Association Between Biomarkers and Clinical Outcomes of Pembrolizumab Monotherapy in Patients With Metastatic Triple-Negative Breast Cancer: KEYNOTE-086 Exploratory Analysis

PURPOSE

In the two-cohort phase II KEYNOTE-086 study (ClinicalTrials.gov identifier: NCT02447003), first-line and second-line or later pembrolizumab monotherapy demonstrated antitumor activity in metastatic triple-negative breast cancer (mTNBC; N = 254). This exploratory analysis evaluates the association between prespecified molecular biomarkers and clinical outcomes.

METHODS

Cohort A enrolled patients with disease progression after one or more systemic therapies for metastatic disease irrespective of PD-L1 status; Cohort B enrolled patients with previously untreated PD-L1-positive (combined positive score [CPS] ≥ 1) metastatic disease. The association between the following biomarkers as continuous variables and clinical outcomes (objective response rate [ORR], progression-free survival [PFS], and overall survival [OS]) was evaluated: PD-L1 CPS (immunohistochemistry), cluster of differentiation 8 (CD8; immunohistochemistry), stromal tumor-infiltrating lymphocyte (sTIL; hematoxylin and eosin staining), tumor mutational burden (TMB; whole-exome sequencing [WES]), homologous recombination deficiency-loss of heterozygosity, mutational signature 3 (WES), mutational signature 2 (apolipoprotein B mRNA editing catalytic polypeptide-like; WES), T-cell-inflamed gene expression profile (Tcell_infGEP; RNA sequencing), and 10 non-Tcell_infGEP signatures (RNA sequencing); Wald test P values were calculated, and significance was prespecified at α = 0.05.

RESULTS

In the combined cohorts (A and B), PD-L1 (P = .040), CD8 (P < .001), sTILs (P = .012), TMB (P = .007), and Tcell_infGEP (P = .011) were significantly associated with ORR; CD8 (P < .001), TMB (P = .034), Signature 3 (P = .009), and Tcell_infGEP (P = .002) with PFS; and CD8 (P < .001), sTILs (P = .004), TMB (P = .025), and Tcell_infGEP (P = .001) with OS. None of the non-Tcell_infGEP signatures were associated with outcomes of pembrolizumab after adjusting for the Tcell_infGEP.

CONCLUSION

In this exploratory biomarker analysis from KEYNOTE-086, baseline tumor PD-L1, CD8, sTILs, TMB, and Tcell_infGEP were associated with improved clinical outcomes of pembrolizumab and may help identify patients with mTNBC who are most likely to respond to pembrolizumab monotherapy.

Putative Biomarkers of Clinical Benefit With Pembrolizumab in Advanced Urothelial Cancer: Results from the KEYNOTE-045 and KEYNOTE-052 Landmark Trials

PURPOSE

In an exploratory analysis, we investigated the association between programmed death ligand 1 (PD-L1), tumor mutational burden (TMB), T-cell-inflamed gene expression profile (TcellinfGEP), and stromal signature with outcomes of pembrolizumab in urothelial carcinoma (UC).

PATIENTS AND METHODS

Patients with advanced UC received first-line pembrolizumab 200 mg every 3 weeks in the single-arm phase II KEYNOTE-052 trial (NCT02335424) and salvage pembrolizumab 200 mg every 3 weeks or chemotherapy (paclitaxel/docetaxel/vinflunine) in the randomized phase III KEYNOTE-045 trial (NCT02256436). The association of each biomarker (continuous variable) with objective response rate (ORR), progression-free survival (PFS), and overall survival (OS) was evaluated using logistic regression (ORR) and Cox PH (PFS, OS), adjusted for ECOG PS; nominal P values were calculated without multiplicity adjustment (one-sided, pembrolizumab; two-sided, chemotherapy). Significance was prespecified at α = 0.05.

RESULTS

In KEYNOTE-052, PD-L1, TMB, and TcellinfGEP were significantly associated with improved outcomes; stromal signature was significantly associated with worse outcomes. In KEYNOTE-045, although findings for TMB and TcellinfGEP with pembrolizumab were consistent with those of KEYNOTE-052, PD-L1 was not significantly associated with improved outcomes, nor was stromal signature associated with worse outcomes with pembrolizumab; chemotherapy was not associated with outcomes in a consistent manner for any of the biomarkers. Hazard ratio (HR) estimates at prespecified cutoffs showed an advantage for pembrolizumab versus chemotherapy regardless of PD-L1 or TMB, with a trend toward lower HRs in the combined positive score ≥10 and the TMB ≥175 mutation/exome subgroup. For TcellinfGEP, PFS and OS HRs were lower in the TcellinfGEP-nonlow subgroup regardless of treatment.

