Paradoxical cancer cell proliferation after FGFR inhibition through decreased p21 signaling in FGFR1-amplified breast cancer cells.

Fibroblast growth factors (FGFs) control various cellular functions through fibroblast growth factor receptor (FGFR) activation, including proliferation, differentiation, migration, and survival. FGFR amplification in ER + breast cancer patients correlate with poor prognosis, and FGFR inhibitors are currently being tested in clinical trials. By comparing three-dimensional spheroid growth of ER + breast cancer cells with and without FGFR1 amplification, our research discovered that FGF2 treatment can paradoxically decrease proliferation in cells with FGFR1 amplification or overexpression. In contrast, FGF2 treatment in cells without FGFR1 amplification promotes classical FGFR proliferative signaling through the MAPK cascade. The growth inhibitory effect of FGF2 in FGFR1 amplified cells aligned with an increase in p21, a cell cycle inhibitor that hinders the G1 to S phase transition in the cell cycle. Additionally, FGF2 addition in FGFR1 amplified cells activated JAK-STAT signaling and promoted a stem cell-like state. FGF2-induced paradoxical effects were reversed by inhibiting p21 or the JAK-STAT pathway and with pan-FGFR inhibitors. Analysis of patient ER + breast tumor transcriptomes from the TCGA and METABRIC datasets demonstrated a strong positive association between expression of FGF2 and stemness signatures, which was further enhanced in tumors with high FGFR1 expression. Overall, our findings reveal a divergence in FGFR signaling, transitioning from a proliferative to stemness state driven by activation of JAK-STAT signaling and modulation of p21 levels. Activation of these divergent signaling pathways in FGFR amplified cancer cells and paradoxical growth effects highlight a challenge in the use of FGFR inhibitors in cancer treatment.

ENDORSE: a prognostic model for endocrine therapy in estrogen-receptor-positive breast cancers.

Advanced and metastatic estrogen receptor-positive (ER+ ) breast cancers are often endocrine resistant. However, endocrine therapy remains the primary treatment for all advanced ER+ breast cancers. Treatment options that may benefit resistant cancers, such as add-on drugs that target resistance pathways or switching to chemotherapy, are only available after progression on endocrine therapy. Here we developed an endocrine therapy prognostic model for early and advanced ER+ breast cancers. The endocrine resistance (ENDORSE) model is composed of two components, each based on the empirical cumulative distribution function of ranked expression of gene signatures. These signatures include a feature set associated with long-term survival outcomes on endocrine therapy selected using lasso-regularized Cox regression and a pathway-based curated set of genes expressed in response to estrogen. We extensively validated ENDORSE in multiple ER+ clinical trial datasets and demonstrated superior and consistent performance of the model over clinical covariates, proliferation markers, and multiple published signatures. Finally, genomic and pathway analyses in patient data revealed possible mechanisms that may help develop rational stratification strategies for endocrine-resistant ER+ breast cancer patients.

Long non-coding RNA transcriptome of uncharacterized samples can be accurately imputed using protein-coding genes.

Long non-coding RNAs (lncRNAs) play an important role in gene regulation and are increasingly being recognized as crucial mediators of disease pathogenesis. However, the vast majority of published transcriptome datasets lack high-quality lncRNA profiles compared to protein-coding genes (PCGs). Here we propose a framework to harnesses the correlative expression patterns between lncRNA and PCGs to impute unknown lncRNA profiles. The lncRNA expression imputation (LEXI) framework enables characterization of lncRNA transcriptome of samples lacking any lncRNA data using only their PCG profiles. We compare various machine learning and missing value imputation algorithms to implement LEXI and demonstrate the feasibility of this approach to impute lncRNA transcriptome of normal and cancer tissues. Additionally, we determine the factors that influence imputation accuracy and provide guidelines for implementing this approach.

Discovering long noncoding RNA predictors of anticancer drug sensitivity beyond protein-coding genes.

