Measuring parameters of the cancer ecosystem for cancer ecological staging.

e14684 Background: Cancer progression through clonal evolution and emergent phenotypic heterogeneity is thought to reflect stochastic events such as genetic drift. This divergence over time in the character of a neoplasm might also reflect genetic selection, analogous to other populations in nature, to maximize niche resource utilization. We hypothesized that selection pressures operate in patients with cancer to drive cancer evolution, are clinically identifiable, their influence measurable. Methods: To develop a system for cancer ecology staging, a feasibility study recruited 15 patients with active cancer from any site, with expected survival of more than 6 months and providing informed consent. A set of clinical parameters obtained from a patient questionnaire, physical exam and laboratory testing was used to generate eight separate ecological profiles of tumor microenvironment, chronic inflammation, energy balance, psychosocial stress, GI microbiome, endocrine environment, skeletal remodeling and environmental mutagenesis. A scoring system, based on evidence of positive selection was designed to quantitate the individual profiles. Profile scores were then aggregated using a 2-D radar plot to generate a polygon, an ‘ecogram’, whose area, it is hypothesized, corresponds to the net level of selection pressure influencing tumor evolution. Results: Ecological profiles were obtained from each of 15 patients allowing determination of the ecogram area (EA) bounded by the polygon. EA determinations ranged widely among the 15 patient, from 0-12.7 arbitrary units (au, mean 5.01± 0.80). Ecograms from individual patients demonstrated unique shapes suggesting specificity for individual patient ecology. EA measurements were then used to inform an ecological staging system based on a simplified dichotomization, low/high, of ecosystem resources and threats. Of 15 patients, 6 were considered to have high resources (EA > 5au) available to support tumor evolution. High anti-tumor threat, measured by CD3 lymphocyte immunohistochemical scoring, was detected in 11 patients. Conclusions: An ecological assessment of patients with active cancer appears feasible. Inter-patient variation in ecogram area and morphology suggests there are potential important differences in genetic selection found between patients and should be correlated with survival outcomes in future studies, validation offering a target for ecosystem ‘restoration’.

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Improving personalized prediction of cancer prognoses with clonal evolution models

10.1101/761510 ◽

2019 ◽

Cited By ~ 1

Author(s):

Yifeng Tao ◽

Ashok Rajaraman ◽

Xiaoyue Cui ◽

Ziyi Cui ◽

Jesse Eaton ◽

...

Keyword(s):

Cancer Progression ◽

Predictive Power ◽

Clonal Evolution ◽

Disease Free Survival ◽

Lung Squamous Cell Carcinoma ◽

Predictive Biomarkers ◽

Tumor Evolution ◽

Structural Variations ◽

Genomic Alterations ◽

Tree Inference

AbstractCancer occurs via an accumulation of somatic genomic alterations in a process of clonal evolution. There has been intensive study of potential causal mutations driving cancer development and progression. However, much recent evidence suggests that tumor evolution is normally driven by a variety of mechanisms of somatic hypermutability, known as mutator phenotypes, which act in different combinations or degrees in different cancers. Here we explore the question of how and to which degree different mutator phenotypes act in a cancer predict its future progression. We develop a computational paradigm using evolutionary tree inference (tumor phylogeny) algorithms to derive features quantifying single-tumor mutational preferences, followed by a machine learning frame-work to identify key features predictive of progression. We build phylogenies tracing the evolution of subclones of cells in tumor tissues using a variety of somatic genomic alterations, including single nucleotide variations, copy number alterations, and structural variations. We demonstrate that mutation preference features derived from the phylogenies are predictive of clinical outcomes of cancer progression – overall survival and disease-free survival – based on the analyses on breast invasive carcinoma, lung adenocarcinoma, and lung squamous cell carcinoma. We further show that mutational phenotypes have predictive power even after accounting for traditional clinical and driver-centric predictors of progression. These results confirm the power of mutational phenotypes as an independent class of predictive biomarkers and suggest a strategy for enhancing the predictive power of conventional clinical or driver-centric genomic features.

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Assessing the contribution of tumor mutational phenotypes to cancer progression risk

PLoS Computational Biology ◽

10.1371/journal.pcbi.1008777 ◽

2021 ◽

Vol 17 (3) ◽

pp. e1008777

Author(s):

Yifeng Tao ◽

Ashok Rajaraman ◽

Xiaoyue Cui ◽

Ziyi Cui ◽

Haoran Chen ◽

...

