Selective Modulation of a Pan-Essential Protein as a Therapeutic Strategy in Cancer

The advent of CRISPR/Cas9 genome-editing technology allows for interrogation of gene function at a genome scale. Dependency maps that profile gene function across hundreds of cancer cell lines have been created, and these data reveal that, as predicted, there are a large number of genes that are not mutated but are nonetheless required for growth and survival of cancer cells (1–3). Some of these previously unstudied dependencies may be tractable drug targets. Indeed, a number of targets identified in these screens have now been validated, demonstrating that the data are robust (4, 5). These efforts allow us to expand our search for new therapeutic targets beyond known oncogenes. Coupled with advances in drug development approaches, such as proteolysis targeting chimeras (PROTAC) and molecular glues, which have pushed the limits of what targets are considered “druggable,” dependency maps have the potential to reveal new avenues for selective killing of cancer cells that have not previously been considered or exploited.

Pediatric cancers have a paucity of genetic alterations relative to adult cancers, and therefore have not benefited from the development of oncogene-targeted therapies to the same degree (6–8). As a result, pediatric cancers are still largely treated with conventional chemotherapies, which are broadly cytotoxic drugs that frequently target pan-essential cell proteins and therefore have significant collateral toxicity (9). The application of dependency maps for novel target identification may therefore be particularly helpful for pediatric cancers. Indeed, a first-generation Pediatric Cancer Dependency Map demonstrates that despite harboring fewer oncogenic mutations, pediatric cancers have a similar number of selective genetic dependencies as adult cancers (10).

Although these dependency maps enable the identification of selective targets (4, 5), prioritizing the most-promising targets remains a challenge. Because these screens assess depletion in vitro at two to three weeks, we cannot readily distinguish between dependencies that slow growth, halt growth, or cause cells to die, nor can we eliminate the subset of the dependencies that could be specific to the cell culture environment and would not be maintained in an in vivo context. The difference between targets in these classes could be very significant when translated clinically. Conventional chemotherapies are largely cytotoxic drugs, but frequently target pan-essential cell proteins and therefore have significant on-target toxicity. Conversely, more recently developed targeted therapies are much more selective in their effects, but these agents are often cytostatic and may not result in as profound a viability effect. We sought to build on dependency map efforts and use additional functional genomic screens to identity in vivo relevant targets that combine the selectivity of a targeted therapy with the cytotoxicity of conventional chemotherapy.

Here, we focused on identifying such targets in neuroblastoma, the most common extracranial solid tumor in children. Overall survival for high-risk disease remains unacceptably low, at about 50%, and children who do survive experience significant and sometimes long-term treatment-induced side effects (11). Approximately 20% to 25% of neuroblastomas harbor genetic amplification of the transcription factor MYCN, which is strongly correlated with high-risk disease, an undifferentiated phenotype, and poor prognosis, and is a disease subset of particular interest (12, 13).

We therefore sought to use additional functional genomic screens to prioritize dependencies for therapeutic development in MYCN-amplified neuroblastoma.

We used the Broad Institute's Cancer Dependency Map (DepMap) data set, inclusive of the Pediatric Cancer Dependency Map, to identify 197 putative genetic dependencies that were strong outliers or selective dependencies for MYCN-amplified neuroblastoma and 17 pan-essential genes as positive controls (Fig. 1A). We generated a single-guide RNA (sgRNA) library consisting of sgRNAs for both CRISPR-mediated gene knockout (CRISPR) and CRISPR-mediated gene interference (CRISPRi), intronic guides to control for copy number–related toxicity effects, and nontargeting and intergenically targeted negative controls. We performed CRISPR and CRISPRi time-course depletion screens at days 7, 14, and 21 in four neuroblastoma cell lines: SKNDZ, CHP-212, SK-N-BE(2), and KELLY. Notably, our results at 21 days correlated with the depletion observed in the original DepMap screen performed at this time-point in these cell lines (r = 0.774, P < 2.2e−16, Fig. 1B, and Supplementary Fig. S1A). The additional time-point information allowed us to prioritize dependencies that deplete rapidly in CRISPR and CRISPRi (Fig. 1C and D), and the correlation between these screening modalities was strong (Fig. 1E). Intronic guides accurately identified potential false positives due to the known copy-number effect of CRISPR (Supplementary Fig. S1B and S1C; ref. 14). Of note, the intron-targeted MYCN sgRNAs depleted similarly to the gene-targeting sgRNAs for MYCN (Supplementary Fig. S1D). An attempt at copy-number correction eliminated both effects. MYCN is a well-established dependency in MYCN-amplified neuroblastoma (15, 16) and an shRNA screen readily detected this dependency (Supplementary Fig. S1E), suggesting that for highly amplified genes such as MYCN, the effect of frequent cutting is so great that additional effect of loss of gene function cannot be accurately assessed in CRISPR depletion screens. Although CRISPR suffers from false positives in copy-gained regions, CRISPRi may produce false negatives for these same regions (Supplementary Fig. S1F and S1G). These data provide support for the strategy of therapeutically exploiting highly amplified oncogenes, such as MYCN, with intron-targeted sgRNAs. These guide RNAs were as effective as eliminating the oncogene and would be expected to have minimal effect in nonamplified cells, as they should not disrupt gene function.

To further narrow potential dependencies to those that are lethal to neuroblastoma when lost, we performed a positive selection screen for Annexin V–positive cells, a marker of dying cells (Fig. 1F; Supplementary Fig. S1H; refs. 17, 18). Disruption of only a few of the validated dependency genes induced cell death as measured by this assay, but we observed a striking consistency across cell lines (Pearson r = 0.576, P < 2.2e−16) with three genes, NXT1, HSPA8, and TYMS, scoring as hits in both cell lines (Fig. 1G). We next performed an in vivo CRISPR screen in a subcutaneous xenograft model of neuroblastoma to filter out dependencies specific to the cell culture environment (Fig. 1H). The overall correlation between in vivo and in vitro in the same cell model was high (Pearson r = 0.774, P < 0.0001). However, there was a notable class of outliers that did not deplete in vivo, suggesting that these dependencies may occur only in the context of cell culture (Fig. 1I; Supplementary Fig. S1I). Notably, the genes that did not validate in vivo were largely related to oxidative stress or metabolism (e.g., SOD2, GPX4, GLRX5, TYMS, SDHB, and SEPHS2), suggesting that special care should be taken to validate dependencies in these classes in vivo before conducting further mechanistic experiments.

At the intersection of these screens, we identified one dependency gene, NXT1, which scored as a strong hit in both CRISPR and CRISPRi time-course screens, scored in the Annexin V screens in both cell lines, and validatedin vivo (Fig. 2A). NXT1, the protein encoded by this gene, is a nuclear export factor whose canonical function is as a cofactor for NXF1-mediated export of mRNAs from the nucleus to the cytoplasm where they can be translated into proteins (19). The DepMap data set has more than doubled since we generated our gene list, but as in the smaller data set, neuroblastoma cell lines (n = 19) are significantly more dependent on NXT1 when compared with all other cancer cell lines (n = 720), suggesting this is a selective effect (Fig. 2B). We confirmed on-target genomic editing by three CRISPR guides targeting NXT1 (Supplementary Fig. S2A) and validated that these sgRNAs induced a profound viability defect in neuroblastoma cell lines compared with control sgRNAs, as well as cell death measured by Annexin V/PI staining and cleavage of PARP (Fig. 2C and D; Supplementary Fig. S2B).

