An Epigenomic fingerprint of human cancers by landscape interrogation of super enhancers at the constituent level.

Super enhancers (SE), large genomic elements that activate transcription and drive cell identity, have been found with cancer-specific gene regulation in human cancers. Recent studies reported the importance of understanding the cooperation and function of SE internal components, i.e., the constituent enhancers (CE). However, there are no pan-cancer studies to identify cancer-specific SE signatures at the constituent level. Here, by revisiting pan-cancer SE activities with H3K27Ac ChIP-seq datasets, we report fingerprint SE signatures for 28 cancer types in the NCI-60 cell panel. We implement a mixture model to discriminate active CEs from inactive CEs by taking into consideration ChIP-seq variabilities between cancer samples and across CEs. We demonstrate that the model-based estimation of CE states provides improved functional interpretation of SE-associated regulation. We identify cancer-specific CEs by balancing their active prevalence with their capability of encoding cancer type identities. We further demonstrate that cancer-specific CEs have the strongest per-base enhancer activities in independent enhancer sequencing assays, suggesting their importance in understanding critical SE signatures. We summarize fingerprint SEs based on the cancer-specific statuses of their component CEs and build an easy-to-use R package to facilitate the query, exploration, and visualization of fingerprint SEs across cancers.

Exploring the Landscape of Immune Checkpoint Inhibitor-Induced Adverse Events Through Big Data Mining of Pan-Cancer Clinical Trials.

PURPOSE: Immune checkpoint inhibitors (ICIs) have significantly improved the survival of patients with cancer and provided long-term durable benefit. However, ICI-treated patients develop a range of toxicities known as immune-related adverse events (irAEs), which could compromise clinical benefits from these treatments. As the incidence and spectrum of irAEs differs across cancer types and ICI agents, it is imperative to characterize the incidence and spectrum of irAEs in a pan-cancer cohort to aid clinical management. DESIGN: We queried >400 000 trials registered at ClinicalTrials.gov and retrieved a comprehensive pan-cancer database of 71 087 ICI-treated participants from 19 cancer types and 7 ICI agents. We performed data harmonization and cleaning of these trial results into 293 harmonized adverse event categories using Medical Dictionary for Regulatory Activities. RESULTS: We developed irAExplorer (https://irae.tanlab.org), an interactive database that focuses on adverse events in patients administered with ICIs from big data mining. irAExplorer encompasses 71 087 distinct clinical trial participants from 343 clinical trials across 19 cancer types with well-annotated ICI treatment regimens and harmonized adverse event categories. We demonstrated a few of the irAE analyses through irAExplorer and highlighted some associations between treatment- or cancer-specific irAEs. CONCLUSION: The irAExplorer is a user-friendly resource that offers exploration, validation, and discovery of treatment- or cancer-specific irAEs across pan-cancer cohorts. We envision that irAExplorer can serve as a valuable resource to cross-validate users' internal datasets to increase the robustness of their findings.

Immune landscape in molecular subtypes of human papillomavirus-negative head and neck cancer.

Head and neck squamous cell carcinomas (HNSCC) remain a poorly understood disease clinically and immunologically. HPV is a known risk factor of HNSCC associated with better outcome, whereas HPV-negative HNSCC are more heterogeneous in outcome. Gene expression signatures have been developed to classify HNSCC into four molecular subtypes (classical, basal, mesenchymal, and atypical). However, the molecular underpinnings of treatment response and the immune landscape for these molecular subtypes are largely unknown. Herein, we described a comprehensive immune landscape analysis in three independent HNSCC cohorts (>700 patients) using transcriptomics data. We assigned the HPV- HNSCC patients into these four molecular subtypes and characterized the tumor microenvironment using deconvolution method. We determined that atypical and mesenchymal subtypes have greater immune enrichment and exhibit a T-cell exhaustion phenotype, compared to classical and basal subtypes. Further analyses revealed different B cell maturation and antibody isotypes enrichment patterns, and distinct immune microenvironment crosstalk in the atypical and mesenchymal subtypes. Taken together, our study suggests that treatments that enhances B cell activity may benefit patients with HNSCC of the atypical subtypes. The rationale can be utilized in the design of future precision immunotherapy trials based on the molecular subtypes of HPV- HNSCC.