Keyword(s):

Cancer Progression ◽

Predictive Power ◽

Clonal Evolution ◽

Large Fraction ◽

Disease Free Survival ◽

Tumor Evolution ◽

Driver Gene ◽

Learning Framework ◽

Tree Inference ◽

Progression Risk

Cancer occurs via an accumulation of somatic genomic alterations in a process of clonal evolution. There has been intensive study of potential causal mutations driving cancer development and progression. However, much recent evidence suggests that tumor evolution is normally driven by a variety of mechanisms of somatic hypermutability, which act in different combinations or degrees in different cancers. These variations in mutability phenotypes are predictive of progression outcomes independent of the specific mutations they have produced to date. Here we explore the question of how and to what degree these differences in mutational phenotypes act in a cancer to predict its future progression. We develop a computational paradigm using evolutionary tree inference (tumor phylogeny) algorithms to derive features quantifying single-tumor mutational phenotypes, followed by a machine learning framework to identify key features predictive of progression. Analyses of breast invasive carcinoma and lung carcinoma demonstrate that a large fraction of the risk of future clinical outcomes of cancer progression—overall survival and disease-free survival—can be explained solely from mutational phenotype features derived from the phylogenetic analysis. We further show that mutational phenotypes have additional predictive power even after accounting for traditional clinical and driver gene-centric genomic predictors of progression. These results confirm the importance of mutational phenotypes in contributing to cancer progression risk and suggest strategies for enhancing the predictive power of conventional clinical data or driver-centric biomarkers.

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In silico learning of tumor evolution through mutational time series

Proceedings of the National Academy of Sciences ◽

10.1073/pnas.1901695116 ◽

2019 ◽

Vol 116 (19) ◽

pp. 9501-9510 ◽

Cited By ~ 4

Author(s):

Noam Auslander ◽

Yuri I. Wolf ◽

Eugene V. Koonin

Keyword(s):

Time Series ◽

Tumor Progression ◽

Cancer Progression ◽

Clinical Decision Making ◽

Short Term Memory ◽

Complex Dynamics ◽

Clinical Decision ◽

Tumor Evolution ◽

Cancer Evolution ◽

Driver Genes

Cancer arises through the accumulation of somatic mutations over time. Understanding the sequence of mutation occurrence during cancer progression can assist early and accurate diagnosis and improve clinical decision-making. Here we employ long short-term memory (LSTM) networks, a class of recurrent neural network, to learn the evolution of a tumor through an ordered sequence of mutations. We demonstrate the capacity of LSTMs to learn complex dynamics of the mutational time series governing tumor progression, allowing accurate prediction of the mutational burden and the occurrence of mutations in the sequence. Using the probabilities learned by the LSTM, we simulate mutational data and show that the simulation results are statistically indistinguishable from the empirical data. We identify passenger mutations that are significantly associated with established cancer drivers in the sequence and demonstrate that the genes carrying these mutations are substantially enriched in interactions with the corresponding driver genes. Breaking the network into modules consisting of driver genes and their interactors, we show that these interactions are associated with poor patient prognosis, thus likely conferring growth advantage for tumor progression. Thus, application of LSTM provides for prediction of numerous additional conditional drivers and reveals hitherto unknown aspects of cancer evolution.

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MYC amplification at diagnosis drives therapy-induced hypermutation of recurrent glioma

10.21203/rs.3.rs-138020/v1 ◽

2021 ◽

Author(s):

Jiguang Wang ◽

Quanhua Mu ◽

Ruichao Chai ◽

Hanjie Liu ◽

Yingxi Yang ◽

...

