N-acetylcysteine overcomes NF1 loss-driven resistance to PI3Kα inhibition in breast cancer

Priska Auf der Maur; Marcel P Trefny; Zora Baumann; Milica Vulin; Ana Luisa Correia; Maren Diepenbruck; Nicolas Kramer; Katrin Volkmann; Bogdan-Tiberius Preca; Pedro Ramos; Cedric Leroy; Tobias Eichlisberger; Katarzyna Buczak; Federica Zilli; Ryoko Okamoto; Roland Rad; Michael Rugaard Jensen; Christine Fritsch; Alfred Zippelius; Michael B Stadler; Mohamed Bentires-Alj

doi:10.1016/j.xcrm.2023.101002

. 2023 Apr 11;4(4):101002. doi: 10.1016/j.xcrm.2023.101002

N-acetylcysteine overcomes NF1 loss-driven resistance to PI3Kα inhibition in breast cancer

Priska Auf der Maur ^1,^∗, Marcel P Trefny ², Zora Baumann ¹, Milica Vulin ^1,³, Ana Luisa Correia ^1,³, Maren Diepenbruck ¹, Nicolas Kramer ¹, Katrin Volkmann ¹, Bogdan-Tiberius Preca ¹, Pedro Ramos ⁴, Cedric Leroy ⁴, Tobias Eichlisberger ³, Katarzyna Buczak ⁵, Federica Zilli ^1,³, Ryoko Okamoto ^1,³, Roland Rad ^6,^7,^8,⁹, Michael Rugaard Jensen ⁴, Christine Fritsch ⁴, Alfred Zippelius ², Michael B Stadler ^3,^10,¹¹, Mohamed Bentires-Alj ^1,^3,^12,^∗∗

PMCID: PMC10140479 PMID: 37044095

Summary

A genome-wide PiggyBac transposon-mediated screen and a resistance screen in a PIK3CA^H1047R-mutated murine tumor model reveal NF1 loss in mammary tumors resistant to the phosphatidylinositol 3-kinase α (PI3Kα)-selective inhibitor alpelisib. Depletion of NF1 in PIK3CA^H1047R breast cancer cell lines and a patient-derived organoid model shows that NF1 loss reduces sensitivity to PI3Kα inhibition and correlates with enhanced glycolysis and lower levels of reactive oxygen species (ROS). Unexpectedly, the antioxidant N-acetylcysteine (NAC) sensitizes NF1 knockout cells to PI3Kα inhibition and reverts their glycolytic phenotype. Global phospho-proteomics indicates that combination with NAC enhances the inhibitory effect of alpelisib on mTOR signaling. In public datasets of human breast cancer, we find that NF1 is frequently mutated and that such mutations are enriched in metastases, an indication for which use of PI3Kα inhibitors has been approved. Our results raise the attractive possibility of combining PI3Kα inhibition with NAC supplementation, especially in patients with drug-resistant metastases associated with NF1 loss.

Keywords: breast cancer, resistance, PI3K alpha inhibition, combination therapy, transposon screen

Graphical abstract

Highlights

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Genetic screen reveals NF1 loss as a resistance mechanism to PI3Kα inhibition
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NF1 is frequently mutated in breast cancer metastasis
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NF1 loss is associated with enhanced glycolysis and MYC dependency
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N-acetyl cysteine sensitizes cells with NF1 loss to PI3Kα inhibition via mTOR inhibition

Auf der Maur et al. discover a mechanism of resistance to PI3Kα inhibition mediated by loss of NF1 using a genome-wide genetic screen in a breast cancel model. Treatment with the antioxidant N-acetyl cysteine reverts the glycolytic phenotype of NF1 KO cells and sensitizes them to PI3Kα inhibition.

Introduction

With over 2.2 million annual cases, breast cancer is the most frequent cancer in women and accounts for 685,000 annual deaths globally.¹ Despite considerably prolonged survival of early-stage breast cancer patients over the last 20 years, the survival of patients with recurring disease has barely improved.² Most recurring metastatic cancers are either drug resistant or will become resistant to therapy.³ Thus, drug resistance is a substantial hurdle in the long-lasting cure of patients.⁴ Mechanisms of resistance to therapy are incompletely understood, and their elucidation is crucial for advancing long-term clinical remission in breast cancer patients.

The most frequently activated oncogenic driver in breast cancer is PIK3CA. One in three breast tumors harbors activating mutations in PIK3CA, and about 70% of all breast cancers show mutations in genes that activate the phosphatidylinositol 3-kinase (PI3K) pathway.⁵^,⁶ Alpelisib (Piqray, BYL719), a PI3Kα-selective inhibitor, was the first US Food and Drug Administration (FDA)-approved drug designated for patients with activating PIK3CA mutations in hormone receptor (HR)⁺/HER2⁻ advanced breast cancer and is applied in combination with fulvestrant, based on the results of the SOLAR-1 trial.⁷^,⁸ Resistance to targeted therapies is very common and involves a multitude of mechanisms. In the case of PI3K inhibitors, pre-existing mutations conferring resistance are rather rare.⁹^,¹⁰ The best-known genetic alterations that confer resistance to PI3K inhibition impair the functionality of PTEN.¹¹ In addition to genetically derived resistance, cancer cells may survive because their metabolic signaling bypasses the PI3K pathway via alternative signaling cascades.⁴^,¹⁰^,¹²^,¹³^,¹⁴^,¹⁵ Thus, the success of PI3K inhibitors is limited by resistance, and better biomarkers for patient stratification as well as more effective combinatorial treatments are needed.

The use of genetic screens to identify and characterize mechanisms of resistance has gained much attention over the last 10 years with the advent of affordable and more efficient sequencing together with better genetic tools that increase the versatility of in vivo screens. Similar to CRISPR-Cas9-mediated screens, transposon-mediated screens have been shown in animal models to be a powerful tool to simultaneously investigate effects of gain- and loss-of-function mutations in an unbiased way.¹⁶^,¹⁷^,¹⁸ Such screens have been used previously by us¹⁹ and others to study genetic drivers of tumor development, metastasis, and resistance (reviewed in Noorani et al.²⁰).

In the present study, we used a genome-wide transposon-mediated mutagenesis screen in a PIK3CA^H1047R-driven mammary tumor model²¹ to seek mutations that promote resistance to PI3Kα inhibition. We found that loss of neurofibromin 1 (NF1), a negative-regulator of RAS with GAP activity,²²^,²³^,²⁴ lowers sensitivity to PI3Kα inhibition in vitro, in vivo, and ex vivo in organoids from a patient-derived xenograft. NF1 is lost more frequently in breast cancer metastases than in primary tumors, which suggests that this contributes to resistance to the initial treatment with the standard of care. Indeed, cells lacking NF1 also show reduced sensitivity toward endocrine therapy and CDK4/6 inhibition ex vivo. Mechanistically, we found that cancer cells lacking NF1 are more glycolytic and have a lower oxidized redox state. We show now that N-acetylcysteine (NAC), a reducing amino acid derivative, synergizes with PI3Kα inhibition and diminishes mTOR signaling, with a subsequent reduction in tumor cell proliferation in vitro and in vivo, especially among cells with NF1 loss. Combining the readily available food supplement NAC with PI3Kα inhibition provides an exciting opportunity to combat therapy resistance in patients with advanced breast cancer.

Results

Parallel in vivo screens identify NF1 loss as a resistance-enhancing event in PI3Kα inhibition

To identify genes whose activation or depletion confers resistance to PI3Kα inhibition in breast cancer, we combined the PiggyBac (PB) transposon system¹⁶ with a murine whey-acidic protein (WAP)-driven and PIK3CA^H1047R mutant mammary tumor model²¹ (Figure S1A). We hypothesized that the transposon-mediated mutagenesis system together with the selection pressure from a PI3Kα inhibitor would yield resistant tumors and that these could be examined for accumulation of resistance-promoting mutations (Figure 1A). Experimental mice were mated to activate the WAP promoter in the mammary gland during late pregnancy, resulting in simultaneous expression of the PIK3CA^H1047R mutation and the PB transposase specifically in mammary cells.¹⁶^,²¹ As expected, mammary tumors developed, and the animals were treated daily with 30 mg/kg alpelisib when tumors reached 250 mm³. Treated tumors that then increased in volume over at least three consecutive measurements in a 3-week period were classified as resistant. Such resistant tumors and tumors from an untreated control group were isolated when the total tumor volume of a mouse reached the allowed maximal size. Overall, genomic DNA from 51 resistant and 94 untreated tumors was isolated and processed by splinkerette PCR for NGS-based transposon integration mapping (Figure S1B). After quality control, we selected genes with a high sum of normalized (per 10⁵ alignments) diversity values over all insertions in a gene in the resistant sample group (>40 sum of normalized diversity values [divNsum]) and 15% enrichment in the resistant compared with the untreated samples (Figures 1B and S1B; Table S1). The genes in this selection were Stxbp5, Kmt2c, Fbxw7, Snd1, Nf1, Opcml, Nmt, Kalrn, Tink, Rapgef4, Hdac8, Depdc5, and Rev1.

Parallel *in vivo* screens reveal *NF1* loss as an event promoting resistance to PI3Kα inhibition

(A) Experimental design of an *in vivo* transposon screen using a WAP-driven *PIK3CA*^H1047R mutant mammary cancer model.

(B) Dot plot of genes with transposon insertions found in untreated (n = 94) and alpelisib-resistant tumors (n = 51). Only insertions mapping to gene-coding regions are displayed. Dot size indicates the divNSum (the sum of normalized diversity values per 10⁵ alignments over all insertions in a gene in alpelisib-treated tumors). Selected hits are highlighted in red.

(C) Experimental design of the generation of tumors resistant to PI3Kα inhibition using a WAP-driven *PIK3CA*^H1047R mutant mammary cancer model.

(D) Tumor volumes of vehicle- or alpelisib-treated mice. Tumors that increased in three consecutive measurements were classified as relapse tumors and are displayed in red, while responding tumors are termed stable and indicated in blue (n = 7 vehicle, n = 7 relapse, n = 7 stable).

(E) Bar graph showing *Nf1* mRNA expression in WAP-driven *PIK3CA*^H1047R mutant tumors treated with vehicle or alpelisib. Displayed are means ± SD and the p-value for the Kruskal-Wallis test (n = 7 per condition). cpm, counts per million.

(F) *NF1* mutation frequency in human breast cancer samples. The bar graph indicates the number of metastases or primary tumor samples in each study. The last column summarizes all studies. The dot plot displays the fraction of samples with *NF1* mutations as a color gradient and as dot size. Displayed are odds ratio (OR) and the p value determined by Fisher’s exact test (n = 2,043 metastases, n = 4,611 primary tumors).

ns, p > 0.05; ∗p < 0.05; ∗∗∗p = 1.143 $\times$ 10⁻⁹.

In a second resistance screen, WAP-Cre/LSL-PIK3CA^H1047R mice (which are reported to develop palpable mammary tumors within 36.8 ± 4.9 days after pregnancy start²¹ were treated daily with 30 mg/kg alpelisib when the tumors reached 250 mm³ (Figures 1C and S1C). Tumors in the treated group were split into relapse (when tumor volume increased over at least three consecutive measurements) and stable tumors (Figure 1D). Using bulk RNA sequencing (RNA-seq), we overlapped transcriptomic changes between vehicle and relapse tumors (Tables S2 and S3) with the data from the in vivo PB transposon-mediated screen. Interestingly, only three of the genes identified in the PB screen were also detected in RNA-seq: Nf1, Kmt2c, and Kalrn (Figures 1E and S1D). Because of the insertion pattern and transposon directionality at the insertion sites, all three genes were predicted to be tumor suppressors in the context of PI3Kα inhibition (Figure S1E). Of those three genes, Kalrn activity did not change significantly between vehicle and stable and relapse tumors; Kmt2c was lower in stable and relapse tumors than in vehicle-treated tumors (Figure S1D). On the other hand, the expression of Nf1 was significantly lower in relapsed than in stable tumors, which made Nf1 a candidate gene in the resistance to PI3Kα inhibition.

To assess the frequency of NF1 mutations in human breast cancer samples, we analyzed several published datasets⁶^,²⁵^,²⁶^,²⁷^,²⁸^,²⁹^,³⁰^,³¹^,³²^,³³^,³⁴^,³⁵^,³⁶ and found NF1 to be mutated in 6.7% of the 6,654 analyzed samples. Strikingly, mutations in NF1 were more frequent in metastatic samples (10.7% mutated samples of 2,043 metastatic samples) than in primary tumor samples (5.8% mutated samples of 4,611 primary tumor samples, odds ratio of 0.53) (Figure 1F; Table S4). Because PI3Kα inhibitors are currently only approved for use in advanced or metastatic breast cancer (based on the SOLAR-1 trial⁸), the loss of NF1 most likely precludes a long-lasting cure. In the same set of human samples, we observed mutations co-occurring with alterations in NF1 in genes including TP53, APC, PREX2, TSC2, ERBB3, ERBB4, and KMT2C (Figure S1F; Table S5). Only NBPF1 was found to be mutually exclusive with alterations in NF1. In primary tumors, among the top genes found to be co-mutated with NF1 were RNF213, HYDIN, ERBB4, TP53, MTOR, and KMT2C. In human metastatic samples, mutations in TP53, ATM, MED12, and PIK3R1 most significantly co-occur with alterations in NF1. In summary, we discovered that mutations conferring loss of Nf1 were enriched in an in vivo PB transposon-mediated resistance screen, and Nf1 was less expressed in alpelisib-resistant tumors in a WAP-Cre/PIK3CA^H1047R mammary tumor model. Additionally, we found that NF1 is frequently mutated in human breast cancer and that such mutations are enriched at metastatic sites.

