The origins and implications of intratumor heterogeneity

Cancer stem cells in meningiomas: novel insights and therapeutic implications

Meningiomas (MGs), which arise from meningothelial cells of the dura mater, represent a significant proportion of primary tumours of the central nervous system (CNS). Despite advances in treatment, the management of malignant meningioma (MMG) remains challenging due to diagnostic, surgical, and resection limitations. Cancer stem cells (CSCs), a subpopulation within tumours capable of self-renewal and differentiation, are highlighted as key markers of tumour growth, metastasis, and treatment resistance. Identifying additional CSC-related markers enhances the precision of malignancy evaluations, enabling advancements in personalised medicine. The review discusses key CSC biomarkers that are associated with high levels of expression, aggressive tumour behaviour, and poor outcomes. Recent molecular research has identified CSC-related biomarkers, including Oct-4, Sox2, NANOG, and CD133, which help maintain cellular renewal, proliferation, and drug resistance in MGs. This study highlights new therapeutic strategies that could improve patient prognosis with more durable tumour regression. The use of combination therapies, such as hydroxyurea alongside diltiazem, suggests more efficient and effective MG management compared to monotherapy. Signalling pathways such as NOTCH and hedgehog also offer additional avenues for therapeutic development. CRISPR/Cas9 technology has also been employed to create meningioma models, uncovering pathways related to cell growth and proliferation. Since the efficacy of traditional therapies is limited in most cases due to resistance mechanisms in CSCs, further studies on the biology of CSCs are warranted to develop therapeutic interventions that are likely to be effective in MG. Consequently, improved diagnostic approaches may lead to personalised treatment plans tailored to the specific needs of each patient.

Evaluation of the prognosis in patients with small-cell lung cancer treated by chemotherapy using tumor shrinkage rate-based radiomics

BACKGROUND

Small-cell lung cancer (SCLC) is a leading cause of cancer-related death. However, the prognostic value of the tumor shrinkage rate (TSR) after chemotherapy for SCLC is still unknown.

METHODS

We performed a retrospective analysis of 235 patients with SCLC. The TSR cutoff was determined based on receiver-operating characteristic curve analysis. The associations of TSR with progression-free survival (PFS) and overall survival (OS) were assessed using univariate and multivariate Cox proportional hazards models. Survival curves were obtained by the Kaplan-Meier method and compared using the log-rank test. Recurrence patterns after first-line treatment were summarized in a pie chart. A nomogram was constructed to validate the predictive role of the TSR in SCLC.

RESULTS

The TSR cutoff was identified to be  - 6.6%. Median PFS and OS were longer in the group with a TSR < -6.6% than in the group with a TSR ≥ - 6.6%. PFS and OS were also longer in patients with extensive SCLC when the TSR was < - 6.6% than when it was > - 6.6%. Brain metastasis-free survival was better in the group with a TSR < - 6.6%. There was a significant positive correlation between TSR and PFS. Furthermore, univariate and multivariate regression analyses showed that the TSR, patient age, and previous radiotherapy were independent prognostic factors for OS while TSR and M stage were independent prognostic factors for PFS.

CONCLUSIONS

The TSR may prove to be a good indicator of OS and PFS in patients receiving chemotherapy-based first-line treatment for SCLC.

Preclinical spheroid models identify BMX as a therapeutic target for metastatic MYCN nonamplified neuroblastoma

The development of targeted therapies offers new hope for patients affected by incurable cancer. However, multiple challenges persist, notably in controlling tumor cell plasticity in patients with refractory and metastatic illness. Neuroblastoma (NB) is an aggressive pediatric malignancy originating from defective differentiation of neural crest-derived progenitors with oncogenic activity due to genetic and epigenetic alterations and remains a clinical challenge for high-risk patients. To identify critical genes driving NB aggressiveness, we performed combined chromatin and transcriptome analyses on matched patient-derived xenografts (PDXs), spheroids, and differentiated adherent cultures derived from metastatic MYCN nonamplified tumors. Bone marrow kinase on chromosome X (BMX) was identified among the most differentially regulated genes in PDXs and spheroids versus adherent models. BMX expression correlated with high tumor stage and poor patient survival and was crucial to the maintenance of the self-renewal and tumorigenic potential of NB spheroids. Moreover, BMX expression positively correlated with the mesenchymal NB cell phenotype, previously associated with increased chemoresistance. Finally, BMX inhibitors readily reversed this cellular state, increased the sensitivity of NB spheroids toward chemotherapy, and partially reduced tumor growth in a preclinical NB model. Altogether, our study identifies BMX as a promising innovative therapeutic target for patients with high-risk MYCN nonamplified NB.

scGAL: unmask tumor clonal substructure by jointly analyzing independent single-cell copy number and scRNA-seq data

BACKGROUND

Accurately deciphering clonal copy number substructure can provide insights into the evolutionary mechanism of cancer, and clustering single-cell copy number profiles has become an effective means to unmask intra-tumor heterogeneity (ITH). However, copy numbers inferred from single-cell DNA sequencing (scDNA-seq) data are error-prone due to technically confounding factors such as amplification bias and allele-dropout, and this makes it difficult to precisely identify the ITH.

RESULTS

We introduce a hybrid model called scGAL to infer clonal copy number substructure. It combines an autoencoder with a generative adversarial network to jointly analyze independent single-cell copy number profiles and gene expression data from same cell line. Under an adversarial learning framework, scGAL exploits complementary information from gene expression data to relieve the effects of noise in copy number data, and learns latent representations of scDNA-seq cells for accurate inference of the ITH. Evaluation results on three real cancer datasets suggest scGAL is able to accurately infer clonal architecture and surpasses other similar methods. In addition, assessment of scGAL on various simulated datasets demonstrates its high robustness against the changes of data size and distribution. scGAL can be accessed at: https://github.com/zhyu-lab/scgal .

CONCLUSIONS

Joint analysis of independent single-cell copy number and gene expression data from a same cell line can effectively exploit complementary information from individual omics, and thus gives more refined indication of clonal copy number substructure.

Heterogeneity of computational pathomic signature predicts drug resistance and intra-tumor heterogeneity of ovarian cancer

BACKGROUND

Chemotherapy resistance is the main cause of ovarian cancer progression and even death. However, there are no clear indicators for predicting the risk of drug resistance in patients. Intra-tumor heterogeneity (ITH) is one of the characteristics of malignant tumors, which is associated with the treatment and prognosis of tumors. Accordingly, our study aims to investigate the correlation between the image features of intra-tumor heterogeneity and drug resistance of ovarian cancer based on artificial intelligence.

METHODS

We obtained hematoxylin and eosin staining frozen histopathological images of ovarian cancer and paracarcinoma tissues from the Cancer Genome Atlas. We extracted quantitative image features of whole-slide images based on the automatic image nuclear segmentation processing technology. After that, we used bioinformatics analysis to find the relationship between image features of intra-tumor heterogeneity and drug resistance.

RESULTS

Our results show that our automatic image processing process based on computer artificial intelligence can extract image features effectively, and the key image features extracted are closely related to ITH. Among them, the Perimeter.sd image feature with the most prominent ITH feature can accurately predict the risk of platinum-based chemotherapy drug resistance in ovarian cancer patients.

CONCLUSION

Automatic image processing and feature extraction based on artificial intelligence have excellent results. Perimeter.sd can be used as a useful image feature indicator for evaluating ITH. ITH is associated with drug resistance of ovarian cancer, so ITH characteristics can be used as an effective indicator to evaluate drug resistance in patients with ovarian cancer.

Prognostic Value of Differential Expression of Polymerase Eta Gene in Nonresponding Patients With Diffuse Large B-cell Lymphoma

Diffuse large B-cell lymphoma (DLBCL) represents the most common subtype of non-Hodgkins lymphoma. After the introduction of rituximab therapy like rituximab, cyclophosphamide, doxorubicin vincristine, prednisolone, there has been considerable improvement in the 5-year overall survival in this group of patients, but the nonresponding patients are a challenge to the clinician. The translesion polymerases are unique polymerases that make cells tolerant to DNA damage. Many point mutations are introduced owing to their inherent property of bypassing the points of lesions, preventing the cell from stalling replication. However, the impaired activity of these polymerases can lead to the development of tumors with aggressive clinical course. In this study, the gene expression levels of polymerase eta ( POLE ) were compared in 2 cohorts of patients with DLBCL: the first cohort, patients who had achieved complete response, and the second cohort, patients who were refractory to the treatment or had relapse within 2 years of treatment. There was a significantly upregulated expression in the refractory/relapse cohort compared with the complete remission cohort ( P = 0.0001). The high POLE expression levels correlated significantly with advanced disease stages (III and IV) and poor disease-free survival in the Kaplan-Meier curve. The high POLE expression levels were correlated with poor disease-free survival in nonresponder patients with DLBCL. The results concluded that patients with DLBCL with a high polymerase gene expression may show nonresponsiveness to chemotherapy; hence the functional impact of upregulated expression of POLE in DLBCL requires an in-depth assessment.

