL5 in DMS114 (Fig. 3F), H446 (Fig. 3G) and H720 (atypical neuroendocrine carcinoid) cells (Supplementary Fig. S4B).

Next, we investigated the importance of STING on transcriptional upregulation of type I/II IFNs and chemokines in SCLC. As before, we performed pharmacological inhibition of STING in SCLC models. Treatment with the STING inhibitor, H151, successfully abrogated NU7441-mediated increased mRNA expression of IFNA, IFNG, and CCL5 in DMS114 cells (Fig. 3H). Next, we investigated whether increase mRNA expression of type-I interferons and chemokines is recapitulated in protein expression level. We performed ELISA both with cell free protein lysate and condition media of H446 and DMS114 to study the internal protein level as well as secreted protein level upon 3 days of NU7441 treatment. Our results indicates increase in IFNα in cell free protein lysate upon NU7441 treatment in both H446 and DMS114 cell lines (Supplementary Fig. S4C). Whereas CCL5 internal protein level as well as secreted protein level increase was only found in DMS114 cell line (Supplementary Fig. S4D). Interestingly in DMS114 cell we further found increased secreted protein expression of another chemokine CXCL10 (Supplementary Fig. S4E).

We demonstrate that DNAPKcs inhibition activates the cGAS/STING pathway in SCLC cells, leading to increased expression of type I and II interferons IFNα, IFNβ, and IFNγ and the pro-inflammatory chemokine, CCL5, in SCLC cells. We further establish the crucial role of STING in TBK1/IRF3 activation and the increase in type I/II interferons and CCL5 in response to DNAPKcs inhibition (Fig. 3I).

Since DNAPKcs activated the cGAS/STING mediated increase in IFN pathway, next we wanted to assess the importance of the intact immune system in DNAPKcs-mediated anti-tumor response. For this we selected GEMM-derived cell lines derived from Rb1-/-, Trp53-/-, and Rbl2-/- (RPP) and Rb1-/-, Trp53-/-, and MYCT58A (RPM) models. RPP and RPM cells were subcutaneously injected in immunocompromised athymic nude mice. Tumor bearing mice were treated with vehicle or NU7441 (10 mg/kg, i.p. 5/7 days). NU7441 treatment caused significant tumor growth delay but not tumor regression in either RPP or RPM tumors grown in immunocompromised nude mice (Fig. 3J, K). This indicates the antitumor response of NU7441 in SCLC.

DNAPKcs inhibition augments the anti-tumor response to chemotherapy in SCLC

Cisplatin-etoposide based chemotherapy has been as the cornerstone of a first-line therapy for SCLC patients, but its activity is limited due to acquired resistance, highlighting the limitations of this treatment paradigm. As cisplatin-etoposide induces replication stress, we hypothesized that DNAPKcs may augment the anti-tumor response of cisplatin-etoposide, inducing potent tumor cell death while also avoiding the potential for overlapping toxicities. To explore this combination further, we tested the effect of NU7441 and cisplatin-etoposide combination on RPP and RPM tumors grown in immunocompetent SCLC in vivo models.

Tumor bearing mice were treated with: (1) Cisplatin-etoposide (3 mg/kg, 3 out of 7 days) alone (CEP) and (2) NU7441 (10 mg/kg, i.p. 5/7 days) and CEP combination (CEPN). NU7441+CEPN combination treatment significantly delayed tumor growth in the RPP model as compared with vehicle treated mice 25 days after treatment. In the RPP model, NU7441 in combination with CEP treatment resulted in 65% regression in average tumor volume compared with the CEP treated group (Fig. 3L). Similarly, in the RPM model, CEP treatment resulted in a 46.4% decrease in tumor volume 25 days after treatment, compared with 67.95% reduction in tumor burden compared to post-treatment in CEPN treatment at day 25 (Fig. 3M). We did not observe any significant changes in body weight in respect to treatment with either CEP or CEPN combination (Supplementary Fig. S4F).

We demonstrate that DNAPKcs inhibition augments the anti-tumor response of cisplatin-etoposide in immunocompetent SCLC models.

DNAPKcs inhibition induces GSK3β-mediated MYC degradation that leads to immune activation

We next wanted to further explore mechanisms by which DNAPKcs inhibition might lead to immune activation in tumors under selective pressure of targeted therapy. As shown before, NU7441 caused better tumor reduction in two out of three PDX models, LX33 (NEUROD1 high) and LX1322 (POU2F3 high) (Fig. 2C–E). We have previously shown that NEUROD1- and POU2F3-driven SCLC models have high MYC expression36–40. We performed bulk transcriptomic analyses on tumors collected from the three PDXs (LX110, LX1322, LX33). In agreement with our previous findings, LX110 showed the lowest mRNA expression of MYC, followed by LX33, and LX1322 had the highest baseline expression of MYC (Fig. 4A), with consistent results observed for MYC protein expression by immunoblot analysis (Fig. 4B). Therefore, the baseline mRNA and protein expression of MYC correlated with the anti-tumor efficacy of DNAPKcs inhibition, i.e., we speculated that high MYC expression might predict improved efficacy of DNAPKcs inhibition.

DNAPKcs directly phosphorylates AKT, which in turn phosphorylates GSK3β to repress its activity41,42. The AKT-GSK3β pathway can regulate phosphorylation of MYC at Thr-58, resulting in the proteasomal degradation of MYC in SCLC42,43. We found that DNAPKcs inhibition led to a significant decrease in phospho-AKT (Ser473) (activating phosphorylation) and phospho-GSK3β (Ser21/9) (inhibitory phosphorylation) (Fig. 4C) in SCLC models. The decrease in GSK3β phosphorylation at the inhibitory Ser21/9 site indicated GSK3β activation. Active GSK3β enhances Thr-58 phosphorylation and ubiquitination of MYC, mediating rapid proteolysis42,44. Immunoblot analysis of DMS114 and H446 cells before and after 4µM NU7441 treatment for 4 days showed a significant decrease in MYC protein expression after DNAPKcs inhibition in both cell lines (Fig. 4C). Furthermore, DNAPKcs inhibition by NU7441 treatment led to the increased expression of phospho-MYC (Thr-58) (Fig. 4C).

We next investigated the role of MYC on STING pathway activation. We performed shRNA-mediated MYC knockdown in H82 cells and examined the expression of cGAS–STING pathway proteins. MYC knockdown led to a notable upregulation of cGAS, p-STING, and p-TBK1 (Fig. 4D). Next, to determine if NU7441-mediated cGAS–STING activation was due to MYC depletion or was independent of MYC, we treated non-targeting control (NTC) and MYC-knockdown cells with NU7441 cells for 24h. NU7441 treatment led to an appreciable increase in the cGAS–STING pathway markers, p-STING (Ser 396) and p-TBK1 (Ser 172) in both NTC and MYC KD cells (Fig. 4E).

Next, to confirm the proteasomal degradation of phosphorylated MYC, we treated the cells with MG132 (proteosome inhibitor) and NU7441. We observe a significant increase in phospho-MYC (Thr58) and total cMYC when proteasomal degradation was blocked (Fig. 4F and Supplementary Fig. S5A). We also found an increase in PD-L1 expression in the proteasome degradation-inhibited condition in response to DNAPKcs inhibition in DMS114 and H446 cell lines (Supplementary Fig. S5B). Next, we aimed to confirm the involvement of GSK3β in the proteasomal degradation of MYC. We treated the two SCLC cell lines with LY2090314 (GSK3 inhibitor) in the presence or absence of NU7441. We observed almost complete depletion of phospho-MYC in the presence of GSK3 inhibitor, confirming GSK3 as the key molecular regulator of proteasomal degradation of MYC, thus affecting MYC stability (Fig. 4G). Further, investigating the protein level expression of total c-MYC we found increased expression in GSK3i treated condition in both DMS114 and H446 (Supplementary Fig. S5A). Interestingly, we further observed an increase in PD-L1 expression when GSK3 was pharmacologically inhibited (Supplementary Fig. S5B).

We next investigated the contribution of MYC in DNAPKcs mediated upregulation of IFNs and chemokines in SCLC models. shRNA-mediated MYC knockdown in H82 upregulated type I/II interferons (IFNA, IFNB, and IFNG); pro-inflammatory chemokine, CCL5; and HLA class-I gene, B2M (Fig. 4H).

Taken together, our results confirm that DNAPKcs inhibition activates GSK3β by inhibiting AKT phosphorylation, which decreases MYC stability, leading to activation of the cGAS/STING pathway and upregulation of IFNs and chemokines.

DNAPKcs inhibition leads to STING-mediated upregulation of MHC-I in SCLC models

One of the major contributing factors leading to the immunosuppressive phenotype of SCLC is the downregulation of MHC class I/II. The majority of SCLC tumors have been shown to have low/ no expression of MHC-I when IHC was performed which is in part due to epigenetic programming5. Hence, we next investigated the effect of DNAPKcs inhibition on HLA genes.

We found that, at baseline, high PRKDC expression is negatively correlated with the expression of two key MHC-I related genes, HLA-B and B2M in SCLC models (Supplementary Fig. S5C). Next, we performed bulk RNA sequencing of DMS114 and H446 cells pre- and post-NU7441 treatment. We observed an appreciable upregulation of HLA class I genes HLA-A, HLA-B, HLA-C, and B2M after NU7441 treatment in DMS114 and H446 cell lines (Supplementary Fig. S5D). This suggests that high PRKDC expression may predict MHC-I suppression and therapeutic targeting of DNAPKcs may increase expression of MHC-I related genes in SCLC.

Next, to assess the effect of DNAPKcs inhibition on the surface expression of the MHC-I in SCLC models, we treated the DMS114 and H446 cell lines with NU7441. Flow cytometry-based analysis indicated a significant increase in the percentage of MHC-I-positive cells post-NU7441 treatment in both models (Fig. 4I, J).

