Noninvasive Early Identification of Therapeutic Benefit from Immune Checkpoint Inhibition.

Although treatment of non-small cell lung cancer (NSCLC) with immune checkpoint inhibitors (ICIs) can produce remarkably durable responses, most patients develop early disease progression. Furthermore, initial response assessment by conventional imaging is often unable to identify which patients will achieve durable clinical benefit (DCB). Here, we demonstrate that pre-treatment circulating tumor DNA (ctDNA) and peripheral CD8 T cell levels are independently associated with DCB. We further show that ctDNA dynamics after a single infusion can aid in identification of patients who will achieve DCB. Integrating these determinants, we developed and validated an entirely noninvasive multiparameter assay (DIREct-On, Durable Immunotherapy Response Estimation by immune profiling and ctDNA-On-treatment) that robustly predicts which patients will achieve DCB with higher accuracy than any individual feature. Taken together, these results demonstrate that integrated ctDNA and circulating immune cell profiling can provide accurate, noninvasive, and early forecasting of ultimate outcomes for NSCLC patients receiving ICIs.

Intratumoral CD4+ T Cells Mediate Anti-tumor Cytotoxicity in Human Bladder Cancer.

Responses to anti-PD-1 immunotherapy occur but are infrequent in bladder cancer. The specific T cells that mediate tumor rejection are unknown. T cells from human bladder tumors and non-malignant tissue were assessed with single-cell RNA and paired T cell receptor (TCR) sequencing of 30,604 T cells from 7 patients. We find that the states and repertoires of CD8+ T cells are not distinct in tumors compared with non-malignant tissues. In contrast, single-cell analysis of CD4+ T cells demonstrates several tumor-specific states, including multiple distinct states of regulatory T cells. Surprisingly, we also find multiple cytotoxic CD4+ T cell states that are clonally expanded. These CD4+ T cells can kill autologous tumors in an MHC class II-dependent fashion and are suppressed by regulatory T cells. Further, a gene signature of cytotoxic CD4+ T cells in tumors predicts a clinical response in 244 metastatic bladder cancer patients treated with anti-PD-L1.

Personalized Mapping of Drug Metabolism by the Human Gut Microbiome.

The human gut microbiome harbors hundreds of bacterial species with diverse biochemical capabilities. Dozens of drugs have been shown to be metabolized by single isolates from the gut microbiome, but the extent of this phenomenon is rarely explored in the context of microbial communities. Here, we develop a quantitative experimental framework for mapping the ability of the human gut microbiome to metabolize small molecule drugs: Microbiome-Derived Metabolism (MDM)-Screen. Included are a batch culturing system for sustained growth of subject-specific gut microbial communities, an ex vivo drug metabolism screen, and targeted and untargeted functional metagenomic screens to identify microbiome-encoded genes responsible for specific metabolic events. Our framework identifies novel drug-microbiome interactions that vary between individuals and demonstrates how the gut microbiome might be used in drug development and personalized medicine.

Follicular helper T cell profiles predict response to costimulation blockade in type 1 diabetes.

Follicular helper T (TFH) cells are implicated in type 1 diabetes (T1D), and their development has been linked to CD28 costimulation. We tested whether TFH cells were decreased by costimulation blockade using the CTLA-4-immunoglobulin (Ig) fusion protein (abatacept) in a mouse model of diabetes and in individuals with new-onset T1D. Unbiased bioinformatics analysis identified that inducible costimulatory molecule (ICOS)+ TFH cells and other ICOS+ populations, including peripheral helper T cells, were highly sensitive to costimulation blockade. We used pretreatment TFH profiles to derive a model that could predict clinical response to abatacept in individuals with T1D. Using two independent approaches, we demonstrated that higher frequencies of ICOS+ TFH cells at baseline were associated with a poor clinical response following abatacept administration. Therefore, TFH analysis may represent a new stratification tool, permitting the identification of individuals most likely to benefit from costimulation blockade.

A Biobank of Breast Cancer Explants with Preserved Intra-tumor Heterogeneity to Screen Anticancer Compounds.