Summarizing internal dynamics boosts differential analysis and functional interpretation of super enhancers.

Super enhancers (SEs) are broad enhancer domains usually containing multiple constituent enhancers that hold elevated activities in gene regulation. Disruption in one or more constituent enhancers causes aberrant SE activities that lead to gene dysregulation in diseases. To quantify SE aberrations, differential analysis is performed to compare SE activities between cell conditions. The state-of-art strategy in estimating differential SEs relies on overall activities and neglect the changes in length and structure of SEs. Here, we propose a novel computational method to identify differential SEs by weighting the combinatorial effects of constituent-enhancer activities and locations (i.e. internal dynamics). In addition to overall activity changes, our method identified four novel classes of differential SEs with distinct enhancer structural alterations. We demonstrate that these structure alterations hold distinct regulatory impact, such as regulating different number of genes and modulating gene expression with different strengths, highlighting the differentiated regulatory roles of these unexplored SE features. When compared to the existing method, our method showed improved identification of differential SEs that were linked to better discernment of cell-type-specific SE activity and functional interpretation.

H3K4me3 remodeling induced acquired resistance through O-GlcNAc transferase.

AIMS: Drivers of the drug tolerant proliferative persister (DTPP) state have not been well investigated. Histone H3 lysine-4 trimethylation (H3K4me3), an active histone mark, might enable slow cycling drug tolerant persisters (DTP) to regain proliferative capacity. This study aimed to determine H3K4me3 transcriptionally active sites identifying a key regulator of DTPPs. METHODS: Deploying a model of adaptive cancer drug tolerance, H3K4me3 ChIP-Seq data of DTPPs guided identification of top transcription factor binding motifs. These suggested involvement of O-linked N-acetylglucosamine transferase (OGT), which was confirmed by metabolomics analysis and biochemical assays. OGT impact on DTPPs and adaptive resistance was explored in vitro and in vivo. RESULTS: H3K4me3 remodeling was widespread in CPG island regions and DNA binding motifs associated with O-GlcNAc marked chromatin. Accordingly, we observed an upregulation of OGT, O-GlcNAc and its binding partner TET1 in chronically treated cancer cells. Inhibition of OGT led to loss of H3K4me3 and downregulation of genes contributing to drug resistance. Genetic ablation of OGT prevented acquired drug resistance in in vivo models. Upstream of OGT, we identified AMPK as an actionable target. AMPK activation by acetyl salicylic acid downregulated OGT with similar effects on delaying acquired resistance. CONCLUSION: Our findings uncover a fundamental mechanism of adaptive drug resistance that governs cancer cell reprogramming towards acquired drug resistance, a process that can be exploited to improve response duration and patient outcomes.

Targeting tumor-derived NLRP3 reduces melanoma progression by limiting MDSCs expansion.

