Non-Genetic Intra-Tumor Heterogeneity Is a Major Predictor of Phenotypic Heterogeneity and Ongoing Evolutionary Dynamics in Lung Tumors

Sharma, Anchal; Merritt, Elise; Hu, Xiaoju; Cruz, Angelique; Jiang, Chuan; Sarkodie, Halle; Zhou, Zhan; Malhotra, Jyoti; Riedlinger, Gregory; De, Subhajyoti

doi:10.1016/j.celrep.2019.10.045

Cited by 95 publications

(77 citation statements)

References 56 publications

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“…Generation of thousands of artificial pseudo-bulk mixtures. Using the testing datasets from the quality control step, we generated matrices containing 1000 pseudobulk mixtures (matrix T in equation (1) from "Computational deconvolution: formulation and methodologies") by adding up count values from the randomly selected individual cells. The minimum number of cells used to create the pseudo-bulk mixtures (pool size) for each of the five datasets was 100 and the maximum possible number was determined by the second most abundant cell type (rounded down to the closest hundred, to avoid non-integer numbers of cells), resulting in n = 100, 700, and 1200 for Baron; n = 100, 300, and 400 for PBMCs; n = 100 and 200 for GSE81547; n = 100 and 200 for the kidney dataset and n = 100 for E-MTAB-5061.…”

Section: Methodsmentioning

confidence: 99%

“…Moreover, understanding differences in cell type composition in diseases, such as cancer will enable researchers to identify discrete cell populations, such as specific cell types that could be targeted therapeutically. For instance, active research on the role of infiltrating lymphocytes and other immune cells in the tumor microenvironment is currently ongoing 1 – 3 (e.g., in the context of immunotherapy) and it has already shown that accounting for the tumor heterogeneity resulted in more sensitive survival analyses and more accurate tumor subtype predictions 4 . For these reasons, many methodologies to infer proportions of individual cell types from bulk transcriptomics data have been developed during the last two decades 5 , along with new methods that use single-cell RNA-sequencing (scRNA-seq) data to infer cell proportions in bulk RNA-sequenced samples.…”

Section: Introductionmentioning

confidence: 99%

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Benchmarking of cell type deconvolution pipelines for transcriptomics data

et al. 2020

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Many computational methods have been developed to infer cell type proportions from bulk transcriptomics data. However, an evaluation of the impact of data transformation, pre-processing, marker selection, cell type composition and choice of methodology on the deconvolution results is still lacking. Using five single-cell RNA-sequencing (scRNA-seq) datasets, we generate pseudo-bulk mixtures to evaluate the combined impact of these factors. Both bulk deconvolution methodologies and those that use scRNA-seq data as reference perform best when applied to data in linear scale and the choice of normalization has a dramatic impact on some, but not all methods. Overall, methods that use scRNA-seq data have comparable performance to the best performing bulk methods whereas semi-supervised approaches show higher error values. Moreover, failure to include cell types in the reference that are present in a mixture leads to substantially worse results, regardless of the previous choices. Altogether, we evaluate the combined impact of factors affecting the deconvolution task across different datasets and propose general guidelines to maximize its performance.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Benchmarking of cell type deconvolution pipelines for transcriptomics data

et al. 2020

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“…Most tumours are comprised of a phenotypically diverse population of cancer cells, driven by a complex array of genetic and phenotypic alterations that disrupt normal cell cycle and cellular processes at multiple levels, including genomic, transcriptomic and influences from the tumour microenvironment ( Prasetyanti and Medema, 2017 ; Shibue and Weinberg, 2017 ; Sharma et al, 2019 ; Tripathi et al, 2020 ). This diversity is known as intra-tumour heterogeneity and is thought to play a crucial role in the development of treatment resistance ( Prasetyanti and Medema, 2017 ).…”

Section: Introductionmentioning

confidence: 99%

The Role of Cancer Stem Cells in Drug Resistance in Gastroesophageal Junction Adenocarcinoma

Dinneen

Baird

Ryan

et al. 2021

Front. Mol. Biosci.

