Targeting CD73 with AB680 (Quemliclustat), a Novel and Potent Small-Molecule CD73 Inhibitor, Restores Immune Functionality and Facilitates Antitumor Immunity.

T cells play a critical role in the control of cancer. The development of immune checkpoint blockers (ICB) aimed at enhancing antitumor T-cell responses has revolutionized cancer treatment. However, durable clinical benefit is observed in only a subset of patients, prompting research efforts to focus on strategies that target multiple inhibitory signals within the tumor microenvironment (TME) to limit tumor evasion and improve patient outcomes. Adenosine has emerged as a potent immune suppressant within the TME, and CD73 is the major enzyme responsible for its extracellular production. CD73 can be co-opted within the TME to impair T-cell-mediated antitumor immunity and promote tumor growth. To target this pathway and block the formation of adenosine, we designed a novel, selective, and potent class of small-molecule inhibitors of CD73, including AB680 (quemliclustat), which is currently being tested in patients with cancer. AB680 effectively restored T-cell proliferation, cytokine secretion, and cytotoxicity that were dampened by the formation of immunosuppressive adenosine by CD73. Furthermore, in an allogeneic mixed lymphocyte reaction where CD73-derived adenosine had a dominant suppressive effect in the presence of PD-1 blockade, AB680 restored T-cell activation and function. Finally, in a preclinical mouse model of melanoma, AB680 inhibited CD73 in the TME and increased the antitumor activity of PD-1 blockade. Collectively, these data provide a rationale for the inhibition of CD73 with AB680 in combination with ICB, such as anti-PD-1, to improve cancer patient outcomes.

Intrinsic and acquired drug resistance to LSD1 inhibitors in small cell lung cancer occurs through a TEAD4-driven transcriptional state.

Small-cell lung cancer (SCLC) is a heterogeneous disease, consisting of intratumoral and intertumoral neuroendocrine (ASCL1 and/or NEUROD1), mesenchymal-like, and YAP-driven transcriptional states. Lysine-specific demethylase 1 (LSD1; also known as KDM1A) inhibitors have recently been progressed to clinical trials in SCLC based on a promising preclinical antitumor activity. A potential clinical limitation of LSD1 inhibitors is the heterogeneous drug responses that have been observed in SCLC cell lines and patient-derived models. Based on these observations, we studied molecular and transcriptional signatures that predict patient response to this class of drug. Employing SCLC patient-derived transcriptional signatures, we define that SCLC cell lines sensitive to LSD1 inhibitors are enriched in neuroendocrine transcriptional markers, whereas cell lines enriched in a mesenchymal-like transcriptional program demonstrate intrinsic resistance to LSD1 inhibitors. We have identified a reversible, adaptive resistance mechanism to LSD1 inhibitors through epigenetic reprogramming to a TEAD4-driven mesenchymal-like state. Our data suggest that only a segment of SCLC patients, with a defined neuroendocrine differentiation state, will likely benefit from LSD1 inhibitors. It provides novel evidence for the selection of a TEAD4-driven mesenchymal-like subpopulation resistant to LSD1 inhibitors in SCLC patients that may require effective drug combinations to sustain effective clinical responses.

Transcriptomic profiling of adjuvant colorectal cancer identifies three key prognostic biological processes and a disease specific role for granzyme B.

BACKGROUND: Colorectal cancer (CRC) is a leading cause of cancer-related deaths, with a 5% 5-year survival rate for metastatic disease, yet with limited therapeutic advancements due to insufficient understanding of and inability to accurately capture high-risk CRC patients who are most likely to recur. We aimed to improve high-risk classification by identifying biological pathways associated with outcome in adjuvant stage II/III CRC. METHODS AND FINDINGS: We included 1062 patients with stage III or high-risk stage II colon carcinoma from the prospective three-arm randomized phase 3 AVANT trial, and performed expression profiling to identify a prognostic signature. Data from validation cohort GSE39582, The Cancer Genome Atlas, and cell lines were used to further validate the prognostic biology. Our retrospective analysis of the adjuvant AVANT trial uncovered a prognostic signature capturing three biological functions-stromal, proliferative and immune-that outperformed the Consensus Molecular Subtypes (CMS) and recurrence prediction signatures like Oncotype Dx in an independent cohort. Importantly, within the immune component, high granzyme B (GZMB) expression had a significant prognostic impact while other individual T-effector genes were less or not prognostic. In addition, we found GZMB to be endogenously expressed in CMS2 tumor cells and to be prognostic in a T cell independent fashion. A limitation of our study is that these results, although robust and derived from a large dataset, still need to be clinically validated in a prospective study. CONCLUSIONS: This work furthers our understanding of the underlying biology that propagates stage II/III CRC disease progression and provides scientific rationale for future high-risk stratification and targeted treatment evaluation in biomarker defined subpopulations of resectable high-risk CRC. Our results also shed light on an alternative GZMB source with context-specific implications on the disease's unique biology.

