Persistent Cancer Cells: The Deadly Survivors.

Persistent cancer cells are the discrete and usually undetected cells that survive cancer drug treatment and constitute a major cause of treatment failure. These cells are characterized by their slow proliferation, highly flexible energy consumption, adaptation to their microenvironment, and phenotypic plasticity. Mechanisms that underlie their persistence offer highly coveted and sought-after therapeutic targets, and include diverse epigenetic, transcriptional, and translational regulatory processes, as well as complex cell-cell interactions. Although the successful clinical targeting of persistent cancer cells remains to be realized, immense progress has been made in understanding their persistence, yielding promising preclinical results.

Plasma proteometabolome in lung cancer: exploring biomarkers through bidirectional Mendelian randomization and colocalization analysis.

Unlike other cancers with widespread screening (breast, colorectal, cervical, prostate, and skin), lung nodule biopsies for positive screenings have higher morbidity with clinical complications. Development of non-invasive diagnostic biomarkers could thereby significantly enhance lung cancer management for at-risk patients. Here, we leverage Mendelian Randomization (MR) to investigate the plasma proteome and metabolome for potential biomarkers relevant to lung cancer. Utilizing bidirectional MR and co-localization analyses, we identify novel associations, highlighting inverse relationships between plasma proteins SFTPB and KDELC2 in lung adenocarcinoma (LUAD) and positive associations of TCL1A with lung squamous cell carcinoma (LUSC) and CNTN1 with small cell lung cancer (SCLC). Additionally, our work reveals significant negative correlations between metabolites such as theobromine and paraxanthine, along with paraxanthine-related ratios, in both LUAD and LUSC. Conversely, positive correlations are found in caffeine/paraxanthine and arachidonate (20:4n6)/paraxanthine ratios with these cancer types. Through single-cell sequencing data of normal lung tissue, we further explore the role of lung tissue-specific protein SFTPB in carcinogenesis. These findings offer new insights into lung cancer etiology, potentially guiding the development of diagnostic biomarkers and therapeutic approaches.

A mathematical model to study the impact of intra-tumour heterogeneity on anti-tumour CD8+ T cell immune response.

Intra-tumour heterogeneity (ITH) has a strong impact on the efficacy of the immune response against solid tumours. The number of sub-populations of cancer cells expressing different antigens and the percentage of immunogenic cells (i.e. tumour cells that are effectively targeted by immune cells) in a tumour are both expressions of ITH. Here, we present a spatially explicit stochastic individual-based model of the interaction dynamics between tumour cells and CD8+ T cells, which makes it possible to dissect out the specific impact of these two expressions of ITH on anti-tumour immune response. The set-up of numerical simulations of the model is defined so as to mimic scenarios considered in previous experimental studies. Moreover, the ability of the model to qualitatively reproduce experimental observations of successful and unsuccessful immune surveillance is demonstrated. First, the results of numerical simulations of this model indicate that the presence of a larger number of sub-populations of tumour cells that express different antigens is associated with a reduced ability of CD8+ T cells to mount an effective anti-tumour immune response. Secondly, the presence of a larger percentage of tumour cells that are not effectively targeted by CD8+ T cells may reduce the effectiveness of anti-tumour immunity. Ultimately, the mathematical model presented in this paper may provide a framework to help biologists and clinicians to better understand the mechanisms that are responsible for the emergence of different outcomes of immunotherapy.

Cytoplasmic STAT3 represses autophagy by inhibiting PKR activity.

In a screen designed to identify novel inducers of autophagy, we discovered that STAT3 inhibitors potently stimulate the autophagic flux. Accordingly, genetic inhibition of STAT3 stimulated autophagy in vitro and in vivo, while overexpression of STAT3 variants, encompassing wild-type, nonphosphorylatable, and extranuclear STAT3, inhibited starvation-induced autophagy. The SH2 domain of STAT3 was found to interact with the catalytic domain of the eIF2α kinase 2 EIF2AK2, best known as protein kinase R (PKR). Pharmacological and genetic inhibition of STAT3 stimulated the activating phosphorylation of PKR and consequent eIF2α hyperphosphorylation. Moreover, PKR depletion inhibited autophagy as initiated by chemical STAT3 inhibitors or free fatty acids like palmitate. STAT3-targeting chemicals and palmitate caused the disruption of inhibitory STAT3-PKR interactions, followed by PKR-dependent eIF2α phosphorylation, which facilitates autophagy induction. These results unravel an unsuspected mechanism of autophagy control that involves STAT3 and PKR as interacting partners.

Model-based design of RNA hybridization networks implemented in living cells.

