Tumour heterogeneity and the evolution of polyclonal drug resistance

Computational frameworks transform antagonism to synergy in optimizing combination therapies

While drug combinations are increasingly important in disease treatment, predicting their therapeutic interactions remains challenging. This review systematically analyzes computational methods for predicting drug combination effects through multi-omics data integration. We comprehensively assess key algorithms including DrugComboRanker and AuDNNsynergy, and evaluate integration approaches encompassing kernel regression and graph networks. The review elucidates artificial intelligence applications in predicting drug synergistic and antagonistic effects.

Gradient boosting reveals spatially diverse cholesterol gene signatures in colon cancer

Colon cancer (CC) is the second most common cause of cancer deaths and the fourth most prevalent cancer in the United States. Recently cholesterol metabolism has been identified as a potential therapeutic avenue due to its consistent association with tumor treatment effects and overall prognosis. We conducted differential gene analysis and KEGG pathway analysis on paired tumor and adjacent-normal samples from the TCGA Colon Adenocarcinoma project, identifying that bile secretion was the only significantly downregulated pathway. To evaluate the relationship between cholesterol metabolism and CC prognosis, we used the genes from this pathway in several statistical models like Cox proportional Hazard (CPH), Random Forest (RF), Lasso Regression (LR), and the eXtreme Gradient Boosting (XGBoost) to identify the genes which contributed highly to the predictive ability of all models, ADCY5, and SLC2A1. We demonstrate that using cholesterol metabolism genes with XGBoost models improves stratification of CC patients into low and high-risk groups compared with traditional CPH, RF and LR models. Spatial transcriptomics (ST) revealed that SLC2A1 (glucose transporter 1, GLUT1) colocalized with small blood vessels. ADCY5 localized to stromal regions in both the ST and protein immunohistochemistry. Interestingly, both these significant genes are expressed in tissues other than the tumor itself, highlighting the complex interplay between the tumor and microenvironment, and that druggable targets may be found in the ability to modify how "normal" tissue interacts with tumors.

Exploring evolutionary trajectories in ovarian cancer patients by longitudinal analysis of ctDNA

OBJECTIVES

We analysed whether temporal heterogeneity of ctDNA encodes evolutionary patterns in ovarian cancer.

METHODS

Targeted sequencing of 275 cancer-associated genes was performed in a primary tumor biopsy and in ctDNA of six longitudinal plasma samples from 15 patients, using the Illumina platform.

RESULTS

While there was low overall concordance between the mutational spectrum of the primary tumor biopsies vs. ctDNA, TP53 variants were the most commonly shared somatic alterations. Up to three variant clusters were detected in each tumor biopsy, likely representing predominant clones of the primary tumor, most of them harbouring a TP53 variant. By tracing these clusters in ctDNA, we propose that liquid biopsy may allow to assess the contribution of ancestral clones of the tumor to relapsed abdominal masses, revealing two evolutionary patterns. In pattern#1, clusters detected in the primary tumor biopsy were likely relapse seeding clones, as they contributed a major share to ctDNA at relapse. In pattern#2, similar clusters were present in tumors and ctDNA; however, they were entirely cleared from liquid biopsy after chemotherapy and were undetectable at relapse. ctDNA private variants were present among both patterns, with some of them mirroring subclonal expansions after chemotherapy.

CONCLUSIONS

We demonstrate that tracing the temporal heterogeneity of ctDNA, even below exome scale resolution, deciphers evolutionary trajectories in ovarian cancer. Furthermore, we describe two evolutionary patterns that may help to identify relapse seeding clones for targeted therapy.

Therapy-induced senescence in breast cancer: an overview

Outcomes for women with breast cancer have improved dramatically in recent decades. However, many patients present with intrinsic drug resistance and others are initially sensitive to anti-cancer drugs but acquire resistance during the course of their treatment, leading to recurrence and/or metastasis. Drug therapy-induced senescence (TIS) is a form of drug resistance characterised by the induction of cell cycle arrest and the emergence of a senescence-associated secretory phenotype (SASP) that can develop in response to chemo- and targeted- therapies. A wide range of anticancer interventions can lead to cell cycle arrest and SASP induction, by inducing genotoxic stress, hyperactivation of signalling pathways or oxidative stress. TIS can be anti-tumorigenic in the short-term, but pro-tumorigenic in the long-term by creating a pro-inflammatory and immunosuppressive microenvironment. Moreover, the SASP can promote angiogenesis and epithelial-mesenchymal transition in neighbouring cells. In this review, we will describe the characteristics of TIS in breast cancer and detail the changes in phenotype that accompany its induction. We also discuss strategies for targeting senescent cancer cells in order to prevent or delay tumour recurrence.

It might be a dead end: immune checkpoint inhibitor therapy in EGFR-mutated NSCLC

Despite innovative advances in molecular targeted therapy, treatment strategies using immune checkpoint inhibitors (ICIs) for epidermal growth factor receptor (EGFR)-mutant non-small cell lung cancer (NSCLC) have not progressed significantly. Accumulating evidence suggests that ICI chemotherapy is inadequate in this population. Biomarkers of ICI therapy, such as programmed cell death ligand 1 (PD-L1) and tumor-infiltrating lymphocytes (TILs), are not biomarkers in patients with EGFR mutations, and the specificity of the tumor microenvironment has been suggested as the reason for this. Combination therapy with PD-L1 and cytotoxic T-lymphocyte-associated protein 4 (CTLA-4) inhibitors is a concern because of its severe toxicity and limited efficacy. However, early-stage NSCLC may differ from advanced-stage NSCLC. In this review, we comprehensively review the current evidence and summarize the potential of ICI therapy in patients with EGFR mutations after acquiring resistance to treatment with EGFR-tyrosine kinase inhibitors (TKIs) with no T790M mutation or whose disease has progressed on osimertinib.

Multi-b-value DWI to evaluate the synergistic antiproliferation and anti-heterogeneity effects of bufalin plus sorafenib in an orthotopic HCC model

BACKGROUND

Multi-b-value diffusion-weighted imaging (DWI) with different postprocessing models allows for evaluating hepatocellular carcinoma (HCC) proliferation, spatial heterogeneity, and feasibility of treatment strategies. We assessed synergistic effects of bufalin+sorafenib in orthotopic HCC-LM3 xenograft nude mice by using intravoxel incoherent motion (IVIM), diffusion kurtosis imaging (DKI), a stretched exponential model (SEM), and a fractional-order calculus (FROC) model.

METHODS

Twenty-four orthotopic HCC-LM3 xenograft mice were divided into bufalin+sorafenib, bufalin, sorafenib treatment groups, and a control group. Multi-b-value DWI was performed using a 3-T scanner after 3 weeks' treatment to obtain true diffusion coefficient Dt, pseudo-diffusion coefficient Dp, perfusion fraction f, mean diffusivity (MD), mean kurtosis (MK), distributed diffusion coefficient (DDC), heterogeneity index α, diffusion coefficient D, fractional order parameter β, and microstructural quantity μ. Necrotic fraction (NF), standard deviation (SD) of hematoxylin-eosin staining, and microvessel density (MVD) of anti-CD31 staining were evaluated. Correlations of DWI parameters with histopathological results were analyzed, and measurements were compared among four groups.

RESULTS

In the final 22 mice, f positively correlated with MVD (r = 0.679, p = 0.001). Significantly good correlations of MK (r = 0.677), α (r = -0.696), and β (r= -0.639) with SD were observed (all p < 0.010). f, MK, MVD, and SD were much lower, while MD, α, β, and NF were higher in bufalin plus sorafenib group than control group (all p < 0.050).

CONCLUSION

Evaluated by IVIM, DKI, SEM, and FROC, bufalin+sorafenib was found to inhibit tumor proliferation and angiogenesis and reduce spatial heterogeneity in HCC-LM3 models.