Controlled tumor heterogeneity in a co-culture system by 3D bio-printed tumor-on-chip model

Cancer treatment resistance is a caused by presence of various types of cells and heterogeneity within the tumor. Tumor cell-cell and cell-microenvironment interactions play a significant role in the tumor progression and invasion, which have important implications for diagnosis, and resistance to chemotherapy. In this study, we develop 3D bioprinted in vitro models of the breast cancer tumor microenvironment made of co-cultured cells distributed in a hydrogel matrix with controlled architecture to model tumor heterogeneity. We hypothesize that the tumor could be represented by a cancer cell-laden co-culture hydrogel construct, whereas its microenvironment can be modeled in a microfluidic chip capable of producing a chemical gradient. Breast cancer cells (MCF7 and MDA-MB-231) and non-tumorigenic mammary epithelial cells (MCF10A) were embedded in the alginate-gelatine hydrogels and printed using a multi-cartridge extrusion bioprinter. Our approach allows for precise control over position and arrangements of cells in a co-culture system, enabling the design of various tumor architectures. We created samples with two different types of cells at specific initial locations, where the density of each cell type was carefully controlled. The cells were either randomly mixed or positioned in sequential layers to create cellular heterogeneity. To study cell migration toward chemoattractant, we developed a chemical microenvironment in a chamber with a gradual chemical gradient. As a proof of concept, we studied different migration patterns of MDA-MB-231 cells toward the epithelial growth factor gradient in presence of MCF10A cells in different ratios using this device. Our approach involves the integration of 3D bioprinting and microfluidic devices to create diverse tumor architectures that are representative of those found in various patients. This provides an excellent tool for studying the behavior of cancer cells with high spatial and temporal resolution.

Upgrading Treatment and Molecular Diagnosis in Endometrial Cancer-Driving New Tools for Endometrial Preservation?

One emerging problem for onco-gynecologists is the incidence of premenopausal patients under 40 years of age diagnosed with stage I Endometrial Cancer (EC) who want to preserve their fertility. Our review aims to define a primary risk assessment that can help fertility experts and onco-gynecologists tailor personalized treatment and fertility-preserving strategies for fertile patients wishing to have children. We confirm that risk factors such as myometrial invasion and The International Federation of Gynecology and Obstetrics (FIGO) staging should be integrated into the novel molecular classification provided by The Cancer Genome Atlas (TCGA). We also corroborate the influence of classical risk factors such as obesity, Polycystic ovarian syndrome (PCOS), and diabetes mellitus to assess fertility outcomes. The fertility preservation options are inadequately discussed with women with a diagnosis of gynecological cancer. A multidisciplinary team of gynecologists, oncologists, and fertility specialists could increase patient satisfaction and improve fertility outcomes. The incidence and death rates of endometrial cancer are rising globally. International guidelines recommend radical hysterectomy and bilateral salpingo-oophorectomy as the standard of care for this cancer; however, fertility-sparing alternatives should be tailored to motivated women of reproductive age, establishing an appropriate cost-benefit balance between childbearing desire and cancer risk. New molecular classifications such as that of TCGA provide a robust supplementary risk assessment tool that can tailor the treatment options to the patient's needs, curtail over- and under-treatment, and contribute to the spread of fertility-preserving strategies.

Quantification of subtype purity in Luminal A breast cancer predicts clinical characteristics and survival

PURPOSE

PAM50 profiling assigns each breast cancer to a single intrinsic subtype based on a bulk tissue sample. However, individual cancers may show evidence of admixture with an alternate subtype that could affect prognosis and treatment response. We developed a method to model subtype admixture using whole transcriptome data and associated it with tumor, molecular, and survival characteristics for Luminal A (LumA) samples.

METHODS

We combined TCGA and METABRIC cohorts and obtained transcriptome, molecular, and clinical data, which yielded 11,379 gene transcripts in common and 1,178 cases assigned to LumA. We used semi-supervised non-negative matrix factorization (ssNMF) to compute the subtype admixture proportions of the four major subtypes-pLumA, pLumB, pHER2, and pBasal-for each case and measured associations with tumor characteristics, molecular features, and survival.

RESULTS

Luminal A cases in the lowest versus highest quartile for pLumA transcriptomic proportion had a 27% higher prevalence of stage > 1, nearly a threefold higher prevalence of TP53 mutation, and a hazard ratio of 2.08 for overall mortality. We found positive associations between pHER2 and HER2 positivity by IHC or FISH; between pLumB and PR negativity; and between pBasal and younger age, node positivity, TP53 mutation, and EGFR expression. Predominant basal admixture, in contrast to predominant LumB or HER2 admixture, was not associated with shorter survival.

CONCLUSION

Bulk sampling for genomic analyses provides an opportunity to expose intratumor heterogeneity, as reflected by subtype admixture. Our results elucidate the striking extent of diversity among LumA cancers and suggest that determining the extent and type of admixture holds promise for refining individualized therapy. LumA cancers with a high degree of basal admixture appear to have distinct biological characteristics that warrant further study.

Understanding cancer stem cells and plasticity: Towards better therapeutics

The ability of cancer cells to finally overcome various lines of treatment in due course has always baffled the scientific community. Even with the most promising therapies, relapse is ultimately seen, and this resilience has proved to be a major hurdle in the management of cancer. Accumulating evidence now attributes this resilience to plasticity. Plasticity is the ability of cells to change their properties and is substantial as it helps in normal tissue regeneration or post-injury repair processes. It also helps in the overall maintenance of homeostasis. Unfortunately, this critical ability of cells, when activated incorrectly, can lead to numerous diseases, including cancer. Therefore, in this review, we focus on the plasticity aspect with an emphasis on cancer stem cells (CSCs). We discuss the various forms of plasticity that provide survival advantages to CSCs. Moreover, we explore various factors that affect plasticity. Furthermore, we provide the therapeutic implications of plasticity. Finally, we provide an insight into the future targeted therapies involving plasticity for better clinical outcomes.

The application of machine learning to imaging in hematological oncology: A scoping review

Background

Here, we conducted a scoping review to (i) establish which machine learning (ML) methods have been applied to hematological malignancy imaging; (ii) establish how ML is being applied to hematological cancer radiology; and (iii) identify addressable research gaps.

Methods

The review was conducted according to the Preferred Reporting Items for Systematic Reviews and Meta-Analysis Extension for Scoping Reviews guidelines. The inclusion criteria were (i) pediatric and adult patients with suspected or confirmed hematological malignancy undergoing imaging (population); (ii) any study using ML techniques to derive models using radiological images to apply to the clinical management of these patients (concept); and (iii) original research articles conducted in any setting globally (context). Quality Assessment of Diagnostic Accuracy Studies 2 criteria were used to assess diagnostic and segmentation studies, while the Newcastle-Ottawa scale was used to assess the quality of observational studies.

Results

Of 53 eligible studies, 33 applied diverse ML techniques to diagnose hematological malignancies or to differentiate them from other diseases, especially discriminating gliomas from primary central nervous system lymphomas (n=18); 11 applied ML to segmentation tasks, while 9 applied ML to prognostication or predicting therapeutic responses, especially for diffuse large B-cell lymphoma. All studies reported discrimination statistics, but no study calculated calibration statistics. Every diagnostic/segmentation study had a high risk of bias due to their case-control design; many studies failed to provide adequate details of the reference standard; and only a few studies used independent validation.

Conclusion

To deliver validated ML-based models to radiologists managing hematological malignancies, future studies should (i) adhere to standardized, high-quality reporting guidelines such as the Checklist for Artificial Intelligence in Medical Imaging; (ii) validate models in independent cohorts; (ii) standardize volume segmentation methods for segmentation tasks; (iv) establish comprehensive prospective studies that include different tumor grades, comparisons with radiologists, optimal imaging modalities, sequences, and planes; (v) include side-by-side comparisons of different methods; and (vi) include low- and middle-income countries in multicentric studies to enhance generalizability and reduce inequity.