CONCLUSIONS

Multiple biomarkers characterizing the tumor microenvironment may help predict response to pembrolizumab monotherapy in UC, and potential clinical utility of these biomarkers may be context-dependent.

Immunosuppressive Myeloid Cells Induce Nitric Oxide-Dependent DNA Damage and p53 Pathway Activation in CD8+ T Cells

Tumor-infiltrating myeloid-derived suppressor cells (MDSC) are associated with poor survival outcomes in many human cancers. MDSCs inhibit T cell-mediated tumor immunity in part because they strongly inhibit T-cell function. However, whether MDSCs inhibit early or later steps of T-cell activation is not well established. Here we show that MDSCs inhibited proliferation and induced apoptosis of CD8⁺ T cells even in the presence of dendritic cells (DC) presenting a high-affinity cognate peptide. This inhibitory effect was also observed with delayed addition of MDSCs to cocultures, consistent with functional data showing that T cells expressed multiple early activation markers even in the presence of MDSCs. Single-cell RNA-sequencing analysis of CD8⁺ T cells demonstrated a p53 transcriptional signature in CD8⁺ T cells cocultured with MDSCs and DCs. Confocal microscopy showed induction of DNA damage and nuclear accumulation of activated p53 protein in a substantial fraction of these T cells. DNA damage in T cells was dependent on the iNOS enzyme and subsequent nitric oxide release by MDSCs. Small molecule-mediated inhibition of iNOS or inactivation of the Nos2 gene in MDSCs markedly diminished DNA damage in CD8⁺ T cells. DNA damage in CD8⁺ T cells was also observed in KPC pancreatic tumors but was reduced in tumors implanted into Nos2-deficient mice compared with wild-type mice. These data demonstrate that MDSCs do not block early steps of T-cell activation but rather induce DNA damage and p53 pathway activation in CD8⁺ T cells through an iNOS-dependent pathway.

Distinct Mesenchymal Cell Populations Generate the Essential Intestinal BMP Signaling Gradient

Intestinal stem cells (ISCs) are confined to crypt bottoms and their progeny differentiate near crypt-villus junctions. Wnt and bone morphogenic protein (BMP) gradients drive this polarity, and colorectal cancer fundamentally reflects disruption of this homeostatic signaling. However, sub-epithelial sources of crucial agonists and antagonists that organize this BMP gradient remain obscure. Here, we couple whole-mount high-resolution microscopy with ensemble and single-cell RNA sequencing (RNA-seq) to identify three distinct PDGFRA⁺ mesenchymal cell types. PDGFRA(hi) telocytes are especially abundant at the villus base and provide a BMP reservoir, and we identified a CD81⁺ PDGFRA(lo) population present just below crypts that secretes the BMP antagonist Gremlin1. These cells, referred to as trophocytes, are sufficient to expand ISCs in vitro without additional trophic support and contribute to ISC maintenance in vivo. This study reveals intestinal mesenchymal structure at fine anatomic, molecular, and functional detail and the cellular basis for a signaling gradient necessary for tissue self-renewal.

TGFβ signaling underlies hematopoietic dysfunction and bone marrow failure in Shwachman-Diamond Syndrome

Shwachman-Diamond Syndrome (SDS) is a rare and clinically-heterogeneous bone marrow (BM) failure syndrome caused by mutations in the Shwachman-Bodian-Diamond Syndrome (SBDS) gene. Although SDS was described over 50 years ago, the molecular pathogenesis is poorly understood due, in part, to the rarity and heterogeneity of the affected hematopoietic progenitors. To address this, we used single cell RNA sequencing to profile scant hematopoietic stem and progenitor cells from SDS patients. We generated a single cell map of early lineage commitment and found that SDS hematopoiesis was left-shifted with selective loss of granulocyte-monocyte progenitors. Transcriptional targets of transforming growth factor-beta (TGFβ) were dysregulated in SDS hematopoietic stem cells and multipotent progenitors, but not in lineage-committed progenitors. TGFβ inhibitors (AVID200 and SD208) increased hematopoietic colony formation of SDS patient BM. Finally, TGFβ3 and other TGFβ pathway members were elevated in SDS patient blood plasma. These data establish the TGFβ pathway as a novel candidate biomarker and therapeutic target in SDS and translate insights from single cell biology into a potential therapy.