As further validation, we used the FKBP12-based degron system for inducible protein degradation (20). We created a cDNA of NXT1 in which a silent mutation had been introduced into the PAM site for one of our CRISPR guides (sgNXT1–3), rendering it resistant to CRISPR editing, and it was fused to the FKBP12^F36V fragment and an HA tag (NXT1^deg; Supplementary Fig. S2C). We generated neuroblastoma cell lines expressing this construct and knocked out endogenous NXT1 using CRISPR/Cas9 (Supplementary Fig. S2D and S2E). Cells with endogenous knockout of NXT1 and exogenous expression of degradable NXT1 grew normally, indicating that tagged NXT1 expression can indeed rescue the effect of NXT1 knockout (Fig. 2E). Treatment with the cereblon-recruiting degrader molecule dTAG-13 caused efficient degradation of exogenous NXT1, induction of cleaved PARP, and a profound dose-responsive viability effect (Fig. 2F and G; Supplementary Fig. S2F and S2G), while a negative control molecule, dTAG-13-NEG, which binds FKBP12^F36V but cannot recruit cereblon (21), had no effect on viability (Supplementary Fig. S2H and S2I).

The in vivo screen confirmed that this dependency is not an artifact of cell culture but did not evaluate the role of NXT1 in an established tumor. To address this question, we generated neuroblastoma cells constitutively expressing Cas9 with sgRNAs under the control of a doxycycline-inducible promoter (22). In tumors with an inducible sgRNA targeting NXT1, we observed arrest of tumor growth in all tumors and tumor regression in a subset of tumors within four days of doxycycline induction, indicating that there is indeed a profound antitumor effect of NXT1 loss (Fig. 2H; Supplementary Fig. S2J). We do not observe these same effects in doxycycline-treated tumors harboring inducible sgChr2-2, demonstrating that this is an on-target effect of NXT1 loss (Fig. 2H; Supplementary Fig. S2K). Further supporting that NXT1 loss is incompatible with tumor cell survival, the tumor cells that do grow out after sgNXT1 induction lack expression of Cas9, show a significant selection against edits in NXT1, and among those edits, a significant selection for in-frame edits that are not predicted to be damaging (Supplementary Fig. S2L–S2N).

NXT1 binds to the NTF2L domain of NXF1, and together they form a heterodimer that binds mRNA and mediates nuclear export (Fig. 3A; ref. 23). Mass spectrometry (MS)–based quantitative multiplexed proteomics revealed that in our inducible degron system, when NXT1 is degraded, NXF1 protein levels also decrease rapidly (Fig. 3B). NXF1 levels also decrease after CRISPR-mediated knockout of NXT1, demonstrating this observation was not due to off-target degradation (Fig. 3C; Supplementary Fig. S3A). In the DepMap data set, NXF1 is a common essential, whereas NXT1 shows a skewed distribution with many lines dependent on NXF1 but not NXT1 (Fig. 3D). Therefore, we were interested in whether NXF1 remains stable in cells that are not dependent on NXT1. Indeed, we identified a small number of neuroblastoma lines that are not dependent on NXT1 and determined that NXF1 levels remain stable after CRISPR knockout or inducible degradation of NXT1 (Fig. 3E–I; Supplementary Fig. S3B and S3C). NXT1 loss, therefore, leads to elimination of the essential protein NXF1 in a context-specific manner, which explains the profound yet selective lethality observed.

We then sought to understand why NXF1 levels decrease after NXT1 loss in sensitive cells. As NXT1 and NXF1 are known to heterodimerize in other contexts (23), we first confirmed a physical interaction between NXT1 and NXF1 in neuroblastoma by coimmunoprecipitation (Fig. 3J; Supplementary Fig. S3D). NXF1 mRNA does not decrease after NXT1 loss. Instead, there is a modest early compensatory increase in NXF1 mRNA followed by a return to baseline at 24 hours after NXT1 degradation, consistent with regulation of NXF1 at the protein rather than at the transcript level (Supplementary Fig. S3E and S3F). We therefore hypothesized that NXF1 might be an obligate heterodimer in this setting and require NXT1 binding for stability. Indeed, when human NXF1 is exogenously expressed in HEK293 cells, coexpression of exogenous NXT1 significantly increases NXF1 protein levels, supporting a role for NXT1 in NXF1 protein stability (24). To interrogate this possibility, we blocked new protein translation with cycloheximide treatment and assessed the stability of the NXF1 protein. In the presence of NXT1, NXF1 was remarkably stable with sustained protein levels up to 24 hours after cycloheximide treatment, while in the absence of NXT1, NXF1 protein levels decrease rapidly, consistent with a loss of protein stability (Supplementary Fig. S3G). Together, these data support a model where NXF1 is an obligate heterodimer, and NXF1 is destabilized at the protein level after NXT1 loss in sensitive cell lines.

To understand the mechanism underlying this difference in NXF1 stability in sensitive and resistant cell lines, we looked for associations in gene expression with NXT1 dependency across the entire DepMap data set of more than 700 cancer cell lines. Lower expression of NXT2, a paralog of NXT1, is strongly associated with dependency on NXT1, and this relationship is maintained within neuroblastoma cell lines (Fig. 4A and B; Supplementary Fig. S4A). In an independent large CRISPR dependency data set generated by the Sanger Institute, this relationship between NXT2 expression and NXT1 dependency is also observed (Supplementary Fig. S4B). NXT2 is poorly expressed in neuroblastoma cell lines and primary human tumors relative to other cancer types in multiple primary tumor data sets, consistent with the enrichment of NXT1 dependency in neuroblastoma (Fig. 4C; Supplementary Fig. S4C and S4D). Furthermore, we could not detect NXT2 protein in three patient-derived xenograft (PDX) models of neuroblastoma (Fig. 4D). In contrast, NXT2 is expressed in most normal tissues (Supplementary Fig. S4E).

NXT2 is a paralog of NXT1, and the two proteins share 73.2% identity and 88.7% similarity (25). NXT2 more closely resembles the gene structure of homologs in other organisms than NXT1, which lacks introns (24). NXT2 is functionally redundant with NXT1 in a constitutive transport element (CTE) containing viral RNA export assay, and NXT2 heterodimerizes with NXF1 with a similar affinity to that of NXT1 (24, 26). Complete functional redundancy between these two paralogs has not been established to date. Zebrafish lacking NXT2 have been reported to have cardiac defects as a result of abnormal development; however, mice lacking NXT2 are viable and fertile with no reported cardiac defects (27, 28), suggesting that NXT1 may be able to compensate for NXT2 in murine development, indicative of some functional redundancy. When NXT2 is exogenously expressed in three different neuroblastoma cell lines that have low endogenous NXT2 expression, NXF1 levels remain stable and cell viability is restored after NXT1 loss through CRISPR knockout or inducible degradation (Fig. 4E–H; Supplementary Fig. S4F–S4I). We confirmed that NXT2 physically interacts with NXF1, and this association is maintained after NXT1 loss in neuroblastoma (Fig. 4I; Supplementary Fig. S5A), raising the possibility that NXT2 binding could stabilize NXF1 in the absence of NXT1. Indeed, after blocking translation with cycloheximide, NXT2 expression restored NXF1 stability after NXT1 loss, consistent with protein stabilization (Supplementary Fig. S5B). Together, these data demonstrate that NXT1 and NXT2 have a functionally redundant role in maintaining NXF1 protein stability in neuroblastoma.

