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Published in final edited form as: Chembiochem. 2023 May 4;24(11):e202200766. doi: 10.1002/cbic.202200766

Differential Chemoproteomics Reveals MARK2/3 as Cell Migration-Relevant Targets of the ALK Inhibitor Brigatinib

Qianqian Hu 1,2, Yi Liao 1, Jessica Cao 1, Bin Fang 3, Sang Y Yun 4, Fumi Kinose 5, Eric B Haura 5, Harshani R Lawrence 4,6, Robert C Doebele 7, John M Koomen 6,8, Uwe Rix 1,6,*
PMCID: PMC10413441  NIHMSID: NIHMS1914793  PMID: 36922348

Abstract

Metastasis poses a major challenge in cancer management, including EML4-ALK-rearranged non-small cell lung cancer (NSCLC). As cell migration is a critical step during metastasis, we assessed the anti-migratory activities of several clinical ALK inhibitors in NSCLC cells and observed differential anti-migratory capabilities despite similar ALK inhibition, with brigatinib displaying superior anti-migratory effects over other ALK inhibitors. Applying an unbiased in-situ mass spectrometry-based chemoproteomics approach, we determined the proteome-wide target profile of brigatinib in EML4-ALK+ NSCLC cells. Dose-dependent and cross-competitive chemoproteomics suggested MARK2 and MARK3 as relevant brigatinib kinase targets. Functional validation showed that combined pharmacological inhibition or genetic modulation of MARK2/3 inhibited cell migration. Consistently, brigatinib treatment induced inhibitory YAP1 phosphorylation downstream of MARK2/3. Collectively, our data suggest that brigatinib exhibits unusual cross-phenotype polypharmacology as, despite similar efficacy for inhibiting EML4-ALK-dependent cell proliferation as other ALK inhibitors, it more effectively prevented migration of NSCLC cells due to co-targeting of MARK2/3.

Keywords: Brigatinib, chemoproteomics, MARK3, migration, polypharmacology

Graphical Abstract:

graphic file with name nihms-1914793-f0001.jpg

Targeting two hallmarks: In addition to inhibiting cancer cell viability, some targeted drugs can also affect cell migration. Using differential quantitative chemoproteomics we identified cross-phenotype polypharmacology of the clinical ALK inhibitor brigatinib, which kills EML4-ALK-positive lung cancer cells through inhibition of ALK and inhibits cancer cell migration through targeting MARK2/3.

