Deciphering Phenotypes from Protein Biomarkers for Translational Research with PIPER.

Liquid chromatography-multiple reaction monitoring mass spectrometry (LC-MRM) has widespread clinical use for detection of inborn errors of metabolism, therapeutic drug monitoring, and numerous other applications. This technique detects proteolytic peptides as surrogates for protein biomarker expression, mutation, and post-translational modification in individual clinical assays and in cancer research with highly multiplexed quantitation across biological pathways. LC-MRM for protein biomarkers must be translated from multiplexed research-grade panels to clinical use. LC-MRM panels provide the capability to quantify clinical biomarkers and emerging protein markers to establish the context of tumor phenotypes that provide highly relevant supporting information. An application to visualize and communicate targeted proteomics data will empower translational researchers to move protein biomarker panels from discovery to clinical use. Therefore, we have developed a web-based tool for targeted proteomics that provides pathway-level evaluations of key biological drivers (e.g., EGFR signaling), signature scores (representing phenotypes) (e.g., EMT), and the ability to quantify specific drug targets across a sample cohort. This tool represents a framework for integrating summary information, decision algorithms, and risk scores to support Physician-Interpretable Phenotypic Evaluation in R (PIPER) that can be reused or repurposed by other labs to communicate and interpret their own biomarker panels.

Integrated proteomics identifies PARP inhibitor-induced prosurvival signaling changes as potential vulnerabilities in ovarian cancer.

BRCA1/2-deficient ovarian carcinoma (OC) has been shown to be particularly sensitive to poly (ADP-ribose) polymerase inhibitors (PARPis). Furthermore, BRCA1/2 mutation status is currently used as a predictive biomarker for PARPi therapy. Despite providing a major clinical benefit to the majority of patients, a significant proportion of BRCA1/2-deficient OC tumors do not respond to PARPis for reasons that are incompletely understood. Using an integrated chemical, phospho- and ADP-ribosylation proteomics approach, we sought here to develop additional mechanism-based biomarker candidates for PARPi therapy in OC and identify new targets for combination therapy to overcome primary resistance. Using chemical proteomics with PARPi baits in a BRCA1-isogenic OC cell line pair, as well as patient-derived BRCA1-proficient and BRCA1-deficient tumor samples, and subsequent validation by coimmunoprecipitation, we showed differential PARP1 and PARP2 protein complex composition in PARPi-sensitive, BRCA1-deficient UWB1.289 (UWB) cells compared to PARPi-insensitive, BRCA1-reconstituted UWB1.289+BRCA1 (UWB+B) cells. In addition, global phosphoproteomics and ADP-ribosylation proteomics furthermore revealed that the PARPi rucaparib induced the cell cycle pathway and nonhomologous end joining (NHEJ) pathway in UWB cells but downregulated ErbB signaling in UWB+B cells. In addition, we observed AKT PARylation and prosurvival AKT-mTOR signaling in UWB+B cells after PARPi treatment. Consistently, we found the synergy of PARPis with DNAPK or AKT inhibitors was more pronounced in UWB+B cells, highlighting these pathways as actionable vulnerabilities. In conclusion, we demonstrate the combination of chemical proteomics, phosphoproteomics, and ADP-ribosylation proteomics can identify differential PARP1/2 complexes and diverse, but actionable, drug compensatory signaling in OC.

Metabolic Changes Are Associated with Melphalan Resistance in Multiple Myeloma.

