Principles and mechanisms of non-genetic resistance in cancer

Signaling dynamics in coexisting monoclonal cell subpopulations unveil mechanisms of resistance to anti-cancer compounds

BACKGROUND

Tumor heterogeneity is a main contributor of resistance to anti-cancer targeted agents though it has proven difficult to study. Unfortunately, model systems to functionally characterize and mechanistically study dynamic responses to treatment across coexisting subpopulations of cancer cells remain a missing need in oncology.

METHODS

Using single cell cloning and expansion techniques, we established monoclonal cell subpopulations (MCPs) from a commercially available epidermal growth factor receptor (EGFR)-mutant non-small cell lung cancer cell line. We then used this model sensitivity to the EGFR inhibitor osimertinib across coexisting cell populations within the same tumor. Pathway-centered signaling dynamics associated with response to treatment and morphological characteristics of the MCPs were assessed using Reverse Phase Protein Microarray. Signaling nodes differentially activated in MCPs less sensitive to treatment were then pharmacologically inhibited to identify target signaling proteins putatively implicated in promoting drug resistance.

RESULTS

MCPs demonstrated highly heterogeneous sensitivities to osimertinib. Cell viability after treatment increased > 20% compared to the parental line in selected MCPs, whereas viability decreased by 75% in other MCPs. Reduced treatment response was detected in MCPs with higher proliferation rates, EGFR L858R expression, activation of EGFR binding partners and downstream signaling molecules, and expression of epithelial-to-mesenchymal transition markers. Levels of activation of EGFR binding partners and MCPs' proliferation rates were also associated with response to c-MET and IGFR inhibitors.

CONCLUSIONS

MCPs represent a suitable model system to characterize heterogeneous biomolecular behaviors in preclinical studies and identify and functionally test biological mechanisms associated with resistance to targeted therapeutics.

CGMega: explainable graph neural network framework with attention mechanisms for cancer gene module dissection

Cancer is rarely the straightforward consequence of an abnormality in a single gene, but rather reflects a complex interplay of many genes, represented as gene modules. Here, we leverage the recent advances of model-agnostic interpretation approach and develop CGMega, an explainable and graph attention-based deep learning framework to perform cancer gene module dissection. CGMega outperforms current approaches in cancer gene prediction, and it provides a promising approach to integrate multi-omics information. We apply CGMega to breast cancer cell line and acute myeloid leukemia (AML) patients, and we uncover the high-order gene module formed by ErbB family and tumor factors NRG1, PPM1A and DLG2. We identify 396 candidate AML genes, and observe the enrichment of either known AML genes or candidate AML genes in a single gene module. We also identify patient-specific AML genes and associated gene modules. Together, these results indicate that CGMega can be used to dissect cancer gene modules, and provide high-order mechanistic insights into cancer development and heterogeneity.

Morphological and Immunocytochemical Characterization of Paclitaxel-Induced Microcells in Sk-Mel-28 Melanoma Cells

Biomarkers, including proteins, nucleic acids, antibodies, and peptides, are essential for identifying diseases such as cancer and differentiating between healthy and abnormal cells in patients. To date, studies have shown that cancer stem cells have DNA repair mechanisms that deter the effects of medicinal treatment. Experiments with cell cultures and chemotherapy treatments of these cultures have revealed the presence of small cells, with a small amount of cytoplasm that can be intensively stained with azure eosin, called microcells. Microcells develop during sporosis from a damaged tumor macrocell. After anticancer therapy in tumor cells, a defective macrocell may produce one or more microcells. This study aims to characterize microcell morphology in melanoma cell lines. In this investigation, we characterized the population of cancer cell microcells after applying paclitaxel treatment to a Sk-Mel-28 melanoma cell line using immunocytochemical cell marker detection and fluorescent microscopy. Paclitaxel-treated cancer cells show stronger expression of stem-associated ALDH2, SOX2, and Nanog markers than untreated cells. The proliferation of nuclear antigens in cells and the synthesis of RNA in microcells indicate cell self-defense, promoting resistance to applied therapy. These findings improve our understanding of microcell behavior in melanoma, potentially informing future strategies to counteract drug resistance in cancer treatment.

From genetic mosaicism to tumorigenesis through indirect genetic effects

Genetic mosaicism has long been linked to aging, and several hypotheses have been proposed to explain the potential connections between mosaicism and susceptibility to cancer. It has been proposed that mosaicism may disrupt tissue homeostasis by affecting intercellular communications and releasing microenvironmental constraints within tissues. The underlying mechanisms driving these tissue-level influences remain unidentified, however. Here, we present an evolutionary perspective on the interplay between mosaicism and cancer, suggesting that the tissue-level impacts of genetic mosaicism can be attributed to Indirect Genetic Effects (IGEs). IGEs can increase the level of cellular stochasticity and phenotypic instability among adjacent cells, thereby elevating the risk of cancer development within the tissue. Moreover, as cells experience phenotypic changes in response to challenging microenvironmental conditions, these changes can initiate a cascade of nongenetic alterations, referred to as Indirect non-Genetic Effects (InGEs), which in turn catalyze IGEs among surrounding cells. We argue that incorporating both InGEs and IGEs into our understanding of the process of oncogenic transformation could trigger a major paradigm shift in cancer research with far-reaching implications for practical applications.

Predictability: A new distinguishing feature of cancer?

Cancer is a consequence of stochastic (mutations, genetic, and epigenetic instabilities) and deterministic (evolutionary bottlenecks) events. Stochastic events are less amenable to prediction, whereas deterministic events yield more predictable results. The relative contribution of these opposing forces determines cancer predictability, which affects the accuracy of our prognostic predictions and is critical for treatment planning. In this study, we attempted to quantify predictability. The predictability index (PI) was defined as the median overall-survival at any time point divided by the standard error at that time. Using data obtained from the SEER program, we found striking differences in the PI of different tumors. Highly predictable tumors were malignancies of the breast, thyroid, prostate, and testis (5-year PI of 3516, 1920, 1919, and 1805, respectively). Less predictable tumors were colorectal, melanoma, and bladder (5-year PI of 1264, 1197, and 760, respectively). Least predictable were pancreatic cancer and chronic myelogenous leukemia (5-year PI of 129, and 42). PI decreased during follow-up in all examined tumors and showed sex differences in some cases. Thyroid cancer was significantly more predictable in women (5-year PI of 2579 vs. 748, p = 0.00017) and bladder cancer more predictable in men (5-year PI of 723 vs. 385, p = 0.012), Predictability is a potentially new distinguishing feature of malignancy. This study sheds light on prognostic accuracy and provides insight into the relative roles of stochastic and deterministic forces during carcinogenesis.

Graphene-Based Photodynamic Therapy and Overcoming Cancer Resistance Mechanisms: A Comprehensive Review

Photodynamic therapy (PDT) is a non-invasive therapy that has made significant progress in treating different diseases, including cancer, by utilizing new nanotechnology products such as graphene and its derivatives. Graphene-based materials have large surface area and photothermal effects thereby making them suitable candidates for PDT or photo-active drug carriers. The remarkable photophysical properties of graphene derivates facilitate the efficient generation of reactive oxygen species (ROS) upon light irradiation, which destroys cancer cells. Surface functionalization of graphene and its materials can also enhance their biocompatibility and anticancer activity. The paper delves into the distinct roles played by graphene-based materials in PDT such as photosensitizers (PS) and drug carriers while at the same time considers how these materials could be used to circumvent cancer resistance. This will provide readers with an extensive discussion of various pathways contributing to PDT inefficiency. Consequently, this comprehensive review underscores the vital roles that graphene and its derivatives may play in emerging PDT strategies for cancer treatment and other medical purposes. With a better comprehension of the current state of research and the existing challenges, the integration of graphene-based materials in PDT holds great promise for developing targeted, effective, and personalized cancer treatments.