The inter- and intra-tumor heterogeneity of breast cancer needs to be adequately captured in pre-clinical models. We have created a large collection of breast cancer patient-derived tumor xenografts (PDTXs), in which the morphological and molecular characteristics of the originating tumor are preserved through passaging in the mouse. An integrated platform combining in vivo maintenance of these PDTXs along with short-term cultures of PDTX-derived tumor cells (PDTCs) was optimized. Remarkably, the intra-tumor genomic clonal architecture present in the originating breast cancers was mostly preserved upon serial passaging in xenografts and in short-term cultured PDTCs. We assessed drug responses in PDTCs on a high-throughput platform and validated several ex vivo responses in vivo. The biobank represents a powerful resource for pre-clinical breast cancer pharmacogenomic studies (http://caldaslab.cruk.cam.ac.uk/bcape), including identification of biomarkers of response or resistance.

Biomarker-Guided Development of DNA Repair Inhibitors.

Anti-cancer drugs targeting the DNA damage response (DDR) exploit genetic or functional defects in this pathway through synthetic lethal mechanisms. For example, defects in homologous recombination (HR) repair arise in cancer cells through inherited or acquired mutations in BRCA1, BRCA2, or other genes in the Fanconi anemia/BRCA pathway, and these tumors have been shown to be particularly sensitive to inhibitors of the base excision repair (BER) protein poly (ADP-ribose) polymerase (PARP). Recent work has identified additional genomic and functional assays of DNA repair that provide new predictive and pharmacodynamic biomarkers for these targeted therapies. Here, we examine the development of selective agents targeting DNA repair, including PARP inhibitors; inhibitors of the DNA damage kinases ataxia-telangiectasia and Rad3 related (ATR), CHK1, WEE1, and ataxia-telangiectasia mutated (ATM); and inhibitors of classical non-homologous end joining (cNHEJ) and alternative end joining (Alt EJ). We also review the biomarkers that guide the use of these agents and current clinical trials with these therapies.

It's diagnostics, stupid.

To stem the spiraling cost of cancer treatment, a concerted effort is urgently needed to develop molecular diagnostics to better identify the patients that respond to expensive targeted therapies. Opportunities and obstacles in the development of such drug response biomarkers are discussed here.

High-dimensional immune profiling identifies a biomarker to monitor dimethyl fumarate response in multiple sclerosis.

Dimethyl fumarate (DMF) is an immunomodulatory treatment for multiple sclerosis (MS). Despite its wide clinical use, the mechanisms underlying clinical response are not understood. This study aimed to reveal immune markers of therapeutic response to DMF treatment in MS. For this purpose, we prospectively collected peripheral blood mononuclear cells (PBMCs) from a highly characterized cohort of 44 individuals with MS before and at 12 and 48 wk of DMF treatment. Single cells were profiled using high-dimensional mass cytometry. To capture the heterogeneity of different immune subsets, we adopted a bioinformatic multipanel approach that allowed cell population-cluster assignment of more than 50 different parameters, including lineage and activation markers as well as chemokine receptors and cytokines. Data were further analyzed in a semiunbiased fashion implementing a supervised representation learning approach to capture subtle longitudinal immune changes characteristic for therapy response. With this approach, we identified a population of memory T helper cells expressing high levels of neuroinflammatory cytokines (granulocyte-macrophage colony-stimulating factor [GM-CSF], interferon γ [IFNγ]) as well as CXCR3, whose abundance correlated with treatment response. Using spectral flow cytometry, we confirmed these findings in a second cohort of patients. Serum neurofilament light-chain levels confirmed the correlation of this immune cell signature with axonal damage. The identified cell population is expanded in peripheral blood under natalizumab treatment, substantiating a specific role in treatment response. We propose that depletion of GM-CSF-, IFNγ-, and CXCR3-expressing T helper cells is the main mechanism of action of DMF and allows monitoring of treatment response.

Network modeling predicts personalized gene expression and drug responses in valve myofibroblasts cultured with patient sera.

