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.

Genetic Intratumor Heterogeneity Remodels the Immune Microenvironment and Induces Immune Evasion in Brain Metastasis of Lung Cancer

INTRODUCTION

Brain metastasis, with the highest incidence in patients with lung cancer, significantly worsens prognosis and poses challenges to clinical management. To date, how brain metastasis evades immune elimination remains unknown.

METHODS

Whole-exome sequencing and RNA sequencing were performed on 30 matched brain metastasis, primary lung adenocarcinoma, and normal tissues. Data from The Cancer Genome Atlas primary lung adenocarcinoma cohort, including multiplex immunofluorescence, were used to support the findings of bioinformatics analysis.

RESULTS

Our study highlights the key role of intratumor heterogeneity of genomic alterations in the metastasis process, mainly caused by homologous recombination deficiency or other somatic copy number alteration-associated mutation mechanisms, leading to increased genomic instability and genomic complexity. We further proposed a selection model of brain metastatic evolution in which intratumor heterogeneity drives immune remodeling, leading to immune escape through different mechanisms under local immune pressure.

CONCLUSIONS

Our findings provide novel insights into the metastatic process and immune escape mechanisms of brain metastasis and pave the way for precise immunotherapeutic strategies for patients with lung cancer with brain metastasis.

AI-powered interpretable imaging phenotypes noninvasively characterize tumor microenvironment associated with diverse molecular signatures and survival in breast cancer

BACKGROUND AND OBJECTIVES

Tumor microenvironment (TME) is a determining factor in decision-making and personalized treatment for breast cancer, which is highly intra-tumor heterogeneous (ITH). However, the noninvasive imaging phenotypes of TME are poorly understood, even invasive genotypes have been largely known in breast cancer.

METHODS

Here, we develop an artificial intelligence (AI)-driven approach for noninvasively characterizing TME by integrating the predictive power of deep learning with the explainability of human-interpretable imaging phenotypes (IMPs) derived from 4D dynamic imaging (DCE-MRI) of 342 breast tumors linked to genomic and clinical data, which connect cancer phenotypes to genotypes. An unsupervised dual-attention deep graph clustering model (DGCLM) is developed to divide bulk tumor into multiple spatially segregated and phenotypically consistent subclusters. The IMPs ranging from spatial heterogeneity to kinetic heterogeneity are leveraged to capture architecture, interaction, and proximity between intratumoral subclusters.

RESULTS

We demonstrate that our IMPs correlate with well-known markers of TME and also can predict distinct molecular signatures, including expression of hormone receptor, epithelial growth factor receptor and immune checkpoint proteins, with the performance of accuracy, reliability and transparency superior to recent state-of-the-art radiomics and 'black-box' deep learning methods. Moreover, prognostic value is confirmed by survival analysis accounting for IMPs.

CONCLUSIONS

Our approach provides an interpretable, quantitative, and comprehensive perspective to characterize TME in a noninvasive and clinically relevant manner.

Evolvability of cancer-associated genes under APOBEC3A/B selection

Evolvability is an emergent hallmark of cancer that depends on intra-tumor heterogeneity and, ultimately, genetic variation. Mutations generated by APOBEC3 cytidine deaminases can contribute to genetic variation and the consequences of APOBEC activation differ depending on the stage of cancer, with the most significant impact observed during the early stages. However, how APOBEC activity shapes evolutionary patterns of genes in the host genome and differential impacts on cancer-associated and non-cancer genes remain unclear. Analyzing over 40,000 human protein-coding transcripts, we identified distinct distribution patterns of APOBEC3A/B TC motifs between cancer-related genes and controls, suggesting unique associations with cancer. Studying a bat species with many more APOBEC3 genes, we found diverse motif patterns in orthologs of cancer genes compared to controls, similar to humans and suggesting APOBEC evolution to reduce impacts on the genome rather than the converse. Simulations confirmed that APOBEC-induced heterogeneity enhances cancer evolution, shaping clonal dynamics through bimodal introduction of mutations in certain classes of genes. Our results suggest that a major consequence of the bimodal distribution of APOBEC affects greater cancer heterogeneity.

ConDoR: tumor phylogeny inference with a copy-number constrained mutation loss model

A tumor contains a diverse collection of somatic mutations that reflect its past evolutionary history and that range in scale from single nucleotide variants (SNVs) to large-scale copy-number aberrations (CNAs). However, no current single-cell DNA sequencing (scDNA-seq) technology produces accurate measurements of both SNVs and CNAs, complicating the inference of tumor phylogenies. We introduce a new evolutionary model, the constrained k-Dollo model, that uses SNVs as phylogenetic markers but constrains losses of SNVs according to clusters of cells. We derive an algorithm, ConDoR, that infers phylogenies from targeted scDNA-seq data using this model. We demonstrate the advantages of ConDoR on simulated and real scDNA-seq data.

Rapid assessment of 3-dimensional intra-tumor heterogeneity through cycling temperature capillary electrophoresis

OBJECTIVE

Tumors are heterogeneous three-dimensional masses populated by numerous cell types, including distinct sub-clones of cancerous cells. Various sub-clones within the same tumor mass may respond differently to cancer treatment, and intra-tumor heterogeneity contributes to acquired therapeutic resistance. Thus, one tissue biopsy will in most cases not be representative of the entire genetic landscape of a tumor mass. In this study, we aimed to establish an easily accessible, low cost method to address intra-tumor heterogeneity in three dimensions, for a limited number of DNA alterations.

RESULTS

This study includes analyses of the three-dimensional (3D) distribution of DNA mutations in human colon cancer and mouse mammary gland tumor tissue samples. We used laser capture microdissection for the unbiased collection of tissue in several XY-planes throughout the tumor masses. Cycling temperature capillary electrophoresis was used to determine mutant allele frequency. High-resolution distribution maps of KRAS and Trp53 mutations were generated for each XY-plane in human and mouse tumor samples, respectively. To provide a holistic interpretation of the mutation distribution, we generated interactive 3D heatmaps giving an easily interpretable understanding of the spatial distribution of the analyzed mutations. The method described herein provides an accessible way of describing intra-tumor heterogeneity for a limited number of mutations.