Leveraging Cancer Phenotypic Plasticity for Novel Treatment Strategies

Cancer cells, like all other organisms, are adept at switching their phenotype to adjust to the changes in their environment. Thus, phenotypic plasticity is a quantitative trait that confers a fitness advantage to the cancer cell by altering its phenotype to suit environmental circumstances. Until recently, new traits, especially in cancer, were thought to arise due to genetic factors; however, it is now amply evident that such traits could also emerge non-genetically due to phenotypic plasticity. Furthermore, phenotypic plasticity of cancer cells contributes to phenotypic heterogeneity in the population, which is a major impediment in treating the disease. Finally, plasticity also impacts the group behavior of cancer cells, since competition and cooperation among multiple clonal groups within the population and the interactions they have with the tumor microenvironment also contribute to the evolution of drug resistance. Thus, understanding the mechanisms that cancer cells exploit to tailor their phenotypes at a systems level can aid the development of novel cancer therapeutics and treatment strategies. Here, we present our perspective on a team medicine-based approach to gain a deeper understanding of the phenomenon to develop new therapeutic strategies.

Multiplexed single-cell lineage tracing of mitotic kinesin inhibitor resistance in glioblastoma

Glioblastoma (GBM) is a deadly brain tumor, and the kinesin motor KIF11 is an attractive therapeutic target with roles in proliferation and invasion. Resistance to KIF11 inhibitors, which has mainly been studied in animal models, presents significant challenges. We use lineage-tracing barcodes and single-cell RNA sequencing to analyze resistance in patient-derived GBM neurospheres treated with ispinesib, a potent KIF11 inhibitor. Similar to GBM progression in patients, untreated cells lose their neural lineage identity and become mesenchymal, which is associated with poor prognosis. Conversely, cells subjected to long-term ispinesib treatment exhibit a proneural phenotype. We generate patient-derived xenografts and show that ispinesib-resistant cells form less aggressive tumors in vivo, even in the absence of drug. Moreover, treatment of human ex vivo GBM slices with ispinesib demonstrates phenotypic alignment with in vitro responses, underscoring the clinical relevance of our findings. Finally, using retrospective lineage tracing, we identify drugs that are synergistic with ispinesib.

Focal adhesion kinase-YAP signaling axis drives drug-tolerant persister cells and residual disease in lung cancer

Targeted therapy is effective in many tumor types including lung cancer, the leading cause of cancer mortality. Paradigm defining examples are targeted therapies directed against non-small cell lung cancer (NSCLC) subtypes with oncogenic alterations in EGFR, ALK and KRAS. The success of targeted therapy is limited by drug-tolerant persister cells (DTPs) which withstand and adapt to treatment and comprise the residual disease state that is typical during treatment with clinical targeted therapies. Here, we integrate studies in patient-derived and immunocompetent lung cancer models and clinical specimens obtained from patients on targeted therapy to uncover a focal adhesion kinase (FAK)-YAP signaling axis that promotes residual disease during oncogenic EGFR-, ALK-, and KRAS-targeted therapies. FAK-YAP signaling inhibition combined with the primary targeted therapy suppressed residual drug-tolerant cells and enhanced tumor responses. This study unveils a FAK-YAP signaling module that promotes residual disease in lung cancer and mechanism-based therapeutic strategies to improve tumor response.

Epigenetic deregulation in breast cancer microenvironment: Implications for tumor progression and therapeutic strategies

Breast cancer comprises a substantial proportion of cancer diagnoses in women and is a primary cause of cancer-related mortality. While hormone-responsive cases generally have a favorable prognosis, the aggressive nature of triple-negative breast cancer presents challenges, with intrinsic resistance to established treatments being a persistent issue. The complexity intensifies with the emergence of acquired resistance, further complicating the management of breast cancer. Epigenetic changes, encompassing DNA methylation, histone and RNA modifications, and non-coding RNAs, are acknowledged as crucial contributors to the heterogeneity of breast cancer. The unique epigenetic landscape harbored by each cellular component within the tumor microenvironment (TME) adds great diversity to the intricate regulations which influence therapeutic responses. The TME, a sophisticated ecosystem of cellular and non-cellular elements interacting with tumor cells, establishes an immunosuppressive microenvironment and fuels processes such as tumor growth, angiogenesis, and extracellular matrix remodeling. These factors contribute to challenging conditions in cancer treatment by fostering a hypoxic environment, inducing metabolic stress, and creating physical barriers to drug delivery. This article delves into the complex connections between breast cancer treatment response, underlying epigenetic changes, and vital interactions within the TME. To restore sensitivity to treatment, it emphasizes the need for combination therapies considering epigenetic changes specific to individual members of the TME. Recognizing the pivotal role of epigenetics in drug resistance and comprehending the specificities of breast TME is essential for devising more effective therapeutic strategies. The development of reliable biomarkers for patient stratification will facilitate tailored and precise treatment approaches.

Three-Dimensional Tumor Models to Study Cancer Stemness-Mediated Drug Resistance

Solid tumors often contain genetically different populations of cancer cells, stromal cells, various structural and soluble proteins, and other soluble signaling molecules. The American Cancer society estimated 1,958,310 new cancer cases and 609,820 cancer deaths in the United States in 2023. A major barrier against successful treatment of cancer patients is drug resistance. Gain of stem cell-like states by cancer cells under drug pressure or due to interactions with the tumor microenvironment is a major mechanism that renders therapies ineffective. Identifying approaches to target cancer stem cells is expected to improve treatment outcomes for patients. Most of our understanding of drug resistance and the role of cancer stemness is from monolayer cell cultures. Recent advances in cell culture technologies have enabled developing sophisticated three-dimensional tumor models that facilitate mechanistic studies of cancer drug resistance. This review summarizes the role of cancer stemness in drug resistance and highlights the various tumor models that are used to discover the underlying mechanisms and test potentially novel therapeutics.

Explore & actuate: the future of personalized medicine in oncology through emerging technologies

PURPOSE OF REVIEW

The future of medicine is aimed to equip the physician with tools to assess the individual health of the patient for the uniqueness of the disease that separates it from the rest. The integration of omics technologies into clinical practice, reviewed here, would open new avenues for addressing the spatial and temporal heterogeneity of cancer. The rising cancer burden patiently awaits the advent of such an approach to personalized medicine for routine clinical settings.

RECENT FINDINGS

To weigh the translational potential, multiple technologies were categorized based on the extractable information from the different types of samples used, to the various omic-levels of molecular information that each technology has been able to advance over the last 2 years. This review uses a multifaceted classification that helps to assess translational potential in a meaningful way toward clinical adaptation.

SUMMARY

The importance of distinguishing technologies based on the flow of information from exploration to actuation puts forth a framework that allows the clinicians to better adapt a chosen technology or use them in combination to enhance their goals toward personalized medicine.