Proteomic and functional characterization of intra-tumor heterogeneity in human endometrial cancer

Endometrial cancer is one of the most frequently diagnosed gynecological cancers worldwide, and its prevalence has increased by more than 50% over the last two decades. Despite the understanding of the major signaling pathways driving the growth and metastasis of endometrial cancer, clinical trials targeting these signals have reported poor outcomes. The heterogeneous nature of endometrial cancer is suspected to be one of the key reasons for the failure of targeted therapies. In this study, we perform a sequential window acquisition of all theoretical fragment ion spectra (SWATH)-based comparative proteomic analysis of 63 tumor biopsies collected from 20 patients and define differences in protein signature in multiple regions of the same tumor. We develop organoids from multiple biopsies collected from the same tumor and show that organoids capture heterogeneity in endometrial cancer growth. Overall, using quantitative proteomics and patient-derived organoids, we define the heterogeneous nature of endometrial cancer within a patient’s tumor.

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Proteomic analysis of endometrial cancer intra-tumor heterogeneity

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Identification of potential biomarkers of tumor volume and invasion

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Protein signatures correlate with pre-and postmenopausal cancers

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Patient-derived organoids capture endometrial cancer heterogeneity

Epigenomic interplay in tumor heterogeneity: Potential of epidrugs as adjunct therapy

"Heterogeneity" in tumor mass has immense importance in cancer progression and therapy. The impact of tumor heterogeneity is an emerging field and not yet fully explored. Tumor heterogeneity is mainly considered as intra-tumor heterogeneity and inter-tumor heterogeneity based on their origin. Intra-tumor heterogeneity refers to the discrepancy within the same cancer mass while inter-tumor heterogeneity refers to the discrepancy between different patients having the same tumor type. Both of these heterogeneity types lead to variation in the histopathological as well as clinical properties of the cancer mass which drives disease resistance towards therapeutic approaches. Cancer stem cells (CSCs) act as pinnacle progenitors for heterogeneity development along with various other genetic and epigenetic parameters that are regulating this process. In recent times epigenetic factors are one of the most studied parameters that drive oxidative stress pathways essential during cancer progression. These epigenetic changes are modulated by various epidrugs and have an impact on tumor heterogeneity. The present review summarizes various aspects of epigenetic regulation in the tumor microenvironment, oxidative stress, and progression towards tumor heterogeneity that creates complications during cancer treatment. This review also explores the possible role of epidrugs in regulating tumor heterogeneity and personalized therapy against drug resistance.

Macrophage induced ERK-TGF-β1 signaling in MCF7 breast cancer cells result in reversible cancer stem cell plasticity and epithelial mesenchymal transition

BACKGROUND

Breast cancer is a heterogenous disease composed of multiple clonal populations and the mechanism by which the tumor microenvironment induces cancer stem cell plasticity is not fully understood.

METHODS

MCF7 breast cancer cells were treated with macrophage conditioned medium (MɸCM). PD98059 and SB431542 were used for ERK and TGF-βR inhibition respectively. Epithelial-mesenchymal transition (EMT) and cancer stem cell markers (CSC) were studied using qRT-PCR and flowcytometry. SCID mice were used for animal experiments.

RESULTS

MɸCM- induced ERK/TGF-β1 signaling led to enrichment of CSC and EMT in MCF7 cells and mammospheres. These effects were abrogated by both MEK inhibitor PD98059 (TGF-β1 synthesis) and SB431542 (TGF-β1 signaling). The increase in CSC was both hybrid (ALDH1⁺) and mesenchymal (CD44⁺ CD24^- cells). Increase in hybrid E/M state was at a single cell level as confirmed by the increase in both claudin-1 (E) and vimentin (M). This did not have any growth advantage in SCID mice and monitoring of CSC and EMT markers before and after growth in SCID mice indicated reversal of these markers in tumor cells recovered from mice. Removal of MɸCM and neutralization of TNF-α, IL-6 and IL-1β in MɸCM abrogated ERK phosphorylation, TGF-β and CSC enrichment indicating the requirement of continuous signaling for maintenance.

CONCLUSIONS

ERK signaling plays an important role in MɸCM- induced EMT and CSC plasticity which is completely reversible upon withdrawal of signals.

GENERAL SIGNIFICANCE

Our experimental observations support the semi-independent nature of EMT-stemness connection which is very dynamic and reversible depending on the microenvironment.

Radiomics analysis of pre-treatment F-18 FDG PET/CT for predicting response to transarterial radioembolization in liver tumors

Aim: To investigate the relationship between the textural features extracted from pre-treatment fluorine-18 fluorodeoxyglucose positron emission with computed tomography (F-18 FDG PET/CT) and the response to treatment in patients undergoing transarterial radioembolization (TARE) due to primary or metastatic liver tumors. Material and Method: A total of 25 liver lesions from the pre-treatment F-18 PET/CT images of 14 patients were segmented manually. Standard uptake value (SUV) metrics and radiomics features were extracted for each lesion. Metabolic treatment response was determined according to PERCIST criteria in 18F-FDG PET/CT imaging performed 2 months after the treatment. Feature selection was done with recursive feature elimination (RFE). The association between selected features and treatment response was evaluated with logistic regression analysis. Results: Eventually, 13 lesions responded to TARE, while 12 lesions remain stable or progressed. All standard uptake values and 27 out of 30 textural heterogeneity indicators were significantly higher in lesions that responded to treatment. SUVmax, kurtosis and dissimilarity features were selected by the RFE algorithm for the prediction of response to TARE. Logistic regression analysis revealed that all three parameters were significantly associated with treatment outcome. Conclusion: Textural features extracted from pre-treatment F-18 FDG PET/CT in patients undergoing TARE due to liver tumors are promising biomarkers that can be potentially used to predict metabolic treatment response.

Clinical experience of tensor-valued diffusion encoding for microstructure imaging by diffusional variance decomposition in patients with breast cancer

BACKGROUND

Diffusion-weighted imaging plays a key role in magnetic resonance imaging (MRI) of breast tumors. However, it remains unclear how to interpret single diffusion encoding with respect to its link with tissue microstructure. The purpose of this retrospective cross-sectional study was to use tensor-valued diffusion encoding to investigate the underlying microstructure of invasive ductal carcinoma (IDC) and evaluate its potential value in a clinical setting.

METHODS

We retrospectively reviewed biopsy-proven breast cancer patients who underwent preoperative breast MRI examination from July 2020 to March 2021. We reviewed the MRI of 29 patients with 30 IDCs, including analysis by diffusional variance decomposition enabled by tensor-valued diffusion encoding. The diffusion parameters of mean diffusivity (MD), total mean kurtosis (MKT), anisotropic mean kurtosis (MKA), isotropic mean kurtosis (MKI), macroscopic fractional anisotropy (FA), and microscopic fractional anisotropy (µFA) were estimated. The parameter differences were compared between IDC and normal fibroglandular breast tissue (FGBT), as well as the association between the diffusion parameters and histopathologic items.

RESULTS

The mean value of MD in IDCs was significantly lower than that of normal FGBT (1.07±0.27 vs. 1.34±0.29, P<0.001); however, MKT, MKA, MKI, FA, and µFA were significantly higher (P<0.005). Among all the diffusion parameters, MKI was positively correlated with the tumor size on both MRI and pathological specimen (rs=0.38, P<0.05 vs. rs=0.54, P<0.01), whereas MKT had a positive correlation with the tumor size in the pathological specimen only (rs=0.47, P<0.02). In addition, the lymph node (LN) metastasis group had significantly higher MKT, MKA, and µFA compared to the metastasis negative group (P<0.05).

CONCLUSIONS

Tensor-valued diffusion encoding enables a useful non-invasive method for characterizing breast cancers with information on tissue microstructures. Particularly, µFA could be a potential imaging biomarker for evaluating breast cancers prior to surgery or chemotherapy.

Modern Approaches to Testing Drug Sensitivity of Patients' Tumors (Review)

Drug therapy is still one of the basic techniques used to treat cancers of different etiology. However, tumor resistance to drugs is a pressing problem limiting drug treatment efficacy. It is obvious for both modern fundamental and clinical oncology that there is the need for an individual approach to treating cancer taking into account the biological properties of a tumor when prescribing chemo- and targeted therapy. One of the promising strategies is to increase the antitumor therapy efficacy by developing predictive tests, which enable to evaluate the sensitivity of a particular tumor to a specific drug or a drug combination before the treatment initiation and, thus, make individual therapy selection possible.