RELEVANCE STATEMENT

Multi-b-value DWI provides potential metrics for evaluating the efficacy of treatment in HCC.

KEY POINTS

• Bufalin plus sorafenib combination may increase the effectiveness of HCC therapy. • Multi-b-value DWI depicted HCC proliferation, angiogenesis, and spatial heterogeneity. • Multi-b-value DWI may be a noninvasive method to assess HCC therapeutic efficacy.

Drug-resilient Cancer Cell Phenotype Is Acquired via Polyploidization Associated with Early Stress Response Coupled to HIF2α Transcriptional Regulation

Therapeutic resistance and recurrence remain core challenges in cancer therapy. How therapy resistance arises is currently not fully understood with tumors surviving via multiple alternative routes. Here, we demonstrate that a subset of cancer cells survives therapeutic stress by entering a transient state characterized by whole-genome doubling. At the onset of the polyploidization program, we identified an upregulation of key transcriptional regulators, including the early stress-response protein AP-1 and normoxic stabilization of HIF2α. We found altered chromatin accessibility, ablated expression of retinoblastoma protein (RB1), and enrichment of AP-1 motif accessibility. We demonstrate that AP-1 and HIF2α regulate a therapy resilient and survivor phenotype in cancer cells. Consistent with this, genetic or pharmacologic targeting of AP-1 and HIF2α reduced the number of surviving cells following chemotherapy treatment. The role of AP-1 and HIF2α in stress response by polyploidy suggests a novel avenue for tackling chemotherapy-induced resistance in cancer.

SIGNIFICANCE

In response to cisplatin treatment, some surviving cancer cells undergo whole-genome duplications without mitosis, which represents a mechanism of drug resistance. This study presents mechanistic data to implicate AP-1 and HIF2α signaling in the formation of this surviving cell phenotype. The results open a new avenue for targeting drug-resistant cells.

A novel approach to evaluation of tumor response for advanced pulmonary adenocarcinoma using the intertumoral heterogeneity response score

Treatment response and prognosis estimation in advanced pulmonary adenocarcinoma are challenged by the significant heterogeneity of the disease. The current Response Evaluation Criteria in Solid Tumors (RECIST) criteria, despite providing a basis for solid tumor response evaluation, do not fully encompass this heterogeneity. To better represent these nuances, we introduce the intertumoral heterogeneity response score (THRscore), a measure built upon and expanding the RECIST criteria. This retrospective study included patients with 3-10 measurable advanced lung adenocarcinoma lesions who underwent first-line chemotherapy or targeted therapy. The THRscore, derived from the coefficient of variation in size for each measurable tumor before and 4-6 weeks posttreatment, unveiled a correlation with patient outcomes. Specifically, a high THRscore was associated with shorter progression-free survival, lower tumor response rate, and a higher tumor mutation burden. These associations were further validated in an external cohort, confirming THRscore's effectiveness in stratifying patients based on progression risk and treatment response, and enhancing the utility of RECIST in capturing complex tumor behaviors in lung adenocarcinoma. These findings affirm the promise of THRscore as an enhanced tool for tumor response assessment in advanced lung adenocarcinoma, extending the RECIST criteria's utility.

Imaging Molecular Targets and Metabolic Pathways in Breast Cancer for Improved Clinical Management: Current Practice and Future Perspectives

Breast cancer is the most frequently diagnosed cancer and leading cause of cancer-related deaths worldwide. Timely decision-making that enables implementation of the most appropriate therapy or therapies is essential for achieving the best clinical outcomes in breast cancer. While clinicopathologic characteristics and immunohistochemistry have traditionally been used in decision-making, these clinical and laboratory parameters may be difficult to ascertain or be equivocal due to tumor heterogeneity. Tumor heterogeneity is described as a phenomenon characterized by spatial or temporal phenotypic variations in tumor characteristics. Spatial variations occur within tumor lesions or between lesions at a single time point while temporal variations are seen as tumor lesions evolve with time. Due to limitations associated with immunohistochemistry (which requires invasive biopsies), whole-body molecular imaging tools such as standard-of-care [18F]FDG and [18F]FES PET/CT are indispensable in addressing this conundrum. Despite their proven utility, these standard-of-care imaging methods are often unable to image a myriad of other molecular pathways associated with breast cancer. This has stimulated interest in the development of novel radiopharmaceuticals targeting other molecular pathways and processes. In this review, we discuss validated and potential roles of these standard-of-care and novel molecular approaches. These approaches' relationships with patient clinicopathologic and immunohistochemical characteristics as well as their influence on patient management will be discussed in greater detail. This paper will also introduce and discuss the potential utility of novel PARP inhibitor-based radiopharmaceuticals as non-invasive biomarkers of PARP expression/upregulation.

Molecular response assessment using circulating tumor DNA (ctDNA) in advanced solid tumors

The therapeutic landscape for patients with advanced malignancies has changed dramatically over the last twenty years. The growing number of targeted therapies and immunotherapeutic options available have improved response rates and survival for a subset of patients, however determining which patients will experience clinical benefit from these therapies in order to avoid potential toxicities and reduce healthcare costs remains a clinical challenge. Cell-free circulating tumor DNA (ctDNA) is shed by tumor cells into systemic circulation and is already an integral part of routine clinical practice for the non-invasive tumor genotyping in advanced non-small cell lung cancer as well as other malignancies. The short half-life of ctDNA offers a unique opportunity to utilize early on-treatment changes in ctDNA for real-time assessment of therapeutic response and outcome, termed molecular response. Here, we provide a summary and review of the use of molecular response for the prediction of outcomes in patients with advanced cancer, including the current state of science, its application in clinic, and next steps for the development of this predictive tool.

Elucidating the clonal relationship of esophageal second primary tumors in patients with laryngeal squamous cell carcinoma

Laryngeal cancer ranks as the second most prevalent upper airway malignancy, following Lung cancer. Although some progress has been made in managing laryngeal cancer, the 5-year survival rate is disappointing. The gradual increase in the incidence of second primary tumors (SPTs) plays a crucial role in determining survival outcomes during long-term follow-up, and the esophagus was the most common site with a worse prognosis. In clinical practice, the treatment of esophageal second primary tumors (ESPT) in patients with laryngeal squamous cell carcinoma (LSCC) has always been challenging. For patients with synchronous tumors, several treatment modalities, such as radiotherapy, chemotherapy and potentially curative surgery are necessary but are typically poorly tolerated. Secondary cancer therapy options for metachronous patients are always constrained by index cancer treatment indications. Therefore, understanding the clonal origin of the second primary tumor may be an important issue in the treatment of patients. LSCC cells demonstrate genetic instability because of two distinct aetiologies (human papillomavirus (HPV)-negative and HPV-positive) disease. Various etiologies exhibit distinct oncogenic mechanisms, which subsequently impact the tissue microenvironment. The condition of the tissue microenvironment plays a crucial role in determining the destiny and clonal makeup of mutant cells during the initial stages of tumorigenesis. This review focuses on the genetic advances of LSCC, the current research status of SPT, and the influence of key carcinogenesis of HPV-positive and HPV-negative LSCC on clonal evolution of ESPT cells. The objective is to gain a comprehensive understanding of the molecular basis underlying the clonal origins of SPT, thereby offering novel perspectives for future investigations in this field.

Nomograms integrating CT radiomic and deep learning signatures to predict overall survival and progression-free survival in NSCLC patients treated with chemotherapy

OBJECTIVES

This study aims to establish nomograms to accurately predict the overall survival (OS) and progression-free survival (PFS) in patients with non-small cell lung cancer (NSCLC) who received chemotherapy alone as the first-line treatment.