Keyword(s):

Machine Learning ◽

Cancer Progression ◽

Target Genes ◽

Clonal Evolution ◽

Active Regions ◽

Open Chromatin ◽

Diffuse Glioma ◽

Cancer Evolution ◽

Loss Of Function ◽

Learning Platform

Abstract Clonal evolution drives cancer progression and therapeutic resistance1-2. Recent longitudinal analyses revealed divergent clonal dynamics in adult diffuse gliomas3–11. However, the early genomic and epigenomic factors that steer post-treatment molecular trajectories remain unknown. To track evolutionary predictors, we analyzed sequencing and clinical data of matched initial-recurrent tumor pairs from 511 adult diffuse glioma patients. Using machine learning we developed methods capable of predicting grade progression and hypermutation from tumor characteristics at diagnosis. Strikingly, MYC copy number gain in initial tumors emerged as a key factor predicting development of hypermutation under temozolomide (TMZ) treatment. The driving role of MYC in TMZ-associated hypermutagenesis has been experimentally validated in a model of TMZ-induced hypermutator using both patient-derived gliomaspheres and established glioma cell lines. Subsequent studies showed that c-Myc binding to open chromatin and transcriptionally active regions increases the vulnerability of genomic regions to TMZ-induced mutagenesis. Consequently, MYC target genes, including the key mismatch repair genes, develop loss-of-function mutations, thus triggering the hypermutation process. This study reveals MYC as an early predictor of cancer evolution and provides a machine learning platform for predicting cancer dynamics to improve patient management.

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Convergence of oncogenic cooperation at single-cell and single-gene levels drives leukemic transformation

Nature Communications ◽

10.1038/s41467-021-26582-4 ◽

2021 ◽

Vol 12 (1) ◽

Author(s):

Yuxuan Liu ◽

Zhimin Gu ◽

Hui Cao ◽

Pranita Kaphle ◽

Junhua Lyu ◽

...

Keyword(s):

Gene Expression ◽

Single Cell ◽

Cancer Progression ◽

Single Gene ◽

Myeloid Cell ◽

Cellular Level ◽

Integrative Approach ◽

Cancer Evolution ◽

Cell Assays

AbstractCancers develop from the accumulation of somatic mutations, yet it remains unclear how oncogenic lesions cooperate to drive cancer progression. Using a mouse model harboring NRasG12D and EZH2 mutations that recapitulates leukemic progression, we employ single-cell transcriptomic profiling to map cellular composition and gene expression alterations in healthy or diseased bone marrows during leukemogenesis. At cellular level, NRasG12D induces myeloid lineage-biased differentiation and EZH2-deficiency impairs myeloid cell maturation, whereas they cooperate to promote myeloid neoplasms with dysregulated transcriptional programs. At gene level, NRasG12D and EZH2-deficiency independently and synergistically deregulate gene expression. We integrate results from histopathology, leukemia repopulation, and leukemia-initiating cell assays to validate transcriptome-based cellular profiles. We use this resource to relate developmental hierarchies to leukemia phenotypes, evaluate oncogenic cooperation at single-cell and single-gene levels, and identify GEM as a regulator of leukemia-initiating cells. Our studies establish an integrative approach to deconvolute cancer evolution at single-cell resolution in vivo.

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The DNA methylation landscape of multiple myeloma shows extensive inter- and intrapatient heterogeneity that fuels transcriptomic variability

10.1101/2020.10.01.321943 ◽

2020 ◽

Author(s):

Jennifer Derrien ◽

Catherine Guérin-Charbonnel ◽

Victor Gaborit ◽

Loïc Campion ◽

Magali Devic ◽

...

Keyword(s):

Dna Methylation ◽

Multiple Myeloma ◽

Environmental Changes ◽

Cpg Island ◽

Entropy Change ◽

Tumor Evolution ◽

Cancer Evolution ◽

High Entropy ◽

Evolutionary Trajectories ◽

Poor Outcomes

AbstractBackgroundCancer evolution depends on epigenetic and genetic diversity. Historically, in multiple myeloma (MM), subclonal diversity and tumor evolution have been investigated mostly from a genetic perspective.ResultsHere, we combined the notions of epipolymorphism and epiallele switching to analyze DNA methylation heterogeneity in MM patients. We show that MM is characterized by the continuous accumulation of stochastic methylation at the promoters of development-related genes. High entropy change is associated with poor outcomes and depends predominantly on partially methylated domains (PMDs). These PMDs, which represent the major source of inter- and intrapatient DNA methylation heterogeneity in MM, are linked to other key epigenetic aberrations, such as CpG island (CGI)/transcription start site (TSS) hypermethylation and H3K27me3 redistribution as well as 3D organization alterations. In addition, transcriptome analysis revealed that intratumor methylation heterogeneity was associated with low-level expression and high variability.ConclusionWe propose that disordered methylation in MM is responsible for high epigenetic and transcriptomic instability allowing tumor cells to adapt to environmental changes by tapping into a pool of evolutionary trajectories.