Multiple myeloma is an incurable hematological malignancy that impacts tens of thousands of people every year in the United States. Treatment for eligible patients involves induction, consolidation with stem cell rescue, and maintenance. High-dose therapy with a DNA alkylating agent, melphalan, remains the primary drug for consolidation therapy in conjunction with autologous stem-cell transplantation; as such, melphalan resistance remains a relevant clinical challenge. Here, we describe a proteometabolomic approach to examine mechanisms of acquired melphalan resistance in two cell line models. Drug metabolism, steady-state metabolomics, activity-based protein profiling (ABPP, data available at PRIDE: PXD019725), acute-treatment metabolomics, and western blot analyses have allowed us to further elucidate metabolic processes associated with melphalan resistance. Proteometabolomic data indicate that drug-resistant cells have higher levels of pentose phosphate pathway metabolites. Purine, pyrimidine, and glutathione metabolisms were commonly altered, and cell-line-specific changes in metabolite levels were observed, which could be linked to the differences in steady-state metabolism of naïve cells. Inhibition of selected enzymes in purine synthesis and pentose phosphate pathways was evaluated to determine their potential to improve melphalan's efficacy. The clinical relevance of these proteometabolomic leads was confirmed by comparison of tumor cell transcriptomes from newly diagnosed MM patients and patients with relapsed disease after treatment with high-dose melphalan and autologous stem-cell transplantation. The observation of common and cell-line-specific changes in metabolite levels suggests that omic approaches will be needed to fully examine melphalan resistance in patient specimens and define personalized strategies to optimize the use of high-dose melphalan.

Intrinsic radiosensitivity, genomic-based radiation dose and patterns of failure of penile cancer in response to adjuvant radiation therapy.

PURPOSE: Optimal postoperative radiation therapy (PORT) dose is unclear in penile squamous cell carcinoma (PeSCC). Herein, we characterized the radiosensitivity index (RSI) and genomic-adjusted radiation dose (GARD) profiles in a cohort of patients with PeSCC, and assessed the application of GARD to personalize PORT. METHODS: A total of 25 PeSCC samples were identified for transcriptomic profiling. The RSI score and GARD were derived for each sample. A cohort of 34 patients was reviewed for clinical correlation. RESULTS: The median RSI for PeSCC was 0.482 (range 0.215-0.682). The majority (n = 21; 84%) of cases were classified as radioresistant. PeSCC GARD ranged from 9.56 to 38.39 (median 18.25), suggesting variable therapeutic effects from PORT. We further determined the optimal GARD-based RT doses to improve locoregional control. We found that therapeutic benefit was only achieved in 52% of PeSCC lesions with PORT of 50 Gy, in contrast to 84% benefit from GARD-modeled PORT of 66 Gy. In the clinical cohort, the majority of patients presented with pathological N2 or N3 disease (n = 31; 91%) and was treated with adjuvant concurrent platinum-based chemoradiotherapy (CRT, n = 30; 88%). Fourteen of the 34 patients (41%) had locoregional recurrence (LRR), of which half had LRR within six months of completion of PORT. CONCLUSIONS: The majority of PeSCC are intrinsically radioresistant with a low GARD-based therapeutic effect from PORT dose of 50 Gy, consistent with the observed high rate of LRR in the clinical cohort. A GARD-based strategy will allow personalizing PORT dose prescription to individual tumor biology and improve outcomes.

Evaluating somatic tumor mutation detection without matched normal samples.

BACKGROUND: Observations of recurrent somatic mutations in tumors have led to identification and definition of signaling and other pathways that are important for cancer progression and therapeutic targeting. As tumor cells contain both an individual's inherited genetic variants and somatic mutations, challenges arise in distinguishing these events in massively parallel sequencing datasets. Typically, both a tumor sample and a "normal" sample from the same individual are sequenced and compared; variants observed only in the tumor are considered to be somatic mutations. However, this approach requires two samples for each individual. RESULTS: We evaluate a method of detecting somatic mutations in tumor samples for which only a subset of normal samples are available. We describe tuning of the method for detection of mutations in tumors, filtering to remove inherited variants, and comparison of detected mutations to several matched tumor/normal analysis methods. Filtering steps include the use of population variation datasets to remove inherited variants as well a subset of normal samples to remove technical artifacts. We then directly compare mutation detection with tumor-only and tumor-normal approaches using the same sets of samples. Comparisons are performed using an internal targeted gene sequencing dataset (n = 3380) as well as whole exome sequencing data from The Cancer Genome Atlas project (n = 250). Tumor-only mutation detection shows similar recall (43-60%) but lesser precision (20-21%) to current matched tumor/normal approaches (recall 43-73%, precision 30-82%) when compared to a "gold-standard" tumor/normal approach. The inclusion of a small pool of normal samples improves precision, although many variants are still uniquely detected in the tumor-only analysis. CONCLUSIONS: A detailed method for somatic mutation detection without matched normal samples enables study of larger numbers of tumor samples, as well as tumor samples for which a matched normal is not available. As sensitivity/recall is similar to tumor/normal mutation detection but precision is lower, tumor-only detection is more appropriate for classification of samples based on known mutations. Although matched tumor-normal analysis is preferred due to higher precision, we demonstrate that mutation detection without matched normal samples is possible for certain applications.