MATERIALS AND METHODS

In a training cohort of 121 NSCLC patients, radiomic features were extracted, selected from intra- and peri-tumoral regions, and used to build signatures (S1 and S2) using a Cox regression model. Deep learning features were obtained from three convolutional neural networks and utilized to build signatures (S3, S4, and S5) that were stratified into over- and under-expression subgroups for survival risk using X-tile. After univariate and multivariate Cox regression analyses, a nomogram incorporating the tumor, node, and metastasis (TNM) stages, radiomic signature, and deep learning signature was established to predict OS and PFS, respectively. The performance was validated using an independent cohort (61 patients).

RESULTS

TNM stages, S2 and S3 were identified as the significant prognosis factors for both OS and PFS; S2 (OS: (HR (95%), 2.26 (1.40-3.67); PFS: (HR (95%), 2.23 (1.36-3.65)) demonstrated the best ability in discriminating patients with over- and under-expression. For the OS nomogram, the C-index (95% CI) was 0.74 (0.70-0.79) and 0.72 (0.67-0.78) in the training and validation cohorts, respectively; for the PFS nomogram, the C-index (95% CI) was 0.71 (0.68-0.81) and 0.72 (0.66-0.79). The calibration curves for the 3- and 5-year OS and PFS were in acceptable agreement between the predicted and observed survival. The established nomogram presented a higher overall net benefit than the TNM stage for predicting both OS and PFS.

CONCLUSION

By integrating the TNM stage, CT radiomic signature, and deep learning signatures, the established nomograms can predict the individual prognosis of NSCLC patients who received chemotherapy. The integrated nomogram has the potential to improve the individualized treatment and precise management of NSCLC patients.

The emergence of RAS mutations in patients with RAS wild-type mCRC receiving cetuximab as first-line treatment: a noninterventional, uncontrolled multicenter study

ABSRTACT

BACKGROUND: Patients treated with anti-epidermal growth factor receptor (anti-EGFR) will ultimately develop acquired resistance promoted by clonal selection, mainly the emergence of mutations in the MAPK pathway (mostly RAS mutations). Baseline assessment of RAS mutations in the blood of patients correlates well with RAS tumour tissue testing and is currently an alternative option in routine clinical practice to guide first-line therapy. The aim of this study was the prevalence of acquired genomic alterations detected in the auxiliary tool of ctDNA testing and investigated the role of RAS ctDNA status for detecting tumour response and predicting benefit to anti-EGFR therapy.

METHODS

Only patients with concordant wild-type formalin-fixed, paraffin-embedded (FFPE) tumour tissue and baseline ctDNA RAS wild-type were included. RAS mutations in plasma were evaluated using MassARRAY platform. Blood samples were collected at baseline, every 3 months during first-line treatment, and at disease progression. The primary endpoint was the detection rate of RAS mutations during cetuximab treatment. The correlation between response and survival outcomes and the emergence of circulating RAS mutations was also analysed.

RESULTS

The detection rate of RAS mutations during treatment was 9.3% (10/108). RAS mutations detection occurred a median of 3 months prior to radiologic documentation. The subgroup of patients with RAS mutations exhibited significantly inferior progression-free survival and overall survival (P = 0.002 and 0.027, respectively) but the baseline characteristics, response rates, disease control rates, and metastatectomy were not significant (all P > 0.05).

CONCLUSIONS

We demonstrated that RAS ctDNA status might be a valuable biomarker for detecting early tumour response and predicting benefit to anti-EGFR therapy.

CLINICAL TRIAL REGISTRATION

NCT03401957 (January 17, 2018).

Disentangling the roles of aneuploidy, chromosomal instability and tumour heterogeneity in developing resistance to cancer therapies

Aneuploidy is defined as the cellular state of having a number of chromosomes that deviates from a multiple of the normal haploid chromosome number of a given organism. Aneuploidy can be present in a static state: Down syndrome individuals stably maintain an extra copy of chromosome 21 in their cells. In cancer cells, however, aneuploidy is usually present in combination with chromosomal instability (CIN) which leads to a continual generation of new chromosomal alterations and the development of intratumour heterogeneity (ITH). The prevalence of cells with specific chromosomal alterations is further shaped by evolutionary selection, for example, during the administration of cancer therapies. Aneuploidy, CIN and ITH have each been individually associated with poor prognosis in cancer, and a wealth of evidence suggests they contribute, either alone or in combination, to cancer therapy resistance by providing a reservoir of potential resistant states, or the ability to rapidly evolve resistance. A full understanding of the contribution and interplay between aneuploidy, CIN and ITH is required to tackle therapy resistance in cancer patients. However, these characteristics often co-occur and are intrinsically linked, presenting a major challenge to defining their individual contributions. Moreover, their accurate measurement in both experimental and clinical settings is a technical hurdle. Here, we attempt to deconstruct the contribution of the individual and combined roles of aneuploidy, CIN and ITH to therapy resistance in cancer, and outline emerging approaches to measure and disentangle their roles as a step towards integrating these principles into cancer therapeutic strategy.

Sulforaphane and bladder cancer: a potential novel antitumor compound

Bladder cancer (BC) is a common form of urinary tract tumor, and its incidence is increasing annually. Unfortunately, an increasing number of newly diagnosed BC patients are found to have advanced or metastatic BC. Although current treatment options for BC are diverse and standardized, it is still challenging to achieve ideal curative results. However, Sulforaphane, an isothiocyanate present in cruciferous plants, has emerged as a promising anticancer agent that has shown significant efficacy against various cancers, including bladder cancer. Recent studies have demonstrated that Sulforaphane not only induces apoptosis and cell cycle arrest in BC cells, but also inhibits the growth, invasion, and metastasis of BC cells. Additionally, it can inhibit BC gluconeogenesis and demonstrate definite effects when combined with chemotherapeutic drugs/carcinogens. Sulforaphane has also been found to exert anticancer activity and inhibit bladder cancer stem cells by mediating multiple pathways in BC, including phosphatidylinositol-3-kinase (PI3K)/protein kinase B (Akt)/mammalian target of rapamycin (mTOR), mitogen-activated protein kinase (MAPK), nuclear factor kappa-B (NF-κB), nuclear factor (erythroid-derived 2)-like 2 (Nrf2), zonula occludens-1 (ZO-1)/beta-catenin (β-Catenin), miR-124/cytokines interleukin-6 receptor (IL-6R)/transcription 3 (STAT3). This article provides a comprehensive review of the current evidence and molecular mechanisms of Sulforaphane against BC. Furthermore, we explore the effects of Sulforaphane on potential risk factors for BC, such as bladder outlet obstruction, and investigate the possible targets of Sulforaphane against BC using network pharmacological analysis. This review is expected to provide a new theoretical basis for future research and the development of new drugs to treat BC.

Guanylation Reactions for the Rational Design of Cancer Therapeutic Agents

The modular synthesis of the guanidine core by guanylation reactions using commercially available ZnEt2 as a catalyst has been exploited as a tool for the rapid development of antitumoral guanidine candidates. Therefore, a series of phenyl-guanidines were straightforwardly obtained in very high yields. From the in vitro assessment of the antitumoral activity of such structurally diverse guanidines, the guanidine termed ACB3 has been identified as the lead compound of the series. Several biological assays, an estimation of AMDE values, and an uptake study using Fluorescence Lifetime Imaging Microscopy were conducted to gain insight into the mechanism of action. Cell death apoptosis, induction of cell cycle arrest, and reduction in cell adhesion and colony formation have been demonstrated for the lead compound in the series. In this work, and as a proof of concept, we discuss the potential of the catalytic guanylation reactions for high-throughput testing and the rational design of guanidine-based cancer therapeutic agents.

Monitoring the Outcomes of Systemic Chemotherapy Including Immune Checkpoint Inhibitor for HER2-Positive Metastatic Gastric Cancer by Liquid Biopsy

PURPOSE

For precision medicine, exploration and monitoring of molecular biomarkers are essential. However, in advanced gastric cancer (GC) with visceral lesions, an invasive procedure cannot be performed repeatedly for the follow-up of molecular biomarkers.

