scholarly journals Distribution-based measures of tumor heterogeneity are sensitive to mutation calling and lack strong clinical predictive power

2018 ◽  
Author(s):  
Javad Noorbakhsh ◽  
Hyunsoo Kim ◽  
Sandeep Namburi ◽  
Jeffrey Chuang

Mutant allele frequency distributions in cancer samples have been used to estimate intratumoral heterogeneity and its implications for patient survival. However, mutation calls are sensitive to the calling algorithm. It remains unknown whether the relationship of heterogeneity and clinical outcome is robust to these variations. To resolve this question, we studied the robustness of allele frequency distributions to the mutation callers MuTect, SomaticSniper, and VarScan in 4722 cancer samples from The Cancer Genome Atlas. We observed discrepancies among the results, particularly a pronounced difference between allele frequency distributions called by VarScan and SomaticSniper. Survival analysis showed little robust predictive power for heterogeneity as measured by Mutant-Allele Tumor Heterogeneity (MATH) score, with the exception of uterine corpus endometrial carcinoma. However, we found that variations in mutant allele frequencies were mediated by variations in copy number. Our results indicate that the clinical predictions associated with MATH score are primarily caused by copy number aberrations that alter mutant allele frequencies. Finally, we present a mathematical model of linear tumor evolution demonstrating why MATH score is insufficient for distinguishing different scenarios of tumor growth. Our findings elucidate the importance of allele frequency distributions as a measure for tumor heterogeneity and their prognostic role.

2018 ◽  
Author(s):  
An-Shun Tai ◽  
Chien-Hua Peng ◽  
Shih-Chi Peng ◽  
Wen-Ping Hsieh

AbstractMultistage tumorigenesis is a dynamic process characterized by the accumulation of mutations. Thus, a tumor mass is composed of genetically divergent cell subclones. With the advancement of next-generation sequencing (NGS), mathematical models have been recently developed to decompose tumor subclonal architecture from a collective genome sequencing data. Most of the methods focused on single-nucleotide variants (SNVs). However, somatic copy number aberrations (CNAs) also play critical roles in carcinogenesis. Therefore, further modeling subclonal CNAs composition would hold the promise to improve the analysis of tumor heterogeneity and cancer evolution. To address this issue, we developed a two-way mixture Poisson model, named CloneDeMix for the deconvolution of read-depth information. It can infer the subclonal copy number, mutational cellular prevalence (MCP), subclone composition, and the order in which mutations occurred in the evolutionary hierarchy. The performance of CloneDeMix was systematically assessed in simulations. As a result, the accuracy of CNA inference was nearly 93% and the MCP was also accurately restored. Furthermore, we also demonstrated its applicability using head and neck cancer samples from TCGA. Our results inform about the extent of subclonal CNA diversity, and a group of candidate genes that probably initiate lymph node metastasis during tumor evolution was also discovered. Most importantly, these driver genes are located at 11q13.3 which is highly susceptible to copy number change in head and neck cancer genomes. This study successfully estimates subclonal CNAs and exhibit the evolutionary relationships of mutation events. By doing so, we can track tumor heterogeneity and identify crucial mutations during evolution process. Hence, it facilitates not only understanding the cancer development but finding potential therapeutic targets. Briefly, this framework has implications for improved modeling of tumor evolution and the importance of inclusion of subclonal CNAs.


Genetics ◽  
2021 ◽  
Vol 217 (1) ◽  
Author(s):  
Smruthy Sivakumar ◽  
F Anthony San Lucas ◽  
Yasminka A Jakubek ◽  
Zuhal Ozcan ◽  
Jerry Fowler ◽  
...  

