Intrinsic resistance to RAS inhibitors is driven by dysregulation of KRAS degradation

Domain: biology
Published: 2025-12-15
Editorial outcome: major_revision

Abstract
Activating mutations in KRAS occur in approximately 30% of lung adenocarcinomas. Despite advances in RAS-targeted therapies, intrinsic resistance limits their long-term efficacy. Here, we identify elevated levels of wild-type KRAS (WT-KRAS) protein as a key driver of intrinsic resistance in KRAS-mutant lung tumors. KRAS accumulation results from impaired LZTR1-mediated degradation, triggered either by LZTR1 loss or pharmacological RAS inhibition. Stabilized WT-KRAS activates the mTOR/HIF1α pathway by promoting lysosomal recruitment of the SLC3A2/SLC7A5 amino acid transporter complex, reprogramming lysosomal amino acid sensing. Shallow deletions of LZTR1, present in up to 40% of KRAS-mutant lung adenocarcinomas, are associated with increased mTOR activity and may contribute to therapeutic resistance to RAS inhibitors. Co-inhibition of mTOR or the SLC3A2/SLC7A5 complex using dactolisib or JPH203 restores sensitivity to KRAS inhibitors in vitro and in vivo. These findings support combinatorial targeting of mTOR signaling or amino acid transport to overcome intrinsic resistance in KRAS-mutant lung cancer.

Introduction
Lung cancer is one of the most common cancers worldwide, with an aggressive clinical course and high mortality rates due to the lack of efficient long-term therapy. Almost 30% of non-small cell lung cancers (NSCLCs) are caused by activating Kirsten rat sarcoma viral oncogene homolog (KRAS) mutations1. Mutations in the KRAS gene occur most frequently at codons G12, G13, and Q61 and activate KRAS signaling by either impairing the intrinsic GTPase activity of KRAS or structurally altering KRAS, which makes it insensitive to GTPase-activating proteins2.

Targeting RAS has been a central focus during the past four decades, and the efforts have intensified over the past years, stirred by the discovery of compounds tethered to the cysteine of KRAS G12C. The KRASG12C mutant-selective inhibitor, sotorasib, is the first FDA-approved targeted therapy against KRAS, in KRASG12C-mutated non–small cell lung cancer (NSCLC). A second KRASG12C inhibitor, adagrasib, received FDA approval in 2023, and several additional RAS inhibitors have now entered clinical investigation3. Mirati Therapeutics (acquired by Bristol Myers Squibb) and Boehringer Ingelheim have also reported KRASG12D mutant-selective inhibitors, MRTX1133 and BI-KRAS-G12D3,4. MRTX1133 has demonstrated potent in vitro and in vivo antitumor efficacy against KRAS-G12D-mutant cancer cells5. In addition, AZD0022 is currently being evaluated by AstraZeneca in a Phase I/II trial as a KRASG12D mutant-selective inhibitor in combination with other anti-cancer agents (NCT06599502). Complementing this effort, LY3962673 and INCB161734 from Lily and Incyte, respectively, demonstrate high potency, oral bioavailability, and robust selectivity for targeting mutant KRAS-G12D (NCT06586515, NCT06179160, respectively).

Sotorasib has demonstrated an overall response rate of approximately 37% in patients with non-small cell lung cancer (NSCLC). However, intrinsic resistance still presents a challenge, as nearly half of the patients experienced stable disease, and all treated patients developed drug resistance during the monitoring phase. This underscores the necessity for further research on resistance mechanisms6. While Boehringer Ingelheim has reported the discovery of direct pan-KRAS inhibitors7, long-term RAS suppression could also be accomplished by simultaneously targeting several RAS family members. Revolution Medicines has developed cyclophilin A-dependent molecular glues, such as RMC6236 or RMC7977, orally available pan-RAS inhibitors8,9. These Pan-RAS inhibitors presented significant antitumor activity9–11, and recent data from phase I trials evaluating RMC6236 indicated tolerability toward the treatment12 (NCT05379985). Preliminary clinical activity of pan-RAS inhibition was demonstrated by the fact that nearly one-third of PDAC and NSCLC patients showed a partial response to the treatment10. Investigating the mechanisms behind intrinsic and acquired resistance to RAS inhibitors is essential for identifying biomarkers that predict treatment response and developing strategies to overcome resistance and improve patient outcomes.

Although numerous studies have examined the role of wild-type (WT) and mutant KRAS allele dosage in tumorigenesis and therapeutic response13, they have overlooked the effect of this allele dosage on KRAS protein expression14. On the other hand, emerging data show that adaptive resistance to KRAS-G12C inhibitors in lung adenocarcinoma is associated with an increase in KRAS protein levels. In addition, genome-wide CRISPR screens have revealed that loss of leucine zipper-like translational regulator 1 (LZTR1), a key regulator of RAS proteostasis, suppresses the response to the KRAS-G12C inhibitor sotorasib15. The LZTR1/Cullin 3 complex mediates the ubiquitination of the RAS family members, such as KRAS, HRAS, NRAS, MRAS, and RIT1, and regulates their activity through either degradative or non-degradative mechanisms16,17. In addition, to sotorasib, loss of LZTR1 is associated with resistance to tyrosine kinase inhibitors18–20 and allosteric inhibitors of SHP221. While these findings suggest that KRAS protein dosage is involved in resistance to anti-RAS therapies, the effects of increased KRAS protein levels and the underlying mechanisms affecting RAS inhibitor response remain unexplored.

Here, we show that the elevated wild-type KRAS (WT-KRAS) protein levels drive intrinsic resistance in KRAS-mutant lung tumors. This accumulation results from impaired degradation by LZTR1, due to either LZTR1 loss or RAS inhibition. Stabilized WT-KRAS activates mTOR/HIF1α signaling by recruiting the SLC3A2/SLC7A5 amino acid transporter to lysosomes, altering nutrient sensing. LZTR1 deletion, found in up to 40% of KRAS-mutant lung cancers, correlate with increased mTOR activity and resistance to RAS inhibitors. Dual targeting of mTOR or the transporter complex with dactolisib or JPH203 restores drug sensitivity, highlighting a strategy to overcome resistance.

Results
RAS inhibitors block LZTR1-mediated degradation of RAS proteins
To investigate the impact of KRAS protein dosage beyond traditional allele modulation techniques, we employed two distinct strategies to increase KRAS protein expression in vivo, using the KrasG12Dlsl/wt mouse model of lung adenocarcinoma induced by an intratracheal administration of a Cre-recombinase expressing virus under the control of the alveolar type II cell-specific surfactant protein C (Sftpc) promoter. As a first approach, we injected a lentivirus encoding Cre-recombinase together with GFP or HA-tagged wild-type (WT-KRAS), driven by the Sftpc-specific promoter, in KrasG12Dlsl/wt mice. Histological analysis validated the expression of KRAS-G12D upon Cre infection in both Kras-G12D; GFP and Kras-G12D; WT-KRAS-OE mice (Fig. 1A, B). We also confirmed HA-tagged KRAS overexpression within the tumor nodules of Kras-G12D; WT-KRAS-OE mice, which developed large adenoma nodules compared to Kras-G12D; GFP mice (Fig. 1A, B). Concordantly, longitudinal micro-computed tomography (micro-CT) volumetric analysis revealed that increased KRAS expression led to accelerated tumor progression and was associated with decreased overall survival, with 30% mortality observed by the tenth week post-injection (Fig. 1C, D).

As a second approach to modulate WT-KRAS protein levels, we employed a Lztr1 knockout model. Previous studies showed that LZTR1 is more prone to interact with GDP-bound RAS proteins22,23 (Fig. 1E), strongly suggesting that LZTR1 is primarily involved in the degradation of KRAS-WT. Consistently, KRAS-WT co-immunoprecipitated with LZTR1 and exhibited increased ubiquitination in LZTR1-overexpressing cells, whereas the KRAS-G12D mutant failed to co-immunoprecipitate with LZTR1 (Supplementary Fig. S1A, B). Moreover, a global protein stability (GPS) analysis using a dual-fluorophore reporter designed to monitor KRAS protein stability revealed that LZTR1 has a markedly stronger effect on the stability of WT-KRAS than on mutant KRAS (Fig. 1F). Immunoblotting with total KRAS and KRAS-G12D-specific antibodies also demonstrated that LZTR1 suppression in lung cancer cell lines increased total KRAS protein levels, without affecting KRAS-G12D abundance, suggesting that the upregulation was restricted to the wild-type protein (Fig. 1G and Supplementary Fig. S1C).

Heterozygous loss of the LZTR1 locus occurs in approximately 40% of KRAS-mutant lung adenocarcinoma (LUAD) cases in the Cancer Genome Atlas (TCGA) dataset (Fig. 1H) and was associated with decreased LZTR1 expression and poorer progression-free survival (Supplementary Fig. S1D). Thus, Lztr1flox/wt; KrasG12D lsl/wt (Kras-G12D; Lztr1-HET) and Lztr1flox/flox, KrasG12D lsl/wt (Kras-G12D; Lztr1-KO) model aligns with the genetic alterations found in human lung cancer. We observed about 50% suppression of Lztr1 in sorted lung tumor cells isolated from either Lztr1-Het or Lztr1-KO mice after Sftpc-Cre recombinase-coding adenovirus injection (Fig. 1I and Supplementary Fig. S1E). This partial loss of LZTR1 could be due to partial Cre recombination upon intratracheal adenoviral injection, which is in line with the previous reports24,25. Nonetheless, this degree of suppression recapitulated the LZTR1 haploinsufficiency observed in human LUAD (Supplementary Fig. S1D). Whereas Cre-mediated recombination induced KRAS-G12D expression, LZTR1 suppression did not alter the levels of mutant KRAS (Fig. 1I). In contrast, LZTR1 loss led to increased levels of total KRAS, suggesting alterations of WT-KRAS protein levels (Fig. 1I). Furthermore, TMT-labeled proteomic analysis of sorted lung tumor cells revealed a significant increase in KRAS protein expression (Fig. 1J), without observing any changes in Kras mRNA levels (Supplementary Fig. S1F). Moreover, we also did not observe any significant upregulation of other RAS proteins in our mouse model (Fig. 1J). Thus, this model enabled us to investigate a physiologically relevant increase in WT-KRAS level mediated by dysregulation of LZTR1-mediated proteostasis.

Micro-CT-based analysis of tumor growth revealed an increase in tumor burden in Lztr1-HET mice (Fig. 1K). Hematoxylin-eosin (H&E) staining showed that lesions appeared in Lztr1-HET lungs at an earlier stage and progressed rapidly into larger, compacted adenoma nodules (Fig. 1L). Immunohistochemical analysis of the lung tissue revealed that the tumor lesions were positive for GATA binding protein 6 (GATA6), cytokeratin 19 (CK19), and transcription termination factor 1 (TTF1) (Supplementary Fig. S1G), recapitulating the characteristics of human papillary lung adenocarcinoma. Altogether, these results demonstrate that elevated KRAS protein levels, resulting from either WT-KRAS overexpression or impaired LZTR1-mediated degradation, accelerate KRAS-driven lung adenocarcinoma progression in vivo.

RAS inhibitors impair LZTR1-mediated RAS degradation
The pivotal role of WT-KRAS protein dosage in tumor development suggests that dysregulation of KRAS proteostasis might also affect therapeutic response. Pan-RAS inhibitors, such as RMC7977, enhance the intrinsic GTPase activity of oncogenic KRAS, thereby shifting it toward its inactive conformation26. Similarly, MRTX1133 stabilizes KRAS in the GDP-bound state27. Given that LZTR1 is known to mediate the degradation of GDP-bound RAS, it is plausible that LZTR1 could contribute to the tumor-suppressive effects of these inhibitors by promoting the degradation of inactive KRAS. However, we observed that RMC7977 treatment resulted in a significant accumulation of KRAS, whereas MRTX1133 treatment only caused a modest increase in KRAS protein levels (Fig. 2A, B). Concordantly, the GPS assay demonstrated that treatment with either RMC7977 or MRTX1133 led to WT-KRAS stabilization in an LZTR1-dependent manner (Fig. 2C, D).

A previous study identified the Switch I region of KRAS as a critical determinant for CRL (Cullin 3-RING Ligase)-mediated degradation26 (Fig. 2E). Specifically, the differential stability (deltaPsi) of KRAS-derived peptides in HEK293T cells treated with DMSO or the CRL inhibitor MLN4924 was assessed using a peptide tiling library cloned into the GPS reporter system. This approach revealed that Switch I was particularly susceptible to CRL-mediated degradation, suggesting that Switch I plays a crucial role in LZTR1-mediated degradation. RMC7977 directly binds to the Switch I region, while MRTX1133 induces conformational changes that propagate toward this domain9,28. A recent structural study solved crystal structures of the LZTR1 Kelch domain bound to KRAS, revealing that the LZTR1-KRAS interface involves loops adjacent to and including portions of the Switch I region29. Altogether, this strongly indicates that both compounds impaired LZTR1-mediated degradation of KRAS.