Loss of NF1 reduces sensitivity to PI3Kα inhibition in vitro and in vivo

To validate our findings from the in vivo resistance screens, we used the human T47D cell line, which harbors a PIK3CA^H1047R mutation and is known to be sensitive to PI3Kα inhibition.³⁷^,³⁸^,³⁹ We knocked out NF1 using CRISPR-Cas9 and found no significant difference between the doubling times of T47D control (CTRL) and NF1 knockout (NF1 KO) cells (Figures S2A–S2D). To test whether NF1 KO cells resist PI3Kα inhibition more than their CTRL counterparts, we assessed their ability to form colonies over a prolonged treatment period. Indeed, NF1 KO cells covered three times more area than CTRL cells after 3 weeks of continuous alpelisib treatment (Figure 2A). These results were confirmed in a cell line derived from a murine PIK3CA^H1047R mutant mammary tumor model (hereafter called 3tg.HR2) in which we depleted Nf1 using two different shRNAs (shNF1_1 and shNF1_2; Figures S2E and S2F).

To investigate whether decreased sensitivity to PI3Kα inhibition is related to a higher proliferation capacity, we measured EdU incorporation in CTRL and NF1 KO T47D cells under PI3Kα inhibition. While we found no difference in EdU incorporation between NF1 KO and CTRL cells under baseline conditions, a higher proportion of EdU⁺ cells was found among NF1 KO cells than among CTRL cells after 48 h of alpelisib treatment, which indicates increased proliferation of NF1 KO cells under treatment pressure (Figure 2B). Similar results were observed in the murine 3tg.HR2 model (Figure S2G).

Because NF1 is a negative regulator of the mitogen-activated protein kinase (MAPK) signaling cascade, we assessed whether the resistance effect observed is due to elevated MAPK signaling by combining alpelisib with binimetinib, a small molecule inhibiting MEK1 and MEK2 (approved for metastatic melanoma in combination with encorafenib⁴⁰) in a colony formation assay. There was no significant effect of MEK inhibition on T47D CTRL or NF1 KO cells under baseline conditions or under PI3Kα inhibition (Figure S2H). Furthermore, in combination with PI3Kα inhibition, MEK inhibition did not lower the colony area of CTRL cells, and the difference between NF1 KO cells treated with the PI3K inhibitor alone or in combination with the MEK inhibitor was also marginal. These data indicate that combining PI3Kα and MEK inhibition only partially restored the response of NF1 KO cells to PI3Kα inhibition.

Having shown the importance of NF1 loss for tumor cell resistance in vitro, we investigated whether this also results in reduced sensitivity to PI3Kα inhibition in vivo. For this purpose, we orthotopically implanted T47D CTRL or NF1 KO cells in the mammary fat pad of non-obese diabetic-severe combined immunodeficiency-Il2rg^null (NSG) mice and treated the mice daily with alpelisib when the tumors had reached 150 mm³ (Figure 2C). Because NF1 KO tumors grew faster than their CTRL counterparts, we only display growth after the start of treatment. Although CTRL and NF1 KO tumors showed some initial response to PI3Kα inhibition, we observed significantly faster growth of NF1 KO tumors under PI3Kα inhibition. This observation was confirmed by the results of a survival analysis (Figure 2D) showing that loss of NF1 reduces sensitivity to PI3Kα inhibition in vitro and in vivo resulting in disease progression.

NF1-depleted cells have reduced reactive oxygen species (ROS) levels and lower mitochondrial respiration

We performed a transcriptome analysis to investigate differences in gene expression between T47D CTRL and NF1 KO cells (Tables S6 and S7). Gene set enrichment analysis indicated that MAPK signaling is more active in NF1 KO than CTRL cells, with three MAPK pathway-related gene sets positively enriched among the top 10 GO terms (Figure 3A). In the MSigDB’s Hallmark gene set collection, we found that the only two positively enriched gene sets in NF1 KO cells are MYC related (Figure S3A). The integrated system for motif activity response analysis (ISMARA) showed that the most significantly enriched transcription factor motif in NF1 KO cells was associated with the transcription factors MXI, MYC, and MYCN (Figure 3B). Moreover, MYC mRNA levels were significantly higher in NF1 KO cells (Figure S3B). Although MAPK inhibition in NF1 KO cells did not overcome the increased resistance to PI3Kα inhibition, we focused on investigating non-transcriptional MYC-associated effects, given that MAPK signaling and MYC activity were the major transcriptional changes observed.

*NF1*-depleted cells have reduced ROS levels and lower mitochondrial respiration

(A) Gene set enrichment analysis comparing gene expression of T47D NF1 KO versus CTRL cells using CAMERA (edgeR) (n = 3 CTRL and n = 3 NF1 KO). Displayed are the 10 most enriched gene sets of the C5 MSigDB GO collection. The dotted line indicates significance (−log₁₀ false discovery rate [FDR] > 1.3). Displayed are the −log₁₀ FDR values; gray and pink indicate positive enrichment in CTRL and NF1 KO cells, respectively.

(B) Top 10 enriched transcription factor (TF) activities calculated by ISMARA.⁴¹ Shown is the Z score; gray and pink indicate positive enrichment in CTRL and NF1 KO cells, respectively.

(C and D) Extracellular acidification rate (ECAR; mpH/min) of T47D CTRL and NF1 KO measured by an extracellular flux analyzer. Addition of glucose (10 mM), oligomycin (1 μM), and 2-DG (50 mM) is indicated. Values in (C) represent the mean ± SD of 3 independent experiments (each with 12 technical replicates) and in (D) a two-way ANOVA with Holm-Šídák multiple-comparisons correction. NGA, non-glycolytic acidification.

(E) ROS levels quantified by DCFDA staining in T47D CTRL and NF1 KO cells. Values are normalized to unstained controls. Displayed are mean ± SD and the p value for the Mann-Whitney test (n = 3 independent experiments).

(F) Mitochondrial superoxide (MitoSOX) in T47D CTRL and NF1 KO cells. Values are normalized to an unstained control and scaled to one. Displayed are mean ± SD and the p value for the Mann-Whitney test (n = 5 independent experiments).

(G) GSH and GSSG levels of T47D CTRL and NF1 KO cells, measured by the luminescence-based GSH/GSSG-Glo assay. Values are the ratio of GSH over GSSG, and displayed are mean ± SD and the p value for the Mann-Whitney test (n = 4 independent experiments).

Ns, p > 0.05; ∗p < 0.05; ∗∗p < 0.01.

Because MAPK signaling enhances aerobic glycolysis (the Warburg effect),⁴²^,⁴³^,⁴⁴ and MYC can increase cell energy production through enhanced oxidative phosphorylation (OXPHOS) but also by augmenting glycolysis,⁴²^,⁴⁵^,⁴⁶^,⁴⁷ we investigated whether NF1 loss impacts glycolysis and mitochondrial respiration. The extracellular acidification rate (ECAR), an indirect measure of lactate production, increased in NF1 KO cells upon addition of glucose, indicating that they are more glycolytic than the CTRL cells (Figures 3C and 3D). Analysis of the oxygen consumption rate (OCR) showed that NF1 loss lowers mitochondrial respiration under baseline conditions (Figures S3C and S3D). Addition of carbonyl cyanide p-trifluoromethoxyphenylhydrazone (FCCP), an uncoupler of the electron transport chain, resulted in a higher maximal OCR in CTRL cells than in NF1 KO cells. Interestingly, NF1 KO cells had no spare respiratory capacity (the difference between maximum and baseline respiration). Next, we quantified mitochondrial content and estimated functional membrane potential using MitoTracker dyes (Figures S3E–S3G). NF1 KO cells had more mitochondria, and these had an even higher membrane potential than their CTRL counterparts. Altogether, these data suggest that NF1 KO cells are less dependent on OXPHOS despite possessing more mitochondria.

Because mitochondrial respiration is the primary source of ROS, we investigated whether NF1 KO cells had lower ROS levels than CTRL cells. In fact, we found lower levels of cellular ROS, as measured by DCFDA staining intensity, and significantly lower levels of mitochondrial superoxides in NF1 KO cells than in CTRLs (Figures 3E and 3F). Cellular ROS balance is generally dependent on the ratio of oxidized glutathione (GSH) to reduced glutathione (GSSG), with GSH being one of the most potent cellular ROS scavengers.⁴⁸^,⁴⁹ We therefore assessed the cellular redox balance by measuring the ratio of GSH to GSSG levels. NF1 KO cells had a considerably higher ratio of GSH/GSSG than CTRL cells, indicating that the former generally have a lower oxidative state (Figure 3G). Collectively, these data show that NF1 loss correlates with a reduced redox state because of lower mitochondrial respiration and enhanced glycolysis.

NAC treatment circumvents resistance to PI3Kα inhibition

Because NF1 KO cells showed lower ROS levels, we postulated that this could be the cause of their lower sensitivity to PI3Kα inhibition. To test this, we treated T47D CTRL and NF1 KO cells with a combination of alpelisib and the antioxidant NAC, hypothesizing that this would increase resistance to PI3Kα inhibition in CTRL cells. Surprisingly, NAC treatment decreased proliferation of CTRL and NF1 KO cells and, more importantly, sensitized NF1 KO cells to PI3Kα inhibition, as measured by EdU incorporation and colony area (Figures 4A and S4A). In vivo, T47D CTRL tumors responded to alpelisib, but co-treatment with NAC had no additive effect. Contrastingly, in T47D NF1 KO tumor-bearing mice, the inhibition of tumor growth was enhanced when PI3Kα inhibition was combined with NAC (Figure 4B). To test whether our findings can be extended to other PIK3CA mutant models, we knocked out NF1 in MCF7 breast cancer cells harboring a PIK3CA^E545K mutation (Figure S4B). We assessed proliferation using EdU incorporation and found that MCF7 cells with NF1 KO are less sensitive to alpelisib treatment compared with intergenic CTRL cells (Figure S4C). Co-treatment with NAC and alpelisib reduced proliferation in NF1 KO cells. This suggests that our findings may be applicable to breast cancers with different PI3Kα-activating mutations.

Next, we knocked out NF1 in a PIK3CA^H1047R mutant breast cancer patient-derived xenograft (PDX) model with an all-in-one CRISPR-Cas9 system and generated PDX-derived organoids to test our findings (Figures 4C and S4D). Tracking of insertions or deletions (indels) by decomposition (TIDE) analysis of the modified genomic locus of NF1 predicted a modification rate of 93%, with 76% being deletions that cause frameshifts (Figure S4E). In line with this, NF1 mRNA transcripts and protein levels were reduced in NF1 KO organoids, and pERK levels increased, which further validated the loss of NF1 in the engineered organoids (Figures S4F and S4G). We then exposed organoids grown in 3D to alpelisib and/or NAC for 7 days. 3D quantification of DAPI⁺ nuclei showed that NF1 KO organoids were less sensitive to PI3Kα inhibition (Figure 4D). Importantly, combination of NAC sensitized NF1 KO organoids to PI3Kα inhibition. Altogether, these data demonstrate that alpelisib combined with NAC circumvents NF1 loss-evoked resistance in a PIK3CA^H1047R mutant tumors in vitro, in vivo using a cell line, and ex vivo in patient-derived organoids.

Because we observed that NF1 KO cells have elevated MYC transcriptional activity and mRNA levels, we tested the dependency of NF1 KO cells on MYC. Pharmacological MYC inhibition using KJ-Pyr-9 lowered proliferation, as measured by EdU incorporation, in CTRL and NF1 KO cells compared with vehicle (Figure 4E). Strikingly, NF1 KO cells were more sensitive to KJ-Pyr-9 treatment compared with CTRL cells, hinting toward enhanced dependence on MYC activity. Interestingly, the combination of NAC with KJ-Pyr-9 had no additional inhibitory effect on proliferation, and combination of MYC inhibition with alpelisib significantly lowered proliferation of NF1 KO cells. Next, we investigated whether pharmacological inhibition of MYC or NAC treatment could reverse the metabolic switch toward glycolysis observed in NF1 KO cells. MYC inhibition could not abolish the difference in glycolysis between CTRL and NF1 KO cells (Figures 4F and S4H). In contrast, 24-h pre-treatment with NAC reversed the increased glycolysis observed in NF1 KO cells compared with CTRL cells. In conclusion, pharmacological MYC inhibition and NAC synergize with PI3Kα inhibition, while only NAC reverses the metabolic phenotype of NF1 KO cells.

To assess whether our findings could be applicable to cancers with other MAPK-activating alterations, we overexpressed the constitutively active KRAS 4B^G12V in CTRL cells (KRAS 4B^G12V). In a colony formation assay, we observed that the colony area formed under alpelisib treatment was larger in KRAS 4B^G12V and NF1 KO cells compared with CTRL cells (Figure S4I). As in NF1 KO cells, KRAS 4B^G12V cells could be sensitized to PI3Kα inhibition when co-treated with NAC. This suggests that NAC co-treatment may also be effective in cancers with other MAPK-activating alterations.