Large-scale cancer cell line screens have identified thousands of protein-coding genes (PCGs) as biomarkers of anticancer drug response. However, systematic evaluation of long noncoding RNAs (lncRNAs) as pharmacogenomic biomarkers has so far proven challenging. Here, we study the contribution of lncRNAs as drug response predictors beyond spurious associations driven by correlations with proximal PCGs, tissue lineage, or established biomarkers. We show that, as a whole, the lncRNA transcriptome is equally potent as the PCG transcriptome at predicting response to hundreds of anticancer drugs. Analysis of individual lncRNAs transcripts associated with drug response reveals nearly half of the significant associations are in fact attributable to proximal cis-PCGs. However, adjusting for effects of cis-PCGs revealed significant lncRNAs that augment drug response predictions for most drugs, including those with well-established clinical biomarkers. In addition, we identify lncRNA-specific somatic alterations associated with drug response by adopting a statistical approach to determine lncRNAs carrying somatic mutations that undergo positive selection in cancer cells. Lastly, we experimentally demonstrate that 2 lncRNAs, EGFR-AS1 and MIR205HG, are functionally relevant predictors of anti-epidermal growth factor receptor (EGFR) drug response.

iMIRAGE: an R package to impute microRNA expression using protein-coding genes.

SUMMARY: MicroRNAs (miRNAs) are critical post-transcriptional regulators of gene expression. Due to challenges in accurate profiling of small RNAs, a vast majority of public transcriptome datasets lack reliable miRNA profiles. However, the biological consequence of miRNA activity in the form of altered protein-coding gene (PCG) expression can be captured using machine-learning algorithms. Here, we present iMIRAGE (imputed miRNA activity from gene expression), a convenient tool to predict miRNA expression using PCG expression of the test datasets. The iMIRAGE package provides an integrated workflow for normalization and transformation of miRNA and PCG expression data, along with the option to utilize predicted miRNA targets to impute miRNA activity from independent test PCG datasets. AVAILABILITY AND IMPLEMENTATION: The iMIRAGE package for R, along with package documentation and vignette, is available at https://aritronath.github.io/iMIRAGE/index.html. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Long Non-Coding RNA ANRIL as a Potential Biomarker of Chemosensitivity and Clinical Outcomes in Osteosarcoma.

Osteosarcoma has a poor prognosis due to chemo-resistance and/or metastases. Increasing evidence shows that long non-coding RNAs (lncRNAs) can play an important role in drug sensitivity and cancer metastasis. Using osteosarcoma cell lines, we identified a positive correlation between the expression of a lncRNA and ANRIL, and resistance to two of the three standard-of-care agents for treating osteosarcoma-cisplatin and doxorubicin. To confirm the potential role of ANRIL in chemosensitivity, we independently inhibited and over-expressed ANRIL in osteosarcoma cell lines followed by treatment with either cisplatin or doxorubicin. Knocking-down ANRIL in SAOS2 resulted in a significant increase in cellular sensitivity to both cisplatin and doxorubicin, while the over-expression of ANRIL in both HOS and U2OS cells led to an increased resistance to both agents. To investigate the clinical significance of ANRIL in osteosarcoma, we assessed ANRIL expression in relation to clinical phenotypes using the osteosarcoma data from the Therapeutically Applicable Research to Generate Effective Treatments (TARGET) dataset. Higher ANRIL expression was significantly associated with increased rates of metastases at diagnosis and death and was a significant predictor of reduced overall survival rate. Collectively, our results suggest that the lncRNA ANRIL can be a chemosensitivity and prognosis biomarker in osteosarcoma. Furthermore, reducing ANRIL expression may be a therapeutic strategy to overcome current standard-of-care treatment resistance.

Discovering novel pharmacogenomic biomarkers by imputing drug response in cancer patients from large genomics studies.

Obtaining accurate drug response data in large cohorts of cancer patients is very challenging; thus, most cancer pharmacogenomics discovery is conducted in preclinical studies, typically using cell lines and mouse models. However, these platforms suffer from serious limitations, including small sample sizes. Here, we have developed a novel computational method that allows us to impute drug response in very large clinical cancer genomics data sets, such as The Cancer Genome Atlas (TCGA). The approach works by creating statistical models relating gene expression to drug response in large panels of cancer cell lines and applying these models to tumor gene expression data in the clinical data sets (e.g., TCGA). This yields an imputed drug response for every drug in each patient. These imputed drug response data are then associated with somatic genetic variants measured in the clinical cohort, such as copy number changes or mutations in protein coding genes. These analyses recapitulated drug associations for known clinically actionable somatic genetic alterations and identified new predictive biomarkers for existing drugs.