Introduction

Following the unprecedented success of the BCR-ABL inhibitor imatinib in chronic myelogenous leukemia (CML) treatment, multiple targeted drugs have been approved and considerably improved the therapeutic outcomes for many cancer patients with various tumor types, such as BRAF-mutant melanoma, EGFR-mutant or EML4-ALK-rearranged non-small cell lung cancer (NSCLC)[1]. However, the complexity of oncogenic signaling in many tumors often requires simultaneous modulation of multiple proteins to achieve optimal clinical response, raising the interest in multi-targeted therapy. Consequently, the clinical application of combinatorial strategies has resulted in higher efficacy and more durable responses in some cases, such as the combination of BRAF and MEK inhibitors in BRAF-mutant melanoma or the combination of aromatase and CDK4/6 inhibitors in hormone receptor-positive breast cancer[2]. Over the last decade, systematic integration of interdisciplinary studies using systems biology, medicinal chemistry and bioinformatics has given rise to the development of a new strategy, which is based on polypharmacology where engaging multiple biologically relevant targets is achieved with a single anticancer drug[3]. Polypharmacology refers to a drug acting on multiple targets within the same pathway or multiple targets across different pathways, which can enable drug repurposing for different tumor types[4]. However, prior research has mostly focused on the polypharmacological effect regarding the same phenotype, such as cell viability or angiogenesis. Examples include the dual BCR-ABL and SRC inhibitor dasatinib for use in CML[5], the bispecific EGFR and MET antibody amivantamab for EGFR-mutant NSCLC[6], and the dual VEGFR and PDGFR inhibitor sunitinib as an antiangiogenic drug for various solid tumors[7]. Here, we report a rare case of polypharmacology spanning two different cancer-relevant phenotypes, namely cell viability and migration, where a single agent treatment leads to the inhibition of one target affecting cell viability, and inhibition of other targets affects cell migration of the same cancer cells. This phenomenon was found in EML4-ALK-rearranged NSCLC cells using a panel of several clinical ALK tyrosine kinase inhibitors (TKIs). EML4-ALK rearrangements have been shown to induce constitutive tyrosine kinase activity of ALK thereby driving tumorigenesis in about 3–7% of NSCLC cases, and ALK-directed targeted therapy has been shown to be highly effective in treating patients with these cancers[1c, 8]. However, metastasis, particularly in the brain, is prevalent and poses a major challenge for treatment of patients with EML4-ALK-rearranged NSCLC[9]. Second-generation ALK TKIs, such as alectinib and brigatinib, were developed to target tumors that were resistant to the first-generation ALK TKI crizotinib. However, they display high efficacy for both crizotinib-naïve and -resistant tumors with secondary ALK mutations[10]. Moreover, results from clinical trials have suggested superior activities of the next-generation ALK TKIs brigatinib and alectinib against brain metastases compared to the first-generation ALK TKI crizotinib[10–11]. Although this has been attributed largely to enhanced brain penetrance of these clinical ALK TKIs, not much is known about their anti-migratory effects suggesting it to be critical to assess these and elucidate the underlying molecular mechanisms and targets. Notably, the high structural similarity and the size of the human protein kinome (>500) often results in broad target profiles of kinase inhibitors and correspondingly complex biological activities. Although kinase activity assays have been widely and successfully used to delineate the target profiles of TKIs in vitro[12], this does not directly allow for conclusive nomination of targets that are relevant in the context of specific cancer cells or phenotypes. In contrast, mass spectrometry (MS)-based chemoproteomics can reveal a more biologically relevant target profile as it pertains to the cellular context of interest[13]. Herein we assessed the anti-migratory activity of a panel of ALK TKIs and then identified the proteome-wide target profile of brigatinib, which displayed the most pronounced migration-inhibitory effects. Quantitative, MS-based cross-competitive chemoproteomics in live cells (in situ) using different ALK TKIs identified MARK2 and MARK3 as unique brigatinib targets, combined inhibition of which was predominantly responsible for mediating its anti-migratory effect. In summary, these data suggested that brigatinib shows rare cross-phenotype polypharmacology with on one hand inhibiting viability and inducing apoptosis of tumor cells via targeting ALK and on the other hand inhibiting tumor cell migration via targeting MARK2/3.