Because loss of NF1 has been implicated in resistance to other treatments,²⁹^,⁵⁰^,⁵¹^,⁵²^,⁵³^,⁵⁴^,⁵⁵^,⁵⁶ we investigated the response of NF1 KO cells to other clinically relevant agents and combinations used in estrogen receptor alpha⁺ PIK3CA^H1047R mutant breast cancer. Proliferation of NF1 KO cells was higher than CTRL cells under endocrine treatment with fulvestrant (Figure 4G). T47D cells are sensitive to CDK4/6 inhibition by ribociclib,⁵⁷ but loss of NF1 decreases the efficacy. Co-treatment with NAC enhanced sensitivity of NF1 KO cells to fulvestrant only marginally. On the other hand, NAC co-treatment enhanced the inhibitory effect of ribociclib more significantly. Thus, NAC reverses the resistance of cells with NF1 loss to CDK4/6 inhibition and only partially to estrogen receptor-targeting agents.

NAC synergizes with PI3Kα inhibition and dampens mTOR signaling

To investigate the mechanism by which NAC overcomes NF1 loss-evoked resistance to PI3Kα inhibition, we measured activation of the PI3Kα and MAPK pathways in lysates of T47D CTRL and NF1 KO cells that were treated for 2 h with a PI3Kα inhibitor, NAC, or the two compounds combined. P^S473AKT was not detected in any PI3Kα-inhibited condition, regardless of the addition of NAC (Figure 5A). NAC alone reduced the p^S473AKT level in NF1 KO lysates compared with CTRL. P^S235/S236S6 levels were only reduced by PI3Kα inhibition. P^T202/Y204ERK 1 and 2 levels were elevated in NF1 KO cells under vehicle conditions. Additionally, p^T202/Y204ERK 1 and 2 levels, although lower in all PI3Kα treatment conditions, remained higher in NF1 KO cells than in CTRL cells.

To explore the effects of NAC in a quantitative and unbiased way, we performed global proteomics profiling of T47D CTRL and NF1 KO cells that were treated for 2 h with a PI3Kα inhibitor or NAC alone or the two in combination (Tables S8, S9, S10, and S11). We quantified phosphorylation of serine, threonine, and tyrosine residues. The only positively enriched Gene Ontology (GO) term of differentially detected post-translationally modified peptides between NF1 KO and CTRL cells without drug treatment was “negative regulation of protein tyrosine kinase activity” (Figure S5A). The terms “establishment of mitochondrion localization” and “cell death in response to oxidative stress” were negatively enriched in NF1 KO cells. We next displayed all significantly changed phospho-peptides of all proteins listed in the “BIOCARTA MAPK pathway” collection and found that most phospho-sites were differently abundant in a genotype-dependent manner (NF1 KO vs. CTRL), while treatment-induced changes were limited to a few peptides from MYC, RPS6KB, MAP3K2, and PAK1 (Figure S5B).

To investigate how NAC sensitizes NF1 KO cells to PI3Kα inhibition, we explored the top five GO terms of differentially detected phospho-peptides among different treatment conditions. This revealed that PI3Kα inhibition had a greater effect on mTOR signaling in CTRL than in NF1 KO cells (Figure 5B). However, NAC synergized with alpelisib to inhibit mTOR signaling in CTRL and NF1 KO cells. Looking at individual phospho-peptides in the mTOR pathway, we found that three activating phosphorylations of S6 (RPS6 at Ser235, Ser236, and Ser240) were markedly reduced in CTRL cells in response to alpelisib alone or combined with NAC (Figures 5C and S5C). However, in NF1 KO cells, the same phosphorylation sites on S6 were only reduced by the combination of alpelisib with NAC. It should also be noted that these changes occurred despite slightly higher S6 protein levels in NF1 KO cells. An additive effect was also observed on p^T2474MTOR, which was reduced further by the combination of alpelisib with NAC than alpelisib alone but without a significant difference between the cell lines (Figure 5D). In conclusion, these data suggest that NAC treatment in combination with PI3Kα inhibition depletes mTOR downstream signaling.

Next, we asked whether mTOR inhibition has a similar effect as NAC in sensitizing NF1 KO cells to alpelisib. Treatment with everolimus, an mTOR inhibitor, was less effective in reducing proliferation in NF1 KO cells compared with CTRL cells (Figure 5E). Interestingly, the combination of everolimus with NAC was more effective compared with single treatments. Combination of either everolimus or NAC with alpelisib was similarly effective at diminishing proliferation of CTRL and NF1 KO cells. In summary, our data indicate that NAC is a promising candidate to combine with PI3Kα inhibition, especially in tumors with NF1 loss, and appears to mediate its effects by inhibiting mTOR signaling.

Discussion

Two independent parallel in vivo screens in PIK3CA^H1047R-driven mammary tumor models revealed that loss of NF1 results in resistance to PI3Kα inhibition, a finding that we confirmed in vitro, in vivo, and in a PDX-derived organoid model. We demonstrate that cancer cells lacking NF1 are metabolically more glycolytic and are overall more reduced in terms of their redox balance than the controls. We show that combining PI3Kα inhibition with NAC reduces proliferation and prolongs tumor control of NF1 KO cancer cells, possibly through augmented inhibition of mTOR signaling.

Cancer cells lacking NF1 have elevated MAPK signaling and a higher activity of MYC, consistent with the fact that NF1 inhibits RAS⁵⁸^,⁵⁹^,⁶⁰^,⁶¹ and RAS/MAPK/ERK signaling activates MYC.⁶²^,⁶³^,⁶⁴ Hyperactive RAS/MAPK/ERK signaling has been associated previously with elevated glycolysis and reduced OXPHOS in various cancer and cell types.⁴²^,⁴³^,⁴⁴ In agreement, we found that NF1 KO cells have lower levels of OXPHOS and ROS than CTRL cells, but NF1 KO cells also have more mitochondria with functional membrane potential. The elevated MYC activity and mRNA level observed in NF1 KO cells could potentially explain the higher mitochondrial content in NF1 KO cells through elevated mitochondrial biogenesis.⁴²^,⁴⁵^,⁴⁶^,⁴⁷^,⁶⁵ We did not investigate mitochondrial function, fusion, and fission, which could be affected by enhanced MAPK and/or MYC activity.⁴⁷ Interestingly, NF1 KO cells were more sensitive to pharmacological MYC inhibition than CTRL cells, and we observed an additive effect of MYC inhibition in combination with PI3Kα inhibition. However, MYC inhibition did not reverse the metabolic phenotype of NF1 KO cells. We therefore speculate that the elevated MYC expression and activity observed in NF1 KO cells is partially responsible for the resistance toward PI3Kα inhibition but not for the elevated glycolysis.

We show that NAC sensitizes cancer cells lacking NF1 to PI3Kα inhibition and that NF1 KO cells are in a more reduced redox state than CTRL cells. Conceivably, NAC may further reduce this redox state, which culminates in proliferation arrest. The biological effects of NAC are highly debated and occur via three different mechanisms:⁶⁶ scavenging of oxidants, replenishment of glutathione, and reduction of disulfide bonds.⁶⁶^,⁶⁷ However, each of these effects are highly debated and might be specific to certain experimental and clinical settings.⁶⁶^,⁶⁸^,⁶⁹ It remains to be demonstrated which of these molecular effects of NAC are responsible for the synergy with PI3Kα inhibition in NF1 KO cells and whether the same mechanisms are responsible for the sensitization toward other therapies, including ribociclib and fulvestrant.

In a phospho-proteomics analysis, we found that NAC reduced phosphorylation of mTOR-associated proteins, including MTOR and RPS6. In agreement, combination of the mTOR inhibitor everolimus or NAC with alpelisib was similarly effective at diminishing proliferation of CTRL and NF1 KO cells. We show that treatment of T47D NF1 KO cells with NAC normalized glycolysis to the level of CTRL cells. Because mTOR signaling is a strong driver of glycolysis, the observed reduction of mTOR signaling by NAC might be partially responsible for this effect. However, it remains to be further investigated whether there is a causal link between NAC’s effects on mTOR signaling, the reduction in the glycolytic phenotype, and its synergy with PI3Kα inhibition. Because our findings were similar in PIK3CA^E545K mutant breast cancer cells, we propose that our findings may apply also to cancer cells with other mechanisms of PI3Kα hyperactivation or pathway amplification.

In the clinical setting, the combination of NAC with PI3Kα inhibition is potentially very interesting. One reason is that NAC is a safe, widely used, and cheap food supplement. Mutations in NF1 occur in about 8% of all breast cancers and, more important, such mutations are enriched in metastatic samples, which highlights their clinical relevance. This finding is also supported by a recent study that listed NF1 loss as a driver event enriched in metastatic samples of hormone⁺/HER2⁻ breast cancer patients and showed that such patients have a lower overall survival than wild-type NF1 patients.⁷⁰ Most of the metastatic samples we analyzed in this study originate from breast cancer patients with prior treatment. Therefore, we assume that many of these metastatic samples are, in fact, treatment resistant. Interestingly, loss of NF1 has been implicated in resistance to other treatments, including tamoxifen, inhibitors of EGFR, HER2, MEK, estrogen receptor α (ERα), SRC, retinoic acid, and chemotherapy in various cancer types.²⁹^,⁵⁰^,⁵¹^,⁵²^,⁵³^,⁵⁴^,⁵⁵^,⁵⁶^,⁷¹^,⁷² In most cases the mechanism of resistance was attributed to increased MAPK signaling. This contrasts with our findings that the NF1 loss-evoked resistance toward PI3Kα inhibition is not strongly MAPK dependent. We speculate that this low sensitivity to MEK inhibition might be due to exogenously enhanced MAPK signaling in otherwise not strongly MAPK-dependent cancer cells. Supporting our findings, a recent study by Bertucci et al.⁷⁰ found that none of five patients harboring NF1-mutated tumors showed an objective response to selumetinib (a MEK inhibitor) in the SAFIR02 trial. This suggests that elevated MAPK signaling might not be the sole driver of NF1 loss tumors. It should be noted that NAC enhances sensitivity to alpelisib in CTRL cells with constitutively active KRAS 4B, hinting that NAC co-treatment could generally be beneficial in malignancies with hyperactive MAPK signaling.

NF1 is mutated frequently (42% missense and 42% truncating mutations) in all major cancer entities, including skin (26%), ovarian (18%), endometrial (10%), lung (8%), and bladder cancer (7%).⁷³ Investigation of whether NAC would sensitize these tumors to other therapies is warranted. Our data indicate that co-treatment with NAC sensitizes T47D with NF1 loss to ribociclib and partially to fulvestrant. This warrants further exploration and could be of high significance for future clinical studies.

In summary, our findings highlight use of genetic screens to identify resistance mechanisms and show that NF1 loss is a resistance-enhancing event in PI3Kα inhibition. This suggests that patients with tumors lacking NF1 are likely to develop resistance to PI3Kα inhibition. Furthermore, we discovered that NAC treatment circumvents resistance to PI3Kα inhibition, likely by dampening glycolytic activity and mTOR signaling, and may be an attractive strategy to be tested in patients with NF1 loss-evoked resistance.

Limitations of the study

We are aware of several limitations of our study. First, we use the PI3Kα inhibitor alpelisib; other PI3Kα inhibitors or pan-PI3K inhibitors were not tested. Second, all of our in vivo experiments were performed in NSG mice, which do not have an intact immune system. Thus, the effects of PI3Kα inhibition and NAC on the immune system have not been evaluated. Third, the only antioxidant we used was NAC, and whether other ROS scavengers have similar effects remains unknown. Also, the exact mechanism by which NAC dampens mTOR signaling and lowers glycolysis needs to be further investigated. Fourth, we found little evidence that the NF1-loss mediated resistance mechanism to PI3Kα inhibition is dependent on MAPK signaling, but this might be limited to the specific readout and time points investigated. Fifth, we did not assess the observed effects on metastases.