[Poly-G for tumor matched samples chronicles the evolution of human colorectal cancer]

Objective: To analyze poly-guanine (poly-G) genotypes and construct the phylogenetic tree of colorectal cancer (CRC) and provide an efficient and convenient method for the study of intra-tumor heterogeneity and tumor metastasis pathway. Methods: The clinicopathological information of patients with primary colorectal cancer resection with regional lymph node metastases were retrospectively collected in the Department of General Surgery, General Hospital of Tianjin Medical University from January 2017 to December 2017. The paraffin sections of the paired tumor samples were performed consecutively, and multi-region microdissection was performed after histogene staining. The phenol-chloroform extraction and ethanol precipitation scheme was used to obtain DNA, and Poly-G multiplex PCR amplification and capillary electrophoresis detection were performed. The correlation between Poly-G mutation frequency and clinicopathological parameters was analyzed. Based on the difference of Poly-G genotypes between paired samples, the distance matrix was calculated, and the phylogenetic tree was constructed to clarify the tumor metastasis pathway. Results: A total of 237 paired samples were collected from 20 patients including 134 primary lesions, 66 lymph node metastases, 37 normal tissues, and Poly-G mutation was detected in 20 patients (100%). The mutation frequency of Poly-G in low and undifferentiated patients was (74.10±23.11)%, higher than that in high and medium differentiated patients [(31.36±12.04)%, P<0.001]. In microsatellite instability patients, the mutation frequency of Poly-G was (68.19±24.80)%, which was higher than that in microsatellite stable patients [(32.40±14.90)%, P=0.003]. The Poly-G mutation frequency was not correlated with age, gender, and pathological staging (all P>0.05). Based on Poly-G genotype difference of the paired samples, the phylogenetic trees of 20 patients were constructed, showing the evolution process of the tumor, especially the subclonal origins of lymph node metastasis. Conclusion: Poly-G mutations accumulate in the occurrence and development of CRC, and can be used as genetic markers to generate reliable maps of intratumor heterogeneity in large numbers of patients with minimal time and cost expenditure.

Extensive intratumor regional epigenetic heterogeneity in clear cell renal cell carcinoma targets kidney enhancers and is associated with poor outcome

BACKGROUND

Clear cell renal cell cancer (ccRCC), the 8th leading cause of cancer-related death in the US, is challenging to treat due to high level intratumoral heterogeneity (ITH) and the paucity of druggable driver mutations. CcRCC is unusual for its high frequency of epigenetic regulator mutations, such as the SETD2 histone H3 lysine 36 trimethylase (H3K36me3), and low frequency of traditional cancer driver mutations. In this work, we examined epigenetic level ITH and defined its relationships with pathologic features, aspects of tumor biology, and SETD2 mutations.

RESULTS

A multi-region sampling approach coupled with EPIC DNA methylation arrays was conducted on a cohort of normal kidney and ccRCC. ITH was assessed using DNA methylation (5mC) and CNV-based entropy and Euclidian distances. We found elevated 5mC heterogeneity and entropy in ccRCC relative to normal kidney. Variable CpGs are highly enriched in enhancer regions. Using intra-class correlation coefficient analysis, we identified CpGs that segregate tumor regions according to clinical phenotypes related to tumor aggressiveness. SETD2 wild-type tumors overall possess greater 5mC and copy number ITH than SETD2 mutant tumor regions, suggesting SETD2 loss contributes to a distinct epigenome. Finally, coupling our regional data with TCGA, we identified a 5mC signature that links regions within a primary tumor with metastatic potential.

CONCLUSION

Taken together, our results reveal marked levels of epigenetic ITH in ccRCC that are linked to clinically relevant tumor phenotypes and could translate into novel epigenetic biomarkers.

Mutant-allele dispersion correlates with prognosis risk in patients with advanced non-small cell lung cancer

BACKGROUND

Intra-tumor heterogeneity (ITH) contributes to lung cancer progression and resistance to therapy. To evaluate ITH and determine whether it may be employed as a predictive biomarker of prognosis in patients with advanced non-small cell lung cancer (NSCLC), we used a novel algorithm called mutant-allele dispersion (MAD).

METHODS

In the study, 103 patients with advanced NSCLC were enrolled. Using a panel of 425 cancer-related genes, next-generation sequencing (NGS) was performed on tumor specimens that had been collected. From NGS data, we derived MAD values, and we next looked into their relationships with clinical variables and different mutation subtypes.

RESULTS

The median MAD among 103 NSCLC patients was 0.73. EGFR mutation, tyrosine kinase inhibitor (TKI) therapy, radiotherapy, and chemotherapy cycles were all substantially correlated with the MAD score. In patients with lung adenocarcinoma (LUAD), correlation analysis revealed that the MAD score was substantially linked with Notch pathway mutation (P = 0.021). A significant relationship between high MAD and shorter progression-free survival (PFS) was found (HR = 2.004, 95%CI 1.269-3.163, P = 0.003). In patients with advanced NSCLC, histological type (P = 0.004), SMARCA4 mutation (P = 0.038), and LRP1B mutation (P = 0.006) were all independently associated with prognosis. The disease control rate was considerably greater in the low MAD group compared to the high MAD group in 19 LUAD patients receiving immunotherapy (92.9% vs. 40%, P = 0.037). TKI-PFS was longer in 37 patients with low MAD who received first-line TKI therapy (P = 0.014).

CONCLUSION

Our findings suggested that MAD is a practical and simple algorithm for assessing ITH, and populations with high MAD values are more likely to have EGFR mutations. MAD can be used as a potential biomarker to predict not only the prognosis of NSCLC but also the efficacy of immunotherapy and TKI therapy in patients with advanced NSCLC.

Relationships of tumor differentiation and immune infiltration in gastric cancers revealed by single-cell RNA-seq analyses

Gastric cancers are highly heterogeneous malignant tumors. To reveal the relationship between differentiation status of cancer cells and tumor immune microenvironments in gastric cancer, single-cell RNA-sequencing was performed on normal mucosa tissue, differentiated gastric cancer (DGC) tissue, poorly differentiated gastric cancer (PDGC) tissue and neuroendocrine carcinoma (NEC) tissue sampled from surgically resected gastric cancer specimens. We identified the signature genes for both DGC and PDGC, and found that signature genes of PDGC strongly enriched in the epithelial-mesenchymal transition (EMT) program. Furthermore, we found that DGC tends to be immune-rich type whereas PDGC tends to be immune-poor type defined according to the density of tumor-infiltrating CD8⁺ T cells. Additionally, interferon alpha and gamma responding genes were specifically expressed in the immune-rich malignant cells compared with immune-poor malignant cells. Through analyzing the mixed adenoneuroendocrine carcinoma, we identified intermediate state malignant cells during the trans-differentiation process from DGC to NEC, which showed double-negative expressions of both DGC marker genes and NEC marker genes. Interferon-related pathways were gradually downregulated along the DGC to NEC trans-differentiation path, which was accompanied by reduced CD8⁺ cytotoxic T-cell infiltration. In summary, molecular features of both malignant cells and immune microenvironment cells of DGC, PDGC and NEC were systematically revealed, which may partially explain the strong tumor heterogeneities of gastric cancer. Especially along the DGC to NEC trans-differentiation path, immune-evasion was gradually enhanced with the decreasing activities of interferon pathway responses in malignant cells.