We next confirmed that resistant neuroblastoma cell lines express NXT2, and indeed endogenous NXT2 protein was readily detected in cell lines that do not depend on NXT1 but was undetectable in cell lines that do depend on NXT1 (Supplementary Fig. S5C). Publicly available epigenetic data revealed H3K4me3 marks at the transcriptional start site for full-length NXT2 and an open chromatin conformation only in neuroblastoma cell lines that highly express NXT2 (Supplementary Fig. S5D and S5E). An internal start site for a shorter isoform of NXT2 is configured similarly across cell lines, but expression of this isoform is uniformly low in neuroblastoma cell lines and not associated with NXT1 dependency, whereas expression of the full-length isoform is highly variable and was strongly associated with NXT1 dependency in neuroblastoma (Supplementary Fig. S5F). Bisulfite sequencing of a previously described string of CpGs in the promoter region of full-length NXT2 uncovered differential methylation patterns between resistant cell lines with high NXT2 expression, which lacked methylation at most CpGs, and sensitive cell lines with low NXT2 expression, which were highly methylated (Supplementary Fig. S5G; ref. 29). Notably, one of these differentially methylated CpGs is within an ETS family recognition sequence, and methylation of the CpG within this sequence has been shown to prevent transcription factor binding in other contexts (30–33). Samples from PDX models that lack NXT2 also had methylated CpGs, and the association between methylation of CpGs in this region, as assessed by DNA methylation array, and NXT2 expression is maintained in neuroblastoma tumors (Supplementary Fig. S5G and S5H).

To establish whether reduced NXT2 expression is sufficient to render resistant neuroblastoma cells sensitive to NXT1 loss, we knocked out NXT2 in two resistant neuroblastoma lines, GIMEN and SK-N-FI, which have high endogenous NXT2 expression. Loss of NXT2 rendered these cells sensitive to subsequent loss of NXT1 by CRISPR knockout or inducible degradation, and NXF1 protein levels also decreased after NXT1 loss (Fig. 4J and K; Supplementary Fig. S5I–S5K). These data support a mechanism of NXT1-selective lethality whereby the cell-essential protein NXF1 is lost upon NXT1 loss in cells that do not express NXT2, and that relatively low levels of NXT2 expression are both necessary and sufficient for NXT1 dependency in neuroblastoma.

Neither GIMEN nor SK-N-FI harbors MYCN amplifications, but NXT2 loss is sufficient to render these lines dependent on NXT1, which suggests that neuroblastoma with endogenously low NXT2 would depend on NXT1 regardless of MYCN amplification status. We were therefore interested in whether NXT2 expression is associated with any common neuroblastoma genetic alterations, including MYCN amplification. Neither MYCN amplification nor ALK mutations correlate with NXT2 expression in neuroblastoma tumors, suggesting that response to loss of NXT1 may not be restricted by disease subtype (Fig. 4L and M; Supplementary Fig. S6A; refs. 34, 35). Recently described adrenergic and mesenchymal cell states similarly did not correlate with NXT2 expression (Supplementary Fig. S6B). These data suggest that nonamplified tumors that lack NXT2 will likely be sensitive to NXT1 loss, although we cannot rule out that MYCN amplification contributes to the dependence on NXT1 independently of NXT2 expression levels.

The profound and precise lethality of NXT1 loss in neuroblastoma indicates that it might be a good candidate for therapeutic exploitation, and therefore we sought to determine whether other cancers might also be vulnerable to NXT1 loss. Strikingly, in human tumor expression data, we observed that medulloblastoma and rhabdomyosarcoma, two other pediatric cancers, also had very low expression of NXT2 (Fig. 4C; Supplementary Fig. S4D). Accordingly, the paralog relationship is conserved in pediatric cancer cell lines (Fig. 5A), and pediatric cancer cell lines are more likely to be dependent on NXT1 than on adult cancer cell lines (Fig. 5B), a difference driven by multiple lineages, including neuroblastoma, rhabdomyosarcoma, medulloblastoma, as well as rhabdoid tumor cell lines (Fig. 5C). To further validate this finding, we confirmed that both embryonal and alveolar rhabdomyosarcomas lack NXT2 expression and are dependent on NXT1 (Fig. 5D and E).

Loss of fitness, as assayed in late time-point genome-scale dropout screens, such as in the DepMap, is a very broad phenotype that can detect minor viability effects caused by disruption of a variety of cellular processes. We provide a framework for mining these data to generate a focused, context-specific library, and for designing follow-up screens that can interrogate precise cell processes. Here we demonstrate that additional CRISPR screening assays can yield critical information about the speed, lethality, and in vivo translatability of many targets simultaneously in a given disease context. Similar approaches could be fruitfully applied to identify genes involved in differentiation or cell-cycle arrest with positive selection assays, for example. Critically, our data suggest that the original screens are highly reproducible, and results are mostly representative of an in vivo setting, although attention should be paid to genes involved in oxidative stress or metabolic processes to verify their in vivo validity.

Our functional genomic screens nominate NXT1 as a precision lethal dependency that functions as an essential gene in neuroblastoma but is dispensable in most other cancer cells. Neuroblastoma, like many other pediatric cancers, is currently treated with high-dose cytotoxic chemotherapy, which is curative for a subset of patients but at the cost of both short- and long-term side effects (36). Accordingly, the targets of these chemotherapies include proteins, such as TOP1 and TOP2A, which behave as common essential genes in the DepMap. NXT1, in contrast, is dispensable in most cell lines, so therapeutic targeting of NXT1 might be expected to be more tolerable than current curative options without a reduction in efficacy.

An important caveat of these data is that “normal” cells cannot readily be screened in vitro without long-term propagation and modifications, rendering them no longer truly representative of normal tissue. The DepMap, therefore, uses all other cancer cell lines as a proxy for assessing pan-essentiality across cell types. However, it is not yet known whether selectivity in this data set is necessarily indicative of selectivity relative to normal tissues in an organism. This is a key question that must be addressed at the organismal level with either conditional knockout animals or a selective molecule targeting NXT1, once it is available. The relatively ubiquitous expression of NXT2 in adult normal tissue, and the selectivity of the target in the DepMap, provide optimism that a therapeutic window can be achieved even if the NXT1–NXT2 synthetic lethal paralog relationship is retained in most normal tissue. Gene-expression patterns in normal developing tissues in children have yet to be carefully profiled, and it is possible that NXT2 is not expressed as highly in developing pediatric tissues as it is in fully mature adult tissue. Efforts are under way to create a normal tissue expression database for pediatric tissues, but until such a resource exists, this limitation will continue to impair target discovery efforts in pediatrics (37).