Notably, RMC7977 treatment completely abrogated the difference in KRAS expression levels between shGFP- and shLZTR1-expressing cells, whereas MRTX1133 only partially mitigated this difference (Fig. 2A–D). Consistently, RMC7977 induced comparable growth inhibition in shGFP- and shLZTR1-expressing cells (Fig. 2F). In contrast, LZTR1 knockdown in KRAS-G12D-mutant A427 lung cancer cells impaired their response to MRTX1133 treatment (Fig. 2G), highlighting the impact of WT-KRAS protein levels in modulating drug response. The reduced drug sensitivity observed in LZTR1-depleted cells was reversed upon re-expression of GFP tagged-LZTR1 (Fig. 2G, H).

We further assessed the impact of LZTR1 loss on the response to MRTX1133 in vivo. We found that, whereas MRTX1133 treatment dramatically suppressed the growth of Kras-G12D; Lztr1-WT tumors, it only modestly delayed the growth of Kras-G12D; Lztr1-HET tumors at earlier stages, with little to no significant effect at later time points (Fig. 2I). These results suggest that LZTR1-mediated proteostatic regulation of RAS proteins might affect the efficacy of RAS inhibitors, whereas LZTR1 downregulation in human cancer could be a common mechanism of intrinsic resistance to RAS inhibitors.

Increased levels of WT-RAS protein activate the mTOR/HIF1a axis
We next explored the mechanism by which increased WT-KRAS protein levels contribute to RAS inhibitor resistance. A gene set enrichment analysis (GSEA) of KRAS-mutant TCGA LUAD samples revealed that LZTR1 loss was associated with altered gene expression signatures linked to the mTOR pathway and hypoxia-related signaling, a transcriptional program downstream of mTOR5 (Fig. 3A). Proteomic and phosphoproteomic analyses of human lung cancer cells showed that mTOR signaling was one of the most significantly altered pathways upon LZTR1 knockdown (Fig. 3B, C; Supplementy Data 1–3). Consistently, LZTR1 suppression promoted mTOR re-localization to lysosomes, as evidenced by increased co-localization with the lysosomal marker LAMP1 (Fig. 3D). This was accompanied by elevated phosphorylation of canonical mTOR targets, p70S6K (70 kDa Ribosomal Protein S6 Kinase) and 4EBP1 (Eukaryotic Translation Initiation Factor 4E Binding Protein 1) (Fig. 3E). The activation of p70 S6K in cells depleted for LZTR1 was rescued by restoring LZTR1 expression (Fig. 3F). In line with mTOR activation, LZTR1 depletion also led to increased mRNA expression and elevated secretion of vascular endothelial growth factor A (VEGFA), a key target of the hypoxic response (Fig. 3G, H).

Proteomic and immunoblotting analyses of lung tumor cells isolated from Lztr1-WT and Lztr1-HET mice also revealed upregulation of mTOR signaling in the Lztr1-HET tumors (Fig. 4A, Supplementary Fig. S2A and Supplementy Data 1–3). Proximity ligation assay (PLA) analysis showed increased interaction between LAMP1 and mTOR in Lztr1-HET lung tumors, further indicating enhanced mTOR activity upon Lztr1 loss (Fig. 4B and Supplementary Fig. S2B). Lztr1 haploinsufficient tumors also exhibited elevated HIF1α nuclear immunostaining (Fig. 4C), while ELISA-based cytokine array analysis demonstrated increased VEGFA secretion in the supernatant of Lztr1-HET lung tumor cells (Fig. 4D). Optical tomography (OPT) of cleared lungs also revealed a significant difference in the blood vessel architecture between Kras-G12D; Lztr1-HET and Kras-G12D; Lztr1-WT tumors. In Lztr1-WT mice, the vasculature surrounding the tumors was organized, with larger vessels branching into smaller ones in a hierarchical pattern. In contrast, in Lztr1-HET mice, the tumor-associated blood vessels were disorganized and lacked a conventional hierarchy, as quantified by vascular tortuosity (Supplementary Fig. S2C). Moreover, the FITC-dextran diffusion assay indicated leaky vessels adjacent to the Lztr1-HET tumors (Supplementary Fig. S2C).

In the Kras-G12D; WT-Kras model, elevated KRAS-WT protein levels also promoted increased mTOR activity, as indicated by higher phosphorylation of p70S6K (Fig. 4E). Enhanced mTOR signaling was accompanied by increased VEGFA secretion and vascular abnormalities (Fig. 4F and Supplementary Fig. S2D). Collectively, these findings demonstrate that KRAS-WT up-regulation contributes to the activation of the mTOR/HIF1α signaling axis and subsequent vascular remodeling.

RMC7977 treatment, which induced KRAS-WT overexpression, also led to the activation of mTOR signaling, as evidenced by increased interaction between mTOR and LAMP1, enhanced phosphorylation of mTOR downstream targets, and elevated VEGFA secretion (Fig. 4G–J). In contrast, these effects were significantly reduced in cells lacking LZTR1, in which RMC7977 treatment did not trigger additional mTOR signaling. This is likely because KRAS degradation was disrupted in these cells, leading to higher baseline levels of KRAS protein (Fig. 4G–J). These findings indicate that either LZTR1 loss or RMC7977 treatment increases KRAS-WT protein levels, thereby promoting mTOR activation and VEGFA upregulation.

Increased wild-type KRAS protein promotes the mTOR via the lysosomal re-localization of the SLC3A2/SLC7A5 complex
We next assessed how elevated KRAS-WT protein levels lead to mTOR activation. Because solute carrier (SLC) transporters regulate amino acid availability and thereby control mTOR activity, we focused on the function of the SLC3A2/SLC7A5 heterodimer, a major transporter of large neutral amino acids. Proximity-based proteomic analyses showed that both wild-type and oncogenic KRAS bind the SLC3A2/SLC7A5 heterodimer at comparable levels (Fig. 5A, B). Concordantly, SLC7A5 showed comparable co‑immunoprecipitation with both WT‑KRAS and KRAS‑G12D (Supplementary Fig. S3A), indicating that this interaction is independent of KRAS mutational status. We confirmed that mTOR activation and VEGF upregulation following LZTR1 loss relied on SLC3A2/SLC7A5 expression (Fig. 5C–E and Supplementary Fig. S3B), comfirming that LZTR1 can modulate mTOR signaling through the SLC3A2/SLC7A5 complex.

Previous studies have indicated that RAS signaling can increase the expression of certain SLC transporters30, we did not detect any changes in SLC3A2 or SLC7A5 expression in cells with LZTR1 depletion or upon treatment with RMC7977 (Fig. 5F). Consistently, amino acid uptake and secretion were comparable between shGFP and shLZTR1 cells, indicating that LZTR1 does not regulate amino acid transport at the plasma membrane (Supplementary Fig. S2E, F). On the other hand, a recent study demonstrated that KRAS can organize a signaling complex with the amino acid transporter, SLC3A2/SLC7A5, and mTOR on endolysosomes, which directly activates mTORC131. Lysosome-IP analysis revealed that elevated KRAS levels increased the recruitment of both the SLC3A2/SLC7A5 complex and mTOR to LAMP1-positive endolysosomal membranes (Fig. 5G). We also observed heightened colocalization of SLC3A2 with the lysosomal marker LAMP1 in lung cancer cells either depleted of LZTR1 or treated with RMC7977 (Fig. 5H).

This suggests that high levels of WT-KRAS could promote mTOR activation by driving endocytic delivery of the SLC3A2/SLC7A5 complex to the endolysosome. In agreement with this idea, we found that the pharmacological inhibition of endocytic trafficking to the lysosome using Dyngo-4a32 eliminated the difference in p70S6K phosphorylation downstream of mTOR between shGFP and shLZTR1 expressing cells (Supplementary Fig. S3C). Once re-localized on lysosomes, the SLC3A2/SLC7A5 heterodimer triggers exchange of intralysosomal glutamine for extracellular leucine, inducing mTOR re-localization on endolysosomes and its activation33,34. Proximity between mTOR and SLC3A2 was confirmed in lung cancer cells with depleted LZTR1 and in lung tumors from Lztr1-HET mice (Fig. 5I, J). Thus, elevated KRAS-WT can induce mTOR signaling through an “in-out” amino-acid delivery pathway that converges on lysosomal nutrient sensing.

Targeting LAT1/mTOR overcomes wild-type KRAS-induced resistance
We next assessed strategies to overcome intrinsic resistance to KRAS inhibitors. Impaired KRAS degradation in LZTR1-deficient cancer cells led to reduced sensitivity to MRTX1133 in vitro, indicating a cell-intrinsic resistance mechanism linked to mTOR pathway activation (Fig. 2F, G). In vivo, elevated WT-KRAS levels also resulted in altered vascular architecture (Supplementary Fig. S2D), potentially affecting drug delivery. Thus, both tumor-intrinsic and microenvironmental factors could contribute to the limited efficacy of RAS inhibitors. Thus, we investigated the potential of LAT1/mTOR inhibition to address both tumor-intrinsic and vascular aspects of resistance to KRAS inhibitors.

Whereas the KRAS inhibitor MRTX1133 failed to reverse the upregulation of VEGFA secretion in A427 cells, the mTOR inhibitors everolimus or the dual PI3K/mTOR inhibitor dactolisib (NVP-BEZ235) abrogated the VEGFA upregulation (Fig. 6A). In contrast to MRTX1133, dactolisib treatment also led to a significant reduction in p70 S6K phosphorylation (Fig. 6B), a decrease in HIF1α nuclear expression, and normalized vasculature in Lztr1-HET tumors (Fig. 6C, D). These findings suggest that LZTR1-mediated KRAS stabilization confers resistance to MRTX1133 by promoting mTOR-driven survival signaling and vascular abnormalities, allowing tumors to evade inhibition of KRAS-G12D. In Kras-G12D; Lztr1-WT mice, MRTX-1133 monotherapy produced profound and sustained tumor regression, with virtually complete inhibition of growth over the treatment period (Supplementary Fig. S4A). By contrast, Kras-G12D; Lztr1-HET tumors showed limited response to MRTX1133 alone, continuing growth despite drug administration. To overcome this resistance, we combined MRTX1133 with dactolisib. This combination not only induced tumor regressions but also prevented rebound growth, producing durable suppression of tumor burden (Fig. 6E). These results indicate that Lztr1 heterozygosity confers partial resistance to KRAS inhibition, which can be effectively overcome by concurrent blockade of the mTOR axis.

Consistent with the role of the SLC3A2/SLC7A5 amino acid transporter complex in WT-KRAS–driven mTOR activation, pharmacological inhibition of LAT1 using JPH203 blocked phosphorylation of downstream mTOR effectors and increased VEGFA secretion (Supplementary Fig. S3D, E). Similar rescue effects were observed in JPH203-treated lung cancer cells overexpressing KRAS-WT (Supplementary Fig. S3F, G). In the Kras-G12D; Lztr1-HET lung cancer model, combining MRTX1133 with JPH203 resulted in sustained tumor growth suppression (Fig. 6F). Importantly, no signs of weight loss were observed during 12 weeks of the combination period with MRTX1133 and either dactolisib or JPH203 (Supplementary Fig. S4B, C), supporting the tolerability of these combination regimens.

We also assessed whether the inhibition of LAT1/mTOR induced by KRAS-WT stabilization sensitizes cancer cells to pan-RAS inhibitor RMC7977. We observed a synergistic interaction in H727 cells when combining RMC7977 with either dactolisib or JPH203. The most pronounced synergy occurred within the concentration range surrounding the IC50 values of both agents, as indicated by the Most Synergistic Area (MSA) analysis (Fig. 6G, H and Supplementary Fig. S5). Together, these findings suggest that pharmacological inhibition of mTOR signaling driven by elevated KRAS-WT protein levels may represent a viable strategy to overcome intrinsic resistance and enhance the therapeutic efficacy of direct RAS inhibitors.

Methods
Genetically modified mice
All procedures involving animals were performed in accordance with the guidelines of the IACUC of KU Leuven and approved project P203/2020. The animal studies were carried on mice, strain C57BL6/Jb, only on males, that were used between 4 weeks and 18 weeks of age. The maximal tumor burden permitted by the ethics committee is 2000 mm3, and this maximal tumor burden was not exceeded.

To generate the genetically engineered mice, Lztr1tm1a(EUCOMM)Wtsi embryonic stem cells were purchased from EUCOMM (EPD0140_5_E07, EPD0140_5_G06) and used for in vitro fertilization of female CD-1 cells. The primers used for genotyping were as follows:

Sequence
neo Forward 5´-AAGATGGATTGCACGCAGGTTCTC-3´
Reverse 5´-GACGAGATCCTCGCCGTCGGGCATGCGCGCC-3´
Lztr1 Forward 5´-AGGTGAGGGGTAGAGGAAGC-3´
Reverse 5´-TTTCTGACCCAACTCCCAAG-3´

Lztr1fl/fl mice (Lztr1tm1c(EUCOMM)Wtsi) were generated by flippase-mediated recombination by breeding Lztr1tm1a(EUCOMM)Wtsi mice with Gt(ROSA)26Sortm1(FLP1)Dym 1 mice. Lztr1fl/fl mice were subsequently backcrossed on C57BL6/J background with B6.129S4-Krastm4Tyj/J (LSL-KrasG12D; RRID:IMSR_JAX:008179) using the project “Creation Sablina 2020” in accordance with the guidelines of the IACUC of KU Leuven. Interbred KrasG12Dlsl/wt, Lztr1fl/fl mice exhibited normal development, were viable and fertile, and were bred on the “GS2 breeding Sablina” project. Naïve mice showed no disease or pain phenotypes.