We next focused on the underlying mechanism of how DNAPKcs inhibition led to the upregulation of the MHC-I genes in SCLC models. DMS114 and H446 cells were treated with NU7441 with or without STING inhibitor, H151. We demonstrate that NU7441 treatment-mediated upregulation of MHC-I surface expression can be appreciably abrogated upon STING inhibition in SCLC (Fig. 4I, J). Since we found MYC-degradation mediated STING activation as a key mechanism of DNAPKcs mediated immune response, we next assessed the role of MYC in MHC-I induction in H82 cells. SCLC cells with either MYC knockdown or pharmacological inhibition of DNAPKcs were treated with STING inhibitor, H151. Flow cytometry-based analysis demonstrated an increase in the surface expression of MHC-I-upon DNAPKcs targeting or MYC knockdown (Supplementary Fig. S5E). However, NU7441 treatment had no added effect on the surface expression of MHC-I in cells which had MYC knockdown. Finally, STING depletion could successfully reverse the enhanced surface expression of MHC-I post NU7441-treatment or MYC-knockdown (Supplementary Fig. S5E). To further validate, we used a second shRNA sequence targeting MYC in H82 cells and treated the MYC KD cell with NU7441. Our data indicated increase in MHC-I positive cells with both NU7441 and MYC sh seq.2 treatment (Supplementary Fig. S5F). Most encouragingly NU7441 treatment upon MYC KD condition further increased the percentage of MHC-I positive cells compared to both only NU7441 treatment and MYC sh treatment alone (Supplementary Fig. S5F). Thus, our results demonstrate that MYC knockdown can induce MHC-I expression, and DNAPKcs inhibition increases MHC-I expression in a MYC-STING dependent manner.

As IFN signaling is a primary regulator of chemokine expression and MHC-I expression12,45, we further examined the expression of IFNs upon DNAPKcs inhibition in SCLC models. SCLC cells with or without MYC knockdown were treated with NU7441. The mRNA expression of IFNA, IFNB1, CCL5, and B2M was significantly enhanced in H82 cells upon treatment with NU7441 (Supplementary Fig. S5G). Similarly, MYC knockdown alone significantly increased the mRNA expression of IFNA and IFNB1, (Supplementary Fig. S5G), confirming our previous data (Fig. 4H). However, IFNA mRNA expressions did not increase further upon NU7441 treatment in SCLC cells (H82) with MYC knockdown (Supplementary Fig. S5G). This indicates that DNAPKcs inhibition increases the mRNA expression of IFNs in a MYC-dependent manner. However, IFNB1 expression further increased upon DNAPKcs inhibition in MYC knockdown cells, suggesting MYC-dependent and MYC-independent transcriptional regulation of IFNβ in SCLC models (Supplementary Fig. S5G).

Next, to ascertain the MYC mediated mechanism of cGAS-STING activation and MHC-I induction we knocked down MYC with 2 independent sh-RNA in H446 cell line. To determine if NU7441-mediated cGAS–STING activation was due to MYC depletion or was independent of MYC, we treated non-targeting control (NTC) and MYC-knockdown cells with NU7441 cells for 96h. Western blot analysis shows significant decrease of MYC protein expression upon MYC shRNA treatment (Fig. 4K and Supplementary Fig. S6A). NU7441 treatment led to an appreciable increase in the cGAS–STING pathway markers, p-STING (Ser396), p-TBK1 (Ser172) and p-IRF3 in NTC (Fig. 4K). Interestingly, both MYC shRNA sequence showed a similar increase in the protein level expression of all the major molecular players of cGAS-STING pathway (Fig. 4K). Interestingly, NU7441 treatment upon MYC KD did not show further appreciable increase in the protein level (Fig. 4K), indicative of a threshold of cGAS-STING activation. Next, we studied the mRNA expression of type-I interferons along with chemokine CCL5 and B2M (Fig. 4L). Our q-rt-PCR results indicated increase in IFNA, IFNB1, CCL5 and B2M mRNA expression upon both MYC sh1 treated condition as well as NU7441 treated condition (Fig. 4L). Similar to our western blot results NU7441 treatment upon MYC KD failed to further induce these mRNA expressions with MYC shRNA sequence1. Interestingly, treating the cells with a second MYC shRNA sequence indicated significant increase in the mRNA expression of IFNB1 and CCL5 upon combination treatment compared to NU7441 alone or MYC shRNA seq2 treatment alone (Supplementary Fig. S6B). Next, we investigated the effect of these 2 independent MYC shRNA sequence upon MHC-I surface expression. We observed increase in the percentage of MHC-I positive H446 cells both upon NU7441 treatment as well as 2 independent MYC shRNA treatment (Fig. 4M and Supplementary Fig. S6C) which were abrogated by STING inhibitor H151 co-treatment. Treating the MYC KD H446 cells with NU7441 did not show further increase in MHC-I positive cells with MYC shRNA sequence1 but for MYC shRNA seq2 (Fig. 4M and Supplementary Fig. S6C).

MYC over-expression reverses NU7441 mediated increase in cGAS-STING, interferon signaling as well as MHC-I induction

Next, we asked whether MYC over expression will reduce the immunogenic efficacy of NU7441 treatment. To investigate we over expressed MYC in H446 cells (Supplementary Fig. S6A). When we treated the non-targeting control and MYC over expressing cells with NU7441 we observe a significant decrease in the protein expression of p-STING, p-TBK1 as well as the p-IRF3 upon MYC overexpression (OE) (Fig. 4N). Furthermore, NU7441 treatment could not increase the p-TBK1 as well as p-IRF3 expression when MYC was overexpressed in H446 cells (Fig. 4N). Similar decrease in mRNA expression of IFNA, IFNB1, CCL5 and B2M was observed upon MYC overexpression (Fig. 4O). NU7441 treatment could increase the mRNA expression of these genes in NTC but not when MYC was overexpressed (Fig. 4O).

Next we investigated the MHC-I suface expression phenotype of H446 cells in both NTC as well as MYC OE cells. Similar to our previous finding NU7441 treatment significantly increased the percentage of MHC-I H446 NTC cells (Fig. 4P). Interestingly, Upon MYC OE percentage of MHC-I cells decreased significantly (Fig. 4P). NU7441 treatment upon MYC OE could not significantly increase the MHC-I positive cell percentage indicating complete reversal of the immunogenic phenotype (Fig. 4P).

Taken together, our results indicate that DNAPKcs inhibition results in MYC degradation and subsequently in STING activation in SCLC. Furthermore, STING activation in response to DNAPKcs inhibition leads to MYC-dependent increase in type I/II interferons and MHC-I proteins [Fig. 4Q].

DNAPKcs inhibition decreases tumor growth, induces immune cell infiltration, and enhances response to ICB therapy in immunocompetent SCLC models

In SCLC clinical samples, PRKDC expression was significantly correlated with CD274 (PD-L1) expression (Supplementary Fig. S6D), suggesting that such immune signaling may also impact the tumor microenvironment and augment the antitumor immune responses of ICB in SCLC.

Therefore, we investigated whether the pharmacological inhibition of DNAPKcs by NU7441 leads to tumor regression and enhances the anti-tumor immune response of anti-PD-L1 antibody in two immunocompetent GEMMs of SCLC. We used cells derived from RPP and RPM models. RPP GEMM closely resembles the transcriptional landscape of the ASCL1-driven subtype of SCLC tumors, and RPM GEMMs closely resemble the transcriptional landscape of the NEUROD1-driven subtype of SCLC tumors. These two major NE subtypes of SCLCs are relatively more immunosuppressed with a low CD8 to regulatory T-cell ratio.

RPP or RPM subcutaneous tumor-bearing mice were randomized into 4 treatment arms: (1) vehicle, (2) PD-L1 alone (300µg/animal, IP, 1/7) (3) NU7441 (10 mg/kg, intraperitoneal (IP) 5/7 days), and (4) NU7441 and PD-L1 combination treatment. In agreement with our previous work12,46 PD-L1 blockade did not show any anti-tumor effect in either RPP or RPM models when compared with the vehicle control (Fig. 5A and C).

In the RPP model, NU7441 treatment alone caused significant tumor regression, with mean tumor volume of 542mm3 on day 25 (59.2% reduction in tumor burden compared with the vehicle group) (Fig. 5A). Most importantly, NU7441 in combination with anti-PD-L1 therapy reduced the tumor volume to an average of 269.6mm3 on day 25, compared with the vehicle tumor volume of 1328.75mm3 on day 25 post-treatment (79.7% reduction in tumor burden) (Fig. 5A). Similar results were obtained for RPM GEMM. NU7441 treatment alone showed a significant reduction in tumor growth, reaching an average tumor volume of 242mm3 compared with 1275mm3 in the vehicle-treated group on day 25 (80% reduction in tumor volume) (Fig. 5C). The combination treatment of NU7441 with anti-PD-L1 therapy showed remarkable tumor regression, reaching a volume of 92.8mm3 post day 25 (Fig. 5C), indicating almost a 92% tumor regression at day 25 post-treatment compared to vehicle or PD-L1 alone. In the RPM models, 1 out of 10 mice showed complete tumor regression on day 25 post-treatment, and 6 out of 10 mice showed less tumor burden than on day 5 (first tumor measurement).

We next assessed the effect of NU7441, either as a single agent or in combination with PD-L1 blockade, on immune subsets in resected tumors using distinct T cell and antigen presenting cell panels (Supplementary Fig. S7A, B). Multicolor flow cytometry demonstrated that NU7441 treatment, either as a single agent or in combination with PD-L1 blockade, led to a significant increase in CD3+ total T-cells, and CD8+ cytotoxic T-cells (Fig. 5B, D) in RPP and RPM GEMMs. Interestingly, PD-1+/TIM3+ exhausted T-cells and CD25+/FOXP3+ regulatory T-cells, known immunosuppressive immune subsets, were significantly decreased in both the RPP and RPM models after NU7441 and NU7441 and PD-L1 combination treatment (Fig. 5B, D). We further observed that NU7441 +/- anti-PD-L1 treatment led to an increase in the CD62L/CD44+ memory-effector T-cells in the RPP and RPM models (Fig. 5B, D). Taken together, our data showed that NU7441 treatment, either as a single agent or in combination with a PD-L1 antibody, significantly enhanced CD8+ T-cell infiltration and decreased T-cell exhaustion and levels in two unique immunocompetent mouse models of SCLC.