We purposely did not include any filters for “druggability” in the criteria for inclusion in our sgRNA library because the notion of what constitutes a druggable target is rapidly evolving. New approaches, such as targeted protein degradation using a PROTAC or molecular glue approach, as well as protein–protein interaction disruptors, have expanded the realm of druggable targets, and any of these approaches could potentially be used to selectively target NXT1 (38–40). Developing new therapeutics using these strategies is not trivial, so pursuing the targets most likely to provide maximal anticancer activity with a broad therapeutic window is key. We demonstrate the power of genome-scale dependency data sets for identifying such targets in cancer and suggest that creative application of additional functional genomic CRISPR screens to other cancer types could help appropriately harness this resource to direct therapeutic development in cancer.

We demonstrate that NXT1 is in a synthetic lethal paralog relationship with NXT2 due to their regulation of the stability of the essential protein NXF1. NXF1 protein levels only decrease in the absence of both NXT1 and NXT2. NXT1 and NXT2 have a high level of similarity and have been shown to be functionally redundant in mRNA export reporter assays. Exogenously expressed NXF1 in HEK293 cells can promote mRNA export alone in these assays, but its protein stability and export function are increased with the addition of either exogenous NXT1 or NXT2 (24). We show that NXT1 and NXT2 similarly promote endogenous NXF1 protein stability in neuroblastoma cells. The Nxt2 knockout mouse has no overt phenotype, suggesting that NXT1 can functionally replace NXT2 (28). Our data provide evidence that NXT2 can similarly replace NXT1 in maintenance of NXF1 stability. An intron-retaining, truncated isoform of NXF1 has also been proposed to be functionally redundant with NXT1 in mRNA export assays, and whether this or other undiscovered mechanisms of replacing NXT1 function beyond NXT2 occur in other cellular settings will be important to determine (41, 42). Our data, however, suggest that within neuroblastoma, expression of either NXT1 or NXT2 is necessary and sufficient for NXF1 stability.

These data support a model for a previously unrecognized mechanism of synthetic lethality whereby a paralog relationship is explained by the loss of stability of a common essential binding partner. The larger DepMap data set may contain other examples in which modulation of a gene is able to regulate stability of another essential complex member in a context-specific manner, and identifying these relationships could yield other promising therapeutic targets. As NXF1 is a common essential gene, direct targeting is anticipated to be broadly toxic, but through exploiting the NXT1–NXT2 paralog relationship, we can restrict these effects to cells with low NXT2. We therefore propose a novel therapeutic strategy of indirect but precise targeting of a common essential protein specifically in cancer cells by exploiting this paralog relationship.

Finally, these data suggest that the mechanism of NXT1 dependency is maintained in other low NXT2 tumor types, and if NXT1 can be effectively targeted, this could have broad benefit for children with several different incurable cancers. Although adult cancers do not display the same lineage enrichment of low NXT2 expression, there are adult cancer cell lines that have low NXT2, and perhaps a subset of adult patients with cancer would similarly benefit from therapeutic exploitation of this target. A key remaining question is why pediatric cancers tend to have low expression of NXT2, while it is more ubiquitously expressed in adult cancers and normal tissues. One possible explanation is that pediatric cancers arise, by definition, through a failure of differentiation, and low NXT2 expression could be more common in undifferentiated cells present during development that give rise to pediatric cancers. Adult cancers, by contrast, generally arise due to an accumulation of mutations in developed tissue where high NXT2 expression may be more common. Future studies will be required to better understand the regulation of NXT2 expression, as well as potential intratumoral heterogeneity of expression. Our finding also reinforces the notion that repurposing of adult oncology drugs for pediatric patients will not always be an effective strategy for drug development, as NXT1 dependency is much more frequently observed in pediatric tumors.

All genome-scale dependency data used in this analysis are publicly available for download at depmap.org and figshare.com (https://figshare.com/articles/dataset/DepMap_20Q1_Public/11791698). All data sets generated in this study are made publicly available at figshare.com (https://figshare.com/s/85c7d5f316522767ff43).

All neuroblastoma cell lines were collected by the Cancer Cell Line Encyclopedia (CCLE; RRID:SCR_013836) and DepMap (Cancer Dependency Map Portal, RRID:SCR_017655) projects as were RD, and SMSCTR. The sources for these lines are listed at DepMap.org, and they can be obtained from their respective sources. Their identities were confirmed by single-nucleotide polymorphism array. RHJT and RH4 were generously provided by Dr. Thomas Look (Dana-Farber Cancer Institute, Boston, MA). HT-29 cells were generously provided by Dr. Karen Cichowski and Brigham and Women's Hospital, Boston, MA. Cell lines were confirmed negative for Mycoplasma infection with a most recent test date of December 18, 2020 (Lonza MycoAlert). Cell line identities were reconfirmed by short tandem repeat (STR) profiling (Genetica). For publicly available STR profiles, matching was performed using the DSMZ STR profile database (43). KELLY (DSMZ catalog no. ACC-355, RRID:CVCL_2092), GIMEN (DSMZ catalog no. ACC-654, RRID:CVCL_1232), RD (ATCC catalog no. CRL-7731, RRID:CVCL_1649), SMSCTR (RRID:CVCL_A770), and SIMA (DSMZ catalog no. ACC-164, RRID:CVCL_1695) were cultured in RPMI 1640 supplemented with 10% FBS. CHP-212 (ATCC catalog no. CRL-2273, RRID:CVCL_1125) and SK-N-BE(2; ATCC catalog no. CRL-2271, RRID:CVCL_0528) were cultured in a 1:1 mix of MEM:F12 supplemented with 10% FBS. SK-N-DZ (ATCC catalog no. CRL-2149, RRID:CVCL_1701) and SK-N-FI (ATCC catalog no.# CRL-2142, RRID:CVCL_1702) were cultured in DMEM supplemented with 10% FBS and nonessential amino acids (10 μmol/L gycine, 10 μmol/L l-alanine, 10 μmol/L l-asparagine, 10 μmol/L l-aspartic acid, 10 μmol/L l-glutamic acid, 10 μmol/L l-proline, 10 μmol/L l-serine). RH4 (RRID:CVCL_5916) and RHJT (RRID:CVCL_VU81) were cultured in DMEM supplemented with 10% FBS.

