Tumor evolution in response to chemotherapy: phenotype versus genotype

Basal-epithelial subpopulations underlie and predict chemotherapy resistance in triple-negative breast cancer

Triple-negative breast cancer (TNBC) is the most aggressive breast cancer subtype, characterized by extensive intratumoral heterogeneity, high metastasis, and chemoresistance, leading to poor clinical outcomes. Despite progress, the mechanistic basis of these aggressive behaviors remains poorly understood. Using single-cell and spatial transcriptome analysis, here we discovered basal epithelial subpopulations located within the stroma that exhibit chemoresistance characteristics. The subpopulations are defined by distinct signature genes that show a frequent gain in copy number and exhibit an activated epithelial-to-mesenchymal transition program. A subset of these genes can accurately predict chemotherapy response and are associated with poor prognosis. Interestingly, among these genes, elevated ITGB1 participates in enhancing intercellular signaling while ACTN1 confers a survival advantage to foster chemoresistance. Furthermore, by subjecting the transcriptional signatures to drug repurposing analysis, we find that chemoresistant tumors may benefit from distinct inhibitors in treatment-naive versus post-NAC patients. These findings shed light on the mechanistic basis of chemoresistance while providing the best-in-class biomarker to predict chemotherapy response and alternate therapeutic avenues for improved management of TNBC patients resistant to chemotherapy.

Combined in vitro/in vivo genome-wide CRISPR screens in triple negative breast cancer identify cancer stemness regulators in paclitaxel resistance

Triple negative breast cancer (TNBC) is defined as lacking the expressions of estrogen receptor (ER), progesterone receptor (PR), and human epidermal growth factor receptor 2 (HER2). TNBC patients exhibit relatively poor clinical outcomes due to lack of molecular markers for targeted therapies. As such chemotherapy often remains the only systemic treatment option for these patients. While chemotherapy can initially help shrink TNBC tumor size, patients eventually develop resistance to drug, leading to tumor recurrence. We report a combined in vitro/in vivo genome-wide CRISPR synthetic lethality screening approach in a relevant TNBC cell line model to identify several targets responsible for the chemotherapy drug, paclitaxel resistance. Computational analysis integrating in vitro and in vivo data identified a set of genes, for which specific loss-of-function deletion enhanced paclitaxel resistance in TNBC. We found that several of these genes (ATP8B3, FOXR2, FRG2, HIST1H4A) act as cancer stemness negative regulators. Finally, using in vivo orthotopic transplantation TNBC models we showed that FRG2 gene deletion reduced paclitaxel efficacy and promoted tumor metastasis, while increasing FRG2 expression by means of CRISPR activation efficiently sensitized TNBC tumors to paclitaxel treatment and inhibited their metastatic abilities. In summary, the combined in vitro/in vivo genome-wide CRISPR screening approach proved effective as a tool to identify novel regulators of paclitaxel resistance/sensitivity and highlight the FRG2 gene as a potential therapeutical target overcoming paclitaxel resistance in TNBC.

Unveiling the novel immune and molecular signatures of ovarian cancer: insights and innovations from single-cell sequencing

Ovarian cancer is a highly heterogeneous and lethal malignancy with limited treatment options. Over the past decade, single-cell sequencing has emerged as an advanced biological technology capable of decoding the landscape of ovarian cancer at the single-cell resolution. It operates at the level of genes, transcriptomes, proteins, epigenomes, and metabolisms, providing detailed information that is distinct from bulk sequencing methods, which only offer average data for specific lesions. Single-cell sequencing technology provides detailed insights into the immune and molecular mechanisms underlying tumor occurrence, development, drug resistance, and immune escape. These insights can guide the development of innovative diagnostic markers, therapeutic strategies, and prognostic indicators. Overall, this review provides a comprehensive summary of the diverse applications of single-cell sequencing in ovarian cancer. It encompasses the identification and characterization of novel cell subpopulations, the elucidation of tumor heterogeneity, the investigation of the tumor microenvironment, the analysis of mechanisms underlying metastasis, and the integration of innovative approaches such as organoid models and multi-omics analysis.

Analysis on heterogeneity of hepatocellular carcinoma immune cells and a molecular risk model by integration of scRNA-seq and bulk RNA-seq

Background

Studies have shown that hepatocellular carcinoma (HCC) heterogeneity is a main cause leading to failure of treatment. Technology of single-cell sequencing (scRNA) could more accurately reveal the essential characteristics of tumor genetics.

Methods

From the Gene Expression Omnibus (GEO) database, HCC scRNA-seq data were extracted. The FindCluster function was applied to analyze cell clusters. Autophagy-related genes were acquired from the MSigDB database. The ConsensusClusterPlus package was used to identify molecular subtypes. A prognostic risk model was built with the Least Absolute Shrinkage and Selection Operator (LASSO)-Cox algorithm. A nomogram including a prognostic risk model and multiple clinicopathological factors was constructed.