Dissection of TBK1 signaling via phosphoproteomics in lung cancer cells.

TANK-binding kinase 1 (TBK1) has emerged as a novel therapeutic target for unspecified subset of lung cancers. TBK1 reportedly mediates prosurvival signaling by activating NF-κB and AKT. However, we observed that TBK1 knockdown also decreased viability of cells expressing constitutively active NF-κB and interferon regulatory factor 3. Basal phospho-AKT level was not reduced after TBK1 knockdown in TBK1-sensitive lung cancer cells, implicating that TBK1 mediates unknown survival mechanisms. To gain better insight into TBK1 survival signaling, we searched for altered phosphoproteins using mass spectrometry following RNAi-mediated TBK1 knockdown. In total, we identified 2,080 phosphoproteins (4,621 peptides), of which 385 proteins (477 peptides) were affected after TBK1 knockdown. A view of the altered network identified a central role of Polo-like kinase 1 (PLK1) and known PLK1 targets. We found that TBK1 directly phosphorylated PLK1 in vitro. TBK1 phosphorylation was induced at mitosis, and loss of TBK1 impaired mitotic phosphorylation of PLK1 in TBK1-sensitive lung cancer cells. Furthermore, lung cancer cell sensitivity to TBK1 was highly correlated with sensitivity to pharmacological PLK inhibition. We additionally found that TBK1 knockdown decreased metadherin phosphorylation at Ser-568. Metadherin was associated with poor outcome in lung cancer, and loss of metadherin caused growth inhibition and apoptosis in TBK1-sensitive lung cancer cells. These results collectively revealed TBK1 as a mitosis regulator through activation of PLK1 and also suggested metadherin as a putative TBK1 downstream effector involved in lung cancer cell survival.

Overexpression of major CDKN3 transcripts is associated with poor survival in lung adenocarcinoma.

BACKGROUND: The cyclin-dependent kinase inhibitor 3 (CDKN3) has been perceived as a tumour suppressor. Paradoxically, CDKN3 is often overexpressed in human cancer. It was unclear if CDKN3 overexpression is linked to alternative splicing variants or mutations that produce dominant-negative CDKN3. METHODS: We analysed CDKN3 expression and its association with patient survival in three cohorts of lung adenocarcinoma. We also examined CDKN3 mutations in the Cancer Genome Atlas (TCGA) and the Moffitt Cancer Center's Total Cancer Care (TCC) projects. CDKN3 transcripts were further analysed in a panel of cell lines and lung adenocarcinoma tissues. CDKN3 mRNA and protein levels in different cell cycle phases were examined. RESULTS: CDKN3 is overexpressed in non small cell lung cancer. High CDKN3 expression is associated with poor overall survival in lung adenocarcinoma. Two CDKN3 transcripts were detected in all samples. These CDKN3 transcripts represent the full length CDKN3 mRNA and a normal transcript lacking exon 2, which encodes an out of frame 23-amino acid peptide with little homology to CDKN3. CDKN3 mutations were found to be very rare. CDKN3 mRNA and protein were elevated during the mitosis phase of cell cycle. CONCLUSIONS: CDKN3 overexpression is prognostic of poor overall survival in lung adenocarcinoma. CDKN3 overexpression in lung adenocarcinoma is not attributed to alternative splicing or mutation but is likely due to increased mitotic activity, arguing against CDKN3 as a tumour suppressor.