Aortic valve stenosis (AVS) patients experience pathogenic valve leaflet stiffening due to excessive extracellular matrix (ECM) remodeling. Numerous microenvironmental cues influence pathogenic expression of ECM remodeling genes in tissue-resident valvular myofibroblasts, and the regulation of complex myofibroblast signaling networks depends on patient-specific extracellular factors. Here, we combined a manually curated myofibroblast signaling network with a data-driven transcription factor network to predict patient-specific myofibroblast gene expression signatures and drug responses. Using transcriptomic data from myofibroblasts cultured with AVS patient sera, we produced a large-scale, logic-gated differential equation model in which 11 biochemical and biomechanical signals were transduced via a network of 334 signaling and transcription reactions to accurately predict the expression of 27 fibrosis-related genes. Correlations were found between personalized model-predicted gene expression and AVS patient echocardiography data, suggesting links between fibrosis-related signaling and patient-specific AVS severity. Further, global network perturbation analyses revealed signaling molecules with the most influence over network-wide activity, including endothelin 1 (ET1), interleukin 6 (IL6), and transforming growth factor ß (TGFß), along with downstream mediators c-Jun N-terminal kinase (JNK), signal transducer and activator of transcription (STAT), and reactive oxygen species (ROS). Lastly, we performed virtual drug screening to identify patient-specific drug responses, which were experimentally validated via fibrotic gene expression measurements in valvular interstitial cells cultured with AVS patient sera and treated with or without bosentan-a clinically approved ET1 receptor inhibitor. In sum, our work advances the ability of computational approaches to provide a mechanistic basis for clinical decisions including patient stratification and personalized drug screening.

Tumor Microenvironment and Immunotherapy: The Whole Picture Is Better Than a Glimpse.

Predicting cancer patients' response to therapy is essential for curing disease and improving quality of life. Garraway and colleagues demonstrate that the frequency and number of neoantigens, non-synonymous mutations, and adaptive immune genes, but not the assessment of individual recurrent neoantigens or mutations, predicts patient responses to immunotherapy.

Tertiary Lymphoid Structures and B cells: Clinical impact and therapeutic modulation in cancer.

Tumors progression is under the control of a heterogeneous microenvironment composed of immune cells, fibroblasts, blood and lymphatic vessels, in which T cells have been demonstrated to be major actors, through their cytotoxic and cytokine producing effector functions and their long term memory that protects against metastasis. In this scenario, lessons from mouse models taught that B cells exert a protumoral role, via macrophage-dependent activation of inflammation. However, it became progressively evident from studies in patients with human cancers that the anti-tumor responses can be generated and controlled in tertiary lymphoid structures (TLS) that concentrate most of the intratumoral B cells and where B cells can differentiate into plasma cells and memory cells. Furthermore, recent studies demonstrated that the presence in tumors of B cells and TLS are associated with favorable outcome in patients treated by immunotherapy, unraveling TLS as a new predictive marker of anti-tumor response human cancers. This review encompasses the characteristics and functions of TLS and of B cells in human tumors, their prognostic and theranostic impact and summarizes the mouse models used to induce TLS neogenesis in tumors.

CeDR Atlas: a knowledgebase of cellular drug response.

Drug response to many diseases varies dramatically due to the complex genomics and functional features and contexts. Cellular diversity of human tissues, especially tumors, is one of the major contributing factors to the different drug response in different samples. With the accumulation of single-cell RNA sequencing (scRNA-seq) data, it is now possible to study the drug response to different treatments at the single cell resolution. Here, we present CeDR Atlas (available at https://ngdc.cncb.ac.cn/cedr), a knowledgebase reporting computational inference of cellular drug response for hundreds of cell types from various tissues. We took advantage of the high-throughput profiling of drug-induced gene expression available through the Connectivity Map resource (CMap) as well as hundreds of scRNA-seq data covering cells from a wide variety of organs/tissues, diseases, and conditions. Currently, CeDR maintains the results for more than 582 single cell data objects for human, mouse and cell lines, including about 140 phenotypes and 1250 tissue-cell combination types. All the results can be explored and searched by keywords for drugs, cell types, tissues, diseases, and signature genes. Overall, CeDR fine maps drug response at cellular resolution and sheds lights on the design of combinatorial treatments, drug resistance and even drug side effects.