Interleukin-1ß (IL-1ß)-mediated inflammation suppresses antitumor immunity, leading to the generation of a tumor-permissive environment, tumor growth, and progression. Here, we demonstrate that nucleotide-binding domain, leucine-rich containing family, pyrin domain-containing-3 (NLRP3) inflammasome activation in melanoma is linked to IL-1ß production, inflammation, and immunosuppression. Analysis of cancer genome datasets (TCGA and GTEx) revealed greater NLRP3 and IL-1ß expression in cutaneous melanoma samples (n = 469) compared to normal skin (n = 324), with a highly significant correlation between NLRP3 and IL-1ß (P < 0.0001). We show the formation of the NLRP3 inflammasome in biopsies of metastatic melanoma using fluorescent resonance energy transfer analysis for NLRP3 and apoptosis-associated speck-like protein containing a CARD. In vivo, tumor-associated NLRP3/IL-1 signaling induced expansion of myeloid-derived suppressor cells (MDSCs), leading to reduced natural killer and CD8+ T cell activity concomitant with an increased presence of regulatory T (Treg) cells in the primary tumors. Either genetic or pharmacological inhibition of tumor-derived NLRP3 by dapansutrile (OLT1177) was sufficient to reduce MDSCs expansion and to enhance antitumor immunity, resulting in reduced tumor growth. Additionally, we observed that the combination of NLRP3 inhibition and anti-PD-1 treatment significantly increased the antitumor efficacy of the monotherapy by limiting MDSC-mediated T cell suppression and tumor progression. These data show that NLRP3 activation in melanoma cells is a protumor mechanism, which induces MDSCs expansion and immune evasion. We conclude that inhibition of NLRP3 can augment the efficacy of anti-PD-1 therapy.

PATH-SURVEYOR: pathway level survival enquiry for immuno-oncology and drug repurposing.

Pathway-level survival analysis offers the opportunity to examine molecular pathways and immune signatures that influence patient outcomes. However, available survival analysis algorithms are limited in pathway-level function and lack a streamlined analytical process. Here we present a comprehensive pathway-level survival analysis suite, PATH-SURVEYOR, which includes a Shiny user interface with extensive features for systematic exploration of pathways and covariates in a Cox proportional-hazard model. Moreover, our framework offers an integrative strategy for performing Hazard Ratio ranked Gene Set Enrichment Analysis and pathway clustering. As an example, we applied our tool in a combined cohort of melanoma patients treated with checkpoint inhibition (ICI) and identified several immune populations and biomarkers predictive of ICI efficacy. We also analyzed gene expression data of pediatric acute myeloid leukemia (AML) and performed an inverse association of drug targets with the patient's clinical endpoint. Our analysis derived several drug targets in high-risk KMT2A-fusion-positive patients, which were then validated in AML cell lines in the Genomics of Drug Sensitivity database. Altogether, the tool offers a comprehensive suite for pathway-level survival analysis and a user interface for exploring drug targets, molecular features, and immune populations at different resolutions.

Systematic evaluation of the predictive gene expression signatures of immune checkpoint inhibitors in metastatic melanoma.

Advances in immunotherapy, including immune checkpoint inhibitors (ICIs), have transformed the standard of care for many types of cancer including melanoma. ICIs have improved the overall outcome of melanoma patients; however, a significant proportion of patients suffer from primary or secondary tumor resistance. Therefore, there is an urgent need to develop predictive biomarkers to better select patients for ICI therapy. Numerous biomarkers that predict the response of melanoma to ICIs have been investigated, including biomarker signatures based on genomics or transcriptomics. Most of these predictive biomarkers have not been systematically evaluated across different cohorts to determine the reproducibility of these signatures in metastatic melanoma. We evaluated 28 previously published predictive biomarkers of ICIs based on gene expression signatures in eight previously published studies with available RNA-sequencing data in public repositories. We found that signatures related to IFN-γ-responsive genes, T and B cell markers, and chemokines in the tumor immune microenvironment are generally predictive of response to ICIs in these patients. In addition, we identified that these predictive biomarkers have higher predictive values in on-treatment samples as compared to pretreatment samples in metastatic melanoma. The most frequently overlapping genes among the top 18 predictive signatures were CXCL10, CXCL9, PRF1, RANTES, IFNG, HLA-DRA, GZMB, and CD8A. From gene set enrichment analysis and cell type deconvolution, we estimated that the tumors of responders were enriched with infiltrating cytotoxic T-cells and other immune cells and the upregulation of genes related to interferon-γ signaling. Conversely, the tumors of non-responders were enriched with stromal-related cell types such as fibroblasts and myofibroblasts, as well as enrichment with T helper 17 cell types across all cohorts. In summary, our approach of validating and integrating multi-omics data can help guide future biomarker development in the field of ICIs and serve the quest for a more personalized therapeutic approach for melanoma patients.