STAR★Methods

Key resources table

REAGENT or RESOURCE	SOURCE	IDENTIFIER
Antibodies

Rabbit polyclonal anti-NF1	Abcam	Cat#ab17963; RRID:AB_444142
Mouse monoclonal anti-ERK2 (D-2)	Santa Cruz Biotechnology	Cat#sc-1647; RRID:AB_627547
Rabbit polyclonal anti-pThr202/pTyr204 ERK1/2	Cell Signaling Technology	Cat#9101; RRID:AB_331646
Mouse monoclonal anti-AKT (40D4)	Cell Signaling Technology	Cat#2920; RRID:AB_1147620
Rabbit monoclonal anti-pSer473 AKT (D9E)	Cell Signaling Technology	Cat#4060; RRID:AB_2315049
Mouse monoclonal anti-S6 (54D2)	Cell Signaling Technology	Cat#2317; RRID:AB_2238583
Rabbit polyclonal anti-pSer235/236 S6	Cell Signaling Technology	Cat#2211; RRID:AB_331679
HRP-conjugated sheep anti-mouse IgG	Merck	Cat#GENA931; RRID:AB_772210
HRP-conjugated donkey anti-rabbit IgG	Merck	Cat#GENA934; RRID:AB_772206
IRDye secondary antibodies goat anti-mouse IgG 680RD	LI-COR	Cat#925-68070; RRID:AB_2651128
IRDye secondary antibodies goat anti-rabbit IgG 800CW	LI-COR	Cat#925-32211; RRID:AB_2651127
APC mouse monoclonal anti-human CD298	BioLegend	Cat#341706; RRID:AB_2564263

Biological samples

Patient-derived xenograft	Gao et al.⁷⁴	X-3077

Chemicals, peptides, and recombinant proteins

Alpelisib	Novartis	N/A
N-Acetyl Cysteine	Sigma	Cat#A7250
Binimetinib	Selleckchem	Cat#S7077
Fulvestrant	Selleckchem	Cat#S1191
Ribociclib	Selleckchem	Cat#S7440
Everolimus	Selleckchem	Cat#S1120
KJ-Pyr-9	MedchemExpress	Cat#HY-19735
Hydrocortisone	Sigma	Cat#H0888
human EGF	Sigma	Cat#E9644
Y-27632	Selleckchem	Cat#S1049
17β-estradiol implant	Belma	Cat#ME2-90
17β-estradiol	Sigma	Cat#E2758

Critical commercial assays

Click-iT EdU Alexa Fluor 647 assay kit	Invitrogen	Cat#C10419
MitoSOX	ThermoFisher	Cat#M36008
ROS-DCFDA	Abcam	Cat#ab186029
MitoTracker Deep Red	ThermoFisher	Cat#M22426
MitoTracker Green	ThermoFisher	Cat#M7514
GSH/GSSG-Glo assay	Promega	Cat#V6611

Deposited data

DNA-seq data of PiggyBac resistance screen (raw and analyzed data)	This paper	Table S1; GEO: GSE207513
mRNA-seq of WAP-Cre/PIK3CA^H1047R murine tumors treated with vehicle/alpelisib (raw and analyzed data)	This paper	Tables S2 and S3; GEO: GSE207512
NF1 mutations in clinical samples	Cancer Genome Atlas Network,⁶ Martelotto et al.,²⁵ Razavi et al.,³⁴ Li et al.,³⁵ Razavi et al.,³⁶ Gao et al.,⁷⁵ Cerami et al.,⁷⁶ Krug et al.,²⁶ Johnson et al.,²⁷ Lefebvre et al.,²⁸ Smith et al.,²⁹ Pereira et al.,³⁰ Curtis et al.,³¹ Nixon et al.,³² Ciriello et al.³³	Table S4 http://cbioportal.org
NF1 co-occurring and mutually exclusive mutations in clinical samples	Cancer Genome Atlas Network,⁶ Martelotto et al.,²⁵ Razavi et al.,³⁴ Li et al.,³⁵ Razavi et al.,³⁶ Gao et al.,⁷⁵ Cerami et al.,⁷⁶ Krug et al.,²⁶ Johnson et al.,²⁷ Lefebvre et al.,²⁸ Smith et al.,²⁹ Pereira et al.,³⁰ Curtis et al.,³¹ Nixon et al.,³² Ciriello et al.³³	Table S5 http://cbioportal.org
mRNA-seq data of T47D CTRL and NF1 KO cells treated with vehicle/ alpelisib (raw and analyzed data)	This paper	Tables S6 and S7; GEO: GSE207514
Phosphorylated peptides of T47D CTRL and NF1 KO cells treated with vehicle/ alpelisib/ NAC (raw and analyzed data)	This paper	Tables S8 and S9; ProteomeXchange: PXD034644
TMT peptides of T47D CTRL and NF1 KO cells treated with vehicle/ alpelisib/ NAC (raw and analyzed data)	This paper	Tables S10 and S11; ProteomeXchange: PXD034644
GSEA (version 6.0)	Broad Institute	https://www.gsea-msigdb.org/gsea/msigdb/

Experimental models: Cell lines

MCF7	ATCC	Cat#HTB-22; RRID:CVCL_0031
T47D	ATCC	Cat#HTB-133; RRID:CVCL_0553
HEK 293T	ATCC	Cat#CRL-3216; RRID:CVCL_0063
3tg.HR2, murine, from WAP-Cre/LSL-PIK3CA^H1047R-GFP/LSL-transposase tumor	This paper	N/A

Experimental models: Organisms/strains

C57Bl/6-Tg(WAP-Cre) mice backcrossed to FVB/N	This paper; Wintermantel et al.⁷⁷	N/A
Balb/c-Tg(LSL- PIK3CA^H1047R) mice backcrossed to FVB/N	This paper; Meyer et al.²¹	N/A
C57Bl/6-Tg(ATP1)	Rad et al.¹⁶	N/A
C57Bl/6-Tg(LSL-PB) mice	Rad et al.¹⁶	N/A
NOD-scid-Il2rg^null	In house facility	N/A

Oligonucleotides

PrimeTime qPCR assay NF1 (human)	Integrated DNA technologies	Cat#Hs.PT.58.27908857
PrimeTime qPCR assay Nf1 (mouse)	Integrated DNA technologies	Cat#Mm.PT.58.17137818
PrimeTime qPCR assay HPRT1 (human)	Integrated DNA technologies	Cat#Hs.PT.58.v.45621572
PrimeTime qPCR assay Hprt1 (mouse)	Integrated DNA technologies	Cat#Mm.PT.39A.22214828
Primers for genotyping of transgenic animals and splinkerette PCR	This paper; Rad et al.,¹⁶ Zilli et al.,¹⁹ Meyer et al.,²¹ Wintermantel et al.,⁷⁷ Friedrich et al.⁷⁸	Table S13
Primer N1 Forward 5′-TAGTGGGGAGAGCGACCAAG-3′	This paper	N/A
Primer N1 Reverse 5′-CTGGGATAAAGGGGATGGAGG-3′	This paper	N/A

Recombinant DNA

PX458	Addgene	Cat#48138; RRID:Addgene_172221
pC2C	Knuckles et al.⁷⁹	N/A
PX458_sgNF1_A	This paper	Table S12
pC2C_sgNF1_B	This paper	Table S12
LentiCRISPR.v2_GFP	Addgene	Cat #82416; RRID:Addgene_82416
LentiCRISPR.v2_mCherry	Addgene	Cat# 99154; RRID:Addgene_99154
LentiCRISPR.v2_GFP_sgINTERGENIC_2261	This paper	Table S12
LentiCRISPR.v2_mCherry_sgINTERGENIC_4649	This paper	Table S12
LentiCRISPR.v2_mCherry_sgNF1_9	This paper	Table S12

Software and algorithms

GraphPad Software (version 9.3.1.)	GraphPad	https://www.graphpad.com
sgRNA design tool	Benchling	https://benchling.com
Integrated CQ1 software	Yokogawa	N/A
ImageJ (version 2.9.0)	Schindelin et al.⁸⁰	https://imagej.nih.gov/ij/download.html
ColonyArea	Guzmán et al.⁸¹	N/A
FlowJo (version 10.7.2)	BD Biosciences	https://www.flowjo.com/solutions/flowjo/downloads
TIDE: Tracking of Indels by Decomposition	Brinkman et al.⁸²	http://shinyapps.datacurators.nl/tide
AssayMAP Bravo platform	Post et al.⁸³	N/A
Progenesis QI software (version 2.0)	Nonlinear Dynamics Limited	N/A
MASCOT	http://www.matrixscience.com/search_form_select.html	http://www.matrixscience.com/search_form_select.html
RStudio (Version 1.2.5033)	https://posit.co/download/rstudio-desktop/	https://posit.co/download/rstudio-desktop/
SafeQuant R script (version 2.3)	Ahrné et al.⁸⁴	https://cran.r-project.org/web/packages/SafeQuant/index.html
SpectroMine software	Biognosis AG	https://biognosys.com/resources/spectromine-3/
eBayes	Ritchie et al.⁸⁵	N/A
edgeR (version 3.28.1)	Chen et al.⁸⁶	https://bioconductor.org/packages/release/bioc/html/edgeR.html
ggplot2 (version 3.3.5)	https://ggplot2.tidyverse.org/	https://ggplot2.tidyverse.org/
ComplexHeatmap (version 2.2.0)	Gu et al.⁸⁷	https://bioconductor.org/packages/release/bioc/html/ComplexHeatmap.html
QuasR (version 1.12.0)	Gaidatzis et al.⁸⁸	https://bioconductor.org/packages/release/bioc/html/QuasR.html
TxDb.Mmusculus.UCSC.mm10.knownGene Bioconductor package (version 3.2.2)	https://bioconductor.org/packages/release/data/annotation/html/TxDb.Mmusculus.UCSC.mm10.knownGene.html	https://bioconductor.org/packages/release/data/annotation/html/TxDb.Mmusculus.UCSC.mm10.knownGene.html
TxDb.Hsapiens.UCSC.hg38.knownGene Bioconductor package (version 3.4.6)	https://bioconductor.org/packages/release/data/annotation/html/TxDb.Hsapiens.UCSC.hg38.knownGene.html	https://bioconductor.org/packages/release/data/annotation/html/TxDb.Hsapiens.UCSC.hg38.knownGene.html
Illumina RTA (version 2.4.11)	Illumina	N/A
Illumina Base calling (version bcl2fastq-2.20.0.422)	Illumina	N/A
STAR (version 2.5.2a-goolf-1.7.20)	Dobin et al.⁸⁹	N/A
SAMtools for the mouse tumors (version 1.3.1-goolf-1.7.20)	https://bioconductor.org/packages/Rsamtools	https://bioconductor.org/packages/Rsamtools
SAMtools for human cell line (version 7-goolf-1.7.20)	https://bioconductor.org/packages/Rsamtools	https://bioconductor.org/packages/Rsamtools
Picard	https://github.com/broadinstitute/picard	https://github.com/broadinstitute/picard
Bamsignals (version 3.6)	https://bioconductor.org/packages/release/bioc/html/bamsignals.html	https://bioconductor.org/packages/release/bioc/html/bamsignals.html
Integrated System for Motif Activity Response Analysis (ISMARA)	Balwierz et al.⁴¹	https://ismara.unibas.ch/mara/

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Resource availability

Lead contact

Further information and requests for resources and reagents should be directed to and will be fulfilled by the lead contact, M. Bentires-Alj (m.bentires-alj@unibas.ch).

Material availability

This study did not generate new unique reagents. Cell lines can be shared upon reasonable request from the lead contact without restriction.

Experimental model and subject details

Cell lines and organoids

For the generation of the murine PIK3CA^H1047R-mutant cell line 3tg.HR2, a tumor from transgenic mouse (WAP-Cre/LSL-PIK3CA^H1047R-GFP/LSL-transposase) was mechanically chopped and then digested with 1× collagenase/hyaluronidase (Stemcell #7912) in DMEM (Sigma #D6429) for 30 min at 37°C. The digested tumor sample was resuspended and supplemented with DNAse I (Qiagen #79254) before passing the cell suspension through a 40-μm cell strainer. Pelleted cells were resuspended in 1 ml of Red Blood Cell Lysis buffer (Sigma #R7757) for 5 min, then diluted in 1 ml of PBS + 2% FBS and subsequently washed again with PBS + 2% FCS + 5 mM EDTA before counting. Cells were stained with anti-mouse CD326 (EpCAM, ThermoFisher Scientific #12-5791-82) and DAPI, and fluorescently activated cell sorted for single/live EpCAM⁺/GFP⁺ cells using a SORP Aria III (BD Biosciences). The mammary tumor-derived cell line (3tg.HR2) was then plated and maintained in DMEM (Sigma #D6429) supplemented with 10% FBS (Gibco #25300-054), Normocin (InvivoGen #ant-nr-1), and 1× penicillin/streptomycin (Sigma #P4333), 15 mM HEPES (Sigma #83264-100ML-F).

T47D, MCF7, and HEK293T cells were obtained from ATCC (#HTB-133, #HTB-22, and #CRL-3216). T47D and MCF7 were cultured in RPMI (Sigma #R8758) and DMEM (Sigma #D6429), respectively. Media for both cell lines was supplemented with 10% FBS (Gibco #25300-054), Normocin (InvivoGen #ant-nr-1), and 1× penicillin/streptomycin (Sigma #P4333), 15 mM HEPES (Sigma #83264-100ML-F), and 1 μg/μl insulin (Sigma #91077C). HEK293T cells were maintained in the same media as MCF7 but without insulin supplementation. Cell lines were all cultured at 37°C with 5% CO₂. All treatments were performed in complete supplemented media. All cell lines were routinely tested for mycoplasma contaminations.

PDX (X-3077; previously described by Gao et al.⁷⁴ derived organoid lines were cultured in a 40 μl drop of Matrigel (Growth factor reduced and phenol red-free, Corning #356231) in 24-well ULA plates that were submerged in organoid media after solidification. Culturing conditions were 37°C with 5% O₂. The organoid media is based on advanced DMEM/F12 (ThermoFisher #12634028) supplemented with 5% FBS (Gibco #25300-054), 1× Normocin (InvivoGen #ant-nr-1), 1× penicillin/streptomycin (Sigma #P4333), 1× HEPES (Sigma #83264-100ML-F), 1× GlutaMAX (ThermoFisher #35050061), 1 μg/ml hydrocortisone (Sigma #H0888), 10 ng/ml human EGF (Sigma #E9644), 50 μg/ml Gentamicin (Genesee #25-533) and 10 μM ROCK inhibitor Y-27632 (Selleckchem #S1049).⁹⁰ For passaging, organoids in Matrigel were collected and incubated 1:1 with dispase (Gibco #17105-041) for 30-60 min at 37°C. After Matrigel digestion, organoids were washed in PBS, resuspended in pre-warmed accutase (Sigma #A6964) and incubated for 5-10 min at 37°C. After disaggregation, the single-cell suspension was washed twice with PBS and reseeded in 40 μl Matrigel or used for an assay.