Clinical evaluation of germline polymorphisms associated with capecitabine toxicity in breast cancer: TBCRC-015.

PURPOSE: Capecitabine is important in breast cancer treatment but causes diarrhea and hand-foot syndrome (HFS), affecting adherence and quality of life. We sought to identify pharmacogenomic predictors of capecitabine toxicity using a novel monitoring tool. METHODS: Patients with metastatic breast cancer were prospectively treated with capecitabine (2000 mg/m2/day, 14 days on/7 off). Patients completed in-person toxicity questionnaires (day 1/cycle) and automated phone-in assessments (days 8, 15). Correlation of genotypes with early and overall toxicity was the primary endpoint. RESULTS: Two hundred and fifty-nine patients were enrolled (14 institutions). Diarrhea and HFS occurred in 52% (17% grade 3) and 69% (9% grade 3), respectively. Only 29% of patients completed four cycles without dose reduction/interruption. In 39%, the highest toxicity grade was captured via phone. Three single nucleotide polymorphisms (SNPs) associated with diarrhea-DPYD*5 (odds ratio [OR] 4.9; P = 0.0005), a MTHFR missense SNP (OR 3.3; P = 0.02), and a SNP upstream of MTRR (OR 3.0; P = 0.03). GWAS elucidated a novel HFS SNP (OR 3.0; P = 0.0007) near TNFSF4 (OX40L), a gene implicated in autoimmunity including autoimmune skin diseases never before implicated in HFS. Genotype-gene expression analyses of skin tissues identified rs11158568 (associated with HFS via GWAS) with expression of CHURC1, a transcriptional activator controlling fibroblast growth factor (beta = - 0.74; P = 1.46 × 10-23), representing a previously unidentified mechanism for HFS. CONCLUSIONS: This is the first cancer pharmacogenomic study to use phone-in self-reporting, permitting augmented toxicity characterization. Three germline toxicity SNPs were replicated, and several novel SNPs/genes having strong functional relevance were discovered. If further validated, these markers could permit personalized capecitabine dosing.

Exploiting collateral sensitivity controls growth of mixed culture of sensitive and resistant cells and decreases selection for resistant cells in a cell line model.

BACKGROUND: CDK4/6 inhibitors such as ribociclib are becoming widely used targeted therapies in hormone-receptor-positive (HR+) human epidermal growth factor receptor 2-negative (HER2-) breast cancer. However, cancers can advance due to drug resistance, a problem in which tumor heterogeneity and evolution are key features. METHODS: Ribociclib-resistant HR+/HER2- CAMA-1 breast cancer cells were generated through long-term ribociclib treatment. Characterization of sensitive and resistant cells were performed using RNA sequencing and whole exome sequencing. Lentiviral labeling with different fluorescent proteins enabled us to track the proliferation of sensitive and resistant cells under different treatments in a heterogeneous, 3D spheroid coculture system using imaging microscopy and flow cytometry. RESULTS: Transcriptional profiling of sensitive and resistant cells revealed the downregulation of the G2/M checkpoint in the resistant cells. Exploiting this acquired vulnerability; resistant cells exhibited collateral sensitivity for the Wee-1 inhibitor, adavosertib (AZD1775). The combination of ribociclib and adavosertib achieved additional antiproliferative effect exclusively in the cocultures compared to monocultures, while decreasing the selection for resistant cells. CONCLUSIONS: Our results suggest that optimal antiproliferative effects in heterogeneous cancers can be achieved via an integrative therapeutic approach targeting sensitive and resistant cancer cell populations within a tumor, respectively.

Rapid fine conformational epitope mapping using comprehensive mutagenesis and deep sequencing.

Knowledge of the fine location of neutralizing and non-neutralizing epitopes on human pathogens affords a better understanding of the structural basis of antibody efficacy, which will expedite rational design of vaccines, prophylactics, and therapeutics. However, full utilization of the wealth of information from single cell techniques and antibody repertoire sequencing awaits the development of a high throughput, inexpensive method to map the conformational epitopes for antibody-antigen interactions. Here we show such an approach that combines comprehensive mutagenesis, cell surface display, and DNA deep sequencing. We develop analytical equations to identify epitope positions and show the method effectiveness by mapping the fine epitope for different antibodies targeting TNF, pertussis toxin, and the cancer target TROP2. In all three cases, the experimentally determined conformational epitope was consistent with previous experimental datasets, confirming the reliability of the experimental pipeline. Once the comprehensive library is generated, fine conformational epitope maps can be prepared at a rate of four per day.