Synthetic gene circuits allow the behavior of living cells to be reprogrammed, and non-coding small RNAs (sRNAs) are increasingly being used as programmable regulators of gene expression. However, sRNAs (natural or synthetic) are generally used to regulate single target genes, while complex dynamic behaviors would require networks of sRNAs regulating each other. Here, we report a strategy for implementing such networks that exploits hybridization reactions carried out exclusively by multifaceted sRNAs that are both targets of and triggers for other sRNAs. These networks are ultimately coupled to the control of gene expression. We relied on a thermodynamic model of the different stable conformational states underlying this system at the nucleotide level. To test our model, we designed five different RNA hybridization networks with a linear architecture, and we implemented them in Escherichia coli. We validated the network architecture at the molecular level by native polyacrylamide gel electrophoresis, as well as the network function at the bacterial population and single-cell levels with a fluorescent reporter. Our results suggest that it is possible to engineer complex cellular programs based on RNA from first principles. Because these networks are mainly based on physical interactions, our designs could be expanded to other organisms as portable regulatory resources or to implement biological computations.

Dynamic signal processing by ribozyme-mediated RNA circuits to control gene expression.

Organisms have different circuitries that allow converting signal molecule levels to changes in gene expression. An important challenge in synthetic biology involves the de novo design of RNA modules enabling dynamic signal processing in live cells. This requires a scalable methodology for sensing, transmission, and actuation, which could be assembled into larger signaling networks. Here, we present a biochemical strategy to design RNA-mediated signal transduction cascades able to sense small molecules and small RNAs. We design switchable functional RNA domains by using strand-displacement techniques. We experimentally characterize the molecular mechanism underlying our synthetic RNA signaling cascades, show the ability to regulate gene expression with transduced RNA signals, and describe the signal processing response of our systems to periodic forcing in single live cells. The engineered systems integrate RNA-RNA interaction with available ribozyme and aptamer elements, providing new ways to engineer arbitrary complex gene circuits.

A new frontier in synthetic biology: automated design of small RNA devices in bacteria.

RNA devices provide synthetic biologists with tools for manipulating post-transcriptional regulation and conditional detection of cellular biomolecules. The use of computational methods to design RNA devices has improved to the stage where it is now possible to automate the entire design process. These methods utilize structure prediction tools that optimize nucleotide sequences, together with fragments of known independent functionalities. Recently, this approach has been used to create an automated method for the de novo design of riboregulators. Here, we describe how it is possible to obtain riboregulatory circuits in prokaryotes by capturing the relevant interactions of RNAs inside the cytoplasm using a physicochemical model. We focus on the regulation of protein expression mediated by intra- or intermolecular interactions of small RNAs (sRNAs), and discuss the design of riboregulators for other functions. The automated design of RNA devices opens new possibilities for engineering fully synthetic regulatory systems that program new functions or reprogram dysfunctions in living cells.

Wedelolactone, a naturally occurring coumestan, enhances interferon-γ signaling through inhibiting STAT1 protein dephosphorylation.

Signal transducers and activators of transcription 1 (STAT1) transduces signals from cytokines and growth factors, particularly IFN-γ, and regulates expression of genes involved in cell survival/death, proliferation, and migration. STAT1 is activated through phosphorylation on its tyrosine 701 by JAKs and is inactivated through dephosphorylation by tyrosine phosphatases. We discovered a natural compound, wedelolactone, that increased IFN-γ signaling by inhibiting STAT1 dephosphorylation and prolonging STAT1 activation through specific inhibition of T-cell protein tyrosine phosphatase (TCPTP), an important tyrosine phosphatase for STAT1 dephosphorylation. More interestingly, wedelolactone inhibited TCPTP through interaction with the C-terminal autoinhibition domain of TCPTP. We also found that wedelolactone synergized with IFN-γ to induce apoptosis of tumor cells. Our data suggest a new target for anticancer or antiproliferation drugs, a new mechanism to regulate PTPs specifically, and a new drug candidate for treating cancer or other proliferation disorders.

Selective induction of tumor cell apoptosis by a novel P450-mediated reactive oxygen species (ROS) inducer methyl 3-(4-nitrophenyl) propiolate.

Induction of tumor cell apoptosis has been recognized as a valid anticancer strategy. However, therapeutic selectivity between tumor and normal cells has always been a challenge. Here, we report a novel anti-cancer compound methyl 3-(4-nitrophenyl) propiolate (NPP) preferentially induces apoptosis in tumor cells through P450-catalyzed reactive oxygen species (ROS) production. A compound sensitivity study on multiple cell lines shows that tumor cells with high basal ROS levels, low antioxidant capacities, and p53 mutations are especially sensitive to NPP. Knockdown of p53 sensitized non-transformed cells to NPP-induced cell death. Additionally, by comparing NPP with other ROS inducers, we show that the susceptibility of tumor cells to the ROS-induced cell death is influenced by the mode, amount, duration, and perhaps location of ROS production. Our studies not only discovered a unique anticancer drug candidate but also shed new light on the understanding of ROS generation and function and the potential application of a ROS-promoting strategy in cancer treatment.