ABCB1-dependent collateral sensitivity of multidrug-resistant colorectal cancer cells to the survivin inhibitor MX106-4C

AIMS

To investigate the collateral sensitivity (CS) of ABCB1-positive multidrug resistant (MDR) colorectal cancer cells to the survivin inhibitor MX106-4C and the mechanism.

METHODS

Biochemical assays (MTT, ATPase, drug accumulation/efflux, Western blot, RT-qPCR, immunofluorescence, flow cytometry) and bioinformatic analyses (mRNA-sequencing, reversed-phase protein array) were performed to investigate the hypersensitivity of ABCB1 overexpressing colorectal cancer cells to MX106-4C and the mechanisms. Synergism assay, long-term selection, and 3D tumor spheroid test were used to evaluate the anti-cancer efficacy of MX106-4C.

RESULTS

MX106-4C selectively killed ABCB1-positive colorectal cancer cells, which could be reversed by an ABCB1 inhibitor, knockout of ABCB1, or loss-of-function ABCB1 mutation, indicating an ABCB1 expression and function-dependent mechanism. MX106-4C's selective toxicity was associated with cell cycle arrest and apoptosis through ABCB1-dependent survivin inhibition and activation on caspases-3/7 as well as modulation on p21-CDK4/6-pRb pathway. MX106-4C had good selectivity against ABCB1-positive colorectal cancer cells and retained this in multicellular tumor spheroids. In addition, MX106-4C could exert a synergistic anti-cancer effect with doxorubicin or re-sensitize ABCB1-positive cancer cells to doxorubicin by reducing ABCB1 expression in the cell population via long-term exposure.

CONCLUSIONS

MX106-4C selectively kills ABCB1-positive MDR colorectal cancer cells via a novel ABCB1-dependent survivin inhibition mechanism, providing a clue for designing CS compound as an alternative strategy to overcome ABCB1-mediated colorectal cancer MDR.

Cellular subpopulations identified using an ensemble average of multiple dielectrophoresis measurements

While the average value measurement approach can successfully analyze and predict the general behavior and biophysical properties of an isogenic cell population, it fails when significant differences among individual cells are generated in the population by intracellular changes such as the cell cycle, or different cellular responses to certain stimuli. Detecting such single-cell differences in a cell population has remained elusive. Here, we describe an easy-to-implement and generalizable platform that measures the dielectrophoretic cross-over frequency of individual cells by decreasing measurement noise with a stochastic method and computing ensemble average statistics. This platform enables multiple, real-time, label-free detection of individual cells with significant dielectric variations over time within an isogenic cell population. Using a stochastic method in combination with the platform, we distinguished cell subpopulations from a mixture of drug-untreated and -treated isogenic cells. Furthermore, we demonstrate that our platform can identify drug-treated isogenic cells with different recovery rates.

Acquired resistance to KRAS G12C small-molecule inhibitors via genetic/nongenetic mechanisms in lung cancer

Inherent or acquired resistance to sotorasib poses a substantialt challenge for NSCLC treatment. Here, we demonstrate that acquired resistance to sotorasib in isogenic cells correlated with increased expression of integrin β4 (ITGB4), a component of the focal adhesion complex. Silencing ITGB4 in tolerant cells improved sotorasib sensitivity, while overexpressing ITGB4 enhanced tolerance to sotorasib by supporting AKT-mTOR bypass signaling. Chronic treatment with sotorasib induced WNT expression and activated the WNT/β-catenin signaling pathway. Thus, silencing both ITGB4 and β-catenin significantly improved sotorasib sensitivity in tolerant, acquired, and inherently resistant cells. In addition, the proteasome inhibitor carfilzomib (CFZ) exhibited synergism with sotorasib by down-regulating ITGB4 and β-catenin expression. Furthermore, adagrasib phenocopies the combination effect of sotorasib and CFZ by suppressing KRAS activity and inhibiting cell cycle progression in inherently resistant cells. Overall, our findings unveil previously unrecognized nongenetic mechanisms underlying resistance to sotorasib and propose a promising treatment strategy to overcome resistance.

The epigenetic modifier HDAC2 and the checkpoint kinase ATM determine the responses of microsatellite instable colorectal cancer cells to 5-fluorouracil

The epigenetic modifier histone deacetylase-2 (HDAC2) is frequently dysregulated in colon cancer cells. Microsatellite instability (MSI), an unfaithful replication of DNA at nucleotide repeats, occurs in about 15% of human colon tumors. MSI promotes a genetic frameshift and consequently a loss of HDAC2 in up to 43% of these tumors. We show that long-term and short-term cultures of colorectal cancers with MSI contain subpopulations of cells lacking HDAC2. These can be isolated as single cell-derived, proliferating populations. Xenografted patient-derived colon cancer tissues with MSI also show variable patterns of HDAC2 expression in mice. HDAC2-positive and HDAC2-negative RKO cells respond similarly to pharmacological inhibitors of the class I HDACs HDAC1/HDAC2/HDAC3. In contrast to this similarity, HDAC2-negative and HDAC2-positive RKO cells undergo differential cell cycle arrest and apoptosis induction in response to the frequently used chemotherapeutic 5-fluorouracil, which becomes incorporated into and damages RNA and DNA. 5-fluorouracil causes an enrichment of HDAC2-negative RKO cells in vitro and in a subset of primary colorectal tumors in mice. 5-fluorouracil induces the phosphorylation of KAP1, a target of the checkpoint kinase ataxia-telangiectasia mutated (ATM), stronger in HDAC2-negative cells than in their HDAC2-positive counterparts. Pharmacological inhibition of ATM sensitizes RKO cells to cytotoxic effects of 5-fluorouracil. These findings demonstrate that HDAC2 and ATM modulate the responses of colorectal cancer cells towards 5-FU.

Multiplexed single-cell lineage tracing of mitotic kinesin inhibitor resistance in glioblastoma

Glioblastoma (GBM) is a deadly brain tumor, and the kinesin motor KIF11 is an attractive therapeutic target because of its dual roles in proliferation and invasion. The clinical utility of KIF11 inhibitors has been limited by drug resistance, which has mainly been studied in animal models. We used multiplexed lineage tracing barcodes and scRNA-seq to analyze drug resistance time courses for patient-derived GBM neurospheres treated with ispinesib, a potent KIF11 inhibitor. Similar to GBM progression in patients, untreated cells lost their neural lineage identity and transitioned to a mesenchymal phenotype, which is associated with poor prognosis. In contrast, cells subjected to long-term ispinesib treatment exhibited a proneural phenotype. We generated patient-derived xenografts to show that ispinesib-resistant cells form less aggressive tumors in vivo, even in the absence of drug. Finally, we used lineage barcodes to nominate drug combination targets by retrospective analysis of ispinesib-resistant clones in the drug-naïve setting and identified drugs that are synergistic with ispinesib.

Cellular Adaptation Takes Advantage of Atavistic Regression Programs during Carcinogenesis

Adaptation of cancer cells to extreme microenvironmental conditions (i.e., hypoxia, high acidity, and reduced nutrient availability) contributes to cancer resilience. Furthermore, neoplastic transformation can be envisioned as an extreme adaptive response to tissue damage or chronic injury. The recent Systemic-Evolutionary Theory of the Origin of Cancer (SETOC) hypothesizes that cancer cells "revert" to "primitive" characteristics either ontogenically (embryo-like) or phylogenetically (single-celled organisms). This regression may confer robustness and maintain the disordered state of the tissue, which is a hallmark of malignancy. Changes in cancer cell metabolism during adaptation may also be the consequence of altered microenvironmental conditions, often resulting in a shift toward lactic acid fermentation. However, the mechanisms underlying the robust adaptive capacity of cancer cells remain largely unknown. In recent years, cancer cells' metabolic flexibility has received increasing attention among researchers. Here, we focus on how changes in the microenvironment can affect cancer cell energy production and drug sensitivity. Indeed, changes in the cellular microenvironment may lead to a "shift" toward "atavistic" biologic features, such as the switch from oxidative phosphorylation (OXPHOS) to lactic acid fermentation, which can also sustain drug resistance. Finally, we point out new integrative metabolism-based pharmacological approaches and potential biomarkers for early detection.