Characterization of transcription factor response kinetics in parallel.

BACKGROUND: Transcription factors (TFs) are effectors of cell signaling pathways that regulate gene expression. TF networks are highly interconnected; one signal can lead to changes in many TF levels, and one TF level can be changed by many different signals. TF regulation is central to normal cell function, with altered TF function being implicated in many disease conditions. Thus, measuring TF levels in parallel, and over time, is crucial for understanding the impact of stimuli on regulatory networks and on diseases. RESULTS: Here, we report the parallel analysis of temporal TF level changes due to multiple stimuli in distinct cell types. We have analyzed short-term dynamic changes in the levels of nuclear factor kappa-light-chain-enhancer of activated B cells (NF-kB), signal transducer and activator of transcription 3 (Stat3), cAMP response element-binding protein (CREB), glucocorticoid receptor (GR), and TATA binding protein (TBP), in breast and liver cancer cells after tumor necrosis factor-alpha (TNF-α) and palmitic acid (PA) exposure. In response to both stimuli, NF-kB and CREB levels were increased, Stat3 decreased, and TBP was constant. GR levels were unchanged in response to TNF-α stimulation and increased in response to PA treatment. CONCLUSIONS: Our results show significant overlap in signaling initiated by TNF-α and by PA, with the exception that the events leading to PA-mediated cytotoxicity likely also include induction of GR signaling. These results further illuminate the dynamics of TF responses to cytokine and fatty acid exposure, while concomitantly demonstrating the utility of parallel TF measurement approaches in the analysis of biological phenomena.

ONC201/TIC10 enhances durability of mTOR inhibitor everolimus in metastatic ER+ breast cancer.

The mTOR inhibitor, everolimus, is an important clinical management component of metastatic ER+ breast cancer (BC). However, most patients develop resistance and progress on therapy, highlighting the need to discover strategies that increase mTOR inhibitor effectiveness. We developed ER+ BC cell lines, sensitive or resistant to everolimus, and discovered that combination treatment of ONC201/TIC10 with everolimus inhibited cell growth in 2D/3D in vitro studies. We confirmed increased therapeutic response in primary patient cells progressing on everolimus, supporting clinical relevance. We show that ONC201/TIC10 mechanism in metastatic ER+ BC cells involves oxidative phosphorylation inhibition and stress response activation. Transcriptomic analysis in everolimus resistant breast patient tumors and mitochondrial functional assays in resistant cell lines demonstrated increased mitochondrial respiration dependency, contributing to ONC201/TIC10 sensitivity. We propose that ONC201/TIC10 and modulation of mitochondrial function may provide an effective add-on therapy strategy for patients with metastatic ER+ BCs resistant to mTOR inhibitors.

Breast cancer is one of the most frequently diagnosed cancers globally, particularly among women. The most common type of breast cancer expresses a receptor for the hormone estrogen. Many treatments block the activity of estrogen and therefore slow or block the development and spread of this type of breast cancer. For patients with advanced breast cancer, hormone-blocking treatments work best in combination with other drugs, including one called everolimus. However, in many patients the cancer cells become resistant to these therapies, leading to disease progression and decreased survival. To explore treatment strategies that could enhance the effectiveness of existing therapies for breast cancer, Farmaki et al. studied how cancer cells which had become resistant to everolimus responded when treated with an experimental drug called ONC201/TIC10. A combination of everolimus and ONC201/TIC10 inhibited growth of resistant cancer cells that had been grown in a three-dimensional arrangement to mimic human tumors. Moreover, the drug combination effectively targeted breast cancer cells collected from patients whose cancer had progressed while being treated with everolimus, suggesting that ONC201/TIC10 could be relevant in a clinical setting. Finally, molecular and biochemical experiments revealed that the drug ONC201/TIC10 works by disrupting the pathways that everolimus-resistant cancer cells use to generate the energy required to grow and proliferate. Taken together these findings suggest that ONC201/TIC10 may provide an effective add-on therapy for patients with certain types of advanced breast cancer that are no longer responding to everolimus. Before this becomes a reality for patients, however, there will have to be more experimental testing of ONC201/TIC10 to determine optimal dosing and timing strategy for future clinical trials.