The present review considers the main approaches to drug sensitivity assessment of patients’ tumors: molecular genetic profiling of tumor cells, and direct efficiency testing of the drugs on tumor cells isolated from surgical or biopsy material. There were analyzed the key directions in research and clinical studies such as: the search for predictive molecular markers, the development of methods to maintain tumor cells or tissue sections viable, i.e. in a condition maximum close to their physiological state, the development of high throughput systems to assess therapy efficiency. Special attention was given to a patient-centered approach to drug therapy in colorectal cancer.

Application of Single-Cell Multi-Omics in Dissecting Cancer Cell Plasticity and Tumor Heterogeneity

Tumor heterogeneity, a hallmark of cancer, impairs the efficacy of cancer therapy and drives tumor progression. Exploring inter- and intra-tumoral heterogeneity not only provides insights into tumor development and progression, but also guides the design of personalized therapies. Previously, high-throughput sequencing techniques have been used to investigate the heterogeneity of tumor ecosystems. However, they could not provide a high-resolution landscape of cellular components in tumor ecosystem. Recently, advance in single-cell technologies has provided an unprecedented resolution to uncover the intra-tumoral heterogeneity by profiling the transcriptomes, genomes, proteomes and epigenomes of the cellular components and also their spatial distribution, which greatly accelerated the process of basic and translational cancer research. Importantly, it has been demonstrated that some cancer cells are able to transit between different states in order to adapt to the changing tumor microenvironment, which led to increased cellular plasticity and tumor heterogeneity. Understanding the molecular mechanisms driving cancer cell plasticity is critical for developing precision therapies. In this review, we summarize the recent progress in dissecting the cancer cell plasticity and tumor heterogeneity by use of single-cell multi-omics techniques.

Baseline 18F-FDG PET textural features as predictors of response to chemotherapy in diffuse large B-cell lymphoma

PURPOSE

We sought to investigate the performance of radiomics analysis on baseline 18F-FDG PET/CT for predicting response to first-line chemotherapy in diffuse large B-cell lymphoma (DLBCL).

MATERIAL AND METHODS

Forty-five patients who received first-line rituximab, cyclophosphamide, doxorubicin, vincristine and prednisone (R-CHOP) chemotherapy for DLBCL were included in the study. Radiomics features and standard uptake value (SUV)-based measurements were extracted from baseline PET images for a total of 147 lesions. The selection of the most relevant features was made using the recursive feature elimination algorithm. A machine-learning model was trained using the logistic regression classifier with cross-validation to predict treatment response. The independent predictors of incomplete response were evaluated with multivariable regression analysis.

RESULTS

A total of 14 textural features were selected by the recursive elimination algorithm, achieving a feature-to-lesion ratio of 1:10. The accuracy and area under the receiver operating characteristic curve of the model for predicting incomplete response were 0.87 and 0.81, respectively. Multivariable analysis revealed that SUVmax and gray level co-occurrence matrix dissimilarity were independent predictors of lesions with incomplete response to first-line R-CHOP chemotherapy.

CONCLUSION

Increased textural heterogeneity in baseline PET images was found to be associated with incomplete response in DLBCL.

Cancer Stem Cell Markers, CD44 and ALDH1, for Assessment of Cancer Risk in OPMDs and Lymph Node Metastasis in Oral Squamous Cell Carcinoma

Tumour heterogeneity in oral cancer is attributed to the presence of cancer stem cells (CSCs). CSCs are the most migratory and metastatic cellular subpopulation within tumours. Assessment of CSC markers as significant predictors of lymph node metastasis may prove valuable in the clinical setting. Furthermore, analysis of this panel of putative stem cell markers in oral dysplasia may additionally inform of the likelihood for oral potentially malignant disorders (OPMDs) to progress to oral squamous cell carcinoma (OSCC). The present study aims to assess the significance of CSC markers in the progression of OPMDs to OSCC and assessment of lymph node metastasis in OSCC. CD44 and ALDH1 were assessed immunohistochemically in 25 normal, 30 OPMDs, and 24 OSCCs. CD44 is a membranous marker and ALDH1 is a cytoplasmic marker. The immunohistochemical expression of these markers were compared between OPMDs with and without dysplasia, as well as between low-risk and high-risk dysplasias. Similarly, expression was compared between OSCC with and without lymph node metastasis and among grades of OSCC. Positive CD44 expression was seen in all normal mucosal tissues. The expression decreased from normal epithelium to OPMDs but increased in OSCC. CD44 expression was positive in 21 cases of OSCC (87.5%) and reduced from well-differentiated to poorly differentiated OSCC. CD44 staining index was higher in OSCC without lymph node metastasis (3.59) when compared with OSCC with lymph node metastasis (1.33). There was a statistically significant difference observed in the ALDH1 staining index among three groups (p < 0.05), with highest expression seen in OSCC. Within OPMDs, the ALDH1 staining index was statistically higher in OPMDs with dysplasia as compared to OPMDs without dysplasia. Furthermore, the expression was higher in OPMDs with high-risk dysplasia when compared with low-risk dysplasia, but this was not statistically significant (p = 0.82). In conclusion, The CD44 positive population possesses properties of CSCs in head and neck carcinoma, and continuous shedding could be found after CD44 down-regulation. The present study reports differences in ALDH1 expression between OPMDs with and without dysplasia, dysplastic and non-dysplastic epithelia, and low-risk and high-risk dysplasia. These findings may suggest ALDH1 as a specific marker for dysplasia. CD44 demonstrated a difference in staining index in OSCC without lymph node metastasis versus OSCC with lymph node metastasis. These findings may suggest CD44 as a marker for lymph node metastasis. Both proteins may play key roles in the tumorigenicity of CSCs in OPMDs and OSCC.

Dioscin Decreases Breast Cancer Stem-like Cell Proliferation via Cell Cycle Arrest by Modulating p38 Mitogen-activated Protein Kinase and AKT/mTOR Signaling Pathways

Dioscin (DS), a steroidal saponin, has been shown to have anti-cancer activity by exerting antioxidant effects and inducing apoptosis. However, the anti-cancer activity of DS in breast cancer-derived stem cells is still controversial. The purpose of this study was to evaluate the effects of DS on migration, invasion, and colony formation in MDA-MB-231 and MCF-7 cell lines and the mechanism by which it inhibits proliferation of breast cancer stem-like cells after inducing differentiation into breast cancer stem cells. DS treatment significantly reduced cellular migration, invasion, and colony formation in MDA-MB-231 and MCF-7 cells. During the differentiation process that induced manifestation of breast cancer stem-like cells, DS significantly inhibited mammosphere formation in a dose-dependent manner and increased the expression of p53 and p21 in breast cancer stem-like cells, reducing the expression of cdc2 and cyclin B1 in MDA-MB-231 cells and cyclin D, cyclin E, CDK4, and CDK2 in MCF-7 cells. Interestingly, DS treatment induced G2/M and G0/G1 cell cycle arrest in the MDA-MB-231 and MCF-7 cells, respectively. DS also increased the phosphorylation of p38 and decreased the expression levels of p-AKT and p-mTOR. These results suggest that DS regulates the p38 mitogen-activated protein kinase and AKT/mTOR signaling pathways to reduce the proliferation of breast cancer stem-like cells through cell cycle arrest. Therefore, these findings suggest that DS may serve as a potential treatment candidate targeting breast cancer stem cells.

Cancer stem cells, epithelial-mesenchymal transition, ATP and their roles in drug resistance in cancer

The cancer stem cell (CSC) state and epithelial-mesenchymal transition (EMT) activation are tightly interconnected. Cancer cells that acquire the EMT/CSC phenotype are equipped with adaptive metabolic changes to maintain low reactive oxygen species levels and stemness, enhanced drug transporters, anti-apoptotic machinery and DNA repair system. Factors present in the tumor microenvironment such as hypoxia and the communication with non-cancer stromal cells also promote cancer cells to enter the EMT/CSC state and display related resistance. ATP, particularly the high levels of intratumoral extracellular ATP functioning through both signaling pathways and ATP internalization, induces and regulates EMT and CSC. The three of them work together to enhance drug resistance. New findings in each of these factors will help us explore deeper into mechanisms of drug resistance and suggest new resistance-associated markers and therapeutic targets.