To create a gene list of putative dependencies, the CRISPR Avana dependency gene effect scores generated in mid-2017 using the CERES algorithm were used (https://figshare.com/s/85c7d5f316522767ff43). For the purposes of this analysis, only neuroblastoma cell lines with MYCN amplification (n = 9) were used. Non-amplified lines were excluded from the analysis (n = 2), and all non-neuroblastoma lines (n = 331) were used for comparison. Inclusion criteria were modeled after those in Durbin and colleagues, but modified to be more inclusive (44). Genes had to meet at least one of these criteria: (i) >4 sigma dependency in mean-centered CERES in at least two MYCN-amplified neuroblastoma cell lines, (ii) >3 sigma outlier in mean-centered CERES in at least three MYCN-amplified neuroblastoma cell lines, (iii) enriched in MYCN-amplified neuroblastoma with an empiric P value <0.0005. Genes were then filtered to require that the gene effect score in CERES was <−0.3. Common essentials were defined as genes for which at least 25% of cell lines had a gene effect score <−0.6 in CERES and the 95th percentile was <−0.4 in CERES. Genes meeting this definition were filtered out. Genes were then filtered for expression in neuroblastoma cell lines and excluded if log₂[transcripts per million (TPM) +1] in neuroblastoma cell lines in CCLE was <−2. Finally, genes were filtered for expression in human tissue data sets in at least one neuroblastoma sample as follows: Affymetrix microarray expression >6 in GSE12460 (45), log₂[reads per million (RPKM)] >2 in GSE49711 (46), or Agilent microarray expression > 10 in GSE73517 (47). For the CRISPR guides, two guide sequences were retained from the Avana library, and three guide sequences were designed de novo. Additionally, three guides targeting intronic regions were generated for each of the 214 genes. Intronic regions were determined using the shared intronic regions across multiple isoforms using NCBI RefSeq UCSC, NCBI REfSeq All (RefSeq, RRID:SCR_003496), and GENGODEv24 knownGene. The 30 bp closest to known splice sites were avoided. The targeted intron regions were screened to make sure they do not fall in an exon of another gene. For seven genes, there were no shared introns or they were single exon genes, in which case neighboring regions that did not have other genes were targeted instead. Five CRISPRi guides per gene were designed as well. All CRISPR, CRISPRi, and intronic guide sequences included in the library are available at figshare.com (https://figshare.com/s/85c7d5f316522767ff43). For the sgRNA library, sgRNAs were cloned into a puromycin selectable sgRNA vector (lentiGuide-Puro, a gift from Feng Zhang; Addgene plasmid # 52963; http://n2t.net/addgene:52963; RRID:Addgene_52963) as described previously (48). These vectors were infected into stable Cas9 or dCas9-KRAB lines as described below.

For inducible CRISPR studies, a guide-only vector containing a doxycycline-inducible sgRNA and constitutive GFP, FgH1tUTG, a gift from Marco Herold (Addgene plasmid # 70183; http://n2t.net/addgene:70183; RRID:Addgene_70183), was used as described previously (22). For NXT2 knockout studies, a hygromycin selectable vector containing Cas9 and the indicated sgRNA (sgLACZ or sgNXT2) was purchased from Vector Builder. For all other experiments, the sgRNA was cloned into lentiCRISPR v2, a gift from Feng Zhang (Addgene plasmid # 52961; http://n2t.net/addgene:52961; RRID:Addgene_52961; ref. 49), containing Cas9 and either puromycin or blasticidin resistance markers. For low-throughput experiments, guide sequences can be found in Supplementary Table S1.

Stable neuroblastoma lines constitutively expressing Streptococcus pyogenes Cas9 have been previously generated and described (2, 10). All cell lines were reassayed for Cas9 activity prior to screening using a GFP Cas9-activity assay. To generate dCas9 lines, parental lines were transduced with a lentivirus expressing a nuclease dead Cas9 (dCas9) fused with a KRAB-transcriptional repressive domain (pLX_311-KRAB-dCas9 was a gift from John Doench, William Hahn, and David Root; Addgene plasmid # 96918; http://n2t.net/addgene:96918; RRID:Addgene_96918; ref. 14). The expression of dCas9 was verified by Western blot, and activity was assayed using a modified version of the GFP activity assay, in which cells were infected with a lentivirus expressing destabilized EGFP (pLX313 EGFP-mODC) under hygromycin selection, and then infected with lentivirus expressing an sgRNA targeting the transcriptional start site of EGFP (sgCiGFP-2, GACCAGGATGGGCACCACCC). dCas9-KRAB activity was further confirmed using a cell viability readout after infection with an sgRNA directing dCas9-KRAB to the transcriptional start site of the essential gene PSMD. Cas9 or dCas9 expressing cell lines were infected with a single sgRNA library containing both CRISPR and CRISPRi sgRNAs (3,985 unique guides) at a multiplicity of infection of ∼30%. Cells were selected with puromycin. For CRISPR screens, cells were maintained under blasticidin and puromycin until puromycin selection was complete (∼day 4). For CRISPRi screens, cells were grown under puromycin and blasticidin selection for the duration of the screen to prevent loss of guides or dCas9 activity. Representation of 500–1,000 cells per guide was achieved at infection and maintained throughout the screen. Cell pellets of 500–1,000× representation were frozen down 7, 14, and 21 days postinfection. Genomic DNA was extracted from these pellets using the Qiagen DNeasy Blood and Tissue kit (cat # 69506). The sgRNA barcode was PCR amplified, and this region was submitted for standard Illumina sequencing as described previously (2). Three replicates were carried out and sequenced for each time point and cell line in each screening assay. No replicates failed or were excluded from the analysis.

To integrate readouts of CRISPR screens at multiple time points, we developed the Chronos model. A manuscript describing and benchmarking this method against existing methods in detail is under preparation; here, we describe the model in sufficient detail for reproducibility.

Chronos assumes that cells infected with an sgRNA are divided into two populations: those where the function of the targeted gene remains intact (e.g., due to in-frame INDEL mutations), which continue proliferating at the original rate, and those where the gene was successfully knocked out, which proliferate at some new, potentially negative rate. For an sgRNA i targeting gene g in cell line c, the number of cells N_cj with the sgRNA at time t after infection will be:

where p_cj is the probability that the sgRNA achieves knockout of its target, R_c is the unperturbed growth rate of the cell line, and r_cg is the fractional change in growth rate caused by knockout of the targeted gene. This last term is the quantity that we will call the gene score, and is what we usually want to learn from the experiment. We exclude sgRNAs targeting more than one gene.

A wide range of efficacy for sgRNAs in abrogating protein function has been reported (48). In addition, we have observed in Project Achilles screens that there is a per-cell-line “screen quality” metric (e.g., due to variable Cas9 activity) which determines the overall separation of essential and nonessential genes in the screens (50). We therefore approximate the knockout probability per sgRNA and cell line as the product of a per-line and per-sgRNA factor, both constrained to the interval [0, 1]: p_ci = p_c p_i. There is some delay between infection and the emergence of the knockout phenotype, which we will call d_g. Finally, we do not observe the number of infected cells N_ci directly, but only the proportion of all reads that map to a particular sgRNA, which we assume have expectation equal to the proportion of cells with that sgRNA: <n_ci> = N_ci/∑_i N_ci. Let Chronos' estimation of n_ci be ν_ci. Then,

where Z_c is a normalization term:

Chronos infers the parameters on the right to maximize the likelihood of the observed read count fractions n_ci according to the NB2 parameterization of the negative binomial distribution. The NB2 cost can be written (up to an additive constant)

where ν_ci is the model prediction of the normalized read counts and k enumerates the time points measured. The overdispersion parameter α_c of the NB2 model is a hyperparameter that may be estimated for each cell line using existing tools, such as edgeR (RRID:SCR_012802; ref. 51), or set to a constant. We have found empirically that the value 0.05 produces good results in a variety of metrics.