Results and Discussion

To be able to better understand any ALK-independent anti-migratory effects of ALK TKIs, we first interrogated their relative cellular potencies across a panel of EML4-ALK-positive NSCLC cell lines using cell viability as a readout. The four clinically relevant ALK TKIs crizotinib (first generation), brigatinib, alectinib (both second generation) and lorlatinib (third generation) demonstrated cellular potencies consistent with the reported literature in the highly TKI-sensitive H3122 and STE-1 cell lines, showing greater potency with later generation TKIs in the order lorlatinib> brigatinib ≈ alectinib > crizotinib (Fig. 1A). However, in H2228 and CUTO9 cells ALK TKIs had generally somewhat weaker effects on cell viability, which were also more similar across the different drugs. We next assessed the anti-migratory activities of these four ALK TKIs in CUTO9 cells, which among these cell lines were the most amenable to wound-healing assays, and observed differential migration-inhibitory potencies. In the absence of ALK inhibitors, all wounds closed after 24 hours. However, 1 μM of brigatinib and alectinib significantly inhibited wound closure by about 40% and 30%, respectively, and 3 μM of these drugs suppressed cell migration by approximately 70% and 45%, respectively (Fig. 1B). Importantly, these concentrations are close to the reported clinically relevant concentrations of 2.9 μM and 1.4 μM for brigatinib and alectinib, respectively[14]. In comparison, crizotinib and lorlatinib, the most potent ALK inhibitor among these TKIs, had no impact on wound closure. Considering similar potencies of the four ALK TKIs for inhibition of viability in CUTO9 cells, this suggested that the anti-migratory activity of brigatinib and alectinib was most likely independent of ALK inhibition, but rather due to an unexpected albeit desirable off-target effect. To further evaluate whether the enhanced anti-migratory activity of brigatinib and alectinib over crizotinib and lorlatinib was indeed ALK-independent, we performed the wound-healing assay also in A549 NSCLC cells, which do not express EML4-ALK fusions and are instead driven by mutant KRAS. Notably, A549 cells recapitulated the effects in CUTO9 cells with wound closure inhibition observed predominantly for brigatinib and alectinib (Fig 1C). Dose-dependent analysis in CUTO9 and A549 cells furthermore showed that these differential anti-migratory effects were not single dose-specific, but discernible across a broad range of concentrations (Fig. S1A/B). Taken together, we observed that, in contrast to crizotinib and lorlatinib, alectinib and particularly brigatinib are potent inhibitors of cell migration and that this effect was likely ALK-independent.

Figure 1. Differential activity of ALK inhibitors on viability and migration inhibitory of NSCLC cells.

Figure 1.

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(A) Dose-dependent viability of different EML4-ALK-positive NSCLC cells to a panel of ALK TKIs after 72 hours as determined by CellTiter-Glo analysis and IC50 values for inhibition of viability. (B/C) Effects of ALK TKIs on cell migration of (B) CUTO9 and (C) A549 cells after 24 and 48 hours, respectively.

To identify and prioritize brigatinib (1) targets in the specific context of CUTO9 cells, which have to express the target(s) responsible for mediating its anti-migratory activity, we employed a dose-dependent, MS-based chemoproteomics strategy that allows for the proteome-wide quantitative determination of target affinity of drugs. To this end, we synthesized an immobilizable brigatinib analogue (i-brigatinib, 2) that allows for tethering to NHS-beads and subsequent affinity enrichment of its protein binding partners by incubation with cell lysate. Considering that, based on