Tumor-associated macrophages (TAMS), including M1 and M2 macrophages, are important components of the TME. Pro-inflammatory M1 macrophages play an antitumor role, whereas anti-inflammatory M2 macrophages have been reported to play immunosuppressive roles in lung cancer47. Multicolor flow cytometry of tumors from RPP GEMMs demonstrated that NU7441 treatment in combination with PD-L1 blockade significantly increased M1 macrophages (Fig. 5B). Interestingly, in the MYC high RPM GEMMs, the M1 macrophage population was upregulated upon NU7441 treatment alone or in combination of NU7441 and PD-L1 (Fig. 5D). Moreover, treatment with NU7441 alone or in combination with anti-PDL-1 significantly decreased the percentage of M2 macrophages in the RPM model (Fig. 5D). In addition, we observed a significant increase in MHC-I in NU7441 treated tumors of the RPM model (Fig. 5D). Encouragingly, none of the treatment groups showed any significant toxicity as demonstrated by the non-significant changes in body weight in both RPP and RPM GEM models (Fig. 5E).

Next, we demonstrate that in 423 SCLC real world clinical samples that PRKDC low tumors have significantly higher percentage of CD8+ T-cells, M1 macrophages as compared to tumors expressing high PRKDC (Fig. 5F). On the other hand, M2 macrophage was significantly decreased in PRKDC low SCLC patient tumors (Fig. 5F).

Taken together our data shows that DNAPKcs inhibition alone or in combination with PD-L1 blockade leads to significant tumor regression and NU7441 treatment augments the anti-tumor immune response in two immunocompetent SCLC GEMMs. Moreover, DNAPKcs inhibition alone or in combination with PD-L1 blockade led to an increase in cytotoxic T-cells, M1 macrophages and MHC-I in SCLC in vivo.

STING depletion reverses the DNAPKcs-mediated antitumor immune response, emphasizing the critical role of STING activation in SCLC

Our in vitro studies showed NU7441 treatment-mediated activation of the innate immune cGAS–STING pathway in a MYC-dependent manner. Hence, next, we assessed the role of the STING pathway in NU7441 treatment-mediated anti-tumor immune response in SCLC models. We silenced STING in RPP and RPM cells (Supplementary Fig. S7C–F). shRNA mediated STING-silenced RPP and RPM cells were injected into immunocompetent mice and such tumor bearing mice were then randomized into four treatment arms: (1) vehicle, (2) PD-L1 alone (300µg/animal, IP, 1/7), (3) only NU7441 (10 mg/kg, IP 5/7 days), and (4) combination of NU7441 and PD-L1 treatment.

In both RPP and RPM tumors harboring knockdown of STING, we observed a significantly decreased degree of tumor shrinkage relative to control arms (Fig. 6A, B, H, I) upon NU7441 treatment. Moreover, contrary to scrambled control (SCR) tumors, all tumors with STING knockdown (shSTING-RPP or shSTING RPM) progressed even with combined NU7441 with or without PD-L1 blockade (Fig. 6C, D and Fig. 6J, K). Our finding confirms the vital role of the STING pathway in DNAPKcs-mediated anti-tumor response in two immunocompetent SCLC models.

The knockdown of STING (TMEM173) was confirmed at the beginning of the treatment in RPP and RPM cells by qRT-PCR (Supplementary Fig S7C, E). STING knockdown was also confirmed at the end of the experiment in tumors resected from all treatment arms. RT-PCR analysis shows significantly lower expression of STING at day 20 in all treatment groups (Supplementary Fig. S7D, F).

Next, to assess the role of STING depletion on immune subsets that were significantly modulated upon NU7441 treatment (i.e., CD8, MHC-I and M1 macrophages) we performed multicolor flow cytometry of resected tumors at Day 20. STING knockdown reversed the increase in CD8+ T-cells upon NU7441 treatment with or without PD-L1 blockade in both RPP and RPM models (Fig. 6E, L). Furthermore, STING knockdown also led to the abrogation of NU7441-mediated upregulation of MHC-I and M1 macrophages in RPP and RPM models (Fig. 6F, G, M, N).

Taken together, we demonstrate that inhibition of DNAPKcs leads to a significant increase in CD8+ cytotoxic T-lymphocytes, MHC-I, and M1 macrophages in a STING-dependent manner and STING depletion can successfully abrogate the anti-tumor immune response of NU7441 with or without PD-L1 blockade.

The anti-tumor immune response of DNAPKcs inhibition can be reversed by CD8 depletion

NU7441 treatment with or without PD-L1 blockade increased the cytotoxic T-cells. Hence, we next tested the role of CD8 cells in the NU7441 effect with or without PD-L1 inhibition-mediated antitumor response. RPP and RPM subcutaneous tumors with or without CD8 depletion were randomized into groups and treated with NU7441 with or without PD-L1 blockade. As before, NU7441 treatment with or without PD-L1 blockade in IgG antibody control groups led to significant tumor regression in both RPP and RPM models (Fig. 7A, C). However, as shown in Fig. 7A, C, in CD8-depleted mice NU7441 treatment with or without PD-L1 blockade led to tumor growth compared to mice treated with an IgG antibody control. CD8 levels in both RPP and RPM models were confirmed by flow cytometry which demonstrates successful depletion of CD8 in RPP and RPM models (Fig. 7B, D).

Therefore, we demonstrate that NU7441 treatment leads to significant tumor regression through CD8+ cytotoxic T-cell infiltration and that depletion of CD8 can reverse the anti-tumor effect by NU7441 with or without anti-PD-L1 antibody.

Taken together, we demonstrate that pharmacological inhibition of DNAPKcs leads to apoptosis, MYC degradation and micronuclei formation. Downregulation of MYC and micronuclei formation can lead to cGAS/STING pathway activation, interferon pathway upregulation, increase in pro-inflammatory chemokines and MHC-I. DNAPKcs inhibition with or without PD-L1 blockade leads to significant anti-tumor response in SCLC models (Fig. 8).

Methods
All studies conducted as part of this work were reviewed by the institutional committee of the Icahn School of Medicine at Mount Sinai.

The animal study was approved by the Institutional Animal Care and Use Committee (IACUC). The identifying number of the approved protocol is 13-07-007. The maximal tumor size/burden permitted by the IACUC ethics committee was 1600mm3. We confirm that this maximal tumor size/burden was never exceeded for the immune competent models, whereas given the highly aggressive nature of these specific small-cell lung cancer PDX models specially LX110 and LX1322, the committee granted a model-specific exception allowing a modest extension of the tumor endpoint criteria. This approval was provided strictly for the LX110 and LX1322 cohorts and was contingent upon enhanced monitoring to ensure animal welfare was fully maintained.

Clinical samples cohort

Real-world patient tumor specimens (N = 179,508) were submitted for comprehensive molecular profiling to a Clinical Laboratory Improvement Amendments (CLIA)-certified laboratory (Caris Life Sciences; Phoenix, AZ). The present study was conducted in accordance with the guidelines of the Declaration of Helsinki, Belmont Report, and US Common Rule and in compliance with policy 45 CFR 46.101(b). The study was conducted using retrospective, de-identified clinical data, and patient consent was therefore not required. All statistical analyses were performed with JMP V13.2.1 (SAS Institute) [Detailed in Supplementary Tables S1–S4]. This study was conducted in accordance with guidelines of the Declaration of Helsinki, Belmont report, and U.S. Common rule. In keeping with 45 CFR 46.104(d)(4), this study was performed utilizing retrospective, deidentified clinical data, and is considered IRB exempt with waivers of patient informed consent. Waiver of patient consent and exempt status was determined by the WCG IRB.

RNA Whole transcriptome sequencing (WTS)

WTS was performed by a hybrid-capture method to pull down the full transcriptome from full formalin-fixed paraffin-embedded (FFPE) tumor samples using the Agilent SureSelect Human All Exon V7 bait panel (Agilent Technologies, Santa Clara, CA) and the Illumina NovaSeq platform (Illumina, Inc., San Diego, CA). FFPE specimens underwent pathology review to discern the percent tumor content and tumor size; a minimum of 10% tumor content in the area selected for microdissection was required to enable enrichment and extraction of tumor-specific RNA. Qiagen RNA FFPE tissue extraction kit was used for extraction, and the RNA quality and quantity were determined using the Agilent TapeStation. Biotinylated RNA baits were hybridized to the synthesized and purified cDNA targets, and the bait-target complexes were amplified in a post-capture PCR reaction. The resultant libraries were quantified and normalized, and the pooled libraries were denatured, diluted, and sequenced. Raw data was demultiplexed using the Illumina DRAGEN FFPE accelerator. FASTQ files were aligned with the STAR aligner (Alex Dobin, release 2.7.4a, GitHub). A full 22,948-gene dataset of expression data was produced by the Salmon, which provides fast and bias-aware quantification of transcript expression21. BAM files from the STAR aligner were further processed for RNA variants using a proprietary custom detection pipeline. The reference genome used was GRCh37/hg19, and analytical validation of this test demonstrated ≥97% Positive Percent Agreement (PPA), ≥99% Negative Percent Agreement (NPA), and ≥99% Overall Percent Agreement (OPA) with a validated comparator method.

Gene expression profiling for SCLC subtyping

For stratification of patient samples (N = 944) into SCLC subtypes, RNA expression values for established and putative lineage-defining transcription factors ASCL1 (A), NEUROD1 (N), POU2F3 (P), and YAP1 (Y) were standardized to Z-scores. Samples with a single positive Z-score among A/N/P/Y were assigned to the respective gene-associated subgroups, samples with multiple positive Z-scores were classified as ‘Mixed’, and samples with negative Z-scores for all 4 genes were classified as ‘Transcription Factor-Negative’ (TF-Neg).