Abstract A176: Overcoming CD8 T-cell suppression in the tumor microenvironment

CD8 T-cell-mediated antitumor immunity is required for the control and elimination of tumors. However, tumors are able to overcome immune response resulting in immunosuppression, tumor growth, and metastatic spread. CD8 T-cells are controlled through a homeostatic network of positive and negative feedback loops. These negative signals are exploited by the tumor and associated suppressive cell populations in the tumor microenvironment. Immunotherapies targeted to receptors that control these negative signals, termed immune checkpoint inhibitors, have provided robust and durable responses to a number of cancers. Unfortunately, only a subset of patients will respond to current immunotherapies. This is due to the accruement of suppressive and inhibitory mechanisms employed by the tumor and its microenvironment. These include expression of inhibitory ligands, release of immune-suppressive factors, and the recruitment of suppressive cell populations. Furthermore, the administration of immune checkpoint inhibitors can enhance tumor acquisition of a more suppressive phenotype, diminishing immune responses through additional inhibitory pathways. We therefore propose that further CD8 T-cell-intrinsic negative regulators most likely exist, which can be targeted to improve antitumor responses. Here, we have used functional genomic approaches to identify pathways that can be targeted to advance anti-tumor responses, either in combination with anti-PD-1 or to overcome suppressive mechanisms employed by the tumor microenvironment. Using both in vivo and in vitro assays, we have identified both a novel target that provides an enhancement to CD8 T-cell effector function when in combination with PD-1 checkpoint blockade, and CD8 T-cell-intrinsic pathways that are modulated by the presence of suppressive cells associated with the tumor microenvironment.

Citation Format: Adam N.R Cartwright, Peng Jiang, Assieh Saadatpour, Guo-Cheng Yuan, Shirley X. Liu, Kai W. Wucherpfennig. Overcoming CD8 T-cell suppression in the tumor microenvironment [abstract]. In: Proceedings of the Fourth CRI-CIMT-EATI-AACR International Cancer Immunotherapy Conference: Translating Science into Survival; Sept 30-Oct 3, 2018; New York, NY. Philadelphia (PA): AACR; Cancer Immunol Res 2019;7(2 Suppl):Abstract nr A176.

Mapping the Mouse Cell Atlas by Microwell-Seq

Single-cell RNA sequencing (scRNA-seq) technologies are poised to reshape the current cell-type classification system. However, a transcriptome-based single-cell atlas has not been achieved for complex mammalian systems. Here, we developed Microwell-seq, a high-throughput and low-cost scRNA-seq platform using simple, inexpensive devices. Using Microwell-seq, we analyzed more than 400,000 single cells covering all of the major mouse organs and constructed a basic scheme for a mouse cell atlas (MCA). We reveal a single-cell hierarchy for many tissues that have not been well characterized previously. We built a web-based "single-cell MCA analysis" pipeline that accurately defines cell types based on single-cell digital expression. Our study demonstrates the wide applicability of the Microwell-seq technology and MCA resource.

Revealing the Critical Regulators of Cell Identity in the Mouse Cell Atlas

Recent progress in single-cell technologies has enabled the identification of all major cell types in mouse. However, for most cell types, the regulatory mechanism underlying their identity remains poorly understood. By computational analysis of the recently published mouse cell atlas data, we have identified 202 regulons whose activities are highly variable across different cell types, and more importantly, predicted a small set of essential regulators for each major cell type in mouse. Systematic validation by automated literature and data mining provides strong additional support for our predictions. Thus, these predictions serve as a valuable resource that would be useful for the broad biological community. Finally, we have built a user-friendly, interactive web portal to enable users to navigate this mouse cell network atlas.

Dynamic Reorganization of Chromatin Accessibility Signatures during Dedifferentiation of Secretory Precursors into Lgr5+ Intestinal Stem Cells

Replicating Lgr5⁺ stem cells and quiescent Bmi1⁺ cells behave as intestinal stem cells (ISCs) in vivo. Disrupting Lgr5⁺ ISCs triggers epithelial renewal from Bmi1⁺ cells, from secretory or absorptive progenitors, and from Paneth cell precursors, revealing a high degree of plasticity within intestinal crypts. Here, we show that GFP⁺ cells from Bmi1^GFP mice are preterminal enteroendocrine cells and we identify CD69⁺CD274⁺ cells as related goblet cell precursors. Upon loss of native Lgr5⁺ ISCs, both populations revert toward an Lgr5⁺ cell identity. While active histone marks are distributed similarly between Lgr5⁺ ISCs and progenitors of both major lineages, thousands of cis elements that control expression of lineage-restricted genes are selectively open in secretory cells. This accessibility signature dynamically converts to that of Lgr5⁺ ISCs during crypt regeneration. Beyond establishing the nature of Bmi1^GFP+ cells, these findings reveal how chromatin status underlies intestinal cell diversity and dedifferentiation to restore ISC function and intestinal homeostasis.