A Mathematical Study of the Influence of Hypoxia and Acidity on the Evolutionary Dynamics of Cancer

Hypoxia and acidity act as environmental stressors promoting selection for cancer cells with a more aggressive phenotype. As a result, a deeper theoretical understanding of the spatio-temporal processes that drive the adaptation of tumour cells to hypoxic and acidic microenvironments may open up new avenues of research in oncology and cancer treatment. We present a mathematical model to study the influence of hypoxia and acidity on the evolutionary dynamics of cancer cells in vascularised tumours. The model is formulated as a system of partial integro-differential equations that describe the phenotypic evolution of cancer cells in response to dynamic variations in the spatial distribution of three abiotic factors that are key players in tumour metabolism: oxygen, glucose and lactate. The results of numerical simulations of a calibrated version of the model based on real data recapitulate the eco-evolutionary spatial dynamics of tumour cells and their adaptation to hypoxic and acidic microenvironments. Moreover, such results demonstrate how nonlinear interactions between tumour cells and abiotic factors can lead to the formation of environmental gradients which select for cells with phenotypic characteristics that vary with distance from intra-tumour blood vessels, thus promoting the emergence of intra-tumour phenotypic heterogeneity. Finally, our theoretical findings reconcile the conclusions of earlier studies by showing that the order in which resistance to hypoxia and resistance to acidity arise in tumours depend on the ways in which oxygen and lactate act as environmental stressors in the evolutionary dynamics of cancer cells.

A verified genomic reference sample for assessing performance of cancer panels detecting small variants of low allele frequency

BACKGROUND

Oncopanel genomic testing, which identifies important somatic variants, is increasingly common in medical practice and especially in clinical trials. Currently, there is a paucity of reliable genomic reference samples having a suitably large number of pre-identified variants for properly assessing oncopanel assay analytical quality and performance. The FDA-led Sequencing and Quality Control Phase 2 (SEQC2) consortium analyze ten diverse cancer cell lines individually and their pool, termed Sample A, to develop a reference sample with suitably large numbers of coding positions with known (variant) positives and negatives for properly evaluating oncopanel analytical performance.

RESULTS

In reference Sample A, we identify more than 40,000 variants down to 1% allele frequency with more than 25,000 variants having less than 20% allele frequency with 1653 variants in COSMIC-related genes. This is 5-100× more than existing commercially available samples. We also identify an unprecedented number of negative positions in coding regions, allowing statistical rigor in assessing limit-of-detection, sensitivity, and precision. Over 300 loci are randomly selected and independently verified via droplet digital PCR with 100% concordance. Agilent normal reference Sample B can be admixed with Sample A to create new samples with a similar number of known variants at much lower allele frequency than what exists in Sample A natively, including known variants having allele frequency of 0.02%, a range suitable for assessing liquid biopsy panels.

CONCLUSION

These new reference samples and their admixtures provide superior capability for performing oncopanel quality control, analytical accuracy, and validation for small to large oncopanels and liquid biopsy assays.

Time to Move to the Single-Cell Level: Applications of Single-Cell Multi-Omics to Hematological Malignancies and Waldenström's Macroglobulinemia-A Particularly Heterogeneous Lymphoma

Single-cell sequencing techniques have become a powerful tool for characterizing intra-tumor heterogeneity, which has been reflected in the increasing number of studies carried out and reported. We have rigorously reviewed and compiled the information about these techniques inasmuch as they are relative to the area of hematology to provide a practical view of their potential applications. Studies show how single-cell multi-omics can overcome the limitations of bulk sequencing and be applied at all stages of tumor development, giving insights into the origin and pathogenesis of the tumors, the clonal architecture and evolution, or the mechanisms of therapy resistance. Information at the single-cell level may help resolve questions related to intra-tumor heterogeneity that have not been previously explained by other techniques. With that in mind, we review the existing knowledge about a heterogeneous lymphoma called Waldenström's macroglobulinemia and discuss how single-cell studies may help elucidate the underlying causes of this heterogeneity.

Glioma Stem Cells as Immunotherapeutic Targets: Advancements and Challenges

Glioblastoma is the most common and lethal primary brain malignancy. Despite major investments in research into glioblastoma biology and drug development, treatment remains limited and survival has not substantially improved beyond 1-2 years. Cancer stem cells (CSC) or glioma stem cells (GSC) refer to a population of tumor originating cells capable of self-renewal and differentiation. While controversial and challenging to study, evidence suggests that GCSs may result in glioblastoma tumor recurrence and resistance to treatment. Multiple treatment strategies have been suggested at targeting GCSs, including immunotherapy, posttranscriptional regulation, modulation of the tumor microenvironment, and epigenetic modulation. In this review, we discuss recent advances in glioblastoma treatment specifically focused on targeting of GCSs as well as their potential integration into current clinical pathways and trials.

A Systematic Review of PET Textural Analysis and Radiomics in Cancer

Background: Although many works have supported the utility of PET radiomics, several authors have raised concerns over the robustness and replicability of the results. This study aimed to perform a systematic review on the topic of PET radiomics and the used methodologies. Methods: PubMed was searched up to 15 October 2020. Original research articles based on human data specifying at least one tumor type and PET image were included, excluding those that apply only first-order statistics and those including fewer than 20 patients. Each publication, cancer type, objective and several methodological parameters (number of patients and features, validation approach, among other things) were extracted. Results: A total of 290 studies were included. Lung (28%) and head and neck (24%) were the most studied cancers. The most common objective was prognosis/treatment response (46%), followed by diagnosis/staging (21%), tumor characterization (18%) and technical evaluations (15%). The average number of patients included was 114 (median = 71; range 20–1419), and the average number of high-order features calculated per study was 31 (median = 26, range 1–286). Conclusions: PET radiomics is a promising field, but the number of patients in most publications is insufficient, and very few papers perform in-depth validations. The role of standardization initiatives will be crucial in the upcoming years.

Single-cell dissection of intratumoral heterogeneity and lineage diversity in metastatic gastric adenocarcinoma

Intratumoral heterogeneity (ITH) is a fundamental property of cancer; however, the origins of ITH remain poorly understood. We performed single-cell transcriptome profiling of peritoneal carcinomatosis (PC) from 15 patients with gastric adenocarcinoma (GAC), constructed a map of 45,048 PC cells, profiled the transcriptome states of tumor cell populations, incisively explored ITH of malignant PC cells and identified significant correlates with patient survival. The links between tumor cell lineage/state compositions and ITH were illustrated at transcriptomic, genotypic, molecular and phenotypic levels. We uncovered the diversity in tumor cell lineage/state compositions in PC specimens and defined it as a key contributor to ITH. Single-cell analysis of ITH classified PC specimens into two subtypes that were prognostically independent of clinical variables, and a 12-gene prognostic signature was derived and validated in multiple large-scale GAC cohorts. The prognostic signature appears fundamental to GAC carcinogenesis and progression and could be practical for patient stratification.

Tumor ablation due to inhomogeneous anisotropic diffusion in generic three-dimensional topologies

In recent decades computer-aided technologies have become prevalent in medicine, however, cancer drugs are often only tested on in vitro cell lines from biopsies. We derive a full three-dimensional model of inhomogeneous -anisotropic diffusion in a tumor region coupled to a binary population model, which simulates in vivo scenarios faster than traditional cell-line tests. The diffusion tensors are acquired using diffusion tensor magnetic resonance imaging from a patient diagnosed with glioblastoma multiform. Then we numerically simulate the full model with finite element methods and produce drug concentration heat maps, apoptosis hotspots, and dose-response curves. Finally, predictions are made about optimal injection locations and volumes, which are presented in a form that can be employed by doctors and oncologists.

Is FDG-PET texture analysis related to intratumor biological heterogeneity in lung cancer?

OBJECTIVES

We aimed at investigating the origin of the correlations between tumor volume and ¹⁸F-FDG-PET texture indices in lung cancer.

METHODS

Eighty-five consecutive patients with newly diagnosed non-small cell lung cancer (NSCLC) underwent a ¹⁸F-FDG-PET/CT scan before treatment. Seven phantom spheres uniformly filled with ¹⁸F-FDG, and covering a range of activities and volumes similar to that found in lung tumors, were also scanned. Established texture indices were computed for lung tumors and homogeneous spheres. The dependence between textural indices and volume in homogeneous spheres was modeled and then used to predict texture indices in lung tumors. Correlation analyses were carried out between predicted and texture features measured in lung tumors. Cox proportional hazards regression was used to investigate the associations between overall survival and volume-adjusted textural features.