Cell culture

Roswell Park Memorial Institute (RPMI) 1640 medium (gibco, #A10491-01) supplemented with 10% Fetal Bovine Serum, penicillin (100 U/mL), streptomycin (50 g/mL) and 2mM L-Glutamine was used to culture SCLC cells that were obtained from the American Type Culture Collection (ATCC), [cell line details have been provided in Supplementary Table S5]. The GEMM-derived SCLC cell lines derived from a conditional triple-knockout model of SCLC, Trp53−/−, p130−/−, Rb1−/− (RPP) were provided by J. Sage, Stanford University California. Rb1−/−, Trp53−/−, and MYCT58A (RPM) cells were provided by T. G. Oliver, University of Utah. We inclubated the cells in 5% carbon dioxide at 37°C with a relative humidity above 90%. We tested and authenticated the cell lines via short tandem repeat profiling (DNA fingerprinting) and routinely tested for mycoplasma species before any we performed any experiments. In general, the cells were used within the first 3-5 passages for experiments.

Drugs used in this study

We obtained NU7441 (Selleckchem, # S2638) and Nedisertib (Selleckchem, # S8586) from Selleckchem. We also obtained STING inhibitor H-151 from Selleckchem (Catalog No. S6652).

Cell viability assay

SCLC cell lines were plated in 96-well plates (2000 cells/well) in triplicate for each concentration. Cells were kept in complete RPMI media containing 10% fetal bovine serum, penicillin (100 U/mL), and streptomycin (50 g/mL) and 2mM L-Glutamine overnight, and then they were treated with dimethyl sulfoxide (DMSO) for control or different concentrations of NU7441 for 110 hours (5 Days). The cell viability was assessed with CellTiter-Glo luminescent cell viability assay reagent (Promega, Madison, WI) according to the manufacturer’s protocol Half-maximal inhibitory concentrations (IC50s) were calculated using GraphPad Prism Ver. 9.0 (GraphPad Software, Inc., San Diego, CA, USA).

Animal studies

The Institutional Animal Care and Use Committee approved all animal experiments. We measured all tumors twice weekly using calipers. To calculated volume, we used the formula width2 x length x 0.5. Twice per week, we measured body weights. When tumor volumes exceeded 2 cm in any dimension, suffered consistent body weight loss exceeding 10%, or in the case of severe tumor ulceration, mice were euthanized. To estimate the tumor growth rate in each treatment group and to test for significant growth attenuation between treatment groups, linear mixed-effects regression models were used.

Nude/PDX mouse model

2×106 cells of the respective PDX models, i.e., LX110, LX33 and LX1322, were mixed with a 1:1 mixture of phosphate-buffered saline (PBS) and Matrigel (#CB40234, Fisher) and were injected subcutaneously into the flanks of 6-week-old female nude mice obtained from ENVIGO (Boyertown, PA). Mice were randomized into treatment groups of control and NU7441 treatment groups (n ≥ 8) once the mean tumor volume reached 100–150mm3. When tumors reached a mean volume of 100–150mm3, the mice were randomized into two groups and treated with either vehicle (5% glucose+0.5% HPMC, IV) or NU7441 (10 mg/kg, i.p. 5/7 days) [n=7 per group].

Immunocompetent mice model

Single-cell suspensions of 2×106 RPP (Trp53−/−, p130−/−, Rb1−/−) or RPM (Rb1−/−, Trp53−/−, MYCT58A) cells derived from GEMMs were injected into 4–6 week-old female B6129F1 mice (for RPP) and B6FVBF1/J mice (for RPM) (The Jackson Laboratory). When tumors reached the mean volume of 100–150mm3, tumor bearing mice were randomized into four different groups. They were treated with either of the four drug regiments: vehicle (n=8 for RPP and RPM) (5% glucose+0.5% HPMC, IV), anti-PD-L1 antibody (n=8 for RPP and RPM) (300µg/animal, once weekly, intraperitoneal injection), NU7441 (n=10 for RPP and RPM) (10 mg/kg, i.p. 5/7 days), or in combination of NU7441 and anti-PD-L1 antibody (n=10 for RPP and RPM. Mice with tumor volumes around 1600 mm3, consistent loss more than 10% body weight or severe ulceration were euthanized.

Flow cytometry analysis

Flow cytometry analysis was performed as per standard protocol. LSRFortessa X-20 and FACSDiva software were used to obtain data and analysis, respectively. For Murine tumors, single-cell suspensions were prepared using the gentleMACS dissociator following the manufacturer’s instructions. Cells were stained with anti-CD16/32 monoclonal antibody and other fluorochrome-conjugated antibodies (Supplementary Table 5) for 30min followed by fixation with 1% Paraformaldehyde. Cytek Aurora (Cytek, Bethesda, MD) flow cytometer was used to obtain data and data analysis was done with FlowJo software (version 10.6, BD Biosciences, Franklin Lakes, NJ). Details of the flow antibodies are provided in supplementary Table S6. For the cell cycle assay, cells were washed once with ice cold PBS and fixed with 70% ice cold ethanol overnight in −20⁰C. Next, day cells were washed again with PBS and stained with FxCycleTM PI/RNase staining solution (Invitrogen) for 15mins at room temperature and analyzed with FACSymphony A5 SE. For in vitro MHC-I surface staining single cell suspensions were prepared, and TruFCX (for human cells) was used to block nonspecific binding. Following the blocking, cells were stained (30min) with anti-HLA-ABC antibody tagged with fluorescent dye. The cells were then washed twice with DPBS and then analyzed by FACSymphony A5 SE.

Annexin V-propidium iodide (PI) assay

Apoptosis was detected by Annexin-V – PI assay according to the manufacturer’s protocol in the FITC annexin V Apoptosis Detection Kit I (BD Biosciences). Briefly, in six-well plates, 4×105 cells were seeded and treated with or without 4μM of NU7441 for 4 days for each cell line. After the treatment, harvested cells were stained with annexin-V and propidium iodide (PI) and analyzed using the FACSymphony A5 SE flow cytometer and with FlowJo software (version 10.6, BD Biosciences).

In vivo STING knockdown study

Single-cell suspensions of 2×106 STING knocked-down cells were injected into the right flank of RPP and RPM GEMM mice, respectively to establish a tumor. When the tumors reached its mean volume of 100–150mm3, mice were randomized into groups into 4 groups and treated as following: (1) vehicle (n=6), (2) anti PD-L1 antibody alone (300µg/animal, IP, 1/7) (n=6) (3) only NU7441 (10 mg/kg, i.p. 5/7 days) (n=6) (4) combination of NU7441 and anti PD-L1 antibody (n=6).

RNA sequencing and analysis

Freshly prepared cell pellets were sent for RNA isolation, followed by library preparation, and RNA sequencing to GENEWIZ. Total RNA extraction was done using RNeasy Plus Universal mini kit (Qiagen, Hilden, Germany) following the manufacturer’s instructions. To prepare RNA seq. library for Illumina, NEBNext Ultra II RNA Library Prep Kit (NEB, Ipswich, MA, USA) was used. Validation and assessment of RNA samples and seq. libraries were performed using Agilent TapeStation (Agilent Technologies, Palo Alto, CA, USA). Library quantification was done by q-PCR (KAPA Biosystems, Wilmington, MA, USA) and by Qubit 2.0 Fluorometer (Invitrogen, Carlsbad, CA).

Illumina HiSeq instrument (4000 or equivalent) was used to load libraries following the manufacturer’s protocol after clustering on one flow cell lane. Samples were sequenced using a 2x150 bp paired end (PE) configuration. Image analysis and base calling were performed using HiSeq Control Software (HCS). Illumina’s bcl2fastq 2.17 software was used to convert raw sequenced data (.bcl files) into fastq files, and were de-multiplexed allowing one mismatch for index sequence identification.

A total of 36 RNA-Seq libraries from six separate cell lines (3 control replicates and 3 NU7441 treatment replicates each) representing three different SCLC subtypes are processed using the same pipeline for compatibility. Quality control has been performed using FastQC (v0.11.8) (Andrews 2010) [https://www.bioinformatics.babraham.ac.uk/projects/fastqc/]. Trim Galore! (version 0.6.5) has been used to trim the adapter sequences with a quality threshold of 20. The human genome reference used was GRCh38.p13, and GENCODE release 36 was used as the transcriptome reference. Samples DMS114 control replicate 3, H196 NU replicate 1, H720 control replicate 3, and H1341 control replicate 2 were removed from the analysis due to poor clustering in the PCA. The alignment is performed by using the STAR aligner (v2.7.5b)61. Gene level read counts are obtained by using Salmon (v1.2.1) for all libraries62. Remaining samples have passed the quality control requirements with >50% of reads uniquely mapping (>20M uniquely mapped reads for each library) using the STAR aligner.

Differential expression and functional analysis

Differential expression analysis was performed using the gene-level read counts and the DESeq2 (v1.28.1) R package63. Genes with less than 5 reads in total across all samples are filtered as inactive genes. Differentially expressed genes were defined as those with an adjusted p-value below 0.05 and an absolute log2 fold change greater than 1, with p-values adjusted using the Benjamin-Hochberg method in DESeq2. The over-representation and gene set enrichment analysis for functional enrichment are both performed using the clusterProfiler R package (v3.16.0)64. The gene sets used for functional analysis are obtained from The Molecular Signatures Database (MSigDB)65–67. The Fisher test was used to determine whether the overlap between the DEGs and the genes in the term is statistically significant (p < 0.05). The bold terms with an asterisk in front are the terms that are significantly enriched (p-adjusted < 0.05).

Principal component analysis, heatmaps and boxplots

Samples were normalized with the use of a variance stabilizing transformation (VST)-DESeq2 package. Principal component analysis (PCA) and Euclidean distance calculations between samples were based on the 1000 most variable genes. Gene expression heatmaps display z-scored VST-normalized gene-level read counts. Heatmaps in Supplementary Figs. S2A, B and S5C are generated using the R package pheatmaps (v1.0.12). Heatmaps in S2A-B represent z-scores of GSEA calculated enrichment scores. The genes were ranked based on correlation with IC50 values of NU7441 and Nedisertib in Supplementary Fig. S2A and S2B respectively to perform GSEA.

Heatmaps of Supplementary Figs. S5C shows z-scored DESeq2-normalized gene-level read counts for the specified genes in each marker group. Boxplots in Supplementary Figs. S1A, 4A are generated using ggplot2 (v3.4.2). Boxplot is Supplementary Fig. S1A represents public data available from CellMinerDB specifying the Cancer Cell Line Encyclopedia dataset and boxplots in 4A represents expression counts from current and previous bulk RNA-seq datasets. Boxplots depict normalized gene expression counts for labeled genes across sample conditions. Except for heatmaps, all visualizations are generated using the plotly R package (v4.9.2.1). R (v4.1.0) is used to perform all bioinformatics analysis.