TISMO: syngeneic mouse tumor database to model tumor immunity and immunotherapy response.

Syngeneic mouse models are tumors derived from murine cancer cells engrafted on genetically identical mouse strains. They are widely used tools for studying tumor immunity and immunotherapy response in the context of a fully functional murine immune system. Large volumes of syngeneic mouse tumor expression profiles under different immunotherapy treatments have been generated, although a lack of systematic collection and analysis makes data reuse challenging. We present Tumor Immune Syngeneic MOuse (TISMO), a database with an extensive collection of syngeneic mouse model profiles with interactive visualization features. TISMO contains 605 in vitro RNA-seq samples from 49 syngeneic cancer cell lines across 23 cancer types, of which 195 underwent cytokine treatment. TISMO also includes 1518 in vivo RNA-seq samples from 68 syngeneic mouse tumor models across 19 cancer types, of which 832 were from immune checkpoint blockade (ICB) studies. We manually annotated the sample metadata, such as cell line, mouse strain, transplantation site, treatment, and response status, and uniformly processed and quality-controlled the RNA-seq data. Besides data download, TISMO provides interactive web interfaces to investigate whether specific gene expression, pathway enrichment, or immune infiltration level is associated with differential immunotherapy response. TISMO is available at http://tismo.cistrome.org.

CTR-DB, an omnibus for patient-derived gene expression signatures correlated with cancer drug response.

To date, only some cancer patients can benefit from chemotherapy and targeted therapy. Drug resistance continues to be a major and challenging problem facing current cancer research. Rapidly accumulated patient-derived clinical transcriptomic data with cancer drug response bring opportunities for exploring molecular determinants of drug response, but meanwhile pose challenges for data management, integration, and reuse. Here we present the Cancer Treatment Response gene signature DataBase (CTR-DB, http://ctrdb.ncpsb.org.cn/), a unique database for basic and clinical researchers to access, integrate, and reuse clinical transcriptomes with cancer drug response. CTR-DB has collected and uniformly reprocessed 83 patient-derived pre-treatment transcriptomic source datasets with manually curated cancer drug response information, involving 28 histological cancer types, 123 drugs, and 5139 patient samples. These data are browsable, searchable, and downloadable. Moreover, CTR-DB supports single-dataset exploration (including differential gene expression, receiver operating characteristic curve, functional enrichment, sensitizing drug search, and tumor microenvironment analyses), and multiple-dataset combination and comparison, as well as biomarker validation function, which provide insights into the drug resistance mechanism, predictive biomarker discovery and validation, drug combination, and resistance mechanism heterogeneity.

Predicting patient response with models trained on cell lines and patient-derived xenografts by nonlinear transfer learning.

Preclinical models have been the workhorse of cancer research, producing massive amounts of drug response data. Unfortunately, translating response biomarkers derived from these datasets to human tumors has proven to be particularly challenging. To address this challenge, we developed TRANSACT, a computational framework that builds a consensus space to capture biological processes common to preclinical models and human tumors and exploits this space to construct drug response predictors that robustly transfer from preclinical models to human tumors. TRANSACT performs favorably compared to four competing approaches, including two deep learning approaches, on a set of 23 drug prediction challenges on The Cancer Genome Atlas and 226 metastatic tumors from the Hartwig Medical Foundation. We demonstrate that response predictions deliver a robust performance for a number of therapies of high clinical importance: platinum-based chemotherapies, gemcitabine, and paclitaxel. In contrast to other approaches, we demonstrate the interpretability of the TRANSACT predictors by correctly identifying known biomarkers of targeted therapies, and we propose potential mechanisms that mediate the resistance to two chemotherapeutic agents.

High-throughput screening and genome-wide analyses of 44 anticancer drugs in the 1000 Genomes cell lines reveals an association of the NQO1 gene with the response of multiple anticancer drugs.