MATERIALS AND METHODS

To verify the clinical implication of serial liquid biopsies targeting circulating tumor DNA (ctDNA) on treatment response, we conducted targeted deep sequencing for serially collected ctDNA of 15 HER2-positive metastatic GC patients treated with anti-PD-1 inhibitor in combination with standard systemic treatment.

RESULTS

In the baseline ctDNAs, 14 patients (93%) harbored more than one genetic alteration. A number of mutations in well-known cancer-related genes, such as KRAS and PIK3CA, were identified. Copy number alterations were identified in eight GCs (53.3%), and amplification of the ERBB2 gene (6/15, 40.0%) was the most recurrent. When we calculated the mean variant allele frequency (VAF) of mutations in each ctDNA as the molecular tumor burden index (mTBI), the mTBI trend was largely consistent with the VAF profiles in both responder and non-responder groups. Notably, in the longitudinal analysis of ctDNA, mTBI provided 2-42 weeks (mean 13.4 weeks) lead time in the detection of disease progression compared to conventional follow-up with CT imaging.

CONCLUSION

Our data indicate that the serial genetic alteration profiling of ctDNA is feasible to predict treatment response in HER2-positive GC patients in a minimally invasive manner. Practically, ctDNA profiles are useful not only for the molecular diagnosis of GC but also for the selection of GC patients with poor prognosis for systemic treatment (ClinicalTrials.gov identifier: NCT02901301).

Application of single-cell RNA sequencing in head and neck squamous cell carcinoma

Single-cell RNA sequencing has been broadly applied to head and neck squamous cell carcinoma (HNSCC) for characterizing the heterogeneity and genomic mutations of HNSCC benefiting from the advantage of single-cell resolution. We summarized most of the current studies and aimed to explore their research methods and ideas, as well as how to transform them into clinical applications. Through single-cell RNA sequencing, we found the differences in tumor cells' expression programs and differentiation tracks. The studies of immune microenvironment allowed us to distinguish immune cell subpopulations, the extensive expression of immune checkpoints, and the complex crosstalk network between immune cells and non-immune cells. For cancer-associated fibroblasts (CAFs), single-cell RNA sequencing had made an irreplaceable contribution to the exploration of their differentiation status, specific CAFs markers, and the interaction with tumor cells and immune cells. In addition, we demonstrated in detail how single-cell RNA sequencing explored the HNSCC epithelial-to-mesenchymal transition (EMT) model and the mechanism of drug resistance, as well as its clinical value.

Identification of collateral sensitivity and evolutionary landscape of chemotherapy-induced drug resistance using cellular barcoding technology

Background: One of the most significant challenges impeding cancer treatment effectiveness is drug resistance. Combining evolutionary understanding with drug resistance can pave the way for the identification of second-line drug options that can overcome drug resistance. Although capecitabine and irinotecan are commonly used therapeutic agents in the treatment of CRC patients, resistance to these agents is common. The underlying clonal dynamics of resistance to these agents using high-resolution barcode technology and identification of effective second-line drugs in this context remain unclear. Methods and materials: Caco-2 and HT-29 cell lines were barcoded, and then capecitabine and irinotecan resistant derivatives of these cell lines were established. The frequencies of barcodes from resistant cell lines and harvested medium, longitudinally, were determined. Collateral drug sensitivity testing was carried out on resistant Caco-2 and HT-29 cell lines using single agents or drug combinations. The SyngeryFinder tool was used to analyse drug combination testing. Results: In Caco-2 and HT-29 cell lines, barcode frequency measurements revealed clonal dynamics of capecitabine and irinotecan formed by both pre-existing and de novo barcodes, indicating the presence of polyclonal drug resistance. The temporal dynamics of clonal evolution in Caco-2 and HT-29 cell lines were demonstrated by longitudinal analysis of pre-existing and de novo barcodes from harvested medium. In Caco-2 and HT-29 cell lines, collateral drug sensitivity revealed a number of drugs that were effective alone and in combination. Conclusion: The use of barcoding technology reveals the clonal dynamics of chemotherapy-induced drug resistance not only from harvested cell populations, but also from longitudinal sampling throughout the course of clonal evolution. Second-line drugs that sensitize drug-resistant CRC cell lines are identified through collateral drug testing.

Progress in genome-inspired treatment decisions for multifocal lung adenocarcinoma

INTRODUCTION

Multifocal lung adenocarcinoma (MFLA) is becoming increasingly recognized as a distinct subset of lung cancer, with unique biology, disease course, and treatment outcomes. While definitions remain controversial, MFLA is characterized by the development and concurrent presence of multiple independent (non-metastatic) lesions on the lung adenocarcinoma spectrum. Disease progression typically follows an indolent course measured in years, with a lower propensity for nodal and distant metastases than other more common forms of non-small cell lung cancer.

AREAS COVERED

Traditional imaging and histopathological analyses of tumor biopsies are frequently unable to fully characterize the disease, prompting interest in molecular diagnosis. We highlight some of the key questions in the field, including accurate definitions to identify and stage MLFA, molecular tests to stratify patients and treatment decisions, and the lack of clinical trial data to delineate best management for this poorly understood subset of lung cancer patients. We review the existing literature and progress toward a genomic diagnosis for this unique disease entity.

EXPERT OPINION

Multifocal lung adenocarcinoma behaves differently than other forms of non-small cell lung cancer. Progress in molecular diagnosis may enhance potential for accurate definition, diagnosis, and optimizing treatment approach.

Clonal selection parallels between normal and cancer tissues

Clonal selection and drift drive both normal tissue and cancer development. However, the biological mechanisms and environmental conditions underpinning these processes remain to be elucidated. Clonal selection models are centered in Darwinian evolutionary theory, where some clones with the fittest features are selected and populate the tissue or tumor. We suggest that different subclasses of stem cells, each of which is responsible for a distinct feature of the selection process, share common features between normal and cancer conditions. While active stem cells populate the tissue, dormant cells account for tissue replenishment/regeneration in both normal and cancerous tissues. We also discuss potential mechanisms that drive clonal drift, their interactions with clonal selection, and their similarities during normal and cancer tissue development.

Estrogen receptor status heterogeneity in breast cancer tumor: role in response to endocrine treatment

Tumor heterogeneity affects diagnosis, prognosis and response to therapy. Heterogeneity is found in both normal and neoplastic human mammary gland. Indeed, luminal ER-negative cells can give rise to various phenotypes, including ER-negative and ER-positive mammary tumors. As a result, the tumor phenotype does not necessarily reflects the cell of origin of cancer. With regard to the ER status, heterogeneity can challenge endocrine therapies, where the elimination of responsive clones could lead to reduced treatment efficacy and tumor relapse through the expansion of the resistant clones. The aim of this study was to investigate breast tumor heterogeneity and its role in endocrine resistance onset. For this purpose, we used ER⁺ (T47D, CAMA1) and triple-negative breast cancer cell lines (TNBC; MDA-MB-231, HCC70), co-cultures using 2D and 3D models. Our results showed that ER status is modulated when ER⁺ cells are cultured in the presence of TNBC cells, leading to a different response to endocrine therapy, demonstrating that the response to treatment can be affected by the influence that different breast cancer cell types exert on each other. In addition, ER⁺ positive cells doubling time was modified after exposure to TNBC cell co-culturing. Further experiments are required to fully elucidate the molecular mechanism of these observations.