Multi-region sequencing with spatial information enables accurate heterogeneity estimation and risk stratification in liver cancer

BACKGROUND

Numerous studies have used multi-region sampling approaches to characterize intra-tumor heterogeneity (ITH) in hepatocellular carcinoma (HCC). However, conventional multi-region sampling strategies do not preserve the spatial details of samples, and thus, the potential influences of spatial distribution on patient-wise ITH (represents the overall heterogeneity level of the tumor in a given patient) have long been overlooked. Furthermore, gene-wise transcriptional ITH (represents the expression pattern of genes across different intra-tumor regions) in HCC is also under-explored, highlighting the need for a comprehensive investigation.

METHODS

To address the problem of spatial information loss, we propose a simple and easy-to-implement strategy called spatial localization sampling (SLS). We performed multi-region sampling and sequencing on 14 patients with HCC, collecting a total of 75 tumor samples with spatial information and molecular data. Normalized diversity score and integrated heterogeneity score (IHS) were then developed to measure patient-wise and gene-wise ITH, respectively.

RESULTS

A significant correlation between spatial and molecular heterogeneity was uncovered, implying that spatial distribution of sampling sites did influence ITH estimation in HCC. We demonstrated that the normalized diversity score had the ability to overcome sampling location bias and provide a more accurate estimation of patient-wise ITH. According to this metric, HCC tumors could be divided into two classes (low-ITH and high-ITH tumors) with significant differences in multiple biological properties. Through IHS analysis, we revealed a highly heterogenous immune microenvironment in HCC and identified some low-ITH checkpoint genes with immunotherapeutic potential. We also constructed a low-heterogeneity risk stratification (LHRS) signature based on the IHS results which could accurately predict the survival outcome of patients with HCC on a single tumor biopsy sample.

CONCLUSIONS

This study provides new insights into the complex phenotypes of HCC and may serve as a guide for future studies in this field.

Computational quantification and characterization of independently evolving cellular subpopulations within tumors is critical to inhibit anti-cancer therapy resistance

Background

Drug resistance continues to be a major limiting factor across diverse anti-cancer therapies. Contributing to the complexity of this challenge is cancer plasticity, in which one cancer subtype switches to another in response to treatment, for example, triple-negative breast cancer (TNBC) to Her2-positive breast cancer. For optimal treatment outcomes, accurate tumor diagnosis and subsequent therapeutic decisions are vital. This study assessed a novel approach to characterize treatment-induced evolutionary changes of distinct tumor cell subpopulations to identify and therapeutically exploit anticancer drug resistance.

Methods

In this research, an information-theoretic single-cell quantification strategy was developed to provide a high-resolution and individualized assessment of tumor composition for a customized treatment approach.

Briefly, this single-cell quantification strategy computes cell barcodes based on at least 100,000 tumor cells from each experiment and reveals a cell-specific signaling signature (CSSS) composed of a set of ongoing processes in each cell.

Results

Using these CSSS-based barcodes, distinct subpopulations evolving within the tumor in response to an outside influence, like anticancer treatments, were revealed and mapped. Barcodes were further applied to assign targeted drug combinations to each individual tumor to optimize tumor response to therapy.

The strategy was validated using TNBC models and patient-derived tumors known to switch phenotypes in response to radiotherapy (RT).

Conclusions

We show that a barcode-guided targeted drug cocktail significantly enhances tumor response to RT and prevents regrowth of once-resistant tumors. The strategy presented herein shows promise in preventing cancer treatment resistance, with significant applicability in clinical use.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13073-022-01121-y.

Systematic investigation of the prognostic impact of clonal status of somatic mutations across multiple cancer types

Tumors are genetically heterogeneous and many mutations are actually present in subclonal populations. The clonal status of mutations is valuable for accurate prognosis, clinical management. The aim of this study was to identify the clonal status of somatic mutations and systematically evaluate their prognostic values across various cancer types. We totally identified 227 clonal and 432 subclonal mutations contributed to prognosis and demonstrated the importance of clonal status in improving mutation-related clinical guidance. We further developed a customized multi-step approach to identify gene-specific prognostic patterns of clonal status at pan-cancer level and found some cancer-specific prognostic patterns. The 'subclonal-dependent risk' subpattern was one of the most common subpatterns, it usually accompanied by high genomic in-stability and high extent of intra-tumor heterogeneity and could be used to improve the accuracy of prognostic analysis. Our results revealed the importance of clonal status, especially subclonal mutation in clinical survival.

Cancer evolution: special focus on the immune aspect of cancer

Cancer is an evolutionary disease. Intra-tumor heterogeneity (ITH), which describes the diversity within individual tumors, sets the foundation for evolution. The fitness of tumor cells is determined by their microenvironment, which exerts intense selection pressure that generally favors cells with survival and proliferation advantages. It has been revealed that host immunity dramatically influences the evolutionary trajectory of cancer. As technologies advance, a refined map of the immune system's involvement in cancer evolution has gradually come to our knowledge. Here we specifically view cancer through the lens of evolutionary immunological biology. We will cover the neoplastic evolution under immunosurveillance, including how the host immunity shapes the tumor evolutionary trajectory and how progressive tumors modulate the host immunity to survive. A comprehensive understanding of the interplay between cancer evolution and cancer immunity provides clues to combating cancer strategically.

Parsimonious Clone Tree Integration in cancer

BACKGROUND

Every tumor is composed of heterogeneous clones, each corresponding to a distinct subpopulation of cells that accumulated different types of somatic mutations, ranging from single-nucleotide variants (SNVs) to copy-number aberrations (CNAs). As the analysis of this intra-tumor heterogeneity has important clinical applications, several computational methods have been introduced to identify clones from DNA sequencing data. However, due to technological and methodological limitations, current analyses are restricted to identifying tumor clones only based on either SNVs or CNAs, preventing a comprehensive characterization of a tumor's clonal composition.

RESULTS

To overcome these challenges, we formulate the identification of clones in terms of both SNVs and CNAs as a integration problem while accounting for uncertainty in the input SNV and CNA proportions. We thus characterize the computational complexity of this problem and we introduce PACTION (PArsimonious Clone Tree integratION), an algorithm that solves the problem using a mixed integer linear programming formulation. On simulated data, we show that tumor clones can be identified reliably, especially when further taking into account the ancestral relationships that can be inferred from the input SNVs and CNAs. On 49 tumor samples from 10 prostate cancer patients, our integration approach provides a higher resolution view of tumor evolution than previous studies.

CONCLUSION

PACTION is an accurate and fast method that reconstructs clonal architecture of cancer tumors by integrating SNV and CNA clones inferred using existing methods.