Integrated digital pathology and transcriptome analysis identifies molecular mediators of T-cell exclusion in ovarian cancer.

Close proximity between cytotoxic T lymphocytes and tumour cells is required for effective immunotherapy. However, what controls the spatial distribution of T cells in the tumour microenvironment is not well understood. Here we couple digital pathology and transcriptome analysis on a large ovarian tumour cohort and develop a machine learning approach to molecularly classify and characterize tumour-immune phenotypes. Our study identifies two important hallmarks characterizing T cell excluded tumours: 1) loss of antigen presentation on tumour cells and 2) upregulation of TGFß and activated stroma. Furthermore, we identify TGFß as an important mediator of T cell exclusion. TGFß reduces MHC-I expression in ovarian cancer cells in vitro. TGFß also activates fibroblasts and induces extracellular matrix production as a potential physical barrier to hinder T cell infiltration. Our findings indicate that targeting TGFß might be a promising strategy to overcome T cell exclusion and improve clinical benefits of cancer immunotherapy.

Genomic Alterations Associated with Recurrence and TNBC Subtype in High-Risk Early Breast Cancers.

The identification of early breast cancer patients who may benefit from adjuvant chemotherapy has evolved to include assessment of clinicopathologic features such as tumor size and nodal status, as well as several gene-expression profiles for ER-positive, HER2-negative cancers. However, these tools do not reliably identify patients at the greatest risk of recurrence. The mutation and copy-number landscape of triple-negative breast cancer (TNBC) subtypes defined by gene expression is also largely unknown, and elucidation of this landscape may shed light on novel therapeutic opportunities. The USO01062 phase III clinical trial of standard chemotherapy (with or without capecitabine) enrolled a cohort of putatively high-risk patients based on clinical features, yet only observed a 5-year disease-free survival event rate of 11.6%. In order to uncover genomic aberrations associated with recurrence, a targeted next-generation sequencing panel was used to compare tumor specimens from patients who had a recurrence event with a matched set who did not. The somatic mutation and copy-number alteration landscapes of high-risk early breast cancer patients were characterized and alterations associated with relapse were identified. Tumor mutational burden was evaluated but was not prognostic in this study, nor did it correlate with PDL1 or CD8 gene expression. However, TNBC subtypes had substantial genomic heterogeneity with a distinct pattern of genomic alterations and putative underlying driver mutations. IMPLICATIONS: The present study uncovers a compendium of genomic alterations with utility to more precisely identify high-risk patients for adjuvant trials of novel therapeutic agents.

Novel Hybrid Phenotype Revealed in Small Cell Lung Cancer by a Transcription Factor Network Model That Can Explain Tumor Heterogeneity.

Small cell lung cancer (SCLC) is a devastating disease due to its propensity for early invasion and refractory relapse after initial treatment response. Although these aggressive traits have been associated with phenotypic heterogeneity, our understanding of this association remains incomplete. To fill this knowledge gap, we inferred a set of 33 transcription factors (TF) associated with gene signatures of the known neuroendocrine/epithelial (NE) and non-neuroendocrine/mesenchymal-like (ML) SCLC phenotypes. The topology of this SCLC TF network was derived from prior knowledge and was simulated using Boolean modeling. These simulations predicted that the network settles into attractors, or TF expression patterns, that correlate with NE or ML phenotypes, suggesting that TF network dynamics underlie the emergence of heterogeneous SCLC phenotypes. However, several cell lines and patient tumor specimens failed to correlate with either the NE or ML attractors. By flow cytometry, single cells within these cell lines simultaneously expressed surface markers of both NE and ML differentiation, confirming the existence of a "hybrid" phenotype. Upon exposure to standard-of-care cytotoxic drugs or epigenetic modifiers, NE and ML cell populations converged toward the hybrid state, suggesting possible escape from treatment. Our findings indicate that SCLC phenotypic heterogeneity can be specified dynamically by attractor states of a master regulatory TF network. Thus, SCLC heterogeneity may be best understood as states within an epigenetic landscape. Understanding phenotypic transitions within this landscape may provide insights to clinical applications. Cancer Res; 77(5); 1063-74. ©2016 AACR.