Animal experiments

Female NOD-scid-Il2rg^null (NSG) mice were bred in specific pathogen-free animal facilities of the Department of Biomedicine. All animal procedures were performed in accordance with Swiss national guidelines on animal well fare and protocols were approved by the cantonal veterinary office of Basel-Stadt. The animal housing was in a light, humidity, and temperature-controlled environment. Mice were acclimatized for at least 7 days prior to experiments. T47D CTRL or NF1 KO cells (2×10⁶) in PBS supplemented with 50% Matrigel (Corning #356237) were injected orthotopically into the mammary fat pad of isoflurane-anaesthetized 7–9-week-old NSG mice. Additionally, mice were supplemented with 17β-estradiol either through an implant (Belma #ME2-90) or with 17β-estradiol in the drinking water at 8 μg/ml (Sigma #E2758). Tumor diameters were measured using Vernier calipers and volume calculated as [length] × [width]² × (π/6). Treatments were started when the average of tumors reached 150 mm³. Mice were then randomized by snake distribution into different treatment groups. Animals were sacrificed when they showed signs of distress or when reaching the maximum tumor volume allowed (1.5 cm³). In some cases, the experiment was terminated before these endpoints were reached.

Transgenic animals

C57Bl/6-Tg(WAP-Cre) mice were originally described by Wintermantel et al.⁷⁷ and then backcrossed to an FVB/N background as described previously.²¹ FVB/N-Tg(WAP-Cre) mice were identified by genotyping using the primers: 5’-GAAAAGCACCAGGAGAAGTCAC-3’ and 5’-GACACAGCATTGGAGTCAGAAG-3’. Balb/c-Tg(LSL-PIK3CA^H1047R) mice were originally described by Meyer et al.²¹ and then backcrossed to an FVB/N background. FVB/N-Tg(LSL-PIK3CA^H1047R) mice were identified by genotyping using the following primers: 5′-TGGCCAGTACCTCATGGATT-3′ and 5′-GCAATACATCTGGGCTACTTCAT-3′. C57Bl/6-Tg(ATP1) mice were described previously by Rad et al.¹⁶ Transgenic animals were identified by genotyping using the primers 5′-CTCGTTAATCGCCGAGCTAC-3′ and 5′-GCCTTATCGCGATTTTACCA-3′. C57Bl/6-Tg(LSL-PB) mice were described previously.¹⁶ Transgenic animals were identified by genotyping using the primers 5′-GCTGGGGATGCGGTGGGCTC-3′ and 5′-GGCGGATCACAAGCAATAATAACCTGTAGTTT-3′, with the first primer binding specifically to the PB-knock-in fragment and the second one binding in the 3′ end of the Rosa locus. Wildtype animals were identified by genotyping using the primers 5’-CCAAAGTCGCTCTGAGTTGTTATCAG-3’ and 5’-GGCGGATCACAAGCAATAATAACCTGTAGTTT-3’, binding in the 5’ end or the 3’ end of the Rosa locus specifically. For generating quadruple-transgenic experimental mice, we first crossed WAP-Cre mice to transgenic LSL-PB mice, generating WAP-Cre/LSL-PB transposase animals. Transgenic LSL-PIK3CA^H1047R mice were crossed with ATP1 mice LSL- PIK3CA^H1047R/ATP1 animals. Finally, quadruple-transgenic experimental mice were generated by crossing WAP-Cre/LSL-PB transposase animals with LSL-PIK3CA^H1047R/ATP1 animals. The final mice had a mixed C57Bl/6 - FVB/N background.

Method details

NF1 mutations in public breast cancer datasets

All breast cancer studies⁶^,²⁵^,²⁶^,²⁷^,²⁸^,²⁹^,³⁰^,³¹^,³²^,³³^,³⁴^,³⁵^,³⁶ were selected in the cBio cancer genomics portal (http://cbioportal.org).⁷⁵^,⁷⁶ Samples with data for both mutations and copy number alterations were selected for our subsequent analysis. One non-breast cancer sample was excluded. Samples associated to the TCGA datasets⁶^,³³ were pooled into one sample group and duplicated samples were excluded. Similarly, samples coming from the MSKCC were pooled into one sample group and duplicated samples were excluded.²⁵^,²⁹^,³²^,³⁴^,³⁵^,³⁶ Clinical data were downloaded for all samples and matched by the sample.ID with the mutation information for NF1. Some samples had to be assigned manually to a sample type (metastasis or primary), e.g., when the complete dataset was of primary origin.

Analysis regarding co-occurring and mutually exclusive mutations with NF1 alterations were performed and downloaded from the cBio cancer genomics portal (http://cbioportal.org).⁷⁵^,⁷⁶ The study.ID and sample.ID of all above used samples were selected and NF1 was used in the query. Copy number alterations and structural variants such as fusions were excluded for this analysis. Genes with mutations in less than 20 samples were excluded.

Doubling time calculation

Cells were cultured as described above and counted (N_t) at each passage. Growth rate (r) and doubling time (DT) was then calculated for each passage interval with the following formula: r = ln(N_t/N₀)/t ; DT= ln(2)/r.

Drugs

The following drugs were used: alpelisib (BYL719; a gift from Novartis) in DMSO or in 0.5% methylcellulose (in H₂O, BioConcept #9-00F14-I) supplemented with 0.5% Tween80 (Sigma #P4780) if used for oral treatment of mice, NAC (Sigma #A7250) in H₂O, binimetinib (Selleckchem #S7077) in DMSO, fulvestrant (Selleckchem #S1191) in DMSO, ribociclib (Selleckchem #S7440) in DMSO, everolimus (Selleckchem #S1120) in DMSO, KJ-Pyr-9 (Medchem #HY-19735) in DMSO.

Generation of stable shRNA cell lines

1×10⁵ 3tg.HR2 cells were mixed with viral particles (multiplicity of infection, MOI, 0.3) in Opti-MEM (ThermoFisher #31985062) and 8 μg/ml polybrene (Sigma #H9268) and plated in 6-well plates. The media was refreshed 24 h later and after 4 days cells were exposed to 1 μg/ml puromycin (InvivoGen #ant-pr-1) for 7 days. The following constructs were used: Sigma MISSION pLKO.1-puro shCTRL: #SHC001, shNf1: #TRCN0000034341 and #TRCN0000034343.

Cloning of CRISPR vectors for NF1 KO

sgRNAs were designed using Benchling (https://benchling.com) or were obtained pre-designed by Park et al.⁹¹ The corresponding annealed and phosphorylated DNA fragments were cloned by Golden Gate Assembly into transient vectors (PX458 or pC2C; Addgene #48138 and Knuckles et al.⁷⁹) or lentiviral vectors (LentiCRISPR.v2-GFP or LentiCRISPR.v2-mCherry; Addgene #82416 and #99154, respectively). The restriction enzymes, sgRNA sequences and vectors used are summarized in Table S12.

Knockout with transient vectors

T47D cells were co-transfected using FuGENE (Promega #E2311) with two sgRNA-containing vectors (PX458_sgNF1_A, pC2C_sgNF1_B) or the two empty vectors. The knockout strategy was to remove the Kozak sequence and exon 1 of NF1, resulting in a 233-bp loss of genomic DNA. 72 h after transient transfection, double-positive cells were fluorescence-assisted single-cell sorted in 96-well plates. Single clones were expanded in 50% sterile-filtered conditioned media with 20% FCS and 30% normal media. gDNA of clones was isolated by adding 40 μl 50 mM NaOH to the cells, boiling them for 1 h at 95°C and then neutralizing with 10 μl 1M Tris (pH 6.8). For PCR, 250 ng of gDNA was used in a PCR mix supplemented with final 2X PCRx enhancer system (Invitrogen #11495-017). Primers used for detecting the 233-base deletion were: Primer_N1_FOR: 5’-TAGTGGGGAGAGCGACCAAG-3’, Primer_N1_REV: 5’-CTGGGATAAAGGGGATGGAGG-3’.

Generation of lentivirus

Low passage HEK293T cells (2.5×10⁶) were seeded in a 15-cm tissue-culture dish. After 72 h, the media was refreshed and cells were transfected in a 2:1:4 ratio with the pCMV-delta8.9 packaging plasmid, the VSV-G envelope plasmid, and a 2^nd generation donor plasmid in Opti-MEM (ThermoFisher #31985062). The plasmid cocktail was mixed with polyethylenimin (Polyscience Inc #23966) in a 1:3 ratio (w/w), mixed and incubated for 30 min before adding to the cells. Conditioned media with viral particles collected after 48 and 72 h was combined and cell debris depleted by centrifugation before filtration through a 0.45-μm PES filter. Viral particles were then enriched 10-100× using Lenti-X Concentrator (Takara #631232), resuspended in plain media or PBS with 0.1% BSA, and stored at -80°C until use.

Infection of T47D and MCF7 cancer cells

5×10⁴ T47D or MCF7 cells in OptiMEM (Gibco #31985062) with 8 μg/ml polybrene (Sigma #H9268) and the virus (MOI 0.3) were mixed and plated. 24 h after infection media was exchanged to complete growth media (see above for details). T47D cells infected with virus containing pLenti-PGK-KRAS4B(G12V)-Hygro (Addgene #35633) were selected with hygromycin B (300 μg/ml; InvivoGen #ant-hm-1). MCF7 infected with LentiCRISPR.v2_Intergenic_4649_mCherry or LentiCRISPR.v2_mCherry_sgNF1_9 were fluorescently activated cell sorted for single/live/mCherry positive cells using a SORP Aria III (BD Biosciences).

Infection of PDX-derived cancer cells

PDX tumors were mechanically chopped and then incubated with liberase (Roche #05466202001) and DNase I (Stemcell #07900) for 1 h at 37°C. The cell suspension was passed stepwise through a 100-μm and a 40-μm cell strainer, washed and incubated briefly with Red Blood Cell Lysis Buffer (Sigma #R7757). Murine cells were depleted with a Mouse Cell Depletion kit (Miltenyi Biotec #130-104-694) using a magnetic separator.

1×10⁵ PDX-derived cells were collected in a well of a 96-well U-bottom ULA plate and resuspended in media containing virus with a lentiviral all-in-one CRISPR/Cas9 vector either targeting an intergenic region (sgIntergenic) or targeting NF1 (sgNF1), both additionally encoding for GFP or mCherry, respectively, (LentiCRISPR.v2_GFP_sgINTER_2261, LentiCRISPR.v2_mCherry_sgNF1_9; MOI 10 based on a titration on HEK293T cells) and 8 μg/ml polybrene (Sigma #H9268). Cells were centrifuged at 37°C for 1 h at 400 x g before placing in the incubator for another 4 h at 37°C. Next, cells were pelleted and collected in Matrigel (growth factor reduced and phenol red-free, Corning #356231) and expanded for 3 weeks as a 3D culture in organoid media. Organoids were collected, washed, and Matrigel was digested with dispase (Gibco #17105-041). Organoids, with 1×10⁵ viable cells, were injected in 50% Matrigel (mixed with PBS) orthotopically in the 4^th mammary fat pad of NSG (NOD-scid-Il2rg^null) mice in both flanks. Tumors were collected when the total tumor volume reached 1 cm³. Cancer cells were then isolated as described above before sorting for GFP or mCherry positivity with a SORP Aria III (BD Bioscience). After sorting, the cells were expanded as organoids (PDXO) in 3D cultures. We verified that the organoids were of human origin by CD298⁺ staining (1:500, BioLegend #341706) (a β-subunit of the Na+/K+ ATPases specific for human cells⁹²).

3D drug response of organoids

A 15 μl layer of 30% Matrigel in organoid media was placed in a 384-well plate (clear bottom, black, Greiner #781091) and incubated 30 min at 37°C for solidification. Organoids were collected and digested as described above and resuspended in 30% Matrigel in organoid media. An aliquot of 15 μl Matrigel containing 1000 cells was then placed on top of the solidified Matrigel layer. After solidification for 30 min at 37°C, 70 μl organoid media was added to each well. Cells were grown as organoids for 4 days. Subsequently, 50% (50 μl) of the media was exchanged with 2x drug-containing media (treatment concentrations are indicated in the legends). After 3 days of treatment, 50% of the treatment media was replaced with 1x drug-containing media for another 4 days. After a total of 7 days treatment, 50 μl media was removed and organoids were washed 3x with 50 μl PBS with 1% BSA. Organoids were then fixed with 50 μl 8% PFA in PBS for 30 min at RT. The fixation was removed, the cells washed 3x with 50 μl PBS with 1% BSA and then permeabilized by adding 50 μl 0.6% Triton-X100 in PBS. Organoids were permeabilized for 20 min and washed again 3x with 50 μl PBS/ 1% BSA. Then DAPI (final 5 μg/ml, ThermoFisher #D1306) in PBS with 1% BSA was added to stain the nuclei. Organoids were imaged using a CQ1 spinning disk confocal microscope (Yokogawa) and nuclei counts were determined with the integrated CQ1 software.

Colony formation assay

Murine 3tg.HR2 or T47D cells (5×10² or 5×10³, respectively) were seeded in each well of a 24-well plate 24 h prior to treatment. Murine 3tg.HR2 or T47D cells were treated for 3 weeks or 4 weeks, respectively. Colonies were fixed using 3.3% trichloroacetic acid (Sigma #T6399) for 1 h at 4°C. The vehicle plates were fixed after 1 week of treatment. Plates with fixed cells were carefully washed in a water bath, air-dried, stained with 0.057% sulforhodamine B, (dissolved in acetic acid 1% vol/vol; Sigma #230162) for 1 h at room temperature, washed 4 times with 1% (vol/vol) acetic acid, and air-dried again at room temperature. For colony quantification, plates were scanned, and images were analyzed with ImageJ (version 2.9.0; FIJI⁸⁰) using the plugin ColonyArea.⁸¹

Cell cycle analysis

T47D cells (5×10⁴) were seeded 24 h prior to treatment in 12-well plates. Cells were treated as indicated for 48 h and incubated with further 10 μM EdU for the last 2 h before collection. Fixation, permeabilization and staining were performed as described in the manufacturer’s protocol for the Click-iT EdU Alexa Fluor 647 assay kit (Invitrogen #C10419). Hoechst 33342 (Invitrogen #H3570) was used to stain DNA. Cells were acquired on a Cytoflex S (Beckmann Coulter) and analyzed with FlowJo (version 10.7.2; BD Biosciences).