Challenges and emerging directions in single-cell analysis

Single-cell analysis is a rapidly evolving approach to characterize genome-scale molecular information at the individual cell level. Development of single-cell technologies and computational methods has enabled systematic investigation of cellular heterogeneity in a wide range of tissues and cell populations, yielding fresh insights into the composition, dynamics, and regulatory mechanisms of cell states in development and disease. Despite substantial advances, significant challenges remain in the analysis, integration, and interpretation of single-cell omics data. Here, we discuss the state of the field and recent advances and look to future opportunities.

A molecular roadmap for induced multi-lineage trans-differentiation of fibroblasts by chemical combinations

Recent advances have demonstrated the power of small molecules in promoting cellular reprogramming. Yet, the full potential of such chemicals in cell fate manipulation and the underlying mechanisms require further characterization. Through functional screening assays, we find that mouse embryonic fibroblast cells can be induced to trans-differentiate into a wide range of somatic lineages simultaneously by treatment with a combination of four chemicals. Genomic analysis of the process indicates activation of multi-lineage modules and relaxation of epigenetic silencing programs. In addition, we identify Sox2 as an important regulator within the induced network. Single cell analysis uncovers a novel priming state that enables transition from fibroblast cells to diverse somatic lineages. Finally, we demonstrate that modification of the culture system enables directional trans-differentiation towards myocytic, glial or adipocytic lineages. Our study describes a cell fate control system that may be harnessed for regenerative medicine.

Single-Cell Analysis in Cancer Genomics

Genetic changes and environmental differences result in cellular heterogeneity among cancer cells within the same tumor, thereby complicating treatment outcomes. Recent advances in single-cell technologies have opened new avenues to characterize the intra-tumor cellular heterogeneity, identify rare cell types, measure mutation rates, and, ultimately, guide diagnosis and treatment. In this paper we review the recent single-cell technological and computational advances at the genomic, transcriptomic, and proteomic levels, and discuss their applications in cancer research.

Characterizing heterogeneity in leukemic cells using single-cell gene expression analysis

BACKGROUND

A fundamental challenge for cancer therapy is that each tumor contains a highly heterogeneous cell population whose structure and mechanistic underpinnings remain incompletely understood. Recent advances in single-cell gene expression profiling have created new possibilities to characterize this heterogeneity and to dissect the potential intra-cancer cellular hierarchy.

RESULTS

Here, we apply single-cell analysis to systematically characterize the heterogeneity within leukemic cells using the MLL-AF9 driven mouse model of acute myeloid leukemia. We start with fluorescence-activated cell sorting analysis with seven surface markers, and extend by using a multiplexing quantitative polymerase chain reaction approach to assay the transcriptional profile of a panel of 175 carefully selected genes in leukemic cells at the single-cell level. By employing a set of computational tools we find striking heterogeneity within leukemic cells. Mapping to the normal hematopoietic cellular hierarchy identifies two distinct subtypes of leukemic cells; one similar to granulocyte/monocyte progenitors and the other to macrophage and dendritic cells. Further functional experiments suggest that these subtypes differ in proliferation rates and clonal phenotypes. Finally, co-expression network analysis reveals similarities as well as organizational differences between leukemia and normal granulocyte/monocyte progenitor networks.

CONCLUSIONS

Overall, our single-cell analysis pinpoints previously uncharacterized heterogeneity within leukemic cells and provides new insights into the molecular signatures of acute myeloid leukemia.

Reprogramming committed murine blood cells to induced hematopoietic stem cells with defined factors

Hematopoietic stem cells (HSCs) sustain blood formation throughout life and are the functional units of bone marrow transplantation. We show that transient expression of six transcription factors Run1t1, Hlf, Lmo2, Prdm5, Pbx1, and Zfp37 imparts multilineage transplantation potential onto otherwise committed lymphoid and myeloid progenitors and myeloid effector cells. Inclusion of Mycn and Meis1 and use of polycistronic viruses increase reprogramming efficacy. The reprogrammed cells, designated induced-HSCs (iHSCs), possess clonal multilineage differentiation potential, reconstitute stem/progenitor compartments, and are serially transplantable. Single-cell analysis revealed that iHSCs derived under optimal conditions exhibit a gene expression profile that is highly similar to endogenous HSCs. These findings demonstrate that expression of a set of defined factors is sufficient to activate the gene networks governing HSC functional identity in committed blood cells. Our results raise the prospect that blood cell reprogramming may be a strategy for derivation of transplantable stem cells for clinical application.