Translational relevance of SOS1 targeting for KRAS-mutant colorectal cancer.

It has been challenging to target mutant KRAS (mKRAS) in colorectal cancer (CRC) and other malignancies. Recent efforts have focused on developing inhibitors blocking molecules essential for KRAS activity. In this regard, SOS1 inhibition has arisen as an attractive approach for mKRAS CRC given its essential role as a guanine nucleotide exchange factor for this GTPase. Here, we demonstrated the translational value of SOS1 blockade in mKRAS CRC. We used CRC patient-derived organoids (PDOs) as preclinical models to evaluate their sensitivity to SOS1 inhibitor BI3406. A combination of in silico analyses and wet lab techniques was utilized to define potential predictive markers for SOS1 sensitivity and potential mechanisms of resistance in CRC. RNA-seq analysis of CRC PDOs revealed two groups of CRC PDOs with differential sensitivities to SOS1 inhibitor BI3406. The resistant group was enriched in gene sets involving cholesterol homeostasis, epithelial-mesenchymal transition, and TNF-α/NFκB signaling. Expression analysis identified a significant correlation between SOS1 and SOS2 mRNA levels (Spearman's ρ 0.56, p < 0.001). SOS1/2 protein expression was universally present with heterogeneous patterns in CRC cells but only minimal to none in surrounding nonmalignant cells. Only SOS1 protein expression was associated with worse survival in patients with RAS/RAF mutant CRC (p = 0.04). We also found that SOS1/SOS2 protein expression ratio >1 by immunohistochemistry (p = 0.03) instead of KRAS mutation (p = 1) was a better predictive marker to BI3406 sensitivity of CRC PDOs, concordant with the significant positive correlation between SOS1/SOS2 protein expression ratio and SOS1 dependency. Finally, we showed that GTP-bound RAS level underwent rebound even in BI3406-sensitive PDOs with no change of KRAS downstream effector genes, thus suggesting upregulation of guanine nucleotide exchange factor as potential cellular adaptation mechanisms to SOS1 inhibition. Taken together, our results show that high SOS1/SOS2 protein expression ratio predicts sensitivity to SOS1 inhibition and support further clinical development of SOS1-targeting agents in CRC.

PARP1 and POLD2 as prognostic biomarkers for multiple myeloma in autologous stem cell transplant.

Multiple Myeloma (MM) is an incurable plasma cell malignancy often treated by autologous stem cell transplant (ASCT). Clinical response to ASCT has been associated with DNA repair efficiency. Here we interrogated the role of the base excision DNA repair (BER) pathway in MM response to ASCT. Across 450 clinical samples and six disease stages, expression levels of genes in the BER pathway were found to be highly upregulated during the development of MM. In a separate cohort of 559 patients with MM treated with ASCT, expression of BER pathway members MPG and PARP3 was positively associated with overall survival (OS) while expression of PARP1, POLD1, and POLD2 was negatively associated with OS. In a validation cohort of 356 patients with MM treated with ASCT, PARP1 and POLD2 findings were replicated. In patients with MM who never received ASCT (n=319), PARP1 and POLD2 were not associated with OS, suggesting that the prognostic effect of these genes may be treatment-dependent. In preclinical models of MM, synergy was observed in anti-tumor activity when poly (ADPribose) polymerase (PARP) inhibitors (olaparib, talazoparib) were used in combination with melphalan. The negative prognosis associated with PARP1 and POLD2 expression along with the apparent melphalan-sensitizing effect of PARP inhibition may suggest this pathway as a potential biomarker in patients with MM in the setting of ASCT. Further understanding of the role of the BER pathway in MM is vital to improve therapeutic strategies related to ASCT.

Neoadjuvant endocrine therapy expands stromal populations that predict poor prognosis in estrogen receptor-positive breast cancer.