Abstract Somatic copy number alterations (SCNAs) serve as hallmarks of tumorigenesis and often result in deviations from one-to-one allelic ratios at heterozygous loci, leading to allelic imbalance (AI). The Cancer Genome Atlas (TCGA) reports SCNAs identified using a circular binary segmentation algorithm, providing segment mean copy number estimates from single-nucleotide polymorphism DNA microarray total intensities (log R ratio), but not allele-specific intensities (“B allele” frequencies) that inform of AI. Our approach provides more sensitive identification of SCNAs by modeling the “B allele” frequencies jointly, thereby bolstering the catalog of chromosomal alterations in this widely utilized resource. Here we present AI summaries for all 33 tumor sites in TCGA, including those induced by SCNAs and copy-neutral loss-of-heterozygosity (cnLOH). We identified AI in 94% of the tumors, higher than in previous reports. Recurrent events included deletions of 17p, 9q, 3p, amplifications of 8q, 1q, 7p, as well as mixed event types on 8p and 13q. We also observed both site-specific and pan-cancer (spanning 17p) cnLOH, patterns which have not been comprehensively characterized. The identification of such cnLOH events elucidates tumor suppressors and multi-hit pathways to carcinogenesis. We also contrast the landscapes inferred from AI- and total intensity-derived SCNAs and propose an automated procedure to improve and adjust SCNAs in TCGA for cases where high levels of aneuploidy obscured baseline intensity identification. Our findings support the exploration of additional methods for robust automated inference procedures and to aid empirical discoveries across TCGA.


2018 ◽  
Author(s):  
Inga H. Rye ◽  
Anne Trinh ◽  
Anna Sætersdal ◽  
Daniel Nebdal ◽  
Ole Christian Lingjærde ◽  
...  

AbstractTargeted therapy for patients with HER2 positive (HER2+) breast cancer has improved the overall survival, but many patients still suffer relapse and death of the disease. Intra-tumor heterogeneity of both estrogen receptor (ER) and HER2 expression has been proposed to play a key role in treatment failure, but little work has been done to comprehensively study this heterogeneity at the single-cell level.In this study, we explored the clinical impact of intra-tumor heterogeneity of ER protein expression, HER2 protein expression, and HER2 gene copy number alterations. Using combined immunofluorescence and in situ hybridization on tissue sections followed by a validated computational approach, we analyzed more than 13,000 single tumor cells across 37 HER2+ breast tumors. The samples were taken both before and after neoadjuvant chemotherapy plus HER2-targeted treatment, enabling us to study tumor evolution as well.We found that intra-tumor heterogeneity for HER2 copy number varied substantially between patient samples. Highly heterogeneous tumors were associated with significantly shorter disease-free survival and fewer long-term survivors. Patients for which HER2 characteristics did not change during treatment had a significantly worse outcome.This work shows the impact of intra-tumor heterogeneity in molecular diagnostics for treatment selection in HER2+ breast cancer patients and the power of computational scoring methods to evaluate in situ molecular markers in tissue biopsies.


Blood ◽  
2006 ◽  
Vol 108 (11) ◽  
pp. 4915-4915
Author(s):  
Roberto H. Nussenzveig ◽  
Sabina Swierczek ◽  
Jaroslav Jelinek ◽  
Srdan Verstovsek ◽  
Jaroslav Prchal ◽  
...  

Abstract Polycythemia vera (PV) arises due to a somatic mutation(s) of a single hematopoietic stem cell leading to clonal hematopoiesis. Greater than 80% of PV patients carry a somatic mutation in JAK2 (V617F). Growing evidence suggests that increased frequency of the JAK2V617F allele may have a prognostic impact on certain clinical aspects of PV, and, possibly, in other myeloproliferative disorders associated with this mutation. We have developed a novel approach to primer design for Real-Time quantitative allele-specific PCR. Allelic discrimination is enhanced by the combined synergistic effects of an artificial mismatch introduced in the −1 position, starting from the 3′ end of the primer, and the use of a locked nucleic acid (LNA) modified nucleoside placed at the −2 position. We provide evidence that the −2 LNA assists in stabilizing the 3′ end, while the −1 mismatch provides specificity but not stability. The difference in cycle number between the two allele-specific reactions is used to calculate the relative allele frequencies. We demonstrate the robustness, sensitivity and reproducibility of our design. The proportion of mutant JAK2 allele determined by pyrosequencing and kinetic allele-specific PCR was highly concordant with an average allele frequency deviation of 2.6%. Repeated determination of allelic ratios in multiple patient samples was highly reproducible with a standard deviation of 1.5%. We have also determined that the design and assay is highly sensitive; as little as 0.1% mutant allele in 40–50 ng of genomic DNA can be detected. We further tested the applicability of this technique to the analysis of individual BFU-E colonies in order to address the question whether the JAK2V617F is the disease initiating mutation. Less than 10% of a single isolated BFU-E colony, originating from a single progenitor, is sufficient for determination of allele frequency. The remainder of the colony may be used for other analyses. A proportion of 0 or 50 or 100 percent JAK2 mutant allele is expected from each individual BFU-E colony, which was indeed observed. However, when we tested granulocytes from PV females, wherein the granulocytes were found to be clonal by the X-chromosome transcriptionally based clonality assay, we found 3 females <50 (27.5 ±11) and 7 females >50 (75 ±10.5)percent mutant JAK2 allele frequencies. This result suggests the presence of a heterogeneous population of cells with differing genotypes regarding the JAK2 mutant allele, and is further supported by our genotyping results with individual BFU-E colonies as described above. Our PV data suggest that the JAK2V617F may not be the PV initiating mutation. This novel primer design is simple, does not require tedious optimization of reaction conditions, and can be applied to any kinetic PCR platform for reliable and sensitive determination of allele frequencies. Potential applications are varied, such as, quantitative determination of mosaicism, proportion of fetal cells in maternal circulation, detection of minimal residual disease associated with known somatic mutation (such as reduction of malignant cells by chemotherapy or reappearance of resistant clone), rapid monitoring of efficacy of new drugs in both “in vitro” systems as well as clinical trials, and many others that require quantitation of allele frequencies.