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CliP: subclonal architecture reconstruction of cancer cells in DNA sequencing data using a penalized likelihood model

10.1101/2021.03.31.437383 ◽

2021 ◽

Author(s):

Yujie Jiang ◽

Kaixian Yu ◽

Shuangxi Ji ◽

Seung Jun Shin ◽

Shaolong Cao ◽

...

Keyword(s):

Prior Knowledge ◽

Penalized Likelihood ◽

Software Tool ◽

Structure Identification ◽

Tumor Evolution ◽

Cancer Evolution ◽

Post Processing ◽

Sequencing Data ◽

Single Nucleotide Variants ◽

Likelihood Model

Subpopulations of tumor cells characterized by mutation profiles may confer differential fitness and consequently influence prognosis of cancers. Understanding subclonal architecture has the potential to provide biological insight in tumor evolution and advance precision cancer treatment. Recent methods comprehensively integrate single nucleotide variants (SNVs) and copy number aberrations (CNAs) to reconstruct subclonal architecture using whole-genome or whole-exome sequencing (WGS, WES) data from bulk tumor samples. However, the commonly used Bayesian methods require a large amount of computational resources, a prior knowledge of the number of subclones, and extensive post-processing. Regularized likelihood modeling approach, never explored for subclonal reconstruction, can inherently address these drawbacks. We therefore propose a model-based method, Clonal structure identification through pair-wise Penalization, or CliP, for clustering subclonal mutations without prior knowledge or post-processing. The CliP model is applicable to genomic regions with or without CNAs. CliP demonstrates high accuracy in subclonal reconstruction through extensive simulation studies. Utilizing the well-established regularized likelihood framework, CliP takes only 16 hours to process WGS data from 2,778 tumor samples in the ICGC-PCAWG study, and 38 hours to process WES data from 9,564 tumor samples in the TCGA study. In summary, a penalized likelihood framework for subclonal reconstruction will help address intrinsic drawbacks of existing methods and expand the scope of computational analysis for cancer evolution in large cancer genomic studies. The associated software tool is freely available at: https://github.com/wwylab/CliP.

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Molecular Profiles of Matched Primary and Metastatic Tumor Samples Support a Linear Evolutionary Model of Breast Cancer

10.1101/667303 ◽

2019 ◽

Author(s):

Runpu Chen ◽

Steve Goodison ◽

Yijun Sun

Keyword(s):

Breast Cancer ◽

Cancer Progression ◽

Evolutionary Model ◽

Metastatic Tumor ◽

Cancer Diagnostics ◽

Tumor Evolution ◽

Paired Data ◽

Cancer Subtypes ◽

Cancer Data ◽

Progression Model

AbstractThe interpretation of accumulating genomic data with respect to tumor evolution and cancer progression requires integrated models. We developed a computational approach that enables the construction of disease progression models using static sample data. Application to breast cancer data revealed a linear, branching evolutionary model with two distinct trajectories for malignant progression. Here, we used the progression model as a foundation to investigate the relationships between matched primary and metastasis breast tumor samples. Mapping paired data onto the model confirmed that molecular breast cancer subtypes can shift during progression, and supported directional tumor evolution through luminal subtypes to increasingly malignant states. Cancer progression modeling through the analysis of available static samples represents a promising breakthrough. Further refinement of a roadmap of breast cancer progression will facilitate the development of improved cancer diagnostics, prognostics and targeted therapeutics.

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Opposing Evolutionary Pressures Drive Clonal Evolution and Health Outcomes in the Aging Blood System

Blood ◽

10.1182/blood-2020-142086 ◽

2020 ◽

Vol 136 (Supplement 1) ◽

pp. 37-37

Author(s):

Kimberly Skead ◽

Armande Ang Houle ◽

Sagi Abelson ◽

Marie-Julie Fave ◽

Boxi Lin ◽

...

