Tumor Heterogeneity, Single-Cell Sequencing, and Drug Resistance

Abstract Malignant tumor is a largely harmful disease worldwide. The cure rate of malignant tumors increases with the continuous discovery of anti-tumor drugs and the optimisation of chemotherapy options. However, drug resistance of tumor cells remains a massive obstacle in the treatment of anti-tumor drugs. The heterogeneity of malignant tumors makes studying it further difficult for us. In recent years, using single-cell sequencing technology to study and analyse circulating tumor cells can avoid the interference of tumor heterogeneity and provide a new perspective for us to understand tumor drug resistance.

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Tumor heterogeneity of acute myeloid leukemia: insights from single-cell sequencing

Blood Science ◽

10.1097/bs9.0000000000000015 ◽

2019 ◽

Vol 1 (1) ◽

pp. 73-76 ◽

Cited By ~ 1

Author(s):

AiLi Chen ◽

ShaoYan Hu ◽

Qian-Fei Wang

Keyword(s):

Acute Myeloid Leukemia ◽

Single Cell ◽

Myeloid Leukemia ◽

Tumor Heterogeneity ◽

Single Cell Sequencing ◽

Acute Myeloid

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Longitudinal cancer evolution from single cells

10.1101/2020.01.14.906453 ◽

2020 ◽

Cited By ~ 4

Author(s):

Daniele Ramazzotti ◽

Fabrizio Angaroni ◽

Davide Maspero ◽

Gianluca Ascolani ◽

Isabella Castiglioni ◽

...

Keyword(s):

Single Cell ◽

Tumor Heterogeneity ◽

Single Cells ◽

Clonal Evolution ◽

Multiple Time ◽

Cancer Evolution ◽

Weighted Likelihood ◽

Time Points ◽

Single Cell Sequencing ◽

The Impact

ABSTRACTThe rise of longitudinal single-cell sequencing experiments on patient-derived cell cultures, xenografts and organoids is opening new opportunities to track cancer evolution in single tumors and to investigate intra-tumor heterogeneity. This is particularly relevant when assessing the efficacy of therapies over time on the clonal composition of a tumor and in the identification of resistant subclones.We here introduce LACE (Longitudinal Analysis of Cancer Evolution), the first algorithmic framework that processes single-cell somatic mutation profiles from cancer samples collected at different time points and in distinct experimental settings, to produce longitudinal models of cancer evolution. Our approach solves a Boolean matrix factorization problem with phylogenetic constraints, by maximizing a weighted likelihood function computed on multiple time points, and we show with simulations that it outperforms state-of-the-art methods for both bulk and single-cell sequencing data.Remarkably, as the results are robust with respect to high levels of data-specific errors, LACE can be employed to process single-cell mutational profiles as generated by calling variants from the increasingly available scRNA-seq data, thus obviating the need of relying on rarer and more expensive genome sequencing experiments. This also allows to investigate the relation between genomic clonal evolution and phenotype at the single-cell level.To illustrate the capabilities of LACE, we show its application to a longitudinal scRNA-seq dataset of patient-derived xenografts of BRAFV600E/K mutant melanomas, in which we characterize the impact of concurrent BRAF/MEK-inhibition on clonal evolution, also by showing that distinct genetic clones reveal different sensitivity to the therapy. Furthermore, the analysis of a longitudinal dataset of breast cancer PDXs from targeted scDNA-sequencing experiments delivers a high-resolution characterization of intra-tumor heterogeneity, also allowing the detection of a late de novo subclone.