PTEN Lipid Phosphatase Activity Suppresses Melanoma Formation by Opposing an AKT/mTOR/FRA1 Signaling Axis.

Inactivating mutations in PTEN are prevalent in melanoma and are thought to support tumor development by hyperactivating the AKT/mTOR pathway. Conversely, activating mutations in AKT are relatively rare in melanoma, and therapies targeting AKT or mTOR have shown disappointing outcomes in preclinical models and clinical trials of melanoma. This has led to the speculation that PTEN suppresses melanoma by opposing AKT-independent pathways, potentially through noncanonical functions beyond its lipid phosphatase activity. In this study, we examined the mechanisms of PTEN-mediated suppression of melanoma formation through the restoration of various PTEN functions in PTEN-deficient cells or mouse models. PTEN lipid phosphatase activity predominantly inhibited melanoma cell proliferation, invasion, and tumor growth, with minimal contribution from its protein phosphatase and scaffold functions. A drug screen underscored the exquisite dependence of PTEN-deficient melanoma cells on the AKT/mTOR pathway. Furthermore, activation of AKT alone was sufficient to counteract several aspects of PTEN-mediated melanoma suppression, particularly invasion and the growth of allograft tumors. Phosphoproteomics analysis of the lipid phosphatase activity of PTEN validated its potent inhibition of AKT and many of its known targets, while also identifying the AP-1 transcription factor FRA1 as a downstream effector. The restoration of PTEN dampened FRA1 translation by inhibiting AKT/mTOR signaling, and FRA1 overexpression negated aspects of PTEN-mediated melanoma suppression akin to AKT. This study supports AKT as the key mediator of PTEN inactivation in melanoma and identifies an AKT/mTOR/FRA1 axis as a driver of melanomagenesis. SIGNIFICANCE: PTEN suppresses melanoma predominantly through its lipid phosphatase function, which when lost, elevates FRA1 levels through AKT/mTOR signaling to promote several aspects of melanomagenesis.

Differential network analysis of ROS1 inhibitors reveals lorlatinib polypharmacology through co-targeting PYK2.

Multiple tyrosine kinase inhibitors (TKIs) are often developed for the same indication. However, their relative overall efficacy is frequently incompletely understood and they may harbor unrecognized targets that cooperate with the intended target. We compared several ROS1 TKIs for inhibition of ROS1-fusion-positive lung cancer cell viability, ROS1 autophosphorylation and kinase activity, which indicated disproportionately higher cellular potency of one TKI, lorlatinib. Quantitative chemical and phosphoproteomics across four ROS1 TKIs and differential network analysis revealed that lorlatinib uniquely impacted focal adhesion signaling. Functional validation using pharmacological probes, RNA interference, and CRISPR-Cas9 knockout uncovered a polypharmacology mechanism of lorlatinib by dual targeting ROS1 and PYK2, which form a multiprotein complex with SRC. Rational multi-targeting of this complex by combining lorlatinib with SRC inhibitors exhibited pronounced synergy. Taken together, we show that systems pharmacology-based differential network analysis can dissect mixed canonical/non-canonical polypharmacology mechanisms across multiple TKIs enabling the design of rational drug combinations.

MIG6 Mediates Adaptive and Acquired Resistance to ALK/ROS1 Fusion Kinase Inhibition through EGFR Bypass Signaling.