Near-Death Cells Cause Chemotherapy-Induced Metastasis via ATF4-Mediated NF-κB Signaling Activation

Cytotoxic chemotherapy is a primary treatment modality for many patients with advanced cancer. Increasing preclinical and clinical observations indicate that chemotherapy can exacerbate tumor metastasis. However, the underlying mechanism remains unclear. Here, it is attempted to identify the mechanisms underlying chemotherapy-induced cancer recurrence and metastasis. It is revealed that a small subpopulation of "near-death cells" (NDCs) with compromised plasma membranes can reverse the death process to enhance survival and repopulation after exposure to lethal doses of cytotoxins. Moreover, these NDCs acquire enhanced tumorigenic and metastatic capabilities, but maintain chemosensitivity in multiple models. Mechanistically, cytotoxin exposure induces activating transcription factor 4 (ATF4)-dependent nonclassical NF-κB signaling activation; ultimately, this results in nuclear translocation of p52 and RelB in NDCs. Deletion of ATF4 in parental cancer cells significantly reduces colony formation and metastasis of NDCs, whereas overexpression of ATF4 activates the nonclassical NF-κB signaling pathway to promote chemotherapy-induced metastasis of NDCs. Overall, these results provide novel mechanistic insights into the chemotherapy-induced metastasis and indicate the pivotal role of NDCs in mediating tumor relapse after cytotoxic therapy. This study also suggests that targeting ATF4 may be an effective approach in improving the efficacy of chemotherapy.

7,8-Dihydroxyflavone induces mitochondrial apoptosis and down-regulates the expression of ganglioside GD3 in malignant melanoma cells

Malignant melanoma is a skin cancer with poor prognosis and high resistance to conventional treatment. 7,8-dihydroxyflavone (7,8-DHF) has shown anti-carcinogenic, anti-inflammatory, anti-oxidant, and pharmacological effects in several types of cancer. However, the relationship between ganglioside expression and the anti-cancer effects of 7,8-DHF in melanoma is not fully understood. In the present study, 7,8-DHF exhibits specific anti-proliferation, anti-migration, and G2/M phase cell-cycle arrest effects on both melanoma cancer cell lines, and induces mitochondrial dysfunction and apoptosis, making it a potent candidate for anti-melanoma treatment. Furthermore, we confirmed that 7,8-DHF significantly reduces the expression levels of ganglioside GD3 and its synthase, which are known to be closely involved in carcinogenesis. Taken together, our findings suggest that 7,8-DHF may be a potent anti-cancer drug candidate for the treatment of malignant melanoma.

Detection of genes with differential expression dispersion unravels the role of autophagy in cancer progression

The majority of gene expression studies focus on the search for genes whose mean expression is different between two or more populations of samples in the so-called "differential expression analysis" approach. However, a difference in variance in gene expression may also be biologically and physiologically relevant. In the classical statistical model used to analyze RNA-sequencing (RNA-seq) data, the dispersion, which defines the variance, is only considered as a parameter to be estimated prior to identifying a difference in mean expression between conditions of interest. Here, we propose to evaluate four recently published methods, which detect differences in both the mean and dispersion in RNA-seq data. We thoroughly investigated the performance of these methods on simulated datasets and characterized parameter settings to reliably detect genes with a differential expression dispersion. We applied these methods to The Cancer Genome Atlas datasets. Interestingly, among the genes with an increased expression dispersion in tumors and without a change in mean expression, we identified some key cellular functions, most of which were related to catabolism and were overrepresented in most of the analyzed cancers. In particular, our results highlight autophagy, whose role in cancerogenesis is context-dependent, illustrating the potential of the differential dispersion approach to gain new insights into biological processes and to discover new biomarkers.

Presence of cells in the polyaneuploid cancer cell (PACC) state predicts the risk of recurrence in prostate cancer

BACKGROUND

The nonproliferating polyaneuploid cancer cell (PACC) state is associated with therapeutic resistance in cancer. A subset of cancer cells enters the PACC state by polyploidization and acts as cancer stem cells by undergoing depolyploidization and repopulating the tumor cell population after the therapeutic stress is relieved. Our aim was to systematically assess the presence and importance of this entity in men who underwent radical prostatectomy with curative intent to treat their presumed localized prostate cancer (PCa).

MATERIALS AND METHODS

Men with National Comprehensive Cancer Network intermediate- or high-risk PCa who underwent radical prostatectomy l from 2007 to 2015 and who did not receive neoadjuvant treatment were included. From the cohort of 2159 patients, the analysis focused on a subcohort of 209 patients and 38 cases. Prostate tissue microarrays (TMAs) were prepared from formalin-fixed, paraffin-embedded blocks of the radical prostatectomy specimens. A total of 2807 tissue samples of matched normal/benign and cancer were arrayed in nine TMA blocks. The presence of PACCs and the number of PACCs on each core were noted.

RESULTS

The total number of cells in the PACC state and the total number of cores with PACCs were significantly correlated with increasing Gleason score (p = 0.0004) and increasing Cancer of the Prostate Risk Assessment Postsurgical (CAPRA-S) (p = 0.004), but no other variables. In univariate proportional hazards models of metastasis-free survival, year of surgery, Gleason score (9-10 vs. 7-8), pathology stage, CAPRA-S, total PACCs, and cores positive for PACCs were all statistically significant. The multivariable models with PACCs that gave the best fit included CAPRA-S. Adding either total PACCs or cores positive for PACCs to CAPRA-S both significantly improved model fit compared to CAPRA-S alone.

CONCLUSION

Our findings show that the number of PACCs and the number of cores positive for PACCs are statistically significant prognostic factors for metastasis-free survival, after adjusting for CAPRA-S, in a case-cohort of intermediate- or high-risk men who underwent radical prostatectomy. In addition, despite the small number of men with complete data to evaluate time to metastatic castration-resistant PCa (mCRPC), the total number of PACCs was a statistically significant predictor of mCRPC in univariate analysis and suggested a prognostic effect even after adjusting for CAPRA-S.

Evolutionary analysis of replicator dynamics about anti-cancer combination therapy

The emergence and growth of drug-resistant cancer cell subpopulations during anti-cancer treatment is a major challenge for cancer therapies. Combination therapies are usually applied for overcoming drug resistance. In the present paper, we explored the evolution outcome of tumor cell populations under different combination schedules of chemotherapy and p53 vaccine, by construction of replicator dynamical model for sensitive cells, chemotherapy-resistant cells and p53 vaccine-resistant cells. The local asymptotic stability analysis of the evolutionary stable points revealed that cancer population could evolve to the population with single subpopulation, or coexistence of sensitive cells and p53 vaccine-resistant cells, or coexistence of chemotherapy-resistant cells and p53 vaccine-resistant cells under different monotherapy or combination schedules. The design of adaptive therapy schedules that maintain the subpopulations under control is also demonstrated by sequential and periodic application of combination treatment strategies based on the evolutionary velocity and evolutionary absorbing regions. Applying a new replicator dynamical model, we further explored the supportive effects of sensitive cancer cells on targeted therapy-resistant cells revealed in mice experiments. It was shown that the supportive effects of sensitive cells could drive the evolution of cell population from sensitive cells to coexistence of sensitive cells and one type of targeted therapy-resistant cells.

Decoding Strategies to Evade Immunoregulators Galectin-1, -3, and -9 and Their Ligands as Novel Therapeutics in Cancer Immunotherapy

Galectins are a family of ß-galactoside-binding proteins that play a variety of roles in normal physiology. In cancer, their expression levels are typically elevated and often associated with poor prognosis. They are known to fuel a variety of cancer progression pathways through their glycan-binding interactions with cancer, stromal, and immune cell surfaces. Of the 15 galectins in mammals, galectin (Gal)-1, -3, and -9 are particularly notable for their critical roles in tumor immune escape. While these galectins play integral roles in promoting cancer progression, they are also instrumental in regulating the survival, differentiation, and function of anti-tumor T cells that compromise anti-tumor immunity and weaken novel immunotherapies. To this end, there has been a surge in the development of new strategies to inhibit their pro-malignancy characteristics, particularly in reversing tumor immunosuppression through galectin-glycan ligand-targeting methods. This review examines some new approaches to evading Gal-1, -3, and -9-ligand interactions to interfere with their tumor-promoting and immunoregulating activities. Whether using neutralizing antibodies, synthetic peptides, glyco-metabolic modifiers, competitive inhibitors, vaccines, gene editing, exo-glycan modification, or chimeric antigen receptor (CAR)-T cells, these methods offer new hope of synergizing their inhibitory effects with current immunotherapeutic methods and yielding highly effective, durable responses.