Results

Eleven cell clusters labeled as various cell types by immune cell markers were obtained from the combined scRNA-seq GSE149614 dataset. ssGSEA revealed that autophagy-related pathways were more enriched in malignant tumors. Two autophagy-related clusters (C1 and C2) were identified, in which C1 predicted a better survival, enhanced immune infiltration, and a higher immunotherapy response. LASSO-Cox regression established an eight-gene signature. Next, the HCCDB18, GSA14520, and GSE76427 datasets confirmed a strong risk prediction ability of the signature. Moreover, the low-risk group had enhanced immune infiltration and higher immunotherapy response. A nomogram which consisted of RiskScore and clinical features had better prediction ability.

Conclusion

To precisely assess the prognostic risk, an eight-gene prognostic stratification signature was developed based on the heterogeneity of HCC immune cells.

Cherenkov Luminescence in Tumor Diagnosis and Treatment: A Review

Malignant tumors rank as a leading cause of death worldwide. Accurate diagnosis and advanced treatment options are crucial to win battle against tumors. In recent years, Cherenkov luminescence (CL) has shown its technical advantages and clinical transformation potential in many important fields, particularly in tumor diagnosis and treatment, such as tumor detection in vivo, surgical navigation, radiotherapy, photodynamic therapy, and the evaluation of therapeutic effect. In this review, we summarize the advances in CL for tumor diagnosis and treatment. We first describe the physical principles of CL and discuss the imaging techniques used in tumor diagnosis, including CL imaging, CL endoscope, and CL tomography. Then we present a broad overview of the current status of surgical resection, radiotherapy, photodynamic therapy, and tumor microenvironment monitoring using CL. Finally, we shed light on the challenges and possible solutions for tumor diagnosis and therapy using CL.

Multi-staged gene expression profiling reveals potential genes and the critical pathways in kidney cancer

Cancer is among the highly complex disease and renal cell carcinoma is the sixth-leading cause of cancer death. In order to understand complex diseases such as cancer, diabetes and kidney diseases, high-throughput data are generated at large scale and it has helped in the research and diagnostic advancement. However, to unravel the meaningful information from such large datasets for comprehensive and minute understanding of cell phenotypes and disease pathophysiology remains a trivial challenge and also the molecular events leading to disease onset and progression are not well understood. With this goal, we have collected gene expression datasets from publicly available dataset which are for two different stages (I and II) for renal cell carcinoma and furthermore, the TCGA and cBioPortal database have been utilized for clinical relevance understanding. In this work, we have applied computational approach to unravel the differentially expressed genes, their networks for the enriched pathways. Based on our results, we conclude that among the most dominantly altered pathways for renal cell carcinoma, are PI3K-Akt, Foxo, endocytosis, MAPK, Tight junction, cytokine-cytokine receptor interaction pathways and the major source of alteration for these pathways are MAP3K13, CHAF1A, FDX1, ARHGAP26, ITGBL1, C10orf118, MTO1, LAMP2, STAMBP, DLC1, NSMAF, YY1, TPGS2, SCARB2, PRSS23, SYNJ1, CNPPD1, PPP2R5E. In terms of clinical significance, there are large number of differentially expressed genes which appears to be playing critical roles in survival.

Sensitizing drug-resistant cancer cells from blood using microfluidic electroporator

Direct assessment of patient samples holds unprecedented potential in the treatment of cancer. Circulating tumor cells (CTCs) in liquid biopsies are a rapidly evolving source of primary cells in the clinic and are ideal candidates for functional assays to uncover real-time tumor information in real-time. However, a lack of routines allowing direct and active interrogation of CTCs directly from liquid biopsy samples represents a bottleneck for the translational use of liquid biopsies in clinical settings. To address this, we present a workflow for using a microfluidic vortex-assisted electroporation system designed for the functional assessment of CTCs purified from blood. Validation of this approach was assessed through drug response assays on wild-type (HCC827 wt) and gefitinib-resistant (HCC827 GR6) non-small cell lung cancer (NSCLC) cells. HCC827 cells trapped within microscale vortices were electroporated to sequentially deliver drug agents into the cytosol. Electroporation conditions facilitating multi-agent delivery were characterized for both cell lines using an automatic single-cell image fluorescence intensity algorithm. HCC827 GR6 cells spiked into the blood to emulate drug-resistant CTCs were able to be collected with high purity, demonstrating the ability of the device to minimize background cell impact for downstream sensitive cell assays. Using our proposed workflow, drug agent combinations to restore gefitinib sensitivity reflected the anticipated cytotoxic response. Taken together, these results represent a microfluidics multi-drug screening panel workflow that can enable functional interrogation of patient CTCs in situ, thereby accelerating the clinical standardization of liquid biopsies.

What do cellular responses to acidity tell us about cancer?

The notion that invasive cancer is a product of somatic evolution is a well-established theory that can be modelled mathematically and demonstrated empirically from therapeutic responses. Somatic evolution is by no means deterministic, and ample opportunities exist to steer its trajectory towards cancer cell extinction. One such strategy is to alter the chemical microenvironment shared between host and cancer cells in a way that no longer favours the latter. Ever since the first description of the Warburg effect, acidosis has been recognised as a key chemical signature of the tumour microenvironment. Recent findings have suggested that responses to acidosis, arising through a process of selection and adaptation, give cancer cells a competitive advantage over the host. A surge of research efforts has attempted to understand the basis of this advantage and seek ways of exploiting it therapeutically. Here, we review key findings and place these in the context of a mathematical framework. Looking ahead, we highlight areas relating to cellular adaptation, selection, and heterogeneity that merit more research efforts in order to close in on the goal of exploiting tumour acidity in future therapies.