Despite the initial benefit from tyrosine kinase inhibitors (TKI) targeting oncogenic ALK and ROS1 gene fusions in non-small cell lung cancer, complete responses are rare and resistance ultimately emerges from residual tumor cells. Although several acquired resistance mechanisms have been reported at the time of disease progression, adaptative resistance mechanisms that contribute to residual diseases before the outgrowth of tumor cells with acquired resistance are less clear. For the patients who have progressed after TKI treatments, but do not demonstrate ALK/ROS1 kinase mutations, there is a lack of biomarkers to guide effective treatments. Herein, we found that phosphorylation of MIG6, encoded by the ERRFI1 gene, was downregulated by ALK/ROS1 inhibitors as were mRNA levels, thus potentiating EGFR activity to support cell survival as an adaptive resistance mechanism. MIG6 downregulation was sustained following chronic exposure to ALK/ROS1 inhibitors to support the establishment of acquired resistance. A higher ratio of EGFR to MIG6 expression was found in ALK TKI-treated and ALK TKI-resistant tumors and correlated with the poor responsiveness to ALK/ROS1 inhibition in patient-derived cell lines. Furthermore, we identified and validated a MIG6 EGFR-binding domain truncation mutation in mediating resistance to ROS1 inhibitors but sensitivity to EGFR inhibitors. A MIG6 deletion was also found in a patient after progressing to ROS1 inhibition. Collectively, this study identifies MIG6 as a novel regulator for EGFR-mediated adaptive and acquired resistance to ALK/ROS1 inhibitors and suggests EGFR to MIG6 ratios and MIG6-damaging alterations as biomarkers to predict responsiveness to ALK/ROS1 and EGFR inhibitors.

Implementation of a High-Accuracy Targeted Gene Expression Panel for Clinical Care.

This study describes the validation of a clinical RNA expression panel with evaluation of concordance between gene copy gain by a next-generation sequencing (NGS) assay and high gene expression by an RNA expression panel. The RNA Salah Targeted Expression Panel (RNA STEP) was designed with input from oncologists to include 204 genes with utility for clinical trial prescreening and therapy selection. RNA STEP was validated with the nanoString platform using remnant formalin-fixed, paraffin-embedded-derived RNA from 102 patients previously tested with a validated clinical NGS panel. The repeatability, reproducibility, and concordance of RNA STEP results with NGS results were evaluated. RNA STEP demonstrated high repeatability and reproducibility, with excellent correlation (r > 0.97, P < 0.0001) for all comparisons. Comparison of RNA STEP high gene expression (log2 ratio ≥ 2) versus NGS DNA-based gene copy number gain (copies ≥ 5) for 38 mutually covered genes revealed an accuracy of 93.0% with a positive percentage agreement of 69.4% and negative percentage agreement of 93.8%. Moderate correlation was observed between platforms (r = 0.53, P < 0.0001). Concordance between high gene expression and gene copy number gain varied by specific gene, and some genes had higher accuracy between assays. Clinical implementation of RNA STEP provides gene expression data complementary to NGS and offers a tool for prescreening patients for clinical trials.

Iterative rank-order normalization of gene expression microarray data.

BACKGROUND: Many gene expression normalization algorithms exist for Affymetrix GeneChip microarrays. The most popular of these is RMA, primarily due to the precision and low noise produced during the process. A significant strength of this and similar approaches is the use of the entire set of arrays during both normalization and model-based estimation of signal. However, this leads to differing estimates of expression based on the starting set of arrays, and estimates can change when a single, additional chip is added to the set. Additionally, outlier chips can impact the signals of other arrays, and can themselves be skewed by the majority of the population. RESULTS: We developed an approach, termed IRON, which uses the best-performing techniques from each of several popular processing methods while retaining the ability to incrementally renormalize data without altering previously normalized expression. This combination of approaches results in a method that performs comparably to existing approaches on artificial benchmark datasets (i.e. spike-in) and demonstrates promising improvements in segregating true signals within biologically complex experiments. CONCLUSIONS: By combining approaches from existing normalization techniques, the IRON method offers several advantages. First, IRON normalization occurs pair-wise, thereby avoiding the need for all chips to be normalized together, which can be important for large data analyses. Secondly, the technique does not require similarity in signal distribution across chips for normalization, which can be important for maintaining biologically relevant differences in a heterogeneous background. Lastly, IRON introduces fewer post-processing artifacts, particularly in data whose behavior violates common assumptions. Thus, the IRON method provides a practical solution to common needs of expression analysis. A software implementation of IRON is available at [http://gene.moffitt.org/libaffy/].