RESULTS

All textural features showed strong, non-linear correlations with volume, both in tumors and homogeneous spheres. Correlations between predicted versus measured texture features were very high for contrast (r² = 0.91), dissimilarity (r² = 0.90), ZP (r² = 0.90), GLNN (r² = 0.86), and homogeneity (r² = 0.82); high for entropy (r² = 0.50) and HILAE (r² = 0.53); and low for energy (r² = 0.30). Cox regressions showed that among volume-adjusted features, only HILAE was associated with overall survival (b = - 0.35, p = 0.008).

CONCLUSION

We have shown that texture indices previously found to be correlated with a number of clinically relevant outcomes might not provide independent information apart from that driven by their correlation with tumor volume, suggesting that these metrics might not be suitable as intratumor heterogeneity markers.

KEY POINTS

• Associations between texture FDG-PET indices and overall survival have been widely reported in lung cancer, with tumor volume also being associated with overall survival, and therefore, it is still unclear whether the predictive power of textural indices is simply driven by this correlation. • Our results demonstrated strong non-linear correlations between textural indices and volume, showing an analogous behavior for lung tumors from patients and homogeneous spheres inserted in phantoms. • Our findings showed that texture FDG-PET indices might not provide independent information apart from that driven by their correlation with tumor volume.

Electrical Impedance Spectroscopy for Monitoring Chemoresistance of Cancer Cells

Electrical impedance spectroscopy (EIS) is an electrokinetic method that allows for the characterization of intrinsic dielectric properties of cells. EIS has emerged in the last decade as a promising method for the characterization of cancerous cells, providing information on inductance, capacitance, and impedance of cells. The individual cell behavior can be quantified using its characteristic phase angle, amplitude, and frequency measurements obtained by fitting the input frequency-dependent cellular response to a resistor-capacitor circuit model. These electrical properties will provide important information about unique biomarkers related to the behavior of these cancerous cells, especially monitoring their chemoresistivity and sensitivity to chemotherapeutics. There are currently few methods to assess drug resistant cancer cells, and therefore it is difficult to identify and eliminate drug-resistant cancer cells found in static and metastatic tumors. Establishing techniques for the real-time monitoring of changes in cancer cell phenotypes is, therefore, important for understanding cancer cell dynamics and their plastic properties. EIS can be used to monitor these changes. In this review, we will cover the theory behind EIS, other impedance techniques, and how EIS can be used to monitor cell behavior and phenotype changes within cancerous cells.

Systems-Level Properties of EGFR-RAS-ERK Signaling Amplify Local Signals to Generate Dynamic Gene Expression Heterogeneity

Intratumoral heterogeneity is associated with aggressive tumor behavior, therapy resistance, and poor patient outcomes. Such heterogeneity is thought to be dynamic, shifting over periods of minutes to hours in response to signaling inputs from the tumor microenvironment. However, models of this process have been inferred from indirect or post-hoc measurements of cell state, leaving the temporal details of signaling-driven heterogeneity undefined. Here, we developed a live-cell model system in which microenvironment-driven signaling dynamics can be directly observed and linked to variation in gene expression. Our analysis reveals that paracrine signaling between two cell types is sufficient to drive continual diversification of gene expression programs. This diversification emerges from systems-level properties of the EGFR-RAS-ERK signaling cascade, including intracellular amplification of amphiregulin-mediated paracrine signals and differential kinetic filtering by target genes including Fra-1, c-Myc, and Egr1. Our data enable more precise modeling of paracrine-driven transcriptional variation as a generator of gene expression heterogeneity. A record of this paper's transparent peer review process is included in the Supplemental Information.

Clonal evolution driven by superdriver mutations

Background

Tumors are widely recognized to progress through clonal evolution by sequentially acquiring selectively advantageous genetic alterations that significantly contribute to tumorigenesis and thus are termned drivers. Some cancer drivers, such as TP53 point mutation or EGFR copy number gain, provide exceptional fitness gains, which, in time, can be sufficient to trigger the onset of cancer with little or no contribution from additional genetic alterations. These key alterations are called superdrivers.

Results

In this study, we employ a Wright-Fisher model to study the interplay between drivers and superdrivers in tumor progression. We demonstrate that the resulting evolutionary dynamics follow global clonal expansions of superdrivers with periodic clonal expansions of drivers. We find that the waiting time to the accumulation of a set of superdrivers and drivers in the tumor cell population can be approximated by the sum of the individual waiting times.

Conclusions

Our results suggest that superdriver dynamics dominate over driver dynamics in tumorigenesis. Furthermore, our model allows studying the interplay between superdriver and driver mutations both empirically and theoretically.

Tumor shrinkage rate as a potential marker for the prediction of long-term outcome in advanced non-small cell lung cancer treated with first-line tyrosine kinase inhibitors

Context

Tyrosine kinase inhibitors (TKIs) targeting epidermal growth factor receptor (EGFR) play an indispensable role in the treatment of non-small cell lung cancer (NSCLC), leading to a survival major breakthrough, but there remains no uniform standard for predicting the efficacy of TKI therapy.

Aims

We retrospectively reviewed the use of EGFR-TKIs for advanced NSCLC between January 2009 and December 2017 in a hospital, which 169 patients who treated with first-line TKIs were enrolled.

Subjects and Methods

Multiple clinical factors, including histology, age, and sex, were analyzed. We calculated the tumor shrinkage rate (TSR) by measuring the longest diameters of the main mass by computed tomography (CT) before TKI therapy and the first CT after TKI therapy. We evaluated overall survival (OS) and progression-free survival (PFS) after first-line TKI therapy, and we assessed factors predicting survival using the Kaplan-Meier method.

Results

Eligible patients were sorted into higher (n = 83) and lower (n = 86) TSR groups according to the mean TSR of 0.49%. The 83 patients with a higher TSR had longer PFS and OS than those in the 86 patients with a lower TSR (14.83 vs. 8.40 months, P < 0.001, and 31.03 vs. 20.10 months, P < 0.001, respectively). Multivariate analyses revealed that TSR was an independent predictor of PFS and OS (PFS hazard ratio [HR]: 0.506, P < 0.001, and OS HR: 0.291, P < 0.001).

Conclusions

These cumulative data support that TSR may be an early predictor of the treatment efficacy in NSCLC with EGFR mutations treated with first-line TKIs.

Evaluation of CT-based radiomics signature and nomogram as prognostic markers in patients with laryngeal squamous cell carcinoma

Background

The aim of this study was to evaluate the prognostic value of radiomics signature and nomogram based on contrast-enhanced computed tomography (CT) in patients after surgical resection of laryngeal squamous cell carcinoma (LSCC).

Methods

All patients (n = 136) were divided into the training cohort (n = 96) and validation cohort (n = 40). The LASSO regression method was performed to construct radiomics signature from CT texture features. Then a radiomics nomogram incorporating the radiomics signature and clinicopathologic factors was established to predict overall survival (OS). The validation of nomogram was evaluated by calibration curve, concordance index (C-index) and decision curve.

Results

Based on three selected texture features, the radiomics signature showed high C-indexes of 0.782 (95%CI: 0.656–0.909) and 0.752 (95%CI, 0.614–0.891) in the two cohorts. The radiomics nomogram had significantly better discrimination capability than cancer staging in the training cohort (C-index, 0.817 vs. 0.682; P = 0.009) and validation cohort (C-index, 0.913 vs. 0.699; P = 0.019), as well as a good agreement between predicted and actual survival in calibration curves. Decision curve analysis also suggested improved clinical utility of radiomics nomogram.

Conclusions

Radiomics signature and nomogram showed favorable prediction accuracy for OS, which might facilitate the individualized risk stratification and clinical decision-making in LSCC patients.

Novel Therapeutic Strategies for Ovarian Cancer Stem Cells

Ovarian cancer (OC) is one of the most lethal gynecologic malignancies. Due to the lack of specific symptoms and screening methods, this disease is usually diagnosed only at an advanced and metastatic stage. The gold-standard treatment for OC patients consists of debulking surgery followed by taxane combined with platinum-based chemotherapy. Most patients show complete clinical remission after first-line therapy, but the majority of them ultimately relapse, developing radio- and chemoresistant tumors. It is now proposed that the cause of recurrence and reduced therapy efficacy is the presence of small populations of cancer stem cells (CSCs). These cells are usually resistant against conventional cancer therapies and for this reason, effective targeted therapies for the complete eradication of CSCs are urgently needed. In this review article, we highlight the mechanisms of CSC therapy resistance, epithelial-to-mesenchymal transition, stemness, and novel therapeutic strategies for ovarian CSCs.