Single-Cell Sequencing Technologies in Precision Oncology

Single-cell sequencing technologies are revolutionizing cancer research and are poised to become the standard for translational cancer studies. Rapidly decreasing costs and increasing throughput and resolution are paving the way for the adoption of single-cell technologies in clinical settings for personalized medicine applications. In this chapter, we review the state of the art of single-cell DNA and RNA sequencing technologies, the computational tools to analyze the data, and their potential application to precision oncology. We also discuss the advantages of single-cell over bulk sequencing for the dissection of intra-tumor heterogeneity and the characterization of subclonal cell populations, the implementation of targeted drug repurposing approaches, and describe advanced methodologies for multi-omics data integration and to assess cell signaling at single-cell resolution.

Subclonal landscape of cancer drives resistance to immune therapy

Tumor mutation burden (TMB) is often used as a biomarker for immunogenicity and prerequisite for immune checkpoint inhibitor (ICI) therapy. However, it is becoming increasingly evident that not all tumors with high TMB respond to ICIs as expected. It has been shown that the ability of T-cells to infiltrate the tumor microenvironment and elicit a specific immune response is dependent not only on the TMB, but also on intra-tumor heterogeneity and the fraction of low-frequency subclonal mutations that make up the tumor. High intra-tumor heterogeneity leads to inefficient recognition of tumor neoantigens by T-cells due to their diluted frequency and spatial heterogeneity. Clinical studies have shown that tumors with a high degree of intra-tumor heterogeneity respond poorly to ICI therapy, and previous cytotoxic treatment may increase the intra-tumor heterogeneity and render second-line ICI therapy less effective. This paper reviews the role of ICI therapy when following chemotherapy or radiation to determine if they may be better suited as first-line therapy in patients with high TMB, low intra-tumor heterogeneity, and high PD-1, PD-L1, or CTLA-4 expression.

Experimental challenges to modeling prostate cancer heterogeneity

Tumor heterogeneity plays a key role in prostate cancer prognosis, therapy selection, relapse, and acquisition of treatment resistance. Prostate cancer presents a heterogeneous diversity at inter- and intra-tumor and inter-patient levels which are influenced by multiple intrinsic and/or extrinsic factors. Recent studies have started to characterize the complexity of prostate tumors and these different tiers of heterogeneity. In this review, we discuss the most common factors that contribute to tumoral diversity. Moreover, we focus on the description of the in vitro and in vivo approaches, as well as high-throughput technologies, that help to model intra-tumoral diversity. Further understanding tumor heterogeneities and the challenges they present will guide enhanced patient risk stratification, aid the design of more precise therapies, and ultimately help beat this chameleon-like disease.

Single-cell DNA and RNA sequencing reveals the dynamics of intra-tumor heterogeneity in a colorectal cancer model

BACKGROUND

Intra-tumor heterogeneity (ITH) encompasses cellular differences in tumors and is related to clinical outcomes such as drug resistance. However, little is known about the dynamics of ITH, owing to the lack of time-series analysis at the single-cell level. Mouse models that recapitulate cancer development are useful for controlled serial time sampling.

RESULTS

We performed single-cell exome and transcriptome sequencing of 200 cells to investigate how ITH is generated in a mouse colorectal cancer model. In the model, a single normal intestinal cell is grown into organoids that mimic the intestinal crypt structure. Upon RNAi-mediated downregulation of a tumor suppressor gene APC, the transduced organoids were serially transplanted into mice to allow exposure to in vivo microenvironments, which play relevant roles in cancer development. The ITH of the transcriptome increased after the transplantation, while that of the exome decreased. Mutations generated during organoid culture did not greatly change at the bulk-cell level upon the transplantation. The RNA ITH increase was due to the emergence of new transcriptional subpopulations. In contrast to the initial cells expressing mesenchymal-marker genes, new subpopulations repressed these genes after the transplantation. Analyses of colorectal cancer data from The Cancer Genome Atlas revealed a high proportion of metastatic cases in human subjects with expression patterns similar to the new cell subpopulations in mouse. These results suggest that the birth of transcriptional subpopulations may be a key for adaptation to drastic micro-environmental changes when cancer cells have sufficient genetic alterations at later tumor stages.

CONCLUSIONS

This study revealed an evolutionary dynamics of single-cell RNA and DNA heterogeneity in tumor progression, giving insights into the mesenchymal-epithelial transformation of tumor cells at metastasis in colorectal cancer.

Clinical Implications of Inter- and Intra-Tumor Heterogeneity of Immune Cell Markers in Lung Cancer

BACKGROUND

Immune cell transcriptome signatures have been widely used to study the lung tumor microenvironment (TME). However, it is unclear to what extent the immune cell composition in the lung TME varies across histological and molecular subtypes (inter-tumor heterogeneity or Inter-TH) and within tumors (intratumor heterogeneity or ITH), and whether ITH has any prognostic relevance.

METHODS

Using RNA sequencing in 269 tumor samples from 160 lung cancer patients we quantified the Inter-TH of immune gene expression and immune cell abundance and evaluated the association of median immune cell abundance with clinical/pathological features and overall survival. In 39 tumors with 132 multi-region samples, we also analyzed the ITH of immune cell abundance in relation to overall survival using a variance-weighted multivariate Cox model not biased by the number of samples per tumor.

RESULTS

Substantial Inter-TH of 14 immune cell types was observed even within the same histological and molecular subtypes, but early-stage tumors had higher lymphocyte infiltration across all tumor types. In multi-region samples, an unbiased estimate of low ITH of overall immune cell composition (hazard ratio [HR] = 0.40, 95% confidence interval [CI] = 0.21 to 0.78; P = .007), dendritic cells (HR = 0.24, 95% CI = 0.096 to 0.58; P = .002) and macrophages (HR = 0.50, 95% CI = 0.30 to 0.84; P = .009) was strongly associated with poor survival. The ITH of three markers, including CD163 and CD68 (macrophages) and CCL13 (dendritic cells), was enough to characterize the ITH of the corresponding immune cell abundances and its association with overall survival.

CONCLUSION

This study suggests that lack of immune cell diversity may facilitate tumor evasion and progression. ITH inferred from CCL13, CD163 and CD68 could be used as a prognostic tool in clinical practice.

Distinguishing linear and branched evolution given single-cell DNA sequencing data of tumors

Background

Cancer arises from an evolutionary process where somatic mutations give rise to clonal expansions. Reconstructing this evolutionary process is useful for treatment decision-making as well as understanding evolutionary patterns across patients and cancer types. In particular, classifying a tumor’s evolutionary process as either linear or branched and understanding what cancer types and which patients have each of these trajectories could provide useful insights for both clinicians and researchers. While comprehensive cancer phylogeny inference from single-cell DNA sequencing data is challenging due to limitations with current sequencing technology and the complexity of the resulting problem, current data might provide sufficient signal to accurately classify a tumor’s evolutionary history as either linear or branched.