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Deciphering Tumor Heterogeneity in Hepatocellular Carcinoma (HCC)—Multi-Omic and Singulomic Approaches

Seminars in Liver Disease ◽

10.1055/s-0040-1722261 ◽

2021 ◽

Author(s):

Renumathy Dhanasekaran

Keyword(s):

Single Cell ◽

Tumor Heterogeneity ◽

Immune Cell ◽

Single Cell Analysis ◽

Sampling Strategies ◽

Cell Analysis ◽

Single Cell Sequencing ◽

Sequencing Technologies ◽

Hepatocellular Carcinomas ◽

Generation Sequencing

AbstractTumor heterogeneity, a key hallmark of hepatocellular carcinomas (HCCs), poses a significant challenge to developing effective therapies or predicting clinical outcomes in HCC. Recent advances in next-generation sequencing-based multi-omic and single cell analysis technologies have enabled us to develop high-resolution atlases of tumors and pull back the curtain on tumor heterogeneity. By combining multiregion targeting sampling strategies with deep sequencing of the genome, transcriptome, epigenome, and proteome, several studies have revealed novel mechanistic insights into tumor initiation and progression in HCC. Advances in multiparametric immune cell profiling have facilitated a deeper dive into the biological complexity of HCC, which is crucial in this era of immunotherapy. Moreover, studies using liquid biopsy have demonstrated their potential to circumvent the need for tissue sampling to investigate heterogeneity. In this review, we discuss how multi-omic and single-cell sequencing technologies have advanced our understanding of tumor heterogeneity in HCC.

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Conifer: Clonal Tree Inference for Tumor Heterogeneity With Single-cell and Bulk Sequencing Data

10.21203/rs.3.rs-263502/v1 ◽

2021 ◽

Author(s):

Leila Baghaarabani ◽

Sama Goliaei ◽

Mohammad-Hadi Foroughmand-Araabi ◽

Seyed Peyman Shariatpanahi ◽

Bahram Goliaei

Keyword(s):

Single Cell ◽

Allele Frequency ◽

Tumor Heterogeneity ◽

Variant Allele ◽

Evolutionary Relationships ◽

Sequencing Data ◽

Variant Allele Frequency ◽

Similar Frequency ◽

Single Cell Sequencing ◽

Tree Inference

Abstract Background: An important and effective step in cancer treatment is understanding the clonal evolution of cancer tumors. Clones are cell populations with different genotypes, resulting from the differences in the somatic mutations that occur and accumulate during cancer development. An appropriate approach for better understanding a tumor population is determining the variant allele frequency with which the mutation occurs in the entire population. Bulk sequencing data can be used to provide that information, but the frequencies are not informative enough in identifying different clones and their evolutionary relationships. On the other hand, single-cell sequencing data provides valuable information about branching events in the evolution of a cancerous tumor. However, in the single-cell sequencing data, the total population of sequenced cells is naturally much smaller than bulk sequencing so it is not precise enough for calculating cell prevalence.Result: In this study, a new method called Conifer (ClONal tree Inference For hEterogeneity of tumoR) is proposed which combines aggregated variant allele frequency from bulk sequencing data with branch evolution information from single-cell sequencing data, in order to better understand clones and their evolutionary relationships. It is proven that the accuracy of clone identification is increased by using Conifer compared to other existing methods in both real and simulated data. Also, it is shown that the approach of Conifer in using single-cell sequencing data together with bulk sequencing data has reduced the possibility of cloning mutations with similar frequency but belonging to different clones.Conclusions: In this study, we provided an accurate and robust method to identify clones of tumor heterogeneity and their evolutionary history by combining single-cell and bulk sequencing data.

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Conifer: clonal tree inference for tumor heterogeneity with single-cell and bulk sequencing data

BMC Bioinformatics ◽

10.1186/s12859-021-04338-7 ◽

2021 ◽

Vol 22 (1) ◽

Author(s):

Leila Baghaarabani ◽

Sama Goliaei ◽

Mohammad-Hadi Foroughmand-Araabi ◽

Seyed Peyman Shariatpanahi ◽

Bahram Goliaei

Keyword(s):