The IKK complex contributes to the induction of autophagy.

In response to stress, cells start transcriptional and transcription-independent programs that can lead to adaptation or death. Here, we show that multiple inducers of autophagy, including nutrient depletion, trigger the activation of the IKK (IkappaB kinase) complex that is best known for its essential role in the activation of the transcription factor NF-kappaB by stress. Constitutively active IKK subunits stimulated autophagy and transduced multiple signals that operate in starvation-induced autophagy, including the phosphorylation of AMPK and JNK1. Genetic inhibition of the nuclear translocation of NF-kappaB or ablation of the p65/RelA NF-kappaB subunit failed to suppress IKK-induced autophagy, indicating that IKK can promote the autophagic pathway in an NF-kappaB-independent manner. In murine and human cells, knockout and/or knockdown of IKK subunits (but not that of p65) prevented the induction of autophagy in response to multiple stimuli. Moreover, the knockout of IKK-beta suppressed the activation of autophagy by food deprivation or rapamycin injections in vivo, in mice. Altogether, these results indicate that IKK has a cardinal role in the stimulation of autophagy by physiological and pharmacological stimuli.

Tet methylcytosine dioxygenase 2 (TET2) deficiency elicits EGFR-TKI (tyrosine kinase inhibitors) resistance in non-small cell lung cancer.

Despite epidermal growth factor receptor (EGFR) tyrosine kinase inhibitors (TKI) have shown remarkable efficacy in patients with EGFR-mutant non-small cell lung cancer (NSCLC), acquired resistance inevitably develops, limiting clinical efficacy. We found that TET2 was poly-ubiquitinated by E3 ligase CUL7FBXW11 and degraded in EGFR-TKI resistant NSCLC cells. Genetic perturbation of TET2 rendered parental cells more tolerant to TKI treatment. TET2 was stabilized by MEK1 phosphorylation at Ser 1107, while MEK1 inactivation promoted its proteasome degradation by enhancing the recruitment of CUL7FBXW11. Loss of TET2 resulted in the upregulation of TNF/NF-κB signaling that confers the EGFR-TKI resistance. Genetic or pharmacological inhibition of NF-κB attenuate the TKI resistance both in vitro and in vivo. Our findings exemplified how a cell growth controlling kinase MEK1 leveraged the epigenetic homeostasis by regulating TET2, and demonstrated an alternative path of non-mutational acquired EGFR-TKI resistance modulated by TET2 deficiency. Therefore, combined strategy exploiting EGFR-TKI and inhibitors of TET2/NF-κB axis holds therapeutic potential for treating NSCLC patients who suffered from this resistance.

Cancer drug-tolerant persister cells: from biological questions to clinical opportunities.

The emergence of drug resistance is the most substantial challenge to the effectiveness of anticancer therapies. Orthogonal approaches have revealed that a subset of cells, known as drug-tolerant 'persister' (DTP) cells, have a prominent role in drug resistance. Although long recognized in bacterial populations which have acquired resistance to antibiotics, the presence of DTPs in various cancer types has come to light only in the past two decades, yet several aspects of their biology remain enigmatic. Here, we delve into the biological characteristics of DTPs and explore potential strategies for tracking and targeting them. Recent findings suggest that DTPs exhibit remarkable plasticity, being capable of transitioning between different cellular states, resulting in distinct DTP phenotypes within a single tumour. However, defining the biological features of DTPs has been challenging, partly due to the complex interplay between clonal dynamics and tissue-specific factors influencing their phenotype. Moreover, the interactions between DTPs and the tumour microenvironment, including their potential to evade immune surveillance, remain to be discovered. Finally, the mechanisms underlying DTP-derived drug resistance and their correlation with clinical outcomes remain poorly understood. This Roadmap aims to provide a comprehensive overview of the field of DTPs, encompassing past achievements and current endeavours in elucidating their biology. We also discuss the prospect of future advancements in technologies in helping to unveil the features of DTPs and propose novel therapeutic strategies that could lead to their eradication.

Multicellular ecotypes shape progression of lung adenocarcinoma from ground-glass opacity toward advanced stages.