Cell facilitation promotes growth and survival under drug pressure in breast cancer.

The interplay of positive and negative interactions between drug-sensitive and resistant cells influences the effectiveness of treatment in heterogeneous cancer cell populations. Here, we study interactions between estrogen receptor-positive breast cancer cell lineages that are sensitive and resistant to ribociclib-induced cyclin-dependent kinase 4 and 6 (CDK4/6) inhibition. In mono- and coculture, we find that sensitive cells grow and compete more effectively in the absence of treatment. During treatment with ribociclib, sensitive cells survive and proliferate better when grown together with resistant cells than when grown in monoculture, termed facilitation in ecology. Molecular, protein, and genomic analyses show that resistant cells increase metabolism and production of estradiol, a highly active estrogen metabolite, and increase estrogen signaling in sensitive cells to promote facilitation in coculture. Adding estradiol in monoculture provides sensitive cells with increased resistance to therapy and cancels facilitation in coculture. Under partial inhibition of estrogen signaling through low-dose endocrine therapy, estradiol supplied by resistant cells facilitates sensitive cell growth. However, a more complete blockade of estrogen signaling, through higher-dose endocrine therapy, diminished the facilitative growth of sensitive cells. Mathematical modeling quantifies the strength of competition and facilitation during CDK4/6 inhibition and predicts that blocking facilitation has the potential to control both resistant and sensitive cancer cell populations and inhibit the emergence of a refractory population during cell cycle therapy.

Ensemble Classification of Cancer Types and Biomarker Identification.

Cancer classification is an important step in biomarker identification. Developing machine learning methods that correctly predict cancer subtypes/types can help in identifying potential cancer biomarkers. In this commentary, we presented ensemble classification approach and compared its performance with single classification approaches. Additionally, the application of cancer classification in identifying biomarkers for drug design was discussed.

Unraveling phenotypic plasticity and evolution in small cell lung cancer.

Malignant cell populations in a tumor often exist in distinct phenotypic states. Deciphering tumor heterogeneity requires determining how many such unique states exist and what the biological traits associated with each are. Archetype analysis of SCLC transcriptomes reveals key phenotypic states in SCLC tumors and their patterns of evolution.

Predicting clinical response to everolimus in ER+ breast cancers using machine-learning.

Endocrine therapy remains the primary treatment choice for ER+ breast cancers. However, most advanced ER+ breast cancers ultimately develop resistance to endocrine. This acquired resistance to endocrine therapy is often driven by the activation of the PI3K/AKT/mTOR signaling pathway. Everolimus, a drug that targets and inhibits the mTOR complex has been shown to improve clinical outcomes in metastatic ER+ breast cancers. However, there are no biomarkers currently available to guide the use of everolimus in the clinic for progressive patients, where multiple therapeutic options are available. Here, we utilized gene expression signatures from 9 ER+ breast cancer cell lines and 23 patients treated with everolimus to develop and validate an integrative machine learning biomarker of mTOR inhibitor response. Our results show that the machine learning biomarker can successfully distinguish responders from non-responders and can be applied to identify patients that will most likely benefit from everolimus treatment.

A machine learning framework for scRNA-seq UMI threshold optimization and accurate classification of cell types.

Recent advances in single cell RNA sequencing (scRNA-seq) technologies have been invaluable in the study of the diversity of cancer cells and the tumor microenvironment. While scRNA-seq platforms allow processing of a high number of cells, uneven read quality and technical artifacts hinder the ability to identify and classify biologically relevant cells into correct subtypes. This obstructs the analysis of cancer and normal cell diversity, while rare and low expression cell populations may be lost by setting arbitrary high cutoffs for UMIs when filtering out low quality cells. To address these issues, we have developed a novel machine-learning framework that: 1. Trains cell lineage and subtype classifier using a gold standard dataset validated using marker genes 2. Systematically assess the lowest UMI threshold that can be used in a given dataset to accurately classify cells 3. Assign accurate cell lineage and subtype labels to the lower read depth cells recovered by setting the optimal threshold. We demonstrate the application of this framework in a well-curated scRNA-seq dataset of breast cancer patients and two external datasets. We show that the minimum UMI threshold for the breast cancer dataset could be lowered from the original 1500 to 450, thereby increasing the total number of recovered cells by 49%, while achieving a classification accuracy of >0.9. Our framework provides a roadmap for future scRNA-seq studies to determine optimal UMI threshold and accurately classify cells for downstream analyses.