2020 ◽  
Author(s):  
Yuying Han ◽  
Xu Liu ◽  
Haihong Ye ◽  
Ye Tian ◽  
Zhengguo Ji

Abstract Background: Bladder cancer displays a broad mutational spectrum and intratumor heterogeneity (ITH), which results in difference in molecular phenotypes and resistance to therapies. However, there are currently no clinically available measures to predict patient prognosis using ITH. We aimed to establish clinically relevant biomarker by using ITH for informing predictive of outcomes.Methods: We used the Bioconductor R package Maftools to efficiently and comprehensively analyze somatic variants of muscle-invasive bladder cancer (MIBC) from The Cancer Genome Atlas (TCGA). We then used a mutant-allele tumor heterogeneity (MATH) algorithm to measure ITH and explored its correlation with clinical parameters as well as mutational subtypes.Results: We observed a broad range of somatic mutations in MIBC from TCGA. MATH value was higher for the high-grade group than for the low-grade group (p < 0.05). There was a strong correlation between higher MATH value and presence of TP53 mutations (p = 0.008), as well as between lower MATH value and presence of FGFR3 mutations (p = 0.006). Patients with FGFR3 mutation and low MATH value exhibit longer overall survival time than that of all BLCA patients (p = 0.044), which was replicated in another bladder cancer database composed of 109 BLCA patients.Conclusion: Measures of tumor heterogeneity may be useful biomarkers for identifying patients with bladder cancer. Low MATH value was an independent risk factor that predicted better prognosis for patients with FGFR3 mutation compared to all BLCA patients.


2020 ◽  
Author(s):  
Yuying Han ◽  
Xu Liu ◽  
Haihong Ye ◽  
Ye Tian ◽  
Zhengguo Ji

Abstract Background: Bladder cancer displays a broad mutational spectrum and intratumor heterogeneity (ITH), which results in difference in molecular phenotypes and resistance to therapies. However, there are currently no clinically available measures to predict patient prognosis using ITH. We aimed to establish clinically relevant biomarker by using ITH for informing predictive of outcomes.Methods: We used the Bioconductor R package Maftools to efficiently and comprehensively analyze somatic variants of muscle-invasive bladder cancer (MIBC) from The Cancer Genome Atlas (TCGA). We then used a mutant-allele tumor heterogeneity (MATH) algorithm to measure ITH and explored its correlation with clinical parameters as well as mutational subtypes.Results: We observed a broad range of somatic mutations in MIBC from TCGA. MATH value was higher for the high-grade group than for the low-grade group (p < 0.05). There was a strong correlation between higher MATH value and presence of TP53 mutations (p = 0.008), as well as between lower MATH value and presence of FGFR3 mutations (p = 0.006). Patients with FGFR3 mutation and low MATH value exhibit longer overall survival time than that of all BLCA patients (p = 0.044), which was replicated in another bladder cancer database composed of 109 BLCA patients.Conclusion: Measures of tumor heterogeneity may be useful biomarkers for identifying patients with bladder cancer. Low MATH value was an independent risk factor that predicted better prognosis for patients with FGFR3 mutation compared to all BLCA patients.