Prof. Anna Sablina is the owner of the mice, according to the MTAs signed by EUCOMM and the Jackson Laboratory.

Plasmids
Plasmid name Source
MSCV-CMV-DsRed-IRES-EGFPDEST Addgene plasmid #41941
pDONR223-KRAS4B Addgene plasmid #81751
pDONR223-KRAS4BG12V Addgene plasmid #81665
pDONR223-KRAS4BG12D Addgene plasmid #81651
pDONR223-KRAS4BG13D Addgene plasmid #82808
pDONR223-KRAS4BG12C Addgene plasmid #81667
pLentiCMVDsRedIRESGFP Gateway recombination KRAS4BWT
pLentiCMVDsRedIRESGFP Gateway recombination KRAS4BG12D
pLentiCMVDsRedIRESGFP Gateway recombination KRAS4BG12C
pLentiCMVDsRedIRESGFP Gateway recombination KRAS4BG12V
pLentiCMVDsRedIRESGFP Gateway recombination KRAS4BG13D
pMT107–6xHis–ubiquitin a gift from Dr. Bohmann (University of Rochester, USA)
shRNA targeting GFP generated by the RNAi Consortium (TRC) TRCN0000072178
SLC3A2 Sigma Mission shRNAs generated by the RNAi Consortium (TRC) TRCN0000043383, TRCN0000043384, TRCN0000043386
SLC7A5 Sigma Mission shRNAs generated by the RNAi Consortium (TRC) TRCN0000043008, TRCN0000043009, TRCN0000043010
LZTR1 shRNAs generated by the RNAi Consortium TRCN0000240480, TRCN0000181026
pLJC6-3XHA-TMEM192 Addgene #104434
pLACMVHALZTR1WT Generated as described in ref. 17
pLACMVGFPLZTR1WT Generated as described in ref. 17
pLV-SPB>HA-mKras4B:IRES:Cre Generated by VectorBuilder
pLV-SPB>EGFP:IRES:Cre Generated by VectorBuilder
pTwistFlagKRAS4BWT Generated by Twist Bioscience
pTwistFlagKRAS4BG12D Generated by Twist Bioscience

All plasmids were verified using DNA sequencing (Eurofins Genomics).
MSCV‑CMV‑DsRed‑IRES‑EGFP‑DEST was gift from Stephen Elledge; pDONR223‑KRAS4B plasmids was gift from Jesse Boehm, William Hahn & David Root; pLJC6‑3XHA‑TMEM192 was gift from David S. Sabatini.

Cell lines and reagents
EPCAM-positive cells were isolated from mouse lung tumors after homogenization and digestion using the Multi Tissue Dissociation Kit 2 (Miltenyi Biotech). The digested mix of cells was first incubated with anti-CD45 beads to remove CD45-positive cells and then isolated with anti-EPCAM magnetic beads (Miltenyi Biotech). The quality of each fraction was evaluated using immunostaining or immunoblotting for CD45, pan-cytokeratin, and EPCAM.

Lentiviral infection was performed as described by the RNAi Consortium (TRC). Infected cells were selected using puromycin for three days (2µg/ml, InvivoGen) or hygromycin B for seven days (500µg/ml, InvivoGen). Cells were transfected using GeneJuice (Merck) or Lipofectamine 3000 (Thermo Fisher Scientific).

Cell growth was measured with the real-time monitoring system, Incucyte (Essen Bioscience). A total of 5000 cells were plated in a 96-well plate in triplicate, and the cell index was measured in at least three independent experiments.

Drugs were purchased from MedChemExpress and used at the dose indicated below:
In vitro In vivo
MRTX1133 1–200nM 10mg/kg
Dactolisib (NVPBEZ235) 1µM 10mg/kg
Dyngo4a (hydroxydynasore) 10µM –
Everolimus 1µM –
JPH203 1µM 10mg/kg
RMC7977 1–100nM –

Amino acids uptake and secretion measurements
Cells were cultured in standard growth medium lacking GlutaMAX supplemented with 5% FBS and 1× penicillin–streptomycin (P/S). To establish baseline metabolite levels, fresh medium (without FBS or P/S) was collected at the start of the experiment. Conditioned medium was sampled in parallel with cell-free control wells.

Metabolites were extracted from 20µL medium using 500µL chloroform and 800µL MeOH/H20 (5:3, v:v) containing 0.5µg/mL glutarate and norvaline as internal standards. Next, samples were shaken and centrifuged to separate the polar and non-polar phase. The polar MeOH/H2O phase was transferred and dried overnight under vacuum at 4°C. Subsequently, samples were resuspended in 50µL 80% methanol in water.

For the analysis of media samples by LC-MS, a 1290 Infinity II liquid chromatography (Agilent Technologies) with a thermal autosampler set at 4°C, coupled to a Q-TOF 6546 mass spectrometer (Agilent Technologies) was used. 5µL sample was injected into an InfinityLab Poroshell 120 HILIC-Z, 2.1×150mm, 2.7µm (Agilent Technologies). The separation of metabolites was achieved using a gradient for 32 min at 50°C with a flow rate of 0.25ml/min (solvent A: 10mM ammonium acetate (pH 9) in water; solvent B: 10mM ammonium acetate (pH 9) in 85% acetonitrile; 0 min: 4% A, 2 min: 4% A, 5.5 min: 12% A, 8.5 min: 12% A, 9 min: 14% A, 14 min: 14% A, 19 min: 18% A, 25 min: 35% A, 27 min: 35% A, 28 min: 4% A, 32 min: 4% A). The MS operated in negative mode (m/z range: 50–1200) using a sheath gas temperature of 350°C (12 L/min) and a gas temperature at 225°C (13 L/min). The nebulizer was set at 35 psi, the fragmentor at 125 V, and the capillary at 3500 V.

Data was analyzed using the Masshunter Profinder software (Agilent Technologies, version 10.0) and normalized by the internal standard. To quantify absolute metabolite concentrations, a standard curve for each metabolite was used. Standards were extracted and analyzed in parallel to the other samples. Specific uptake or secretion rates (q) were calculated as (Δmetabolite × growth rate) / Δcell number, where Δmetabolite = (metabolite concentration – blank).

Intratracheal Adenoviral injection
The Adenovirus expressing Cre recombinase under the Surfactant Protein C (Sftpc) promoter was purchased from The University of Iowa Gene Transfer Vector Core (https://vector-core.medicine.uiowa.edu/collections/ad5/products/ad5mspc-cre). The virus was added to MEM and prepared in a sterile syringe with a 23-20-gauge tracheal tubing or a 22-gauge venous catheter. The injected volume was ~0.05 ml. Mice were anesthetized using an appropriate method, such as an intraperitoneal injection of ketamine and xylazine. The mouse was placed in the prone position on a surgical platform. The neck region of each mouse was exposed and disinfected using povidone-iodine. A small incision was made in the skin and subcutaneous tissue to expose the trachea. Alternatively, a non-invasive method that did not require an incision was used, such as inserting the tubing through the mouth and into the trachea. The tubing was carefully inserted into the trachea to prevent damage to the surrounding tissues. The substance was slowly injected into the trachea to prevent leakage and reflux. The chest movements of mice were monitored to confirm that the substance had reached the lungs. The tubing was withdrawn, and the incision was closed with sutures. The mice were monitored until they recovered from anesthesia, and analgesia was provided if needed.

Computed micro-computed tomography (micro-CT)
Before the start of the imaging sessions, the animals underwent at least one week of acclimatization to the new environment. The animals were anesthetized using isoflurane (induction at 3% in 100% oxygen and maintenance at 1-2% in 100% oxygen). The animals were scanned using a dedicated in vivo micro-CT scanner (SkyScan 1278, Bruker micro-CT, Kontich, Belgium) at an X-ray dose that was well tolerated47–49. The images were acquired with the following parameters: 65 kVp X-ray source voltage and 350 μA current combined with an X-ray filter of 1mm aluminum, 150ms exposure time per projection, three projections averaged per view, acquiring projections with 0.9° increments over a total angle of 220°, producing reconstructed 3D datasets with a 50μm isotropic reconstructed voxel size. The total scanning time per mouse was approximately 2.5min, resulting in a measured delivered radiation dose of approximately 60–80mGy per scan. A baseline scan was performed before the adenoviral injection, and four weeks after the injection, scans were performed every two weeks. The total nodule volume was evaluated after segmentation of the lung using MIPAV (NIH). Next, each region was analyzed to extract features, such as shape, texture, and intensity, which can help identify the nodules. The nodules were then separated from the blood vessels using a mathematical formula called the sphericity parameter, which measures the sphericity of an object. The more spherical an object is, the more likely it is that it is a nodule and not a vessel. Finally, the volume of each nodule was calculated using a technique called volumetric analysis, which estimates the volume occupied by nodules using the volumetric neuroimaging analysis extension of MIPAV, according to the methodology described before50.

Optical projection tomography of cleared lungs
Before sacrifice, FITC dextran (70 kDa) was injected in the ventricle and left for circulation for 5 min in adult mice (500 μl at 10 mg/ml). Lung clearing was then performed according to the Life Canvas guidelines, using the passive clearing protocol for the Passive Clearing Kit (C-PCK-250). Immunostaining for Smooth Muscle Actin (SMA) was performed according to the guidelines of Life Canvas. The cleared lungs were scanned using dedicated optical projection tomography Bioptonics 3001 (Skyscan, Kontich, Belgium). The images were acquired with the following parameters: TexasRed laser power, excitation 560/40nm, emission 610nm, 100ms exposure time per projection, three projections averaged per view, acquiring projections with 0.9° increments over a total angle of 360°, producing reconstructed 3D datasets with a 6.7μm isotropic reconstructed voxel size. Quantification of vascular tortuosity on volumes after reconstruction of the vascular network using the Bitplane Imaris Surpass mode, skeletonization of the network using the Skeletonize 3D plugin, and quantification of morphometric parameters using the Analyze Skeleton plugin, according to approaches validated previously51,52.

Cytokine array and ELISA assay
Cytokine arrays to detect mouse and human cytokines were performed according to the manufacturer’s protocol (RayBiotech). The array was scanned using the TECCAN Powerscanner and analyzed using the provided software. Mouse Cytokine Array C1 (AAM-CYT-1) (RayBiotech). An ELISA assay to measure VEGFA levels was performed according to the manufacturer’s protocol (BioLegend, 430204) using the lung cancer cell supernatant.

Antibodies
The following antibodies/dyes were used:
Mouse Anti-Vinculin (Sigma Aldrich, clone hVIN-1, V9131), monoclonal anti KRAS-G12D mutant (GTX635362, GeneTex), Anti-RIT1 (GTX117329, GeneTex), Anti-Actin α-Smooth Muscle-Cy3 (Sigma Aldrich, C6198), anti CD107a (LAMP-1) (Biolegend, 328601), Anti-4EBP1 Phospho(Thr70) (Biolegend, 619601), Anti-mTOR (Biolegend, 659202), anti CD98 (SLC3A2) (Biolegend, 315602), Anti-Actin, α-Smooth Muscle-Cy3 antibody (Sigma-Aldrich, C6198), Anti-Flag M2 Antibody (Sigma-Aldrich, F3165).
Rabbit Anti-Cytokeratin 19 (10712-1-AP, Proteintech), Anti-FLAG Epitope (DYKDDDDK) (740001, ThermoFisher), Anti-GATA6 (55435-1-AP, Proteintech), Anti-GFP (NB600-308, Novus Biologicals), anti HIF1a (H1alpha67, Novus Biologicals), Anti-KRAS (12063-1-AP, Proteintech), anti TTF1 (66034-1-Ig, Proteintech), Anti-Phospho-p70S6 Kinase (Thr389) (108D2) (Cell Signaling, 9234S), Anti-p70S6 Kinase (108D2) (Cell Signaling, 9202S), Anti-4EBP1 (Sanbio, 60246), Anti-mTOR (7C10) (Cell Signaling, #2983), LAT1/SLC7A5 (E9O4D) (Cell Signaling, 32683S), Anti-4F2hc/SLC3A2 (D6O3P) (Cell Signaling, 13180S), anti-ubiquitin (ab7780, Abcam)

The following secondary antibodies were used: Donkey or Goat Alexa 488- or Alexa 568 or Alexa 594- or Alexa 647-conjugated secondary antibodies (ThermoFisher, Molecular Probes) (A10037, A10042, A11005, A11006, A11012, A11034, A11057, A21201, A21245, A21247, A21447) or HRP-labeled antibodies (DAKO) (P0447, P0448).