published co-crystal data (pdb: 5J7H)[15], the N-methyl group of brigatinib’s piperidine moiety does not meaningfully contribute to the binding of brigatinib with ALK and extends into the solvent space, we introduced an aminopropyl linker to this moiety (Fig. 2A, Fig. S2A). Subsequent characterization of i-brigatinib via in vitro kinase assay validated conservation of activity against wild-type ALK (Fig. S2B), which according to previous reports suggested broader utility for a range of kinase targets[16]. Therefore, using bead-linked i-brigatinib as a “bait” we performed chemoproteomics using CUTO9 cell lysates. To better eliminate unspecific binding and quantify target binding, unmodified brigatinib was used at a serial dose dilution for pre-treatment of cells prior to the drug-bead incubation with cell lysates. Thus, upon free drug binding, the abundance of bona fide targets in the pulldown eluate were expected to decrease in a dose-dependent fashion whereas background proteins would be unaltered (Fig. 2B). Unlike in vitro competitive chemoproteomics, where the pre-treatment takes place in the cell lysates, such in situ chemical proteomics assay has the potential advantage that drugs engage their targets inside live cells in a more physiologically relevant milieu. We therefore treated cells for a period of 24 hours, which allows for capturing also targets that may be transcriptionally or post-translationally up- or downregulated in response to drug treatment as this would likely occur similarly in tumors. Subsequent LC-MS/MS analysis allowed quantification of the proteome-wide binding affinity of brigatinib targets and determination of the target-specific Ki for inhibition of cellular binding using the Cheng-Prusoff equation as described previously (Table S1)[17]. To validate the proteins highlighted by competitive chemoproteomics, results were first benchmarked by characterizing the affinity pattern of several putative targets identified by reported in vitro kinase inhibition data[18]. As expected, several known targets, such as ALK and EGFR, showed decreasing intensities in a dose-dependent manner in the chemoproteomics results (Fig. 2C), which was further confirmed by immunoblotting (Fig. 2D). We next prioritized the chemoproteomics-derived target candidates based on their experimentally determined Ki. Among the 84 protein kinases identified by MS analysis, 62 were defined as potential targets of brigatinib as they were observed in both DMSO pretreatment samples, showed stable curve fitting and displayed a minimum average intensity of 5 × 106. Of these, most showed a Ki of less than the reported Cmax 2.5 μM and had been also captured by in vitro kinase assay screens. In addition, in vitro chemoproteomics using cell lysates identified a similar set of targets thereby on one hand providing further qualitative support for these targets and at the same time illustrating that despite potential advantages of in situ chemoproteomics, actual differences in this case were minor (Fig. 2E, Fig. S3A, Table S2). Comparing the MS-derived cellular Ki with the published IC50 via kinase assay, we generally observed good congruence as for instance ALK and EGFR showed Ki values of 2.2 nM and 126 nM similar to the reported IC50 values of 0.6 nM and 67 nM, respectively[18]. This congruence was important as it allowed a more nuanced target prioritization considering that inhibition of cell migration was most prominent between 1–3 μM and the responsible target(s) were therefore likely of moderate to weak potency, i.e. in the mid-nanomolar to sub-micromolar rather than the low nanomolar range. Taken together, quantitative chemoproteomics in CUTO9 cells identified a large number of brigatinib targets and enabled further differentiation of highly potent and less potent binders.