2020 ◽  
Vol 18 (1) ◽  
Author(s):  
Yuying Han ◽  
Xu Liu ◽  
Haihong Ye ◽  
Ye Tian ◽  
Zhengguo Ji

Abstract Background Bladder cancer displays a broad mutational spectrum and intratumor heterogeneity (ITH), which results in difference in molecular phenotypes and resistance to therapies. However, there are currently no clinically available measures to predict patient prognosis using ITH. We aimed to establish a clinically relevant biomarker by using ITH for informing predictive of outcomes. Methods We used the Bioconductor R package Maftools to efficiently and comprehensively analyze somatic variants of muscle-invasive bladder cancer (MIBC) from The Cancer Genome Atlas (TCGA). We then used a mutant-allele tumor heterogeneity (MATH) algorithm to measure ITH and explored its correlation with clinical parameters as well as mutational subtypes. Results We observed a broad range of somatic mutations in MIBC from TCGA. MATH value was higher for the high-grade group than for the low-grade group (p < 0.05). There was a strong correlation between higher MATH value and presence of TP53 mutations (p = 0.008), as well as between lower MATH value and presence of FGFR3 mutations (p = 0.006). Patients with FGFR3 mutation and low MATH value exhibit longer overall survival time than that of all BLCA patients (p = 0.044), which was replicated in another bladder cancer database composed of 109 BLCA patients. Conclusion Measures of tumor heterogeneity may be useful biomarkers for identifying patients with bladder cancer. Low MATH value was an independent risk factor that predicted better prognosis for patients with FGFR3 mutation compared to all BLCA patients.


Blood ◽  
2018 ◽  
Vol 132 (Supplement 1) ◽  
pp. 5574-5574
Author(s):  
Andrea Raspadori ◽  
Claudio Forcato ◽  
Petrini Edoardo ◽  
Francesca Marzia Papadopulos ◽  
Alberto Ferrarini ◽  
...  

Abstract Introduction: Multiple myeloma (MM) is a malignancy of terminally differentiated plasma cells. The high heterogeneity of MM cells is one of the major cause of disease relapse. Detection of circulating MM cells (CMMC) from peripheral blood is a useful procedure to investigate tumor heterogeneity and provides a painless alternative to the classic bone marrow biopsy to monitor disease progression. Here we demonstrate that the synergy between CellSearch® (CS) and DEPArray™ (DA) technologies can be used to identify, isolate and characterize at the genetic level single and pure CMMCs . Methods: 4.0 ml of peripheral blood samples were obtained from 3 patients with MM. Putative CMMCs were enriched with CS using anti-CD138 or anti-CD138/CD38 as positive selection marker and subsequently stained with CD38-PE, CD19/CD45-APC immunofluorescent probes. Cells detection and enumeration was performed based on the co-localization of nuclei DAPI staining and CD38-PE. Single CMMCs (CD38+/CD19- and CD45-/DAPI+) and White Blood Cells (WBCs: CD38-/CD19+ or CD45+/DAPI+) were then isolated using the DA NxT system. Single cells genomic DNA was amplified using Ampli1™ Whole Genome Amplification (WGA) kit and Illumina®-compatible libraries were obtained using Ampli1™ LowPass kit and a high-throughput, customized automated protocol using Hamilton STARLet Liquid handler. Highly-multiplexed, genome-wide single-cell Low-Pass Copy Number Alteration (LPCNA) analysis was performed using HiSeq 2500 Illumina® platform. Results: CS and DA workflow* enabled the isolation of 215 single CMMC, selected for LPCNA analysis. 42 single WBCs were also included as normal controls. Copy-number profiles of single CMMCs showed relevant gains and losses of chromosomal segments, as result of a high-level genomic instability. Notably, intra-patient CMMCs revealed overall conserved CNA patterns with subclonal alterations, suggesting a certain level of branched tumor evolution. Conversely, a higher degree of heterogeneity in CMMCs CNA profiles was observed among different patients. Interestingly, CNAs detected in all patients are located in regions containing genes involved in cell cycle regulation (MAPK, NOTCH pathways) and cell signaling (IL6R), which might be involved in proliferative processes and immuno-surveillance escape. Conclusion: The combination of CS and DA workflow* with a streamlined automated protocol allowed to obtain hundreds of genomic libraries from pure single CMMCs. The presented workflow constitutes a non-invasive, rapid and high-throughput approach for characterizing MM tumor heterogeneity and progression, suggesting a possible future implementation in clinical applications. *For Research Use Only. Not for use in diagnostic procedures. Disclosures Raspadori: Menarini Silicon Biosystems: Employment. Forcato:Menarini Silicon Biosystems: Employment. Edoardo:Menarini Silicon Biosystems: Employment. Papadopulos:Menarini Silicon Biosystems: Employment. Ferrarini:Menarini Silicon Biosystems: Employment. Del Monaco:Menarini Silicon Biosystems: Employment. Terracciano:Menarini Silicon Biosystems: Employment. Morano:Menarini Silicon Biosystems: Employment. Gross:Menarini Silicon Biosystems: Employment. Bolognesi:Menarini Silicon Biosystems: Employment. Buson:Menarini Silicon Biosystems: Employment. Fontana:Menarini Silicon Biosystems: Employment. Connelly:Menarini Silicon Biosystems, Inc.: Employment, Other: Chief R&D Officer, USA. Simonelli:Menarini Silicon Biosystems: Employment. Medoro:Menarini Silicon Biosystems: Employment. Manaresi:Menarini Silicon Biosystems: Employment.