Quantification of MitoSOX and ROS-DCFDA

Cells were trypsinized, washed and stained in 96-well plates according to the manufacturer’s protocol for 15 min with MitoSOX (final 5 μM, ThermoFisher #M36008) or for 30 min with ROS-DCFDA (final concentration 10 μM, Abcam #ab186029), both at 37°C. After washing, cells were stained with DAPI (final concentration 1 μg/ml, ThermoFisher #D1306) for 10 min and then acquired with a Cytoflex S (Beckmann Coulter) flow cytometer. Analysis was done with FlowJo (version 10.7.2; BD Biosciences).

Quantification of mitochondrial content

CTRL or NF1 KO T47D cells (5×10⁴) were seeded per well of a 12-well plate 24 h before the assay. Cells were stained for 20 min at 37°C with MitoTracker Deep Red (MTDR, final concentration10 nM, ThermoFisher #M22426) and MitoTracker Green (MTG, final concentration 50 nM, ThermoFisher #M7514). After washing, cells were stained with DAPI (final concentration 1 μg/ml, ThermoFisher #D1306) for 10 min and then the fluorescent signal was acquired at a Cytoflex S (Beckmann) flow cytometer. MTDR is retained only in mitochondria with an active membrane potential whereas MTG stains all mitochondria. Analysis was performed with FlowJo (version 10.7.2; BD Biosciences).

Quantification of GSSG/GSH levels

Total glutathione and oxidized glutathione were measured separately according to the manufacturer’s protocol for the GSH/GSSG-Glo assay (Promega #V6611). CTRL or NF1 KO cells 1×10⁴ were seeded per well of a 96-well plate 24 h before the assay. Luminescence was measured with a Synergy H1 microplate reader (BioTek).

Seahorse experiments

T47D cells (2.5×10⁴) were seeded in a Seahorse XF96 Cell Culture microplate (Agilent #102416-100) in 80 μl normal growth media 24 h prior to the assay. For treatments (KJ-Pyr-3, NAC), cells were plated 5 hours prior to treatment start and incubated for 24h with the drugs present. Prior to the assay, the cells were washed with Glyco Stress Test assay media. The assay was performed according to the manufacturer’s Mito Stress Test or Glyco Stress Test protocol. In brief, the assay media used for the Glyco Stress Test was composed of Seahorse XF RPMI media (Agilent #103576-100) supplemented with 2 mM L-glutamine (Agilent #103579-100). The assay media used for the Mito Stress Test additionally contained 10 mM glucose (Agilent #103577-100) and 1 mM pyruvate (Agilent #103578-100).

For the Mito Stress Test, we used the following compounds: 1 μM (final concentration per assay well) oligomycin (Sigma #75351), 1 μM carbonylcyanid-4-(trifluormethoxy)phenylhydrazon (FCCP, Sigma #C2920), and a mix of rotenone (Sigma #R8875)/ and antimycin A (Sigma #A8674) each at a concentration of 2.5 μM. All compounds were prepared as 100× stocks in DMSO and then diluted in media to load the respective plate ports at 10×. For the Glyco Stress Test, we used the following compounds (final concentration per assay well): 10 mM D-(+)-glucose (Sigma #G5400), 1 μM oligomycin (Sigma #75351), and 50 mM 2-deoxy-D-glucose (2DG, Sigma #D8375). Oligomycin was prepared as 100× stocks in DMSO and then diluted in media to load the respective plate ports at 10×. Glucose and 2DG was prepared in assay media and then loaded into the respective plate ports at 10×. Assays were performed as described by the manufacturer’s protocol (Seahorse XF96, Agilent). Data were corrected for cell number using total protein or the non-mitochondrial respiration (Mito Stress Test) or the non-mitochondrial acidification (Glyco Stress Test) of each experiment and analyzed in R Studio.

Basal respiration is the difference in OCR before adding oligomycin and the non-mitochondrial respiration. Maximum respiration is the difference in OCR after addition of FCCP and the non-mitochondrial respiration. The spare capacity represents the difference between the basal respiration and the maximum respiration. The glycolysis level is defined by the ECAR increase after glucose addition. The glycolytic capacity is the difference between the maximum ECAR after adding oligomycin and the non-mitochondrial acidification. The glycolytic reserve is the difference between the glycolytic capacity and the glycolysis.

Immunoblotting

Cells were washed in ice-cold PBS before they were lysed in 8 M urea (in H₂O, Cell Signaling #7900) supplemented with 0.5% Triton-X100 (Merck #1.08643.1000), 1× cOmplete mini protease inhibitor cocktail (Roche #11836153001), and phosphatase inhibitor cocktail (Sigma #P0044). DNA was sheared by sonication and then pelleted before samples were complemented with 5x Laemmli buffer (2% SDS, 5% 2β-mercaptoethanol, 10% glycerol, 0.002% bromophenol blue in 62.5 mM Tris-HCl) and boiled at 95°C for 5 min. Denaturized proteins and Precision Plus Protein Dual Color Standards (BioRad #1610374) were separated by SDS-PAGE and later transferred to a PVDF membrane (Immobilon-P, Sigma #IPVH85R) at 90 V for 90 min in tris/glycine buffer (BioRad #1610771) supplemented with 20% methanol. The membrane was blocked for 1 h at room temperature with 5% BSA or 5% milk in TBS-T (TBS with 0.05% Tween 20). Membranes were incubated with primary antibodies overnight at 4°C or at room temperature for 2 h followed by an incubation with HRP-conjugated (Merck #GENA931 or #GENA934) or IRDye secondary antibodies (LI-COR #925-68070 and #925-32211) for 1 h at room temperature. The following primary antibodies were used: anti-NF1 (Abcam #ab17963), anti-ERK2 (SCBT #sc-1647), anti-pThr202/pTyr204 ERK1/2 (Cell Signaling #9101), anti-AKT (Cell Signaling #2920), anti-pSer473 AKT (Cell Signaling #4060), anti-pSer235/236 S6 (Cell Signaling #2211), and anti-S6 (Cell Signaling #2317). All primary antibodies were used at a dilution of 1:1000 in TBS-T with 5% BSA, except for anti-NF1, which was diluted in TBS-T with 5% milk. Blots were developed with WesternBright Sirius HRP substrate (Advansta #K-12043-C20) and imaged with a Vilber fusion system or directly scanned by with a LI-COR Odyssey CLx imager. Signal intensities were quantified by ImageJ (version 2.9.0; FIJI⁸⁰).

Quantitative real-time PCR

Total RNA was collected from cells with the RNeasy Plus Mini kit (Qiagen #74136) according to the manufacturer’s protocol. RNA (500 ng) was then reverse transcribed using the iScript cDNA synthesis kit (BioRad #170-8891) according to the manufacturer’s protocol (IDT #1055771). Quantitative real-time PCR was performed according to the manufacturer’s protocol using the PrimeTime Gene Expression Master Mix (IDT #1055771), the equivalent of 12.5 ng cDNA, and PrimeTime qPCR assay probes (IDT) in a volume of 10 μl. A ViiA™ 7 Real-Time PCR System (Applied Biosystems) was used for the fluorescence readout. The following assay probes were used: NF1 (Hs.PT.58.27908857), Nf1 (Mm.PT.58.17137818), and the house keeping gene HPRT1 (Hs.PT.58.v.45621572) or Hprt1 (Mm.PT.39A.22214828).

Tracking of indels by decomposition

gDNA of organoids was isolated using the QIAamp DNA blood mini kit (Qiagen #51106). A 614-bp long PCR amplicon was generated using the same PCR setup and primers as described in “Knockout with transient vectors”. PCR was Sanger sequenced using the Primer_N1_REV. Sequencing results (.ab1 files) were uploaded to http://shinyapps.datacurators.nl/tide and analyzed with standard settings, only the indel size range was set to 50 bp. This method is described in detail in Brinkman et al.⁸²

Mass spectrometry proteomics

Sample preparation

T47D cells (2.5×10⁶) were seeded in complete medium 24 h prior to treatment. The following day media was replaced with the indicated drug-supplemented media for 2 h. Cells were washed and collected in ice-cold PBS. Cells were lysed in buffer containing 2 M guanidine hydrochloride, 0.1 M ammonium bicarbonate, 5 mM TCEP and phosphatase inhibitors (Sigma #P5726 & #P0044) using strong ultra-sonication (Bioruptor, 10 cycles, 30 s on/off, Diagenode), followed by 10 min incubation at 95°C.

Protein concentration was determined by BCA assay (Thermo Fisher Scientific) using a small sample aliquot. Proteins (200 μg) were alkylated with 10 mM chloroacetamide for 30 min at 37°C. Samples were then diluted with 100 mM ammonium bicarbonate buffer to a final guanidine hydrochloride concentration of 0.5 M. Proteins were digested by incubation with sequencing-grade modified trypsin (1/50, w/w; Promega) overnight at 37°C. After acidification using 5% TFA, peptides were desalted on C18 reversed-phase spin columns according to the manufacturer’s instructions (Macrospin) and dried under vacuum.

Peptide samples were enriched for phosphorylated peptides using Fe(III)-IMAC cartridges on an AssayMAP Bravo platform as described previously.⁸³

The peptide sample flow-through obtained after IMAC enrichment was dried and 10 μg of peptides labeled with tandem mass isobaric tags (TMT 18-plex, Thermo Fisher Scientific) according to the manufacturer’s instructions. Samples were distributed across two TMT set and the two reference channels (containing mix of all samples) were included in each of the sets. After pooling the TMT-labeled peptide samples, peptides were again desalted on C18 reversed-phase spin columns according to the manufacturer’s instructions (Macrospin) and dried under vacuum. TMT-labeled peptides were fractionated by high-pH reversed phase separation using a XBridge Peptide BEH C18 column (3,5 μm, 130 Å, 1 mm x 150 mm, Waters) on an Agilent 1260 Infinity HPLC system. Peptides were loaded on the column in buffer A (ammonium formate (20 mM, pH 10) in water) and eluted using a two-step linear gradient starting from 2% to 10% in 5 min and then to 50% (v/v) buffer B (90% acetonitrile/ 10% ammonium formate (20 mM, pH 10) over 55 min at a flow rate of 42 μl/min. Elution of peptides was monitored with a UV detector (215 nm, 254 nm). A total of 36 fractions were collected, pooled into 12 fractions using a post-concatenation strategy as previously described⁹³ and dried under vacuum.

Data acquisition of phosphorylated peptides

Phospho-enriched peptides were resuspended in 0.1% aqueous formic acid and subjected to LC–MS/MS analysis using a Orbitrap Fusion Lumos Mass Spectrometer fitted with an EASY-nLC 1200 (both Thermo Fisher Scientific) and a custom-made column heater set to 60°C. Peptides were resolved using a RP-HPLC column (75 μm × 36 cm) packed in-house with C18 resin (ReproSil-Pur C18–AQ, 1.9 μm resin; Dr. Maisch GmbH) at a flow rate of 0.2 μl/min. The following gradient was used for peptide separation: from 5% B to 8% B over 5 min to 20% B over 45 min to 25% B over 15 min to 30% B over 10 min to 35% B over 7 min to 42% B over 5 min to 50% B over 3min to 95% B over 2 min followed by 18 min at 95% B. Buffer A was 0.1% formic acid in water and buffer B was 80% acetonitrile, 0.1% formic acid in water.

The mass spectrometer was operated in DDA mode with a cycle time of 3 s between master scans. Each master scan was acquired in the Orbitrap at a resolution of 120’000 FWHM (at 200 m/z) and a scan range from 375 to 1600 m/z followed by MS2 scans of the most intense precursors in the Orbitrap at a resolution of 30’000 FWHM (at 200 m/z) with isolation width of the quadrupole set to 1.4 m/z. Maximum ion injection time was set to 50 ms (MS1) and 54 ms (MS2) with an AGC target set to 250% and “Standard”, respectively. Only peptides with charge state 2 – 5 were included in the analysis. Monoisotopic precursor selection (MIPS) was set to Peptide, and the Intensity Threshold was set to 2.5×10⁴. Peptides were fragmented by HCD (Higher-energy collisional dissociation) with collision energy set to 30%, and one microscan was acquired for each spectrum. The dynamic exclusion duration was set to 30 s.

Data acquisition of TMT peptides

Dried peptides were resuspended in 0.1% aqueous formic acid and subjected to LC–MS/MS analysis using an Orbitrap Eclipse Tribrid Mass Spectrometer fitted with Ultimate 3000 nano system and a FAIMS Pro interface (all Thermo Fisher Scientific) and a custom-made column heater set to 60°C. Peptides were resolved using a RP-HPLC column (75 μm × 30 cm) packed in-house with C18 resin (ReproSil-Pur C18–AQ, 1.9 μm resin; Dr. Maisch GmbH) at a flow rate of 0.3 μl/min. The following gradient was used for peptide separation: from 2% B to 12% B over 5 min to 30% B over 70 min to 50% B over 15 min to 95% B over 2 min followed by 18 min at 95% B then back to 2% B over 2 min followed by 8 min at 2% B. Buffer A was 0.1% formic acid in water and buffer B was 80% acetonitrile, 0.1% formic acid in water.