Perturbation-Driven Entropy as a Source of Cancer Cell Heterogeneity

Intratumor heterogeneity is a key hallmark of cancer that contributes to progression and therapeutic resistance. Phenotypic heterogeneity is in part caused by Darwinian selection of subclones that arise by random (epi)genetic aberrations. In addition, cancer cells are endowed with increased cellular plasticity compared with their normal counterparts, further adding to their heterogeneous behavior. However, the molecular mechanisms underpinning cancer cell plasticity are incompletely understood. Here, I outline the hypothesis that cancer-associated perturbations collectively disrupt normal gene regulatory networks (GRNs) by increasing their entropy. Importantly, in this model both somatic driver and passenger alterations contribute to 'perturbation-driven entropy', thereby increasing phenotypic heterogeneity and evolvability. This additional layer of heterogeneity may contribute to our understanding of cancer evolution and therapeutic resistance.

The Role Of Circulating Tumor DNA In Therapeutic Resistance

The application of precision medicine in cancer treatment has partly succeeded in reducing the side effects of unnecessary chemotherapeutics and in improving the survival rate of patients. However, with the long-term use of therapy, the dynamically changing intratumoral and intertumoral heterogeneity eventually gives rise to therapeutic resistance. In recent years, a novel testing technology (termed liquid biopsy) using circulating tumor DNAs (ctDNAs) extracted from peripheral blood samples from patients with cancer has brought about new expectations to the medical community. Using ctDNAs, clinicians can trace the heterogeneity pattern to duly adjust individual therapy and prolong overall survival for patients with cancer. Technological advances in detecting and characterizing ctDNAs (eg, development of next-generation sequencing) have provided clinicians with a valuable tool for genotyping tumors individually and identifying genetic and epigenetic alterations of the entire tumor to capture mutations associated with therapeutic resistance.

Characterization of Hepatocellular Carcinoma Cell Lines Using a Fractionation-Then-Sequencing Approach Reveals Nuclear-Enriched HCC-Associated lncRNAs

Background: Advances in sequencing technologies have greatly improved our understanding of long noncoding RNA (lncRNA). These transcripts with lengths of >200 nucleotides may play significant regulatory roles in various biological processes. Importantly, the dysregulation of better characterized lncRNAs has been associated with multiple types of cancers, including hepatocellular carcinoma (HCC). There are many studies on altered lncRNA expression levels, very few, however, have focused on their subcellular localizations, from which accumulating evidences have indicated their close relationships to lncRNA functions. A transcriptome-wide investigation of the subcellular distributions of lncRNAs might thus provide new insights into their roles and functions in cancers.

Results: In this study, we subjected eight patient-derived HCC cell lines to subcellular fractionation and independently sequenced RNAs from the nuclear and cytoplasmic compartments. With the integration of tumor and tumor-adjacent RNA-seq datasets of liver hepatocellular carcinoma (LIHC) from The Cancer Genome Atlas (TCGA), de novo transcriptome assembly and differential expression analysis were conducted successively and identified 26 nuclear-enriched HCC-associated lncRNAs shared between the HCC samples and the TCGA datasets, including the reported cancer driver PXN-AS1. The majority of nuclear-enriched HCC-associated lncRNAs were associated with the survival outcomes of HCC patients, exhibited characteristics similar to those of many experimentally supported HCC prognostic lncRNAs, and were co-expressed with protein-coding genes that have been linked to disease progression in various cancer types.

Conclusion: We adopted a fractionation-then-sequencing approach on multiple patient-derived HCC samples and identified nuclear-enriched, HCC-associated lncRNAs that could serve as important targets for HCC diagnosis and therapeutic development. This approach could be widely applicable to other studies into the disease etiologies of lncRNA.

Artificial intelligence in digital pathology - new tools for diagnosis and precision oncology

In the past decade, advances in precision oncology have resulted in an increased demand for predictive assays that enable the selection and stratification of patients for treatment. The enormous divergence of signalling and transcriptional networks mediating the crosstalk between cancer, stromal and immune cells complicates the development of functionally relevant biomarkers based on a single gene or protein. However, the result of these complex processes can be uniquely captured in the morphometric features of stained tissue specimens. The possibility of digitizing whole-slide images of tissue has led to the advent of artificial intelligence (AI) and machine learning tools in digital pathology, which enable mining of subvisual morphometric phenotypes and might, ultimately, improve patient management. In this Perspective, we critically evaluate various AI-based computational approaches for digital pathology, focusing on deep neural networks and 'hand-crafted' feature-based methodologies. We aim to provide a broad framework for incorporating AI and machine learning tools into clinical oncology, with an emphasis on biomarker development. We discuss some of the challenges relating to the use of AI, including the need for well-curated validation datasets, regulatory approval and fair reimbursement strategies. Finally, we present potential future opportunities for precision oncology.

Heterogeneity of breast cancer: The importance of interaction between different tumor cell populations

INTRODUCTION

Breast cancer is the most common cancer and the second leading cause of cancer-related death in women worldwide. Despite the early detection of breast cancer and increasing knowledge of its biology and chemo-resistance, metastatic breast cancer is largely incurable disease. We provide a review of the intertumor and intratumor heterogeneity, explain the differences between triple-negative breast cancer subtypes. Also, we describe the interaction of breast tumor cells with their microenvironment cells and explain how this interaction contributes to the tumor progression, metastasis formation and resistance to the treatment.

DISCUSSION

One of the main causes that complicate the treatment is tumor heterogeneity. It is observed among patients (intertumor heterogeneity) and in each individual tumor (intratumor heterogeneity). In the case of intratumor heterogeneity, the tumor consists of different phenotypical cell populations. During breast cancer subtype identification, a small piece of solid tumor tissue does not necessarily represent all the tumor composition. Breast tumor cell phenotypical differences may appear due to cell localization in different tumor sites, unique response to the treatment, cell interaction with tumor microenvironment or tumor cell interaction with each other. This heterogeneity may lead to breast cancer aggressiveness and challenging treatment.

CONCLUSION

Understanding the molecular and cellular mechanisms of tumor heterogeneity that are relevant to the development of treatment resistance is a major area of research. Identification of differences between populations and their response to anticancer drugs would help to predict the potential resistance to chemotherapy and thus would help to select the most suitable anticancer treatment.

Absence of an embryonic stem cell DNA methylation signature in human cancer

BACKGROUND

Differentiated cells that arise from stem cells in early development contain DNA methylation features that provide a memory trace of their fetal cell origin (FCO). The FCO signature was developed to estimate the proportion of cells in a mixture of cell types that are of fetal origin and are reminiscent of embryonic stem cell lineage. Here we implemented the FCO signature estimation method to compare the fraction of cells with the FCO signature in tumor tissues and their corresponding nontumor normal tissues.

METHODS

We applied our FCO algorithm to discovery data sets obtained from The Cancer Genome Atlas (TCGA) and replication data sets obtained from the Gene Expression Omnibus (GEO) data repository. Wilcoxon rank sum tests, linear regression models with adjustments for potential confounders and non-parametric randomization-based tests were used to test the association of FCO proportion between tumor tissues and nontumor normal tissues. P-values of < 0.05 were considered statistically significant.

RESULTS

Across 20 different tumor types we observed a consistently lower FCO signature in tumor tissues compared with nontumor normal tissues, with 18 observed to have significantly lower FCO fractions in tumor tissue (total n = 6,795 tumor, n = 922 nontumor, P < 0.05). We replicated our findings in 15 tumor types using data from independent subjects in 15 publicly available data sets (total n = 740 tumor, n = 424 nontumor, P < 0.05).

CONCLUSIONS

The results suggest that cancer development itself is substantially devoid of recapitulation of normal embryologic processes. Our results emphasize the distinction between DNA methylation in normal tightly regulated stem cell driven differentiation and cancer stem cell reprogramming that involves altered methylation in the service of great cell heterogeneity and plasticity.

The role of clonal communication and heterogeneity in breast cancer

Background

Cancer is a rapidly evolving, multifactorial disease that accumulates numerous genetic and epigenetic alterations. This results in molecular and phenotypic heterogeneity within the tumor, the complexity of which is further amplified through specific interactions between cancer cells. We aimed to dissect the molecular mechanisms underlying the cooperation between different clones.