Immunoblotting
Cells were washed in cold PBS and scraped on ice in NP40 lysis buffer (50mM Tris-HCl pH 7.5, 150mM NaCl, 1% NP40, and 5% glycerol) or RIPA lysis buffer (50mM Tris-HCl pH 7.5, 150mM NaCl, 1% NP40, 0.5% sodium deoxycholate, and 0.1% SDS) containing a protease inhibitor cocktail (Merck Millipore) and phosphatase inhibitors (Sigma-Aldrich). The cell lysates were cleared for 15 min at 16,000 x g and 4°C. Protein concentration was quantified using the Pierce BCA Protein Assay Kit (Thermo Fisher). For immunoblotting, the proteins were separated using SDS-PAGE gels (Thermo Fisher) and transferred to nitrocellulose membranes. The membranes were immunoblotted with primary and secondary HRP-conjugated antibodies. Chemiluminescence was detected with a chemiluminescent substrate kit (Thermo Fisher Scientific) using a digital developer.

Immunoprecipitation and lysosome immunoprecipitation (LysoIP)
For immunoprecipitation, cells were washed twice in cold PBS and scraped on ice in lysis buffer (50mM Tris-HCl pH 7.5, 150mM NaCl, 1% NP-40) containing protease inhibitor, phosphatase inhibitor cocktails (Roche). Samples were subsequently cleared by centrifugation for 10 minutes at 16,000 g at 4°C. Protein concentration was measured using the Pierce BCA assay, equal amounts of proteins were incubated with anti-GFP Nanobody Affinity Gel (BioLegend cat. # 689304) or anti-FLAG M2 affinity gel (Sigma Aldrich, Anti-FLAG M2 affinity gel, A2220) overnight at 4°C, washed five times with cold co-immunoprecipitation buffer, and immunoprecipitants eluted by boiling or by competition using 3X FLAG peptide.

For immunoprecipitation of ubiquitinated proteins, HEK293T cells were co-transfected with 6xHis–ubiquitin and Flag–KRAS. Ubiquitinated proteins were purified as described previously53. Briefly, cells were lysed in a co-immunoprecipitation buffer containing an EDTA-free protease inhibitor cocktail (Roche). Cell lysates were mixed with His-buffer A (PBS, pH 8.0, 6M guanidinium-HCl, 0.1% NP-40, and 1mM β-ME) and added to TALON beads (Clontech). After binding, the resin was washed with His-buffer B (PBS, pH 8.0, 0.1% NP-40, 5% glycerol, 20mM imidazole).

To isolate intact lysosomes, we employed the LysoIP method using anti-HA magnetic beads to capture lysosomal membranes expressing TMEM192-3xHA. Cells were infected with pCHMWS-pLJC6-3XHA-TMEM192 and selected with blasticidin. After selection, cells were cultured in 15 cm dishes to reach 90% confluency. After medium refreshment, cells were collected on ice, washed with ice-cold PBS, and lysed in KPBS buffer supplemented with 1mM EGTA and protease inhibitors (KPBS/EGTA). The lysates were homogenized using a 33mm 29G needle, aspirated up-and-down 15-20x, and then centrifuged. All supernatants, which include organelles, were pre-cleared by incubation with Protein A agarose beads at 4°C for 10 mins, followed by centrifugation. All pre-cleared samples were dosed using the Pierce BCA Protein Assay Kit (Thermo Fisher), and an equal amount of total proteins were loaded and incubated with Pierce Anti-HA Magnetic Beads for 15min at 4°C. Following incubation, the beads were washed three times with KPBS/EGTA buffer to remove non-specifically bound proteins. Bound lysosomal proteins were eluted by buffer containing 50mM HEPES, 100mM NaCl, 1% Triton after final wash. Protein concentration was measured using the BCA assay, and the eluted proteins were prepared for subsequent western blot analysis.

Immunocytochemistry and immunohistochemistry
For immunocytochemistry, 2×104 cells per well were seeded on an 8-well chamber glass slide (Ibidi). The cells were fixed with 4% paraformaldehyde in PBS for 15 min at room temperature. Cells were permeabilized with 0.1% Triton-X100 in PBS and blocked with 1% goat serum for 1 h at room temperature. Primary antibodies and goat fluo-conjugated secondary antibodies were diluted in 1% goat serum-blocking buffer. Cells were mounted using Vectashield antifade mounting medium with DAPI (Vector Laboratories).

For immunohistochemistry, tumors were fixed in 4% paraformaldehyde and embedded in paraffin. The paraffin slides were rehydrated and treated with hydrogen peroxide. Antigen retrieval was performed by heating samples in Tris-ethylenediaminetetraacetic acid (pH 9.0). The sections were incubated with primary and secondary antibodies, and diaminobenzidine (Dako) was used for detection, followed by hematoxylin counterstaining. Quantification of IHC immunostaining was performed using the ImageJ IHC Profiler plug-in.

Microscopic analysis was performed using a Leica DCF-6000 confocal microscope for all fluorescent images, and images were acquired using LAS X software. IHC images were acquired using a Nikon SMZ25 stereozoom microscope (Perfect zoom 3.15−315x) and ZEN Blue (ZEISS). Image analysis was performed using Imaris (Bitplane), Zeiss Aviovision, or ImageJ.

Quantification fields were randomly selected on tissue sections or cell monolayers, on at least 8 fields per replicate, with N corresponding to the number of replicates. Colocalization was measured using the Imaris Coloc module in Imaris (Bitplane) Surpass mode. Both intensity-based and object-based approaches were applied to assess overlapping signals and calculate voxel correlations, giving a colocalization score between two immunostainings. Representative images were chosen after quantification as the most accurate depiction of the phenotype of the control compared to the experimental conditions.

Proximity ligation assay
Proximity ligation assay was performed according to the manufacturer’s instructions (Merck Sigma). The Duolink PLA Mouse minus (DUO92001) and Rabbit plus (DUO92005) probes were used in combination with the Duolink In Situ Detection Reagents Far Red (DUO92013). The combinations of antibodies used for PLA on human cells are listed below:

Mouse minus SLC3A2 (Biolegend, 315602)
LAMP1 (Biolegend, 121601)
Rabbit plus mTOR (Cell Signaling, #2983)

On the tumor tissue, a different combination was used: Goat Minus probe (DUO92006) with anti-LAMP1 (Novus Biologicals, AF4320) or SLC3A2 /LAT1 Antibody (Novus Biologicals, NBP1-51935, combined with Rabbit Plus probe (DUO92005) with mTOR (Cell Signaling, #2983). Quantification fields were randomly selected on tissue sections or cell monolayers, on at least 8 fields per replicate, with N corresponding to the number of replicates. Quantification of the number of PLA dots per cell was performed by surfacing the dots and cell nuclei using the Surpass mode of Bitplane Imaris, in line with previous publications54.

Protein stability assay
24 hours after plating, HEK293T cells were transfected with RAS stability reporter plasmids (DsRed–IRES–EGFP–RAS). At 24 h post‑transfection, cells were treated with DMSO or RAS inhibitors and harvested after an additional 24h. Single live cells were analyzed on a MACSQuant VYB flow cytometer (Miltenyi Biotec), measuring EGFP and DsRed expression levels. The EGFP:DsRed fluorescence ratio, which reflects relative protein stability, was calculated for each cell using FlowJo software (BD Biosciences).

(Phospho)Proteomic analyses
Sample digestion. Cell pellets and tissues were lysed with lysis buffer consisting of 6M Guanidinium Hydrochloride, 10mM TCEP, 40mM CAA, and 50mM HEPES pH 8.5. Samples were boiled at 95oC °C for 5 min, processed using the Tissue Lyser for 2 times for 1 min from 3 to 30 hz and sonicated at a high for 5×60 son and 30 soff in a Bioruptor Pico sonication water bath (Diagenode) at 4oC °C. Sample concentrations were determined using BCA, and 200µg of the sample was taken forward for digestion. Samples were diluted 1:3 with 10% acetonitrile and 50mM HEPES pH 8.5, LysC (MS grade, Wako) was added at a 1:50 (enzyme to protein) ratio, and samples were incubated at 37oC °C for 4h. Samples were further diluted to a final ratio of 1:10 with 10% acetonitrile, 50mM HEPES pH 8.5, trypsin (MS grade, Sigma) was added at a 1:100 (enzyme to protein) ratio, and samples were incubated overnight at 37oC °C. Enzyme activity was quenched by adding 2% trifluoroacetic acid (TFA) to a final concentration of 1%. Prior to further processing, the peptides were desalted on a SOLAµ SPE plate (HRP, Thermo). For each sample, the filters were activated with 200µl of 100% methanol (HPLC grade, Sigma-Aldrich), then 200µl of 80% acetonitrile, and 0.1% formic acid. The filters were subsequently equilibrated 2x with 200µl of 1% TFA and 3% acetonitrile, after which the sample was loaded by centrifugation at 3000 g. After washing the tips twice with 200µl of 0.1% formic acid, the peptides were eluted into clean 1.5ml Eppendorf tubes using 40% acetonitrile and 0.1% formic acid. The eluted peptides were concentrated on an Eppendorf Speedvac column and re-constituted in 50mM HEPES (pH 8.5).

TMT quantitative proteomics analysis. For TMT labeling with 16 plex tags (Thermo Fischer, TFA was added to the samples to reduce the acetonitrile concentration to less than 5%. Prior to mass spectrometry analysis, the peptides were fractionated using an offline Thermo Fisher Ultimate 3000 liquid chromatography system with high-pH fractionation (5mM Ammonium Bicarbonate, pH 10) at 5µl/min flow rate. 15µg of peptides were separated over a 120 min gradient (5–35% acetonitrile), and fractions were collected every 130 sec. The resulting 60 fractions were pooled into 30 final fractions, acidified to pH < 2 with 1% TFA, and loaded onto EvoSep stages according to the manufacturer’s protocol.

For each fraction, peptides were analyzed using the pre-set ’30 samples per day’ method on an EvoSep One instrument. Peptides were eluted over a 44-min gradient and analyzed with an Orbitrap EclipseTM TribridTM instrument (Thermo Fisher) with a FAIMS ProTM Interface (Thermo Fisher) switched between CVs of −50V and −70V with cycle times of 2s and 1.5s respectively. Full MS spectra were collected at a resolution of 120,000, with the normalized AGC target set to 100%, maximum injection time of 50ms, and scan range of 375–1500 m/z. MS1 precursors with an intensity of >5×103 and a charge state of 2–7 were selected for MS2 analysis. Dynamic exclusion was set to 120s, the exclusion list was shared between CV values and Advanced Peak Determination was set to ‘off’. The precursor fit threshold was set to 70% with a fit window of 0.7 m/z for MS2. Precursors selected for MS2 were isolated in the quadrupole with a 0.7 m/z window. Ions were collected for a maximum injection time of 35ms, and the normalized AGC target was set to 300%. Fragmentation was performed with a HCD-normalized collision energy of 30%, and MS2 spectra were acquired in the IT at a rapid scan rate. The MS2 spectra were subjected to RTS using the UniProt protein database Homosapiens, and trypsin was used as the enzyme. Static modifications included TMTpro on Lysine (K), N-terminus, and carbamidomethyl on cysteine (C). Methionine (M) oxidation was used as a variable modification. The Maximum missed cleavage parameter was set to 1, and the maximum variable modification was set to 2. FDR filtering was enabled, maximum search time was set to 35ms, and scoring threshold was set to 1× corr, 0 dCn, and 5 ppm precursor tolerance. Use as a trigger only was disabled, and close-out was enabled with the maximum number of peptides per protein set to four. Precursors were subsequently filtered with isobaric tag loss exclusion of TMT, and precursor mass exclusion was set to 25 ppm low and 25 ppm high. Precursors identified by RTS were isolated for an MS3 scan using a quadrupole with a 2 m/z window, and ions were collected for a maximum injection time of 86ms and a normalized AGC target of 300%. The Turbo TMT was deactivated, and the number of dependent scans was set to 10. Isolated precursors were fragmented again with 50% normalized HCD collision energy, and MS3 spectra were acquired in the Orbitrap at 50000 resolution with a scan range of 100–500 m/z.

MS performance was verified for consistency by running a complex cell lysate quality control standard.

The raw files were analyzed using Proteome Discoverer 2.4 (Thermo Fisher) and have been deposited and are accessible in the PRIDE. TMT reporter ion quantitation was performed in the processing and consensus steps, and the spectra were matched against the UniProt Homo sapiens database, including reviewed and unreviewed proteins. Dynamic modifications were set as oxidation (M) and acetyl on the protein N-termini. Cysteine carbamidomethyl (on C residues) and TMT 16-plex (on peptide N-termini and K residues) were used for static modifications. All results were filtered to a 1% FDR, and protein quantitation was performed using the built-in Minora Feature Detector, with statistical significance testing performed using the built-in t-test.