Modeling the effect of acquired resistance on cancer therapy outcomes

Adaptive therapy (AT) is an evolution-based treatment strategy that exploits cell-cell competition. Acquired resistance can change the competitive nature of cancer cells in a tumor, impacting AT outcomes. We aimed to determine if adaptive therapy can still be effective with cell's acquiring resistance. We developed an agent-based model for spatial tumor growth considering three different types of acquired resistance: random genetic mutations during cell division, drug-induced reversible (plastic) phenotypic changes, and drug-induced irreversible phenotypic changes. These three resistance mechanisms lead to different spatial distributions of resistant cells. To quantify the spatial distribution, we propose an extension of Ripley's K-function, Sampled Ripley's K-function (SRKF), which calculates the non-randomness of the resistance distribution over the tumor domain. Our model predicts that the emergent spatial distribution of resistance can determine the time to progression under both adaptive and continuous therapy (CT). Notably, a high rate of random genetic mutations leads to quicker progression under AT than CT due to the emergence of many small clumps of resistant cells. Drug-induced phenotypic changes accelerate tumor progression irrespective of the treatment strategy. Low-rate switching to a sensitive state reduces the benefits of AT compared to CT. Furthermore, we also demonstrated that drug-induced resistance necessitates aggressive treatment under CT, regardless of the presence of cancer-associated fibroblasts. However, there is an optimal dose that can most effectively delay tumor relapse under AT by suppressing resistance. In conclusion, this study demonstrates that diverse resistance mechanisms can shape the distribution of resistance and thus determine the efficacy of adaptive therapy.

Clinical trial designs for evaluating and exploiting cancer evolution

The evolution of drug-resistant cell subpopulations causes cancer treatment failure. Current preclinical evidence shows that it is possible to model herding of clonal evolution and collateral sensitivity where an initial treatment could favourably influence the response to a subsequent one. Novel therapy strategies exploiting this understanding are being considered, and clinical trial designs for steering cancer evolution are needed. Furthermore, preclinical evidence suggests that different subsets of drug-sensitive and resistant clones could compete between themselves for nutrients/blood supply, and clones that populate a tumour do so at the expense of other clones. Treatment paradigms based on this clinical application of exploiting cell-cell competition include intermittent dosing regimens or cycling different treatments before progression. This will require clinical trial designs different from the conventional practice of evaluating responses to individual therapy regimens. Next-generation sequencing to assess clonal dynamics longitudinally will improve current radiological assessment of clinical response/resistance and be incorporated into trials exploiting evolution. Furthermore, if understood, clonal evolution can be used to therapeutic advantage, improving patient outcomes based on a new generation of clinical trials.

Unraveling the Drivers of Tumorigenesis in the Context of Evolution: Theoretical Models and Bioinformatics Tools

Cancer originates from somatic cells that have accumulated mutations. These mutations alter the phenotype of the cells, allowing them to escape homeostatic regulation that maintains normal cell numbers. The emergence of malignancies is an evolutionary process in which the random accumulation of somatic mutations and sequential selection of dominant clones cause cancer cells to proliferate. The development of technologies such as high-throughput sequencing has provided a powerful means to measure subclonal evolutionary dynamics across space and time. Here, we review the patterns that may be observed in cancer evolution and the methods available for quantifying the evolutionary dynamics of cancer. An improved understanding of the evolutionary trajectories of cancer will enable us to explore the molecular mechanism of tumorigenesis and to design tailored treatment strategies.

The recent progress of deep-learning-based in silico prediction of drug combination

Drug combination therapy has become a common strategy for the treatment of complex diseases. There is an urgent need for computational methods to efficiently identify appropriate drug combinations owing to the high cost of experimental screening. In recent years, deep learning has been widely used in the field of drug discovery. Here, we provide a comprehensive review on deep-learning-based drug combination prediction algorithms from multiple aspects. Current studies highlight the flexibility of this technology in integrating multimodal data and the ability to achieve state-of-art performance; it is expected that deep-learning-based prediction of drug combinations should play an important part in future drug discovery.

Effective dose window for containing tumor burden under tolerable level

A maximum-tolerated dose (MTD) reduces the drug-sensitive cell population, though it may result in the competitive release of drug resistance. Alternative treatment strategies such as adaptive therapy (AT) or dose modulation aim to impose competitive stress on drug-resistant cell populations by maintaining a sufficient number of drug-sensitive cells. However, given the heterogeneous treatment response and tolerable tumor burden level of individual patients, determining an effective dose that can fine-tune competitive stress remains challenging. This study presents a mathematical model-driven approach that determines the plausible existence of an effective dose window (EDW) as a range of doses that conserve sufficient sensitive cells while maintaining the tumor volume below a threshold tolerable tumor volume (TTV). We use a mathematical model that explains intratumor cell competition. Analyzing the model, we derive an EDW determined by TTV and the competitive strength. By applying a fixed endpoint optimal control model, we determine the minimal dose to contain cancer at a TTV. As a proof of concept, we study the existence of EDW for a small cohort of melanoma patients by fitting the model to longitudinal tumor response data. We performed identifiability analysis, and for the patients with uniquely identifiable parameters, we deduced patient-specific EDW and minimal dose. The tumor volume for a patient could be theoretically contained at the TTV either using continuous dose or AT strategy with doses belonging to EDW. Further, we conclude that the lower bound of the EDW approximates the minimum effective dose (MED) for containing tumor volume at the TTV.

Unraveling non-genetic heterogeneity in cancer with dynamical models and computational tools

Individual cells within an otherwise genetically homogenous population constantly undergo fluctuations in their molecular state, giving rise to non-genetic heterogeneity. Such diversity is being increasingly implicated in cancer therapy resistance and metastasis. Identifying the origins of non-genetic heterogeneity is therefore crucial for making clinical breakthroughs. We discuss with examples how dynamical models and computational tools have provided critical multiscale insights into the nature and consequences of non-genetic heterogeneity in cancer. We demonstrate how mechanistic modeling has been pivotal in establishing key concepts underlying non-genetic diversity at various biological scales, from population dynamics to gene regulatory networks. We discuss advances in single-cell longitudinal profiling techniques to reveal patterns of non-genetic heterogeneity, highlighting the ongoing efforts and challenges in statistical frameworks to robustly interpret such multimodal datasets. Moving forward, we stress the need for data-driven statistical and mechanistically motivated dynamical frameworks to come together to develop predictive cancer models and inform therapeutic strategies.