Serum circulating free DNA of syncytin-1 as a novel molecular marker for early diagnosis of non-small-cell lung cancer

Background: Liquid biopsy has been receiving attention as an emerging detection technology in the clinical application of non-small-cell lung cancer (NSCLC). Methods: We quantified serum circulating free DNA (cfDNA) of syncytin-1 in 126 patients and 106 controls, analyzed the correlation of level with pathological parameters and explored diagnostic utility. Results: The cfDNA of syncytin-1 levels in NSCLC patients were higher than healthy controls (p < 0.0001). These levels were associated with smoking history (p = 0.0393). The area under the curve of cfDNA of syncytin-1 was 0.802, and combination of cfDNA of syncytin-1/cytokeratin 19 fragment antigen 21-1/carcinoembryonic antigen markers improved diagnostic efficiency. Conclusion: The cfDNA of syncytin-1 was detected in NSCLC patients and can be used as a novel molecular marker for early diagnosis.

Liquid biopsy to detect resistance mutations against anti-epidermal growth factor receptor therapy in metastatic colorectal cancer

Colorectal cancer (CRC) is a major cause of mortality worldwide, associated with a steadily growing prevalence. Notably, the identification of KRAS, NRAS, and BRAF mutations has markedly improved targeted CRC therapy by affording treatments directed against the epidermal growth factor receptor (EGFR) and other anti-angiogenic therapies. However, the survival benefit conferred by these therapies remains variable and difficult to predict, owing to the high level of molecular heterogeneity among patients with CRC. Although classification into consensus molecular subtypes could optimize response prediction to targeted therapies, the acquisition of resistance mutations to targeted therapy is, in part, responsible for the lack of response in some patients. However, the acquisition of such mutations can induce challenges in clinical practice. The utility of liquid biopsy to detect resistance mutations against anti-EGFR therapy has recently been described. This approach may constitute a new standard in the decision algorithm for targeted CRC therapy.

Single-cell biology uncovers apoptotic cell death and its spatial organization as a potential modifier of tumor diversity in HCC

BACKGROUND AND AIMS

HCC is a highly aggressive and heterogeneous cancer type with limited treatment options. Identifying drivers of tumor heterogeneity may lead to better therapeutic options and favorable patient outcomes. We investigated whether apoptotic cell death and its spatial architecture is linked to tumor molecular heterogeneity using single-cell in situ hybridization analysis.

APPROACH AND RESULTS

We analyzed 254 tumor samples from two HCC cohorts using tissue microarrays. We developed a mathematical model to quantify cellular diversity among HCC samples using two tumor markers, cyclin-dependent kinase inhibitor 3 and protein regulator of cytokinesis 1 as surrogates for heterogeneity and caspase 3 (CASP3) as an apoptotic cell death marker. We further explored the impact of potential dying-cell hubs on tumor cell diversity and patient outcome by density contour mapping and spatial proximity analysis. We also developed a selectively controlled in vitro model of cell death using CRISPR/CRISPR-associated 9 to determine therapy response and growth under hypoxic conditions. We found that increasing levels of CASP3⁺ tumor cells are associated with higher tumor diversity. Interestingly, we discovered regions of densely populated CASP3⁺ , which we refer to as CASP3⁺ cell islands, in which the nearby cellular heterogeneity was found to be the greatest compared to cells farther away from these islands and that this phenomenon was associated with survival. Additionally, cell culture experiments revealed that higher levels of cell death, accompanied by increased CASP3 expression, led to greater therapy resistance and growth under hypoxia.

CONCLUSIONS

These results are consistent with the hypothesis that increased apoptotic cell death may lead to greater tumor heterogeneity and thus worse patient outcomes.

Predicting chemotherapy response in non-small-cell lung cancer via computed tomography radiomic features: Peritumoral, intratumoral, or combined?

Purpose

This study aims to evaluate the ability of peritumoral, intratumoral, or combined computed tomography (CT) radiomic features to predict chemotherapy response in non-small cell lung cancer (NSCLC).

Methods

After excluding subjects with incomplete data or other types of treatments, 272 (Dataset 1) and 43 (Dataset 2, external validation) NSCLC patients who were only treated with chemotherapy as the first-line treatment were enrolled between 2015 and 2019. All patients were divided into response and nonresponse based on the response evaluation criteria in solid tumors, version 1.1. By using 3D slicer and morphological operations in python, the intra- and peritumoral regions of lung tumors were segmented from pre-treatment CT images (unenhanced) and confirmed by two experienced radiologists. Then radiomic features (the first order, texture, shape, et al.) were extracted from the above regions of interest. The models were trained and tested in Dataset 1 and further validated in Dataset 2. The performance of models was compared using the area under curve (AUC), confusion matrix, accuracy, precision, recall, and F1-score.

Results

The radiomic model using features from the peritumoral region of 0–3 mm outperformed that using features from 3–6, 6–9, 9–12 mm peritumoral region, and intratumoral region (AUC: 0.95 versus 0.87, 0.86, 0.85, and 0.88). By the fusion of features from 0–3 and 3–6 mm peritumoral regions, the logistic regression model achieved the best performance, with an AUC of 0.97. This model achieved an AUC of 0.85 in the external cohort. Moreover, among the 20 selected features, seven features differed significantly between the two groups (p < 0.05).

Conclusions

CT radiomic features from both the peri- and intratumoral regions can predict chemotherapy response in NSCLC using machine learning models. Combined features from two peritumoral regions yielded better predictions.

Molecular imaging to support cancer immunotherapy

The advent of immune checkpoint inhibitors has reinvigorated the field of immuno-oncology. These monoclonal antibody-based therapies allow the immune system to recognize and eliminate malignant cells. This has resulted in improved survival of patients across several tumor types. However, not all patients respond to immunotherapy therefore predictive biomarkers are important. There are only a few Food and Drug Administration-approved biomarkers to select patients for immunotherapy. These biomarkers do not consider the heterogeneity of tumor characteristics across lesions within a patient. New molecular imaging tracers allow for whole-body visualization with positron emission tomography (PET) of tumor and immune cell characteristics, and drug distribution, which might guide treatment decision making. Here, we summarize recent developments in molecular imaging of immune checkpoint molecules, such as PD-L1, PD-1, CTLA-4, and LAG-3. We discuss several molecular imaging approaches of immune cell subsets and briefly summarize the role of FDG-PET for evaluating cancer immunotherapy. The main focus is on developments in clinical molecular imaging studies, next to preclinical studies of interest given their potential translation to the clinic.