Label-free quantitative phosphoproteomic analysis. Phosphopeptides were enriched using MagResyn Ti-IMAC beads (LabLife). 25µL of bead particles were first washed with 200µL of 70% ethanol, then with 100µL of 1% NH4OH, and finally washed three times in a solution of 80% Acetonitrile, 1M glycolic acid, and 5% TFA (loading buffer). The samples were mixed in a 1:1 ratio with the same loading buffer before incubation with the beads for 30min at RT. The supernatant was removed, and the beads were wash with 400µL loading buffer. The beads were then washed twice in 400µL 80% ACN with 1% TFA and twice in 400µL 10% ACN with 0.1% TFA. After the final wash, the beads were transferred to clean Eppendorf tubes and incubated three times in 80µL 1% NH 4 OH to elute peptides from the beads. The eluted samples were speedvacced for 30mins at 60°C before being acidified and desalted on a SOLAµ SPE plate (HRP, Thermo Fischer) following the same procedure as previously described. Dried peptides were reconstituted in 12µL of 2% ACN and 1% TFA/20µL of HEPES 50mM pH 8.5).

Peptides were loaded onto a 2cm C18 trap column (Thermo Fisher 164946), connected in-line to a 15cm C18 reverse-phase analytical column (Thermo EasySpray ES904) using 100% Buffer A (0.1% formic acid in water) at 750 bar, using the Thermo EasyLC 1200 HPLC system, and the column oven was operated at 30°C. Peptides were eluted over a 70 min gradient ranging from 10% to 60% of 80% acetonitrile and 0.1% formic acid at 250 nl/min, and the Orbitrap EclipseTM TribridTM instrument (Thermo Fisher) was run in DDA mode with a FAIMS ProTM Interface (Thermo Fisher) switched between CVs of -50V and -70V with cycle times of 2s and 1.5s, respectively. Full MS spectra were collected at a resolution of 120,000, with an AGC target of 100% or maximum injection time set to ‘auto’ and a scan range of 375–1500 m/z. The MS2 spectra were obtained in the orbitrap operating at a resolution of 60.000, with an AGC target of 100% or maximum injection time set to ‘auto,’ a normalized HCD collision energy of 30 and an intensity threshold of 2.5e4. Dynamic exclusion was set to 60s, and ions with charge states of <2, >7, or unknown were excluded.

MS performance was verified for consistency by running complex cell lysate quality control standards, and chromatography was monitored to check for reproducibility.

Raw files were analyzed using Proteome Discoverer 2.4. Label-free quantitation (LFQ) was performed in the processing and consensus steps, and the spectra were matched against the UniProt Homo sapiens database, including reviewed and unreviewed proteins. Dynamic modifications were set as oxidation (M), phospho (S, T, Y), and acetyl at the protein N-termini. Cysteine carbamidomethyl was used as a static modification. All results were filtered to 1% FDR, and protein quantitation was performed using the Minora Feature Detector.

Pathway analysis. Proteomic and phosphoproteomic datasets were analyzed using Ingenuity Pathway Analysis (IPA) software (QIAGEN Inc.). The differentially expressed or phosphorylated proteins were identified based on a permutation-adjusted q-value < 0.05 (correcting for multiple hypothesis testing) to limit false positives while accounting for multiple hypothesis testing and an absolute fold change ≥ 20%. To uncover pathway-level perturbations, Canonical Pathway Analysis55 was applied, mapping the dataset against curated signaling and metabolic pathways. This method identifies statistically enriched pathways (Fisher’s exact test, p < 0.05). To identify causal upstream regulators, Causal Network Analysis55 was employed using standard parameters defined by Qiagen, inferring upstream regulators likely responsible for the observed proteomic or phosphoproteomic changes. By integrating experimentally validated cause-effect relationships, this method predicts activation states of upstream regulators (z-score ≥ |2|) and their downstream functional consequences. This approach prioritizes key regulatory nodes and pathways. The outcomes of both analyses were visually represented using dot plots.

Gene expression analysis of the TCGA-LUAD cohort
The count files for the TCGA-LUAD samples were acquired from the GDC portal (https://portal.gdc.cancer.gov/projects/TCGA-LUAD). Only samples with KRAS mutations identified using WGS data from the TCGA Cancer Atlas were included in subsequent analyses. Patients were classified according to LZTR1 copy number variation status as LZTR1-WT (diploid) or LZTR1-HETLOSS (shallow deletion) based on WGS information obtained from cBioPortal. After conducting differential gene expression analysis using DEseq2 (v1.49.3, R), pre-ranked GSEA analysis was performed using GSEA 4.3.3.

Proxytome analysis
The KRAS proximitome was reconstructed from raw datasets reported in refs. 23, 56. This proximity labeling approach involved fusing KRAS to a mutant biotin ligase, which biotinylates neighboring proteins in live cells. These labeled proteins were then enriched using streptavidin affinity purification and identified by mass spectrometry, enabling the characterization of the KRAS-associated protein network

mRNA expression analysis
RNA was isolated using the NucleoSpin RNA kit (Machery Nagel). RNA (500ng) was reverse transcribed using a SensiFast cDNA Synthesis Kit (Bioline). RT-qPCR was performed with LightCycler 480 SYBR Green I Master reagent using the primers listed below, that were all tested by Primerbank (Harvard University) and produced by IDT-DNA:

Murine Sequences
mDnm1FW AATATGCCGAGTTCCTGCACT
mDnm1RV GTCTCAGCCTCGATCTCCAG
mLztr1FW GATGAAGGAGTTTGAGCGCC
mLztr1RV ATGTCACAGAACTCCGAGCC
mHMBSFW GAAACTCTGCTTCGCTGCATT
mHMBSRV TGCCCATCTTTCATCACTGTATG
mACTNFW TCCACACCCGCCACCAG
mACTNRV CCTCGTCACCCACATAGGAG
mB2MFW GGTGCTTGTCTCACTGACC
mB2MRV CCCGTTCTTCAGCATTTGGA
mHRASFW GATTGCTTCAGTCTGACCTATCC
mHRASRV CACGTAGCCATCACCCAAGT
mKRASFW CAAGAGCGCCTTGACGATACA
mKRASRV CCAAGAGACAGGTTTCTCCATC
mMRASFW TGTTCCAAGTGAAAACCTTCCC
mMRASRV GGGTCGTAGTCAGGCACAAA
mNRASFW ACTGAGTACAAACTGGTGGTGG
mNRASRV TCGGTAAGAATCCTCTATGGTGG
mRIT1FW GCCACCGATTCCCAGAAGAC
mRIT1RV GATCCCGCATGGCTGTAAACT

Human Sequences
hHMBSFW CAGACGGGGTCCTTTCATTC
hHMBSRV AAGACGTTTGTGTGCAGTTCG
hLZTR1FW TCTCTTTACTCAGGGGGTTACA
hLZTR1RV TTTCTGCAAAGTTTCTCACCGTC
hTBPFW TGGTGAAGAACTGATGACTTTAGA
hTBPRV AAAATAAGACTGACGAAGTTTGC
hB2MFW AAACGCAAATCCCACTGTCAC
hB2MRV AAATGGTTGAGTTGGACCCGA
hVEGFAFW AGGGCAGAATCATCACGAAGT
hVEGFARV AGGGTCTCGATTGGATGGCA
hKRASFW GAGTACAGTGCAATGAGGGAC
hKRASRV CCTGAGCCTGTTTTGTGTCTAC
hSLC3A2FW GTGCTGGGTCCAATTCACAAG
hSLC3A2RV CACCCCGGTAGTTGGGAGTA
hSLC7A5FW CCGTGAACTGCTACAGCGT
hSLC7A5RV CTTCCCGATCTGGACGAAGC

Statistics & reproducibility
Data entry and analysis were performed blindly. All statistical analyses were performed using the GraphPad Prism software, assuming non-parametric parameters. Statistical significance was calculated using the Wilcoxon Mann-Whitney or Wilcoxon matched-pair signed-rank test under two experimental conditions. Continuous variables between more than two groups were compared using the Kruskal-Wallis test, and Dunn’s Multiple Comparison Test was used if statistical significance was observed. Two-dimensional data were analyzed using a mixed model analysis in GraphPad PRISM version 9. The graphs show each replicate as a dot. No experiment-wide multiple-test corrections were applied. No statistical method was used to predetermine sample size. No data were excluded from the analyses. The experiments were not randomized. The Investigators were not blinded to allocation during experiments and outcome assessment.

Ethical approval
All procedures involving animals were performed in accordance with the guidelines of the IACUC of KU Leuven and approved project P203/2020.

Reporting summary
Further information on research design is available in the Nature Portfolio Reporting Summary linked to this article.

Data availability
All data and materials are available upon request to anna.sablina@kuleuven.be. Source data are provided as a Source Data file. Source data are provided in this paper.