Systemic Alterations of Cancer Cells and Their Boost by Polyploidization: Unicellular Attractor (UCA) Model

Using meta-analyses, we introduce a unicellular attractor (UCA) model integrating essential features of the ‘atavistic reversal’, ‘cancer attractor’, ‘somatic mutation’, ‘genome chaos’, and ‘tissue organization field’ theories. The ‘atavistic reversal’ theory is taken as a keystone. We propose a possible mechanism of this reversal, its refinement called ‘gradual atavism’, and evidence for the ‘serial atavism’ model. We showed the gradual core-to-periphery evolutionary growth of the human interactome resulting in the higher protein interaction density and global interactome centrality in the UC center. In addition, we revealed that UC genes are more actively expressed even in normal cells. The modeling of random walk along protein interaction trajectories demonstrated that random alterations in cellular networks, caused by genetic and epigenetic changes, can result in a further gradual activation of the UC center. These changes can be induced and accelerated by cellular stress that additionally activates UC genes (especially during cell proliferation), because the genes involved in cellular stress response and cell cycle are mostly of UC origin. The functional enrichment analysis showed that cancer cells demonstrate the hyperactivation of energetics and the suppression of multicellular genes involved in communication with the extracellular environment (especially immune surveillance). Collectively, these events can unleash selfish cell behavior aimed at survival at all means. All these changes are boosted by polyploidization. The UCA model may facilitate an understanding of oncogenesis and promote the development of therapeutic strategies.

Ribonucleotide reductase subunit switching in hepatoblastoma drug response and relapse

Prognosis of children with high-risk hepatoblastoma (HB), the most common pediatric liver cancer, remains poor. In this study, we found ribonucleotide reductase (RNR) subunit M2 (RRM2) was one of the key genes supporting cell proliferation in high-risk HB. While standard chemotherapies could effectively suppress RRM2 in HB cells, they induced a significant upregulation of the other RNR M2 subunit, RRM2B. Computational analysis revealed distinct signaling networks RRM2 and RRM2B were involved in HB patient tumors, with RRM2 supporting cell proliferation and RRM2B participating heavily in stress response pathways. Indeed, RRM2B upregulation in chemotherapy-treated HB cells promoted cell survival and subsequent relapse, during which RRM2B was gradually replaced back by RRM2. Combining an RRM2 inhibitor with chemotherapy showed an effective delaying of HB tumor relapse in vivo. Overall, our study revealed the distinct roles of the two RNR M2 subunits and their dynamic switching during HB cell proliferation and stress response.

Memory of stochastic single-cell apoptotic signaling promotes chemoresistance in neuroblastoma

Gene expression noise is known to promote stochastic drug resistance through the elevated expression of individual genes in rare cancer cells. However, we now demonstrate that chemoresistant neuroblastoma cells emerge at a much higher frequency when the influence of noise is integrated across multiple components of an apoptotic signaling network. Using a JNK activity biosensor with longitudinal high-content and in vivo intravital imaging, we identify a population of stochastic, JNK-impaired, chemoresistant cells that exist because of noise within this signaling network. Furthermore, we reveal that the memory of this initially random state is retained following chemotherapy treatment across a series of in vitro, in vivo, and patient models. Using matched PDX models established at diagnosis and relapse from individual patients, we show that HDAC inhibitor priming cannot erase the memory of this resistant state within relapsed neuroblastomas but improves response in the first-line setting by restoring drug-induced JNK activity within the chemoresistant population of treatment-naïve tumors.

BdLT-Seq as a barcode decay-based method to unravel lineage-linked transcriptome plasticity

Cell plasticity is a core biological process underlying a myriad of molecular and cellular events taking place throughout organismal development and evolution. It has been postulated that cellular systems thrive to balance the organization of meta-stable states underlying this phenomenon, thereby maintaining a degree of populational homeostasis compatible with an ever-changing environment and, thus, life. Notably, albeit circumstantial evidence has been gathered in favour of the latter conceptual framework, a direct observation of meta-state dynamics and the biological consequences of such a process in generating non-genetic clonal diversity and divergent phenotypic output remains largely unexplored. To fill this void, here we develop a lineage-tracing technology termed Barcode decay Lineage Tracing-Seq. BdLT-Seq is based on episome-encoded molecular identifiers that, supported by the dynamic decay of the tracing information upon cell division, ascribe directionality to a cell lineage tree whilst directly coupling non-genetic molecular features to phenotypes in comparable genomic landscapes. We show that cell transcriptome states are both inherited, and dynamically reshaped following constrained rules encoded within the cell lineage in basal growth conditions, upon oncogene activation and throughout the process of reversible resistance to therapeutic cues thus adjusting phenotypic output leading to intra-clonal non-genetic diversity.

Zebrafish Cancer Avatars: A Translational Platform for Analyzing Tumor Heterogeneity and Predicting Patient Outcomes

The increasing number of available anti-cancer drugs presents a challenge for oncologists, who must choose the most effective treatment for the patient. Precision cancer medicine relies on matching a drug with a tumor's molecular profile to optimize the therapeutic benefit. However, current precision medicine approaches do not fully account for intra-tumoral heterogeneity. Different mutation profiles and cell behaviors within a single heterogeneous tumor can significantly impact therapy response and patient outcomes. Patient-derived avatar models recapitulate a patient's tumor in an animal or dish and provide the means to functionally assess heterogeneity's impact on drug response. Mouse xenograft and organoid avatars are well-established, but the time required to generate these models is not practical for clinical decision-making. Zebrafish are emerging as a time-efficient and cost-effective cancer avatar model. In this review, we highlight recent developments in zebrafish cancer avatar models and discuss the unique features of zebrafish that make them ideal for the interrogation of cancer heterogeneity and as part of precision cancer medicine pipelines.

Ribonucleotide Reductase Subunit Switching in Hepatoblastoma Drug Response and Relapse

Prognosis of children with high-risk hepatoblastoma (HB), the most common pediatric liver cancer, remains poor. In this study, we found ribonucleotide reductase (RNR) subunit M2 ( RRM2 ) was one of the key genes supporting cell proliferation in high-risk HB. While standard chemotherapies could effectively suppress RRM2 in HB cells, they induced a significant upregulation of the other RNR M2 subunit, RRM2B . Computational analysis revealed distinct signaling networks RRM2 and RRM2B were involved in HB patient tumors, with RRM2 supporting cell proliferation and RRM2B participating heavily in stress response pathways. Indeed, RRM2B upregulation in chemotherapy-treated HB cells promoted cell survival and subsequent relapse, during which RRM2B was gradually replaced back by RRM2. Combining an RRM2 inhibitor with chemotherapy showed an effective delaying of HB tumor relapse in vivo. Overall, our study revealed the distinct roles of the two RNR M2 subunits and their dynamic switching during HB cell proliferation and stress response.

A Systems Biology Approach for Addressing Cisplatin Resistance in Non-Small Cell Lung Cancer

Translational research in medicine, defined as the transfer of knowledge and discovery from the basic sciences to the clinic, is typically achieved through interactions between members across scientific disciplines to overcome the traditional silos within the community. Thus, translational medicine underscores 'Team Medicine', the partnership between basic science researchers and clinicians focused on addressing a specific goal in medicine. Here, we highlight this concept from a City of Hope perspective. Using cisplatin resistance in non-small cell lung cancer (NSCLC) as a paradigm, we describe how basic research scientists, clinical research scientists, and medical oncologists, in true 'Team Science' spirit, addressed cisplatin resistance in NSCLC and identified a previously approved compound that is able to alleviate cisplatin resistance in NSCLC. Furthermore, we discuss how a 'Team Medicine' approach can help to elucidate the mechanisms of innate and acquired resistance in NSCLC and develop alternative strategies to overcome drug resistance.