On-target editing of NXT1 and chromosome 2 (negative control) was confirmed by amplifying the appropriate region of genomic DNA and then performing Sanger sequencing with deconvolution by the TIDE or ICE algorithms. For NXT1, the region targeted by all three guides was amplified using the following primers: forward 5′-TGGCTGAATCTGTGGATGCAAAAC-3′; reverse 5′-GCACTGTCTCCGCAACAAC-3′. The gene desert region on chromosome 2 containing the target of sgChr2-2 was amplified with the following primers: forward 5′-TTTGAGGCTTATGGGGGCAG-3′ and reverse 5′-AAGGGCCCCGATTTTCTCAA-3′. PCR products were purified using the QiaQuick PCR purification kit and sent for Sanger sequencing with the following sequencing primers: sgChr2-2; 5′-ATGGGTAAGGAATCTGAGCATGG-3′ and sgNXT1-1/2/3 5′-GCCACAGTGGTAATCCCATC-3′. On-target editing was then determined using either the TIDE algorithm https://tide.nki.nl/ (52) or ICE v. 2.0 by Synthego (https://ice.synthego.com/#/) as indicated.

Cas9-expressing cell lines were generated, validated, and infected as described above. Seven days after infection, floating cells were collected, and adherent cells were detached from the plate using Accutase. Cells were combined, and an appropriate number of cells from each replicate (1,000× representation) was pelleted and frozen down. The remaining cells (4,000× representation) were incubated with magnetic beads (Miltenyi Biotec; 130-090-201) and passed over a magnetic column (Miltenyi Biotec 130-042-201). Cells were washed 3× with PBS, and then the bound cells were eluted from the column. The purified cell population was then frozen down by cell pellet. Three biological replicates were collected. A small number of cells were then incubated with fluorescent Annexin V, and the purity of the purified population relative to input was assessed by flow cytometry. Genomic DNA was extracted and sequenced as described above. No replicates failed or were excluded from the analysis.

This study was approved by the Institutional Animal Care and Use Committee (IACUC) of the Broad Institute under animal protocol 0194-01-18. IACUC guidelines on the ethical use and care of animals were followed. KELLY cells constitutively expressing Cas9 were infected with our sgRNA library as described above. Cells were put into puromycin selection 24 hours after infection. Once cells were fully selected, 72 hours after infection, 8.0 × 10⁶ cells (∼2,000× library representation) were implanted into the flanks of NRG (NOD-Rag1^null IL2rg^null) mice bilaterally. Twenty-six days after implantation, when tumors were approximately 100–200 mm³, mice were euthanized and their tumors (n = 6) were harvested and flash-frozen. Tumors were homogenized with a Precellys tissue homogenizer, and DNA was extracted with a QIAamp DNA Blood Maxi Kit, after tissue lysis in Qiagen buffer ATL. Genomic DNA was sequenced as described above. No tumors failed or were excluded from the analysis.

This study was approved by the IACUC of Dana-Farber Cancer Institute and performed under protocol 15-029. IACUC guidelines on the ethical use and care of animals were followed. KELLY cells constitutively expressing Cas9 were infected with inducible sgRNAs targeting Chr2-2 or NXT1. Cells (6.0e6) were implanted into the flanks of NRG (NOD-Rag1^null IL2rg^null) mice. When tumors were ∼100–200 mm³, mice were randomized to either normal chow or chow containing doxycycline at 625 mg/kg. Tumors were measured by Vernier caliper, and volume was determined using the standard formula. Animals were euthanized once they reached a humane endpoint, and tumor tissue was flash-frozen for later protein and genomic DNA extraction.

For low-throughput viability assays, cells were transfected with a vector containing Cas9 and the indicated sgRNA as described above, and four days after infection seeded into 384-well plates or 96-well plates. CellTiter-Glo was used according to the manufacturer's instructions to assess the relative viability at days 3, 5, and 7, normalized to day 0. Luminescence was determined using an Envision Plate reader. Experiments were done in triplicate, and technical replicates in each experiment were at least triplicate.

Cells were seeded into 96-well or 384-well plates. Twenty-four hours after plating, compound was added either manually or with a robot (HP D300e digital dispenser) at the concentrations indicated. Seventy-two hours after addition of drug, viability was assessed by CellTiter-Glo according to the manufacturer's instructions. Luminescence was determined using an Envision Plate reader. Viability was normalized to the DMSO condition. dTAG13 and dTAG-13-NEG, the inactive analogue, were generously provided by Dr. Nathanael Gray's laboratory at Dana-Farber Cancer Institute.

To render it resistant to sgNXT1-3, the PAM sequence in NXT1 was mutated (C→T at position 590). This NXT1 sequence was cloned into pLEX_305-C-dTAG, a gift from James Bradner and Behnam Nabet (Addgene plasmid #91798; http://n2t.net/addgene:91798; RRID:Addgene_91798; ref. 20). To generate the NXT1^deg lines with knockout of endogenous NXT1 and degron-tagged exogenous NXT1, cell lines were coinfected with a puromycin-resistant vector containing NXT1 with a c-terminal FKBP12^F36V-2XHA tag and a blasticidin-resistant vector containing an sgRNA targeting NXT1 (sgNXT1-3). Cell lines were selected with both blasticidin and puromycin. A vector containing the NXT2 orf with a c-terminal MYC tag under neomycin selection was purchased from Origene (RC213204). Cell lines were transfected with this vector, and stable integrations were selected with neomycin.

Three PDX models (COGN-424X, COGN-557X, and COGN-603X) were obtained from the Children's Oncology Group's (COG) Childhood Cancer Repository. Written informed consent and institutional review board approval were obtained according to COG protocols, and studies were conducted in accordance with recognized ethical guidelines. Models were propagated in mice, and tumors were flash-frozen before gDNA or protein was extracted.

Cells were lysed in Cell Signaling Lysis Buffer (9803) supplemented with protease (Roche; catalog no. 11836170001) and phosphatase inhibitors (Roche; cat. #04906845001). Lysates were quantified using a BCA assay (Pierce) and normalized. SDS-PAGE gels were used to separate proteins, and proteins were transferred to a PVDF membrane. Primary antibodies used in this study were NXT2 (Abcam, catalog no. ab121797, RRID:AB_11127842), GAPDH (Santa Cruz Biotechnology, catalog no. sc-47724), PARP (Cell Signaling Technology, catalog no. 9542), NXF1 (Abcam, catalog no. ab50609), MYC-tag (Cell Signaling Technology, catalog no. 9402), HA-tag (Cell Signaling Technology, catalog no. 2367 and #3724), CAS9 (Cell Signaling Technology, catalog no. 7A9-3A3), and vinculin (Cell Signaling Technology, catalog no. 4650). Membranes were incubated with secondary antibodies (LI-COR, catalog nos. 926-68070 and 926-32211) and imaged on a LI-COR Odyssey.

Neuroblastoma cell lines were plated, and 24 hours later treated with 50 μg/mL cycloheximide. Cells were then lysed 2, 6, and 24 hours after cycloheximide treatment, and protein levels were assessed by Western blot analysis.