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "In the manuscript by Ivanisevic et al., the authors utilize a lung cancer mouse model with LZTR1 loss to demonstrate that\nLZTR1 deficiency contributes to KRAS inhibitor resistance. This resistance arises from an increase in KRAS-WT protein\nabundance, which subsequently activates mTOR/HIF1α signaling and promotes vascular remodeling. The study identifies\nthe SLC3A2/7A5 complex as a key upstream regulator of mTOR activation, highlighting the therapeutic potential of targeting\nthe mTOR pathway or SLC3A2/7A5 in combination with KRAS inhibitors to overcome resistance. This manuscript provides\nnovel insights by showing that upregulation of WT KRAS protein—induced by LZTR1 loss or treatment with the pan-RAS\ninhibitor RMC-7977—plays a critical role in resistance mechanisms. However, multiple limitations were identified, and I\nsuggest the paper be rejected, at least until a substantial revision were submitted to the journal. In particular, many western\nblots are of very poor quality and no quantification is provided.\nMajor Comments:\nFigure 1H: Does the LZTR1 mutational status predict KRAS inhibitor response in clinical settings?\nFigure 1C: Is the total KRAS upregulation reproducible? Please also provide the quantification. In general, all western blots\nare of quite variable quality and numbers of biological replicates should be provided to indicate reproducibility.\nFigure 3C: Does Dactolisib treatment reduce HIF1α staining? In addition, higher magnification of HIF1α staining images\nshould be provided. It is impossible to gauge how convincing the data are with the image provided.\nFigure 3G", "reviewer_h": "How does the vasculature appear when Dactolisib is administered concurrently with MRTX1133? Does the\ncombination still mitigate the vascular tortuosity?\nDoes the disorganized vascular architecture directly contribute to KRAS inhibitor resistance in LZTR1-deficient tumors, or is\nit simply a consequence of the larger tumor sizes observed in LZTR1-deficient mice? It is important for the authors to\ncomment on this point.\nFigure 4F: Can KRAS-WT be deleted in MRTX1133 treated KRAS G12D, Lztr1-HET mice to see if vasculature is\nnormalized- this will provide a solid proof that KRAS-WT directly contributes to VEGFA secretion, since KRAS-WT OE could\nresult in supraphysiological KRAS, which just increases anabolic programs overall.\nDo MRTX treated Lztr1 het mice show upregulated KRAS-WT protein? In other words, does MRTX only increase KRAS-WT\nprotein abundance upon Lztr1 loss but not when Lztr1 is intact?\nMechanism of RMC-7977: Comment on the mechanism by which the pan-RAS inhibitor RMC-7977 upregulates RAS-WT\nand VEGFA intensity even in the absence of Lztr1 loss. Does RMC-7977 treatment decrease Lztr1?\nFor Figures 5L and 5M, the authors need to provide data on mouse body weights to confirm the combination treatment’s lack\nof toxicity.\nLong-Term Efficacy: For Figures 5L and 5M, the authors need to include longer in vivo data (beyond 8 weeks) to assess\nwhether treatment relapse occurs in the combination group during ongoing therapy. This appears to be the case at 7-8\nweeks in the figure.\nDoes RMC-7977 synergize with Dactolisib and JHP203 in vivo in the Lztr1 WT mice?\nMinor Comments:\nFigure 2F: Re-run the Western blot for A427 and SKUL1 cell lines, particularly for 4EBP1 and p-4EBP1 in SKUL1. Please\nprovide western blot quantification.\nFigures 2G and 2H: Verify the figure legends. The legend suggests that Figure 2G corresponds to qPCR data and Figure 2H\nto ELISA, but the data presentation appears to be reversed.\nFigure 4D: Re-run the Western blot for p70-S6K and 4EBP1 to get better signal.\nFigure 4M: Re-run the Western blot for p70-S6K to improve data quality.\nFigures 5L and 5M: Include p-values for comparisons such as MRTX1133 vs. MRTX1133 + Dactolisib and MRTX1133 vs.\nMRTX1133 + JHP203 to enhance data interpretation.", "reviewer_2": "The study by Ivanisevic et al. investigates the intrinsic resistance to RAS inhibitors in KRAS-mutant lung adenocarcinomas,\nhighlighting the potential role of elevated wild-type KRAS protein levels. Loss of LZTR1, a regulator of Ras GTPase\ndegradation, is linked to the activation the mTOR/HIF1α signaling pathway. This pathway drives vascular remodeling and\npro-survival mechanisms, undermining the efficacy of RAS-targeted therapies. Combination therapies targeting mTOR or the\nlysosomal SLC3A2/SLC7A5 complex with RAS inhibitors showed promise in overcoming resistance, suggesting a path\nforward for more effective treatments.\nThe data presented are intriguing and offer provocative insights, but they remain preliminary. For instance, critical\nexperiments that would solidify the proposed mechanisms are either missing or not sufficiently detailed, making it difficult to\nfully substantiate the claims made in the study. Consequently, while the data open interesting avenues for future research,\nparticularly the impact of LZTR1 deletion on remodeling of the tumor adjacent vasculature, stronger and more\ncomprehensive evidence is needed to support the conclusions definitively.\nThis study suggests that deletion of one copy of Lztr1 promotes an increase in protein levels of Ras family GTPases.\nHowever, recent reports from two independent groups using independently derived models of LZTR1 indicate that loss of\nboth alleles is necessary for the upregulation of Ras family GTPase protein levels in mouse tissues and MEFs (PMID:\n39352760, 30872527). Given this conflicting evidence, could the authors discuss what factors may be contributing to the\nhaploinsufficiency phenotype observed in their model that may be missing from the other models?\nCan the authors clarify how the “sorted lung tumor cells” in Figures 1B and C were isolated, and why the average relative\nexpression of Lztr1 mRNA in KO tumor cells is above 0.5 and not 0? Is this due to contaminating non-tumor cells or is there\na possibility that Cre recombination of both floxed alleles in less than 50% efficient? Furthermore, it would be favorable if the\nauthors could show a partial and complete loss of LZTR1 protein in their Heterozygous and KO tumor cells, respectively, as\nthis would support their claim that “even a partial loss of LZTR1 causes a complete loss of function” (lines 121/122) which is\nundermined by the transcript data in Fig. 1B.\nInhibition of KRAS(G12D) failed to reduce mTOR pathway activation caused by LZTR1 knockdown. The authors claim that\nthis data “suggest[s] a role of wild-type KRAS (RAS-WT) in mTOR upregulation” (lines 194/195). However, a WT RAS-\nindependent mechanism is equally plausible. To demonstrate dependency on WT RAS, the authors should show that KO of\nWT RAS rescues the effect of LZTR1 knockdown.\nThe authors claim that “LZTR1 has higher affinity for KRAS-WT compared to mutant RAS” based on increased detection of\nLZTR1:KRAS(WT) association in proximity ligation assays (Fig. 4A and B). However, given that LZTR1 preferentially binds\nto GDP-bound RAS proteins (PMID: 30872527), the increased association to KRAS(WT) detected in these assays can be\nexplained by the difference in proportion of GDP and GTP-bound RAS between WT and mutant variants in cells.\nThe authors show that ectopic expression of FLAG-tagged KRAS, but not HRAS, NRAS, MRAS, or RIT1 (Fig. 4C) promotes\nVEGFA secretion, indicating that only KRAS overexpression recapitulates LZTR1 knockdown. It would be useful for the\nauthors to show the ratio of ectopic to endogenous expression for each GTPase to more adequately draw this conclusion\ngiven that some GTPases may be modestly overexpressed compared with others.\nThe Flag-tagged GTPases expressed in Fig. 4C are expected to migrate at different rates by SDS-PAGE in correlation with\ntheir molecular weight (21 kDa for H/N/KRAS, and 24 for MRAS, and 25 kDa for RIT1) as shown by others (PMID:\n39009833; 15143186). However, the immunoblot in Fig. 4C suggest no difference in electrophoretic mobility between the\nconstructs. Could the authors clarify this discrepancy?\nThe authors suggest RMC-7977:CYPA blocks LZTR1-mediated degradation of KRAS via steric hinderance. However,\nRMC-7977:CYPA targets active GTP-bound state of KRAS, whereas LZTR1 binds to the GDP-bound state of KRAS and\nshould therefore not compete for binding. The LZTR1-dependent increase in KRAS protein levels observed in Fig. 4K could\ninstead be caused by RMC-7977 inhibition of KRAS signaling. To explore this possibility the authors could evaluate\nwhether inhibition of effector pathways (e.g., MAPK and PI3K) stabilize KRAS protein levels in an LZTR1 dependent\nmanner.\nThe “GPS” assay used to generate the data in Figures 4H and 4I was not described in any detail in the main text, figure\nlegends, nor in the supplementary material, preventing an assessment of these data and the author’s conclusions.\nThe authors indicate that ectopic overexpression of WT KRAS in Kras-G12D lung tumors led to an increase in phospho-p70\nS6 kinase relative to a GFP control (Fig. 4E). In the image of the GFP control, it is not apparent that a tumor is present. Can\nthe authors clarify where the tumor is located as validated by IHC analysis of tumor markers?\nOverexpression of WT KRAS and Lztr1 deletion in Kras-G12D lung tumors were shown to have a similar impact on the\narchitecture of the tumor adjacent vasculature. Did WT-KRAS overexpression have a similar effect on tumor growth as the\ndeletion of Lztr1?\nIn Figure 3E, the legend reports a sample size of n=5 mice per group, but there are six datapoints per group in the figure.\nLine 216, Figure 4J is referenced instead of Figure 4I.\nFigure 2G and 2H are mislabeled in the legend.", "reviewer_3": "I co-reviewed this manuscript with one of the reviewers who provided the listed reports. This is part of the Nature\nCommunications initiative to facilitate training in peer review and to provide appropriate recognition for Early Career\nResearchers who co-review manuscripts.", "reviewer_4": "Anna Sablina and her team describe in their manuscript that “KRAS protein dosage reprograms the amino acid sensing\nmachinery to confer intrinsic resistance to RAS inhibitors.” Their work focuses on the molecular mechanisms that confer\nresistance to RAS inhibitors in lung cancer cells carrying oncogenic KRAS. They propose that this resistance is partly due to\nthe upregulation of WT-KRAS in lung carcinoma cells, driven by impaired RAS degradation, such as the loss of LZTR1-\nCul3-mediated RAS ubiquitination or treatment with RMC7977. Upregulated KRAS, in turn, stimulates amino acid transport\nvia the essential amino acid transporter SLC7A5/SLC3A2 across the lysosomal membrane for mTORC1 activation.\nA key strength of the manuscript lies in its integration of patient data, mouse models, and mechanistic work in cancer cells.\nHowever, a number of experiments require additional controls, and the manuscript would benefit from a careful re-evaluation\nand reinterpretation of certain data (particularly regarding the role of SLC3A2-SLC7A5).\nMajor Points:\nFigure 1A: The relative cell growth of A427 lung cancer cells expressing shGFP or shLZTR1 was analyzed. How effective\nwas the reduction of LZTR1 protein levels? The authors do not demonstrate in their manuscript that LZTR1 protein levels\nwere indeed reduced and do not provide the appropriate rescue experiments. e.g.: Can the increased resistance to\nMRTX1133 of A427 cells expressing shLZTR1 be rescued?\nThe relative growth curves are interpolated with non-existing data points.\nFigure 1B", "reviewer_c": "How are LZTR1 protein levels affected by the 50-70% reduction in LZTR1 mRNA levels in the sorted lung\ntumor cells?\nminor point: In the third lane, why does the second KRAS-G12D band disappear in LZTR1 homozygous KO cells?\nFigure 2A-D: Bioinformatics analysis of gene expression datasets and phosphoproteomics indicates that mTOR and\nhypoxia signaling are upregulated pathways following LZTR1 loss. The manuscript would profit from a better rational\ndescription of these complex experiments.\nFigure 2E: It appears that H727 cells expressing shLZTR1 have a higher number of LAMP1-positive objects. Please\nsubstantiate statements like “increased co-localization” with quantitative data.\nThe upper image (“wt cells”) has two overlapping scale bars and shows a pixel shift between red and green channels,\nsuggesting a possible error in overlay processing.\nFigure 2F: LZTR1 blots are missing.\nPlease provide a definition or background for SKUL1 cells, as they are not mentioned elsewhere.\nFigure 3B: The rationale for using protein-protein interaction (PLA) between LAMP1 and TOR to argue for increased mTOR\nsignaling remains unclear. What motivated this experiment? If demonstrating increased co-localization is the goal, a proper\nexperiment with quantification should be performed. This also applies to other PLA assays (e.g., Figure 4).\nFigure 4A", "reviewer_b": "This is a key experiment. Perform an independent/orthoglonal analysis to validate the interaction of RAS (or\nmutant RAS) with the SLC3A2-SLC7A5 heterodimer.\nFigure 5C", "reviewer_d": "The antibody used to detect SLC3A2 targets its extracellular domain, and SLC3A2 forms heterodimers with\nother SLCs. These facts questions the interpretation of the results. Please investigate whether SLC3A2-SLC7A5 protein\nlevels or localization change in response to LZTR1 loss or RMC7977 treatment.\nFigure 5E: Include Western blot controls for SLC3A2-SLC7A5 knockdown.\nIt is expected that loss of SLC7A5-SLC3A2 dramatically reduces mTORC1 signaling. As these transporters mediate\nessential amino acid uptake, their loss mimics amino acid starvation.\nFrom the current experiments, the conclusion that lysosomal amino acid transport hyperactivates mTOR signaling is not\nsupported. Instead, it appears that RAS upregulation alters essential amino acid uptake via SLC3A2-SLC7A5, driving\nmTOR signaling. Previous studies have noted similar connections between RAS, SLC7A5-SLC3A2, and mTOR (e.g.,\n10.1038/s41588-020-00753-3)."}, "author_response": ""}, {"round": 2, "reviewer_comments": {"reviewer_1": "The authors have been very responsive to the prior concerns and the paper is now ready for publication.", "reviewer_2": "Anna Sablina and colleagues have addressed our concerns and made considerable improvements to the manuscript.