The mass spectrometer was operated in DDA mode with a cycle time of 3 s between master scans. Throughout each acquisition, the FAIMS Pro interface switched between CVs of −40 V and −70 V with cycle times of 1.5 s and 1.5 s, respectively. MS1 spectra were acquired in the Orbitrap at a resolution of 120’000 and a scan range of 400 to 1600 m/z, AGC target set to “Standard” and maximum injection time set to “Auto”. Precursors were filtered with precursor selection range set to 400–1600 m/z, monoisotopic peak determination set to “Peptide”, charge state set to 2 to 6, a dynamic exclusion of 45 s, a precursor fit of 50% in a window of 0.7 m/z and an intensity threshold of 5×10³.

Precursors selected for MS2 analysis were isolated in the quadrupole with a 0.7 m/z window and collected for a maximum injection time of 35 ms with AGC target set to “Standard”. Fragmentation was performed with a CID collision energy of 30% and MS2 spectra were acquired in the IT at scan rate “Turbo”. MS2 spectra were subjected to real time search using a human database (uniport) with the following settings: enzyme was set to “Trypsin”, TMTpro16plex (K and N-term) and Carbamidomethyl (C) were set as fixed modification, Oxidation (M) was set as variable modifications, maximum missed cleavages was set to 1 and maximum variable modifications to 2. Maximum search time was set to 100 ms, the scoring threshold was set to 1.4 XCorr, 0.1 dCn, 10 ppm precursor tolerance, charge state 2 and “TMT SPS MS3 Mode” was enabled. Subsequently, spectra were filtered with a precursor selection range filter of 400–1600 m/z, precursor ion exclusion set to 25 ppm low and 25 ppm high and isobaric tag loss exclusion set to “TMTpro”. MS/MS product ions of precursors identified via RTS were isolated for an MS3 scan using the quadrupole with a 2 m/z window and ions were collected for a maximum injection time of 200 ms with a normalized AGC target set to 200%. SPS was activated and the number of SPS precursors was set to 10. Isolated fragments were fragmented with normalized HCD collision energy set to 55% and MS3 spectra were acquired in the orbitrap with a resolution of 50’000 and a scan range of 100 to 500 m/z.

Phosphoproteome data analysis

The acquired raw files were imported into the Progenesis QI software (version 2.0, Nonlinear Dynamics Limited), which was used to extract peptide precursor ion intensities across all samples applying the default parameters. The generated mgf-file was searched using MASCOT against a human protein database (Uniprot) using the following search criteria: full tryptic specificity was required (cleavage after lysine or arginine residues, unless followed by proline); 3 missed cleavages were allowed; carbamidomethylation (C) was set as fixed modification; oxidation (M) and phosphorylation (STY) were applied as variable modifications; mass tolerance of 10 ppm (precursor) and 0.02 Da (fragments). The database search results were filtered using the ion score to set the false discovery rate (FDR) to 1% on the peptide and protein level, respectively, based on the number of reverse protein sequence hits in the datasets. Quantitative analysis results from label-free quantification were processed using the SafeQuant R package⁸⁴ (version 2.3) to obtain peptide relative abundances. This analysis included global data normalization by equalizing the total peak/reporter areas across all LC-MS runs, data imputation using the knn algorithm, summation of peak areas per protein and LC-MS/MS run, followed by calculation of peptide abundance ratios. Only isoform specific peptide ion signals were considered for quantification.

TMT data analysis

The acquired raw files were analyzed using the SpectroMine software (Biognosis AG). Spectra were searched against a human proteome database (Uniprot). Standard Pulsar search settings for TMT 18 pro (“TMTpro_Quantification”) were used. The raw reported intensities were exported. The different TMT experiments were combined using the pool channel based internal reference scaling (IRS) approach described previously.⁹⁴ In brief, for each protein a geometric mean of pool channels from all TMT sets was calculated. Subsequently, for each TMT set, a protein specific scaling factor was calculated as ratio of geometric mean to the reference channel intensity. The IRS corrected protein intensities were then used for differential analysis using the SafeQuant R package⁸⁴ (version 2.3).

Analysis

Modifications of amino acid residues from multiple detected peptides per protein were summarized using R and shown in the nomenclature “Protein_AminoAcid_ResidueNumber”. Differential protein and phosphopeptide abundance analyses were performed using the limma-voom framework with the eBayes function.⁸⁵ Pathway enrichment analyses for GO and Biocarta gene sets (GSEA version 6.0, Broad Institute) were performed using camera (within edgeR). Results were plotted in R using the ggplot2 (version 3.3.5) and ComplexHeatmap packages⁸⁷ (version 2.2.0).

The mass spectrometry proteomics data have been deposited in the ProteomeXchange Consortium via the PRIDE⁹⁵ partner repository with the dataset identifier PXD034644.

PiggyBac in vivo resistance screen

Quantitative insertion site sequencing

The protocol for the splinkerette PCR was adapted from Friedrich et al.⁷⁸ and is also described by Zilli et al.¹⁹ In detail, gDNA of 94 untreated tumors and 51 resistant tumors (from a total of 53 mice) was isolated with a DNeasy Blood and Tissue Kit (Qiagen #69504) and fragmented by sonication (Covaris sonicator) to a size of 250 bp. DNA fragments were end repaired, A-tailed, purified and then ligated to the splinkerette adaptor (annealed 5’-gttcccatggtactactcatataatacgactcactataggtgacagcgagcgct-3’ with 5’-gcgctcgctgtcacctatagtgagtcgtattataatttttttttcaaaaaaa-3’). Transposon containing DNA fragments were enriched by PCR using a primer specific for the 5’-transposon arm (5’-gacggattcgcgctatttagaaagagag-3’) and the common splinkerette primer (5’-gttcccatggtactactcata-3’). An additional PCR was performed for barcoding the individual samples by using a primer specific for the 5’ arm of the transposon (5’- aatgatacggcgaccaccgagatctacacatgcgtcaattttacgcagactatc-3’) and another primer specific tor the splinkerette side (5’-caagcagaagacggcatacgagatcggtXXXXXXXXtaatacgactcactatagg-3’; Xs standing for the eight-base barcode). Purified libraries were sequenced on Illumina HiSeq 2500 (Paired-End, 2 × 100 bp, rapid run, 200 cycles).

Mapping and transposon integration sites

Most analysis steps (pre-processing, mapping, integration site identification and quantification and gene wise analysis) are shared with the publication by Zilli et al.¹⁹ In detail, the expected transposon-derived sequence (5’-TAGGGTTAA-3’) was removed from the beginning of the first read (read pairs with non-matching first reads were discarded) using the preprocessReads function from the QuasR package (version 1.12.0).⁸⁸ Read pairs were aligned to the mouse genome obtained from TxDb.Mmusculus.UCSC.mm10.knownGene Bioconductor package (version 3.2.2) using the QuasR⁸⁸ (version 1.12.0) qAlign function with the parameters alignmentParameter = “-m 1 --best --strata --maxins 1000”; this will report only uniquely mapping pairs with up to 1000 bp between-pair distance. Mapping rates were recorded, and non-mapped read pairs were further aligned against the non-mobilized transposon sequence to estimate the probable fraction of read pairs that originate from non-mobilized copies of the transposon. For each aligned read pair, the PiggyBac insertion coordinate was identified as the coordinate of the first (most 5’) base of the first read.

Integration site quantification

For each unique integration site, the number of distinct supporting alignments (distinct read pairs, referred to as “diversity”), and the genomic sequence from the four base pairs on the same strand as the first read directly upstream of the insertion site were recorded. These diversity values were normalized by dividing through the total alignments in a sample and then multiplied by 10⁵ (normalized diversity values “divN”). Only integration sites with the expected upstream TTAA sequence were used for the downstream analysis.

Association of integration sites with genes

Coordinates of known genes (exons/introns, 5’-untranslated region [UTR], coding sequence [CDS], 3’UTR) were obtained from the TxDb.Mmusculus.UCSC.mm10.knownGene Bioconductor package (version 3.2.2). Promoter regions were defined as regions 2000 bp upstream of known transcript start sites. Integration sites were matched against these genomic regions to identify overlaps on any strand and orientation, selecting the first overlap in the case of multiple overlaps. Integration sites were classified hierarchically as follows: sites without overlaps to any transcript were labelled as promoter sites (in the case of an overlap with a promoter region) or intergenic sites. All other sites were labelled with the first region type that they overlapped, in the following order: 5’UTR, CDS, 3’UTR, intron or ncRNA (defined as an overlap with a transcript without annotated CDS). Sites were further labelled according to their orientation with respect to the associated gene (same or opposite). Finally, sites were grouped according to the gene they overlapped (including promoter sites) or, for intergenic sites, according to the pair of flanking genes.

Enrichment of unique integration sites

For each insert the normalized diversity values were calculated as described above. The normalized diversity values of all unique insertions sites in a gene were summed over one category of samples (divNsum). We identified a total of 61’241 insertions in the resistant samples and 98’373 insertions in the untreated controls, corresponding to a total of 24’032 affected genes. We excluded two genes that were found in all analyzed resistance samples as a potential PCR contamination. To find putative resistance genes we focused on the genes with a higher number of unique transposon integrations in the alpelisib treated samples compared to the untreated samples.

The sequencing data, a table with all integration sites per sample and an annotated table with the gene centered analysis per ample group have been deposited in the Gene Expression Omnibus with the dataset identifier GSE207513.

Transcriptomic analysis

Cell RNA isolation and library preparation

2×10⁶ T47D CTRL or NF1 KO cells were seeded 24 h prior to treatment and then treated for 48 h with vehicle (DMSO) or alpelisib. Plates were briefly washed with cold PBS and then total RNA was extracted with a RNeasy Plus Mini kit (Qiagen #74136). RNA quality was analyzed on an Agilent 2100 Bioanalyzer (Agilent Technologies) using the Agilent RNA 6000 Nano Chip (Agilent #5067-1511) - RIN (RNA Integrity Number) was 8.6-10 (average 9.7). RNA was quantified by Fluorometry using the QuantiFluor RNA System (Promega #E3310). Library preparation was performed, starting from 200 ng total RNA. The library was prepared with the Illumina TruSeq stranded mRNA-seq preparation kit (Illumina #20020595) and the TruSeq RNA CD Index Plate (Illumina #20019792) according to the manufacturer’s protocol. Size and quality of the library were evaluated on the Fragment Analyzer (Advanced Analytical) with the Standard Sensitivity NGS Fragment Analysis Kit (Advanced Analytical, #DNF-473). Samples were pooled to equimolar concentrations. Libraries complemented with 1% PhiX were sequenced single-reads 76 bases (in addition: 8 bases for index 1 and 8 bases for index 2) using 2 NextSeq 500 High Output Kit 75-cycles (Illumina, Cat# FC-404-1005). Primary data analysis was performed with the Illumina RTA (version 2.4.11) and Base-calling (version bcl2fastq-2.20.0.422). Two NextSeq runs were performed to compile enough reads.

Tumor RNA isolation and library preparation

Tumors of WAP-Cre/PIK3CA^H1047R transgenic mice (vehicle or alpelisib treated animals) were collected, snap frozen in liquid N₂ and then ground to powder with a mortar. Total RNA was extracted with a RNeasy Plus Mini kit (Qiagen #74136). RNA quality was analyzed on an Agilent 2100 Bioanalyzer (Agilent Technologies). The library was prepared with the Illumina TruSeq stranded mRNA-seq preparation kit (Illumina #20020595) and the TruSeq RNA CD Index Plate (Illumina #20019792) according to the manufacturer’s protocol. Size and quality of the library were evaluated on the Fragment Analyzer (Advanced Analytical) with the Standard Sensitivity NGS Fragment Analysis Kit (Advanced Analytical, #DNF-473). Samples were then pooled in equimolar ratios. Single-end libraries were sequenced with an Illumina HiSeq 2500 (50-nt read length).

Alignment and differential gene expression

Reads were aligned to the mouse genome (TxDb.Mmusculus.UCSC.mm10.knownGene Bioconductor package, version 3.2.2) or the human genome (TxDb.Hsapiens.UCSC.hg38.knownGene Bioconductor package (version 3.4.6) with STAR⁸⁹ (version 2.5.2a-goolf-1.7.20) using the multi-map settings outFilterMultimapNmax = 10 and outSAMmultNmax = 1. The output was sorted and indexed with SAMtools (for the mouse tumors: version 1.3.1-goolf-1.7.20; for human cell line: version 1.7-goolf-1.7.20) and picard markDuplicates (version 2.9.2) was used to collapse samples run on different sequencing lanes. Stand-specific coverage tracks per sample were generated by tiling the genome in 20-bp windows and counting overlapping reads in each window using the bamCount function from the Bioconductor package bamsignals (version 3.6). These window counts were then exported in bigWig format using the export function rtracklayer of the Bioconductor package. The function gQCReport of the Bioconductor package QuasR⁸⁸ (version 1.12.0) was used for assessing read and alignment quality. The number of reads (5’ ends) overlapping the exons of each gene was counted with the gCount function of QuasR⁸⁸ (version 1.12.0), assuming an exon union model. Differential gene expression analysis was achieved using the edgeR framework⁸⁶ (version 3.28.1). Gene set enrichments were performed using CAMERA.⁹⁶

The sequencing data and a table with annotated counts have been deposited in the Gene Expression Omnibus with the dataset identifier GSE207512 and GSE207514.