Survival analysis

Survival analysis was performed using a publicly available dataset of small-cell lung cancer samples from study GSE30219. Samples were split into high or low categories by ranking the average expression of PRKDC and dividing the samples into the two groups. P-value is calculated using Cox regression. Plots and analysis were done using R packages Survminer (v0.4.9) and Survival (v3.2-11).

For survival analysis with the IMPower133 dataset which includes 271 patients of various ages, genders, treated with either Atezo or placebo. First, we set out to test for the association of PRKDC expression with progression-free survival (PFS). We used a Cox regression model with censored time-to-disease progression (in months) as the dependent variable, and PRKDC as the independent variable (continuous, tpm), exploring possible covariates such as age, sex, and treatment, as well as interaction terms. Examination of the scaled Schoenfeld residuals indicated that the proportional hazards assumption had been met. Next, we utilized the same statistical approach to test for the association of PRKDC expression with the overall survival (OS) of SCLC patients. Statistical analysis was performed using the “coxph”, “cox.zph”, and “ggsurvplot” functions from the R packages “survminer” version 0.4.9 and “survival” version 3.7.0.

Scatterplots and correlation analysis

Scatter plots and correlation analyses were performed using R packages ggplot2 (v3.4.2) and ggpubr (v0.4.0). Each dot in the plot represents one sample, the blue line represents a linear model applied to the dataset, and the gray shading depicts the standard error of the model. R value and p value are reported on plot, calculated as Pearson correlations. Expression data used in Supplementary Figs. S1C, S1D, and S5A, B are normalized counts from publicly available data from CellMiner CBD representing NCI-DTP SCLC cell lines. IC-50 values used in Supplementary Figs. are from publicly available data from GSE7316230.

Western blot

Protein extraction was done in a previously described method68. Briefly, post-treatment cells were collected in ice-cold PBS followed by centrifugation at 1000×g for 3 min. Cell pellets were resuspended in 1X RIPA buffer (Thermo Fisher, #89901) containing 5µM of Sodium Fluoride and 1X protease - phosphatase inhibitor cocktail (Thermo Fisher, #78446) and incubated on ice for 1h with intermittent vortexing every 5 min followed by centrifugation at 13000×g for 20 min at 4°C. Supernatant was transferred to fresh tubes were quantified using Coomassie Protein Assay reagent (Thermo Scientific, #1856209). Laemmli Sample Buffer (Bio-Rad, #1610747) was used to prepare samples and were run on 10-15% SDS-PAGE followed by transfer onto 0.45μm Immobilon-FL PVDF membrane (Millipore, #IPFL00010). Membrane was blocked with Blocker Casein (Thermo Scientific, #37532) in TBS (Fisher bioreagents, #BP2471-1) containing Tween 20 (Fisher bioreagents, #BP337-500) for 1 hour at room temperature. Then the membranes were probed with specific primary antibodies [listed in Supplementary Table S7] at 1:1000 dilution and incubated overnight at 4°C. Following 2 TBST washes for 5 mins each in shaking condition the blots were incubated with 1:10000 dilution of secondary antibodies containing horseradish peroxidase enzyme for 1 hour at room temperature. Following 5 times washes with TBST, each for 5 mins in shaking condition, blots were developed in iBright Western Blot Imaging Systems (Thermo Fisher) using SuperSignal West Pico PLUS Chemiluminescent Substrate (Thermo Scientific, #34577).

Micronuclei assay

Cells were seeded in 8 chambered slides and were treated with either vehicle [DMSO] or 4µM NU7441 for 1 day. 4 hours before the endpoint 3μg/ml Cytochalasin B was added. After the endpoint of treatment slides were washed twice with DPBS. The cells were lysed using a weak hypotonic solution (0.075M KCl/0.9 % saline, 1:9) by incubating at 37°C for five min. 3:1 methanol:acetic acid solution was used to fix the cells followed by washing with DPBS. The cells were blocked with 1% BSA by incubating at 37⁰C for 45 mins followed by 2 washes with PBS each for 5 mins. After washing, cells were incubated with cGAS and Lamin B1 primary antibody (1:300 dilution) at 4°C in a humidified chamber for 12–16 h. Then the cells were washed with PBS twice, each for 5 mins and incubated with secondary antibodies (Alexa Fluor 488 Rabbit Monoclonal and Alexa Fluor 647 mouse Monoclonal antibodies) (1:500 dilution) for an hour at 37°C in a humidified chamber and washed twice with PBS, each for 5 mins. Following the wash cells were counterstained with DAPI (2ug/ml) for 30 mins at room temp. Finally, stained cells were imaged with Leica STED3X at 63X lens.

Transfection of siRNAs

10nM of Silencer Select siRNAs (Invitrogen) against TMEM173 (STING) or PRKDC was transfected into cells using lipofectamine RNAi-MAX (Invitrogen) per the manufacturer’s protocol. As the scrambled control, Silencer Select siRNA for Negative Control no.1 (AM4611, Invitrogen) was used. We confirmed knockdown with quantitative RT-PCR.

shRNA mediated knockdown

MYC shRNA1 construct ref. seq. NM_002467 (Target seq: CCTGAGACAGATCAGCAACAA) was obtained from Sigma Aldrich, shRNA sequence2 (HSH088534) was bought from Genecopoeia and transduced into SCLC cell lines using lenti-viral vector according to the manufacturer’s instructions.

RNA isolation and cDNA preparation

Trizol method was used for RNA isolation. Post-treatment, cells were harvested in PBS, centrifuged and the pellets were resuspended in Trizol (ambion by life technologies #15596026). To 1ml of trizol, 0.2ml of 1-Bromo-3-chloropropane (Thermo Scientific, #106862500) was added and was mixed by inversion. The tubes were then incubated on ice for 2 min and then centrifugated for 15 mins at 12000×g in 4°C. The top layer (aqueous phase) was carefully taken out and transferred to new tubes. Following the transfer, 0.5ml of isopropanol was added to the solution and incubated for 10 min followed by centrifugation at 12000g for 10 minutes at 4°C. The RNA pellets were then washed with 1ml of 75% ethanol and centrifuged at 7500×g for 5 minutes at 4°C. This washing step was repeated twice and supernatants were discarded. The pellets were dissolved in DEPC water after air-drying the pellets. The dissolved RNA was incubated at 58°C for 10–12 min then immediately chilled on ice. RNA concentrations were measured with Thermo Scientific Nanodrop one, and cDNA was prepared with 2µg of RNA with Verso cDNA synthesis kit (Thermo Scientific #AB-1452/B).

Quantitative rt-PCR

To perform Quantitative PCR, prepared cDNA were diluted with DEPC treated water (1:4 or 1:5 ratio). MAstermix were prepared with diluted cDNA, PowerUp SYBR Green Master Mix (applied biosystems, #A25742) and forward and reverse primers to final volume of 10µl. The PCR cycle contained the following steps: initial denaturation at 95°C for 10 min followed by 40 cycles of denaturation (95°C for 30s), annealing (60°C for 30s) and extension (72°C 30s) and a final extension for 10 mins at 72°C. After the final extension, melting curve analysis was performed. Detailed primer list of the genes along with sequences are provided in Supplementary Table S8.

ELISA

SCLC cells (0.5×106) were seeded in 6-well plate in complete RPMI-1640 media. The next day cells were treated with either DMSO or 4µM NU7441 for 3 days. After the treatment condition media as well as the cell-free protein extract were collected. Elisa was performed to quantify INFa, IFNb, CCL5, CXCL10 (R&D Systems, Minneapolis, MN, USA) [Kits detailed in Supplementary Table S8] as per manufacturer’s instruction. Protein concentrations of each protein (pg/ml) was assessed from the standard curve.

Statistics and reproducibility

Linear mixed-effects regression models were used to estimate the association between time and tumor size of each treatment group for all the in vivo experiments (Figs. 2C–E, 3J–M, 5A, C, 6A–D, H–K, 7A, D). These models included tumor-volume as the dependent variable (Y), while considering time (days) and Treatment Group (Vehicle, NU7441 etc) as independent variables (X) along with interaction terms between Time and Treatment. To address the repeated measurement over time of the same mouse, a random intercept and and random slope for Mouse ID were included assuming an auto-regressive (AR) covariance structure across time.

Quantitative rt-PCR, flow cytometry and ELISA data were expressed as means±standard error of mean (SE). Whereas tumor volume data of the in vivo studies were expressed as means±standard deviation (SD). For the comparison between two groups paired two-tailed Students’ t-test was performed following ANOVA or dunnett’s test to assess significance among multiple group using GraphPad Prism Ver. 9.0 software. p-values of students’ t-test less than 0.05 was considered to be statistically significant (ns>0.05, ∗p<0.05, ∗∗p<0.01, ∗∗∗p<0.001). Data are expressed as the mean±SEM. All the in vitro experiments have been repeated at least twice for reproducibility.