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Gastroesophageal junction adenocarcinomas (GEJA) have dramatically increased in incidence in the western world since the mid-20th century. Their prognosis is poor, and conventional anti-cancer therapies do not significantly improve survival outcomes. These tumours are comprised of a heterogenous population of both cancer stem cells (CSC) and non-CSCs, with the former playing a crucial role in tumorigenesis, metastasis and importantly drug resistance. Due to the ability of CSCs to self-replicate indefinitely, their resistance to anti-cancer therapies poses a significant barrier to effective treatment of GEJA. Ongoing drug development programmes aim to target and eradicate CSCs, however their characterisation and thus identification is difficult. CSC regulation is complex, involving an array of signalling pathways, which are in turn influenced by a number of entities including epithelial mesenchymal transition (EMT), microRNAs (miRNAs), the tumour microenvironment and epigenetic modifications. Identification of CSCs commonly relies on the expression of specific cell surface markers, yet these markers vary between different malignancies and indeed are often co-expressed in non-neoplastic tissues. Development of targeted drug therapies against CSCs thus requires an understanding of disease-specific CSC markers and regulatory mechanisms. This review details the current knowledge regarding CSCs in GEJA, with particular emphasis on their role in drug resistance.

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“…Inter-patient heterogeneity in NSCLC is due in part to the presence of cell subtypes: squamous, adenocarcinoma, and large-cell. Intra-patient heterogeneity is manifested by multiple primaries and dissemination of a primary tumors to distant organs [ 9 ]., intra-tumor heterogeneity has been proven through single-cell sequencing of lung cancer [ 10 ]. Tumor heterogeneity creates a challenge for treatment planning as well as prediction of response to treatment.…”

Section: Introductionmentioning

confidence: 99%

Mathematical model predicts response to chemotherapy in advanced non-resectable non-small cell lung cancer patients treated with platinum-based doublet

et al. 2020

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We developed a computational platform including machine learning and a mechanistic mathematical model to find the optimal protocol for administration of platinum-doublet chemotherapy in a palliative setting. The platform has been applied to advanced metastatic non-small cell lung cancer (NSCLC). The 42 NSCLC patients treated with palliative intent at Maria Sklodowska-Curie National Research Institute of Oncology, Gliwice Branch, were collected from a retrospective cohort of patients diagnosed in 2004-2014. Patients were followed-up, for three years. Clinical data collected include complete information about the clinical course of the patients including treatment schedule, response according to RECIST classification, and survival. The core of the platform is the mathematical model, in the form of a system of ordinary differential equations, describing dynamics of platinum-sensitive and platinum-resistant cancer cells and interactions reflecting competition for space and resources. The model is simulated stochastically by sampling the parameter values from a joint probability distribution function. The machine learning model is applied to calibrate the mathematical model and to fit it to the overall survival curve. The model simulations faithfully reproduce the clinical cohort at three levels long-term response (OS), the initial response (according to RECIST criteria), and the relationship between the number of chemotherapy cycles and time between two consecutive chemotherapy cycles. In addition, we investigated the relationship between initial and long-term response. We showed that those two variables do not correlate which means that we cannot predict patient survival solely based on the initial response. We also tested several chemotherapy schedules to find the best one for patients treated with palliative intent. We found that the optimal treatment schedule depends, among others, on the strength of competition among various subclones in a tumor. The computational platform developed allows optimizing chemotherapy protocols, within admissible limits of toxicity, for palliative treatment of metastatic NSCLC. The simplicity of the method allows its application to chemotherapy optimization in different cancers.

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Non-Genetic Intra-Tumor Heterogeneity Is a Major Predictor of Phenotypic Heterogeneity and Ongoing Evolutionary Dynamics in Lung Tumors

Cited by 95 publications

References 56 publications

Benchmarking of cell type deconvolution pipelines for transcriptomics data

Benchmarking of cell type deconvolution pipelines for transcriptomics data

The Role of Cancer Stem Cells in Drug Resistance in Gastroesophageal Junction Adenocarcinoma

Mathematical model predicts response to chemotherapy in advanced non-resectable non-small cell lung cancer patients treated with platinum-based doublet

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