Time-course screen data for the individual time points were collapsed to a gene-level score using the MAGeCK algorithm with copy-number correction for CRISPR screens (53). No copy-number correction was applied to CRISPRi screens. Plasmid pool was used as the reference. To collapse time-point data to a single gene-level score that accounts for rate of depletion, we used the Chronos algorithm, without copy-number correction (see Chronos algorithm methods section above for detailed description). For the in vivo screen, MAGeCK was used to collapse to gene-level scores as above, but gDNA from the cells at the time of inoculation was used as the reference. For the Annexin V–positive selection screen, the log₂ fold change of guides relative to input gDNA was calculated and averaged across replicates. The mean log fold change of the top two guides per gene was considered the Annexin V enrichment score. For all screens, genes were considered a strong hit if they exceeded the effect of the median of the positive controls in that assay. For copy-number analysis and corrections, gene-level copy-number calls for each cell line were calculated by taking the log₂ of the copy-number segment mean of DepMap data (20Q1 Public CN Segments available at DepMap.org).

Cells were treated as indicated and then the cytoplasmic fraction was lysed in a lysis buffer of 50 mmol/L Tris ph7.5, 0.1% NP-40, 1 mmol/L EDTA, and 1 mmol/L MgCl₂ supplemented with protease (Roche, catalog no. 11836170001) and phosphatase inhibitors (Roche, catalog no. 04906845001). The nuclear fraction was pelleted by centrifugation and then lysed in a nuclear lysis buffer (50 mmol/L Tris pH 7.5, 1% NP-40, 1 mmol/L EDTA, 1 mmol/L MgCl₂, 300 mmol/L NaCl supplemented with protease and phosphatase inhibitors as above). DTT (1 μmol/L) was added and 2 μg of antibody. IPs were incubated overnight at 4°C. The next day 30 μL of Protein A or G Dynabeads (Thermo Fisher Scientific, 10006D and 10007D) were added for 2–4 hours, and then beads were washed 5× in lysis buffer and eluted in LDS.

Genomic DNA was extracted using the Qiagen DNeasy Blood and Tissue kit (catalog no. 69506). DNA was then bisulfite converted using the Qiagen EpiTect Bisulfite kit (catalog no. 59104). Bisulfite-converted DNA was then amplified with EpiMark HotStart Taq (NEB #M0490) using the following primers: forward 5′-TTGGGAGAATATAAAAGTTTG-3′ and reverse 5′-ATCTCCCTAAAACCAATAAC-3′ from Sung and colleagues (29). PCR products were purified using the QiaQuick PCR purification kit, and Sanger sequenced with the reverse primer.

To create the list of initial dependencies, the CRISPR Avana dependency gene effect scores generated in mid-2017 using the CERES algorithm were used. These data are available for download at figshare.com (https://figshare.com/s/85c7d5f316522767ff43). We used the CRISPR DepMap Public 20Q1 gene effect, probability, copy number, and CCLE expression and transcript-level expression data sets; data are available for download at depmap.org and figshare.com (https://figshare.com/articles/dataset/DepMap_20Q1_Public/11791698). We used the Wellcome Sanger Institute Dependency Map data (PROJECT SCORE) processed with the CERES algorithm, which can be downloaded from depmap.org and figshare.com (https://figshare.com/authors/Broad_DepMap/5514062). Human tumor microarray data were downloaded from the R2 database (r2.amc.nl). Human tumor RNA-sequencing data (Tumor Compendium v11 Public PolyA April 2020) were downloaded from the Treehouse project (https://treehousegenomics.soe.ucsc.edu/). Normal tissue RNA-sequencing data were downloaded from the Genotype-Tissue Expression project (V8; GTEXportal.org). ATAC-sequencing and ChIP-sequencing data for neuroblastoma cell lines were downloaded from the Gene Expression Omnibus (GEO; RRID:SCR_005012) with GEO accession numbers GSE138293 and GSE138314, respectively (54). Gene expression for these lines was downloaded from the GEO database with accession number GSE89413 (55). Tumor mutation, copy number, and expression data for neuroblastoma tumors from the Gabriela Miller Kid's First provisional data set (Discovering the Genetic Basis of Human Neuroblastoma (Maris/GMKF dbGaP phs001436.v1.p1, Provisional) were accessed through the pediatric cBioPortal (pedcbioportal.org; refs. 56, 57).

For statistical tests of significance, the statistical test and P value are described in the respective figure legends. All t tests are two-sided unless otherwise indicated. A P value of 0.05 was used as the cutoff for significance unless otherwise indicated. These values were calculated in GraphPad Prism (RRID:SCR_002798) or R 3.63. Error bars represent SD unless otherwise indicated. All duplicate measures were taken from distinct samples rather than repeated measures of the same sample. For null hypotheses with multiple groups, a two-way ANOVA was used, followed by a Tukey multiple comparisons test if the ANOVA was significant.

Isobaric TMT reagents and the BCA protein concentration assay kit were from Thermo Fisher Scientific. Empore-C18 material for in-house made Stage Tips was from 3M. Sep-Pak cartridges (100 mg size) were purchased from Waters. All solvents used for liquid chromatography (LC) were purchased from J.T. Baker. MS-grade trypsin and Lys-C protease were purchased from Thermo Fisher Scientific and Wako, respectively. Complete protease inhibitors were from Millipore Sigma. Unless otherwise noted, all other chemicals were purchased from Thermo Fisher Scientific.

KELLY cells with endogenous NXT1 knocked-out and expressing degron-tagged exogenous NXT1 (KELLY+ NXT1^deg) were treated with 500 nmol/L dTAG-13 for 2 hours. Cell pellets were then collected by cell scraping and frozen at −80°C until future use. Cell pellets were lysed using 8 mol/L urea, 200 mmol/L 4-(2-hydroxyethyl)-1-piperazinepropanesulfonic acid (EPPS) at pH 8.5 with protease inhibitors (one tablet per 10 mL of lysis buffer). Samples were further homogenized, and DNA was sheared via sonication using a probe sonicator (20 × 0.5 second pulses; level 3). Total protein was determined using a BCA assay, and proteins were stored at −80°C until future use. A total of 25 μg of protein was aliquoted for each condition, and TMT channel for further downstream processing. Protein extracts were reduced using 5 mmol/L tris-(2-carboxyethyl) phosphine (TCEP) for 15 minutes at room temperature. Next samples were alkylated with 10 mmol/L iodoacetamide for 30 minutes in the dark at room temperature. To facilitate the removal of incompatible reagents, proteins were precipitated using chloroform and methanol. Briefly, to 100 μL of each sample, 400 μL of methanol was added, followed by 100 μL of chloroform with thorough vortexing. Next, 300 μL of HPLC-grade water was added and samples were vortexed thoroughly. Each sample was centrifuged at 14,000 × g for 5 minutes at room temperature. The top aqueous layer was removed, and the protein pellet was washed twice with methanol and centrifuged at 14,000 × g for 5 minutes at room temperature. Protein pellets were resolubilized in 200 mmol/L EPPS buffer and digested overnight with Lys-C (1:100, enzyme:protein ratio) at room temperature. The next day, trypsin (1:100 ratio) was added and incubated at 37°C for an additional 6 hours in a ThermoMixer set to 1,000 rpm.