The Multiple Dimensions of Networks in Cancer: A Perspective

This perspective article gathers the latest developments in mathematical and computational oncology tools that exploit network approaches for the mathematical modelling, analysis, and simulation of cancer development and therapy design. It instigates the community to explore new paths and synergies under the umbrella of the Special Issue “Networks in Cancer: From Symmetry Breaking to Targeted Therapy”. The focus of the perspective is to demonstrate how networks can model the physics, analyse the interactions, and predict the evolution of the multiple processes behind tumour-host encounters across multiple scales. From agent-based modelling and mechano-biology to machine learning and predictive modelling, the perspective motivates a methodology well suited to mathematical and computational oncology and suggests approaches that mark a viable path towards adoption in the clinic.

Single-Cell RNA Sequencing of Ovarian Cancer: Promises and Challenges

Ovarian cancer remains the leading cause of death from gynecologic malignancy in the Western world. Tumors are comprised of heterogeneous populations of various cancer, immune, and stromal cells; it is hypothesized that rare cancer stem cells within these subpopulations lead to disease recurrence and treatment resistance. Technological advances now allow for the analysis of tumor genomes and transcriptomes at the single-cell level, which provides the resolution to potentially identify these rare cancer stem cells within the larger tumor.In this chapter, we review the evolution of next-generation RNA sequencing techniques, the methodology of single-cell isolation and sequencing, sequencing data analysis, and the potential applications in ovarian cancer. We also summarize the current published work using single-cell sequencing in ovarian cancer.By utilizing this novel technique to characterize the gene expression of rare subpopulations, new targets and treatment pathways may be identified in ovarian cancer to change treatment paradigms.

High-Precision Quantitative Analysis Reveals Carcinoembryonic Protein Expression Differs Among Colorectal Cancer Primary Foci and Metastases to Different Sites

The expression of carcinoembryonic protein (CEA) is an important biological marker and therapeutic target in colorectal cancer (CRC). CEA expression heterogeneity confers resistance to CEA-targeting immunotherapy antibodies. Thus, quantification of the CEA-positive cell ratio among all tumor cells would be important in identifying patients that would benefit from CEA-targeted therapies. However, the proportion of tumor cells that express CEA within primary and metastasized tumors at different sites has not been studied. Therefore, the present study aimed to determine CEA positive cell proportion in paired CRC primary foci, liver metastases, and lymph node (LN) metastases, and whether proportion of CEA positive cell differs among colorectal cancer primary foci, liver metastases, and LN metastases from 26 patients. The CEA expression was detected by immunohistochemical assay. Then we set up a quantification approach to quantify the proportion of CEA-positive cells based on the TissueGnostics (TG) system. Then the proportion of CEA positive cells were measured and compared among primary foci, liver metastases, and LN metastases. As a result, the proportion of CEA positive tumor cells was slightly higher in liver metastases than in primary foci (89.8% ± 2.71% vs 82.1% ± 5.05%, P < 0.001). The proportion of CEA-positive cells was significantly lower in LN metastases than in primary foci (82.3% ± 4.32% vs 70.28% ± 5.04%, P < 0.001). In 8 cases with matched CRC primary foci, liver metastases, and LN metastases, the proportions of CEA proportion in liver metastasis was the highest, followed by primary foci and LNs metastasis. In conclusion, this study provided an new approach for quantification of CEA positive cell in tumors and proved the percentage of CEA-positive cells varied in different metastases.

Single cell RNA sequencing for breast cancer: present and future

Breast cancer is one of the most common malignant tumors in women. It is a heterogeneous disease related to genetic and environmental factors. Presently, the treatment of breast cancer still faces challenges due to recurrence and metastasis. The emergence of single-cell RNA sequencing (scRNA-seq) technology has brought new strategies to deeply understand the biological behaviors of breast cancer. By analyzing cell phenotypes and transcriptome differences at the single-cell level, scRNA-seq reveals the heterogeneity, dynamic growth and differentiation process of cells. This review summarizes the application of scRNA-seq technology in breast cancer research, such as in studies on cell heterogeneity, cancer cell metastasis, drug resistance, and prognosis. scRNA-seq technology is of great significance to deeply analyze the mechanism of breast cancer occurrence and development, identify new therapeutic targets and develop new therapeutic approaches for breast cancer.

SYTL4 downregulates microtubule stability and confers paclitaxel resistance in triple-negative breast cancer

Background: Taxanes are frontline chemotherapeutic drugs for patients with triple-negative breast cancer (TNBC); however, chemoresistance reduces their effectiveness. We hypothesized that the molecular profiling of tumor samples before and after neoadjuvant chemotherapy (NAC) would help identify genes associated with drug resistance.

Methods: We sequenced 10 samples by RNA-seq from 8 NAC patients with TNBC: 3 patients with a pathologic complete response (pCR) and the other 5 with non-pCR. Differentially expressed genes that predicted chemotherapy response were selected for in vitro functional screening via a small-scale siRNAs pool. The clinical and functional significance of the gene of interest in TNBC was further investigated in vitro and in vivo, and biochemical assays and imaging analysis were applied to study the mechanisms.