Single Cell ◽

Tumor Heterogeneity ◽

Temporal Order ◽

Variant Allele ◽

Evolutionary Relationships ◽

Sequencing Data ◽

Variant Allele Frequency ◽

Single Cell Sequencing ◽

Tree Inference ◽

Cell Data

Abstract Background Genetic heterogeneity of a cancer tumor that develops during clonal evolution is one of the reasons for cancer treatment failure, by increasing the chance of drug resistance. Clones are cell populations with different genotypes, resulting from differences in somatic mutations that occur and accumulate during cancer development. An appropriate approach for identifying clones is determining the variant allele frequency of mutations that occurred in the tumor. Although bulk sequencing data can be used to provide that information, the frequencies are not informative enough for identifying different clones with the same prevalence and their evolutionary relationships. On the other hand, single-cell sequencing data provides valuable information about branching events in the evolution of a cancerous tumor. However, the temporal order of mutations may be determined with ambiguities using only single-cell data, while variant allele frequencies from bulk sequencing data can provide beneficial information for inferring the temporal order of mutations with fewer ambiguities. Result In this study, a new method called Conifer (ClONal tree Inference For hEterogeneity of tumoR) is proposed which combines aggregated variant allele frequency from bulk sequencing data with branching event information from single-cell sequencing data to more accurately identify clones and their evolutionary relationships. It is proven that the accuracy of clone identification and clonal tree inference is increased by using Conifer compared to other existing methods on various sets of simulated data. In addition, it is discussed that the evolutionary tree provided by Conifer on real cancer data sets is highly consistent with information in both bulk and single-cell data. Conclusions In this study, we have provided an accurate and robust method to identify clones of tumor heterogeneity and their evolutionary history by combining single-cell and bulk sequencing data.

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Identifying effective single-cell sequencing strategies for intra-tumor heterogeneity estimation

European Journal of Cancer ◽

10.1016/s0959-8049(16)61104-8 ◽

2016 ◽

Vol 61 ◽

pp. S33

Author(s):

J. Alves

Keyword(s):

Single Cell ◽

Tumor Heterogeneity ◽

Single Cell Sequencing

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Single-cell DNA sequencing reveals complex mechanisms of resistance to quizartinib

Blood Advances ◽

10.1182/bloodadvances.2020003398 ◽

2021 ◽

Vol 5 (5) ◽

pp. 1437-1441

Author(s):

Cheryl A. C. Peretz ◽

Lisa H. F. McGary ◽

Tanya Kumar ◽

Hunter Jackson ◽

Jose Jacob ◽

...

Keyword(s):

Dna Sequencing ◽

Single Cell ◽

Tumor Heterogeneity ◽

Clonal Evolution ◽

Mechanisms Of Resistance ◽

Single Cell Sequencing ◽

Key Points ◽

Complex Mechanisms

Key Points Single-cell sequencing exposes previously unmeasurable complexity of tumor heterogeneity and clonal evolution on quizartinib. Single-cell sequencing reveals on- and off-target mechanisms of resistance to quizartinib, which can preexist therapy.

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Inference of clonal selection in cancer populations using single-cell sequencing data

10.1101/465211 ◽

2018 ◽

Author(s):

Pavel Skums ◽

Vyacheslau Tsivina ◽

Alex Zelikovsky

Keyword(s):

Single Cell ◽

Cancer Progression ◽

Tumor Heterogeneity ◽

Evolutionary Dynamics ◽

Clonal Selection ◽

Fitness Landscapes ◽

Sequencing Data ◽

Evolutionary Mechanisms ◽

Single Cell Sequencing ◽

Insight Into

AbstractIntra-tumor heterogeneity is one of the major factors influencing cancer progression and treatment outcome. However, evolutionary dynamics of cancer clone populations remain poorly understood. Quantification of clonal selection and inference of fitness landscapes of tumors is a key step to understanding evolutionary mechanisms driving cancer. These problems could be addressed using single cell sequencing, which provides an unprecedented insight into intra-tumor heterogeneity allowing to study and quantify selective advantages of individual clones. Here we present SCIFIL, a computational tool for inference of fitness landscapes of heterogeneous cancer clone populations from single cell sequencing data. SCIFIL allows to estimate maximum likelihood fitnesses of clone variants, measure their selective advantages and order of appearance by fitting an evolutionary model into the tumor phylogeny. We demonstrate the accuracy and utility of our approach on simulated and experimental data. SCIFIL can be used to provide new insight into the evolutionary dynamics of cancer. Its source code is available at https://github.com/compbel/SCIFIL

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Investigation of the Heterogeneity Evolution and Drug Resistance Mechanism of Diffuse Large B-Cell Lymphoma Under the Treatment of a Novel HDAC Inhibitor LAQ824

Blood ◽

10.1182/blood-2021-152006 ◽

2021 ◽

Vol 138 (Supplement 1) ◽

pp. 3988-3988

Author(s):

Hui Jin ◽

Luqiao Wang ◽

Lei Fan ◽

Zijuan Wu ◽

Xueying Lu ◽

...