To each digested sample, 30% anhydrous acetonitrile was added, and 25 μg of peptides was labeled using approximately 55 μg of TMTPro reagents (TMT1-TMT16) for 1 hour at room temperature with constant agitation. Following labeling, 5% hydroxylamine was added to quench excess TMT reagent. To equalize protein loading, a ratio check was performed by pooling approximately 2 μg of each TMT-labeled sample. Samples were pooled and desalted using an in-house packed C18 Stage Tip and analyzed by LC/MS-MS. Normalization factors derived from the ratio check were used to pool samples 1:1 across all TMT channels, and the combined sample was desalted using a 100-mg Sep-Pak solid phase extraction cartridge. Eluted peptides were further fractionated using basic-pH reversed-phase (bRP) on an Agilent 300 extend C18 column and were collected into a 96 deep-well plate. Samples were consolidated into 24 fractions as previously described, and 12 nonadjacent fractions were desalted using Stage Tips prior to analyses using LC/MS-MS (58–60).

All MS data were acquired using an Orbitrap Fusion Lumos mass spectrometer in-line with a Proxeon nanoLC-1200 Ultra performance LC (UPLC) system. TMT-labeled peptides were separated using an in-house packed 100-μm capillary column with 35 cm of Accucore 150 resin (2.6 μmol/L, 150 Å; Thermo Fisher Scientific) using a 120-minute LC gradient from 4% to 24% acetonitrile in 0.125% formic acid per run. Eluted peptides were acquired using synchronous precursor selection (SPS-MS3) method for TMT quantification. Briefly, MS1 spectra were acquired at 120K resolving power with a maximum of 50 ms ion injection in the Orbitrap. MS2 spectra were acquired by selecting the top 10 most abundant features via collisional induced dissociation in the ion trap using an automatic gain control (AGC) of 15K, quadrupole isolation width of 0.5 m/z and a maximum ion time of 50 ms. These spectra were passed in real time to the external computer for database searching. Intelligent data acquisition using real-time searching was performed using Orbiter as previously described (61, 62). Peptide spectral matches were analyzed using the Comet search algorithm designed for spectral acquisition speed (63, 64). Real-time access to spectral data was enabled by the Thermo Fisher Scientific Fusion API. Briefly, peptides were filtered using simple filters that included the following: not a match to a reversed sequence, maximum PPM error 50, minimum XCorr of 0.5, minimum deltaCorr of 0.10, and minimum peptide length of 7. If peptide spectra matched to above criteria, an SPS-MS3 scan was performed using up to 10 b- and y-type fragment ions as precursors with an AGC of 200K for a maximum of 200 ms with a normalized collision energy setting of 45.

All acquired data were searched using the open-source Comet algorithm using a previously described informatics pipeline (65–67). We acknowledge Dr. Steven Gygi for use of a custom CORE data analysis software as part of the pipeline. Briefly, peptide spectral libraries were first filtered to a peptide false discovery rate (FDR) of less than 1% using linear discriminant analysis using a target–decoy strategy. Spectral searches were done using a custom fasta-formatted database that included common contaminants, reversed sequences (Uniprot Human, 2014) with custom NXT1–FKBP12^F36V entries and the following parameters: 50 PPM precursor tolerance, fully tryptic peptides, fragment ion tolerance of 0.9 kDa, and a static modification by TMT (+304.2071 Da) on lysine and peptide N termini. Carbamidomethylation of cysteine residues (+57.021 Da) was set as a static modification while oxidation of methionine residues (+15.995 Da) was set as a variable modification. Resulting peptides were further filtered to obtain a 1% protein FDR, and proteins were collapsed into groups. Reporter ion intensities were adjusted to correct for impurities during synthesis of different TMT reagents according to the manufacturer's specifications. Finally, protein quantitative values were column normalized so that the sum of the signal for all protein in each channel was equal to account for sample loading differences, and a total sum signal-to-noise of all report ion ions of 100 was required for analysis.

C.F. Malone reports grants from NCI and The Pussycat Foundation Helen Gurley Brown Fellowship during the conduct of the study. N.V. Dharia reports grants from St. Baldrick's Foundation during the conduct of the study; outside the submitted work, N.V. Dharia is a current employee of Genentech, Inc., a member of the Roche Group. B.R. Paolella reports grants from Novo Ventures outside the submitted work. D.E. Root reports grants from AbbVie, BMS, Janssen, Merck, and Vir outside the submitted work. T.R. Golub reports grants from Dependency Map Consortium during the conduct of the study; personal fees from GlaxoSmithKline, Sherlock Biosciences, and Forma Therapeutics outside the submitted work. F. Vazquez reports grants from Novo Ventures during the conduct of the study. K. Stegmaier reports grants from NIH, St. Baldrick's Foundation Robert J. Arceci Innovation Award, and grants from Friends for Life during the conduct of the study; grants from Novartis, personal fees from Kronos Bio, Auron Therapeutics, and personal fees from Rigel Pharmaceuticals outside the submitted work. No disclosures were reported by the other authors.

C.F. Malone: Conceptualization, investigation, visualization, methodology, writing–original draft, writing–review and editing. N.V. Dharia: Conceptualization, software, formal analysis, visualization, methodology, writing–review and editing. G. Kugener: Software, formal analysis, visualization, writing–review and editing. A.B. Forman: Investigation, writing–review and editing. M.V. Rothberg: Investigation, writing–review and editing. M. Abdusamad: Investigation, writing–review and editing. A. Gonzalez: Investigation, writing–review and editing. M. Kuljanin: Investigation, writing–review and editing. A.L. Robichaud: Investigation, writing–review and editing. A. Saur Conway: Investigation, writing–review and editing.J.M. Dempster: Software, formal analysis, writing–review and editing. B.R. Paolella: Supervision, methodology, writing–review and editing. N. Dumont: Investigation, methodology, writing–review and editing. V. Hovestadt: Formal analysis, writing–review and editing. J.D. Mancias: Resources, supervision, writing–review and editing. S.T. Younger: Software, formal analysis, methodology, writing–review and editing. D.E. Root: Methodology, writing–review and editing. T.R. Golub: Funding acquisition, writing–review and editing. F. Vazquez: Conceptualization, supervision, funding acquisition, methodology, writing–review and editing. K. Stegmaier: Conceptualization, supervision, funding acquisition, methodology, writing–original draft, writing–review and editing.

We acknowledge Dr. Steven Gygi for the use of a custom CORE data analysis software as part of the mass spectrometry pipeline. We also thank Andrew Tang for graphic design assistance. This work was funded by NIH R35 CA210030 (to K. Stegmaier), the St. Baldrick's Foundation Robert J. Arceci Innovation Award (to K. Stegmaier), Friends for Life (to K. Stegmaier), NIH 1P01 CA217959 (to K. Stegmaier), and the Slim Initiative in Genomic Medicine for the Americas (Sigma), a joint U.S.–Mexico project funded by the Carlos Slim Foundation (to F. Vazquez and T.R. Golub). C.F. Malone was supported by a Helen Gurley Brown Presidential Initiative Fellowship, and by the NIH under a Ruth L. Kirschstein National Research Service Award (F32CA243266). N.V. Dharia was supported by the Julia's Legacy of Hope St. Baldrick's Foundation Fellowship.

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