Results

We introduce the Linear Perfect Phylogeny Flipping (LPPF) problem as a means of testing two alternative hypotheses for the pattern of evolution, which we prove to be NP-hard. We develop Phyolin, which uses constraint programming to solve the LPPF problem. Through both in silico experiments and real data application, we demonstrate the performance of our method, outperforming a competing machine learning approach.

Conclusion

Phyolin is an accurate, easy to use and fast method for classifying an evolutionary trajectory as linear or branched given a tumor’s single-cell DNA sequencing data.

PhyliCS: a Python library to explore scCNA data and quantify spatial tumor heterogeneity

BACKGROUND

Tumors are composed by a number of cancer cell subpopulations (subclones), characterized by a distinguishable set of mutations. This phenomenon, known as intra-tumor heterogeneity (ITH), may be studied using Copy Number Aberrations (CNAs). Nowadays ITH can be assessed at the highest possible resolution using single-cell DNA (scDNA) sequencing technology. Additionally, single-cell CNA (scCNA) profiles from multiple samples of the same tumor can in principle be exploited to study the spatial distribution of subclones within a tumor mass. However, since the technology required to generate large scDNA sequencing datasets is relatively recent, dedicated analytical approaches are still lacking.

RESULTS

We present PhyliCS, the first tool which exploits scCNA data from multiple samples from the same tumor to estimate whether the different clones of a tumor are well mixed or spatially separated. Starting from the CNA data produced with third party instruments, it computes a score, the Spatial Heterogeneity score, aimed at distinguishing spatially intermixed cell populations from spatially segregated ones. Additionally, it provides functionalities to facilitate scDNA analysis, such as feature selection and dimensionality reduction methods, visualization tools and a flexible clustering module.

CONCLUSIONS

PhyliCS represents a valuable instrument to explore the extent of spatial heterogeneity in multi-regional tumour sampling, exploiting the potential of scCNA data.

Establishment and characterization of 38 novel patient-derived primary cancer cell lines using multi-region sampling revealing intra-tumor heterogeneity of gallbladder carcinoma

Gallbladder carcinoma (GBC) is a lethal biliary tract malignant neoplasm. Patient-derived primary cancer cell lines (PDPCs) are appropriate models to explore biological characteristics and potential therapeutics; however, there is a lack of PDPCs in GBC. In this study, we aimed to establish and characterize the GBC PDPCs, and further investigated the intra-tumor heterogeneity (ITH). Multi-region sampling (3–9 regions) of the operable tumor tissue samples was used to establish PDPCs. Short tandem repeat genotyping for cell authentication and karyotyping was performed, followed by whole-exome sequencing and RNA sequencing to assess the ITH at the genetic and transcriptional levels, respectively. Thirty-eight PDPCs were successfully established from seven GBC patients and characterized. ITH was observed with a median of 38.3% mutations being heterogeneous (range, 26.6–59.4%) across all patients. Similar with other tumor types, TP53 mutations were always truncal. In addition, there were three genes, KMT2C, CDKN2A, and ARID1A, with truncal mutations in at least two patients. A median of 370 differentially expressed genes (DEGs) was identified per patient. Distinct expression patterns were observed between major histocompatibility complex (MHC) class I and II genes. We found the expression of MHC class II genes in the PDPC samples was closely regulated by CIITA, while that of MHC class I genes were not correlated with CIITA expression. The PDPCs established from GBC patients can serve as novel in vitro models to identify the ITH, which may pave a crucial molecular foundation for enhanced understanding of tumorigenesis and progression.

Single-cell RNA-seq reveals dynamic change in tumor microenvironment during pancreatic ductal adenocarcinoma malignant progression

Background

Pancreatic ductal adenocarcinoma (PDAC) is most aggressive among all gastrointestinal tumors. The complex intra-tumor heterogeneity and special tumor microenvironment in PDAC bring great challenges for developing effective treatment strategies. We aimed to delineate dynamic changes of tumor microenvironment components during PDAC malignant progression utilizing single-cell RNA sequencing.

Methods

A total of 11 samples (4 PDAC I, 4 PDAC II, 3 PDAC III) were used to construct expression matrix. After identifying distinct cell clusters, subcluster analysis for each cluster was performed. New cancer associated fibroblasts (CAFs) subset was validated by weighted gene co-expression network analysis, RNA in situ hybridization and immunofluorescence.

Findings

We found that ductal cells were not dominant component while tumor infiltrating immune cells and pancreatic stellate cells gradually accumulated during tumor development. We defined several new Treg and exhausted T cell signature genes, including DUSP4, FANK1 and LAIR2. The analysis of TCGA datasets showed that patients with high expression of DUSP4 had significantly worse prognosis. In addition, we identified a new CAFs subset (complement-secreting CAFs, csCAFs), which specifically expresses complement system components, and constructed csCAFs-related module by weighted gene co-expression network analysis. The csCAFs were located in the tissue stroma adjacent to malignant ductal cells only in early PDAC.

Interpretation

We systematically explored PDAC heterogeneity and identified csCAFs as a new CAFs subset special to PDAC, which may be valuable for understanding the crosstalk inside tumor.

Funding

This study was supported by The Natural Science Foundation of China (NO.81572339, 81672353, 81871954) and the Youth Clinical Research Project of Peking University First Hospital (2018CR28).

Methods for copy number aberration detection from single-cell DNA-sequencing data

Copy number aberrations (CNAs), which are pathogenic copy number variations (CNVs), play an important role in the initiation and progression of cancer. Single-cell DNA-sequencing (scDNAseq) technologies produce data that is ideal for inferring CNAs. In this review, we review eight methods that have been developed for detecting CNAs in scDNAseq data, and categorize them according to the steps of a seven-step pipeline that they employ. Furthermore, we review models and methods for evolutionary analyses of CNAs from scDNAseq data and highlight advances and future research directions for computational methods for CNA detection from scDNAseq data.

SimuSCoP: reliably simulate Illumina sequencing data based on position and context dependent profiles

BACKGROUND

A number of simulators have been developed for emulating next-generation sequencing data by incorporating known errors such as base substitutions and indels. However, their practicality may be degraded by functional and runtime limitations. Particularly, the positional and genomic contextual information is not effectively utilized for reliably characterizing base substitution patterns, as well as the positional and contextual difference of Phred quality scores is not fully investigated. Thus, a more effective and efficient bioinformatics tool is sorely required.