Results: Synaptotagmin-like 4 (SYTL4), a Rab effector in vesicle transport, was identified as a leading functional candidate. High SYTL4 expression indicated a poor prognosis in multiple TNBC cohorts, specifically in taxane-treated TNBCs. SYTL4 was identified as a novel chemoresistant gene as validated in TNBC cells, a mouse model and patient-derived organoids. Mechanistically, downregulating SYTL4 stabilized the microtubule network and slowed down microtubule growth rate. Furthermore, SYTL4 colocalized with microtubules and interacted with microtubules through its middle region containing the linker and C2A domain. Finally, we found that SYTL4 was able to bind microtubules and inhibit the in vitro microtubule polymerization.

Conclusion: SYTL4 is a novel chemoresistant gene in TNBC and its upregulation indicates poor prognosis in taxane-treated TNBC. Further, SYTL4 directly binds microtubules and decreases microtubule stability.

Oncogenic TRIM37 Links Chemoresistance and Metastatic Fate in Triple-Negative Breast Cancer

The majority of clinical deaths in patients with triple-negative breast cancer (TNBC) are due to chemoresistance and aggressive metastases, with high prevalence in younger women of African ethnicity. Although tumorigenic drivers are numerous and varied, the drivers of metastatic transition remain largely unknown. Here, we uncovered a molecular dependence of TNBC tumors on the TRIM37 network, which enables tumor cells to resist chemotherapeutic as well as metastatic stress. TRIM37-directed histone H2A monoubiquitination enforces changes in DNA repair that rendered TP53-mutant TNBC cells resistant to chemotherapy. Chemotherapeutic drugs triggered a positive feedback loop via ATM/E2F1/STAT signaling, amplifying the TRIM37 network in chemoresistant cancer cells. High expression of TRIM37 induced transcriptomic changes characteristic of a metastatic phenotype, and inhibition of TRIM37 substantially reduced the in vivo propensity of TNBC cells. Selective delivery of TRIM37-specific antisense oligonucleotides using antifolate receptor 1-conjugated nanoparticles in combination with chemotherapy suppressed lung metastasis in spontaneous metastatic murine models. Collectively, these findings establish TRIM37 as a clinically relevant target with opportunities for therapeutic intervention. SIGNIFICANCE: TRIM37 drives aggressive TNBC biology by promoting resistance to chemotherapy and inducing a prometastatic transcriptional program; inhibition of TRIM37 increases chemotherapy efficacy and reduces metastasis risk in patients with TNBC.

Modeling Tumor Evolutionary Dynamics to Predict Clinical Outcomes for Patients with Metastatic Colorectal Cancer: A Retrospective Analysis

Over 50% of colorectal cancer patients develop resistance after a transient response to therapy. Understanding tumor resistance from an evolutionary perspective leads to better predictions of treatment outcomes. The objectives of this study were to develop a computational framework to analyze tumor longitudinal measurements and recapitulate the individual evolutionary dynamics in metastatic colorectal cancer (mCRC) patients. A stochastic modeling framework was developed to depict the whole spectrum of tumor evolution prior to diagnosis and during and after therapy. The evolutionary model was optimized using a nonlinear mixed effect (NLME) method based on the longitudinal measurements of liver metastatic lesions from 599 mCRC patients. The deterministic limits in the NLME model were applied to optimize the stochastic model for each patient. Cox proportional hazards models coupled with the least absolute shrinkage and selection operator (LASSO) algorithm were applied to predict patients' progression-free survival (PFS) and overall survival (OS). The stochastic evolutionary model well described the longitudinal profiles of tumor sizes. The evolutionary parameters optimized for each patient indicated substantial interpatient variability. The number of resistant subclones at diagnosis was found to be a significant predictor to survival, and the hazard ratios with 95% CI were 1.09 (0.79-1.49) and 1.54 (1.01-2.34) for patients with three or more resistant subclones. Coupled with several patient characteristics, evolutionary parameters strongly predict patients' PFS and OS. A stochastic computational framework was successfully developed to recapitulate individual patient evolutionary dynamics, which could predict clinical survival outcomes in mCRC patients. SIGNIFICANCE: A data analysis framework depicts the individual evolutionary dynamics of mCRC patients and can be generalized to project patient survival outcomes.

Assessing reliability of intra-tumor heterogeneity estimates from single sample whole exome sequencing data

Tumors are made of evolving and heterogeneous populations of cells which arise from successive appearance and expansion of subclonal populations, following acquisition of mutations conferring them a selective advantage. Those subclonal populations can be sensitive or resistant to different treatments, and provide information about tumor aetiology and future evolution. Hence, it is important to be able to assess the level of heterogeneity of tumors with high reliability for clinical applications. In the past few years, a large number of methods have been proposed to estimate intra-tumor heterogeneity from whole exome sequencing (WES) data, but the accuracy and robustness of these methods on real data remains elusive. Here we systematically apply and compare 6 computational methods to estimate tumor heterogeneity on 1,697 WES samples from the cancer genome atlas (TCGA) covering 3 cancer types (breast invasive carcinoma, bladder urothelial carcinoma, and head and neck squamous cell carcinoma), and two distinct input mutation sets. We observe significant differences between the estimates produced by different methods, and identify several likely confounding factors in heterogeneity assessment for the different methods. We further show that the prognostic value of tumor heterogeneity for survival prediction is limited in those datasets, and find no evidence that it improves over prognosis based on other clinical variables. In conclusion, heterogeneity inference from WES data on a single sample, and its use in cancer prognosis, should be considered with caution. Other approaches to assess intra-tumoral heterogeneity such as those based on multiple samples may be preferable for clinical applications.