Figure 2. Proteome-wide target profile of brigatinib in CUTO9 cells.

Figure 2.

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(A) Chemical structures of brigatinib and its immobilizable analogue i-brigatinib. (B) Workflow of in situ dose-dependent mass spectrometry-based chemical proteomics. (C) Dose-dependent pulldown protein intensity curves of known brigatinib targets. (D) Immunoblot confirmation of pulldowns of indicated select targets. (E) Mapping of brigatinib targets and their respective chemoproteomics-derived Ki value ranges on the phylogenetic tree of the humane protein kinome. Kinases not observed in both DMSO pretreatment samples, requiring exclusion of more than 2 values for stable curve fitting or displaying average intensities of lower than 5 × 106 were not considered high confidence targets and are not displayed. Illustration reproduced courtesy of Cell Signaling Technology, Inc. (www.cellsignal.com).

Considering the broad target profile of brigatinib, we hypothesized that comparison with the other ALK TKIs in CUTO9 cells would allow for enhanced candidate selection. We therefore also performed chemoproteomics with i-brigatinib beads and cross-competition with all four ALK TKIs via pre-treatment of CUTO9 cells with 2.5 μM TKI for 24 hours, 2.5 μM being the plasma Cmax of brigatinib and a relevant concentration for the cell migration phenotype observed here (Fig. 3A). This led to differential intensities in proteins that were pulled down by i-brigatinib indicating shared and exclusive targets of brigatinib compared with the other three ALK TKIs. We posited that the targets that contribute to the differential anti-migratory effects across all four ALK TKIs should a) be in the mid-nanomolar to sub-micromolar potency range and b) exhibit superior affinity for brigatinib and, to slightly lower extent, alectinib, but less to no affinity for lorlatinib and crizotinib. We therefore determined the ratio of protein intensities of brigatinib- or alectinib-treated samples versus that of lorlatinib and crizotinib competition. Integration of the cross-competition information with the Ki values obtained from dose-dependent chemoproteomics enabled prioritization of moderately potent to weaker targets (Fig. 3B, Table S3). Detailed comparison of cross-competition data highlighted only seven kinases that were significantly less competed for by lorlatinib and crizotinib than brigatinib (Fig. 3C). Further filtering for kinases that showed (only) moderately reduced affinity for alectinib revealed that only EGFR and MARK3 met this additional criterion. While EGFR is not usually associated with a migration phenotype, this left MARK3 as a prime candidate considering that its affinity pattern across the four TKIs closely matched the differential anti-migratory effects in CUTO9 cells and its cellular Ki of 1079 nM reflected a brigatinib target of weaker potency (Fig. 3B, Fig. S3B), which was in reasonably good agreement with the previously reported in vitro IC50 of 127 nM[18]. Furthermore, the closely related family member MARK2, in contrast for instance to the generally migration-associated target FAK, showed a very similar cross-competition pattern (Fig. S3B) and with a cellular Ki of 213 nM also qualified as a moderately potent brigatinib target, which was in excellent agreement with its reported in vitro IC50 of 93 nM[18]. Finally, immunoblotting of cross-competition pulldowns for MARK3 and MARK2 validated the higher binding affinity for brigatinib and alectinib compared with crizotinib and lorlatinib, as alectinib and particularly brigatinib more completely competed for binding of MARK2/3 to i-brigatinib beads (Fig. 3E). In contrast, competition for EML4-ALK followed the reported order of in vitro potency for the various ALK TKIs with crizotinib showing the least, and lorlatinib and brigatinib the strongest extent of competition. Notably, MARK3, also known as microtubule affinity-regulating kinase 3, has been reported to be required for the migration of multiple cell types via regulating migration-relevant pathways like Hippo signaling[19]. Thus, integration of cross-competitive chemoproteomics across multiple ALK TKIs with quantitative chemoproteomics highlighted the non-canonical kinase targets MARK2 and MARK3 as candidates for mediating brigatinib’s and alectinib’s anti-migratory activity.

Figure 3. Integrated analysis of dose-dependent and cross-competitive chemical proteomics to identify exclusive targets of brigatinib and alectinib.

Figure 3.

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(A) Workflow of in situ cross-competitive mass spectrometry-based chemical proteomics. (B) Circular heatmap depicting the differential target affinity of ALK TKIs based on cross-competition of i-brigatinib beads in comparison to mass spectrometry signal intensity of uncompeted i-brigatinib beads (Intensity; grey) and brigatinib −logKi values (purple) determined by dose-dependent brigatinib competition (see Figure 2). n.d.: not determined. (C) Cross-competitive chemoproteomics-based prioritization (red quadrant) of brigatinib-selective targets over crizotinib and lorlatinib. Brigatinib targets with moderate to low potency are shown in black, others in grey. Kinases with p value (Briga vs. DMSO) < 0.05, log2 ratio (Briga vs. Alec) > 0, and valid p value (Briga vs. Crizo/Lorla) are shown. (D) Cross-competitive chemoproteomics-based prioritization (blue quadrant) of brigatinib targets that are shared with alectinib. Brigatinib targets with moderate to low potency are shown in black, others in grey. (E) Immunoblot analysis of cross-competition profiles of prioritized targets.