2021 ◽  
Author(s):  
Gryte Satas ◽  
Simone Zaccaria ◽  
Mohammed El-Kebir ◽  
Benjamin J. Raphael

AbstractMost tumors are heterogeneous mixtures of normal cells and cancer cells, with individual cancer cells distinguished by somatic mutations that accumulated during the evolution of the tumor. The fundamental quantity used to measure tumor heterogeneity from somatic single-nucleotide variants (SNVs) is the Cancer Cell Fraction (CCF), or proportion of cancer cells that contain the SNV. However, in tumors containing copy-number aberrations (CNAs) – e.g. most solid tumors – the estimation of CCFs from DNA sequencing data is challenging because a CNA may alter the mutation multiplicity, or number of copies of an SNV. Existing methods to estimate CCFs rely on the restrictive Constant Mutation Multiplicity (CMM) assumption that the mutation multiplicity is constant across all tumor cells containing the mutation. However, the CMM assumption is commonly violated in tumors containing CNAs, and thus CCFs computed under the CMM assumption may yield unrealistic conclusions about tumor heterogeneity and evolution. The CCF also has a second limitation for phylogenetic analysis: the CCF measures the presence of a mutation at the present time, but SNVs may be lost during the evolution of a tumor due to deletions of chromosomal segments. Thus, SNVs that co-occur on the same phylogenetic branch may have different CCFs.In this work, we address these limitations of the CCF in two ways. First, we show how to compute the CCF of an SNV under a less restrictive and more realistic assumption called the Single Split Copy Number (SSCN) assumption. Second, we introduce a novel statistic, the descendant cell fraction (DCF), that quantifies both the prevalence of an SNV and the past evolutionary history of SNVs under an evolutionary model that allows for mutation losses. That is, SNVs that co-occur on the same phylogenetic branch will have the same DCF. We implement these ideas in an algorithm named DeCiFer. DeCiFer computes the DCFs of SNVs from read counts and copy-number proportions and also infers clusters of mutations that are suitable for phylogenetic analysis. We show that DeCiFer clusters SNVs more accurately than existing methods on simulated data containing mutation losses. We apply DeCiFer to sequencing data from 49 metastatic prostate cancer samples and show that DeCiFer produces more parsimonious and reasonable reconstructions of tumor evolution compared to previous approaches. Thus, DeCiFer enables more accurate quantification of intra-tumor heterogeneity and improves downstream inference of tumor evolution.Code availabilitySoftware is available at https://github.com/raphael-group/decifer


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