\nThe authors have included additional experimental data that strengthen their original claims. Importantly, the text has been\namended to better contextualize and address the study’s limitations and interpretation of the data. We are in support of the\nmanuscript’s publication.", "reviewer_3": "I co-reviewed this manuscript with one of the reviewers who provided the listed reports. This is part of the Nature\nCommunications initiative to facilitate training in peer review and to provide appropriate recognition for Early Career\nResearchers who co-review manuscripts.", "reviewer_4": "Unfortunately, the new results presented in the revised version of the manuscript remain insufficient to demonstrate that\nLZTR1 directly regulates RAS protein levels. While LZTR1 loss is associated with increased RAS abundance, the\nunderlying mechanism is still not well understood. Furthermore, it remains unresolved whether mTOR activation is driven by\nlysosomal SLC3A2–SLC7A5 complexes or by those located at the cell surface. The statement, “We suggested that\naccumulation of KRAS can drive clustering of anionic lipids into nanodomains that generate negative curvature and might\ninduce endocytic uptake (32) of KRAS itself and co-associated transporters, delivering them to the endolysosomal\nmembrane,” also lacks any experimental support. Reference 32 in supporting this claim is ambiguous. Finally, there is no\nexperiment addressing that SLC3A2-SLC7A5 has to reach the surface of lysosomes to simulate mTOR.\nA number of points raised where insufficiently addressed:\nFigure 1A: The relative cell growth of A427 lung cancer cells expressing shGFP or shLZTR1 was\nanalyzed. How effective was the reduction of LZTR1 protein levels?\nWe have performed immunoblotting analysis of LZTR1 expression, showing significant reduction\nin every tested cell model (Revised Figure 1G, 1I, 3E, 4E).\nminor point: The knockdown of LZTR1 is only partially in Figures 1G, 1I and highly variable throughout the manuscript.\nThe authors do not demonstrate in their manuscript that LZTR1 protein levels were indeed\nreduced and do not provide the appropriate rescue experiments. e.g.: Can the increased\nresistance to MRTX1133 of A427 cells expressing shLZTR1 be rescued?\nWe have performed rescue experiments using the overexpression of a synthetic LZTR7 coding\nsequence in the presence of a shRNA that recognizes the endogenous but not ectopic sequence\nof LZTR7. The rescue experiments showed that the restoration of LZTR1 expression in LZTR7-\ndepleted cells abrogated either LZTR1-induced mTOR activation (Revised Figure 3F) or decreased\nresponse to MRTX1133 treatment (Revised Figure 2H).\nmajor point: These experiments remain incomplete since the expression levels of GFP-tagged LZTR1 are not compared to\nendogenous LZTR1.\nThe relative growth curves are interpolated with non-existing data points.\nWe have replotted the growth graphs (Revised Figure 2F , H). We disabled spline or polynomial\ninterpolation in GraphPad Prism. Since we used GraphPad Prism and included only growth rate at\nspecified doses in the statistical analysis, interpolation does not impact the reported statistical\noutcomes.\nOK\nFigure 1B", "reviewer_c": "How are LZTR1 protein levels affected by the 50-70% reduction in LZTR1 mRNA levels\nin the sorted lung tumor cells?\nWe have added the immunoblotting analysis of LZTR1 expression on mouse tumor cells, showing\na significant decrease in LZTR1 protein level in the sorted LztrL7-deleted lung tumor cells (Revised\nFigure 1I). and in human lung cancer expressing shLZTR7 (Revised Figure 1G).\nSee comment to first point.\nMinor point: In the third lane, why does the second KRAS-G12D band disappear in LZTR1\nhomozygous KO cells?\nThe identity of the second band observed in our assays remains uncertain. One possibility is that\nit represents a ubiquitinated form of KRAS, whose disappearance upon LZTR1 loss aligns with the\nrole of LZTR1 in facilitating KRAS ubiquitination. Alternatively, this band might be non-specific,\nand its absence in LZTR1 knockout cells could result from alterations in the ubiquitin system.\nOK\nFigure 2A-D: Bioinformatics analysis of gene expression datasets and phosphoproteomics\nindicates that mTOR and hypoxia signaling are upregulated pathways following LZTR1 loss. The\nmanuscript would profit from a better, rational description of these complex experiments.\nWe have added explanations for the proteomic analyses to the Revised Materials and Methods\nsection.\nMinor point: I would be advisable to better explain these experiments, otherwise the analysis appears superficial.\nFigure 2E: It appears that H727 cells expressing shLZTR1 have a higher number of LAMP1-positive\nobjects. Please substantiate statements like “increased co-localization” with quantitative data.\nWe have added the quantification for the colocalization of LAMP1 and mTOR (Revised Figure 3D,\n4G), confirming that LZTR1 loss leads to increased LAMP1/mTOR colocalization.\nMajor point: The increase in the number of lysosomes has not been considered. If the number of lysosomes increases, so\nwill co-localization…this should be considered in the quantification.\nThe upper image (“wt cells”) has two overlapping scale bars and shows a pixel shift between red\nand green channels, suggesting a possible error in overlay processing.\nWe have replaced the image of mTOR/LAMP1 immunostaining in the revised manuscript.\nOK\nFigure 2F: LZTR1 blots are missing.\nWe have added immunoblotting analysis of LZTR1 expression to the Revised Figure 3E.\nOK\nPlease provide a definition or background for SKUL1 cells, as they are not mentioned elsewhere.\nSKLU1 is a commercial lung adenocarcinoma cell line, which harbors a heterozygous KRAS-G12D\nmutation, meaning they possess one mutated and one wildtype KRAS allele. We have added this\ninformation to the revised manuscript.\nOK\nFigure 3B: The rationale for using protein-protein interaction (PLA) between LAMP1 and TOR to\nargue for increased mTOR signaling remains unclear. What motivated this experiment? If\ndemonstrating increased co-localization is the goal, a proper experiment with quantification\nshould be performed. This also applies to other PLA assays (e.g., Figure 4).\nWe appreciate your comment regarding the use of the PLA to assess the proximity between LAMP1\nand mTOR in Revised Figure 4B. Our objective with this experiment was to investigate the spatial\nproximity between mTOR and lysosomal compartments, as lysosomal localization is a critical\ndeterminant of mTOR activation. The PLA technique enables the detection of proteins in close\nproximity, providing evidence of potential interactions or co-localization events that may not be\ndiscernible through conventional immunofluorescence microscopy. This method has been\neffectively utilized in previous studies to demonstrate nutrient-dependent associations between\nmTOR and lysosomal markers (e.g. PMID: 33635313; PMID: 36115844).\nIn our study, PLA allowed us to demonstrate the translocation of mTOR to lysosomal membranes.\nWe have complemented the PLA data with quantitative co-localization analyses using confocal\nmicroscopy (Revised Figure 3D, 4G), as well as lysosome immunoprecipitation (Revised Figure\n5G).\nMajor point: together with the new data, it now remains unclear why only few cells show mTOR on lysosomes (revised\nfigures mentioned above). For example: In Figure 3D, only few LZTR1 knock-down cells show a signal for mTOR. The same\nholds true for Figure 4G (DMSO treated) – curiously these cells also have more lysosomes….see also comment above. It\nremains unclear what is going on.\nFigure 4A", "reviewer_b": "This is a key experiment. Perform an independent/orthoglonal analysis to\nvalidate the interaction of RAS (or mutant RAS) with the SLC3A2-SLC7A5 heterodimer.\nA recent study reported an interaction between KRAS and SLC3A2 based on\nimmunoprecipitation (PMID: 36115844, Figure 2A). In agreement with this observation,\nwe have found that both WT-KRAS and the KRAS-G12D mutant co-immunoprecipitated\nwith SLC7A5 (Revised Figure S3A).\nMinor point: I cannot follow this argument, since Figure 5C shows a decrease in SLC3A2\nprotein levels upon KD of LZTR1, whereas in Figure 5 SLC3A2 protein levels do not change.\nIn Figure 5F, the quantification of SLC3A2 levels normalized to vinculin revealed no\nsignificant decrease. Immunoblot analyses across multiple experiments confirmed that\nLZTR1 loss does not significantly affect the total protein levels of either SLC3A2 or\nSLC7A5.\nPoint-by-Point Response to Reviewer Comments\nReviewer #4. Also, Figures 1 and 2 from the rebuttal letter should be included for the\npublication of the final version of the manuscript, as they provide a good sense of the\nmagnitude of the observed effects.\nIn response, Figures 1 and 2 from the rebuttal letter have been included in the final\nmanuscript as Figure S1B and Figure S2E–F, respectively.", "reviewer_point": "together with the new data, it now remains unclear why only few cells show\nmTOR on lysosomes (revised figures mentioned above). For example: In Figure 3D, only\nfew LZTR1 knock-down cells show a signal for mTOR. The same holds true for Figure 4G\n(DMSO treated) – curiously these cells also have more lysosomes….see also comment\nabove. It remains unclear what is going on.\nMajor point: It is difficult to understand why only few cells express SLC3A2 in Figure 5H.\nBoth mTOR and SLC3A2 are expected to be robustly expressed in lung cancer cells.\nmTOR displays both diffuse cytoplasmic localization and dynamic recruitment to lysosomal\nmembranes. While cytoplasmic mTOR is widely distributed, its diffuse nature results in\nrelatively low fluorescence intensity per unit volume, making it less visible under confocal\nmicroscopy. In contrast, lysosome-associated mTOR produces stronger, more easily\ndetectable signal. SLC3A2 is known to localize to both the plasma membrane and\nintracellular compartments, including lysosomes.\nBecause confocal microscopy captures fluorescence within a narrow optical section,\nsignal detection strongly depends on whether the focal plane intersects regions of\nconcentrated protein, such as lateral membranes or lysosome-rich perinuclear zones.\nCells not aligned with these planes may appear negative or show weak signal, despite\nexpressing the protein. This is a recognized limitation of single-plane imaging and\ncolocalization analysis (PMID: 19564400; PMID: 21898338).\nMajor points: This explanation is insufficient, and orthogonal quantification/ experiment\nevidence is advised. The conclusion that LZTR1 has different affinities for wild-type RAS\nversus mutant RAS therefore remains speculative. Without experimental evidence, the\nmanuscript remains descriptive and lacks experimental insight.\nOur co-immunoprecipitation experiments showed LZTR1 binding to WT-KRAS but not\nKRAS-G12D (Revised Figure S1A).\nFigure 5A", "reviewer_d": "The antibody used to detect SLC3A2 targets its extracellular domain, and SLC3A2\nforms heterodimers with other SLCs. These facts questions the interpretation of the results.\nPlease investigate whether SLC3A2-SLC7A5 protein levels or localization change in response to\nLZTR1 loss or RMC7977 treatment.\nOur immunoblot analysis showed that LZTR1 loss did not significantly alter the protein levels of\nSLC3A2 or SLC7A5 (Revised Figure 5F). However, LZTR1 depletion resulted in increased lysosomal\nlocalization of SLC3A2, as indicated by enhanced enrichment in the lysosomal fraction (Revised\nFigure 5G) and co-localization with the lysosomal marker LAMP1 (Revised Figure 5H). Similarly,\ntreatment with RMC-7977 did not affect the expression of SLC3A2 or SLC7A5, regardless of LZTR1\nstatus (Revised Figure 5F).\nminor point: I cannot follow this argument, since Figure 5C shows a decrease in SLC3A2 protein levels upon KD of LZTR1,\nwhereas in Figure 5 SLC3A2 protein levels do not change.\nFigure 5E: Include Western blot controls for SLC3A2-SLC7A5 knockdown.\nWe have performed immunoblot analyses to assess SLC3A2 and SLC7A5 protein levels following\ntheir knockdowns. The results confirmed the effective knockdown of both proteins (Revised\nFigure 5C).\nIt is expected that loss of SLC7A5-SLC3A2 dramatically reduces mTORC1 signaling. As these\ntransporters mediate essential amino acid uptake, their loss mimics amino acid starvation. From\nthe current experiments, the conclusion that lysosomal amino acid transport hyperactivates\nmTOR signaling is not supported. Instead, it appears that RAS upregulation alters essential amino\nacid uptake via SLC3A2-SLC7A5, driving mTOR signaling. Previous studies have noted similar\nconnections between RAS, SLC7A5-SLC3A2, and mTOR (e.g., 10.1038/s41588-020-00753-3).\nWe have clarified how elevated KRAS-WT protein levels could lead to mTOR activation. A recent\nstudy demonstrated that KRAS can drive mTOR activation on lysosomes as siRNA-mediated KRAS\nknockdown abolished mTOR activation on LAMP1-positive membranes, and re-expression of\nKRAS restored both lysosomal mTOR recruitment and downstream signaling. Moreover, they\nreported that KRAS activates mTOR via the bidirectional amino acid transporter SLC3A2/SLC7A5\n(PMID: 36115844). While this study focused on oncogenic RAS, proximity-based proteomic\nanalyses showed that both wild-type and oncogenic KRAS bind the SLC3A2/SLC7A5 heterodimer\nat comparable levels, indicating that this mechanism could be independent of KRAS mutational\nstatus (Revised Figure 5A, B).\nConcordantly, we found that the depletion of either SLC3A2 or SLC7A5 abolished the differences in mTOR activation\nbetween cells expressing shGFP and shLZTR7 (Revised Figure 5C, Figure S3A). The suppression of SLC3A2 also\ndiminished the LZTR1- driven increase in VEGFA expression and secretion, indicating that LZTR1 could mediate mTOR\nsignaling through the SLC3A2/SLC7A5 complex (Revised Figure 5D, E).\nmajor point: There is no experiment addressing that SLC3A2-SLC7A5 has to reach the surface of lysosomes to simulate\nmTOR. All other conclusions regarding this essential amino acid transporter are not surprising: SLC3A2-SLC7A5 forms\nobligatory heterodimers, and loss of SLC3A2 or SLC7A5 mimicks amino acid starvation (regardless of where the transporter\nlocalizes).\nAlthough previous studies have indicated that elevated RAS signaling can increase the expression\nof certain SLC transporters, we did not detect any changes in SLC3A2 or SLC7A5 expression in\ncells with LZTR1 depletion or upon treatment with RMC7977 (Revised Figure 5F). Instead,\nLysosome-IP analysis revealed that LZTR1 loss levels enhanced the recruitment of the\nSLC3A2/SLC7A5 complex to LAMP1-positive endolysosomal membranes (Revised Figure 5G).