The role of mitosis in generating fitness heterogeneity

Cancer cells have heterogeneous fitness, and this heterogeneity stems from genetic and epigenetic sources. Here, we sought to assess the contribution of asymmetric mitosis (AM) and time on the variability of fitness in sister cells. Around one quarter of sisters had differences in fitness, assessed as the intermitotic time (IMT), from 330 to 510 min. Phenotypes related to fitness, such as ERK activity (herein referring to ERK1 and ERK2, also known as MAPK3 and MAPK1, respectively), DNA damage and nuclear morphological phenotypes were also asymmetric at mitosis or turned asymmetric over the course of the cell cycle. The ERK activity of mother cell was found to influence the ERK activity and the IMT of the daughter cells, and cells with ERK asymmetry at mitosis produced more offspring with AMs, suggesting heritability of the AM phenotype for ERK activity. Our findings demonstrate how variabilities in sister cells can be generated, contributing to the phenotype heterogeneities in tumor cells.

screenwerk: a modular tool for the design and analysis of drug combination screens

MOTIVATION

There is a rapidly growing interest in high-throughput drug combination screening to identify synergizing drug interactions for treatment of various maladies, such as cancer and infectious disease. This creates the need for pipelines that can be used to design such screens, perform quality control on the data and generate data files that can be analyzed by synergy-finding bioinformatics applications.

RESULTS

screenwerk is an open-source, end-to-end modular tool available as an R-package for the design and analysis of drug combination screens. The tool allows for a customized build of pipelines through its modularity and provides a flexible approach to quality control and data analysis. screenwerk is adaptable to various experimental requirements with an emphasis on precision medicine. It can be coupled to other R packages, such as bayesynergy, to identify synergistic and antagonistic drug interactions in cell lines or patient samples. screenwerk is scalable and provides a complete solution for setting up drug sensitivity screens, read raw measurements and consolidate different datasets, perform various types of quality control and analyze, report and visualize the results of drug sensitivity screens.

AVAILABILITY AND IMPLEMENTATION

The R-package and technical documentation is available at https://github.com/Enserink-lab/screenwerk; the R source code is publicly available at https://github.com/Enserink-lab/screenwerk under GNU General Public License v3.0; bayesynergy is accessible at https://github.com/ocbe-uio/bayesynergy. Selected modules are available through Galaxy, an open-source platform for FAIR data analysis at https://oncotools.elixir.no.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Acquired drug resistance interferes with the susceptibility of prostate cancer cells to metabolic stress

Background

Metformin is an inhibitor of oxidative phosphorylation that displays an array of anticancer activities. The interference of metformin with the activity of multi-drug resistance systems in cancer cells has been reported. However, the consequences of the acquired chemoresistance for the adaptative responses of cancer cells to metformin-induced stress and for their phenotypic evolution remain unaddressed.

Methods

Using a range of phenotypic and metabolic assays, we assessed the sensitivity of human prostate cancer PC-3 and DU145 cells, and their drug-resistant lineages (PC-3_DCX20 and DU145_DCX20), to combined docetaxel/metformin stress. Their adaptation responses have been assessed, in particular the shifts in their metabolic profile and invasiveness.

Results

Metformin increased the sensitivity of PC-3 wild-type (WT) cells to docetaxel, as illustrated by the attenuation of their motility, proliferation, and viability after the combined drug application. These effects correlated with the accumulation of energy carriers (NAD(P)H and ATP) and with the inactivation of ABC drug transporters in docetaxel/metformin-treated PC-3 WT cells. Both PC-3 WT and PC-3_DCX20 reacted to metformin with the Warburg effect; however, PC-3_DCX20 cells were considerably less susceptible to the cytostatic/misbalancing effects of metformin. Concomitantly, an epithelial–mesenchymal transition and Cx43 upregulation was seen in these cells, but not in other more docetaxel/metformin-sensitive DU145_DCX20 populations. Stronger cytostatic effects of the combined fenofibrate/docetaxel treatment confirmed that the fine-tuning of the balance between energy supply and expenditure determines cellular welfare under metabolic stress.

Conclusions

Collectively, our data identify the mechanisms that underlie the limited potential of metformin for the chemotherapy of drug-resistant tumors. Metformin can enhance the sensitivity of cancer cells to chemotherapy by inducing their metabolic decoupling/imbalance. However, the acquired chemoresistance of cancer cells impairs this effect, facilitates cellular adaptation to metabolic stress, and prompts the invasive front formation.

Supplementary Information

The online version contains supplementary material available at 10.1186/s11658-022-00400-1.

Multiplexed and reproducible high content screening of live and fixed cells using Dye Drop

High-throughput measurement of cells perturbed using libraries of small molecules, gene knockouts, or different microenvironmental factors is a key step in functional genomics and pre-clinical drug discovery. However, it remains difficult to perform accurate single-cell assays in 384-well plates, limiting many studies to well-average measurements (e.g., CellTiter-Glo®). Here we describe a public domain Dye Drop method that uses sequential density displacement and microscopy to perform multi-step assays on living cells. We use Dye Drop cell viability and DNA replication assays followed by immunofluorescence imaging to collect single-cell dose-response data for 67 investigational and clinical-grade small molecules in 58 breast cancer cell lines. By separating the cytostatic and cytotoxic effects of drugs computationally, we uncover unexpected relationships between the two. Dye Drop is rapid, reproducible, customizable, and compatible with manual or automated laboratory equipment. Dye Drop improves the tradeoff between data content and cost, enabling the collection of information-rich perturbagen-response datasets.

Long-Term Fenofibrate Treatment Stimulates the Phenotypic Microevolution of Prostate Cancer Cells In Vitro

Fenofibrate is a widely used anti-hyperlipidemic agonist of peroxisome proliferator-activated receptor alpha (PPARα). As a metabolic blocker, fenofibrate interferes with cancer promotion/progression via its misbalancing effects on cellular metabolism. However, the consequences of its long-term application for patients with diagnosed drug-resistant cancers are unknown. We addressed this point by tracing the phenotypic microevolution of naïve and drug-resistant prostate cancer PC3_DCX20 cells that underwent a long-term exposition to 10 μM and 50 μM fenofibrate. Their resistance to fenofibrate, metabolic profile and invasive phenotype were estimated in the control conditions and under fenofibrate-induced stress. Apparently, drug efflux systems are not effective against the cytostatic FF action. However, wtPC3 and PC3_DCX20 cells that survived the long-term 50 μM fenofibrate treatment gave rise to lineages that displayed an increased proliferation rate, lower motility in the control conditions and enhanced fenofibrate resistance. Attenuated fenofibrate bioavailability modified the pattern of PC3 microevolution, as illustrated by phenotypic differences between wtPC3/PC3_DCX20 lineages propagated in the presence of 50 μM and 10 μM fenofibrate. Collectively, our observations indicate that fenofibrate acts as a selective factor that affects prostate cancer microevolution. We also pinpoint potential consequences of long-term exposition of prostate cancer patients to metabolic blockers.