EGFR, HER2, and HER3 protein expression in paired primary tumor and lymph node metastasis of colorectal cancer

Due to the difficulty in sampling of metastatic tumors, patient selection is commonly based on results of primary tumor samples when metastatic samples are not available. However, due to tumor heterogeneity, metastatic tumors may be different from primary tumors in their phenotypes. The aim of this study was to investigate the expression of EGFR, HER2, and HER3 between primary and lymph node metastatic lesions of colorectal cancer. Paired primary tumors and lymph node metastases from 79 patients with colorectal cancer were retrospectively collected and analyzed for EGFR, HER2, and HER3 expression. High EGFR, HER2, and HER3 expression (2+ and 3+) was found in 64.2%, 66.0%, and 85.0% of primary tumors, and 56.8%, 46.0%, and 76.0% of lymph node metastases, respectively. Correlation rates between primary and metastatic lesions were 67.1%, 63.3%, and 74.7% for EGFR, HER2, and HER3, respectively. Stage IV tumors (with distant metastasis) had higher correlation rates of HER2 expression compared to stage III tumors (without distant metastasis) (P = 0.050). Moderate correlation rates in EGFR, HER2, and HER3 expression were observed between primary and metastatic lesions of colorectal cancer. Tumor stage or existence of distant metastasis could serve as potential predictive markers for the correlation of HER2 expression between primary tumors and lymph node metastases of colorectal cancer.

Progressing Towards a Human-Centric Approach in Cancer Research

Despite the advancement in research methodologies and technologies for cancer research, there is a high rate of anti-cancer drug attrition. In this review, we discuss different conventional and modern approaches in cancer research and how human-centric models can improve on the voids conferred by more traditional animal-centric models, thereby offering a more reliable platform for drug discovery. Advanced three-dimensional cell culture methodologies, along with in silico computational analysis form the core of human-centric cancer research. This can provide a holistic understanding of the research problems and help design specific and accurate experiments that could lead to the development of better cancer therapeutics. Here, we propose a new human-centric research roadmap that promises to provide a better platform for cancer research and drug discovery.

Establishing and characterizing a novel doxorubicin-resistant acute myeloid leukaemia cell line

Drug resistance is a major setback in cancer treatment, thus models to study its mechanisms are needed. Our work aimed to establish and characterize a resistant cell line from a sensitive acute myeloid leukaemia (AML) cell line - HL60 - by treating the sensitive cells with increasing concentrations of doxorubicin. We confirmed (cell viability assays) that the established subline, HL60-CDR, was resistant to doxorubicin for at least 30 days without drug treatment. The HL60-CDR cells were also resistant to three other drugs (cisplatin, etoposide and daunorubicin), exhibiting a multidrug resistant (MDR) profile. We verified (Western Blotting) that the MDR cells do not express drug efflux pumps, nor present altered expression of apoptotic proteins, when compared with the parental cell line. HL60-CDR cells presented alterations in the cell cycle profile, and in the expression levels of proteins involved in DNA repair mechanisms and drug metabolism, when compared with their drug sensitive counterpart. Proteomic analysis revealed that HL60-CDR cells presented an upregulation of proteins involved in oncogenic pathways, such as TSC2, PDPK1, Annexin A2, among others. Overall, we established an AML MDR subline - HL60-CDR - which presents several resistance mechanisms, providing an in vitro model to test new compounds to circumvent MDR in AML.

Next-Generation Sequencing on Circulating Tumor DNA in Advanced Solid Cancer: Swiss Army Knife for the Molecular Tumor Board? A Review of the Literature Focused on FDA Approved Test

FDA-approved next-generation sequencing assays based on cell-free DNA offers new opportunities in a molecular-tumor-board context thanks to the noninvasiveness of liquid biopsy, the diversity of analyzed parameters and the short turnaround time. It gives the opportunity to study the heterogeneity of the tumor, to elucidate complex resistance mechanisms and to adapt treatment strategies. However, lowering the limit of detection and increasing the panels' size raise new questions in terms of detection of incidental germline alterations, occult malignancies and clonal hematopoiesis of indeterminate potential mutations. In this review, after a technological discussion and description of the common problematics encountered, we establish recommendations in properly using these FDA-approved tests in a molecular-tumor-board context.

Prognostic Significance of Lineage Diversity in Bladder Cancer Revealed by Single-Cell Sequencing

Bladder cancer is the most common malignant tumor of the urinary system. We investigated the clinical implications of cell lineages in bladder cancer by integrating single-cell and bulk transcriptome data. By investigating the single-cell transcriptional profiles of 12,424 cells from normal bladder, eleven cell types and five types of epithelial sub-population were identified. Based on the signature of cell types identified in single-cell profiles, deconvolution analysis was employed to estimate cell types and epithelial lineages in the bulk RNA sequencing bladder cancer cohort. Cancer subtypes with clinical implications were further identified based on the heterogeneity of the epithelial lineage across patients. This study suggests that the EMT-like subtype is robustly correlated with poor prognosis and the umbrella subtype is a positive factor for the patient survival. Our research has a high potential for accurate prognostic and therapeutic stratification of bladder cancer.

Synergy between sublethal doses of shikonin and metformin fully inhibits breast cancer cell migration and reverses epithelial-mesenchymal transition

BACKGROUND

Shikonin is a natural multipotent anti-tumorigenic compound. We investigated potential synergy between shikonin and anti-diabetic metformin against tumorigenic properties of breast cancer cell line MCF-7.

METHODS AND RESULTS

The IC₅₀ of shikonin and metformin was determined after a single treatment of two cell lines MCF-7 and MDA-MB-231. We then measured optimal doses of each drug, used in combination, in MCF-7 cells. These sub-IC₅₀ doses were co-applied for all subsequent combined treatments to evaluate their synergistic effects on MCF-7 tumorigenic properties. Next, we examined expression levels of the genes crucial for apoptosis, cell growth, and EMT using RT-PCR or real-time PCR and monitored CD44/CD24 ratios using flow cytometry. Binding energies between shikonin and growth molecules were measured by in silico simulation. Shikonin caused significantly reduced cell survival that was accelerated by the synergizing presence of metformin. Drug combination induced apoptosis and ROS levels while fully blocking cell migration and reverting EMT. RT-PCR showed strong suppression of BCL-2 but induction of BAX and PTEN. Prolonged shikonin treatment caused a total loss of the nuclear membrane, whereas metformin prevented this damage while promoting apoptotic morphologies. Our real-time PCR detected reduced levels of EMT genes but increases in the anti-EMT gene CDH1. Combined treatment also reduced CD44/CD24 ratios in favor of chemosensitivity. Binding energies strongly favored shikonin interactions with growth-signaling molecules.

CONCLUSIONS

Shikonin and metformin synergize in inhibiting the tumorigenic activities of MCF-7 cells including their proliferation, invasiveness, and EMT with a potential to inhibit multidrug resistance.

Spatial structure impacts adaptive therapy by shaping intra-tumoral competition

Background

Adaptive therapy aims to tackle cancer drug resistance by leveraging resource competition between drug-sensitive and resistant cells. Here, we present a theoretical study of intra-tumoral competition during adaptive therapy, to investigate under which circumstances it will be superior to aggressive treatment.

Methods

We develop and analyse a simple, 2-D, on-lattice, agent-based tumour model in which cells are classified as fully drug-sensitive or resistant. Subsequently, we compare this model to its corresponding non-spatial ordinary differential equation model, and fit it to longitudinal prostate-specific antigen data from 65 prostate cancer patients undergoing intermittent androgen deprivation therapy following biochemical recurrence.

Results

Leveraging the individual-based nature of our model, we explicitly demonstrate competitive suppression of resistance during adaptive therapy, and examine how different factors, such as the initial resistance fraction or resistance costs, alter competition. This not only corroborates our theoretical understanding of adaptive therapy, but also reveals that competition of resistant cells with each other may play a more important role in adaptive therapy in solid tumours than was previously thought. To conclude, we present two case studies, which demonstrate the implications of our work for: (i) mathematical modelling of adaptive therapy, and (ii) the intra-tumoral dynamics in prostate cancer patients during intermittent androgen deprivation treatment, a precursor of adaptive therapy.

Conclusion

Our work shows that the tumour’s spatial architecture is an important factor in adaptive therapy and provides insights into how adaptive therapy leverages both inter- and intra-specific competition to control resistance.