Lung adenocarcinoma is a type of cancer that exhibits a wide range of clinical radiological manifestations, from ground-glass opacity (GGO) to pure solid nodules, which vary greatly in terms of their biological characteristics. Our current understanding of this heterogeneity is limited. To address this gap, we analyze 58 lung adenocarcinoma patients via machine learning, single-cell RNA sequencing (scRNA-seq), and whole-exome sequencing, and we identify six lung multicellular ecotypes (LMEs) correlating with distinct radiological patterns and cancer cell states. Notably, GGO-associated neoantigens in early-stage cancers are recognized by CD8+ T cells, indicating an immune-active environment, while solid nodules feature an immune-suppressive LME with exhausted CD8+ T cells, driven by specific stromal cells such as CTHCR1+ fibroblasts. This study also highlights EGFR(L858R) neoantigens in GGO samples, suggesting potential CD8+ T cell activation. Our findings offer valuable insights into lung adenocarcinoma heterogeneity, suggesting avenues for targeted therapies in early-stage disease.

Theoretical and experimental analysis of the forced LacI-AraC oscillator with a minimal gene regulatory model.

Oscillatory dynamics have been observed in multiple cellular functions and synthetic constructs; and here, we study the behavior of a synthetic oscillator under temporal perturbations. We use a minimal model, involving a single transcription factor with delayed self-repression and enzymatic degradation, together with a first-order perturbative approach, to derive an analytical expression for the power spectrum of the system, which characterizes its response to external forces and molecular noise. Experimentally, we force and monitor the dynamics of the LacI-AraC oscillator in single cells during long time intervals by constructing a microfluidics device. Pulse dynamics of IPTG with different periods serve to perturb this system. Due to the resonance of the system, we predict theoretically and confirm experimentally the dependence on the forcing frequency of the variability in gene expression with time and the synchronization of the population to the input signal. The reported results show that the engineering of gene circuits can provide test cases for dynamical models, which could be further exploited in synthetic biology.

Immunotherapy for Recurrent Glioma-From Bench to Bedside.

Glioma is the most aggressive malignant tumor of the central nervous system, and most patients suffer from a recurrence. Unfortunately, recurrent glioma often becomes resistant to established chemotherapy and radiotherapy treatments. Immunotherapy, a rapidly developing anti-tumor therapy, has shown a potential value in treating recurrent glioma. Multiple immune strategies have been explored. The most-used ones are immune checkpoint blockade (ICB) antibodies, which are barely effective in monotherapy. However, when combined with other immunotherapy, especially with anti-angiogenesis antibodies, ICB has shown encouraging efficacy and enhanced anti-tumor immune response. Oncolytic viruses and CAR-T therapies have shown promising results in recurrent glioma through multiple mechanisms. Vaccination strategies and immune-cell-based immunotherapies are promising in some subgroups of patients, and multiple new tumor antigenic targets have been discovered. In this review, we discuss current applicable immunotherapies and related mechanisms for recurrent glioma, focusing on multiple preclinical models and clinical trials in the last 5 years. Through reviewing the current combination of immune strategies, we would like to provide substantive thoughts for further novel therapeutic regimes treating recurrent glioma.

Drug-tolerant persister cells in cancer: the cutting edges and future directions.

Drug-tolerant persister (DTP) cell populations were originally discovered in antibiotic-resistant bacterial biofilms. Similar populations with comparable features have since been identified among cancer cells and have been linked with treatment resistance that lacks an underlying genomic alteration. Research over the past decade has improved our understanding of the biological roles of DTP cells in cancer, although clinical knowledge of the role of these cells in treatment resistance remains limited. Nonetheless, targeting this population is anticipated to provide new treatment opportunities. In this Perspective, we aim to provide a clear definition of the DTP phenotype, discuss the underlying characteristics of these cells, their biomarkers and vulnerabilities, and encourage further research on DTP cells that might improve our understanding and enable the development of more effective anticancer therapies.

Spatial patterns of the cap-binding complex eIF4F in human melanoma cells.

As a central node of protein synthesis, the cap-binding complex, eukaryotic translation initiation factor 4 F (eIF4F), is involved in cell homeostasis, development and tumorigenesis. A large body of literature exists on the regulation and function of eIF4F in cancer cells, however the intracellular localization patterns of this complex are largely unknown. Since different subsets of mRNAs are translated in distinct subcellular compartments, understanding the distribution of translation initiation factors in the cell is of major interest. Here, we developed an in situ detection method for eIF4F at the single cell level. By using an image-based spot feature analysis pipeline as well as supervised machine learning, we identify five distinct spatial patterns of the eIF4F translation initiation complex in human melanoma cells. The quantity of eIF4F complex per cell correlated with the global mRNA translation activity, and its variation is dynamically regulated by cell state or extracellular stimuli. In contrast, the spatial patterns of eIF4F complexes at the single cell level could distinguish melanoma cells harboring different oncogenic driver mutations. This suggests that different tumorigenic contexts differentially regulate the subcellular localization of mRNA translation, with specific localization of eIF4F potentially associated with melanoma cell chemoresistance.