RESULTS

Here, we introduce a novel tool, SimuSCoP, to reliably emulate complex DNA sequencing data. The base substitution patterns and the statistical behavior of quality scores in Illumina sequencing data are fully explored and integrated into the simulation model for reliably emulating datasets for different applications. In addition, an integrated and easy-to-use pipeline is employed in SimuSCoP to facilitate end-to-end simulation of complex samples, and high runtime efficiency is achieved by implementing the tool to run in multithreading with low memory consumption. These features enable SimuSCoP to gets substantial improvements in reliability, functionality, practicality and runtime efficiency. The tool is comprehensively evaluated in multiple aspects including consistency of profiles, simulation of genomic variations and complex tumor samples, and the results demonstrate the advantages of SimuSCoP over existing tools.

CONCLUSIONS

SimuSCoP, a new bioinformatics tool is developed to learn informative profiles from real sequencing data and reliably mimic complex data by introducing various genomic variations. We believe that the presented work will catalyse new development of downstream bioinformatics methods for analyzing sequencing data.

Implications of non-uniqueness in phylogenetic deconvolution of bulk DNA samples of tumors

Background

Tumors exhibit extensive intra-tumor heterogeneity, the presence of groups of cellular populations with distinct sets of somatic mutations. This heterogeneity is the result of an evolutionary process, described by a phylogenetic tree. In addition to enabling clinicians to devise patient-specific treatment plans, phylogenetic trees of tumors enable researchers to decipher the mechanisms of tumorigenesis and metastasis. However, the problem of reconstructing a phylogenetic tree T given bulk sequencing data from a tumor is more complicated than the classic phylogeny inference problem. Rather than observing the leaves of T directly, we are given mutation frequencies that are the result of mixtures of the leaves of T. The majority of current tumor phylogeny inference methods employ the perfect phylogeny evolutionary model. The underlying Perfect Phylogeny Mixture (PPM) combinatorial problem typically has multiple solutions.

Results

We prove that determining the exact number of solutions to the PPM problem is #P-complete and hard to approximate within a constant factor. Moreover, we show that sampling solutions uniformly at random is hard as well. On the positive side, we provide a polynomial-time computable upper bound on the number of solutions and introduce a simple rejection-sampling based scheme that works well for small instances. Using simulated and real data, we identify factors that contribute to and counteract non-uniqueness of solutions. In addition, we study the sampling performance of current methods, identifying significant biases.

Conclusions

Awareness of non-uniqueness of solutions to the PPM problem is key to drawing accurate conclusions in downstream analyses based on tumor phylogenies. This work provides the theoretical foundations for non-uniqueness of solutions in tumor phylogeny inference from bulk DNA samples.

A multi-labeled tree dissimilarity measure for comparing "clonal trees" of tumor progression

We introduce a new dissimilarity measure between a pair of "clonal trees", each representing the progression and mutational heterogeneity of a tumor sample, constructed by the use of single cell or bulk high throughput sequencing data. In a clonal tree, each vertex represents a specific tumor clone, and is labeled with one or more mutations in a way that each mutation is assigned to the oldest clone that harbors it. Given two clonal trees, our multi-labeled tree dissimilarity (MLTD) measure is defined as the minimum number of mutation/label deletions, (empty) leaf deletions, and vertex (clonal) expansions, applied in any order, to convert each of the two trees to the maximum common tree. We show that the MLTD measure can be computed efficiently in polynomial time and it captures the similarity between trees of different clonal granularity well.

Associating somatic mutations to clinical outcomes: a pan-cancer study of survival time

We developed subclone multiplicity allocation and somatic heterogeneity (SMASH), a new statistical method for intra-tumor heterogeneity (ITH) inference. SMASH is tailored to the purpose of large-scale association studies with one tumor sample per patient. In a pan-cancer study of 14 cancer types, we studied the associations between survival time and ITH quantified by SMASH, together with other features of somatic mutations. Our results show that ITH is associated with survival time in several cancer types and its effect can be modified by other covariates, such as mutation burden. SMASH is available at https://github.com/Sun-lab/SMASH .

Intra-tumor molecular heterogeneity in breast cancer: definitions of measures and association with distant recurrence-free survival

PURPOSE

The purpose of the study was to define quantitative measures of intra-tumor heterogeneity in breast cancer based on histopathology data gathered from multiple samples on individual patients and determine their association with distant recurrence-free survival (DRFS).

METHODS

We collected data from 971 invasive breast cancers, from 1st January 2000 to 23rd March 2014, that underwent repeat tumor sampling at our institution. We defined and calculated 31 measures of intra-tumor heterogeneity including ER, PR, and HER2 immunohistochemistry (IHC), proliferation, EGFR IHC, grade, and histology. For each heterogeneity measure, Cox proportional hazards models were used to determine whether patients with heterogeneous disease had different distant recurrence-free survival (DRFS) than those with homogeneous disease.

RESULTS

The presence of heterogeneity in ER percentage staining was prognostic of reduced DRFS with a hazard ratio of 4.26 (95% CI 2.22-8.18, p < 0.00002). It remained significant after controlling for the ER status itself (p < 0.00062) and for patients that had chemotherapy (p < 0.00032). Most of the heterogeneity measures did not show any association with DRFS despite the considerable sample size.

CONCLUSIONS

Intra-tumor heterogeneity of ER receptor status may be a predictor of patient DRFS. Histopathologic data from multiple tissue samples may offer a view of tumor heterogeneity and assess recurrence risk.

Cancer cell plasticity: Impact on tumor progression and therapy response

Most tumors exhibit intra-tumor heterogeneity, which is associated with disease progression and an impaired response to therapy. Cancer cell plasticity has been proposed as being an important mechanism that, along with genetic and epigenetic alterations, promotes cancer cell diversity and contributes to intra-tumor heterogeneity. Plasticity endows cancer cells with the capacity to shift dynamically between a differentiated state, with limited tumorigenic potential, and an undifferentiated or cancer stem-like cell (CSC) state, which is responsible for long-term tumor growth. In addition, it confers the ability to transit into distinct CSC states with different competence to invade, disseminate and seed metastasis. Cancer cell plasticity has been linked to the epithelial-to-mesenchymal transition program and relies not only on cell-autonomous mechanisms, but also on signals provided by the tumor microenvironment and/or induced in response to therapy. We provide an overview of the dynamic transition for cancer cell states, the mechanisms governing cell plasticity and their impact on tumor progression, metastasis and therapy response. Understanding the mechanisms involved in cancer cell plasticity will provide insights for establishing new therapeutic interventions.