Motif activity response analysis

Transcription factor activity was computationally inferred using the Integrated System for Motif Activity Response Analysis (ISMARA) by Balwierz et al.⁴¹ We compared significantly enriched transcription factor motifs in T47D CTRL and NF1 KO cells.

Quantification and statistical analysis

Statistical analysis and generation of graphs was performed using the Prism software (GraphPad Software, version 9.3.1.) and R Studio. Data are displayed as scatter plot where applicable and single points represent replicates. In general, replicates are independent experiments or individual tumors of mice, technical replicates are indicated as such in the figure legend. Data are shown as mean ± standard deviation (SD). Depending on the type of experiment, data were tested for normal distribution and analyzed using one-way ANOVA, 2way ANOVA, Kruskal-Wallis test, unpaired t-test, and Mann-Whitney test as indicated in the figure legends. Data were considered statistically significant at P < 0.05 or with FDR < 0.05. P-values were corrected for multiple comparison testing as indicated in the figure legend.

Acknowledgments

We thank members of the Bentires-Alj laboratory for advice and discussions; Marie-May Coissieux and Sherlyn Sok Lin Foo for helping out with injections and treatment of mice; Saadia Iftikhar for help with image analysis; Matyas Flemr from Marc Bühler’s lab for providing the pC2P Cas9-2A-mCherry vector; Jonas Lötscher and Philippe Georg Dehio from Christoph Hess’ lab for advice on the extracellular flux experiments; Novartis for providing us with alpelisib; the ATP1-S2 and PB mice, which were generated by Roland Rad at the Sanger Institute (Hinxton, UK) in the lab of Alan Bradley; Philipp Demougin, Christian Beisel, and team for genomic library preparation and mRNA sequencing; the FMI sequencing facility; Atul Sethi, Michal Kloc, and Julien Roux and Robert Ivanek from the DBM bioinformatics facility for assisting with data transfer and omics analysis expertise; the scientific computing center at the University of Basel (sciCORE; http://scicore.unibas.ch/); the DBM imaging core facility, specifically Loïc Sauter; Emmanuel Traunecker, Telma Lopez, Lorenzo Raeli, and Stella Stefanova from the DBM flow cytometry facility for assisting with FACS; the animal facilities of the University of Basel and FMI; Diego Calabrese from the histology facility for advice and histological staining; and Sarah-Maria Fendt and Karen Cichowski for discussions and advice. The results on primary and metastatic clinical samples are in part based on data generated by the TCGA Research Network (https://www.cancer.gov/tcga) and the Metastatic Breast Cancer Project (www.mbcproject.org). This research was funded by the Swiss National Science Foundation (310030_184673 20). Research in the Bentires-Alj laboratory is supported by the Swiss Initiative for Systems Biology-SystemsX, the European Research Council (ERC advanced grant 694033 STEM-BCPC), the Swiss National Science Foundation, the Krebsliga Beider Basel, the Swiss Cancer League, the Swiss Personalized Health Network (Swiss Personalized Oncology driver project), and the Department of Surgery of the University Hospital Basel.

Author contributions

Conceptualization, P.A.d.M., M.P.T., M.V., A.L.C., P.R., and M.B.-A.; methodology, P.A.d.M., M.P.T., Z.B., M.V., A.L.C., M.D., K.V., B.-T.P., P.R., C.L., T.E., R.R., and M.R.J.; software, P.A.d.M., M.P.T., and M.B.S.; formal analysis, P.A.d.M., M.P.T., and M.B.S.; investigation, P.A.d.M., M.P.T., Z.B., M.D., N.K., K.V., P.R., C.L., T.E., K.B., F.Z., and R.O.; resources, K.B., R.R., M.R.J., C.F., A.Z., M.B.S., and M.B.-A.; data curation, P.A.d.M., M.P.T., and M.B.S.; writing – original draft, P.A.d.M.; writing – review & editing, P.A.d.M., M.P.T., Z.B., M.V., A.L.C., N.K., M.D., N.K., M.B.S., and M.B.-A.; visualization, P.A.d.M. and M.B.S.; supervision, M.B.-A.; project administration, P.A.d.M. and M.B.-A; funding acquisition, M.B.-A.

Declaration of interests

P.R. and F.Z. are employees of Novartis Pharma AG. M.R.J. and C.F. are employees and shareholders of Novartis Pharma AG. C.L. is an employee of Idorsia Pharmaceuticals Ltd. A.Z. received consulting/advisor fees from BMS, MSD, Hoffmann-La Roche, NBE Therapeutics, Secarna, ACM Pharma, and Hookipa and maintains further non-commercial research agreements with Secarna, Hookipa, Roche, and BeyondSpring. M.B.-A. owns equities in and received laboratory support and compensation from Novartis and served as a consultant for Basilea.

Inclusion and diversity

We support inclusive, diverse, and equitable conduct of research.

Published: April 11, 2023

Footnotes

Supplemental information can be found online at https://doi.org/10.1016/j.xcrm.2023.101002.

Contributor Information

Priska Auf der Maur, Email: priska.aufdermaur@unibas.ch.

Mohamed Bentires-Alj, Email: m.bentires-alj@unibas.ch.

Supplemental information

Document S1. Figures S1–S5

mmc1.pdf^{(1.3MB, pdf)}

Table S1. PB combined analysis, related to Figure 1

mmc2.xlsx^{(4.4MB, xlsx)}

Table S2. RNA-seq WAP tumor count, related to Figure 1

mmc3.xlsx^{(3.5MB, xlsx)}

Table S3. RNA-seq WAP tumor differential gene expression, related to Figure 1

mmc4.xlsx^{(1.4MB, xlsx)}

Table S4. NF1 mutations in clinical samples, related to Figure 1

mmc5.xlsx^{(1.1MB, xlsx)}

Table S5. NF1 co-occurring and mutually exclusive mutations in clinical samples, related to Figure 1

mmc6.xlsx^{(2.2MB, xlsx)}

Table S6. RNA-seq T47D CTRL NF1KO count, related to Figure 3

mmc7.xlsx^{(2.4MB, xlsx)}

Table S7. RNA-seq T47D CTRL NF1KO differential gene expression, related to Figure 3

mmc8.xlsx^{(1.5MB, xlsx)}

Table S8. Proteomics count, related to Figure 5

mmc9.xlsx^{(2.6MB, xlsx)}

Table S9. Proteomics differential peptide analysis, related to Figure 5

mmc10.xlsx^{(34.1KB, xlsx)}

Table S10. Phosphoproteomics count, related to Figure 5

mmc11.xlsx^{(5MB, xlsx)}

Table S11. Phosphoproteomics differential phosphopeptide analysis, related to Figure 5

mmc12.xlsx^{(529.6KB, xlsx)}

Table S12. Vectors, related to Method details and Key resources table

mmc13.xlsx^{(9.5KB, xlsx)}

Table S13. Oligonucleotides, related to Method details and Key resources table

mmc14.xlsx^{(10.1KB, xlsx)}

Document S2. Article plus supplemental information

mmc15.pdf^{(6.1MB, pdf)}

Data and code availability

•
All sequencing data are available as a SuperSeries on the Gene Expression Omnibus (accession GSE207515). This SuperSeries contains DNA-seq data of the PiggyBac resistance screen: GEO accession GSE207513 (differential insertion analysis: Table S1); mRNA-seq of WAP-Cre/PIK3CA^H1047R murine tumors treated with vehicle/alpelisib: GEO accession GSE207512 (counts and differential gene expression: Tables S2 and S3); mRNA-seq data of T47D CTRL and NF1 KO cells treated with vehicle/ alpelisib: GEO accession GSE207514 (counts and differential gene expression: Tables S6 and S7). Mass spectrometry proteomics data are deposited on ProteomeXchange (project accession PXD034644). For counts and differential peptide/phosphopeptide analysis see Tables S8, S9, S10, and S11. Consolidated data derived from publicly available data at cBioportal used for mutation analysis of NF1 in patient samples: Tables S4 and S5. All accession numbers and analysis tables are also listed in the key resources table. Additional data reported in this paper will be shared by the lead contact upon request.
•
This paper does not contain original code.
•
Any additional information required to reanalyze the data reported in this paper is available from the lead contact upon request.

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Document S1. Figures S1–S5

mmc1.pdf^{(1.3MB, pdf)}

Table S1. PB combined analysis, related to Figure 1

mmc2.xlsx^{(4.4MB, xlsx)}

Table S2. RNA-seq WAP tumor count, related to Figure 1

mmc3.xlsx^{(3.5MB, xlsx)}

Table S3. RNA-seq WAP tumor differential gene expression, related to Figure 1

mmc4.xlsx^{(1.4MB, xlsx)}

Table S4. NF1 mutations in clinical samples, related to Figure 1

mmc5.xlsx^{(1.1MB, xlsx)}

Table S5. NF1 co-occurring and mutually exclusive mutations in clinical samples, related to Figure 1

mmc6.xlsx^{(2.2MB, xlsx)}

Table S6. RNA-seq T47D CTRL NF1KO count, related to Figure 3

mmc7.xlsx^{(2.4MB, xlsx)}

Table S7. RNA-seq T47D CTRL NF1KO differential gene expression, related to Figure 3

mmc8.xlsx^{(1.5MB, xlsx)}

Table S8. Proteomics count, related to Figure 5

mmc9.xlsx^{(2.6MB, xlsx)}

Table S9. Proteomics differential peptide analysis, related to Figure 5

mmc10.xlsx^{(34.1KB, xlsx)}

Table S10. Phosphoproteomics count, related to Figure 5

mmc11.xlsx^{(5MB, xlsx)}

Table S11. Phosphoproteomics differential phosphopeptide analysis, related to Figure 5

mmc12.xlsx^{(529.6KB, xlsx)}

Table S12. Vectors, related to Method details and Key resources table

mmc13.xlsx^{(9.5KB, xlsx)}

Table S13. Oligonucleotides, related to Method details and Key resources table

mmc14.xlsx^{(10.1KB, xlsx)}

Document S2. Article plus supplemental information

mmc15.pdf^{(6.1MB, pdf)}

Data Availability Statement

•
All sequencing data are available as a SuperSeries on the Gene Expression Omnibus (accession GSE207515). This SuperSeries contains DNA-seq data of the PiggyBac resistance screen: GEO accession GSE207513 (differential insertion analysis: Table S1); mRNA-seq of WAP-Cre/PIK3CA^H1047R murine tumors treated with vehicle/alpelisib: GEO accession GSE207512 (counts and differential gene expression: Tables S2 and S3); mRNA-seq data of T47D CTRL and NF1 KO cells treated with vehicle/ alpelisib: GEO accession GSE207514 (counts and differential gene expression: Tables S6 and S7). Mass spectrometry proteomics data are deposited on ProteomeXchange (project accession PXD034644). For counts and differential peptide/phosphopeptide analysis see Tables S8, S9, S10, and S11. Consolidated data derived from publicly available data at cBioportal used for mutation analysis of NF1 in patient samples: Tables S4 and S5. All accession numbers and analysis tables are also listed in the key resources table. Additional data reported in this paper will be shared by the lead contact upon request.
•
This paper does not contain original code.
•
Any additional information required to reanalyze the data reported in this paper is available from the lead contact upon request.

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PERMALINK

N-acetylcysteine overcomes NF1 loss-driven resistance to PI3Kα inhibition in breast cancer

Priska Auf der Maur

Marcel P Trefny

Zora Baumann

Milica Vulin

Ana Luisa Correia

Maren Diepenbruck

Nicolas Kramer

Katrin Volkmann

Bogdan-Tiberius Preca

Pedro Ramos

Cedric Leroy

Tobias Eichlisberger

Katarzyna Buczak

Federica Zilli

Ryoko Okamoto

Roland Rad

Michael Rugaard Jensen

Christine Fritsch

Alfred Zippelius

Michael B Stadler

Mohamed Bentires-Alj

Summary

Graphical abstract

Highlights

Introduction

Results

Parallel in vivo screens identify NF1 loss as a resistance-enhancing event in PI3Kα inhibition

Figure 1.

Loss of NF1 reduces sensitivity to PI3Kα inhibition in vitro and in vivo

Figure 2.

NF1-depleted cells have reduced reactive oxygen species (ROS) levels and lower mitochondrial respiration

Figure 3.

NAC treatment circumvents resistance to PI3Kα inhibition

Figure 4.

NAC synergizes with PI3Kα inhibition and dampens mTOR signaling

Figure 5.

Discussion

Limitations of the study

STAR★Methods

Key resources table

Resource availability

Lead contact

Material availability

Experimental model and subject details

Cell lines and organoids

Animal experiments

Transgenic animals

Method details

NF1 mutations in public breast cancer datasets

Doubling time calculation

Drugs

Generation of stable shRNA cell lines

Cloning of CRISPR vectors for NF1 KO

Knockout with transient vectors

Generation of lentivirus

Infection of T47D and MCF7 cancer cells

Infection of PDX-derived cancer cells

3D drug response of organoids

Colony formation assay

Cell cycle analysis

Quantification of MitoSOX and ROS-DCFDA

Quantification of mitochondrial content

Quantification of GSSG/GSH levels

Seahorse experiments

Immunoblotting

Quantitative real-time PCR

Tracking of indels by decomposition

Mass spectrometry proteomics

Sample preparation

Data acquisition of phosphorylated peptides

Data acquisition of TMT peptides

Phosphoproteome data analysis

TMT data analysis

Analysis

PiggyBac in vivo resistance screen

Quantitative insertion site sequencing

Mapping and transposon integration sites

Integration site quantification