Cancer patients exhibit a broad range of inter-individual variability in response and toxicity to widely used anticancer drugs, and genetic variation is a major contributor to this variability. To identify new genes that influence the response of 44 FDA-approved anticancer drug treatments widely used to treat various types of cancer, we conducted high-throughput screening and genome-wide association mapping using 680 lymphoblastoid cell lines from the 1000 Genomes Project. The drug treatments considered in this study represent nine drug classes widely used in the treatment of cancer in addition to the paclitaxel + epirubicin combination therapy commonly used for breast cancer patients. Our genome-wide association study (GWAS) found several significant and suggestive associations. We prioritized consistent associations for functional follow-up using gene-expression analyses. The NAD(P)H quinone dehydrogenase 1 (NQO1) gene was found to be associated with the dose-response of arsenic trioxide, erlotinib, trametinib, and a combination treatment of paclitaxel + epirubicin. NQO1 has previously been shown as a biomarker of epirubicin response, but our results reveal novel associations with these additional treatments. Baseline gene expression of NQO1 was positively correlated with response for 43 of the 44 treatments surveyed. By interrogating the functional mechanisms of this association, the results demonstrate differences in both baseline and drug-exposed induction.

IFN-γ: A cytokine at the right time, is in the right place.

Interferon gamma has long been studied as a critical mediator of tumor immunity. In recent years, the complexity of cellular interactions that take place in the tumor microenvironment has become better appreciated in the context of immunotherapy. While checkpoint inhibitors have dramatically improved remission rates in cancer treatment, IFN-γ and related effectors continue to be identified as strong predictors of treatment success. In this review, we provide an overview of the multiple immunosuppressive barriers that IFN-γ has to overcome to eliminate tumors, and potential avenues for modulating the immune response in favor of tumor rejection.

Phase I clinical trial of HC-1119 soft capsule in Chinese healthy adult male subjects: Pharmacokinetics and safety of single-dose proportionality and effects of food.

BACKGROUND: Preclinical studies showed that HC-1119, a deuterated version of enzalutamide, could competitively inhibit androgen binding to androgen receptor by blocking the transmission of androgen receptor signaling pathway as enzalutamide, inducing apoptosis of prostate cancer cells and reducing the proliferation of prostate cancer cells. Animal pharmacokinetic studies also show that deuterization of enzalutamide as HC-1119 could retain the basic properties of mother drug, increases the stability of compounds to metabolic enzymes and the drug exposure in vivo, prolong the half-life and reduce the production of metabolites, which may lead to a better efficacy and safety of HC-1119 compared with enzalutamide. METHODS: To evaluate the pharmacokinetics and safety of HC-1119 and the effects of food on pharmacokinetics in healthy adult Chinese men after single-dose administration of HC-1119. A total of 47 Chinese healthy adult male subjects received HC-1119 soft capsule at a single oral dose of 40, 80, or 160 mg followed on fasting or 160 mg after high-fat meal respectively. HC-1119 prototype and its metabolites M1 and M2 in plasma were collected individually in a total 23 time points. Pharmacokinetics were determined by sensitive LC/MS/MS for dose-proportionality study. RESULTS: In subjects taking HC-1119 soft capsules on fasting, Cmax of HC-1119 prototype increased dose-dependently. Either Cmax and AUC0-∞ of M1 or Cmax of M2 showed statistically significant difference. Dose-proportionality evaluation showed linear pharmacokinetic characteristics in Cmax of HC-1119 prototype, Cmax and AUC0-∞ of M2 in dose range of 40-160 mg. Cmax of HC-1119 was significantly different between the two groups as 160 mg HC-1119 on fasting or after a high-fat diet respectively, while the other parameter were not. HC-1119 and its metabolites M1 and M2 showed a linear dynamic trend. CONCLUSIONS: HC-1119 is expected to have lower clinical dose than the similar drug enzalutamide. The absorption of HC-1119 and the main pharmacokinetic parameters of HC-1119 and its metabolites M1 and M2 were not affected by high-fat diet. The clinical application of HC-1119 soft capsule in the later stage can be recommended for both fasting and postprandial. The safety and tolerance were good in this population.