The multiple roles of LDH in cancer.

High serum lactate dehydrogenase (LDH) levels are typically associated with a poor prognosis in many cancer types. Even the most effective drugs, which have radically improved outcomes in patients with melanoma over the past decade, provide only marginal benefit to those with high serum LDH levels. When viewed separately from the oncological, biochemical, biological and immunological perspectives, serum LDH is often interpreted in very different ways. Oncologists usually see high serum LDH only as a robust biomarker of a poor prognosis, and biochemists are aware of the complexity of the various LDH isoforms and of their key roles in cancer metabolism, whereas LDH is typically considered to be oncogenic and/or immunosuppressive by cancer biologists and immunologists. Integrating these various viewpoints shows that the regulation of the five LDH isoforms, and their enzymatic and non-enzymatic functions is closely related to key oncological processes. In this Review, we highlight that serum LDH is far more than a simple indicator of tumour burden; it is a complex biomarker associated with the activation of several oncogenic signalling pathways as well as with the metabolic activity, invasiveness and immunogenicity of many tumours, and constitutes an extremely attractive target for cancer therapy.

A PD-1/PD-L1 Proximity Assay as a Theranostic Marker for PD-1 Blockade in Patients with Metastatic Melanoma.

PURPOSE: Less than 50% of patients with melanoma respond to anti-programmed cell death protein 1 (anti-PD-1), and this treatment can induce severe toxicity. Predictive markers are thus needed to improve the benefit/risk ratio of immune checkpoint inhibitors (ICI). Baseline tumor parameters such as programmed death ligand 1 (PD-L1) expression, CD8+ T-cell infiltration, mutational burden, and various transcriptomic signatures are associated with response to ICI, but their predictive values are not sufficient. Interaction between PD-1 and its main ligand, PD-L1, appears as a valuable target of anti-PD-1 therapy. Thus, instead of looking at PD-L1 expression only, we evaluated the predictive value of the proximity between PD-1 and its neighboring PD-L1 molecules in terms of response to anti-PD-1 therapy. EXPERIMENTAL DESIGN: PD-1/PD-L1 proximity was assessed by proximity ligation assay (PLA) on 137 samples from two cohorts (exploratory n = 66 and validation n = 71) of samples from patients with melanoma treated with anti-PD-1±anti-CTLA-4. Additional predictive biomarkers, such as PD-L1 expression (MELscore), CD8+ cells density, and NanoString RNA signature, were also evaluated. RESULTS: A PD-1/PD-L1 PLA model was developed to predict tumor response in an exploratory cohort and further evaluated in an independent validation cohort. This score showed higher predictive ability (AUC = 0.85 and 0.79 in the two cohorts, respectively) for PD-1/PD-L1 PLA as compared with other parameters (AUC = 0.71-0.77). Progression-free and overall survival were significantly longer in patients with high PLA values (P = 0.00019 and P < 0.0001, respectively). CONCLUSIONS: The proximity between PD-1 and PD-L1, easily assessed by this PLA on one formalin-fixed paraffin-embedded section, appears as a new biomarker of anti-PD-1 efficacy.

The Role of mRNA Translational Control in Tumor Immune Escape and Immunotherapy Resistance.

Tremendous advances have been made in cancer immunotherapy over the last decade. Among the different steps of gene expression, translation of mRNA is emerging as an essential player in both cancer and immunity. Changes in mRNA translation are both rapid and adaptive, and translational reprogramming is known to be necessary for sustaining cancer cell proliferation. However, the role of mRNA translation in shaping an immune microenvironment permissive to tumors has not been extensively studied. Recent studies on immunotherapy approaches have indicated critical roles of mRNA translation in regulating the expression of immune checkpoint proteins, tuning the secretion of inflammation-associated factors, modulating the differentiation of immune cells in the tumor microenvironment, and promoting cancer resistance to immunotherapies. Careful consideration of the role of mRNA translation in the tumor-immune ecosystem could suggest more effective therapeutic strategies and may eventually change the current paradigm of cancer immunotherapy. In this review, we discuss recent advances in understanding the relationship between mRNA translation and tumor-associated immunity, the potential mechanisms of immunotherapy resistance in cancers linked to translational reprogramming, and therapeutic perspectives and potential challenges of modulating translational regulation in cancer immunotherapy.