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.

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.

Phylogenetic inference from single-cell RNA-seq data.

Tumors are comprised of subpopulations of cancer cells that harbor distinct genetic profiles and phenotypes that evolve over time and during treatment. By reconstructing the course of cancer evolution, we can understand the acquisition of the malignant properties that drive tumor progression. Unfortunately, recovering the evolutionary relationships of individual cancer cells linked to their phenotypes remains a difficult challenge. To address this need, we have developed PhylinSic, a method that reconstructs the phylogenetic relationships among cells linked to their gene expression profiles from single cell RNA-sequencing (scRNA-Seq) data. This method calls nucleotide bases using a probabilistic smoothing approach and then estimates a phylogenetic tree using a Bayesian modeling algorithm. We showed that PhylinSic identified evolutionary relationships underpinning drug selection and metastasis and was sensitive enough to identify subclones from genetic drift. We found that breast cancer tumors resistant to chemotherapies harbored multiple genetic lineages that independently acquired high K-Ras and ß-catenin, suggesting that therapeutic strategies may need to control multiple lineages to be durable. These results demonstrated that PhylinSic can reconstruct evolution and link the genotypes and phenotypes of cells across monophyletic tumors using scRNA-Seq.

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.

Multiomic analysis of homologous recombination-deficient end-stage high-grade serous ovarian cancer.

High-grade serous ovarian cancer (HGSC) is frequently characterized by homologous recombination (HR) DNA repair deficiency and, while most such tumors are sensitive to initial treatment, acquired resistance is common. We undertook a multiomics approach to interrogate molecular diversity in end-stage disease, using multiple autopsy samples collected from 15 women with HR-deficient HGSC. Patients had polyclonal disease, and several resistance mechanisms were identified within most patients, including reversion mutations and HR restoration by other means. We also observed frequent whole-genome duplication and global changes in immune composition with evidence of immune escape. This analysis highlights diverse evolutionary changes within HGSC that evade therapy and ultimately overwhelm individual patients.

RAGE ablation attenuates glioma progression and enhances tumor immune responses by suppressing galectin-3 expression.

BACKGROUND: Malignant gliomas consist of heterogeneous cellular components that have adopted multiple overlapping escape mechanisms that overcome both targeted and immune-based therapies. The receptor for advanced glycation end products (RAGE) is a member of the immunoglobulin superfamily that is activated by diverse proinflammatory ligands present in the tumor microenvironment. Activation of RAGE by its ligands stimulates multiple signaling pathways that are important in tumor growth and invasion. However, treatment strategies that only target the interaction of RAGE with its ligands are ineffective as cancer therapies due to the abundance and diversity of exogenous RAGE ligands in gliomas. METHODS: As an alternative approach to RAGE ligand inhibition, we evaluated the genetic ablation of RAGE on the tumorigenicity of 2 syngeneic murine glioma models. RAGE expression was inhibited in the GL261 and K-Luc gliomas by shRNA and CRSPR/Cas9 techniques prior to intracranial implantation. Tumor growth, invasion, and inflammatory responses were examined by histology, survival, Nanostring, and flow cytometry. RESULTS: Intracellular RAGE ablation abrogated glioma growth and invasion by suppressing AKT and ERK1/2 activities and by downregulating MMP9 expression. Interestingly, RAGE inhibition in both glioma models enhanced tumor inflammatory responses by downregulating the expression of galectin-3 and potentiated immunotherapy responses to immune checkpoint blockade. CONCLUSIONS: We demonstrated that intracellular RAGE ablation suppresses multiple cellular pathways that are important in glioma progression, invasion, and immune escape. These findings strongly support the development of RAGE ablation as a treatment strategy for malignant gliomas.

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.

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.

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.

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.

Influence of Estrogen Treatment on ESR1+ and ESR1- Cells in ER+ Breast Cancer: Insights from Single-Cell Analysis of Patient-Derived Xenograft Models.