Chemical Proteomics with Novel Fully Functionalized Fragments and Stringent Target Prioritization Identifies the Glutathione-Dependent Isomerase GSTZ1 as a Lung Cancer Target.

Photoreactive fragment-like probes have been applied to discover target proteins that constitute novel cellular vulnerabilities and to identify viable chemical hits for drug discovery. Through forming covalent bonds, functionalized probes can achieve stronger target engagement and require less effort for on-target mechanism validation. However, the design of probe libraries, which directly affects the biological target space that is interrogated, and effective target prioritization remain critical challenges of such a chemical proteomic platform. In this study, we designed and synthesized a diverse panel of 20 fragment-based probes containing natural product-based privileged structural motifs for small-molecule lead discovery. These probes were fully functionalized with orthogonal diazirine and alkyne moieties and used for protein crosslinking in live lung cancer cells, target enrichment via "click chemistry," and subsequent target identification through label-free quantitative liquid chromatography-tandem mass spectrometry analysis. Pair-wise comparison with a blunted negative control probe and stringent prioritization via individual cross-comparisons against the entire panel identified glutathione S-transferase zeta 1 (GSTZ1) as a specific and unique target candidate. DepMap database query, RNA interference-based gene silencing, and proteome-wide tyrosine reactivity profiling suggested that GSTZ1 cooperated with different oncogenic alterations by supporting survival signaling in refractory non-small cell lung cancer cells. This finding may form the basis for developing novel GSTZ1 inhibitors to improve the therapeutic efficacy of oncogene-directed targeted drugs. In summary, we designed a novel fragment-based probe panel and developed a target prioritization scheme with improved stringency, which allows for the identification of unique target candidates, such as GSTZ1 in refractory lung cancer.

Dynamic proteome analysis of Cyanothece sp. ATCC 51142 under constant light.

Understanding the dynamic nature of protein abundances provides insights into protein turnover not readily apparent from conventional, static mass spectrometry measurements. This level of data is particularly informative when surveying protein abundances in biological systems subjected to large perturbations or alterations in environment such as cyanobacteria. Our current analysis expands upon conventional proteomic approaches in cyanobacteria by measuring dynamic changes of the proteome using a (13)C(15)N-l-leucine metabolic labeling in Cyanothece ATCC51142. Metabolically labeled Cyanothece ATCC51142 cells grown under nitrogen-sufficient conditions in continuous light were monitored longitudinally for isotope incorporation over a 48 h period, revealing 414 proteins with dynamic changes in abundances. In particular, proteins involved in carbon fixation, pentose phosphate pathway, cellular protection, redox regulation, protein folding, assembly, and degradation showed higher levels of isotope incorporation, suggesting that these biochemical pathways are important for growth under continuous light. Calculation of relative isotope abundances (RIA) values allowed the measurement of actual active protein synthesis over time for different biochemical pathways under high light exposure. Overall results demonstrated the utility of "non-steady state" pulsed metabolic labeling for systems-wide dynamic quantification of the proteome in Cyanothece ATCC51142 that can also be applied to other cyanobacteria.

Global proteomics reveal an atypical strategy for carbon/nitrogen assimilation by a cyanobacterium under diverse environmental perturbations.