Addressing Drug Resistance in Cancer: A Team Medicine Approach

Drug resistance remains one of the major impediments to treating cancer. Although many patients respond well initially, resistance to therapy typically ensues. Several confounding factors appear to contribute to this challenge. Here, we first discuss some of the challenges associated with drug resistance. We then discuss how a ‘Team Medicine’ approach, involving an interdisciplinary team of basic scientists working together with clinicians, has uncovered new therapeutic strategies. These strategies, referred to as intermittent or ‘adaptive’ therapy, which are based on eco-evolutionary principles, have met with remarkable success in potentially precluding or delaying the emergence of drug resistance in several cancers. Incorporating such treatment strategies into clinical protocols could potentially enhance the precision of delivering personalized medicine to patients. Furthermore, reaching out to patients in the network of hospitals affiliated with leading academic centers could help them benefit from such innovative treatment options. Finally, lowering the dose of the drug and its frequency (because of intermittent rather than continuous therapy) can also have a significant impact on lowering the toxicity and undesirable side effects of the drugs while lowering the financial burden carried by the patient and insurance providers.

Novel epigenetic therapeutic strategies and targets in cancer

The critical role of dysregulated epigenetic pathways in cancer genesis, development, and therapy has typically been established as a result of scientific and technical innovations in next generation sequencing. RNA interference, histone modification, DNA methylation and chromatin remodelling are epigenetic processes that control gene expression without causing mutations in the DNA. Although epigenetic abnormalities are thought to be a symptom of cell tumorigenesis and malignant events that impact tumor growth and drug resistance, physicians believe that related processes might be a key therapeutic target for cancer treatment and prevention due to the reversible nature of these processes. A plethora of novel strategies for addressing epigenetics in cancer therapy for immuno-oncological complications are currently available - ranging from basic treatment to epigenetic editing. - and they will be the subject of this comprehensive review. In this review, we cover most of the advancements made in the field of targeting epigenetics with special emphasis on microbiology, plasma science, biophysics, pharmacology, molecular biology, phytochemistry, and nanoscience.

MODIG: Integrating Multi-Omics and Multi-Dimensional Gene Network for Cancer Driver Gene Identification based on Graph Attention Network Model

MOTIVATION

Identifying genes that play a causal role in cancer evolution remains one of the biggest challenges in cancer biology. With the accumulation of high-throughput multi-omics data over decades, it becomes a great challenge to effectively integrate these data into the identification of cancer driver genes.

RESULTS

Here, we propose MODIG, a graph attention network (GAT)-based framework to identify cancer driver genes by combining multi-omics pan-cancer data (mutations, copy number variants, gene expression, and methylation levels) with multi-dimensional gene networks. First, we established diverse types of gene relationship maps based on protein-protein interactions (PPI), gene sequence similarity, KEGG pathway co-occurrence, gene co-expression patterns, and Gene Ontology (GO). Then, we constructed a multi-dimensional gene network consisting of approximately 20,000 genes as nodes and five types of gene associations as multiplex edges. We applied a GAT to model within-dimension interactions to generate a gene representation for each dimension based on this graph. Moreover, we introduced a joint learning module to fuse multiple dimension-specific representations to generate general gene representations. Finally, we used the obtained gene representation to perform a semi-supervised driver gene identification task. The experiment results show that MODIG outperforms the baseline models in terms of area under precision-recall curves (AUPR) and area under the receiver operating characteristic curves (AUROC).

AVAILABILITY

The MODIG program is available at https://github.com/zjupgx/modig. The code and data underlying this article are also available on Zenodo, at https://doi.org/10.5281/zenodo.7057241.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Model-informed experimental design recommendations for distinguishing intrinsic and acquired targeted therapeutic resistance in head and neck cancer

The promise of precision medicine has been limited by the pervasive resistance to many targeted therapies for cancer. Inferring the timing (i.e., pre-existing or acquired) and mechanism (i.e., drug-induced) of such resistance is crucial for designing effective new therapeutics. This paper studies cetuximab resistance in head and neck squamous cell carcinoma (HNSCC) using tumor volume data obtained from patient-derived tumor xenografts. We ask if resistance mechanisms can be determined from this data alone, and if not, what data would be needed to deduce the underlying mode(s) of resistance. To answer these questions, we propose a family of mathematical models, with each member of the family assuming a different timing and mechanism of resistance. We present a method for fitting these models to individual volumetric data, and utilize model selection and parameter sensitivity analyses to ask: which member(s) of the family of models best describes HNSCC response to cetuximab, and what does that tell us about the timing and mechanisms driving resistance? We find that along with time-course volumetric data to a single dose of cetuximab, the initial resistance fraction and, in some instances, dose escalation volumetric data are required to distinguish among the family of models and thereby infer the mechanisms of resistance. These findings can inform future experimental design so that we can best leverage the synergy of wet laboratory experimentation and mathematical modeling in the study of novel targeted cancer therapeutics.

Stochastic population dynamics of cancer stemness and adaptive response to therapies

Intratumoral heterogeneity can exist along multiple axes: Cancer stem cells (CSCs)/non-CSCs, drug-sensitive/drug-tolerant states, and a spectrum of epithelial-hybrid-mesenchymal phenotypes. Further, these diverse cell-states can switch reversibly among one another, thereby posing a major challenge to therapeutic efficacy. Therefore, understanding the origins of phenotypic plasticity and heterogeneity remains an active area of investigation. While genomic components (mutations, chromosomal instability) driving heterogeneity have been well-studied, recent reports highlight the role of non-genetic mechanisms in enabling both phenotypic plasticity and heterogeneity. Here, we discuss various processes underlying phenotypic plasticity such as stochastic gene expression, chromatin reprogramming, asymmetric cell division and the presence of multiple stable gene expression patterns ('attractors'). These processes can facilitate a dynamically evolving cell population such that a subpopulation of (drug-tolerant) cells can survive lethal drug exposure and recapitulate population heterogeneity on drug withdrawal, leading to relapse. These drug-tolerant cells can be both pre-existing and also induced by the drug itself through cell-state reprogramming. The dynamics of cell-state transitions both in absence and presence of the drug can be quantified through mathematical models. Such a dynamical systems approach to elucidating patterns of intratumoral heterogeneity by integrating longitudinal experimental data with mathematical models can help design effective combinatorial and/or sequential therapies for better clinical outcomes.

In Vitro Characterization of Renal Drug Transporter Activity in Kidney Cancer

The activity of drug transporters is central to the secretory function of the kidneys and a defining feature of renal proximal tubule epithelial cells (RPTECs). The expression, regulation, and function of these membrane-bound proteins is well understood under normal renal physiological conditions. However, the impact of drug transporters on the pathophysiology of kidney cancer is still elusive. In the present study, we employed different renal cell carcinoma (RCC) cell lines and a prototypical non-malignant RPTEC cell line to characterize the activity, expression, and potential regulatory mechanisms of relevant renal drug transporters in RCC in vitro. An analysis of the uptake and efflux activity, the expression of drug transporters, and the evaluation of cisplatin cytotoxicity under the effects of methylation or epidermal growth factor receptor (EGFR) inhibition showed that the RCC cells retained substantial drug transport activity. In RCC cells, P-glycoprotein was localized in the nucleus and its pharmacological inhibition enhanced cisplatin toxicity in non-malignant RPTECs. On the other hand, methylation inhibition enhanced cisplatin toxicity by upregulating the organic cation uptake activity in RCC cells. Differential effects of methylation and EGFR were observed in transporter expression, showing regulatory heterogeneity in these cells. Interestingly, the non-malignant RPTEC cell line that was used lacked the machinery responsible for organic cation transport, which reiterates the functional losses that renal cells undergo in vitro.