Clustering and classification methods for single-cell RNA-sequencing data

Appropriate ways to measure the similarity between single-cell RNA-sequencing (scRNA-seq) data are ubiquitous in bioinformatics, but using single clustering or classification methods to process scRNA-seq data is generally difficult. This has led to the emergence of integrated methods and tools that aim to automatically process specific problems associated with scRNA-seq data. These approaches have attracted a lot of interest in bioinformatics and related fields. In this paper, we systematically review the integrated methods and tools, highlighting the pros and cons of each approach. We not only pay particular attention to clustering and classification methods but also discuss methods that have emerged recently as powerful alternatives, including nonlinear and linear methods and descending dimension methods. Finally, we focus on clustering and classification methods for scRNA-seq data, in particular, integrated methods, and provide a comprehensive description of scRNA-seq data and download URLs.

Ongoing chromosomal instability and karyotype evolution in human colorectal cancer organoids

Chromosome segregation errors cause aneuploidy and genomic heterogeneity, which are hallmarks of cancer in humans. A persistent high frequency of these errors (chromosomal instability (CIN)) is predicted to profoundly impact tumor evolution and therapy response. It is unknown, however, how prevalent CIN is in human tumors. Using three-dimensional live-cell imaging of patient-derived tumor organoids (tumor PDOs), we show that CIN is widespread in colorectal carcinomas regardless of background genetic alterations, including microsatellite instability. Cell-fate tracking showed that, although mitotic errors are frequently followed by cell death, some tumor PDOs are largely insensitive to mitotic errors. Single-cell karyotype sequencing confirmed heterogeneity of copy number alterations in tumor PDOs and showed that monoclonal lines evolved novel karyotypes over time in vitro. We conclude that ongoing CIN is common in colorectal cancer organoids, and propose that CIN levels and the tolerance for mitotic errors shape aneuploidy landscapes and karyotype heterogeneity.

Clinical implications of changes in the diversity of c-MYC copy number variation after neoadjuvant chemotherapy in breast cancer

Chemotherapy can alter the makeup of a tumor cell population by exerting selection pressure. We examined the change in Shannon index, a mathematical diversity measure used in ecology, for c-MYC copy number variation (CNV) after neoadjuvant chemotherapy and evaluated its clinical significance in breast cancer. Associations between Shannon indices for c-MYC CNV in pre- and post-neoadjuvant chemotherapy breast cancer samples and clinicopathologic features of tumors as well as patient survival were analyzed in 144 patients. A change in c-MYC amplification and copy number gain status was found in 14.3% and 33.6% with most cases showing positive to negative conversion. The chemo-sensitive group showed a significant decrease in Shannon index after neoadjuvant chemotherapy. However, there was no difference in diversity indices between pre- and post-neoadjuvant chemotherapy specimens in the chemo-resistant group. In survival analyses, high Shannon indices for c-MYC CNV in post-neoadjuvant chemotherapy samples as well as those in pre-neoadjuvant chemotherapy samples were revealed as independent prognostic factors for poor disease-free survival not only in the whole group but also in the chemo-resistant subgroup. These findings suggest that a change in Shannon index for c-MYC CNV after neoadjuvant chemotherapy reflects chemo-responsiveness and that Shannon indices after neoadjuvant chemotherapy have a prognostic value in breast cancer patients who receive neoadjuvant chemotherapy.

Dissecting human disease with single-cell omics: application in model systems and in the clinic

Probing cellular population diversity at single-cell resolution became possible only in recent years. The popularity of single-cell 'omic' approaches, which allow researchers to dissect sample heterogeneity and cell-to-cell variation, continues to grow. With continuous technological improvements, single-cell omics are becoming increasingly prevalent and contribute to the discovery of new and rare cell types, and to the deciphering of disease pathogenesis and outcome. Animal models of human diseases have significantly facilitated our understanding of the mechanisms driving pathologies and resulted in the development of more efficient therapies. The application of single-cell omics to animal models improves the precision of the obtained insights, and brings single-cell technology closer to the clinical field. This Review focuses on the use of single-cell omics in cellular and animal models of diseases, as well as in samples from human patients. It also highlights the potential of these approaches to further improve the diagnosis and treatment of various pathologies, and includes a discussion of the advantages and remaining challenges in implementing these technologies into clinical practice.

Analyzing Circulating Tumor Cells One at a Time

Whole-genome sequencing has made a significant impact on cancer research, but traditional bulk methods fail to detect information from rare cells. Recently developed single-cell sequencing methods have provided new insights and unprecedented details about cancer progression and diversity. These advancements also enable the investigation of rare cells, such as circulating tumor cells (CTCs) derived from cancer patients. In this review, we outline various single-cell sequencing techniques that can elucidate the molecular properties of CTCs. In addition, we explain the drawbacks that need to be overcome for each method.