Keyword(s):

Drug Resistance ◽

Single Cell ◽

Cell Lymphoma ◽

Resistance Mechanism ◽

Treatment Strategies ◽

B Cell Lymphoma ◽

Drug Pressure ◽

Single Cell Sequencing ◽

Large B Cell Lymphoma ◽

Large B Cell

Abstract Aims: Diffuse large B-cell lymphoma (DLBCL), the most frequent malignant lymphoma subtype, is a group of highly invasive diseases with great heterogeneity in genomic alterations, clinical characteristics, morphological manifestations, treatment response and prognosis. Although most DLBCL patients can be cured by immunochemotherapy, nearly 40% of DLBCL patients still develop drug resistance and relapse. For relapsed/ refractory (R/R) DLBCL patients, there is still no optimal treatment. The heterogeneity and clonal evolution of tumor cells are the core driving forces for the occurrence and development of DLBCL, and the root causes for their refractory, recurrence and drug resistance. In this study, we screened out a novel small molecule compound effectively killing DLBCL cells, and analyzed its potential mechanism of anti-tumor. Meanwhile, by using single cell sequencing technology, we try to further investigate the heterogeneity and clona evolution and drug resistance mechanism of DLBCL under different drug pressure, explore core driver factors of drug resistance, evaluate and develop new treatment strategies. Methods: In this study, GEXSCOPE microfluidic platform was used for single-cell transcriptome sequencing. Seruat software was used for cell type recognition and clustering analysis. In order to further investigate the molecular mechanism of LAQ824 inducing the apoptosis of DLBCL cells and explore the target of LAQ824, antibody chip was performed to detect the phosphorylation of related signaling pathway. Gene expression was detected by real-time qPCR and Western blot. ChIP, RNA interfering (RNAi) and dual-luciferase activity assay were performed to validate the potential drug resistance targets for LAQ824. Moreover, WES of 21 DLBCL cell lines were performed to map mutations and analyze the correlation between related mutations and LAQ824 resistance. In this study, we established DLBCL animal models using NOD SCID mice transplanted with DLBCL cell lines, by which we could evaluate the tumor inhibition efficiency of LAQ824 alone and/or combination with other small molecular inhibitors. Results: Using GDSC database, we screened out Dacinostat (LAQ824), a novel HDAC inhibitor, was highly sensitive that could effectively induce the apoptosis of most DLBCL cells at low concentrations. Functional assay showed that LAQ824 could inhibit cell proliferation and promote apoptosis of tumor cells. LAQ824 treatment could significantly upregulate the acetylation level of histone H3 within a certain concentration range, and the DNA damage repair function of DLBCL cells was supressed by inhibiting Chk2 expression, thus significantly inducing cell apoptosis and effectively killing DLBCL cells. Meanwhile, through single-cell sequencing analysis, it was found that c-Fos could be activated under certain drug pressure of LAQ824. As a potential drug-resistant core driver gene, the expression level of c-Fos is highly correlated with IC50 of LAQ824 and the prognosis of patients with DLBCL, which can be used as a sensitivity indicator of LAQ824. Treatment with c-Fos inhibitor combined with LAQ824 can significantly improve the tumor inhibition rate, validated both in vitro and in vivo, which is expected to alleviate the recurrence and drug resistance of DLBCL patients. Conclusions: In general, we explores potential therapeutic drugs for DLBCL parients, adjusts and explores new clinical treatment strategies on this basis, and provides theoretical basis and data support for the realization of individualized precise treatment and the solution of DLBCL recurrence and drug resistance. Disclosures No relevant conflicts of interest to declare.

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