Cancer therapy traditionally focuses on maximising tumour cell kill with the aim of achieving a cure, but such aggressive treatment can open up space for drug-resistant cells to grow. In contrast, adaptive therapy aims to leverage competition between drug-sensitive and resistant cells by adjusting treatment to maintain the tumour at a tolerable size, whilst preserving drug-sensitive cells. This approach is being tested in trials but is not yet widely used as deeper understanding of cell-cell competition is required. Here, we used a mathematical model to investigate how strongly, and with whom, resistant cells compete during continuous and adaptive therapy, and applied our insights to hormone therapy in prostate cancer where adaptive therapy has recently been successfully trialed. Our results provide new insights into how adaptive therapy works and show that, by shaping cell competition, the tumour’s spatial architecture is important in determining therapy response.

Strobl et al. develop an agent-based spatial model of drug resistance in tumour cells under adaptive therapy. Using this model, they investigate how the tumour’s spatial architecture impacts intratumoural competitive dynamics of drug-sensitive vs. -resistant clones in response to therapy.

Understanding Drug Sensitivity and Tackling Resistance in Cancer

Decades of research into the molecular mechanisms of cancer and the development of novel therapeutics have yielded a number of remarkable successes. However, our ability to broadly assign effective, rationally targeted therapies in a personalized manner remains elusive for many patients, and drug resistance persists as a major problem. This is in part due to the well-documented heterogeneity of cancer, including the diversity of tumor cell lineages and cell states, the spectrum of somatic mutations, the complexity of microenvironments, and immune-suppressive features and immune repertoires, which collectively require numerous different therapeutic approaches. Here, we describe a framework to understand the types and biological causes of resistance, providing translational opportunities to tackle drug resistance by rational therapeutic strategies.

Extrachromosomal Circular DNA: A New Target in Cancer

Genomic instability and amplification are intrinsically important traits determining the development and heterogeneity of tumors. The role of extrachromosomal circular DNA (eccDNA) in tumors has recently been highlighted. EccDNAs are unique genetic materials located off the chromosomal DNA. They have been detected in a variety of tumors. This review analyzes the mechanisms involved in the formation of eccDNAs and their genetic characteristics. In addition, the high-copy number and transcriptional levels of oncogenes located in eccDNA molecules contribute to the acceleration of tumor evolution and drug resistance and drive the development of genetic heterogeneity. Understanding the specific genomic forms of eccDNAs and characterizing their potential functions will provide new strategies for tumor therapy. Further research may yield new targets and molecular markers for the early diagnosis and treatment of human cancer.

Subcellular progression of mesenchymal transition identified by two discrete synchronous cell lines derived from the same glioblastoma

Glioblastomas (GBM) exhibit intratumoral heterogeneity of various oncogenic evolutional processes. We have successfully isolated and established two distinct cancer cell lines with different morphological and biological characteristics that were derived from the same tissue sample of a GBM. When we compared their genomic and transcriptomic characteristics, each cell line harbored distinct mutation clusters while sharing core driver mutations. Transcriptomic analysis revealed that one cell line was undergoing a mesenchymal transition process, unlike the other cell line. Furthermore, we could identify four tumor samples containing our cell line-like clusters from the publicly available single-cell RNA-seq data, and in a set of paired longitudinal GBM samples, we could confirm three pairs where the recurrent sample was enriched in the genes specific to our cell line undergoing mesenchymal transition. The present study provides direct evidence and a valuable source for investigating the ongoing process of subcellular mesenchymal transition in GBM, which has prognostic and therapeutic implications.

Dynamics of RAS/BRAF Mutations in cfDNA from Metastatic Colorectal Carcinoma Patients Treated with Polychemotherapy and Anti-EGFR Monoclonal Antibodies

Analysis of plasma-derived cell-free DNA (cfDNA) might allow for the early identification of resistance in metastatic colorectal carcinoma (mCRC) patients receiving anti-EGFR monoclonal antibodies. We tested plasma samples from the Erbitux Metastatic Colorectal Cancer Strategy (ERMES) phase III trial of FOLFIRI+Cetuximab in first-line treatment of RAS/BRAF wild-type mCRC. Samples were collected at baseline (n = 37), at 8 weeks of treatment (n = 32), progressive disease (PD; n = 36) and 3 months after PD (n = 21). cfDNA testing was performed using the Idylla™ ctKRAS and ctNRAS-BRAF tests and the Oncomine Pan-Cancer Cell-Free Assay. Analysis of basal samples revealed RAS/BRAF mutations in 6/37 cases. A transient RAS positivity not associated with PD was observed at 8 weeks in five cases that showed no mutations at baseline and PD. The frequency of mutant cases increased at PD (33.3%) and decreased again at 3 months after PD (9.5%). The median progression-free survival (mPFS) of patients RAS/BRAF mutant at PD was 7.13 months versus 7.71 months in wild-type patients (p = 0.3892). These data confirm that the occurrence of RAS/BRAF mutations in mCRC patients receiving anti-EGFR agents is relatively frequent. However, the cfDNA dynamics of RAS mutations in patients treated with anti-EGFR agents plus polychemotherapy are complex and might not be directly associated with resistance to treatment.

Options to Improve the Action of PROTACs in Cancer: Development of Controlled Delivery Nanoparticles

Classical targeting in cancer focuses on the development of chemical structures able to bind to protein pockets with enzymatic activity. Some of these molecules are designed to bind the ATP side of the kinase domain avoiding protein activation and the subsequent oncogenic activity. A further improvement of these agents relies on the generation of non-allosteric inhibitors that once bound are able to limit the kinase function by producing a conformational change at the protein and, therefore, augmenting the antitumoural potency. Unfortunately, not all oncogenic proteins have enzymatic activity and cannot be chemically targeted with these types of molecular entities. Very recently, exploiting the protein degradation pathway through the ubiquitination and subsequent proteasomal degradation of key target proteins has gained momentum. With this approach, non-enzymatic proteins such as Transcription Factors can be degraded. In this regard, we provide an overview of current applications of the PROteolysis TArgeting Chimeras (PROTACs) compounds for the treatment of solid tumours and ways to overcome their limitations for clinical development. Among the different constraints for their development, improvements in bioavailability and safety, due to an optimized delivery, seem to be relevant. In this context, it is anticipated that those targeting pan-essential genes will have a narrow therapeutic index. In this article, we review the advantages and disadvantages of the potential use of drug delivery systems to improve the activity and safety of PROTACs.

Stability of scRNA-Seq Analysis Workflows is Susceptible to Preprocessing and is Mitigated by Regularized or Supervised Approaches

Background:

Statistical methods developed to address various questions in single-cell datasets show increased variability to different parameter regimes. In order to delineate further the robustness of commonly utilized methods for single-cell RNA-Seq, we aimed to comprehensively review scRNA-Seq analysis workflows in the setting of dimension reduction, clustering, and trajectory inference.

Methods:

We utilized datasets with temporal single-cell transcriptomics profiles from public repositories. Combining multiple methods at each level of the workflow, we have performed over 6 k analysis and evaluated the results of clustering and pseudotime estimation using adjusted rand index and rank correlation metrics. We have further integrated neural network methods to assess whether models with increased complexity can show increased bias/variance trade-off.

Results:

Combinatorial workflows showed that utilizing non-linear dimension reduction techniques such as t-SNE and UMAP are sensitive to initial preprocessing steps hence clustering results on dimension reduced space of single-cell datasets should be utilized carefully. Similarly, pseudotime estimation methods that depend on previous non-linear dimension reduction steps can result in highly variable trajectories. In contrast, methods that avoid non-linearity such as WOT can result in repeatable inferences of temporal gene expression dynamics. Furthermore, imputation methods do not improve clustering or trajectory inference results substantially in terms of repeatability. In contrast, the selection of the normalization method shows an increased effect on downstream analysis where ScTransform reduces variability overall.