Altered TGF-α/ß signaling drives cooperation between breast cancer cell populations.

The role of tumor heterogeneity in regulating disease progression is poorly understood. We hypothesized that interactions between subpopulations of cancer cells can affect the progression of tumors selecting for a more aggressive phenotype. We developed an in vivo assay based on the immortalized nontumorigenic breast cell line MCF10A and its Ras-transformed derivatives AT1 (mildly tumorigenic) and CA1d (highly tumorigenic). CA1d cells outcompeted MCF10A, forming invasive tumors. AT1 grafts were approximately 1% the size of CA1d tumors when initiated using identical cell numbers. In contrast, CA1d/AT1 mixed tumors were larger than tumors composed of AT1 alone (100-fold) or CA1d (3-fold), suggesting cooperation in tumor growth. One of the mechanisms whereby CA1d and AT1 were found to cooperate was by modulation of TGF-α and TGF-ß signaling. Both of these molecules were sufficient to induce changes in AT1 proliferative potential in vitro. Reisolation of AT1 tumor-derived (AT1-TD) cells from these mixed tumors revealed that AT1-TD cells grew in vivo, forming tumors as large as tumorigenic CA1d cells. Cooperation between subpopulations of cancer epithelium is an understudied mechanism of tumor growth and invasion that may have implications on tumor resistance to current therapies.-Franco, O. E., Tyson, D. R., Konvinse, K. C., Udyavar, A. R., Estrada, L., Quaranta, V., Crawford, S. E., Hayward, S. W. Altered TGF-α/ß signaling drives cooperation between breast cancer cell populations.

Co-expression network analysis identifies Spleen Tyrosine Kinase (SYK) as a candidate oncogenic driver in a subset of small-cell lung cancer.

BACKGROUND: Oncogenic mechanisms in small-cell lung cancer remain poorly understood leaving this tumor with the worst prognosis among all lung cancers. Unlike other cancer types, sequencing genomic approaches have been of limited success in small-cell lung cancer, i.e., no mutated oncogenes with potential driver characteristics have emerged, as it is the case for activating mutations of epidermal growth factor receptor in non-small-cell lung cancer. Differential gene expression analysis has also produced SCLC signatures with limited application, since they are generally not robust across datasets. Nonetheless, additional genomic approaches are warranted, due to the increasing availability of suitable small-cell lung cancer datasets. Gene co-expression network approaches are a recent and promising avenue, since they have been successful in identifying gene modules that drive phenotypic traits in several biological systems, including other cancer types. RESULTS: We derived an SCLC-specific classifier from weighted gene co-expression network analysis (WGCNA) of a lung cancer dataset. The classifier, termed SCLC-specific hub network (SSHN), robustly separates SCLC from other lung cancer types across multiple datasets and multiple platforms, including RNA-seq and shotgun proteomics. The classifier was also conserved in SCLC cell lines. SSHN is enriched for co-expressed signaling network hubs strongly associated with the SCLC phenotype. Twenty of these hubs are actionable kinases with oncogenic potential, among which spleen tyrosine kinase (SYK) exhibits one of the highest overall statistical associations to SCLC. In patient tissue microarrays and cell lines, SCLC can be separated into SYK-positive and -negative. SYK siRNA decreases proliferation rate and increases cell death of SYK-positive SCLC cell lines, suggesting a role for SYK as an oncogenic driver in a subset of SCLC. CONCLUSIONS: SCLC treatment has thus far been limited to chemotherapy and radiation. Our WGCNA analysis identifies SYK both as a candidate biomarker to stratify SCLC patients and as a potential therapeutic target. In summary, WGCNA represents an alternative strategy to large scale sequencing for the identification of potential oncogenic drivers, based on a systems view of signaling networks. This strategy is especially useful in cancer types where no actionable mutations have emerged.