Chemoresistance Evolution in Triple-Negative Breast Cancer Delineated by Single-Cell Sequencing

Triple-negative breast cancer (TNBC) is an aggressive subtype that frequently develops resistance to chemotherapy. An unresolved question is whether resistance is caused by the selection of rare pre-existing clones or alternatively through the acquisition of new genomic aberrations. To investigate this question, we applied single-cell DNA and RNA sequencing in addition to bulk exome sequencing to profile longitudinal samples from 20 TNBC patients during neoadjuvant chemotherapy (NAC). Deep-exome sequencing identified 10 patients in which NAC led to clonal extinction and 10 patients in which clones persisted after treatment. In 8 patients, we performed a more detailed study using single-cell DNA sequencing to analyze 900 cells and single-cell RNA sequencing to analyze 6,862 cells. Our data showed that resistant genotypes were pre-existing and adaptively selected by NAC, while transcriptional profiles were acquired by reprogramming in response to chemotherapy in TNBC patients.

Programmed death-ligand 1 expression by digital image analysis advances thyroid cancer diagnosis among encapsulated follicular lesions

Recognition of noninvasive follicular thyroid neoplasms with papillary-like nuclear features (NIFTP) that distinguishes them from invasive malignant encapsulated follicular variant of papillary thyroid carcinoma (EFVPTC) can prevent overtreatment of NIFTP patients. We and others have previously reported that programmed death-ligand 1 (PD-L1) is a useful biomarker in thyroid tumors; however, all reports to date have relied on manual scoring that is time consuming as well as subject to individual bias. Consequently, we developed a digital image analysis (DIA) protocol for cytoplasmic and membranous stain quantitation (ThyApp) and evaluated three tumor sampling methods [Systemic Uniform Random Sampling, hotspot nucleus, and hotspot nucleus/3,3'-Diaminobenzidine (DAB)]. A patient cohort of 153 cases consisting of 48 NIFTP, 44 EFVPTC, 26 benign nodules and 35 encapsulated follicular lesions/neoplasms with lymphocytic thyroiditis (LT) was studied. ThyApp quantitation of PD-L1 expression revealed a significant difference between invasive EFVPTC and NIFTP; but none between NIFTP and benign nodules. ThyApp integrated with hotspot nucleus tumor sampling method demonstrated to be most clinically relevant, consumed least processing time, and eliminated interobserver variance. In conclusion, the fully automatic DIA algorithm developed using a histomorphological approach objectively quantitated PD-L1 expression in encapsulated thyroid neoplasms and outperformed manual scoring in reproducibility and higher efficiency.

Cell state plasticity, stem cells, EMT, and the generation of intra-tumoral heterogeneity

Cellular heterogeneity in cancer represents a significant challenge. In order to develop effective and lasting therapies, it is essential to understand the source of this heterogeneity, and its role in tumor progression and therapy resistance. Here, we consider not only genetic and epigenetic mechanisms, but also inflammation and cell state reprogramming in creating tumor heterogeneity. We discuss similarities between normal mammary epithelial developmental states and various breast cancer molecular sub-types, and the cells that are thought to propagate them. We emphasize that while stem cell phenotypes and mesenchymal character have often been conflated, existing data suggest that the combination of intrinsic genetic and epigenetic changes, and microenvironmental influences generate multiple types of tumor propagating cells distinguishable by their positions along a continuum of epithelial to mesenchymal, stem to differentiated and embryonic to mature cell states. Consequently, in addition to the prospect of stem cell-directed tumor therapies, there is a need to understand interrelationships between stem cell, epithelial–mesenchymal, and tumor-associated reprogramming events to develop new therapies that mitigate cell state plasticity and minimize the evolution of tumor heterogeneity.

New paradigms in clonal evolution: punctuated equilibrium in cancer

Evolutionary theories are themselves subject to evolution. Clonal evolution - the model that describes the initiation and progression of cancer - is entering a period of profound change, brought about largely by technological developments in genome analysis. A flurry of recent publications, using modern mathematical and bioinformatics techniques, have revealed both punctuated and neutral evolution phenomena that are poorly explained by the conventional graduated perspectives. In this review, we propose that a hybrid model, inspired by the evolutionary model of punctuated equilibrium, could better explain these recent observations. We also discuss the conceptual changes and clinical implications of variable evolutionary tempos. Copyright © 2016 Pathological Society of Great Britain and Ireland. Published by John Wiley & Sons, Ltd.