Cyanobacteria, the only prokaryotes capable of oxygenic photosynthesis, are present in diverse ecological niches and play crucial roles in global carbon and nitrogen cycles. To proliferate in nature, cyanobacteria utilize a host of stress responses to accommodate periodic changes in environmental conditions. A detailed knowledge of the composition of, as well as the dynamic changes in, the proteome is necessary to gain fundamental insights into such stress responses. Toward this goal, we have performed a large-scale proteomic analysis of the widely studied model cyanobacterium Synechocystis sp. PCC 6803 under 33 different environmental conditions. The resulting high-quality dataset consists of 22,318 unique peptides corresponding to 1955 proteins, a coverage of 53% of the predicted proteome. Quantitative determination of protein abundances has led to the identification of 1198 differentially regulated proteins. Notably, our analysis revealed that a common stress response under various environmental perturbations, irrespective of amplitude and duration, is the activation of atypical pathways for the acquisition of carbon and nitrogen from urea and arginine. In particular, arginine is catabolized via putrescine to produce succinate and glutamate, sources of carbon and nitrogen, respectively. This study provides the most comprehensive functional and quantitative analysis of the Synechocystis proteome to date, and shows that a significant stress response of cyanobacteria involves an uncommon mode of acquisition of carbon and nitrogen.

Inhibition of Epidermal Growth Factor Receptor Signaling by Antisense Oligonucleotides as a Novel Approach to Epidermal Growth Factor Receptor Inhibition.

We report a novel method to inhibit epidermal growth factor receptor (EGFR) signaling using custom morpholino antisense oligonucleotides (ASOs) to drive expression of dominant negative mRNA isoforms of EGFR by ASO-induced exon skipping within the transmembrane (16) or tyrosine kinase domains (18 and 21). In vivo ASO formulations induced >95% exon skipping in several models of nonsmall cell lung cancer (NSCLC) and were comparable in efficacy to erlotinib in reducing colony formation, cell viability, and migration in EGFR mutant NSCLC (PC9). However, unlike erlotinib, ASOs maintained their efficacy in both erlotinib-resistant subclones (PC9-GR) and wild-type overexpressing EGFR models (H292), in which erlotinib had no significant effect. The most dramatic ASO-induced phenotype resulted from targeting the EGFR kinase domain directly, which resulted in maximal inhibition of phosphorylation of EGFR, Akt, and Erk in both PC9 and PC9GR cells. Phosphoproteomic mass spectrometry confirmed highly congruent impacts of exon 16-, 18-, and 21-directed ASOs compared with erlotinib on PC9 genome-wide cell signaling. Furthermore, EGFR-directed ASOs had no impact in EGFR-independent NSCLC models, confirming an EGFR-specific therapeutic mechanism. Further exploration of synergy of ASOs with existing tyrosine kinase inhibitors may offer novel clinical models to improve EGFR-targeted therapies for both mutant and wild-type NSCLC patients.

TCR-Independent Metabolic Reprogramming Precedes Lymphoma-Driven Changes in T-cell Fate.

Chronic T-cell receptor (TCR) signaling in the tumor microenvironment is known to promote T-cell dysfunction. However, we reasoned that poorly immunogenic tumors may also compromise T cells by impairing their metabolism. To address this, we assessed temporal changes in T-cell metabolism, fate, and function in models of B-cell lymphoma driven by Myc, a promoter of energetics and repressor of immunogenicity. Increases in lymphoma burden most significantly impaired CD4+ T-cell function and promoted regulatory T cell (Treg) and Th1-cell differentiation. Metabolomic analyses revealed early reprogramming of CD4+ T-cell metabolism, reduced glucose uptake, and impaired mitochondrial function, which preceded changes in T-cell fate. In contrast, B-cell lymphoma metabolism remained robust during tumor progression. Finally, mitochondrial functions were impaired in CD4+ and CD8+ T cells in lymphoma-transplanted OT-II and OT-I transgenic mice, respectively. These findings support a model, whereby early, TCR-independent, metabolic interactions with developing lymphomas limits T cell-mediated immune surveillance.

Drepmel-A Multi-Omics Melanoma Drug Repurposing Resource for Prioritizing Drug Combinations and Understanding Tumor Microenvironment.