SiFit: inferring tumor trees from single-cell sequencing data under finite-sites models

Single-cell sequencing enables the inference of tumor phylogenies that provide insights on intra-tumor heterogeneity and evolutionary trajectories. Recently introduced methods perform this task under the infinite-sites assumption, violations of which, due to chromosomal deletions and loss of heterozygosity, necessitate the development of inference methods that utilize finite-sites models. We propose a statistical inference method for tumor phylogenies from noisy single-cell sequencing data under a finite-sites model. The performance of our method on synthetic and experimental data sets from two colorectal cancer patients to trace evolutionary lineages in primary and metastatic tumors suggests that employing a finite-sites model leads to improved inference of tumor phylogenies.

The Emerging Role of "Liquid Biopsies," Circulating Tumor Cells, and Circulating Cell-Free Tumor DNA in Lung Cancer Diagnosis and Identification of Resistance Mutations. Curr Oncol Rep. 2017;19:1. [PMID: 28110461 DOI: 10.1007/s11912-017-0564-y]

Therapeutic advances in the treatment of lung cancer are in part due to a more complete understanding of its genomic portrait. The serial monitoring of tumor genotypes, which are instable and prone to changes under selective pressure, is becoming increasingly needed. Although tumor biopsies remain the reference standard for the diagnosis and genotyping of lung cancer, they are invasive and not always feasible. The "liquid biopsies" have the potential to overcome many of these hurdles, allowing a rapid and accurate identification of de novo and resistant genetic alterations and a real-time monitoring of treatment responses. In this review, we provide insights into new liquid diagnostic platforms and discuss the role of circulating tumor cells and circulating tumor DNA in the diagnosis and identification of resistance mutations in lung cancer.

Real-time monitoring of microdistribution of antibody-photon absorber conjugates during photoimmunotherapy in vivo

Photoimmunotherapy (PIT) is an emerging low side effect cancer therapy based on a monoclonal antibody (mAb) conjugated with a near-infrared (NIR) phthalocyanine dye IRDye 700DX. IR700 is fluorescent, can be used as an imaging agent, and also is phototoxic. It induces rapid cell death after exposure to NIR light. PIT induces highly selective cancer cell death, while leaving most of tumor blood vessels unharmed, leading to an effect called super-enhanced permeability and retention (SUPR). SUPR significantly improves the effectiveness of the anticancer drug. Currently, the therapeutic effects of PIT are monitored using the IR700 fluorescent signal based on macroscopic fluorescence reflectance imagery. This technique, however, lacks the resolution and depth information to reveal the intratumor heterogeneity of mAb-IR700 distribution. We applied a minimally invasive two-channel fluorescence fiber imaging system by combining the traditional fluorescence imaging microscope with two imaging fiber bundles (~0.85mm). This method monitored mAb-IR700 distribution and therapeutic effects during PIT at different intratumor locations (e.g., tumor surface vs. deep tumor) in situ and in real time simultaneously. This enabled evaluation of the therapeutic effects in vivo and treatment regimens. The average IR700 fluorescence intensity recovery after PIT to the tumor surface is 91.50%, while it is 100.63% in deep tumors. To verify the results, two-photon microscopy combined with a microprism was also used to record the mAb-IR700 distribution and fluorescence intensity of green fluorescent protein (GFP) at different tumor depths during PIT. After PIT treatment, there was significantly higher IR700 fluorescence recovery in deep tumor than in the tumor surface. This phenomenon can be explained by increased vascular permeability immediately after NIR-PIT. Fluorescence intensity of GFP at the tumor surface decreased significantly more compared to that of deep tumor and in controls (no PIT).

CLImAT-HET: detecting subclonal copy number alterations and loss of heterozygosity in heterogeneous tumor samples from whole-genome sequencing data

Background

Copy number alterations (CNA) and loss of heterozygosity (LOH) represent a large proportion of genetic structural variations of cancer genomes. These aberrations are continuously accumulated during the procedure of clonal evolution and patterned by phylogenetic branching. This invariably results in the emergence of multiple cell populations with distinct complement of mutational landscapes in tumor sample. With the advent of next-generation sequencing technology, inference of subclonal populations has become one of the focused interests in cancer-associated studies, and is usually based on the assessment of combinations of somatic single-nucleotide variations (SNV), CNA and LOH. However, cancer samples often have several inherent issues, such as contamination of normal stroma, tumor aneuploidy and intra-tumor heterogeneity. Addressing these critical issues is imperative for accurate profiling of clonal architecture.

Methods

We present CLImAT-HET, a computational method designed for capturing clonal diversity in the CNA/LOH dimensions by taking into account the intra-tumor heterogeneity issue, in the case where a reference or matched normal sample is absent. The algorithm quantitatively represents the clonal identification problem using a factorial hidden Markov model, and takes an integrated analysis of read counts and allele frequency data. It is able to infer subclonal CNA and LOH events as well as the fraction of cells harboring each event.

Results

The results on simulated datasets indicate that CLImAT-HET has high power to identify CNA/LOH segments, it achieves an average accuracy of 0.87. It can also accurately infer proportion of each clonal population with an overall Pearson correlation coefficient of 0.99 and a mean absolute error of 0.02. CLImAT-HET shows significant advantages when compared with other existing methods. Application of CLImAT-HET to 5 primary triple negative breast cancer samples demonstrates its ability to capture clonal diversity in the CAN/LOH dimensions. It detects two clonal populations in one sample, and three clonal populations in one other sample.

Conclusions

CLImAT-HET, a novel algorithm is introduced to infer CNA/LOH segments from heterogeneous tumor samples. We demonstrate CLImAT-HET’s ability to accurately recover clonal compositions using tumor WGS data without a match normal sample.

Electronic supplementary material

The online version of this article (doi:10.1186/s12920-017-0255-4) contains supplementary material, which is available to authorized users.

Primary breast cancer cell culture yields intra-tumor heterogeneous subpopulations expressing exclusive patterns of receptor tyrosine kinases

Background

It has become evident that intra-tumor heterogeneity of breast cancer impact on several biological processes such as proliferation, migration, cell death and also might contribute to chemotherapy resistance. The expression of Receptor Tyrosine Kinases (RTKs) has not been analyzed in the context of intra-tumor heterogeneity in a primary breast cancer cell culture. Several subpopulations were isolated from the MBCDF (M serial-breast cancer ductal F line) primary breast cancer cells and were successfully maintained in culture and divided in two groups according to their morphology and RTKs expression pattern, and correlated with biological processes like proliferation, migration, anchorage-independent cell growth, and resistance to cytotoxic chemotherapy drugs and tyrosine kinase inhibitors (TKIs).

Methods

Subpopulations were isolated from MBCDF primary breast cancer cell culture by limiting dilution. RTKs and hormone receptors were examined by Western blot. Proliferation was measure by 3-[4,5-dimethylthiazol-2-yl]-2,5-diphenyl-tetrazolium bromide (MTT assay). Cell viability was evaluated by Crystal Violet. Migration was assessed using Boyden chambers. Anchorage-independent cell growth was evaluated by colony formation in soft agar.