The tumor microenvironment (TME) is an important modulator of response and resistance to endocrine therapy in estrogen receptor alpha (ER) positive breast cancer. Endocrine therapy is highly effective at reducing tumor burden and preventing recurrence in most estrogen receptor alpha (ER) positive breast cancers. Existing drugs work either directly by targeting tumor-cell ER or indirectly by inhibiting estrogen production in stromal cells with aromatase inhibitors (AI). However, many stromal cells also express ER and the direct impact of endocrine therapies on ER + stromal cells remain unclear. In this study, we investigated how neoadjuvant endocrine therapy (NET) directly effects stromal cells by measuring changes in stomal components of the TME that favor tumor progression. We previously defined two major subsets of tumor-associated stromal cells (TASCs): CD146 positive/CDCP1 negative (TASCCD146 ), CD146 negative/CDCP1 positive (TASCCDCP1 ), and generated a differentially expressed genes list associated with each type. Here, we applied the TASC gene list for classification and an algorithm that estimates immune cell abundance (TIMEx) to METABRIC transcriptomic data for ER + breast cancer patients coupled with multiplex imaging and analysis of paired tissue samples pre- and post- NET with the AI exemestane. TASCCDCP1 composition predicted for decreased patient survival in the METABRIC cohort. Exemestane treatment significantly increased expression of TASCCDCP1 and decreased expression of TASCCD146 . The posttreatment shift toward TASCCDCP1 composition correlated with increased macrophage infiltration and increased CD8+ T-cell, B cell, and general stromal components. The effectiveness of NET is currently based solely on the reduction of ER+ breast cancer cells. Here, we show NET displays clear TME effects that promote the expansion of the less favorable TASCCDCP1 population which are correlated with TME remodeling and reshaping immune infiltration supportive of tumor progression. Our findings highlight the need to further understand the role of endocrine therapy on TME remodeling, tumor progression, and patient outcomes.

TIMEx: tumor-immune microenvironment deconvolution web-portal for bulk transcriptomics using pan-cancer scRNA-seq signatures.

SUMMARY: The heterogeneous cell types of the tumor-immune microenvironment (TIME) play key roles in determining cancer progression, metastasis and response to treatment. We report the development of TIMEx, a novel TIME deconvolution method emphasizing on estimating infiltrating immune cells for bulk transcriptomics using pan-cancer single-cell RNA-seq signatures. We also implemented a comprehensive, user-friendly web-portal for users to evaluate TIMEx and other deconvolution methods with bulk transcriptomic profiles. AVAILABILITY AND IMPLEMENTATION: TIMEx web-portal is freely accessible at http://timex.moffitt.org. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Enhanced immune activation within the tumor microenvironment and circulation of female high-risk melanoma patients and improved survival with adjuvant CTLA4 blockade compared to males.