A 100% ER positivity is not required for an endocrine therapy response. Furthermore, while estrogen typically promotes the progression of hormone-dependent breast cancer via the activation of estrogen receptor (ER)-α, estrogen-induced tumor suppression in ER+ breast cancer has been clinically observed. With the success in establishing estrogen-stimulated (SC31) and estrogen-suppressed (GS3) patient-derived xenograft (PDX) models, single-cell RNA sequencing analysis was performed to determine the impact of estrogen on ESR1+ and ESR1- tumor cells. We found that 17ß-estradiol (E2)-induced suppression of GS3 transpired through wild-type and unamplified ERα. E2 upregulated the expression of estrogen-dependent genes in both SC31 and GS3; however, E2 induced cell cycle advance in SC31, while it resulted in cell cycle arrest in GS3. Importantly, these gene expression changes occurred in both ESR1+ and ESR1- cells within the same breast tumors, demonstrating for the first time a differential effect of estrogen on ESR1- cells. E2 also upregulated a tumor-suppressor gene, IL-24, in GS3. The apoptosis gene set was upregulated and the G2M checkpoint gene set was downregulated in most IL-24+ cells after E2 treatment. In summary, estrogen affected pathologically defined ER+ tumors differently, influencing both ESR1+ and ESR1- cells. Our results also suggest IL-24 to be a potential marker of estrogen-suppressed tumors.

Phenotypic Consequences of SLC25A40-ABCB1 Fusions beyond Drug Resistance in High-Grade Serous Ovarian Cancer.

Despite high response rates to initial chemotherapy, the majority of women diagnosed with High-Grade Serous Ovarian Cancer (HGSOC) ultimately develop drug resistance within 1-2 years of treatment. We previously identified the most common mechanism of acquired resistance in HGSOC to date, transcriptional fusions involving the ATP-binding cassette (ABC) transporter ABCB1, which has well established roles in multidrug resistance. However, the underlying biology of fusion-positive cells, as well as how clonal interactions between fusion-negative and positive populations influences proliferative fitness and therapeutic response remains unknown. Using a panel of fusion-negative and positive HGSOC single-cell clones, we demonstrate that in addition to mediating drug resistance, ABCB1 fusion-positive cells display impaired proliferative capacity, elevated oxidative metabolism, altered actin cellular morphology and an extracellular matrix/inflammatory enriched transcriptional profile. The co-culture of fusion-negative and positive populations had no effect on cellular proliferation but markedly altered drug sensitivity to doxorubicin, paclitaxel and cisplatin. Finally, high-throughput screening of 2907 FDA-approved compounds revealed 36 agents that induce equal cytotoxicity in both pure and mixed ABCB1 fusion populations. Collectively, our findings have unraveled the underlying biology of ABCB1 fusion-positive cells beyond drug resistance and identified novel therapeutic agents that may significantly improve the prognosis of relapsed HGSOC patients.

Simplifying the development of portable, scalable, and reproducible workflows.

Command-line software plays a critical role in biology research. However, processes for installing and executing software differ widely. The Common Workflow Language (CWL) is a community standard that addresses this problem. Using CWL, tool developers can formally describe a tool's inputs, outputs, and other execution details. CWL documents can include instructions for executing tools inside software containers. Accordingly, CWL tools are portable-they can be executed on diverse computers-including personal workstations, high-performance clusters, or the cloud. CWL also supports workflows, which describe dependencies among tools and using outputs from one tool as inputs to others. To date, CWL has been used primarily for batch processing of large datasets, especially in genomics. But it can also be used for analytical steps of a study. This article explains key concepts about CWL and software containers and provides examples for using CWL in biology research. CWL documents are text-based, so they can be created manually, without computer programming. However, ensuring that these documents conform to the CWL specification may prevent some users from adopting it. To address this gap, we created ToolJig, a Web application that enables researchers to create CWL documents interactively. ToolJig validates information provided by the user to ensure it is complete and valid. After creating a CWL tool or workflow, the user can create 'input-object' files, which store values for a particular invocation of a tool or workflow. In addition, ToolJig provides examples of how to execute the tool or workflow via a workflow engine. ToolJig and our examples are available at https://github.com/srp33/ToolJig.

Serial single-cell genomics reveals convergent subclonal evolution of resistance as early-stage breast cancer patients progress on endocrine plus CDK4/6 therapy.