Results

Several subpopulations were isolated from the MBCDF breast cancer cells that were divided into two groups according to their morphology. Analysis of RTKs expression pattern showed that HER1, HER3, c-Met and VEGFR2 were expressed exclusively in cells from group 1, but not in cells from group 2. PDGFR was expressed only in cells from group 2, but not in cells from group 1. HER2, HER4, c-Kit, IGF1-R were expressed in all subpopulations. Biological processes correlated with the RTKs expression pattern. Group 2 subpopulations present the highest rate of cell proliferation, migration and anchorage-independent cell growth. Analysis of susceptibility to chemotherapy drugs and TKIs showed that only Paclitaxel and Imatinib behaved differently between groups. Group 1-cells were resistant to both Paclitaxel and Imatinib.

Conclusions

We demonstrated that subpopulations from MBCDF primary cell culture could be divided into two groups according to their morphology and a RTKs excluding-expression pattern. The differences observed in RTKs expression correlate with the biological characteristics and chemoresistance of each group. These results suggest that intra-tumor heterogeneity contributes to generate groups of subpopulations with a more aggressive phenotype within the tumor.

Electronic supplementary material

The online version of this article (doi:10.1186/s12885-016-2769-0) contains supplementary material, which is available to authorized users.

CloneCNA: detecting subclonal somatic copy number alterations in heterogeneous tumor samples from whole-exome sequencing data

Background

Copy number alteration is a main genetic structural variation that plays an important role in tumor initialization and progression. Accurate detection of copy number alterations is necessary for discovering cancer-causing genes. Whole-exome sequencing has become a widely used technology in the last decade for detecting various types of genomic aberrations in cancer genomes. However, there are several major issues encountered in these detection problems, including normal cell contamination, tumor aneuploidy, and intra-tumor heterogeneity. Especially, deciphering the intra-tumor heterogeneity is imperative for identifying clonal and subclonal copy number alterations.

Results

We introduce CloneCNA, a novel bioinformatics tool for efficiently addressing these issues and automatically detecting clonal and subclonal somatic copy number alterations from heterogeneous tumor samples. CloneCNA fully explores the log ratio of read counts between paired tumor-normal samples and tumor B allele frequency of germline heterozygous SNP positions, further employs efficient statistical models to quantitatively represent copy number status of tumor sample containing multiple clones. We examine CloneCNA on simulated heterogeneous and real tumor samples, and the results demonstrate that CloneCNA has higher power to detect copy number alterations than existing methods.

Conclusions

CloneCNA, a novel algorithm is developed to efficiently and accurately identify somatic copy number alterations from heterogeneous tumor samples. We demonstrate the statistical framework of CloneCNA represents a remarkable advance for tumor whole-exome sequencing data. We expect that CloneCNA will promote cancer-focused studies for investigating the role of clonal evolution and elucidating critical events benefiting tumor tumourigenesis and progression.

Electronic supplementary material

The online version of this article (doi:10.1186/s12859-016-1174-7) contains supplementary material, which is available to authorized users.

Liquid biopsies for solid tumors: Understanding tumor heterogeneity and real time monitoring of early resistance to targeted therapies

In the era of personalized medicine detection of the molecular drivers of tumors and of specific DNA mutations predicting response or resistance to targeted agents has become routine practice in clinical oncology. The tumor biopsy depicts only a single timeframe from a single site, and might be inadequate to characterize a tumor because of intratumoral and intermetastatic heterogeneity. Circulating tumor DNA offers a "real time" tool for serially monitoring tumor genomes in a non-invasive manner providing accessible genetic biomarkers for cancer diagnosis, prognosis, and response to therapy. The liquid biopsy can be used for a variety of clinical and investigational applications. Future development will have to provide a cost effective analysis mainly identifying the genes known to be recurrently mutated in each tumor. Therefore, developing standardized methodologies for DNA analyses and validation in large prospective clinical studies is mandatory to implement the 'liquid biopsy' approach in the clinical management of cancer patients. In our review, we will focus on the clinical applications of liquid biopsies and on the recent findings in this field.

Mediated coalescence: a possible mechanism for tumor cellular heterogeneity

Recently, we demonstrated that tumorigenic cell lines and fresh tumor cells seeded in a 3D Matrigel model, first grow as clonal islands (primary aggregates), then coalesce through the formation and contraction of cellular cables. Non-tumorigenic cell lines and cells from normal tissue form clonal islands, but do not form cables or coalesce. Here we show that as little as 5% tumorigenic cells will actively mediate coalescence between primary aggregates of majority non-tumorigenic or non-cancerous cells, by forming cellular cables between them. We suggest that this newly discovered, specialized characteristic of tumorigenic cells may explain, at least in part, why tumors contain primarily non-tumorigenic cells.

A novel framework for analyzing somatic copy number aberrations and tumor subclones for paired heterogeneous tumor samples

Application of the Next generation sequencing (NGS) technology has demonstrated that most tumor samples exhibit intra-tumor heterogeneity. Here we proposed SAPPH (Somatic Aberrations Prediction for Paired Heterogeneous tumor samples), as a new method for estimating tumor somatic copy number aberrations as well as inferring tumor subclone proportions from heterogeneous tumor sequencing data. This method is based on CBS and local proportion clustering strategy. When SAPPH is applied on simulated tumor samples, the agreement between the results analyzed by SAPPH and the sequencing signals suggests that SAPPH can find the solution to best fit the signal distributions. We benchmark the performance of SAPPH and show that it outperforms existing method in estimating tumor copy number aberrations.

Therapeutic implications of intratumor heterogeneity for TP53 mutational status in Burkitt lymphoma

Therapeutic implications of intra-tumor heterogeneity are still undefined. In this study we report a genetic and functional analysis aimed at defining the mechanisms of chemoresistance in a 43-year old woman affected by stage IVB Burkitt lymphoma with bulky abdominal masses and peritoneal effusion. The patient, despite a transient initial response to chemotherapy with reduction of the bulky masses, rapidly progressed and died of her disease. Targeted TP53 sequencing found that the bulky mass was wild-type whereas peritoneal fluid cells harbored a R282W mutation. Functional studies on TP53 mutant cells demonstrated an impaired p53-mediated response, resistance to ex vivo doxorubicin administration, overexpression of DNA damage response (DDR) activation markers and high sensitivity to pharmacologic DDR inhibition. These findings suggest that intra-tumor heterogeneity for TP53 mutational status may occur in MYC-driven cancers, and that DDR inhibitors could be effective in targeting hidden TP53 mutant clones in tumors characterized by genomic instability and prone to intra-tumor heterogeneity.