BACKGROUND: We hypothesized that a gender difference in clinical response may exist to adjuvant CTLA4 blockade with ipilimumab versus high-dose IFNα (HDI). We investigated differences in candidate immune biomarkers in the circulation and tumor microenvironment (TME). PATIENTS AND METHODS: This gender-based analysis was nested within the E1609 trial that tested adjuvant therapy with ipilimumab 3 mg/kg (ipi3) and 10 mg/kg (ipi10) versus HDI in high risk resected melanoma. We investigated gender differences in treatment efficacy with ipi3 and ipi10 versus HDI while adjusting for age, stage, ECOG performance (PS), ulceration, primary tumor status and lymph node number. Forest plots were created to compare overall survival (OS) and relapse free survival (RFS) between ipi and HDI. Gene expression profiling (GEP) was performed on tumors of 718 (454 male, 264 female) patients. Similarly, serum and peripheral blood mononuclear cells (PBMC) samples were tested for soluble and cellular biomarkers (N = 321 patients; 109 female and 212 male). RESULTS: The subgroups of female, stage IIIC, PS = 1, ulcerated primary, in-transit metastasis demonstrated significant improvement in RFS and/or OS with ipi3 versus HDI. Female gender was significant for both OS and RFS and was further explored. In the RFS comparison, a multivariate Cox regression model including significant variables indicated a significant interaction between gender and treatment (P = 0.024). In peripheral blood, percentages of CD3+ T cells (P = 0.024) and CD3+ CD4+ helper T cells (P = 0.0001) were higher in females compared to males. Trends toward higher circulating levels of IL1ß (P = 0.07) and IL6 (P = 0.06) were also found in females. Males had higher percentages of monocytes (P = 0.03) with trends toward higher percentages of regulatory T cells (T-reg). Tumor GEP analysis supported enhanced infiltration with immune cells including gammadelta T cells (P = 0.005), NK cells (P = 0.01), dendritic cells (P = 0.01), CD4+ T cells (P = 0.03), CD8+ T cells (P = 0.03) and T-reg (P = 0.008) in the tumors of females compared to males and a higher T-effector and IFNγ gene signature score (P = 0.0244). CONCLUSION: Female gender was associated with adjuvant CTLA4 blockade clinical benefits and female patients were more likely to have evidence of type1 immune activation within the TME and the circulation. Trial registration ClinicalTrials.gov NCT01274338. Registered 11 January 2011, https://www. CLINICALTRIALS: gov/ct2/show/NCT01274338.

Crowdsourced identification of multi-target kinase inhibitors for RET- and TAU- based disease: The Multi-Targeting Drug DREAM Challenge.

A continuing challenge in modern medicine is the identification of safer and more efficacious drugs. Precision therapeutics, which have one molecular target, have been long promised to be safer and more effective than traditional therapies. This approach has proven to be challenging for multiple reasons including lack of efficacy, rapidly acquired drug resistance, and narrow patient eligibility criteria. An alternative approach is the development of drugs that address the overall disease network by targeting multiple biological targets ('polypharmacology'). Rational development of these molecules will require improved methods for predicting single chemical structures that target multiple drug targets. To address this need, we developed the Multi-Targeting Drug DREAM Challenge, in which we challenged participants to predict single chemical entities that target pro-targets but avoid anti-targets for two unrelated diseases: RET-based tumors and a common form of inherited Tauopathy. Here, we report the results of this DREAM Challenge and the development of two neural network-based machine learning approaches that were applied to the challenge of rational polypharmacology. Together, these platforms provide a potentially useful first step towards developing lead therapeutic compounds that address disease complexity through rational polypharmacology.

The mutational landscape of mucosal melanoma.

Mucosal melanoma is a rare and aggressive subtype of melanoma that has a less favorable prognosis due to the lack of understanding and identification of oncogenic drivers. Recently, whole genome and whole exome sequencing have unveiled the molecular landscape and potential oncogenic drivers of mucosal melanoma, which remains distinct from cutaneous melanoma. In this review, we provide an overview of the genomic landscape of mucosal melanoma, with a focus on molecular studies identifying potential oncogenic drivers allowing for a better mechanistic understanding of the biology of mucosal melanoma. We summarized the published genomics and clinical data supporting the observations that mucosal melanoma harbors distinct genetic alterations and oncogenic drivers from cutaneous melanoma, and thus should be treated accordingly. The common drivers (BRAF and NRAS) found in cutaneous melanoma have lower mutation rate in mucosal melanoma. In contrast, SF3B1 and KIT have higher mutation rate in mucosal melanoma as compared to cutaneous melanoma. From the meta-analysis, we also observed that the mutational profiles are slightly different between the "upper" and "lower" regions of mucosal melanoma, providing new insights and therapeutic options for the mucosal melanoma patients. Mutations identified in mucosal melanoma should be incorporated into routine clinical testing, as there are targeted therapies already developed for treating patients with these mutations in the precision medicine era.

Novel Human-Derived RET Fusion NSCLC Cell Lines Have Heterogeneous Responses to RET Inhibitors and Differential Regulation of Downstream Signaling.