Methods

We produced clonal cell lines derived from the MDA-MB-231 breast cancer cell line, using the UbC-StarTrack system, which allowed tracking of multiple clones by color: GFP C3, mKO E10 and Sapphire D7. Characterization of these clones was performed by growth rate, cell metabolic activity, wound healing, invasion assays and genetic and epigenetic arrays. Tumorigenicity was tested by orthotopic and intravenous injections. Clonal cooperation was evaluated by medium complementation, co-culture and co-injection assays.

Results

Characterization of these clones in vitro revealed clear genetic and epigenetic differences that affected growth rate, cell metabolic activity, morphology and cytokine expression among cell lines. In vivo, all clonal cell lines were able to form tumors; however, injection of an equal mix of the different clones led to tumors with very few mKO E10 cells. Additionally, the mKO E10 clonal cell line showed a significant inability to form lung metastases. These results confirm that even in stable cell lines heterogeneity is present. In vitro, the complementation of growth medium with medium or exosomes from parental or clonal cell lines increased the growth rate of the other clones. Complementation assays, co-growth and co-injection of mKO E10 and GFP C3 clonal cell lines increased the efficiency of invasion and migration.

Conclusions

These findings support a model where interplay between clones confers aggressiveness, and which may allow identification of the factors involved in cellular communication that could play a role in clonal cooperation and thus represent new targets for preventing tumor progression.

Electronic supplementary material

The online version of this article (10.1186/s12885-019-5883-y) contains supplementary material, which is available to authorized users.

Molecular heterogeneity in lung cancer: from mechanisms of origin to clinical implications

Molecular heterogeneity is a frequent event in cancer responsible of several critical issues in diagnosis and treatment of oncologic patients. Lung tumours are characterized by high degree of molecular heterogeneity associated to different mechanisms of origin including genetic, epigenetic and non-genetic source. In this review, we provide an overview of recognized mechanisms underlying molecular heterogeneity in lung cancer, including epigenetic mechanisms, mutant allele specific imbalance, genomic instability, chromosomal aberrations, tumor mutational burden, somatic mutations. We focus on the role of spatial and temporal molecular heterogeneity involved in therapeutic implications in lung cancer patients.

Differences in mutational processes and intra-tumour heterogeneity between organs: The local selective filter hypothesis

Extensive diversity (genetic, cytogenetic, epigenetic and phenotypic) exists within and between tumours, but reasons behind these variations, as well as their consistent hierarchical pattern between organs, are poorly understood at the moment. We argue that these phenomena are, at least partially, explainable by the evolutionary ecology of organs' theory, in the same way that environmental adversity shapes mutation rates and level of polymorphism in organisms. Organs in organisms can be considered as specialized ecosystems that are, for ecological and evolutionary reasons, more or less efficient at suppressing tumours. When a malignancy does arise in an organ applying strong selection pressure on tumours, its constituent cells are expected to display a large range of possible surviving strategies, from hyper mutator phenotypes relying on bet-hedging to persist (high mutation rates and high diversity), to few poorly variable variants that become invisible to natural defences. In contrast, when tumour suppression is weaker, selective pressure favouring extreme surviving strategies is relaxed, and tumours are moderately variable as a result. We provide a comprehensive overview of this hypothesis. Lay summary: Different levels of mutations and intra-tumour heterogeneity have been observed between cancer types and organs. Anti-cancer defences are unequal between our organs. We propose that mostly aggressive neoplasms (i.e. higher mutational and ITH levels), succeed in emerging and developing in organs with strong defences.

The nuclear export inhibitor aminoratjadone is a potent effector in extracellular-targeted drug conjugates

The concept of targeted drug conjugates has been successfully translated to clinical practice in oncology. Whereas the majority of cytotoxic effectors in drug conjugates are directed against either DNA or tubulin, our study aimed to validate nuclear export inhibition as a novel effector principle in drug conjugates. For this purpose, a semisynthetic route starting from the natural product ratjadone A, a potent nuclear export inhibitor, has been developed. The biological evaluation of ratjadones functionalized at the 16-position revealed that oxo- and amino-analogues had very high potencies against cancer cell lines (e.g. 16R-aminoratjadone 16 with IC50 = 260 pM against MCF-7 cells, or 19-oxoratjadone 14 with IC50 = 100 pM against A-549 cells). Mechanistically, the conjugates retained a nuclear export inhibitory activity through binding CRM1. To demonstrate a proof-of-principle for cellular targeting, folate- and luteinizing hormone releasing hormone (LHRH)-based carrier molecules were synthesized and coupled to aminoratjadones as well as fluorescein for cellular efficacy and imaging studies, respectively. The Trojan-Horse conjugates selectively addressed receptor-positive cell lines and were highly potent inhibitors of their proliferation. For example, the folate conjugate FA-7-Val-Cit-pABA-16R-aminoratjadone had an IC50 of 34.3 nM, and the LHRH conjugate d-Orn-Gose-Val-Cit-pABA-16R-aminoratjadone had an IC50 of 12.8 nM. The results demonstrate that nuclear export inhibition is a promising mode-of-action for extracellular-targeted drug conjugate payloads.

Ovarian Teratoid Carcinosarcoma Is an Aggressive Tumor of Probable Mullerian Derivation with a Carcinosarcomatous and Mixed Germ-Cell Morphology

Ovarian carcinosarcoma is also referred to as malignant mixed Mullerian tumor (MMMT). It is a rare neoplasm, and although it represents less than 5% of malignant ovarian tumors, it remains generally well-known among clinicians and pathologists. Rarer yet is ovarian teratoid carcinosarcoma, defined as carcinosarcoma with the added feature of immature neuroectodermal tissue, with or without elements of primitive germ cell tumor. To our knowledge, six ovarian teratoid carcinosarcomas have been reported in the literature [Matsuura et al. J Obstet Gynaecol Res. 2010 Aug; 36(4): 907-11]. These tumors resemble nasopharyngeal tumors of the same name. We report a 55-year-old woman seen at Orlando Health's division of gynecological oncology whose pathology showed ovarian teratoid carcinosarcoma, and present what we believe to be a seventh report of this entity.

Quantification of intrinsic subtype ambiguity in Luminal A breast cancer and its relationship to clinical outcomes

Background

PAM50 gene profiling assigns each cancer to a single intrinsic subtype. However, individual cancers vary in their adherence to a prototype, and due to bulk tissue sampling, some may exhibit expression patterns that indicate intra-tumor admixture of multiple subtypes. Our objective was to develop admixture metrics from PAM50 gene expression profiles in order to stratify Luminal A (LumA) cases according to their degree of subtype admixture, and then relate such admixture to clinical and molecular variables.

Methods

We re-constructed scaled, normalized PAM50 profiles for 1980 cases (674 LumA) in the METABRIC cohort and for each case computed its Mahalanobis (M-) distance from its assigned centroid and M-distance from all other centroids. We used t-SNE plots to visualize overlaps in subtype clustering. With Normal-like cases excluded, we developed two metrics: Median Distance Criteria (MDC) classified pure cases as those located within the 50th percentile of the LumA centroid and > =50th percentile from any other centroid. Distance Ratio Criteria (DRC) was computed as the ratio of M-distances from the LumA centroid to the nearest non-assigned centroid. Pure and admixed LumA cases were compared on clinical/molecular traits. TCGA LumA cases (n = 509) provided independent validation.

Results

Compared to pure cases in METABRIC, admixed ones had older age at diagnosis, larger tumor size, and higher grade and stage. These associations were stronger for the DRC metric compared to MDC. Admixed cases were associated with HER2 gain, high proliferation, higher PAM50 recurrence scores, more frequent TP53 mutation, and less frequent PIK3CA mutation. Similar results were observed in the TCGA validation cohort, which also showed a positive association between admixture and number of clonal populations estimated by PyClone. LumA-LumB confusion predominated, but other combinations were also present. Degree of admixture was associated with overall survival in both cohorts, as was disease-free survival in TCGA, independent of age, grade and stage (HR = 2.85, Tertile 3 vs.1).

Conclusions

Luminal A breast cancers subgrouped based on PAM50 subtype purity support the hypothesis that admixed cases have worse clinical features and survival. Future analyses will explore more extensive genomic metrics for admixture and their spatial significance within a single tumor.

Electronic supplementary material

The online version of this article (10.1186/s12885-019-5392-z) contains supplementary material, which is available to authorized users.