Boolean modeling of biological regulatory networks: a methodology tutorial

Given the complexity and interactive nature of biological systems, constructing informative and coherent network models of these systems and subsequently developing efficient approaches to analyze the assembled networks is of immense importance. The integration of network analysis and dynamic modeling enables one to investigate the behavior of the underlying system as a whole and to make experimentally testable predictions about less-understood aspects of the processes involved. In this paper, we present a tutorial on the fundamental steps of Boolean modeling of biological regulatory networks. We demonstrate how to infer a Boolean network model from the available experimental data, analyze the network using graph-theoretical measures, and convert it into a predictive dynamic model. For each step, the pitfalls one may encounter and possible ways to circumvent them are also discussed. We illustrate these steps on a toy network as well as in the context of the Drosophila melanogaster segment polarity gene network.

Dynamical and structural analysis of a T cell survival network identifies novel candidate therapeutic targets for large granular lymphocyte leukemia

The blood cancer T cell large granular lymphocyte (T-LGL) leukemia is a chronic disease characterized by a clonal proliferation of cytotoxic T cells. As no curative therapy is yet known for this disease, identification of potential therapeutic targets is of immense importance. In this paper, we perform a comprehensive dynamical and structural analysis of a network model of this disease. By employing a network reduction technique, we identify the stationary states (fixed points) of the system, representing normal and diseased (T-LGL) behavior, and analyze their precursor states (basins of attraction) using an asynchronous Boolean dynamic framework. This analysis identifies the T-LGL states of 54 components of the network, out of which 36 (67%) are corroborated by previous experimental evidence and the rest are novel predictions. We further test and validate one of these newly identified states experimentally. Specifically, we verify the prediction that the node SMAD is over-active in leukemic T-LGL by demonstrating the predominant phosphorylation of the SMAD family members Smad2 and Smad3. Our systematic perturbation analysis using dynamical and structural methods leads to the identification of 19 potential therapeutic targets, 68% of which are corroborated by experimental evidence. The novel therapeutic targets provide valuable guidance for wet-bench experiments. In addition, we successfully identify two new candidates for engineering long-lived T cells necessary for the delivery of virus and cancer vaccines. Overall, this study provides a bird's-eye-view of the avenues available for identification of therapeutic targets for similar diseases through perturbation of the underlying signal transduction network.

T-LGL leukemia is a blood cancer characterized by an abnormal increase in the abundance of a type of white blood cell called T cell. Since there is no known curative therapy for this disease, identification of potential therapeutic targets is of utmost importance. Experimental identification of manipulations capable of reversing the disease condition is usually a long, arduous process. Mathematical modeling can aid this process by identifying potential therapeutic interventions. In this work, we carry out a systematic analysis of a network model of T cell survival in T-LGL leukemia to get a deeper insight into the unknown facets of the disease. We identify the T-LGL status of 54 components of the system, out of which 36 (67%) are corroborated by previous experimental evidence and the rest are novel predictions, one of which we validate by follow-up experiments. By deciphering the structure and dynamics of the underlying network, we identify component perturbations that lead to programmed cell death, thereby suggesting several novel candidate therapeutic targets for future experiments.

Attractor analysis of asynchronous Boolean models of signal transduction networks

Prior work on the dynamics of Boolean networks, including analysis of the state space attractors and the basin of attraction of each attractor, has mainly focused on synchronous update of the nodes' states. Although the simplicity of synchronous updating makes it very attractive, it fails to take into account the variety of time scales associated with different types of biological processes. Several different asynchronous update methods have been proposed to overcome this limitation, but there have not been any systematic comparisons of the dynamic behaviors displayed by the same system under different update methods. Here we fill this gap by combining theoretical analysis such as solution of scalar equations and Markov chain techniques, as well as numerical simulations to carry out a thorough comparative study on the dynamic behavior of a previously proposed Boolean model of a signal transduction network in plants. Prior evidence suggests that this network admits oscillations, but it is not known whether these oscillations are sustained. We perform an attractor analysis of this system using synchronous and three different asynchronous updating schemes both in the case of the unperturbed (wild-type) and perturbed (node-disrupted) systems. This analysis reveals that while the wild-type system possesses an update-independent fixed point, any oscillations eventually disappear unless strict constraints regarding the timing of certain processes and the initial state of the system are satisfied. Interestingly, in the case of disruption of a particular node all models lead to an extended attractor. Overall, our work provides a roadmap on how Boolean network modeling can be used as a predictive tool to uncover the dynamic patterns of a biological system under various internal and environmental perturbations.