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "It still remains largely unclear about the mechanisms preventing immune checkpoint blockade (ICB) from effectively reactivating antitumor immunity in SCLC. In this manuscript, Chakraborty et al. proposed an important role for nonhomologous DNA end joining (NHEJ) signaling in regulating immune evasion of SCLC. The authors found that the NHEJ regulator PRKDC, also known as DNAPKcs, was frequently overexpressed in SCLC and associated with unfavorable patient outcomes. They further showed that PRKDC expression was negatively correlated with the expression of key immune-related genes, and predictive of poor responses to immunotherapy in SCLC. Through functional studies, they demonstrated that inhibition of DNAPKCs by NU7441, a DNAPKcs inhibitor currently undergoing clinical trials, decreased tumor growth, induced immune cell infiltration, and enhanced the response to ICB therapy in two immunocompetent SCLC models. Mechanistically, DNAPKCs inhibition triggered GSK3β-mediated MYC degradation and subsequent activation of the STING pathway, which led to increased cytotoxic T-cell infiltration, pro-inflammatory M1 macrophage population, and upregulation of MHC-I, thus conversing immune ‘cold’ tumors into immune ‘hot’ tumors. Collectively, these findings highlight DNAPKcs as a potential therapeutic target to enhance the treatment response of ICB in SCLC. This work is interesting, aligning well with their previous studies showing that targeting DNA damage response pathways is an effective strategy to improve the anti-tumor immune response of ICB in SCLC preclinical models. Below, I list some concerns for the authors to further improve the manuscript.\n1. Fig. 1D and 1G show that high PRKDC expression predicted shorter OS in SCLC patients, both with or without chemotherapy. However, the criteria used to classify high and low PRKDC expression in these two panels appear to be different (11:10 in Fig. 1D, 164: 62 in Fig. 1G). Could the authors clarify the rationale for choosing different cut-off values in these cases? Additionally, the authors mentioned in Fig. 1G that a total of 423 patients treated with cisplatin were included in the survival analysis (Line 240 on page 8), yet the figure shows only 224 patients at the starting point. Could the authors address this discrepancy?\n2. The authors stated that “IFNγ response, IFNα response were found to be enriched in SCLC clinical samples with low baseline expression of PRKDC (Fig. 1E). Whereas E2F target genes, G2M checkpoint and MYC target genes were observed to be enriched in SCLC patient samples with high expression of PRKDC (Fig. 1F)” (Lines 201-204 on page 7). However, the direction of the enrichment profile curves in Fig. 1F appears unusual as they are in the opposite direction compared to the curves in Fig. 1E.\n3. Results from Fig. 3C and 3E show that NU7441 treatment significantly reduced the protein levels of total STING while simultaneously inducing STING signaling activation in DMS114 and H446 cells. However, the data in Fig. 3D indicate that PRKDC knockdown did not affect the protein levels of total STING in these cell lines. These data suggest that the mechanisms of action underlying pharmacological inhibition and genetic knockdown of DNAPKcs might be different. Did NU7441 treatment also inhibit the transcriptional levels of STING or affect its protein stability?\n4. From the results in Fig. 4E, the authors concluded that “NU7441 treatment led to an appreciable increase in the cGAS-STING pathway markers, p-TBK1 (Ser 172) and p-IRF3 (Ser396) in NTC cells which was relatively abrogated upon MYC knockdown (Fig. 4E)” (Lines 472-474 on page 15). Nevertheless, if take a close look at the result, it’s hard to conclude that MYC knockdown abrogated the effect of NU7441 on cGAS-STING activation. To confirm the role of MYC, the authors should perform MYC overexpression experiment in this setting to determine if MYC overexpression can reverse NU7441-mediated cGAS-STING activation.\n5. The proposed working model of this study suggests that DNAPKcs inhibition leads to MYC degradation and subsequent STING activation, which induces upregulation of interferon pathway and MHC-I expression. However, the data in Fig. 4L seem to contradict this model, as the combined effect of NU7441 treatment and MYC knockdown on the upregulation of MHC-I expression appears much less than the effect of either NU7441 treatment alone or MYC knockdown alone.\n6. Similarly, the data from Fig. 4P and 4Q show that MYC knockdown significantly reversed the NU7441-induced upregulation of CCL5 and B2M, suggesting that the presence, rather than the absence, of MYC is essential for the NU7441-induced upregulation of these genes. This finding also seems somewhat contradictory to the proposed working model of this manuscript.\n7. In Fig. 7A-B, why it was the NU7441 monotherapy group (Orange dotted line) but not the combination treatment group (Red dotted line) in the IgG control arm showed the greatest tumor regression, although the latter displayed the strongest CD8+ T cell infiltration? Additionally, as the authors did not present data on mice body weight changes following drug treatments, it remains unclear whether the combination treatment have any systemic toxicity.\n8. The current findings support that DNAPKcs might serve as a potential therapeutic target to enhance the response of ICB in SCLC. However, it is unclear whether DNAPKcs inhibition is more effective specifically in PRKDC-high SCLC. It would be informative for the authors to show the protein levels of PRKDC in the mouse models (RPP and RPM) and PDX models (LX110, LX33, and LX1322) tested.\n9. Some of the data are not convincing and should be improved. For example, in Fig. 2I, it appears that PRKDC knockdown did not induce an increase in cleaved PARP in DMS114 cells, nor did it induce a notable increase in cleaved caspase-3 and γ-H2AX in H446 cells. In Fig. 4E, NU7441 treatment did not induce an appreciable upregulation of p-STING. The protein levels of MYC in Fig. 4E-G should be included to better support the findings.", "reviewer_2": "In this manuscript, Chakraborty et al. investigate the role of nonhomologous DNA end-joining (NHEJ), specifically PRKDC/DNAPKcs, in small-cell lung cancer (SCLC) and discover the potential of clinically targeting this molecule to improve both chemotherapy and immune checkpoint blockade (ICB) immunotherapy. They show that PRKDC is overexpressed in SCLC and the patients with the highest PRKDC experience worse clinical outcomes. In addition, lower PRKDC expression predicts better responses to both chemotherapy alone and combined immunotherapy–chemotherapy treatments. They demonstrate that PRKDC inhibition with a small molecule inhibitor (NU7441), in addition to another inhibitor as well as siRNA-mediated knock-down, leads to increased cell cycle arrest, apoptosis, and micronuclei formation. Through a series of clever experiments, they show that PRKDC inhibition causes MYC degradation (through the AKT-GSK-beta signaling axis), which in turn activates the cGAS-STING pathway to upregulate several genes, including IFN family members, antigen presentation, and PD-L1. Using well-established mouse models, they show that NU7441 treatment alone, as well as in combination with anti-PD-L1 ICB, significantly decreases tumor growth, which is accompanied by several alterations in the tumor immune microenvironment towards a more active anti-tumor state.\nThis manuscript explores a relatively understudied NHEJ in a very aggressive cancer type, SCLC, and identifies PRKDC targeting as a novel treatment strategy to sensitize these tumors to both chemotherapy and immunotherapy. The authors employed multiple cell lines, mouse models, and patient-derived xenograft (PDX) models that cover various molecular subtypes of the heterogeneous SCLC to test their hypothesis. Their finding is significant to the field as both chemotherapy and ICB work relatively poorly (i.e., the effects are rather short-lived) for SCLC, and there is an immediate clinical need to improve these treatments through combination therapies. The manuscript is very well-written, and experiments were performed thoroughly with complementary approaches and various models. I firmly believe their findings will be valuable to the cancer community.\nPlease see below my comments that aim to suggest minor improvements:\n1) The authors show elevated PRKDC in SCLC and its predictive power for patient outcomes. Can you please also comment on mutations in this gene? How frequent are PRKDC mutations in SCLC, and do they show any correlations with clinical outcomes? In addition, is it possible to survey whether NHEJ as a pathway is enriched in tumors with high PRKDC levels?\n2) Does any of the key lineage transcription factors (i.e., ASCL1, NEUROD1, POU2F3, and YAP1) directly regulate the expression of the PRKDC gene?\n3) Figure 1D. The survival plot data is based on only 21 patients. Could this analysis be extended to larger cohorts, such as TCGA?\n4) Figure S1C. Is DDL3 correlation with PRKDC levels due to PRKDC being enriched in SCLC-A (as shown in Figure 1C)?\n5) Line 244. Please write the full drug name and its target; i.e. Atezolizumab (anti-PD-L1). Also, please clarify in the text that this clinical trial tests Atezolizumab in combination with chemotherapy.\n6) Figure 1I. The graph shows that tumors with lower PRKDC expression benefit ICB better. Since the authors also show that PRKDC can regulate both MYC and IFN, which are known to regulate PD-L1, it would be useful to know whether the PRKDC levels show any correlation with PD-L1 levels in this cohort. In other words, can PRKDC levels predict ICB response independent of PD-L1 levels?\n7) Figures 2C-E. Do PRKDC levels or MYC levels in these PDXs predict response to NU7441?\n8) Figures 3J-K. In line 409, the authors mention the importance of an intact immune system in PRKDC-mediated anti-tumor response but proceed to test the treatment in only athymic nude mice (immunocompromised). Would it not be more useful to test the drug on RPP and RPM transplants in both immunocompetent and immunocompromised mice side by side to address this question? Given that they later perform CD8 T cell depletion (Figure 7), the question is partially answered. So, additional experimentation would only be marginally useful, but it would be beneficial to revise the relevant text to better explain that there are both cancer cell-intrinsic (as NU7441 is effective even in nude mice) and extrinsic (and immune-mediated) effects (as CD8 T cell depletion decreases the effectiveness of the drug) of the drug.\n9) MYC T58 phosphorylation site is labeled as Thr-158P-cMYC) in multiple figures (e.g. Figure 4B). Please correct the labeling.\n10) Figure 4E. The drug treatment (i.e., PRKDC inhibition) does not seem to induce p-STING or p-IRF3 but only p-TBK1 on the western blots. As a reference, compare this to Figure 3C for the same cell line (i.e., H82).\n11) Figure 4F. While this experiment is useful, it would be more useful to test whether GSK3-beta inhibition could rescue the effect of NU7441 on MYC phosphorylation and degradation.\n12) Figure 4K-L. Why does H151 (STING inhibitor) treatment alone induce MHC-I? Should it not decrease MHC-I levels?\n13) Related to Figures 5-7. The authors should acknowledge in their discussion that the tumor immune microenvironment (TIME) of subcutaneous tumors is different than the TIME of orthotopic tumors and that it is a potential weakness of their study to use subcutaneous transplant models.