Rearranged during transfection (RET) rearrangements occur in 1% to 2% of lung adenocarcinomas as well as other malignancies and are now established targets for tyrosine kinase inhibitors. We developed three novel RET fusion-positive (RET+) patient-derived cancer cell lines, CUTO22 [kinesin 5B (KIF5B)-RET fusion], CUTO32 (KIF5B-RET fusion), and CUTO42 (echinoderm microtubule-associated protein-like 4-RET fusion), to study RET signaling and response to therapy. We confirmed each of our cell lines expresses the RET fusion protein and assessed their sensitivity to RET inhibitors. We found that the CUTO22 and CUTO42 cell lines were sensitive to multiple RET inhibitors, whereas the CUTO32 cell line was >10-fold more resistant to three RET inhibitors. We discovered that our RET+ cell lines had differential regulation of the mitogen-activated protein kinase and phosphoinositide 3-kinase/protein kinase B (AKT) pathways. After inhibition of RET, the CUTO42 cells had robust inhibition of phosphorylated AKT (pAKT), whereas CUTO22 and CUTO32 cells had sustained AKT activation. Next, we performed a drug screen, which revealed that the CUTO32 cells were sensitive (<1 nM IC50) to inhibition of two cell cycle-regulating proteins, polo-like kinase 1 and Aurora kinase A. Finally, we show that two of these cell lines, CUTO32 and CUTO42, successfully establish xenografted tumors in nude mice. We demonstrated that the RET inhibitor BLU-667 was effective at inhibiting tumor growth in CUTO42 tumors but had a much less profound effect in CUTO32 tumors, consistent with our in vitro experiments. These data highlight the utility of new RET+ models to elucidate differences in response to tyrosine kinase inhibitors and downstream signaling regulation. Our RET+ cell lines effectively recapitulate the interpatient heterogeneity observed in response to RET inhibitors and reveal opportunities for alternative or combination therapies. SIGNIFICANCE STATEMENT: We have derived and characterized three novel rearranged during transfection (RET) fusion non-small cell lung cancer cell lines and demonstrated that they have differential responses to RET inhibition as well as regulation of downstream signaling, an area that has previously been limited by a lack of diverse cell line modes with endogenous RET fusions. These data offer important insight into regulation of response to RET tyrosine kinase inhibitors and other potential therapeutic targets.

Effects of checkpoint kinase 1 inhibition by prexasertib on the tumor immune microenvironment of head and neck squamous cell carcinoma.

Prognosis for patients with recurrent and/or metastatic head and neck squamous cell carcinoma (HNSCC) remains poor. Development of more effective and less toxic targeted therapies is necessary for HNSCC patients. Checkpoint kinase 1 (CHK1) plays a vital role in cell cycle regulation and is a promising therapeutic target in HNSCC. Prexasertib, a CHK1 inhibitor, induces DNA damage and cell death, however, its effect on the tumor immune microenvironment (TIME) is largely unknown. Therefore, we evaluated a short-term and long-term effects of prexasertib in HNSCC and its TIME. Prexasertib caused increased DNA damage and cell death in vitro and significant tumor regression and improved survival in vivo. The gene expression and multiplex immunohistochemistry (mIHC) analyses of the in vivo tumors demonstrated increased expression of genes that are related to T-cell activation and increased immune cell trafficking, and decreased expression of genes that related to immunosuppression. However, increased expression of genes related to immunosuppression emerged over time suggesting evasion of immune surveillances. These findings in gene expression analyses were confirmed using mIHC which showed differential modulation of TIME in the tumor margins and as well as cores over time. These results suggest that evasion of immune surveillance, at least in part, may contribute to the acquired resistance to prexasertib in HNSCC.

Loss of prdm1a accelerates melanoma onset and progression.