Population genetics of clonally transmissible cancers

Populations of cancer cells are subject to the same core evolutionary processes as asexually reproducing, unicellular organisms. Transmissible cancers are particularly striking examples of these processes. These unusual cancers are clonal lineages that can spread through populations via physical transfer of living cancer cells from one host individual to another, and they have achieved long-term success in the colonization of at least eight different host species. Population genetic theory provides a useful framework for understanding the shift from a multicellular sexual animal into a unicellular asexual clone and its long-term effects on the genomes of these cancers. In this Review, we consider recent findings from transmissible cancer research with the goals of developing an evolutionarily informed perspective on transmissible cancers, examining possible implications for their long-term fate and identifying areas for future research on these exceptional lineages.

Single-cell characterization of step-wise acquisition of carboplatin resistance in ovarian cancer

The molecular underpinnings of acquired resistance to carboplatin are poorly understood and often inconsistent between in vitro modeling studies. After sequential treatment cycles, multiple isogenic clones reached similar levels of resistance, but significant transcriptional heterogeneity. Gene-expression based virtual synchronization of 26,772 single cells from 2 treatment steps and 4 resistant clones was used to evaluate the activity of Hallmark gene sets in proliferative (P) and quiescent (Q) phases. Two behaviors were associated with resistance: (1) broad repression in the P phase observed in all clones in early resistant steps and (2) prevalent induction in Q phase observed in the late treatment step of one clone. Furthermore, the induction of IFNα response in P phase or Wnt-signaling in Q phase were observed in distinct resistant clones. These observations suggest a model of resistance hysteresis, where functional alterations of the P and Q phase states affect the dynamics of the successive transitions between drug exposure and recovery, and prompts for a precise monitoring of single-cell states to develop more effective schedules for, or combination of, chemotherapy treatments.

GDF15 Is an Eribulin Response Biomarker also Required for Survival of DTP Breast Cancer Cells

Simple Summary

Drug tolerant persister (DTP) cells are a unique, small sub-population of cancer cells that maintain viability under anti-cancer cytotoxic treatments. These cells enter into a reversible drug-tolerant state, which is believed to be the root of tumor recurrence. Therefore, there is a great need to find novel ways to monitor and eliminate DTP cells. We have identified the secretion of GDF15 as a response biomarker of eribulin treatment, as well as a specific biomarker of DTP cells in breast cancer. GDF15 expression is low or absent in cells sensitive to eribulin, strongly upregulated during response to the drug, and then downregulated when stable resistance is ultimately established. We have also shown that GDF15 plays a direct role in the survival of DTP cells. Thus, targeting GDF15 could help eradicate DTP cells and block the onset of stable acquired resistance. Most importantly, our data suggest that the combination of eribulin plus a GDF15 neutralizing antibody might be beneficial in the treatment of breast cancer.

Abstract

Drug tolerant persister (DTP) cells enter into a reversible slow-cycling state after drug treatment. We performed proteomic characterization of the breast cancer (BC) DTP cell secretome after eribulin treatment. We showed that the growth differentiation factor 15 (GDF15) is a protein significantly over-secreted upon eribulin treatment. The biomarker potential of GDF15 was confirmed in 3D-cell culture models using BC cells lines and PDXs, as well as in a TNBC in vivo model. We also found that GDF15 is required for survival of DTP cells. Direct participation of GDF15 and its receptor GFRAL in eribulin-induction of DTPs was established by the enhanced cell killing of DTPs by eribulin seen under GDF15 and GFRAL loss of function assays. Finally, we showed that combination therapy of eribulin plus an anti-GDF15 antibody kills BC-DTP cells. Our results suggest that targeting GDF15 may help eradicate DTP cells and block the onset of acquired resistance.

Functionalized Lineage Tracing Can Enable the Development of Homogenization-Based Therapeutic Strategies in Cancer

The therapeutic landscape across many cancers has dramatically improved since the introduction of potent targeted agents and immunotherapy. Nonetheless, success of these approaches is too often challenged by the emergence of therapeutic resistance, fueled by intratumoral heterogeneity and the immense evolutionary capacity inherent to cancers. To date, therapeutic strategies have attempted to outpace the evolutionary tempo of cancer but frequently fail, resulting in lack of tumor response and/or relapse. This realization motivates the development of novel therapeutic approaches which constrain evolutionary capacity by reducing the degree of intratumoral heterogeneity prior to treatment. Systematic development of such approaches first requires the ability to comprehensively characterize heterogeneous populations over the course of a perturbation, such as cancer treatment. Within this context, recent advances in functionalized lineage tracing approaches now afford the opportunity to efficiently measure multimodal features of clones within a tumor at single cell resolution, enabling the linkage of these features to clonal fitness over the course of tumor progression and treatment. Collectively, these measurements provide insights into the dynamic and heterogeneous nature of tumors and can thus guide the design of homogenization strategies which aim to funnel heterogeneous cancer cells into known, targetable phenotypic states. We anticipate the development of homogenization therapeutic strategies to better allow for cancer eradication and improved clinical outcomes.

Epigenome erosion and SOX10 drive neural crest phenotypic mimicry in triple-negative breast cancer

Intratumoral heterogeneity is caused by genomic instability and phenotypic plasticity, but how these features co-evolve remains unclear. SOX10 is a neural crest stem cell (NCSC) specifier and candidate mediator of phenotypic plasticity in cancer. We investigated its relevance in breast cancer by immunophenotyping 21 normal breast and 1860 tumour samples. Nuclear SOX10 was detected in normal mammary luminal progenitor cells, the histogenic origin of most TNBCs. In tumours, nuclear SOX10 was almost exclusive to TNBC, and predicted poorer outcome amongst cross-sectional (p = 0.0015, hazard ratio 2.02, n = 224) and metaplastic (p = 0.04, n = 66) cases. To understand SOX10’s influence over the transcriptome during the transition from normal to malignant states, we performed a systems-level analysis of co-expression data, de-noising the networks with an eigen-decomposition method. This identified a core module in SOX10’s normal mammary epithelial network that becomes rewired to NCSC genes in TNBC. Crucially, this reprogramming was proportional to genome-wide promoter methylation loss, particularly at lineage-specifying CpG-island shores. We propose that the progressive, genome-wide methylation loss in TNBC simulates more primitive epigenome architecture, making cells vulnerable to SOX10-driven reprogramming. This study demonstrates potential utility for SOX10 as a prognostic biomarker in TNBC and provides new insights about developmental phenotypic mimicry—a major contributor to intratumoral heterogeneity.

Cancer progression as a learning process

Drug resistance and metastasis—the major complications in cancer—both entail adaptation of cancer cells to stress, whether a drug or a lethal new environment. Intriguingly, these adaptive processes share similar features that cannot be explained by a pure Darwinian scheme, including dormancy, increased heterogeneity, and stress-induced plasticity. Here, we propose that learning theory offers a framework to explain these features and may shed light on these two intricate processes. In this framework, learning is performed at the single-cell level, by stress-driven exploratory trial-and-error. Such a process is not contingent on pre-existing pathways but on a random search for a state that diminishes the stress. We review underlying mechanisms that may support this search, and show by using a learning model that such exploratory learning is feasible in a high-dimensional system as the cell. At the population level, we view the tissue as a network of exploring agents that communicate, restraining cancer formation in health. In this view, disease results from the breakdown of homeostasis between cellular exploratory drive and tissue homeostasis.