\n14) Figure 5C. MYC is stably overexpressed due to the T58A mutation in the RPM model. Hence, one would expect that NU7441 treatment cannot induce STING pathway activation (as the previous figures suggest that STING activation is dependent on MYC degradation) in this model. Yet, the treatment is quite effective, and there is significant TIME alteration that resembles STING-IFN-mediated effects. Could you please expand your discussion of these findings and propose your explanation as to why the treatment is so effective and leads to TIME alteration despite stable MYC?\n15) Related to Figures 5 and 6. Please provide your flow-gating strategy and representative dot plots as a supplementary figure.\n16) Figure 7A. Could you please comment on why NU7441 alone worked better than its combination with anti-PD-L1 in the IgG control arm? As a reference, please compare to Figure 5A.", "reviewer_3": "In this manuscript, the authors report that PRKDC is highly expressed in SCLC tumor tissues and cell lines. The authors showed that PRKDC expression correlates with lower expression of immune activation signaling and predicts a poor response to multiple therapies. DNA-PKcs depletion leads to cytoplasmic DNA accumulation and micronuclei formation, thereby activating cGAS-STING signaling. Moreover, DNA-PKcs depletion promotes the proteasomal degradation of MYC, which seems to further enhance cGAS-STING signaling activation. DNA-PK inhibitors show a synergistic effect when combined with PD-L1 inhibitors, and this effect is dependent on CD8+ T cells. While this study provides insights into the role of DNA-PKcs in SCLC, substantial issues need to be addressed to consolidate the study.\nGeneral Comments:\n1.The role of the DNA-PK complex in cGAS-STING signaling has been extensively explored in the literature, with some studies presenting conflicting results. It would be beneficial for the authors to reference these studies and engage with the existing literature.\n2.The accumulation of cytoplasmic DNA and micronuclei following DNA damage or genome instability is widely accepted as a common mechanism for cGAS activation. However, DNA-PKcs inhibition or depletion typically results in a relatively weak DNA damage signal, and the accumulation of damage signaling takes a long time frame. This seems at odds with the treatment time frame in the study. The authors may clarify this point.\n3.MYC is a well-known oncogene and its role in suppressing cGAS-STING signaling has been explored. However, the mechanisms by which MYC suppresses cGAS-STING signaling in SCLC are not elaborated in this study. Moreover, the connection between MYC, DNA-PKcs, and cGAS/STING signaling shall be further strengthened.\n4.The study relies heavily on the use of inhibitors, but the potential off-target effects of these inhibitors are a critical consideration. When gene loss-of-function experiments were conducted, only one siRNA or shRNA was used. It would be more convincing if at least two independent siRNAs/shRNAs/sgRNAs were employed to confirm the phenotypes.\n5.Many immunoblot images presented lack sufficient quality, including uneven internal controls and high backgrounds.\nSpecific Comments:\n1.Following treatment with NU7441, the presence of micronuclei was confirmed by confocal microscopy (Fig. 2K and 2L). However, a time-dependent increase in micronuclei after NU7441 treatment was not observed.\n2.For Figure 2, protein levels are generally more reliable than mRNA expression for evaluating IFN-α, IFN-β, IFN-γ, and CCL5. Therefore, ELISA quantification is recommended to provide more strong evidence.\n3.In lines 450-453, the authors suggest that the baseline mRNA and protein levels of MYC correlate with the anti-tumor efficacy of DNA-PKcs inhibition. However, in the cases of LX1322 and LX33, the MYC mRNA levels do not fully correspond with its protein levels. The authors may provide explanation.\n4.In lines 513-515, the authors stated that NU7441 treatment did not further affect the surface expression of MHC-I in MYC knockdown cells. However, the flow cytometry data (Fig. 4K) show that MHC-I expression is further reduced in the MYC knockdown + NU7441 treatment group compared to MYC knockdown alone. This needs to be clarified.\n5.In Figure 4M-4P, some of the genes, such as IFNB1, showed enhanced transcription upon MYC knockdown+NU7441 treatment, while other genes do not display the effect. The authors may provide explanation. Additionally, the authors should use gene names (e.g., IFNB1) rather than protein names (e.g., IFNβ) in this context.\n6.In Figures 7A and 7C, the depletion of CD8+ T cells does not seem to promote SCLC tumor growth. This is unexpected, and the role of CD8+ T cells in SCLC could be further evaluated or at least discussed."}, "author_response": "Response: We thank the reviewer for the question.\nFigure 1D represents data from a publicly available GEO dataset consisting of 21 small cell lung cancer patient samples. This dataset was not stratified by any treatment status, and given the small sample size, we categorized patients into high and low PRKDC expression groups using a median split to maximize statistical power within this limited cohort.\nFigure 1G uses an independent, real-world clinical dataset (from Caris Life Sciences) comprising 423 patients with small cell lung cancer who were treated with cisplatin-based chemotherapy. For this larger dataset, we stratified patients by PRKDC expression into quartiles. We then compared patient prognosis between the highest and lowest quartiles. As a result, Figure 1G reflects survival data from the top and bottom quartiles only, totaling 224 patients (112 per group), which explains the apparent discrepancy with the full cohort size of 423 patients mentioned in the manuscript text.\n2. The authors stated that “IFNγ response, IFNα response were found to be enriched in SCLC clinical samples with low baseline expression of PRKDC (Fig. 1E). Whereas E2F target genes, G2M checkpoint and MYC target genes were observed to be enriched in SCLC patient samples with high expression of PRKDC (Fig. 1F)” (Lines 201-204 on page 7). However, the direction of the enrichment profile curves in Fig. 1F appears unusual as they are in the opposite direction compared to the curves in Fig. 1E.\nResponse: We thank the reviewer for the comment.\nThe enrichment curves in Figure 1F were originally shown curving toward the right because they reflected gene set enrichment in the high PRKDC expression group. In contrast, Figure 1E showed enrichment in the low PRKDC group, with curves curving to the left. This difference in the direction of the curves was due to how the data were plotted.\nThese figures continue to support our conclusion that low PRKDC expression is associated with interferon responses, while high PRKDC expression correlates with enrichment of E2F targets, G2M checkpoint, and MYC target genes. We believe this revision fully addresses the comment.\n3. Results from Fig. 3C and 3E show that NU7441 treatment significantly reduced the protein levels of total STING while simultaneously inducing STING signaling activation in DMS114 and H446 cells. However, the data in Fig. 3D indicate that PRKDC knockdown did not affect the protein levels of total STING in these cell lines. These data suggest that the mechanisms of action underlying pharmacological inhibition and genetic knockdown of DNAPKcs might be different. Did NU7441 treatment also inhibit the transcriptional levels of STING or affect its protein stability?\nResponse: We thank the reviewer for the question.\nTo clarify the apparent difference between pharmacological inhibition and genetic knockdown of DNAPKcs, we have repeated the STING western blots and confirmed that NU7441 treatment consistently reduces total STING protein levels compared to vehicle-treated DMS114 and H446 cells, as shown in Figure 3C and Figure 3E (also pasted below; R1A). To further investigate, we examined RNA sequencing data following NU7441 treatment.\nWe observed a trend toward reduced STING (TMEM173) RNA expression in four of five SCLC cell lines (figure below R1B), although the suppression was not statistically significant. This suggests that NU7441 may also influence STING at the transcriptional level to a limited extent.\nOne of the discrepancies found in this study in respect to pharmacological and genetic inhibition of DNAPKcs was protein expression of total STING. Given the dynamics signaling proteins, stability of STING not only depends on transcriptional and translational induction of STING but also degradation mechanism. ESCRT mediated micro-autophagy has been reported to be a significant contributor to STING degradation pathway [PMID:36918692, PMID:36739287]. Both mTOR and PI3K have been known to play a crucial role in initiating autophagy and lysosome mediated degradation of long lived / impaired proteins. Thus, it is possible that upon NU7441 mediated pharmacological inhibition of DNAPKcs mTOR or PI3K signaling may play a role in decreasing STING expression which is not found in case of siRNA mediated genetic inhibition of DNAPKcs.\nHowever, PRKDC knockdown by siRNA (Figure 3D) did not significantly reduce total STING protein levels in these cell lines. These findings support the notion that pharmacological inhibition with NU7441 and genetic knockdown of PRKDC may elicit partially distinct cellular responses, potentially due to differences in DNAPKcs complex dynamics or drug-induced protein destabilization. Pharmacological inhibition may affect STING through mechanisms involving protein stability or post-translational modification.\nWe have included the RNA sequencing results in the rebuttal and are happy to add them as a supplementary figure in the revised manuscript upon request. We have now included this apparent difference between genetic and pharmacological DNAPKcs inhibition in the discussion of the revised manuscript, highlighting it as an important observation that encourages further mechanistic validation in future studies. (page 27-28).\nFig. R1A\n4. From the results in Fig. 4E, the authors concluded that “NU7441 treatment led to an appreciable increase in the cGAS-STING pathway markers, p-TBK1 (Ser 172) and p-IRF3 (Ser396) in NTC cells which was relatively abrogated upon MYC knockdown (Fig. 4E)” (Lines 472-474 on page 15). Nevertheless, if take a close look at the result, it’s hard to conclude that MYC knockdown abrogated the effect of NU7441 on cGAS-STING activation. To confirm the role of MYC, the authors should perform MYC overexpression experiment in this setting to determine if MYC overexpression can reverse NU7441-mediated cGAS-STING activation.\nResponse: We thank the reviewer and editor for this insightful comment.\nAs correctly noted, we have revised the language in the updated manuscript to more accurately reflect our findings (Page 16). In addition, we performed a new set of experiments involving MYC overexpression in the H446 cell line, as suggested.\nThe new data, included in the revised manuscript, strongly supports and extends our original claims, confirming the functional link between MYC and the cGAS STING pathway. Upon MYC overexpression, we observed a reversal of the phenotype previously seen with MYC knockdown and DNAPKcs inhibition. Specifically:\n• Western blot analysis revealed downregulation of key cGAS STING signaling proteins following MYC overexpression, even in the presence of NU7441 treatment.\n• Quantitative RT PCR showed a decreasing trend in mRNA levels of type I interferons, CCL5, and B2M, indicating suppression of the innate immune signaling and antigen presentation regulators.\n• Flow cytometry demonstrated significant downregulation of MHC I surface expression in MYC-overexpressing cells, including under NU7441 treatment conditions.