Melanoma is an aggressive, deadly skin cancer derived from melanocytes, a neural crest cell derivative. Melanoma cells mirror the developmental program of neural crest cells in that they exhibit the same gene expression patterns and utilize similar cellular mechanisms, including increased cell proliferation, epithelial-mesenchymal transition, and migration. Here we studied the role of neural crest regulator PRDM1 in melanoma onset and progression. In development, Prdm1a functions to promote neural crest progenitor fate, and in melanoma, we found that PRDM1 has reduced copy number and is recurrently deleted in both zebrafish and humans. When examining expression of neural crest and melanocyte development genes, we show that sox10 progenitor expression is high in prdm1a-/- mutants, while more differentiated melanocyte markers are reduced, suggesting that normally Prdm1a is required for differentiation. Data mining of human melanoma datasets indicates that high PRDM1 expression in human melanoma is correlated with better patient survival and decreased PRDM1 expression is common in metastatic tumors. When one copy of prdm1a is lost in the zebrafish melanoma model Tg(mitfa:BRAFV600E );p53-/- ;prdm1a+/- , melanoma onset occurs more quickly, and the tumors that form have a larger area with increased expression of sox10. These data demonstrate a novel role for PRDM1 as a tumor suppressor in melanoma.

ReSimNet: drug response similarity prediction using Siamese neural networks.

MOTIVATION: Traditional drug discovery approaches identify a target for a disease and find a compound that binds to the target. In this approach, structures of compounds are considered as the most important features because it is assumed that similar structures will bind to the same target. Therefore, structural analogs of the drugs that bind to the target are selected as drug candidates. However, even though compounds are not structural analogs, they may achieve the desired response. A new drug discovery method based on drug response, which can complement the structure-based methods, is needed. RESULTS: We implemented Siamese neural networks called ReSimNet that take as input two chemical compounds and predicts the CMap score of the two compounds, which we use to measure the transcriptional response similarity of the two compounds. ReSimNet learns the embedding vector of a chemical compound in a transcriptional response space. ReSimNet is trained to minimize the difference between the cosine similarity of the embedding vectors of the two compounds and the CMap score of the two compounds. ReSimNet can find pairs of compounds that are similar in response even though they may have dissimilar structures. In our quantitative evaluation, ReSimNet outperformed the baseline machine learning models. The ReSimNet ensemble model achieves a Pearson correlation of 0.518 and a precision@1% of 0.989. In addition, in the qualitative analysis, we tested ReSimNet on the ZINC15 database and showed that ReSimNet successfully identifies chemical compounds that are relevant to a prototype drug whose mechanism of action is known. AVAILABILITY AND IMPLEMENTATION: The source code and the pre-trained weights of ReSimNet are available at https://github.com/dmis-lab/ReSimNet. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

PAGER 2.0: an update to the pathway, annotated-list and gene-signature electronic repository for Human Network Biology.

Integrative Gene-set, Network and Pathway Analysis (GNPA) is a powerful data analysis approach developed to help interpret high-throughput omics data. In PAGER 1.0, we demonstrated that researchers can gain unbiased and reproducible biological insights with the introduction of PAGs (Pathways, Annotated-lists and Gene-signatures) as the basic data representation elements. In PAGER 2.0, we improve the utility of integrative GNPA by significantly expanding the coverage of PAGs and PAG-to-PAG relationships in the database, defining a new metric to quantify PAG data qualities, and developing new software features to simplify online integrative GNPA. Specifically, we included 84 282 PAGs spanning 24 different data sources that cover human diseases, published gene-expression signatures, drug-gene, miRNA-gene interactions, pathways and tissue-specific gene expressions. We introduced a new normalized Cohesion Coefficient (nCoCo) score to assess the biological relevance of genes inside a PAG, and RP-score to rank genes and assign gene-specific weights inside a PAG. The companion web interface contains numerous features to help users query and navigate the database content. The database content can be freely downloaded and is compatible with third-party Gene Set Enrichment Analysis tools. We expect PAGER 2.0 to become a major resource in integrative GNPA. PAGER 2.0 is available at http://discovery.informatics.uab.edu/PAGER/.