Combining cyclin-dependent kinase (CDK) inhibitors with endocrine therapy improves outcomes for metastatic estrogen receptor positive (ER+) breast cancer patients but its value in earlier stage patients is unclear. We examined evolutionary trajectories of early-stage breast cancer tumors, using single cell RNA sequencing (scRNAseq) of serial biopsies from the FELINE clinical trial (#NCT02712723) of endocrine therapy (letrozole) alone or combined with the CDK inhibitor ribociclib. Despite differences in subclonal diversity evolution across patients and treatments, common resistance phenotypes emerged. Resistant tumors treated with combination therapy showed accelerated loss of estrogen signaling with convergent up-regulation of JNK signaling through growth factor receptors. In contrast, cancer cells maintaining estrogen signaling during mono- or combination therapy showed potentiation of CDK4/6 activation and ERK upregulation through ERBB4 signaling. These results indicate that combination therapy in early-stage ER+ breast cancer leads to emergence of resistance through a shift from estrogen to alternative growth signal-mediated proliferation.

Novel temporal and spatial patterns of metastatic colonization from breast cancer rapid-autopsy tumor biopsies.

BACKGROUND: Metastatic breast cancer is a deadly disease with a low 5-year survival rate. Tracking metastatic spread in living patients is difficult and thus poorly understood. METHODS: Via rapid autopsy, we have collected 30 tumor samples over 3 timepoints and across 8 organs from a triple-negative metastatic breast cancer patient. The large number of sites sampled, together with deep whole-genome sequencing and advanced computational analysis, allowed us to comprehensively reconstruct the tumor's evolution at subclonal resolution. RESULTS: The most unique, previously unreported aspect of the tumor's evolution that we observed in this patient was the presence of "subclone incubators," defined as metastatic sites where substantial tumor evolution occurs before colonization of additional sites and organs by subclones that initially evolved at the incubator site. Overall, we identified four discrete waves of metastatic expansions, each of which resulted in a number of new, genetically similar metastasis sites that also enriched for particular organs (e.g., abdominal vs bone and brain). The lung played a critical role in facilitating metastatic spread in this patient: the lung was the first site of metastatic escape from the primary breast lesion, subclones at this site were likely the source of all four subsequent metastatic waves, and multiple sites in the lung acted as subclone incubators. Finally, functional annotation revealed that many known drivers or metastasis-promoting tumor mutations in this patient were shared by some, but not all metastatic sites, highlighting the need for more comprehensive surveys of a patient's metastases for effective clinical intervention. CONCLUSIONS: Our analysis revealed the presence of substantial tumor evolution at metastatic incubator sites in a patient, with potentially important clinical implications. Our study demonstrated that sampling of a large number of metastatic sites affords unprecedented detail for studying metastatic evolution.

Tepoxalin increases chemotherapy efficacy in drug-resistant breast cancer cells overexpressing the multidrug transporter gene ABCB1.

Effective cancer chemotherapy treatment requires both therapy delivery and retention by malignant cells. Cancer cell overexpression of the multidrug transmembrane transporter gene ABCB1 (MDR1, multi-drug resistance protein 1) thwarts therapy retention, leading to a drug-resistant phenotype. We explored the phenotypic impact of ABCB1 overexpression in normal human mammary epithelial cells (HMECs) via acute adenoviral delivery and in breast cancer cell lines with stable integration of inducible ABCB1 expression. One hundred sixty-two genes were differentially expressed between ABCB1-expressing and GFP-expressing HMECs, including the gene encoding the cyclooxygenase-2 protein, PTGS2. Several breast cancer cell lines with inducible ABCB1 expression demonstrated sensitivity to the 5-lipoxygenase, cyclooxygenase-1/2 inhibitor tepoxalin in two-dimensional drug response assays, and combination treatment of tepoxalin either with chemotherapies or with histone deacetylase (HDAC) inhibitors improved therapeutic efficacy in these lines. Moreover, selection for the ABCB1-expressing cell population was reduced in three-dimensional co-cultures of ABCB1-expressing and GFP-expressing cells when chemotherapy was given in combination with tepoxalin. Further study is warranted to ascertain the clinical potential of tepoxalin, an FDA-approved therapeutic for use in domesticated mammals, to restore chemosensitivity and improve drug response in patients with ABCB1-overexpressing drug-resistant breast cancers.

Evolution of core archetypal phenotypes in progressive high grade serous ovarian cancer.