Deciphering and Targeting Oncogenic Mutations and Pathways in Breast Cancer

: Advances in DNA and RNA sequencing revealed substantially greater genomic complexity in breast cancer than simple models of a few driver mutations would suggest. Only very few, recurrent mutations or copy-number variations in cancer-causing genes have been identified. The two most common alterations in breast cancer are TP53 (affecting the majority of triple-negative breast cancers) and PIK3CA (affecting almost half of estrogen receptor-positive cancers) mutations, followed by a long tail of individually rare mutations affecting <1%-20% of cases. Each cancer harbors from a few dozen to a few hundred potentially high-functional impact somatic variants, along with a much larger number of potentially high-functional impact germline variants. It is likely that it is the combined effect of all genomic variations that drives the clinical behavior of a given cancer. Furthermore, entirely new classes of oncogenic events are being discovered in the noncoding areas of the genome and in noncoding RNA species driven by errors in RNA editing. In light of this complexity, it is not unexpected that, with the exception of HER2 amplification, no robust molecular predictors of benefit from targeted therapies have been identified. In this review, we summarize the current genomic portrait of breast cancer, focusing on genetic aberrations that are actively being targeted with investigational drugs.

IMPLICATIONS FOR PRACTICE

Next-generation sequencing is now widely available in the clinic, but interpretation of the results is challenging, and its impact on treatment selection is often limited. This work provides an overview of frequently encountered molecular abnormalities in breast cancer and discusses their potential therapeutic implications. This review emphasizes the importance of administering investigational targeted therapies, or off-label use of approved targeted drugs, in the context of a formal clinical trial or registry programs to facilitate learning about the clinical utility of tumor target profiling.

The first five years of single-cell cancer genomics and beyond

Single-cell sequencing (SCS) is a powerful new tool for investigating evolution and diversity in cancer and understanding the role of rare cells in tumor progression. These methods have begun to unravel key questions in cancer biology that have been difficult to address with bulk tumor measurements. Over the past five years, there has been extraordinary progress in technological developments and research applications, but these efforts represent only the tip of the iceberg. In the coming years, SCS will greatly improve our understanding of invasion, metastasis, and therapy resistance during cancer progression. These tools will also have direct translational applications in the clinic, in areas such as early detection, noninvasive monitoring, and guiding targeted therapy. In this perspective, I discuss the progress that has been made and the myriad of unexplored applications that still lie ahead in cancer research and medicine.

"Combo" nanomedicine: Co-delivery of multi-modal therapeutics for efficient, targeted, and safe cancer therapy

The dynamic and versatile nature of diseases such as cancer has been a pivotal challenge for developing efficient and safe therapies. Cancer treatments using a single therapeutic agent often result in limited clinical outcomes due to tumor heterogeneity and drug resistance. Combination therapies using multiple therapeutic modalities can synergistically elevate anti-cancer activity while lowering doses of each agent, hence, reducing side effects. Co-administration of multiple therapeutic agents requires a delivery platform that can normalize pharmacokinetics and pharmacodynamics of the agents, prolong circulation, selectively accumulate, specifically bind to the target, and enable controlled release in target site. Nanomaterials, such as polymeric nanoparticles, gold nanoparticles/cages/shells, and carbon nanomaterials, have the desired properties, and they can mediate therapeutic effects different from those generated by small molecule drugs (e.g., gene therapy, photothermal therapy, photodynamic therapy, and radiotherapy). This review aims to provide an overview of developing multi-modal therapies using nanomaterials ("combo" nanomedicine) along with the rationale, up-to-date progress, further considerations, and the crucial roles of interdisciplinary approaches.

Cancer modelling in the NGS era - Part I: Emerging technology and initial modelling

It is today indisputable that great progresses have been made in our molecular understanding of cancer cells, but an effective implementation of such knowledge into dramatic cancer-cures is still belated and yet desperately needed. This review gives a snapshot at where we stand today in this search for cancer understanding and definitive treatments, how far we have progressed and what are the major obstacles we will have to overcome both technologically and for disease modelling. In the first part, promising 3rd/4th Generation Sequencing Technologies will be summarized (particularly IonTorrent and OxfordNanopore technologies). Cancer modelling will be then reviewed from its origin in XIX Century Germany to today's NGS applications for cancer understanding and therapeutic interventions. Developments after Molecular Biology revolution (1953) are discussed as successions of three phases. The first, PH1, labelled "Clonal Outgrowth" (from 1960s to mid 1980s) was characterized by discoveries in cytogenetics (Nowell, Rowley) and viral oncology (Dulbecco, Bishop, Varmus), which demonstrated clonality. Treatments were consequently dominated by a "cytotoxic eradication" strategy with chemotherapeutic agents. In PH2, (from the mid 1980s to our days) the description of cancer as "Gene Networks" led to targeted-gene-therapies (TGTs). TGTs are the focus of Section 3: in view of their apparent failing (Ephemeral Therapies), alternative strategies will be discussed in review part II (particularly cancer immunotherapy, CIT). Additional Pitfalls impinge on the concepts of tumour heterogeneity (inter/intra; ITH). The described pitfalls set the basis for a new phase, PH3, which is called "NGS Era" and will be also discussed with ten emerging cancer models in the Review 2nd part.