\nThis suggests that high levels of non-active KRAS could promote mTOR activation by driving\nendocytic delivery of the SLC3A2/SLC7A5 complex to the endolysosome. We suggested that\naccumulation of KRAS can drive the clustering of anionic lipids into nanodomains that generate\nnegative curvature and induce endocytic uptake of KRAS itself and co-associated transporters,\ndelivering them to the endolysosomal membrane. Consistent with this model, we observed\nincreased colocalization of SLC3A2 with the lysosomal marker LAMP1 in lung cancer cells that\nwere either depleted of LZTR1 or treated with RMC7977. While SLC3A2 was mostly expressed at\nthe cytoplasmic membrane in control cells, it re-localized to cytosolic punctae that partially\ncolocalized with LAMP1 upon either LZTR1 depletion or RMC7977 treatment (Revised Figure 5H).\nmajor point: It is difficult to understand why only few cells express SLC3A2 in Figure 5H.\nOnce re-localized on lysosomes, the SLC3A2/SLC7A5 heterodimer exchanges intralysosomal\nglutamine for extracellular leucine, inducing mTOR re-localization on endolysosomes and its\nactivation (PMID: 37779156; PMID:PMID: 19203585). mTOR and SLC3A2 proximity was also\nconfirmed in lung cancer cells depleted of LZTR1, as well as in lung tumors from Lztr7-\nheterozygous mice (Revised Figure 5I, J). Thus, elevated KRAS-WT can induce mTOR signaling\nthrough an “in-out” amino-acid delivery pathway that converges on lysosomal nutrient sensing.", "reviewer_author_response": "Point-by-Point Response to Reviewer Comments\nUnfortunately, the new results presented in the revised version of the manuscript remain\ninsufficient to demonstrate that LZTR1 directly regulates RAS protein levels. While LZTR1\nloss is associated with increased RAS abundance, the underlying mechanism is still not\nwell understood.\nMajor point: To conclude that LZTR1 ubiquitinates RAS and thereby triggers its degradation, this sequence of events should\nbe demonstrated experimentally. Without experimental evidence, the manuscript remains descriptive and lacks experimental\ninsight.\nSeveral previous studies from different groups have already demonstrated the role of\nLZTR1 in the control of KRAS ubiquitination: PMID: 30442762 – Figure S6D; PMID:\n31337872 - Figure 2C; and PMID: 30442766 – Figure S16D. We have reproduced these\nresults showing that overexpression of LZTR1 led to increased KRAS ubiquitination\n(Rebuttal Figure 1).\nRebuttal Figure 1. LZTR1 regulates KRAS\nubiquitination. Ubiquitinated KRAS was purified from\nHEK293T cells expressing the indicated constructs by\nCo2+ metal affinity chromatography and detected by\nimmunoblotting. Numbers on the left are molecular masses in\nkDa. EV, empty vector; WCL, whole-cell lysate.\nHA-LZTR1\nFlag-wt-KRAS\nUbiquitin\nThe statement, “We suggested that accumulation of KRAS can drive clustering of anionic\nlipids into nanodomains that generate negative curvature and might induce endocytic\nuptake (32) of KRAS itself and co-associated transporters, delivering them to the\nendolysosomal membrane,” also lacks any experimental support. Reference 32 in\nsupporting this claim is ambiguous.\nThe statement was based on a theoretical model inspired by biophysical studies of KRAS-\nlipid interactions. We consistently used cautious language, such as suggest, can, and\nmight, to indicate that the suggested mechanism is speculative. In response to the\nreviewer’s comment, we have removed this sentence from the revised manuscript.\nIt remains unresolved whether mTOR activation is driven by lysosomal SLC3A2–SLC7A5\ncomplexes or by those located at the cell surface.\nVE\n1RTZL-AH\nHis-Ub\nMW,\nkDa\n50\nKRAS-1xUb\nFinally, there is no experiment addressing that SLC3A2-SLC7A5 has to reach the surface\nof lysosomes to simulate mTOR.\nMajor point: There is no experiment addressing that SLC3A2-SLC7A5 has to reach the\nsurface of lysosomes to simulate mTOR. All other conclusions regarding this essential\namino acid transporter are not surprising: SLC3A2-SLC7A5 forms obligatory\nheterodimers, and loss of SLC3A2 or SLC7A5 mimics amino acid starvation (regardless of\nwhere the transporter localizes).\nWe first tested whether LZTR1 can affect the plasma membrane amino acid transport\nfunction of SLC3A2–SLC7A5 by analyzing amino acid uptake and secretion by the cells\nwith different LZTR1 statuses. These experiments revealed no clear differences between\nshGFP and shLZTR7 cells, indicating that LZTR1 does not affect amino acid transport at\nthe plasma membrane (Rebuttal Fig. 2).\nA427 A427\nshGFP shLZTR1\nHistidine\nLysine\nMethionine\nPhenylalanine\nThreonine\nTyrosine\nValine\nA\nnoiterceS/ekatpU\ndicaonimA\nA427 A427\nshGFP shLZTR1\n-0.002\nGlutamate\n-0.004\nGlutamine\n-0.006\nIso/Leucine\n-0.008\nnoiterceS\n/ekatpU\ndicaonimA\n0.02\n0\n-0.02\n-0.04\nH727 H727\nshGFP shLZTR1\nHistidine\nLysine\nMethionine\nPhenylalanine\nThreonine\nTyrosine\nValine\nB\nnoiterceS\n/ekatpU\ndicaonimA\nH727 H727\nshGFP shLZTR1\n-0.002\nGlutamate\n-0.004\nGlutamine\n-0.006\nIso/Leucine\n-0.008\nnoiterceS\n/ekatpU\ndicaonimA\n0\n-0.05\nRebuttal Figure 2: The effect of LZTR1 loss on amino acid uptake/secretion. A427 (A) and\nH727 (B) cells expressing either shGFP or shLZTR1 were cultured in standard growth medium\nlacking GlutaMAX supplemented with 5% FBS and 1× penicillin–streptomycin (P/S). To\nestablish baseline metabolite levels, fresh medium (without FBS or P/S) was collected at the\nstart of the experiment. Conditioned medium was sampled in parallel with cell-free control\nwells. Extracellular metabolite concentrations were quantified by liquid chromatography–\ntandem mass spectrometry (LC–MS/MS) and corrected by subtracting the corresponding cell-\nfree control values at each time point. Specific uptake or secretion rates (q) were calculated as\n(Δmetabolite × growth rate) / Δcell number, where Δmetabolite = (metabolite concentration –\nblank). Heatmap shows values from three replicates.\nOn the other hand, we observed redistribution of the SLC3A2–SLC7A5 heterodimer to\nlysosomes upon LZTR1 loss (Figure 5), whereas recent findings indicate that SLC3A2–\nSLC7A5 accumulation at lysosomal membranes establishes a local amino acid–sensing\nplatform capable of engaging and activating mTORC1 (PMID: 19203585; PMID 25998567).\nThis suggests that high levels of WT-KRAS could promote mTOR activation by driving re-\nlocalization of the SLC3A2/SLC7A5 complex to the endolysosomes. In agreement with\nthis idea, we found that pharmacological inhibition of endocytic trafficking to the\nlysosome using Dyngo4a (PMID: 24025110) eliminated the difference in p70 S6K\nphosphorylation downstream of mTOR between shGFP and shLZTR7 expressing cells\n(Revised Figure S3C). Taken together, these data demonstrate that the LZTR1-dependent\ndifferences in mTOR signaling arise not from changes in net amino acid uptake but from\nthe spatial redistribution of SLC3A2–SLC7A5 to lysosomes.\nMajor point: These experiments remain incomplete since the expression levels of GFP-\ntagged LZTR1 are not compared to endogenous LZTR1.\nImmunoblotting with an LZTR1-specific antibody confirmed efficient knockdown of\nendogenous LZTR1 in shRNA-expressing cells, while GFP–LZTR1 re-expression restored\nLZTR1 levels to those observed in control cells (Revised Figure 2G).\nMinor point: The knockdown of LZTR1 is only partially in Figures 1G, 1I and highly variable throughout the manuscript.\nWe acknowledge that some variability in the extent of protein depletion exists among\nindividual mice and across different cell lines. This reflects expected biological\nheterogeneity, including differences in Cre recombination efficiency in the Lztr7-\nconditional knockout model and shRNA transduction and expression efficiency in lung\ncancer cell lines. Nevertheless, in both murine and human systems, the observed\nreductions in protein levels were reproducible across biological replicates and sufficient\nto induce downstream phenotypic changes, supporting the functional relevance of LZTR1\nloss.\nMinor point: In the third lane, why does the second KRAS-G12D band disappear in\nLZTR1homozygous KO cells?\nThe identity of the second band observed in the KRAS-G12D immunoblot remains\nunclear; it may be non-specific. As this band is not consistently detected and its\nbiological relevance remains uncertain, we have not pursued its characterization further\nin this study.\nFigure 2A-D: Bioinformatics analysis of gene expression datasets and\nphosphoproteomics indicates that mTOR and hypoxia signaling are upregulated\npathways following LZTR1 loss. The manuscript would profit from a better, rational\ndescription of these complex experiments. We have added explanations for the\nproteomic analyses to the Revised Materials and Methods section.\nMinor point: I would be advisable to better explain these experiments, otherwise the\nanalysis appears superficial.\nWe have further revised the methodology following the suggestions of the reviewer.\nMajor point: The increase in the number of lysosomes has not been considered. If the\nnumber of lysosomes increases, so will co-localization…this should be considered in the\nquantification.\nIn Figure 2E, H727 cells expressing shLZTR1 display a visual increase in LAMP1-positive\nstructures, suggesting a possible rise in lysosome abundance. While not quantitatively\nconfirmed, we took this observation into account when we analyzed the co-localization\nbetween mTOR and LAMP1. We used the coloc module in Bitplane Imaris (Figures 3D and\n4G), which provides voxel-based quantification and normalizes colocalization values to\ntotal LAMP1 signal. This module calculates the degree of spatial overlap between LAMP1\nand mTOR signals on 3D volumes by generating a colocalization coefficient, which is then\ncompared across experimental conditions. This approach allows us to distinguish\nbetween genuine changes in molecular association and those attributable to altered\norganelle abundance (PMID: 36672153"}, "author_response": ""}, {"round": 3, "reviewer_comments": {"reviewer_4": "The authors have now added more experiments that provide some level of clarity and underline some of the main findings.\nWhile not all of my points have been addressed, and I am still not entirely convinced that the proposed model is correct,\nI think there is sufficient data to argue along the lines of the authors’ model. Hence, in light of the other two reviewers’\ncomments and the new experiments (in particular, the discrimination between binding of LZTR1 to WT-KRAS but not to\nKRAS-G12D, and the co-immunoprecipitation of WT-KRAS and the KRAS-G12D mutant with SLC7A5), I agree that the\nmanuscript has improved.\nAlso, Figures 1 and 2 from the rebuttal letter should be included for the publication of the final version of the manuscript,\nas they provide a good sense of the magnitude of the observed effects."}, "author_response": "We thank the reviewers for their insightful and constructive feedback, which has substantially\nimproved the manuscript.\nReviewer #1:\nFigure 1H: Does the LZTR1 mutational status predict KRAS inhibitor response in clinical settings?\nAlthough several RAS inhibitors have gained regulatory approval, most remain in (pre)clinical\ndevelopment, and reliable predictive biomarkers for their therapeutic efficacy are still lacking. The\npotential of LZTR7 mutational status as a determinant of sensitivity to KRAS-targeted therapies\nhas not yet been systematically explored. Notably, LZTR7.is absent from most clinical sequencing\npanels, particularly those used for lung cancer patients. Our findings underscore the importance\nof incorporating LZTR7 genetic alterations into such panels, as we demonstrate its critical role in\nmodulating therapeutic response. This recommendation is further reinforced by emerging clinical\ndata associating LZTR7 mutations with resistance to EGFR inhibitors (PMID: 40114953),\nemphasizing its broader relevance in the context of targeted therapy responsiveness.\nA similar precedent was seen in Noonan syndrome and schwannomatosis, where LZTR7 was\ninitially not included in standard diagnostic panels. However, functional studies, including our\nown, have provided compelling evidence for its pathogenic relevance, and it was included in the\ndiagnostic panels. In light of our findings and the growing clinical use of RAS inhibitors, we\nadvocate for the systematic inclusion of LZTR7 in diagnostic and stratification frameworks.\nFigure 1C: Is the total KRAS upregulation reproducible?\nTo further support our findings on KRAS upregulation, we have performed additional immunoblot\nanalyses of both mutant and total KRAS protein levels in tumor cells isolated from Lztr7-deleted\nmice (Revised Figure 1I). In parallel, we have examined the effects of LZTR7 knockdown in human\nlung cancer cell lines (Revised Figure 1G). In line with our previous observations, the loss of LZTR1\nled to a significant stabilization of total KRAS protein, while levels of mutant KRAS remained\nunchanged. This was further confirmed by immunoblot quantification analysis. These results\nwere also independently validated by mass spectrometry–based quantification of KRAS protein\nlevels, which showed a consistent increase in KRAS protein abundance in LZTR1-depleted murine\ntumors (Revised Figure 1J). In contrast, RT-qPCR analysis revealed no significant changes in KRAS\nmRNA expression upon LZTR1 deletion (Revised Figure S1D), indicating that LZTR1 regulates\nKRAS expression at the post-transcriptional level. Finally, global protein stability assays\ndemonstrated that LZTR1 loss selectively destabilizes wild-type KRAS, with less effect on mutant\nKRAS proteins (Revised Figure 1F).\nIn particular, many western blots are of very poor quality and no quantification is provided.\nPlease also provide the quantification. In general, all western blots are of quite variable quality\nand numbers of biological replicates should be provided to indicate reproducibility.\nWe have provided immunoblotting images with better resolution for all panels. We have also\nperformed quantification analysis of all immunoblots and provided the number of replicates.\nFigure 3C: Does Dactolisib treatment reduce HIF1α staining?\nWe have performed immunohistochemistry analysis of HIF1α expression in dactolisib-treated\nmice (Revised Figure 6C). Whereas dactolisib treatment did not affect HIF1α expression in Kras‗\nG78D·.Lztr7‗WT mice, it led to a decrease of HIF1α nuclear staining in Kras‗G78D·.Lztr7‗HET mice.\nThis confirms the impact of dactolisib on tumor vascular normalization in LZTR7-HET mice.\nIn addition, higher magnification of HIF1α staining images should be provided. It is impossible to\ngauge how convincing the data are with the image provided.\nWe have provided higher-magnification images of HIF1α immunostaining in tumors from Kras‗\nG78D·.Lztr7‗WT and Kras‗G78D·.Lztr7‗HET. mice following treatment with vehicle, dactolisib, or\nMRTX1133 (Revised Figure 4C, 6C).\nFigure 3G"}]

Decision letter