Although substantial progress has been made in treating patients with advanced melanoma with targeted and immuno-therapies, de novo and acquired resistance is commonplace. After treatment failure, therapeutic options are very limited and novel strategies are urgently needed. Combination therapies are often more effective than single agents and are now widely used in clinical practice. Thus, there is a strong need for a comprehensive computational resource to define rational combination therapies. We developed a Shiny app, DRepMel to provide rational combination treatment predictions for melanoma patients from seventy-three thousand combinations based on a multi-omics drug repurposing computational approach using whole exome sequencing and RNA-seq data in bulk samples from two independent patient cohorts. DRepMel provides robust predictions as a resource and also identifies potential treatment effects on the tumor microenvironment (TME) using single-cell RNA-seq data from melanoma patients. Availability: DRepMel is accessible online.

Integrated Proteomics-Based Physical and Functional Mapping of AXL Kinase Signaling Pathways and Inhibitors Define Its Role in Cell Migration.

To better understand the signaling complexity of AXL, a member of the tumor-associated macrophage (TAM) receptor tyrosine kinase family, we created a physical and functional map of AXL signaling interactions, phosphorylation events, and target-engagement of three AXL tyrosine kinase inhibitors (TKI). We assessed AXL protein complexes using proximity-dependent biotinylation (BioID), effects of AXL TKI on global phosphoproteins using mass spectrometry, and target engagement of AXL TKI using activity-based protein profiling. BioID identifies AXL-interacting proteins that are mostly involved in cell adhesion/migration. Global phosphoproteomics show that AXL inhibition decreases phosphorylation of peptides involved in phosphatidylinositol-mediated signaling and cell adhesion/migration. Comparison of three AXL inhibitors reveals that TKI RXDX-106 inhibits pAXL, pAKT, and migration/invasion of these cells without reducing their viability, while bemcentinib exerts AXL-independent phenotypic effects on viability. Proteomic characterization of these TKIs demonstrates that they inhibit diverse targets in addition to AXL, with bemcentinib having the most off-targets. AXL and EGFR TKI cotreatment did not reverse resistance in cell line models of erlotinib resistance. However, a unique vulnerability was identified in one resistant clone, wherein combination of bemcentinib and erlotinib inhibited cell viability and signaling. We also show that AXL is overexpressed in approximately 30% to 40% of nonsmall but rarely in small cell lung cancer. Cell lines have a wide range of AXL expression, with basal activation detected rarely. IMPLICATIONS: Our study defines mechanisms of action of AXL in lung cancers which can be used to establish assays to measure drug targetable active AXL complexes in patient tissues and inform the strategy for targeting it's signaling as an anticancer therapy.

The non-canonical target PARP16 contributes to polypharmacology of the PARP inhibitor talazoparib and its synergy with WEE1 inhibitors.

PARP inhibitors (PARPis) display single-agent anticancer activity in small cell lung cancer (SCLC) and other neuroendocrine tumors independent of BRCA1/2 mutations. Here, we determine the differential efficacy of multiple clinical PARPis in SCLC cells. Compared with the other PARPis rucaparib, olaparib, and niraparib, talazoparib displays the highest potency across SCLC, including SLFN11-negative cells. Chemical proteomics identifies PARP16 as a unique talazoparib target in addition to PARP1. Silencing PARP16 significantly reduces cell survival, particularly in combination with PARP1 inhibition. Drug combination screening reveals talazoparib synergy with the WEE1/PLK1 inhibitor adavosertib. Global phosphoproteomics identifies disparate effects on cell-cycle and DNA damage signaling thereby illustrating underlying mechanisms of synergy, which is more pronounced for talazoparib than olaparib. Notably, silencing PARP16 further reduces cell survival in combination with olaparib and adavosertib. Together, these data suggest that PARP16 contributes to talazoparib's overall mechanism of action and constitutes an actionable target in SCLC.