\nThese results, now presented in new Figure 4 as well as supplementary figure S5 and S6, provide compelling mechanistic evidence that MYC negatively regulates cGAS STING signaling and immune activation, and that MYC overexpression can override the immune-stimulatory effects of DNAPKcs inhibition.\nWe believe this new dataset not only fully addresses the reviewer’s concern but also strengthens the mechanistic foundation of our study and further validates the central conclusions of our manuscript.\n5. The proposed working model of this study suggests that DNAPKcs inhibition leads to MYC degradation and subsequent STING activation, which induces upregulation of interferon pathway and MHC-I expression. However, the data in Fig. 4L seem to contradict this model, as the combined effect of NU7441 treatment and MYC knockdown on the upregulation of MHC-I expression appears much less than the effect of either NU7441 treatment alone or MYC knockdown alone.\nResponse: We thank the reviewer for the thoughtful comment.\nSpecifically, we repeated the flow cytometry analysis in the H82 cell line and extended our study to include an independent SCLC line, H446, to verify the robustness of our findings. We also employed a second, independent MYC-shRNA sequence (MYC sh2) to validate the reproducibility of the observed phenotype.\nOur results confirm that both NU7441 treatment and MYC knockdown individually significantly increase the percentage of MHC I positive cells, consistent with our model that MYC repression leads to cGAS STING pathway activation and immune stimulation.\nImportantly, in the combination treatment group (NU7441 plus MYC knockdown), we observed that:\n• In H82 cells, combination treatment showed a further increase in MHC I positive cells compared to NU7441 alone (see Fig. S5E).\n• In H82 cells, using MYC sh2, we found that the combination of MYC sh2 and NU7441 treatment resulted in increased MHC I expression compared to MYC sh2 alone (new Supplementary Figure S5F).\n• Further in H446 cells MYC over expression significantly reversed the NU7441 induced increase in MHC-I positive H446 cell population (Fig. 4P)\nThe data supports our model that MYC suppression is a critical mediator of NU7441-induced immune activation in SCLC. However, the magnitude of response may vary depending on the baseline level of MYC activity and the dynamic range of cGAS STING pathway activation. The potential for a ceiling effect at downstream effectors of this pathway may limit additive effects when MYC is already maximally suppressed.\nWe have now explicitly addressed this point in the revised discussion (Page no. 27-28), including a section on the mechanistic convergence and the need for future validation to dissect potential feedback regulation and pathway saturation effects.\nThese new data strengthen our conclusions and reinforce the mechanistic link between DNAPKcs inhibition, MYC suppression, cGAS STING activation, and enhanced immune visibility via MHC I expression.\n6. Similarly, the data from Fig. 4P and 4Q show that MYC knockdown significantly reversed the NU7441-induced upregulation of CCL5 and B2M, suggesting that the presence, rather than the absence, of MYC is essential for the NU7441-induced upregulation of these genes. This finding also seems somewhat contradictory to the proposed working model of this manuscript.\nResponse: We thank the reviewer for raising this important point.\nTo directly address this issue, we conducted new RT-PCR experiments in the H82 cell line and extended our analysis to an independent model, H446, using two independent MYC shRNA constructs. Our data show that both NU7441 treatment and MYC knockdown independently and significantly upregulate CCL5 and B2M mRNA levels (new data in Figure 4 and newly added Figure S5 and S6). However, when MYC knockdown was combined with NU7441 treatment, we did not observe further increases beyond MYC knockdown alone.\nThese results suggest that NU7441-induced MYC suppression may be a dominant mechanism driving the upregulation of CCL5 and B2M, and once MYC is sufficiently suppressed, the downstream immune-stimulatory response may reach a functional plateau. This ceiling effect likely explains the apparent lack of synergy in the combination condition and does not contradict the proposed model but rather emphasizes the central role of MYC in mediating this response.\nTo further test our hypothesis, we performed MYC overexpression in H446 cells. The results provide compelling support for our model:\n• Western blot analysis revealed that MYC overexpression attenuates the NU7441-induced upregulation of key cGAS-STING pathway components.\n• RT-PCR analysis showed a decrease in CCL5, B2M, and Type I interferon mRNAs upon MYC overexpression in the presence of NU7441.\n4\n• Flow cytometry demonstrated a significant reduction in MHC I positive cells with MYC overexpression, even when NU7441 was present (Figure 4P).\nThese findings strongly support our working model that DNAPKcs inhibition induces MYC degradation, which in turn activates the cGAS-STING pathway and promotes immune gene expression and antigen presentation.\nWe have now included these new data and interpretations in the revised manuscript and have explicitly discussed this mechanistic nuance in the discussion (Page no. 27-28) and future validation section, emphasizing the need for further investigation into the saturation dynamics of this pathway and feedback regulation mechanisms.\nOverall, the new experiments reinforce and strengthen our claims, provide mechanistic clarity, and we believe that we have fully addressed this point satisfactorily.\n7. In Fig. 7A-B, why it was the NU7441 monotherapy group (Orange dotted line) but not the combination treatment group (Red dotted line) in the IgG control arm showed the greatest tumor regression, although the latter displayed the strongest CD8+ T cell infiltration? Additionally, as the authors did not present data on mice body weight changes following drug treatments, it remains unclear whether the combination treatment have any systemic toxicity.\nResponse: We thank the reviewer for this insightful comment.\nWe have carefully re-examined the tumor regression data and confirmed that the color coding for treatment groups in the original Figure 7A-B was inadvertently reversed between the NU7441 monotherapy and the combination treatment groups. We sincerely apologize for this oversight. In the revised manuscript, this has been corrected to accurately reflect the treatment conditions, and the figure now clearly shows that the combination treatment group (correctly labeled in red) displayed the greatest tumor regression, as expected based on our proposed model and consistent with the observed enhanced CD8+ T cell infiltration.\nTo directly address the reviewer’s concern regarding systemic toxicity, we have now included a new figure (Figure 5E and S4F) in the revised manuscript that presents the body weight profiles of mice across all treatment groups. These data demonstrate no significant changes in body weight throughout the duration of treatment, confirming that NU7441 monotherapy, anti-PD-1 monotherapy, and the combination treatment were all well tolerated, with no evidence of systemic toxicity.\n8. The current findings support that DNAPKcs might serve as a potential therapeutic target to enhance the response of ICB in SCLC. However, it is unclear whether DNAPKcs inhibition is more effective specifically in PRKDC-high SCLC. It would be informative for the authors to show the protein levels of PRKDC in the mouse models (RPP and RPM) and PDX models (LX110, LX33, and LX1322) tested.\nResponse: We thank the reviewer for this insightful comment.\nTo further investigate whether DNAPKcs inhibition is more effective in PRKDC-high SCLC, we performed new analyses on our preclinical models. We conducted a protein dot blot assay of the tumors from the three PDX models (LX110, LX33, LX1322) included in our study to assess DNAPKcs protein levels (Fig. R2A). In parallel, we evaluated PRKDC RNA expression using RNA-seq data from the tissue of the same PDX models (Fig. R2B).\nWe found that LX33 and LX1322 exhibited higher PRKDC RNA expressions, but this did not fully correlate with protein expression across all models. Notably, LX1322, the most responsive model to DNAPKcs inhibition, showed both elevated mRNA and protein levels, while LX110, the least responsive model, had the lowest expression. This supports the idea that PRKDC-high tumors may be more susceptible to DNAPKcs-targeted therapy.\nFurthermore, we performed western blot analysis in the two immunocompetent SCLC models, KP11 (RPP) and PB115 (RPM), to directly assess the expression of DNAPKcs and cMYC. As shown in the data below (Figure\nWe are providing the data in the rebuttal and will be happy to incorporate them as a new supplementary figure in the revised manuscript if the reviewer recommends so. This new analysis further strengthens our claims and addresses the reviewer’s concern comprehensively.\n9. Some of the data are not convincing and should be improved. For example, in Fig. 2I, it appears that PRKDC knockdown did not induce an increase in cleaved PARP in DMS114 cells, nor did it induce a notable increase in cleaved caspase-3 and γ-H2AX in H446 cells. In Fig. 4E, NU7441 treatment did not induce an appreciable upregulation of p-STING. The protein levels of MYC in Fig. 4E-G should be included to better support the findings.\nResponse: We thank the reviewer for these thoughtful comments and have taken several steps to address all concerns raised. Our revised manuscript includes new data and optimized experiments that strengthen our original findings:\n1. We repeated the Western blots for PRKDC knockdown using both the original siRNA and an independent siRNA sequence. These experiments show appreciable induction of phospho H2AX and cleaved PARP in both DMS114 and H446 cells. These results confirm activation of DNA damage and apoptotic responses and are presented in Figure 3, Figure 4 and Supplementary Figure S2.\n2. To further validate DNA damage, we performed confocal immunofluorescence microscopy for phospho H2AX in DMS114 and H446 cells, with or without NU7441 treatment. These data, now included in new Figure 2H and Figure 2I, demonstrate a significant increase in phospho H2AX signal following DNAPKcs inhibition.\n3. The cleaved caspase 3 Western blot was removed from the revised manuscript due to suboptimal signal quality, which we believe could detract from the overall clarity.\n4. In response to the concern about phospho STING levels in the original Figure 4E, we optimized and repeated the experiment. The revised blot now shows appreciable upregulation of phospho STING\n6\nnoisserpxE\nscKPAND\n)ytisned\ndetargetni\ndezilamroN(\nFig. R2A Fig. R2B\nFig. R2C\nfollowing NU7441 treatment, confirming pathway activation. This updated data is shown in the revised Figure 4E.\n5. As requested, we included MYC protein levels in the revised Figures 4E -G, demonstrating that NU7441 treatment leads to downregulation of MYC. These data support our model linking DNAPKcs inhibition to MYC suppression and subsequent STING pathway activation (cMYC for Figure 4E has been added in the same figure and total cMYC expression of 4F-G is in Supplementary Figure S5A).\nThese revisions directly address all points raised by the reviewer, and the new data further confirm and strengthen the mechanistic insights proposed in our study.\n7"}, {"round": 2, "reviewer_comments": {"reviewer_1": "The authors have answered my concerns and the manuscript is ready for the publication.", "reviewer_2": "The authors' responses and revisions are satisfactory. I have no further questions or comments.", "reviewer_3": "The authors have addressed my concerns, and I have no further comments."}, "author_response": ""}]

Decision letter