Early Changes in Alpha-Fetoprotein Are a Useful Predictor of Efficacy of Atezolizumab plus Bevacizumab Treatment in Patients with Advanced Hepatocellular Carcinoma.

INTRODUCTION: The aim of this study was to investigate the early changes in alpha-fetoprotein (AFP) and des-γ-carboxy prothrombin (DCP) levels in patients with advanced hepatocellular carcinoma (HCC) treated with atezolizumab plus bevacizumab and to evaluate the relationship between changes in these tumor markers and treatment efficacy. METHODS: Of 58 consecutive patients who started atezolizumab plus bevacizumab at our institution, 50 patients with information on antitumor response obtained at 6 weeks after therapy were enrolled in this study and their treatment outcomes were retrospectively evaluated. RESULTS: According to the Response Evaluation Criteria in Solid Tumors at 6 weeks, the objective response (OR) rate was 22.0% and the disease control (DC) rate was 78.0%. In patients who achieved OR at 6 weeks, median AFP and DCP ratios at weeks 1, 2, 3, and 6 were significantly lower than those in patients who did not achieve OR. AFP ratios in patients who did not achieve DC at 6 weeks (Non-6W-DC group) were significantly higher than in those who achieved DC at week 6 (6W-DC group). Median overall survival in the Non-6W-DC group was significantly shorter than in the 6W-DC group (156 days vs. not reached, p = 0.0008). An AFP ratio of 1.4 or higher at 3 weeks had a specificity of 88.0% and a sensitivity of 88.9% for predicting Non-6W-DC. Median progression-free survival was significantly shorter in patients with an AFP ratio of 1.4 or higher at 3 weeks than in those with an AFP ratio of <1.4 (42 days vs. 210 days, p = 0.0003). CONCLUSION: Early changes in AFP might be useful for predicting the antitumor efficacy of atezolizumab plus bevacizumab in patients with advanced HCC. An AFP ratio of 1.4 or higher at 3 weeks might be an early predictor of refractoriness to atezolizumab plus bevacizumab therapy.

Hyperandrogenism and menstrual imbalance are the best predictors of metformin response in PCOS patients.

BACKGROUND: Moving from the correlation between insulin-resistance and PCOS, metformin has been administered in some PCOS women improving ovulatory and metabolic functions and decreasing androgen levels. Inconsistency and unpredictability of response to metformin limit its extensive use. Aim of this study was to identify reliable predictors of response to metformin therapy for weight loss and reduction in plasma androgen levels using ANNs (artificial neural networks). METHODS: One hundred eight consecutive women with PCOS (ESHRE/ASRM 2003 Rotterdam criteria) treated with metformin 1500 mg/day, at inclusion and every 6 months underwent to a complete clinical, endocrine/metabolic assessment and ultrasonographic evaluation. Therapy outcomes were BMI reduction (≥1 kg/m2) in overweight/obese and free-androgen-index (FAI) decrease (≥1%) in hyperandrogenemic women. Semantic connectivity maps (SCMs) were obtained through Auto-CM, a fourth generation ANN, to compare patients' baseline clinical features to the treatment outcomes. Multivariate logistic regression analysis was used to assess the major predictor in drop-out patients and the associated risk. RESULTS: At 6 months 54 out of 103 (52,4%) obese patients showed BMI reduction and 45 out of 89 (50,6%) hyperandrogenemic women showed FAI decrease. The further response rates at 12 months were 30,6 and 47%, respectively. SCMs showed a clear polarization for both the outcomes with elevated accuracy. Treatment responsiveness resulted strictly related to oligo-amenorrhea and hyperandrogenemia at baseline. In addition, lower serum testosterone levels at baseline were found to be the major predictor of treatment discontinuation. CONCLUSIONS: In women with PCOS, menstrual pattern imbalance and ovarian androgens excess are the best predictors of metformin response. They may pave the way for a rethinking of the criteria for evaluating hyperandrogenism in order to better define the large population included in the diagnosis of PCOS. Baseline plasma testosterone level can serve as a sensitive marker to predict treatment compliance.