Keyword(s):

Gene Disruption ◽

Large Scale ◽

Evolutionary Dynamics ◽

Clonal Evolution ◽

Neutral Evolution ◽

Driver Mutations ◽

Cancer Evolution ◽

Sequencing Data ◽

High Coverage ◽

Passenger Mutations

The age-associated accumulation of somatic mutations and large-scale structural variants (SVs) in the early hematopoietic hierarchy have been linked to premalignant stages for cancer and cardiovascular disease (CVD). However, only a small proportion of individuals harboring these mutations progress to disease, and mechanisms driving the transformation to malignancy remains unclear. Hematopoietic evolution, and cancer evolution more broadly, has largely been studied through a lens of adaptive evolution and the contribution of functionally neutral or mildly damaging mutations to early disease-associated clonal expansions has not been well characterised despite comprising the majority of the mutational burden in healthy or tumoural tissues. Through combining deep learning with population genetics, we interrogate the hematopoietic system to capture signatures of selection acting in healthy and pre-cancerous blood populations. Here, we leverage high-coverage sequencing data from healthy and pre-cancerous individuals from the European Prospective Investigation into Cancer and Nutrition Study (n=477) and dense genotyping from the Canadian Partnership for Tomorrow's Health (n=5,000) to show that blood rejects the paradigm of strictly adaptive or neutral evolution and is subject to pervasive negative selection. We observe clear age associations across hematopoietic populations and the dominant class of selection driving evolutionary dynamics acting at an individual level. We find that both the location and ratio of passenger to driver mutations are critical in determining if positive selection acting on driver mutations is able to overwhelm regulated hematopoiesis and allow clones harbouring disease-predisposing mutations to rise to dominance. Certain genes are enriched for passenger mutations in healthy individuals fitting purifying models of evolution, suggesting that the presence of passenger mutations in a subset of genes might confer a protective role against disease-predisposing clonal expansions. Finally, we find that the density of gene disruption events with known pathogenic associations in somatic SVs impacts the frequency at which the SV segregates in the population with variants displaying higher gene disruption density segregating at lower frequencies. Understanding how blood evolves towards malignancy will allow us to capture cancer in its earliest stages and identify events initiating departures from healthy blood evolution. Further, as the majority of mutations are passengers, studying their contribution to tumorigenesis, will unveil novel therapeutic targets thus enabling us to better understand patterns of clonal evolution in order to diagnose and treat disease in its infancy. Disclosures Dick: Bristol-Myers Squibb/Celgene: Research Funding.

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Prostaglandin E-Major Urinary Metabolite (PGE-MUM) as a Tumor Marker for Lung Adenocarcinoma

Cancers ◽

10.3390/cancers11060768 ◽

2019 ◽

Vol 11 (6) ◽

pp. 768 ◽

Cited By ~ 1

Author(s):

Hironori Kawamoto ◽

Hiromichi Hara ◽

Jun Araya ◽

Akihiro Ichikawa ◽

Yu Fujita ◽

...

Keyword(s):

Lung Adenocarcinoma ◽

Cancer Progression ◽

Stage Iv ◽

Staging System ◽

Tnm Staging ◽

Tumor Burden ◽

Urinary Metabolite ◽

Prostaglandin E ◽

Major Urinary Metabolite ◽

Embryonic Antigen

Background: Prostaglandin E2 (PGE2) is metabolized to prostaglandin E-major urinary metabolite (PGE-MUM). Enhanced cyclooxygenase-2 (COX-2) expression demonstrated in lung adenocarcinoma indicates increased PGE-MUM levels in patients with lung adenocarcinoma. Objectives: We aimed to elucidate the clinical usefulness of measuring PGE-MUM as an indicator of tumor burden in patients with lung adenocarcinoma. Methods: PGE-MUM was measured by a radioimmunoassay in control healthy volunteers (n = 124) and patients with lung adenocarcinoma (n = 54). Associations between PGE-MUM levels and clinical characteristics of the patients (including lung cancer stage and TNM factors (T: Tumor, N: Node, M: Metastasis) were examined. Results: PGE-MUM levels were significantly elevated in patients with lung adenocarcinoma. A PGE-MUM level of 14.9 μg/g∙Cr showed 70.4% sensitivity and 67.7% specificity for the diagnosis of lung adenocarcinoma. PGE-MUM levels tended to be positively correlated with cancer progression as determined by the TNM staging system. Advanced stage (stage III, stage IV, and recurrence) was significantly associated with high PGE-MUM levels by logistic regression analysis. No apparent correlation was demonstrated between PGE-MUM and carcinoma embryonic antigen (CEA) levels. Conclusions: PGE-MUM can be a promising biomarker reflecting the systemic tumor burden of lung adenocarcinoma.

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