RAGE Inhibitors as Alternatives to Dexamethasone for Managing Cerebral Edema Following Brain Tumor Surgery.

Resection of brain tumors frequently causes injury to the surrounding brain tissue that exacerbates cerebral edema by activating an inflammatory cascade. Although corticosteroids are often utilized peri-operatively to alleviate the symptoms associated with brain edema, they increase operative morbidities and suppress the efficacy of immunotherapy. Thus, novel approaches to minimize cerebral edema caused by neurosurgical procedures will have significant utility in the management of patients with brain tumors. We have studied the role of the receptor for advanced glycation end products (RAGE) and its ligands on inflammatory responses to neurosurgical injury in mice and humans. Blood-brain barrier (BBB) integrity and neuroinflammation were characterized by Nanostring, flow cytometry, qPCR, and immunoblotting of WT and RAGE knockout mice brains subjected to surgical brain injury (SBI). Human tumor tissue and fluid collected from the resection cavity of patients undergoing craniotomy were also analyzed by single-cell RNA sequencing and ELISA. Genetic ablation of RAGE significantly abrogated neuroinflammation and BBB disruption in the murine SBI model. The inflammatory responses to SBI were associated with infiltration of S100A9-expressing myeloid-derived cells into the brain. Local release of pro-inflammatory S100A9 was confirmed in patients following tumor resection. RAGE and S100A9 inhibitors were as effective as dexamethasone in attenuating neuroinflammation. However, unlike dexamethasone and S100A9 inhibitor, RAGE inhibition did not diminish the efficacy of anti-PD-1 immunotherapy in glioma-bearing mice. These observations confirm the role of the RAGE axis in surgically induced neuroinflammation and provide an alternative therapeutic option to dexamethasone in managing post-operative cerebral edema.

Leveraging Single-Cell Approaches in Cancer Precision Medicine.

Cancer precision medicine aims to improve patient outcomes by tailoring treatment to the unique genomic background of a tumor. However, efforts to develop prognostic and drug response biomarkers largely rely on bulk 'omic' data, which fails to capture intratumor heterogeneity (ITH) and deconvolve signals from normal versus tumor cells. These shortcomings in measuring clinically relevant features are being addressed with single-cell technologies, which provide a fine-resolution map of the genetic and phenotypic heterogeneity in tumors and their microenvironment, as well as an improved understanding of the patterns of subclonal tumor populations. Here we present recent advances in the application of single-cell technologies, towards gaining a deeper understanding of ITH and evolution, and potential applications in developing personalized therapeutic strategies.

Palmitate-Induced IRE1-XBP1-ZEB Signaling Represses Desmoplakin Expression and Promotes Cancer Cell Migration.

Elevated uptake of saturated fatty acid palmitate is associated with metastatic progression of cancer cells; however, the precise signaling mechanism behind the phenomenon is unclear. The loss of cell adhesion proteins, such as desmoplakin (DSP), is a key driving event in the transformation of cancer cells to more aggressive phenotypes. Here, we investigated the mechanism by which palmitate induces the loss of DSP in liver and breast cancer cells. We propose that palmitate activates the IRE1-XBP1 branch of the endoplasmic reticulum (ER) stress pathway to upregulate the ZEB transcription factor, leading to transcriptional repression of DSP. Using liver and breast cancer cells treated with palmitate, we found loss of DSP leads to increased cell migration independent of E-cadherin. We report that the ZEB family of transcription factors function as direct transcriptional repressors of DSP. CRISPR-mediated knockdown of IRE1 confirmed that the transcription of ZEB, loss of DSP, and enhanced migration in the presence of palmitate is dependent on the IRE1-XBP1 pathway. In addition, by analyzing the somatic expression and copy number variation profiles of over 11,000 tumor samples, we corroborate our hypothesis and establish the clinical relevance of DSP loss via ZEB in human cancers. IMPLICATIONS: Provides mechanistic link on palmitate-induced activation of IRE1α to cancer cell migration.