As chemoproteomics analysis highlighted MARK2/3 as potential mediators of brigatinib’s anti-migratory effects, but multiple MARK family members have been shown to have similar roles in regulating cancer cell migration[19], there was a possibility that they would be able to compensate for each other’s function. Thus, we next examined the functional roles of both MARK3 and MARK2 in the wound-healing of CUTO9 cells using both genetic and chemical tools. Although single knockdown of either MARK3 or MARK2 using small interfering RNA (siRNA) had no effect by itself, double knockdown of both genes significantly impaired the wound healing capability of CUTO9 cells thereby recapitulating the migration inhibitory effects of brigatinib (Fig. 4A). Interestingly, in the case of A549 cells, single knockdown of MARK3 alone either with pooled or different individual siRNAs significantly inhibited cellular migration, whereas MARK2 silencing only made minimal contributions in these cells, indicating a major role of MARK3 in regulating the migration of A549 cells (Fig. 4B, Fig. S3C). Targeting MARK3 cells by CRISPR-Cas9 also significantly reduced migration of A549 cells (Fig. S3D). Thus, while migration of A549 cells was prominently affected by targeting MARK3 alone, most likely MARK2 and MARK3 were both critical in migration regulation of CUTO9 cells, but suppression of either target could lead to compensatory signaling by the other. Consistently, the pan-specific MARK family kinase inhibitor MRT67307[20] inhibited CUTO9 cell migration in a dose-dependent manner where 1 μM and 3 μM reduced cell migration by approximately 25% and 40%, respectively (Fig. 4C). Notably, the magnitude of this effect is slightly lower than what is achieved by treatment with 3 μM of brigatinib. This entails the possibility that additional targets could make further contributions, albeit smaller than what is observed with MARK2/3 targeting, which would represent a yet more complex polypharmacology effect. Additional candidate targets could be for instance FAK or its orthologue PYK2, the relevance of which for migration and metastasis are well documented in other cells[21]. However, there may be a significant degree of context specificity as FAK and PYK2 are also potent targets of lorlatinib[22], which has no anti-migratory activity in the cell lines interrogated here. Consistently, the dual FAK/PYK2 inhibitor defactinib shows minor, but notable effects on migration of CUTO9 and A549 cells (Fig. S3E/F), which was consistent with previous reports[23]. However, defactinib did not enhance inhibition of migration over what was achieved with dual MARK2/3 silencing, suggesting that targeting of MARK2/3 is primarily responsible for brigatinib’s anti-migratory effect, at least in these cells. Intriguingly, a recent study reported that MARK2 can act as an upstream activator of FAK and promote focal adhesion formation[24], which is consistent with the observations here. It is possible, though, that inhibition of cell migration by brigatinib may be mediated by a somewhat different combination of targets, including FAK or PYK2, in other cancer cells.

Figure 4. Effects of targeting MARK2/3 on cell migration and Hippo signaling.

Figure 4.

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(A/B) Wound healing assay following knockdown of MARK2/3 in (A) CUTO9 and (B) A549 cells with corresponding immunoblotting for MARK2/3. NT: non-targeting. (C) Wound width determination following treatment of CUTO9 cells with the MARK inhibitor MRT67307. (D) Immunoblot analysis and quantification of Hippo signaling in CUTO9 cells upon treatment with brigatinib, lorlatinib (3 μM each) and MRT67307 (1 μM) for 2 hours.

MARK family kinases have been reported as negative regulators of the Hippo pathway by phosphorylating and inhibiting MST1/2 in cancer cells, thereby interrupting the interaction and phosphoactivation of LATS1/2, which in turn is responsible for phosphorylation of YAP1 at Ser127 subsequently leading to YAP1 degradation[19a]. Thus, MARK kinases indirectly stabilize YAP1 protein and promote its activity as a pro-tumorigenic transcriptional co-activator. Consistently, immunoblotting indicated that treatment of CUTO9 cells with the MARK inhibitors MRT67307 or brigatinib induced phosphorylation of YAP1 at S127, thus reducing YAP1 activity, which in contrast was not observed by treatment with the MARK-sparing ALK TKI lorlatinib (Fig. 4D). Taken together, these data suggest that inhibition of MARK3 and MARK2 makes a major contribution to the anti-migratory effect of brigatinib and its regulation of Hippo signaling in NSCLC cells.