The evolution of resistance in high-grade serous ovarian cancer (HGSOC) cells following chemotherapy is only partially understood. To understand the selection of factors driving heterogeneity before and through adaptation to treatment, we profile single-cell RNA-sequencing (scRNA-seq) transcriptomes of HGSOC tumors collected longitudinally during therapy. We analyze scRNA-seq data from two independent patient cohorts to reveal that HGSOC is driven by three archetypal phenotypes, defined as oncogenic states that describe the majority of the transcriptome variation. Using a multi-task learning approach to identify the biological tasks of each archetype, we identify metabolism and proliferation, cellular defense response, and DNA repair signaling as consistent cell states found across patients. Our analysis demonstrates a shift in favor of the metabolism and proliferation archetype versus cellular defense response archetype in cancer cells that received multiple lines of treatment. While archetypes are not consistently associated with specific whole-genome driver mutations, they are closely associated with subclonal populations at the single-cell level, indicating that subclones within a tumor often specialize in unique biological tasks. Our study reveals the core archetypes found in progressive HGSOC and shows consistent enrichment of subclones with the metabolism and proliferation archetype as resistance is acquired to multiple lines of therapy.

Usefulness of Circulating Tumor DNA in Identifying Somatic Mutations and Tracking Tumor Evolution in Patients With Non-small Cell Lung Cancer.

BACKGROUND: The usefulness of circulating tumor DNA (ctDNA) in detecting mutations and monitoring treatment response has not been well studied beyond a few actionable biomarkers in non-small cell lung cancer (NSCLC). RESEARCH QUESTION: How does the usefulness of ctDNA analysis compare with that of solid tumor biopsy analysis in patients with NSCLC? METHODS: We retrospectively evaluated 370 adult patients with NSCLC treated at the City of Hope between November 2015 and August 2019 to assess the usefulness of ctDNA in mutation identification, survival, concordance with matched tissue samples in 32 genes, and tumor evolution. RESULTS: A total of 1,688 somatic mutations were detected in 473 ctDNA samples from 370 patients with NSCLC. Of the 473 samples, 177 showed at least one actionable mutation with currently available Food and Drug Administration-approved NSCLC therapies. MET and CDK6 amplifications co-occurred with BRAF amplifications (false discovery rate [FDR], < 0.01), and gene-level mutations were mutually exclusive in KRAS and EGFR (FDR, 0.0009). Low cumulative percent ctDNA levels were associated with longer progression-free survival (hazard ratio [HR], 0.56; 95% CI, 0.37-0.85; P = .006). Overall survival was shorter in patients harboring BRAF mutations (HR, 2.35; 95% CI, 1.24-4.6; P = .009), PIK3CA mutations (HR, 2.77; 95% CI, 1.56-4.9; P < .001) and KRAS mutations (HR, 2.32; 95% CI, 1.30-4.1; P = .004). Gene-level concordance was 93.8%, whereas the positive concordance rate was 41.6%. More mutations in targetable genes were found in ctDNA than in tissue biopsy samples. Treatment response and tumor evolution over time were detected in repeated ctDNA samples. INTERPRETATION: Although ctDNA analysis exhibited similar usefulness to tissue biopsy analysis, more mutations in targetable genes were missed in tissue biopsy analyses. Therefore, the evaluation of ctDNA in conjunction with tissue biopsy samples may help to detect additional targetable mutations to improve clinical outcomes in advanced NSCLC.

Knowledge-based classification of fine-grained immune cell types in single-cell RNA-Seq data.

Single-cell RNA sequencing (scRNA-Seq) is an emerging strategy for characterizing immune cell populations. Compared to flow or mass cytometry, scRNA-Seq could potentially identify cell types and activation states that lack precise cell surface markers. However, scRNA-Seq is currently limited due to the need to manually classify each immune cell from its transcriptional profile. While recently developed algorithms accurately annotate coarse cell types (e.g. T cells versus macrophages), making fine distinctions (e.g. CD8+ effector memory T cells) remains a difficult challenge. To address this, we developed a machine learning classifier called ImmClassifier that leverages a hierarchical ontology of cell type. We demonstrate that its predictions are highly concordant with flow-based markers from CITE-seq and outperforms other tools (+15% recall, +14% precision) in distinguishing fine-grained cell types with comparable performance on coarse ones. Thus, ImmClassifier can be used to explore more deeply the heterogeneity of the immune system in scRNA-Seq experiments.

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.