A novel 96well-formatted micro-gap plate enabling drug response profiling on primary tumour samples

Drug-based treatments are the most widely used interventions for cancer management. Personalized drug response profiling remains inherently challenging with low cell count harvested from tumour sample. We present a 96well-formatted microfluidic plate with built-in micro-gap that preserves up to 99.2% of cells during multiple assay/wash operation and only 9,000 cells needed for a single reagent test (i.e. 1,000 cells per test spot x 3 selected concentration x triplication), enabling drug screening and compatibility with conventional automated workstations. Results with MCF7 and MDA-MB-231 cell lines showed that no statistical significance was found in dose-response between the device and conventional 96-well plate control. Primary tumour samples from breast cancer patients tested in the device also showed good IC₅₀ prediction. With drug screening of primary cancer cells must consider a wide range of scenarios, e.g. suspended/attached cell types and rare/abundant cell availability, the device enables high throughput screening even for suspended cells with low cell count since the signature microfluidic cell-trapping feature ensures cell preservation in a multiple solution exchange protocol.

Deciphering the cellular source of tumor relapse identifies CD44 as a major therapeutic target in pancreatic adenocarcinoma

It has been commonly found that in patients presenting Pancreatic Ductal Adenocarcinoma (PDAC), after a period of satisfactory response to standard treatments, the tumor becomes non-responsive and patient death quickly follows. This phenomenon is mainly due to the rapid and uncontrolled development of the residual tumor. The origin and biological characteristics of residual tumor cells in PDAC still remain unclear. In this work, using PDACs from patients, preserved as xenografts in nude mice, we demonstrated that a residual PDAC tumor originated from a small number of CD44+ cells present in the tumor. During PDAC relapse, proliferating CD44+ cells decrease expression of ZEB1, while overexpressing the MUC1 protein, and gain morphological and biological characteristics of differentiation. Also, we report that CD44+ cells, in primary and residual PDAC tumors, are part of a heterogeneous population, which includes variable numbers of CD133+ and EpCAM+ cells. We confirmed the propagation of CD44+ cells in samples from cases of human relapse, following standard PDAC treatment. Finally, using systemic administration of anti-CD44 antibodies in vivo, we demonstrated that CD44 is an efficient therapeutic target for treating tumor relapse, but not primary PDAC tumors. We conclude that CD44+ cells generate the relapsing tumor and, as such, are themselves promising therapeutic targets for treating patients with recurrent PDAC.

Cancer genomics: one cell at a time

The study of single cancer cells has transformed from qualitative microscopic images to quantitative genomic datasets. This paradigm shift has been fueled by the development of single-cell sequencing technologies, which provide a powerful new approach to study complex biological processes in human cancers.

Intratumor DNA methylation heterogeneity reflects clonal evolution in aggressive prostate cancer

Despite much evidence on epigenetic abnormalities in cancer, it is currently unclear to what extent epigenetic alterations can be associated with tumors' clonal genetic origins. Here, we show that the prostate intratumor heterogeneity in DNA methylation and copy-number patterns can be explained by a unified evolutionary process. By assaying multiple topographically distinct tumor sites, premalignant lesions, and lymph node metastases within five cases of prostate cancer, we demonstrate that both DNA methylation and copy-number heterogeneity consistently reflect the life history of the tumors. Furthermore, we show cases of genetic or epigenetic convergent evolution and highlight the diversity in the evolutionary origins and aberration spectrum between tumor and metastatic subclones. Importantly, DNA methylation can complement genetic data by serving as a proxy for activity at regulatory domains, as we show through identification of high epigenetic heterogeneity at androgen-receptor-bound enhancers. Epigenome variation thereby expands on the current genome-centric view on tumor heterogeneity.

From genotype to functional phenotype: unraveling the metabolomic features of colorectal cancer

Much effort in recent years has been expended in defining the genomic and epigenetic alterations that characterize colorectal adenocarcinoma and its subtypes. However, little is known about the functional ramifications related to various subtypes. Metabolomics, the study of small molecule intermediates in disease, provides a snapshot of the functional phenotype of colorectal cancer. Data, thus far, have characterized some of the metabolic perturbations that accompany colorectal cancer. However, further studies will be required to identify biologically meaningful metabolic subsets, including those corresponding to specific genetic aberrations. Moreover, further studies are necessary to distinguish changes due to tumor and the host response to tumor.

Circulating Tumor Cells: What Is in It for the Patient? A Vision towards the Future

Knowledge on cellular signal transduction pathways as drivers of cancer growth and metastasis has fuelled development of “targeted therapy” which “targets” aberrant oncogenic signal transduction pathways. These drugs require nearly invariably companion diagnostic tests to identify the tumor-driving pathway and the cause of the abnormal pathway activity in a tumor sample, both for therapy response prediction as well as for monitoring of therapy response and emerging secondary drug resistance. Obtaining sufficient tumor material for this analysis in the metastatic setting is a challenge, and circulating tumor cells (CTCs) may provide an attractive alternative to biopsy on the premise that they can be captured from blood and the companion diagnostic test results are correctly interpreted. We discuss novel companion diagnostic directions, including the challenges, to identify the tumor driving pathway in CTCs, which in combination with a digital pathology platform and algorithms to quantitatively interpret complex CTC diagnostic results may enable optimized therapy response prediction and monitoring. In contrast to CTC-based companion diagnostics, CTC enumeration is envisioned to be largely replaced by cell free tumor DNA measurements in blood for therapy response and recurrence monitoring. The recent emergence of novel in vitro human model systems in the form of cancer-on-a-chip may enable elucidation of some of the so far elusive characteristics of CTCs, and is expected to contribute to more efficient CTC capture and CTC-based diagnostics.