Conclusion

Collectively, applying a MS-based competitive in situ chemical proteomics strategy that enabled proteome-wide quantitative target profiling, we here elucidated the molecular mechanism for the anti-migratory activity of the clinical ALK TKI brigatinib in lung cancer cells. Cross-comparison with other ALK TKIs identified MARK2/3 as the brigatinib-specific targets predominantly responsible for mediating this anti-migratory effect in NSCLC cells. Considering prevalent metastasis in ALK+ NSCLC, the anti-migratory effect of brigatinib may be beneficial to patients for the prevention of metastasis development in addition to its primary mechanism of action through potent inhibition of ALK kinase activity, the oncogenic driving force of EML4-ALK-rearranged NSCLC. Notably, as for some cells dual inhibition of both MARK3 and MARK2 was required, this constituted both, a polypharmacology mechanism regarding inhibition of migration, as well as polypharmacology across different cancer-relevant phenotypes, namely migration and viability. Similar cases have been reported for the multitargeted TKIs sunitinib and cabozantinib, which target the oncogenic drivers KIT and PDGFR in gastrointestinal stromal tumors (sunitinib) and RET in thyroid cancers (cabozantinib), but also act as strong antiangiogenic agents through inhibition of VEGFR and PDGFR (sunitinib) and VEGFR and MET (cabozantinib)[7, 25]. Only few specific examples for such cross-phenotype polypharmacology have been described so far, although it is likely that comprehensive systems biology studies would uncover many more, which could lead to novel therapeutic strategies involving improved biomarkers and drugs with optimized multi-target profiles or drug combinations to improve patient outcomes.

Methods

See also Supporting Information.

Supplementary Material

Table S3
Table S2
Table S1
Supinfo

Acknowledgments

This work was supported by the NIH/NCI R01 CA219347 (to U.R./E.B.H.), the NIH/NCI R50 CA211447 (to H.R.L.), the Moffitt Lung Cancer Center of Excellence and the H. Lee Moffitt Cancer Center and Research Institute. We furthermore wish to acknowledge the Moffitt Chemical Biology (Chemistry Unit), Analytic Microscopy, and Proteomics and Metabolomics Core Facilities, which are supported by the National Cancer Institute (award no. P30-CA076292) as a Cancer Center Support Grant. The Proteomics and Metabolomics Core is also supported by the U.S. Army Medical Research and Material Command (award no. W81XWH-08-2-0101) for a National Functional Genomics Center, the Moffitt Foundation, and the Bankhead-Coley Cancer Research program of the Florida Department of Health (09BE-04).

Footnotes

Conflict of Interest

The authors declare the following competing financial interest(s): U.R., J.M.K., H.R.L., and E.B.H. hold patents or patent applications to MARK inhibitors. R.C.D. is an employee and shareholder of Rain Therapeutics. J.M.K. reports partial salary support from Bristol Myers Squibb, which is unrelated to this project. E.B.H. serves in a consulting or advisory role to Amgen, Ellipses Pharma, Janssen Oncology, Janssen Research & Development and Revolution Medicines; reports research funding (paid to his institution) from AstraZeneca, Genentech, Incyte, Janssen, Novartis, Revolution Medicines and Spectrum Pharmaceuticals; and reports patents, royalties or other intellectual property from Protein–Protein Interactions as Biomarkers Patent. The other authors declare no competing conflicts.

Data Availability

The mass spectrometry proteomics data have been deposited at the ProteomeXchange Consortium via the PRIDE partner repository (http://www.ebi.ac.uk/pride) with the dataset identifiers PXD036716, and are publicly available as of the date of publication. Searched data is also provided as supporting information with this manuscript.

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Associated Data

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

Supplementary Materials

Table S3
NIHMS1914793-supplement-Table_S3.xlsx (92.4KB, xlsx)
Table S2
NIHMS1914793-supplement-Table_S2.xlsx (120.5KB, xlsx)
Table S1
NIHMS1914793-supplement-Table_S1.xlsx (355.8KB, xlsx)
Supinfo
NIHMS1914793-supplement-Supinfo.pdf (1.6MB, pdf)

Data Availability Statement

The mass spectrometry proteomics data have been deposited at the ProteomeXchange Consortium via the PRIDE partner repository (http://www.ebi.ac.uk/pride) with the dataset identifiers PXD036716, and are publicly available as of the date of publication. Searched data is also provided as supporting information with this manuscript.

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